fix(oss): cap v3 add ingestion fanout

feat: support Gemini via Vertex AI as LLM provider (#4030 )
Co-authored-by: kartik-mem0 <kartik.labhshetwar@mem0.ai>
2026-06-16 17:05:20 -07:00 · 2026-06-16 16:59:06 +05:30 · 2026-06-16 16:10:57 +05:30 · 2026-06-16 15:51:27 +05:30 · 2026-06-16 15:50:30 +05:30 · 2026-06-16 15:49:26 +05:30
88 changed files with 2777 additions and 3020 deletions
@@ -0,0 +1,4 @@
+[submodule "evaluation"]
+	path = evaluation
+	url = https://github.com/mem0ai/memory-benchmarks
+	branch = main
@@ -12,7 +12,7 @@ This file provides context for AI coding assistants (Claude Code, Cursor, GitHub

 ## Repository Structure

-This is a **polyglot monorepo** containing Python and TypeScript packages, CLIs, servers, plugins, documentation, and evaluation tooling.
+This is a **polyglot monorepo** containing Python and TypeScript packages, CLIs, servers, plugins, and documentation.

 ### Key Directories

@@ -32,7 +32,7 @@ This is a **polyglot monorepo** containing Python and TypeScript packages, CLIs,
 | `skills/` | Claude Code skill definitions. Reference skills (SDK knowledge, always-on): `mem0/`, `mem0-cli/`, `mem0-vercel-ai-sdk/`. Pipeline skills (run on demand): `mem0-integrate/`, `mem0-test-integration/`, `mem0-oss-to-platform/` |
 | `docs/` | Documentation site (Mintlify) |
 | `tests/` | Python SDK tests (pytest) |
-| `evaluation/` | Benchmarking framework — LOCOMO evals, experiment runner, score generation |
+| `evaluation/` | Submodule → [`mem0ai/memory-benchmarks`](https://github.com/mem0ai/memory-benchmarks) — benchmarking (LOCOMO, LongMemEval, BEAM) lives in that repo |
 | `examples/` | Sample projects & runnable demos — apps, Chrome extension, multi-agent patterns, and Jupyter notebooks (`notebooks/`) |
 | `pr-reviews/` | Pull request review materials |
 | `scripts/` | Repo-wide utility scripts (e.g., `check-llms-txt-coverage.py` for docs/llms.txt sync) |
@@ -246,18 +246,19 @@ make docs                          # or: cd docs && mintlify dev
 - **API spec:** `docs/openapi.json`
 - **Structure:** `api-reference/`, `open-source/`, `platform/`, `integrations/`, `cookbooks/`, `core-concepts/`

-### Evaluation (`evaluation/`)
+### Evaluation / Benchmarking
+
+Benchmarking lives in the external [`mem0ai/memory-benchmarks`](https://github.com/mem0ai/memory-benchmarks) repo (LOCOMO + LongMemEval + BEAM). The in-repo `evaluation/` path is a **git submodule** pinned to that repo's `main` — populate it with `git submodule update --init evaluation` (or clone mem0 with `--recurse-submodules`), or clone the benchmarks repo standalone:

 ```bash
-cd evaluation
-make run-mem0-add                  # Run mem0 add experiments
-make run-mem0-search               # Run mem0 search experiments
-make run-mem0-plus-add             # With graph memory
-make run-mem0-plus-search          # With graph memory
-make run-rag                       # RAG baseline
-make run-full-context              # Full context baseline
-make run-langmem                   # LangMem comparison
-make run-openai                    # OpenAI comparison
+git clone https://github.com/mem0ai/memory-benchmarks.git
+cd memory-benchmarks
+pip install -r requirements.txt
+
+# Run a benchmark (Mem0 Cloud; use docker compose for OSS)
+python -m benchmarks.locomo.run --project-name my-test --backend cloud --mem0-api-key $MEM0_API_KEY
+python -m benchmarks.longmemeval.run --project-name my-test --backend cloud --mem0-api-key $MEM0_API_KEY --all-questions
+python -m benchmarks.beam.run --project-name my-test --backend cloud --mem0-api-key $MEM0_API_KEY --chat-sizes 100K --conversations 0-9
 ```

 ## Core APIs
@@ -44,5 +44,15 @@
    "vitest": "^4.1.0",
    "@biomejs/biome": "^1.7.0",
    "@types/node": "^20.0.0"
+  },
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+      "postcss@<8.5.10": ">=8.5.10",
+      "esbuild": ">=0.28.1"
+    }
  }
 }
@@ -10,6 +10,7 @@ overrides:
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  picomatch@<2.3.2: ^2.3.2
  postcss@<8.5.10: '>=8.5.10'
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-    resolution: {integrity: sha512-uqZMTLr/zR/ed4jIGnwSLkaHmPjOjJvnm6TVVitAa08SLS9Z0VM8wIRx7gWbJB5/J54YuIMInDquWyYvQLZkgw==}
+  '@esbuild/linux-x64@0.28.1':
+    resolution: {integrity: sha512-u/anNYF2mmVOEDwLtnQ1wOr3EZ9sTNGLWrsYGYwHWzGA3Si84IOkHXlbWTD1NB+9/1lcnweYKO54uhxZydNzfA==}
    engines: {node: '>=18'}
    cpu: [x64]
    os: [linux]

-  '@esbuild/linux-x64@0.27.4':
-    resolution: {integrity: sha512-S5qOXrKV8BQEzJPVxAwnryi2+Iq5pB40gTEIT69BQONqR7JH1EPIcQ/Uiv9mCnn05jff9umq/5nqzxlqTOg9NA==}
-    engines: {node: '>=18'}
-    cpu: [x64]
-    os: [linux]
-
-  '@esbuild/netbsd-arm64@0.25.12':
-    resolution: {integrity: sha512-xXwcTq4GhRM7J9A8Gv5boanHhRa/Q9KLVmcyXHCTaM4wKfIpWkdXiMog/KsnxzJ0A1+nD+zoecuzqPmCRyBGjg==}
+  '@esbuild/netbsd-arm64@0.28.1':
+    resolution: {integrity: sha512-oks0DYbLwWMmaakTsCb+zL4E+aHRVLom9IJZOAthMQEPiQmydXHkziYEsGYRx0uNV/IjEKGAV941JzH02pflqw==}
    engines: {node: '>=18'}
    cpu: [arm64]
    os: [netbsd]

-  '@esbuild/netbsd-arm64@0.27.4':
-    resolution: {integrity: sha512-xHT8X4sb0GS8qTqiwzHqpY00C95DPAq7nAwX35Ie/s+LO9830hrMd3oX0ZMKLvy7vsonee73x0lmcdOVXFzd6Q==}
-    engines: {node: '>=18'}
-    cpu: [arm64]
-    os: [netbsd]
-
-  '@esbuild/netbsd-x64@0.25.12':
-    resolution: {integrity: sha512-Ld5pTlzPy3YwGec4OuHh1aCVCRvOXdH8DgRjfDy/oumVovmuSzWfnSJg+VtakB9Cm0gxNO9BzWkj6mtO1FMXkQ==}
+  '@esbuild/netbsd-x64@0.28.1':
+    resolution: {integrity: sha512-aeL6lAnN89Hz43Mlh1G8ARasbuoYvSITDEx0tHh5b7jJnHcssqgjy9Yx430GDpmCa6OyrKoS0aNRjKundRizGg==}
    engines: {node: '>=18'}
    cpu: [x64]
    os: [netbsd]

-  '@esbuild/netbsd-x64@0.27.4':
-    resolution: {integrity: sha512-RugOvOdXfdyi5Tyv40kgQnI0byv66BFgAqjdgtAKqHoZTbTF2QqfQrFwa7cHEORJf6X2ht+l9ABLMP0dnKYsgg==}
-    engines: {node: '>=18'}
-    cpu: [x64]
-    os: [netbsd]
-
-  '@esbuild/openbsd-arm64@0.25.12':
-    resolution: {integrity: sha512-fF96T6KsBo/pkQI950FARU9apGNTSlZGsv1jZBAlcLL1MLjLNIWPBkj5NlSz8aAzYKg+eNqknrUJ24QBybeR5A==}
+  '@esbuild/openbsd-arm64@0.28.1':
+    resolution: {integrity: sha512-MEFJe5C3R8pwXdZ5Y21oo6m7ePiS0d9pWucn99O/wvyJZChoIQKrQDxKrGeW8F5+T0okTHesAmDeiHDTIq0V/Q==}
    engines: {node: '>=18'}
    cpu: [arm64]
    os: [openbsd]

-  '@esbuild/openbsd-arm64@0.27.4':
-    resolution: {integrity: sha512-2MyL3IAaTX+1/qP0O1SwskwcwCoOI4kV2IBX1xYnDDqthmq5ArrW94qSIKCAuRraMgPOmG0RDTA74mzYNQA9ow==}
-    engines: {node: '>=18'}
-    cpu: [arm64]
-    os: [openbsd]
-
-  '@esbuild/openbsd-x64@0.25.12':
-    resolution: {integrity: sha512-MZyXUkZHjQxUvzK7rN8DJ3SRmrVrke8ZyRusHlP+kuwqTcfWLyqMOE3sScPPyeIXN/mDJIfGXvcMqCgYKekoQw==}
+  '@esbuild/openbsd-x64@0.28.1':
+    resolution: {integrity: sha512-i/ZLIOafE0Z8cI/XANJAixoJL/uRAoS2xOA3rb0xN+KK0K177cMAsQYkzHtBrtMXAKuAc7HGgcWiZ/sRC1Nxgw==}
    engines: {node: '>=18'}
    cpu: [x64]
    os: [openbsd]

-  '@esbuild/openbsd-x64@0.27.4':
-    resolution: {integrity: sha512-u8fg/jQ5aQDfsnIV6+KwLOf1CmJnfu1ShpwqdwC0uA7ZPwFws55Ngc12vBdeUdnuWoQYx/SOQLGDcdlfXhYmXQ==}
-    engines: {node: '>=18'}
-    cpu: [x64]
-    os: [openbsd]
-
-  '@esbuild/openharmony-arm64@0.25.12':
-    resolution: {integrity: sha512-rm0YWsqUSRrjncSXGA7Zv78Nbnw4XL6/dzr20cyrQf7ZmRcsovpcRBdhD43Nuk3y7XIoW2OxMVvwuRvk9XdASg==}
+  '@esbuild/openharmony-arm64@0.28.1':
+    resolution: {integrity: sha512-ge+Z7EXFNt2BO1oAMsVpiQ8EwndV9i1xXerAeTIK7AtPs3bKFXQM7nlRxDSIUIMeueR1CNXxqztLzdNeReKBJg==}
    engines: {node: '>=18'}
    cpu: [arm64]
    os: [openharmony]

-  '@esbuild/openharmony-arm64@0.27.4':
-    resolution: {integrity: sha512-JkTZrl6VbyO8lDQO3yv26nNr2RM2yZzNrNHEsj9bm6dOwwu9OYN28CjzZkH57bh4w0I2F7IodpQvUAEd1mbWXg==}
-    engines: {node: '>=18'}
-    cpu: [arm64]
-    os: [openharmony]
-
-  '@esbuild/sunos-x64@0.25.12':
-    resolution: {integrity: sha512-3wGSCDyuTHQUzt0nV7bocDy72r2lI33QL3gkDNGkod22EsYl04sMf0qLb8luNKTOmgF/eDEDP5BFNwoBKH441w==}
+  '@esbuild/sunos-x64@0.28.1':
+    resolution: {integrity: sha512-BEjgtECkL3vY+SaSQ6nzVfiALUeFxpawyp8Jmf5PtYhf1Ug40N1h/hxlhts+f1FvSvarEigdxS3BlSMI2PJLcQ==}
    engines: {node: '>=18'}
    cpu: [x64]
    os: [sunos]

-  '@esbuild/sunos-x64@0.27.4':
-    resolution: {integrity: sha512-/gOzgaewZJfeJTlsWhvUEmUG4tWEY2Spp5M20INYRg2ZKl9QPO3QEEgPeRtLjEWSW8FilRNacPOg8R1uaYkA6g==}
-    engines: {node: '>=18'}
-    cpu: [x64]
-    os: [sunos]
-
-  '@esbuild/win32-arm64@0.25.12':
-    resolution: {integrity: sha512-rMmLrur64A7+DKlnSuwqUdRKyd3UE7oPJZmnljqEptesKM8wx9J8gx5u0+9Pq0fQQW8vqeKebwNXdfOyP+8Bsg==}
+  '@esbuild/win32-arm64@0.28.1':
+    resolution: {integrity: sha512-lCv9eK/H6ZJWbE7bh2nw54CZ9M2nupBxJcTsdk/QQnWkdSjKGuxmmH8/GWrlT1eMmZfn4dGcCjRte397WqfQXA==}
    engines: {node: '>=18'}
    cpu: [arm64]
    os: [win32]

-  '@esbuild/win32-arm64@0.27.4':
-    resolution: {integrity: sha512-Z9SExBg2y32smoDQdf1HRwHRt6vAHLXcxD2uGgO/v2jK7Y718Ix4ndsbNMU/+1Qiem9OiOdaqitioZwxivhXYg==}
-    engines: {node: '>=18'}
-    cpu: [arm64]
-    os: [win32]
-
-  '@esbuild/win32-ia32@0.25.12':
-    resolution: {integrity: sha512-HkqnmmBoCbCwxUKKNPBixiWDGCpQGVsrQfJoVGYLPT41XWF8lHuE5N6WhVia2n4o5QK5M4tYr21827fNhi4byQ==}
+  '@esbuild/win32-ia32@0.28.1':
+    resolution: {integrity: sha512-zvb/mB2bSCoJOpoCBgYKKpX6YM6mJBlBUVUtVj41DlZJVEB6/0CKlRYxP5wWl1C1ILiCoAU5wZZ4q1P3qeS6Eg==}
    engines: {node: '>=18'}
    cpu: [ia32]
    os: [win32]

-  '@esbuild/win32-ia32@0.27.4':
-    resolution: {integrity: sha512-DAyGLS0Jz5G5iixEbMHi5KdiApqHBWMGzTtMiJ72ZOLhbu/bzxgAe8Ue8CTS3n3HbIUHQz/L51yMdGMeoxXNJw==}
-    engines: {node: '>=18'}
-    cpu: [ia32]
-    os: [win32]
-
-  '@esbuild/win32-x64@0.25.12':
-    resolution: {integrity: sha512-alJC0uCZpTFrSL0CCDjcgleBXPnCrEAhTBILpeAp7M/OFgoqtAetfBzX0xM00MUsVVPpVjlPuMbREqnZCXaTnA==}
-    engines: {node: '>=18'}
-    cpu: [x64]
-    os: [win32]
-
-  '@esbuild/win32-x64@0.27.4':
-    resolution: {integrity: sha512-+knoa0BDoeXgkNvvV1vvbZX4+hizelrkwmGJBdT17t8FNPwG2lKemmuMZlmaNQ3ws3DKKCxpb4zRZEIp3UxFCg==}
+  '@esbuild/win32-x64@0.28.1':
+    resolution: {integrity: sha512-bm4Mowrv+GXMlpWX++EcXw/iLyd1o3+bJkC2DkWXYVvgZCqD/bSj9ctZeAMC3cIxgjRVR2Dufaiu4YPxr5gW1A==}
    engines: {node: '>=18'}
    cpu: [x64]
    os: [win32]
@@ -475,79 +316,66 @@ packages:
    resolution: {integrity: sha512-RzeBwv0B3qtVBWtcuABtSuCzToo2IEAIQrcyB/b2zMvBWVbjo8bZDjACUpnaafaxhTw2W+imQbP2BD1usasK4g==}
    cpu: [arm]
    os: [linux]
-    libc: [glibc]

  '@rollup/rollup-linux-arm-musleabihf@4.60.0':
    resolution: {integrity: sha512-Sf7zusNI2CIU1HLzuu9Tc5YGAHEZs5Lu7N1ssJG4Tkw6e0MEsN7NdjUDDfGNHy2IU+ENyWT+L2obgWiguWibWQ==}
    cpu: [arm]
    os: [linux]
-    libc: [musl]

  '@rollup/rollup-linux-arm64-gnu@4.60.0':
    resolution: {integrity: sha512-DX2x7CMcrJzsE91q7/O02IJQ5/aLkVtYFryqCjduJhUfGKG6yJV8hxaw8pZa93lLEpPTP/ohdN4wFz7yp/ry9A==}
    cpu: [arm64]
    os: [linux]
-    libc: [glibc]

  '@rollup/rollup-linux-arm64-musl@4.60.0':
    resolution: {integrity: sha512-09EL+yFVbJZlhcQfShpswwRZ0Rg+z/CsSELFCnPt3iK+iqwGsI4zht3secj5vLEs957QvFFXnzAT0FFPIxSrkQ==}
    cpu: [arm64]
    os: [linux]
-    libc: [musl]

  '@rollup/rollup-linux-loong64-gnu@4.60.0':
    resolution: {integrity: sha512-i9IcCMPr3EXm8EQg5jnja0Zyc1iFxJjZWlb4wr7U2Wx/GrddOuEafxRdMPRYVaXjgbhvqalp6np07hN1w9kAKw==}
    cpu: [loong64]
    os: [linux]
-    libc: [glibc]

  '@rollup/rollup-linux-loong64-musl@4.60.0':
    resolution: {integrity: sha512-DGzdJK9kyJ+B78MCkWeGnpXJ91tK/iKA6HwHxF4TAlPIY7GXEvMe8hBFRgdrR9Ly4qebR/7gfUs9y2IoaVEyog==}
    cpu: [loong64]
    os: [linux]
-    libc: [musl]

  '@rollup/rollup-linux-ppc64-gnu@4.60.0':
    resolution: {integrity: sha512-RwpnLsqC8qbS8z1H1AxBA1H6qknR4YpPR9w2XX0vo2Sz10miu57PkNcnHVaZkbqyw/kUWfKMI73jhmfi9BRMUQ==}
    cpu: [ppc64]
    os: [linux]
-    libc: [glibc]

  '@rollup/rollup-linux-ppc64-musl@4.60.0':
    resolution: {integrity: sha512-Z8pPf54Ly3aqtdWC3G4rFigZgNvd+qJlOE52fmko3KST9SoGfAdSRCwyoyG05q1HrrAblLbk1/PSIV+80/pxLg==}
    cpu: [ppc64]
    os: [linux]
-    libc: [musl]

  '@rollup/rollup-linux-riscv64-gnu@4.60.0':
    resolution: {integrity: sha512-3a3qQustp3COCGvnP4SvrMHnPQ9d1vzCakQVRTliaz8cIp/wULGjiGpbcqrkv0WrHTEp8bQD/B3HBjzujVWLOA==}
    cpu: [riscv64]
    os: [linux]
-    libc: [glibc]

  '@rollup/rollup-linux-riscv64-musl@4.60.0':
    resolution: {integrity: sha512-pjZDsVH/1VsghMJ2/kAaxt6dL0psT6ZexQVrijczOf+PeP2BUqTHYejk3l6TlPRydggINOeNRhvpLa0AYpCWSQ==}
    cpu: [riscv64]
    os: [linux]
-    libc: [musl]

  '@rollup/rollup-linux-s390x-gnu@4.60.0':
    resolution: {integrity: sha512-3ObQs0BhvPgiUVZrN7gqCSvmFuMWvWvsjG5ayJ3Lraqv+2KhOsp+pUbigqbeWqueGIsnn+09HBw27rJ+gYK4VQ==}
    cpu: [s390x]
    os: [linux]
-    libc: [glibc]

  '@rollup/rollup-linux-x64-gnu@4.60.0':
    resolution: {integrity: sha512-EtylprDtQPdS5rXvAayrNDYoJhIz1/vzN2fEubo3yLE7tfAw+948dO0g4M0vkTVFhKojnF+n6C8bDNe+gDRdTg==}
    cpu: [x64]
    os: [linux]
-    libc: [glibc]

  '@rollup/rollup-linux-x64-musl@4.60.0':
    resolution: {integrity: sha512-k09oiRCi/bHU9UVFqD17r3eJR9bn03TyKraCrlz5ULFJGdJGi7VOmm9jl44vOJvRJ6P7WuBi/s2A97LxxHGIdw==}
    cpu: [x64]
    os: [linux]
-    libc: [musl]

  '@rollup/rollup-openbsd-x64@4.60.0':
    resolution: {integrity: sha512-1o/0/pIhozoSaDJoDcec+IVLbnRtQmHwPV730+AOD29lHEEo4F5BEUB24H0OBdhbBBDwIOSuf7vgg0Ywxdfiiw==}
@@ -658,7 +486,7 @@ packages:
    resolution: {integrity: sha512-3WrrOuZiyaaZPWiEt4G3+IffISVC9HYlWueJEBWED4ZH4aIAC2PnkdnuRrR94M+w6yGWn4AglWtJtBI8YqvgoA==}
    engines: {node: ^12.20.0 || ^14.13.1 || >=16.0.0}
    peerDependencies:
-      esbuild: '>=0.18'
+      esbuild: '>=0.28.1'

  cac@6.7.14:
    resolution: {integrity: sha512-b6Ilus+c3RrdDk+JhLKUAQfzzgLEPy6wcXqS7f/xe1EETvsDP6GORG7SFuOs6cID5YkqchW/LXZbX5bc8j7ZcQ==}
@@ -738,13 +566,8 @@ packages:
  es-module-lexer@2.1.0:
    resolution: {integrity: sha512-n27zTYMjYu1aj4MjCWzSP7G9r75utsaoc8m61weK+W8JMBGGQybd43GstCXZ3WNmSFtGT9wi59qQTW6mhTR5LQ==}

-  esbuild@0.25.12:
-    resolution: {integrity: sha512-bbPBYYrtZbkt6Os6FiTLCTFxvq4tt3JKall1vRwshA3fdVztsLAatFaZobhkBC8/BrPetoa0oksYoKXoG4ryJg==}
-    engines: {node: '>=18'}
-    hasBin: true
-
-  esbuild@0.27.4:
-    resolution: {integrity: sha512-Rq4vbHnYkK5fws5NF7MYTU68FPRE1ajX7heQ/8QXXWqNgqqJ/GkmmyxIzUnf2Sr/bakf8l54716CcMGHYhMrrQ==}
+  esbuild@0.28.1:
+    resolution: {integrity: sha512-HrJrvZv5ayxBzPfwphOoNzkzOIIlifzk0KJrGK2c8R4+LKpMtpYLQeUdjnwjWv/LZlkH2laZk+4w78pi99D4Vw==}
    engines: {node: '>=18'}
    hasBin: true

@@ -1164,160 +987,82 @@ snapshots:
  '@colors/colors@1.5.0':
    optional: true

-  '@esbuild/aix-ppc64@0.25.12':
+  '@esbuild/aix-ppc64@0.28.1':
    optional: true

-  '@esbuild/aix-ppc64@0.27.4':
+  '@esbuild/android-arm64@0.28.1':
    optional: true

-  '@esbuild/android-arm64@0.25.12':
+  '@esbuild/android-arm@0.28.1':
    optional: true

-  '@esbuild/android-arm64@0.27.4':
+  '@esbuild/android-x64@0.28.1':
    optional: true

-  '@esbuild/android-arm@0.25.12':
+  '@esbuild/darwin-arm64@0.28.1':
    optional: true

-  '@esbuild/android-arm@0.27.4':
+  '@esbuild/darwin-x64@0.28.1':
    optional: true

-  '@esbuild/android-x64@0.25.12':
+  '@esbuild/freebsd-arm64@0.28.1':
    optional: true

-  '@esbuild/android-x64@0.27.4':
+  '@esbuild/freebsd-x64@0.28.1':
    optional: true

-  '@esbuild/darwin-arm64@0.25.12':
+  '@esbuild/linux-arm64@0.28.1':
    optional: true

-  '@esbuild/darwin-arm64@0.27.4':
+  '@esbuild/linux-arm@0.28.1':
    optional: true

-  '@esbuild/darwin-x64@0.25.12':
+  '@esbuild/linux-ia32@0.28.1':
    optional: true

-  '@esbuild/darwin-x64@0.27.4':
+  '@esbuild/linux-loong64@0.28.1':
    optional: true

-  '@esbuild/freebsd-arm64@0.25.12':
+  '@esbuild/linux-mips64el@0.28.1':
    optional: true

-  '@esbuild/freebsd-arm64@0.27.4':
+  '@esbuild/linux-ppc64@0.28.1':
    optional: true

-  '@esbuild/freebsd-x64@0.25.12':
+  '@esbuild/linux-riscv64@0.28.1':
    optional: true

-  '@esbuild/freebsd-x64@0.27.4':
+  '@esbuild/linux-s390x@0.28.1':
    optional: true

-  '@esbuild/linux-arm64@0.25.12':
+  '@esbuild/linux-x64@0.28.1':
    optional: true

-  '@esbuild/linux-arm64@0.27.4':
+  '@esbuild/netbsd-arm64@0.28.1':
    optional: true

-  '@esbuild/linux-arm@0.25.12':
+  '@esbuild/netbsd-x64@0.28.1':
    optional: true

-  '@esbuild/linux-arm@0.27.4':
+  '@esbuild/openbsd-arm64@0.28.1':
    optional: true

-  '@esbuild/linux-ia32@0.25.12':
+  '@esbuild/openbsd-x64@0.28.1':
    optional: true

-  '@esbuild/linux-ia32@0.27.4':
+  '@esbuild/openharmony-arm64@0.28.1':
    optional: true

-  '@esbuild/linux-loong64@0.25.12':
+  '@esbuild/sunos-x64@0.28.1':
    optional: true

-  '@esbuild/linux-loong64@0.27.4':
+  '@esbuild/win32-arm64@0.28.1':
    optional: true

-  '@esbuild/linux-mips64el@0.25.12':
+  '@esbuild/win32-ia32@0.28.1':
    optional: true

-  '@esbuild/linux-mips64el@0.27.4':
-    optional: true
-
-  '@esbuild/linux-ppc64@0.25.12':
-    optional: true
-
-  '@esbuild/linux-ppc64@0.27.4':
-    optional: true
-
-  '@esbuild/linux-riscv64@0.25.12':
-    optional: true
-
-  '@esbuild/linux-riscv64@0.27.4':
-    optional: true
-
-  '@esbuild/linux-s390x@0.25.12':
-    optional: true
-
-  '@esbuild/linux-s390x@0.27.4':
-    optional: true
-
-  '@esbuild/linux-x64@0.25.12':
-    optional: true
-
-  '@esbuild/linux-x64@0.27.4':
-    optional: true
-
-  '@esbuild/netbsd-arm64@0.25.12':
-    optional: true
-
-  '@esbuild/netbsd-arm64@0.27.4':
-    optional: true
-
-  '@esbuild/netbsd-x64@0.25.12':
-    optional: true
-
-  '@esbuild/netbsd-x64@0.27.4':
-    optional: true
-
-  '@esbuild/openbsd-arm64@0.25.12':
-    optional: true
-
-  '@esbuild/openbsd-arm64@0.27.4':
-    optional: true
-
-  '@esbuild/openbsd-x64@0.25.12':
-    optional: true
-
-  '@esbuild/openbsd-x64@0.27.4':
-    optional: true
-
-  '@esbuild/openharmony-arm64@0.25.12':
-    optional: true
-
-  '@esbuild/openharmony-arm64@0.27.4':
-    optional: true
-
-  '@esbuild/sunos-x64@0.25.12':
-    optional: true
-
-  '@esbuild/sunos-x64@0.27.4':
-    optional: true
-
-  '@esbuild/win32-arm64@0.25.12':
-    optional: true
-
-  '@esbuild/win32-arm64@0.27.4':
-    optional: true
-
-  '@esbuild/win32-ia32@0.25.12':
-    optional: true
-
-  '@esbuild/win32-ia32@0.27.4':
-    optional: true
-
-  '@esbuild/win32-x64@0.25.12':
-    optional: true
-
-  '@esbuild/win32-x64@0.27.4':
+  '@esbuild/win32-x64@0.28.1':
    optional: true

  '@jridgewell/gen-mapping@0.3.13':
@@ -1492,9 +1237,9 @@ snapshots:
      widest-line: 4.0.1
      wrap-ansi: 8.1.0

-  bundle-require@5.1.0(esbuild@0.27.4):
+  bundle-require@5.1.0(esbuild@0.28.1):
    dependencies:
-      esbuild: 0.27.4
+      esbuild: 0.28.1
      load-tsconfig: 0.2.5

  cac@6.7.14: {}
@@ -1547,63 +1292,34 @@ snapshots:

  es-module-lexer@2.1.0: {}

-  esbuild@0.25.12:
+  esbuild@0.28.1:
    optionalDependencies:
-      '@esbuild/aix-ppc64': 0.25.12
-      '@esbuild/android-arm': 0.25.12
-      '@esbuild/android-arm64': 0.25.12
-      '@esbuild/android-x64': 0.25.12
-      '@esbuild/darwin-arm64': 0.25.12
-      '@esbuild/darwin-x64': 0.25.12
-      '@esbuild/freebsd-arm64': 0.25.12
-      '@esbuild/freebsd-x64': 0.25.12
-      '@esbuild/linux-arm': 0.25.12
-      '@esbuild/linux-arm64': 0.25.12
-      '@esbuild/linux-ia32': 0.25.12
-      '@esbuild/linux-loong64': 0.25.12
-      '@esbuild/linux-mips64el': 0.25.12
-      '@esbuild/linux-ppc64': 0.25.12
-      '@esbuild/linux-riscv64': 0.25.12
-      '@esbuild/linux-s390x': 0.25.12
-      '@esbuild/linux-x64': 0.25.12
-      '@esbuild/netbsd-arm64': 0.25.12
-      '@esbuild/netbsd-x64': 0.25.12
-      '@esbuild/openbsd-arm64': 0.25.12
-      '@esbuild/openbsd-x64': 0.25.12
-      '@esbuild/openharmony-arm64': 0.25.12
-      '@esbuild/sunos-x64': 0.25.12
-      '@esbuild/win32-arm64': 0.25.12
-      '@esbuild/win32-ia32': 0.25.12
-      '@esbuild/win32-x64': 0.25.12
-
-  esbuild@0.27.4:
-    optionalDependencies:
-      '@esbuild/aix-ppc64': 0.27.4
-      '@esbuild/android-arm': 0.27.4
-      '@esbuild/android-arm64': 0.27.4
-      '@esbuild/android-x64': 0.27.4
-      '@esbuild/darwin-arm64': 0.27.4
-      '@esbuild/darwin-x64': 0.27.4
-      '@esbuild/freebsd-arm64': 0.27.4
-      '@esbuild/freebsd-x64': 0.27.4
-      '@esbuild/linux-arm': 0.27.4
-      '@esbuild/linux-arm64': 0.27.4
-      '@esbuild/linux-ia32': 0.27.4
-      '@esbuild/linux-loong64': 0.27.4
-      '@esbuild/linux-mips64el': 0.27.4
-      '@esbuild/linux-ppc64': 0.27.4
-      '@esbuild/linux-riscv64': 0.27.4
-      '@esbuild/linux-s390x': 0.27.4
-      '@esbuild/linux-x64': 0.27.4
-      '@esbuild/netbsd-arm64': 0.27.4
-      '@esbuild/netbsd-x64': 0.27.4
-      '@esbuild/openbsd-arm64': 0.27.4
-      '@esbuild/openbsd-x64': 0.27.4
-      '@esbuild/openharmony-arm64': 0.27.4
-      '@esbuild/sunos-x64': 0.27.4
-      '@esbuild/win32-arm64': 0.27.4
-      '@esbuild/win32-ia32': 0.27.4
-      '@esbuild/win32-x64': 0.27.4
+      '@esbuild/aix-ppc64': 0.28.1
+      '@esbuild/android-arm': 0.28.1
+      '@esbuild/android-arm64': 0.28.1
+      '@esbuild/android-x64': 0.28.1
+      '@esbuild/darwin-arm64': 0.28.1
+      '@esbuild/darwin-x64': 0.28.1
+      '@esbuild/freebsd-arm64': 0.28.1
+      '@esbuild/freebsd-x64': 0.28.1
+      '@esbuild/linux-arm': 0.28.1
+      '@esbuild/linux-arm64': 0.28.1
+      '@esbuild/linux-ia32': 0.28.1
+      '@esbuild/linux-loong64': 0.28.1
+      '@esbuild/linux-mips64el': 0.28.1
+      '@esbuild/linux-ppc64': 0.28.1
+      '@esbuild/linux-riscv64': 0.28.1
+      '@esbuild/linux-s390x': 0.28.1
+      '@esbuild/linux-x64': 0.28.1
+      '@esbuild/netbsd-arm64': 0.28.1
+      '@esbuild/netbsd-x64': 0.28.1
+      '@esbuild/openbsd-arm64': 0.28.1
+      '@esbuild/openbsd-x64': 0.28.1
+      '@esbuild/openharmony-arm64': 0.28.1
+      '@esbuild/sunos-x64': 0.28.1
+      '@esbuild/win32-arm64': 0.28.1
+      '@esbuild/win32-ia32': 0.28.1
+      '@esbuild/win32-x64': 0.28.1

  estree-walker@3.0.3:
    dependencies:
@@ -1840,12 +1556,12 @@ snapshots:

  tsup@8.5.1(postcss@8.5.15)(tsx@4.21.0)(typescript@5.9.3):
    dependencies:
-      bundle-require: 5.1.0(esbuild@0.27.4)
+      bundle-require: 5.1.0(esbuild@0.28.1)
      cac: 6.7.14
      chokidar: 4.0.3
      consola: 3.4.2
      debug: 4.4.3
-      esbuild: 0.27.4
+      esbuild: 0.28.1
      fix-dts-default-cjs-exports: 1.0.1
      joycon: 3.1.1
      picocolors: 1.1.1
@@ -1868,7 +1584,7 @@ snapshots:

  tsx@4.21.0:
    dependencies:
-      esbuild: 0.27.4
+      esbuild: 0.28.1
      get-tsconfig: 4.13.7
    optionalDependencies:
      fsevents: 2.3.3
@@ -1883,7 +1599,7 @@ snapshots:

  vite@6.4.3(@types/node@20.19.37)(tsx@4.21.0):
    dependencies:
-      esbuild: 0.25.12
+      esbuild: 0.28.1
      fdir: 6.5.0(picomatch@4.0.4)
      picomatch: 4.0.4
      postcss: 8.5.15
@@ -11,3 +11,4 @@ overrides:
  tar-fs@>=2.0.0 <2.1.4: ^2.1.4
  picomatch@<2.3.2: ^2.3.2
  "postcss@<8.5.10": ">=8.5.10"
+  "esbuild": ">=0.28.1"
@@ -1,31 +0,0 @@
-
-# Run the experiments
-run-mem0-add:
-	python run_experiments.py --technique_type mem0 --method add
-
-run-mem0-search:
-	python run_experiments.py --technique_type mem0 --method search --output_folder results/ --top_k 30
-
-run-mem0-plus-add:
-	python run_experiments.py --technique_type mem0 --method add --is_graph
-
-run-mem0-plus-search:
-	python run_experiments.py --technique_type mem0 --method search --is_graph --output_folder results/ --top_k 30
-
-run-rag:
-	python run_experiments.py --technique_type rag --chunk_size 500 --num_chunks 1 --output_folder results/
-
-run-full-context:
-	python run_experiments.py --technique_type rag --chunk_size -1 --num_chunks 1 --output_folder results/
-
-run-langmem:
-	python run_experiments.py --technique_type langmem --output_folder results/
-
-run-zep-add:
-	python run_experiments.py --technique_type zep --method add --output_folder results/
-
-run-zep-search:
-	python run_experiments.py --technique_type zep --method search --output_folder results/
-
-run-openai:
-	python run_experiments.py --technique_type openai --output_folder results/
@@ -1,198 +0,0 @@
-# Mem0: Building Production‑Ready AI Agents with Scalable Long‑Term Memory
-
-[![arXiv](https://img.shields.io/badge/arXiv-Paper-b31b1b.svg)](https://arxiv.org/abs/2504.19413)
-[![Website](https://img.shields.io/badge/Website-Project-blue)](https://mem0.ai/research)
-
-This repository contains the code and dataset for our paper: **Mem0: Building Production‑Ready AI Agents with Scalable Long‑Term Memory**.
-
-## 📋 Overview
-
-This project evaluates Mem0 and compares it with different memory and retrieval techniques for AI systems:
-
-1. **Established LOCOMO Benchmarks**: We evaluate against five established approaches from the literature: LoCoMo, ReadAgent, MemoryBank, MemGPT, and A-Mem.
-2. **Open-Source Memory Solutions**: We test promising open-source memory architectures including LangMem, which provides flexible memory management capabilities.
-3. **RAG Systems**: We implement Retrieval-Augmented Generation with various configurations, testing different chunk sizes and retrieval counts to optimize performance.
-4. **Full-Context Processing**: We examine the effectiveness of passing the entire conversation history within the context window of the LLM as a baseline approach.
-5. **Proprietary Memory Systems**: We evaluate OpenAI's built-in memory feature available in their ChatGPT interface to compare against commercial solutions.
-6. **Third-Party Memory Providers**: We incorporate Zep, a specialized memory management platform designed for AI agents, to assess the performance of dedicated memory infrastructure.
-
-We test these techniques on the LOCOMO dataset, which contains conversational data with various question types to evaluate memory recall and understanding.
-
-## 🔍 Dataset
-
-The LOCOMO dataset used in our experiments can be downloaded from our Google Drive repository:
-
-[Download LOCOMO Dataset](https://drive.google.com/drive/folders/1L-cTjTm0ohMsitsHg4dijSPJtqNflwX-?usp=drive_link)
-
-The dataset contains conversational data specifically designed to test memory recall and understanding across various question types and complexity levels.
-
-Place the dataset files in the `dataset/` directory:
- `locomo10.json`: Original dataset
- `locomo10_rag.json`: Dataset formatted for RAG experiments
-
-## 📁 Project Structure
-
-```
-.
-├── src/                  # Source code for different memory techniques
-│   ├── mem0/             # Implementation of the Mem0 technique
-│   ├── openai/           # Implementation of the OpenAI memory
-│   ├── zep/              # Implementation of the Zep memory
-│   ├── rag.py            # Implementation of the RAG technique
-│   └── langmem.py        # Implementation of the Language-based memory
-├── metrics/              # Code for evaluation metrics
-├── results/              # Results of experiments
-├── dataset/              # Dataset files
-├── evals.py              # Evaluation script
-├── run_experiments.py    # Script to run experiments
-├── generate_scores.py    # Script to generate scores from results
-└── prompts.py            # Prompts used for the models
-```
-
-## 🚀 Getting Started
-
-### Prerequisites
-
-Create a `.env` file with your API keys and configurations. The following keys are required:
-
-```
-# OpenAI API key for GPT models and embeddings
-OPENAI_API_KEY="your-openai-api-key"
-
-# Mem0 API keys (for Mem0 and Mem0+ techniques)
-MEM0_API_KEY="your-mem0-api-key"
-MEM0_PROJECT_ID="your-mem0-project-id"
-MEM0_ORGANIZATION_ID="your-mem0-organization-id"
-
-# Model configuration
-MODEL="gpt-4o-mini"  # or your preferred model
-EMBEDDING_MODEL="text-embedding-3-small"  # or your preferred embedding model
-ZEP_API_KEY="api-key-from-zep"
-```
-
-### Running Experiments
-
-You can run experiments using the provided Makefile commands:
-
-#### Memory Techniques
-
-```bash
-# Run Mem0 experiments
-make run-mem0-add         # Add memories using Mem0
-make run-mem0-search      # Search memories using Mem0
-
-# Run Mem0+ experiments (with graph-based search)
-make run-mem0-plus-add    # Add memories using Mem0+
-make run-mem0-plus-search # Search memories using Mem0+
-
-# Run RAG experiments
-make run-rag              # Run RAG with chunk size 500
-make run-full-context     # Run RAG with full context
-
-# Run LangMem experiments
-make run-langmem          # Run LangMem
-
-# Run Zep experiments
-make run-zep-add          # Add memories using Zep
-make run-zep-search       # Search memories using Zep
-
-# Run OpenAI experiments
-make run-openai           # Run OpenAI experiments
-```
-
-Alternatively, you can run experiments directly with custom parameters:
-
-```bash
-python run_experiments.py --technique_type [mem0|rag|langmem] [additional parameters]
-```
-
-#### Command-line Parameters:
-
-| Parameter | Description | Default |
-|-----------|-------------|---------|
-| `--technique_type` | Memory technique to use (mem0, rag, langmem) | mem0 |
-| `--method` | Method to use (add, search) | add |
-| `--chunk_size` | Chunk size for processing | 1000 |
-| `--top_k` | Number of top memories to retrieve | 30 |
-| `--filter_memories` | Whether to filter memories | False |
-| `--is_graph` | Whether to use graph-based search | False |
-| `--num_chunks` | Number of chunks to process for RAG | 1 |
-
-### 📊 Evaluation
-
-To evaluate results, run:
-
-```bash
-python evals.py --input_file [path_to_results] --output_file [output_path]
-```
-
-This script:
-1. Processes each question-answer pair
-2. Calculates BLEU and F1 scores automatically
-3. Uses an LLM judge to evaluate answer correctness
-4. Saves the combined results to the output file
-
-### 📈 Generating Scores
-
-Generate final scores with:
-
-```bash
-python generate_scores.py
-```
-
-This script:
-1. Loads the evaluation metrics data
-2. Calculates mean scores for each category (BLEU, F1, LLM)
-3. Reports the number of questions per category
-4. Calculates overall mean scores across all categories
-
-Example output:
-```
-Mean Scores Per Category:
-         bleu_score  f1_score  llm_score  count
-category                                       
-1           0.xxxx    0.xxxx     0.xxxx     xx
-2           0.xxxx    0.xxxx     0.xxxx     xx
-3           0.xxxx    0.xxxx     0.xxxx     xx
-
-Overall Mean Scores:
-bleu_score    0.xxxx
-f1_score      0.xxxx
-llm_score     0.xxxx
-```
-
-## 📏 Evaluation Metrics
-
-We use several metrics to evaluate the performance of different memory techniques:
-
-1. **BLEU Score**: Measures the similarity between the model's response and the ground truth
-2. **F1 Score**: Measures the harmonic mean of precision and recall
-3. **LLM Score**: A binary score (0 or 1) determined by an LLM judge evaluating the correctness of responses
-4. **Token Consumption**: Number of tokens required to generate final answer.
-5. **Latency**: Time required during search and to generate response.
-
-## 📚 Citation
-
-If you use this code or dataset in your research, please cite our paper:
-
-```bibtex
-@article{mem0,
-  title={Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory},
-  author={Chhikara, Prateek and Khant, Dev and Aryan, Saket and Singh, Taranjeet and Yadav, Deshraj},
-  journal={arXiv preprint arXiv:2504.19413},
-  year={2025}
-}
-```
-
-## 📄 License
-
-[MIT License](LICENSE)
-
-## 👥 Contributors
-
- [Prateek Chhikara](https://github.com/prateekchhikara)
- [Dev Khant](https://github.com/Dev-Khant)
- [Saket Aryan](https://github.com/whysosaket)
- [Taranjeet Singh](https://github.com/taranjeet)
- [Deshraj Yadav](https://github.com/deshraj)
-
@@ -1,81 +0,0 @@
-import argparse
-import concurrent.futures
-import json
-import threading
-from collections import defaultdict
-
-from metrics.llm_judge import evaluate_llm_judge
-from metrics.utils import calculate_bleu_scores, calculate_metrics
-from tqdm import tqdm
-
-
-def process_item(item_data):
-    k, v = item_data
-    local_results = defaultdict(list)
-
-    for item in v:
-        gt_answer = str(item["answer"])
-        pred_answer = str(item["response"])
-        category = str(item["category"])
-        question = str(item["question"])
-
-        # Skip category 5
-        if category == "5":
-            continue
-
-        metrics = calculate_metrics(pred_answer, gt_answer)
-        bleu_scores = calculate_bleu_scores(pred_answer, gt_answer)
-        llm_score = evaluate_llm_judge(question, gt_answer, pred_answer)
-
-        local_results[k].append(
-            {
-                "question": question,
-                "answer": gt_answer,
-                "response": pred_answer,
-                "category": category,
-                "bleu_score": bleu_scores["bleu1"],
-                "f1_score": metrics["f1"],
-                "llm_score": llm_score,
-            }
-        )
-
-    return local_results
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Evaluate RAG results")
-    parser.add_argument(
-        "--input_file", type=str, default="results/rag_results_500_k1.json", help="Path to the input dataset file"
-    )
-    parser.add_argument(
-        "--output_file", type=str, default="evaluation_metrics.json", help="Path to save the evaluation results"
-    )
-    parser.add_argument("--max_workers", type=int, default=10, help="Maximum number of worker threads")
-
-    args = parser.parse_args()
-
-    with open(args.input_file, "r") as f:
-        data = json.load(f)
-
-    results = defaultdict(list)
-    results_lock = threading.Lock()
-
-    # Use ThreadPoolExecutor with specified workers
-    with concurrent.futures.ThreadPoolExecutor(max_workers=args.max_workers) as executor:
-        futures = [executor.submit(process_item, item_data) for item_data in data.items()]
-
-        for future in tqdm(concurrent.futures.as_completed(futures), total=len(futures)):
-            local_results = future.result()
-            with results_lock:
-                for k, items in local_results.items():
-                    results[k].extend(items)
-
-    # Save results to JSON file
-    with open(args.output_file, "w") as f:
-        json.dump(results, f, indent=4)
-
-    print(f"Results saved to {args.output_file}")
-
-
-if __name__ == "__main__":
-    main()
@@ -1,34 +0,0 @@
-import json
-
-import pandas as pd
-
-# Load the evaluation metrics data
-with open("evaluation_metrics.json", "r") as f:
-    data = json.load(f)
-
-# Flatten the data into a list of question items
-all_items = []
-for key in data:
-    all_items.extend(data[key])
-
-# Convert to DataFrame
-df = pd.DataFrame(all_items)
-
-# Convert category to numeric type
-df["category"] = pd.to_numeric(df["category"])
-
-# Calculate mean scores by category
-result = df.groupby("category").agg({"bleu_score": "mean", "f1_score": "mean", "llm_score": "mean"}).round(4)
-
-# Add count of questions per category
-result["count"] = df.groupby("category").size()
-
-# Print the results
-print("Mean Scores Per Category:")
-print(result)
-
-# Calculate overall means
-overall_means = df.agg({"bleu_score": "mean", "f1_score": "mean", "llm_score": "mean"}).round(4)
-
-print("\nOverall Mean Scores:")
-print(overall_means)
@@ -1,130 +0,0 @@
-import argparse
-import json
-from collections import defaultdict
-
-import numpy as np
-from openai import OpenAI
-
-from mem0.memory.utils import extract_json
-
-client = OpenAI()
-
-ACCURACY_PROMPT = """
-Your task is to label an answer to a question as ’CORRECT’ or ’WRONG’. You will be given the following data:
-    (1) a question (posed by one user to another user), 
-    (2) a ’gold’ (ground truth) answer, 
-    (3) a generated answer
-which you will score as CORRECT/WRONG.
-
-The point of the question is to ask about something one user should know about the other user based on their prior conversations.
-The gold answer will usually be a concise and short answer that includes the referenced topic, for example:
-Question: Do you remember what I got the last time I went to Hawaii?
-Gold answer: A shell necklace
-The generated answer might be much longer, but you should be generous with your grading - as long as it touches on the same topic as the gold answer, it should be counted as CORRECT. 
-
-For time related questions, the gold answer will be a specific date, month, year, etc. The generated answer might be much longer or use relative time references (like "last Tuesday" or "next month"), but you should be generous with your grading - as long as it refers to the same date or time period as the gold answer, it should be counted as CORRECT. Even if the format differs (e.g., "May 7th" vs "7 May"), consider it CORRECT if it's the same date.
-
-Now it's time for the real question:
-Question: {question}
-Gold answer: {gold_answer}
-Generated answer: {generated_answer}
-
-First, provide a short (one sentence) explanation of your reasoning, then finish with CORRECT or WRONG. 
-Do NOT include both CORRECT and WRONG in your response, or it will break the evaluation script.
-
-Just return the label CORRECT or WRONG in a json format with the key as "label".
-"""
-
-
-def evaluate_llm_judge(question, gold_answer, generated_answer):
-    """Evaluate the generated answer against the gold answer using an LLM judge."""
-    response = client.chat.completions.create(
-        model="gpt-4o-mini",
-        messages=[
-            {
-                "role": "user",
-                "content": ACCURACY_PROMPT.format(
-                    question=question, gold_answer=gold_answer, generated_answer=generated_answer
-                ),
-            }
-        ],
-        response_format={"type": "json_object"},
-        temperature=0.0,
-    )
-    label = json.loads(extract_json(response.choices[0].message.content))["label"]
-    return 1 if label == "CORRECT" else 0
-
-
-def main():
-    """Main function to evaluate RAG results using LLM judge."""
-    parser = argparse.ArgumentParser(description="Evaluate RAG results using LLM judge")
-    parser.add_argument(
-        "--input_file",
-        type=str,
-        default="results/default_run_v4_k30_new_graph.json",
-        help="Path to the input dataset file",
-    )
-
-    args = parser.parse_args()
-
-    dataset_path = args.input_file
-    output_path = f"results/llm_judge_{dataset_path.split('/')[-1]}"
-
-    with open(dataset_path, "r") as f:
-        data = json.load(f)
-
-    LLM_JUDGE = defaultdict(list)
-    RESULTS = defaultdict(list)
-
-    index = 0
-    for k, v in data.items():
-        for x in v:
-            question = x["question"]
-            gold_answer = x["answer"]
-            generated_answer = x["response"]
-            category = x["category"]
-
-            # Skip category 5
-            if int(category) == 5:
-                continue
-
-            # Evaluate the answer
-            label = evaluate_llm_judge(question, gold_answer, generated_answer)
-            LLM_JUDGE[category].append(label)
-
-            # Store the results
-            RESULTS[index].append(
-                {
-                    "question": question,
-                    "gt_answer": gold_answer,
-                    "response": generated_answer,
-                    "category": category,
-                    "llm_label": label,
-                }
-            )
-
-            # Save intermediate results
-            with open(output_path, "w") as f:
-                json.dump(RESULTS, f, indent=4)
-
-            # Print current accuracy for all categories
-            print("All categories accuracy:")
-            for cat, results in LLM_JUDGE.items():
-                if results:  # Only print if there are results for this category
-                    print(f"  Category {cat}: {np.mean(results):.4f} ({sum(results)}/{len(results)})")
-            print("------------------------------------------")
-        index += 1
-
-    # Save final results
-    with open(output_path, "w") as f:
-        json.dump(RESULTS, f, indent=4)
-
-    # Print final summary
-    print("PATH: ", dataset_path)
-    print("------------------------------------------")
-    for k, v in LLM_JUDGE.items():
-        print(k, np.mean(v))
-
-
-if __name__ == "__main__":
-    main()
@@ -1,211 +0,0 @@
-"""
-Borrowed from https://github.com/WujiangXu/AgenticMemory/blob/main/utils.py
-
-@article{xu2025mem,
-    title={A-mem: Agentic memory for llm agents},
-    author={Xu, Wujiang and Liang, Zujie and Mei, Kai and Gao, Hang and Tan, Juntao
-           and Zhang, Yongfeng},
-    journal={arXiv preprint arXiv:2502.12110},
-    year={2025}
-}
-"""
-
-import statistics
-from collections import defaultdict
-from typing import Dict, List, Union
-
-import nltk
-from bert_score import score as bert_score
-from nltk.translate.bleu_score import SmoothingFunction, sentence_bleu
-from nltk.translate.meteor_score import meteor_score
-from rouge_score import rouge_scorer
-from sentence_transformers import SentenceTransformer
-
-# from load_dataset import load_locomo_dataset, QA, Turn, Session, Conversation
-from sentence_transformers.util import pytorch_cos_sim
-
-# Download required NLTK data
-try:
-    nltk.download("punkt", quiet=True)
-    nltk.download("wordnet", quiet=True)
-except Exception as e:
-    print(f"Error downloading NLTK data: {e}")
-
-# Initialize SentenceTransformer model (this will be reused)
-try:
-    sentence_model = SentenceTransformer("all-MiniLM-L6-v2")
-except Exception as e:
-    print(f"Warning: Could not load SentenceTransformer model: {e}")
-    sentence_model = None
-
-
-def simple_tokenize(text):
-    """Simple tokenization function."""
-    # Convert to string if not already
-    text = str(text)
-    return text.lower().replace(".", " ").replace(",", " ").replace("!", " ").replace("?", " ").split()
-
-
-def calculate_rouge_scores(prediction: str, reference: str) -> Dict[str, float]:
-    """Calculate ROUGE scores for prediction against reference."""
-    scorer = rouge_scorer.RougeScorer(["rouge1", "rouge2", "rougeL"], use_stemmer=True)
-    scores = scorer.score(reference, prediction)
-    return {
-        "rouge1_f": scores["rouge1"].fmeasure,
-        "rouge2_f": scores["rouge2"].fmeasure,
-        "rougeL_f": scores["rougeL"].fmeasure,
-    }
-
-
-def calculate_bleu_scores(prediction: str, reference: str) -> Dict[str, float]:
-    """Calculate BLEU scores with different n-gram settings."""
-    pred_tokens = nltk.word_tokenize(prediction.lower())
-    ref_tokens = [nltk.word_tokenize(reference.lower())]
-
-    weights_list = [(1, 0, 0, 0), (0.5, 0.5, 0, 0), (0.33, 0.33, 0.33, 0), (0.25, 0.25, 0.25, 0.25)]
-    smooth = SmoothingFunction().method1
-
-    scores = {}
-    for n, weights in enumerate(weights_list, start=1):
-        try:
-            score = sentence_bleu(ref_tokens, pred_tokens, weights=weights, smoothing_function=smooth)
-        except Exception as e:
-            print(f"Error calculating BLEU score: {e}")
-            score = 0.0
-        scores[f"bleu{n}"] = score
-
-    return scores
-
-
-def calculate_bert_scores(prediction: str, reference: str) -> Dict[str, float]:
-    """Calculate BERTScore for semantic similarity."""
-    try:
-        P, R, F1 = bert_score([prediction], [reference], lang="en", verbose=False)
-        return {"bert_precision": P.item(), "bert_recall": R.item(), "bert_f1": F1.item()}
-    except Exception as e:
-        print(f"Error calculating BERTScore: {e}")
-        return {"bert_precision": 0.0, "bert_recall": 0.0, "bert_f1": 0.0}
-
-
-def calculate_meteor_score(prediction: str, reference: str) -> float:
-    """Calculate METEOR score for the prediction."""
-    try:
-        return meteor_score([reference.split()], prediction.split())
-    except Exception as e:
-        print(f"Error calculating METEOR score: {e}")
-        return 0.0
-
-
-def calculate_sentence_similarity(prediction: str, reference: str) -> float:
-    """Calculate sentence embedding similarity using SentenceBERT."""
-    if sentence_model is None:
-        return 0.0
-    try:
-        # Encode sentences
-        embedding1 = sentence_model.encode([prediction], convert_to_tensor=True)
-        embedding2 = sentence_model.encode([reference], convert_to_tensor=True)
-
-        # Calculate cosine similarity
-        similarity = pytorch_cos_sim(embedding1, embedding2).item()
-        return float(similarity)
-    except Exception as e:
-        print(f"Error calculating sentence similarity: {e}")
-        return 0.0
-
-
-def calculate_metrics(prediction: str, reference: str) -> Dict[str, float]:
-    """Calculate comprehensive evaluation metrics for a prediction."""
-    # Handle empty or None values
-    if not prediction or not reference:
-        return {
-            "exact_match": 0,
-            "f1": 0.0,
-            "rouge1_f": 0.0,
-            "rouge2_f": 0.0,
-            "rougeL_f": 0.0,
-            "bleu1": 0.0,
-            "bleu2": 0.0,
-            "bleu3": 0.0,
-            "bleu4": 0.0,
-            "bert_f1": 0.0,
-            "meteor": 0.0,
-            "sbert_similarity": 0.0,
-        }
-
-    # Convert to strings if they're not already
-    prediction = str(prediction).strip()
-    reference = str(reference).strip()
-
-    # Calculate exact match
-    exact_match = int(prediction.lower() == reference.lower())
-
-    # Calculate token-based F1 score
-    pred_tokens = set(simple_tokenize(prediction))
-    ref_tokens = set(simple_tokenize(reference))
-    common_tokens = pred_tokens & ref_tokens
-
-    if not pred_tokens or not ref_tokens:
-        f1 = 0.0
-    else:
-        precision = len(common_tokens) / len(pred_tokens)
-        recall = len(common_tokens) / len(ref_tokens)
-        f1 = 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0.0
-
-    # Calculate all scores
-    bleu_scores = calculate_bleu_scores(prediction, reference)
-
-    # Combine all metrics
-    metrics = {
-        "exact_match": exact_match,
-        "f1": f1,
-        **bleu_scores,
-    }
-
-    return metrics
-
-
-def aggregate_metrics(
-    all_metrics: List[Dict[str, float]], all_categories: List[int]
-) -> Dict[str, Dict[str, Union[float, Dict[str, float]]]]:
-    """Calculate aggregate statistics for all metrics, split by category."""
-    if not all_metrics:
-        return {}
-
-    # Initialize aggregates for overall and per-category metrics
-    aggregates = defaultdict(list)
-    category_aggregates = defaultdict(lambda: defaultdict(list))
-
-    # Collect all values for each metric, both overall and per category
-    for metrics, category in zip(all_metrics, all_categories):
-        for metric_name, value in metrics.items():
-            aggregates[metric_name].append(value)
-            category_aggregates[category][metric_name].append(value)
-
-    # Calculate statistics for overall metrics
-    results = {"overall": {}}
-
-    for metric_name, values in aggregates.items():
-        results["overall"][metric_name] = {
-            "mean": statistics.mean(values),
-            "std": statistics.stdev(values) if len(values) > 1 else 0.0,
-            "median": statistics.median(values),
-            "min": min(values),
-            "max": max(values),
-            "count": len(values),
-        }
-
-    # Calculate statistics for each category
-    for category in sorted(category_aggregates.keys()):
-        results[f"category_{category}"] = {}
-        for metric_name, values in category_aggregates[category].items():
-            if values:  # Only calculate if we have values for this category
-                results[f"category_{category}"][metric_name] = {
-                    "mean": statistics.mean(values),
-                    "std": statistics.stdev(values) if len(values) > 1 else 0.0,
-                    "median": statistics.median(values),
-                    "min": min(values),
-                    "max": max(values),
-                    "count": len(values),
-                }
-
-    return results
@@ -1,147 +0,0 @@
-ANSWER_PROMPT_GRAPH = """
-    You are an intelligent memory assistant tasked with retrieving accurate information from 
-    conversation memories.
-
-    # CONTEXT:
-    You have access to memories from two speakers in a conversation. These memories contain 
-    timestamped information that may be relevant to answering the question. You also have 
-    access to knowledge graph relations for each user, showing connections between entities, 
-    concepts, and events relevant to that user.
-
-    # INSTRUCTIONS:
-    1. Carefully analyze all provided memories from both speakers
-    2. Pay special attention to the timestamps to determine the answer
-    3. If the question asks about a specific event or fact, look for direct evidence in the 
-       memories
-    4. If the memories contain contradictory information, prioritize the most recent memory
-    5. If there is a question about time references (like "last year", "two months ago", 
-       etc.), calculate the actual date based on the memory timestamp. For example, if a 
-       memory from 4 May 2022 mentions "went to India last year," then the trip occurred 
-       in 2021.
-    6. Always convert relative time references to specific dates, months, or years. For 
-       example, convert "last year" to "2022" or "two months ago" to "March 2023" based 
-       on the memory timestamp. Ignore the reference while answering the question.
-    7. Focus only on the content of the memories from both speakers. Do not confuse 
-       character names mentioned in memories with the actual users who created those 
-       memories.
-    8. The answer should be less than 5-6 words.
-    9. Use the knowledge graph relations to understand the user's knowledge network and 
-       identify important relationships between entities in the user's world.
-
-    # APPROACH (Think step by step):
-    1. First, examine all memories that contain information related to the question
-    2. Examine the timestamps and content of these memories carefully
-    3. Look for explicit mentions of dates, times, locations, or events that answer the 
-       question
-    4. If the answer requires calculation (e.g., converting relative time references), 
-       show your work
-    5. Analyze the knowledge graph relations to understand the user's knowledge context
-    6. Formulate a precise, concise answer based solely on the evidence in the memories
-    7. Double-check that your answer directly addresses the question asked
-    8. Ensure your final answer is specific and avoids vague time references
-
-    Memories for user {{speaker_1_user_id}}:
-
-    {{speaker_1_memories}}
-
-    Relations for user {{speaker_1_user_id}}:
-
-    {{speaker_1_graph_memories}}
-
-    Memories for user {{speaker_2_user_id}}:
-
-    {{speaker_2_memories}}
-
-    Relations for user {{speaker_2_user_id}}:
-
-    {{speaker_2_graph_memories}}
-
-    Question: {{question}}
-
-    Answer:
-    """
-
-
-ANSWER_PROMPT = """
-    You are an intelligent memory assistant tasked with retrieving accurate information from conversation memories.
-
-    # CONTEXT:
-    You have access to memories from two speakers in a conversation. These memories contain 
-    timestamped information that may be relevant to answering the question.
-
-    # INSTRUCTIONS:
-    1. Carefully analyze all provided memories from both speakers
-    2. Pay special attention to the timestamps to determine the answer
-    3. If the question asks about a specific event or fact, look for direct evidence in the memories
-    4. If the memories contain contradictory information, prioritize the most recent memory
-    5. If there is a question about time references (like "last year", "two months ago", etc.), 
-       calculate the actual date based on the memory timestamp. For example, if a memory from 
-       4 May 2022 mentions "went to India last year," then the trip occurred in 2021.
-    6. Always convert relative time references to specific dates, months, or years. For example, 
-       convert "last year" to "2022" or "two months ago" to "March 2023" based on the memory 
-       timestamp. Ignore the reference while answering the question.
-    7. Focus only on the content of the memories from both speakers. Do not confuse character 
-       names mentioned in memories with the actual users who created those memories.
-    8. The answer should be less than 5-6 words.
-
-    # APPROACH (Think step by step):
-    1. First, examine all memories that contain information related to the question
-    2. Examine the timestamps and content of these memories carefully
-    3. Look for explicit mentions of dates, times, locations, or events that answer the question
-    4. If the answer requires calculation (e.g., converting relative time references), show your work
-    5. Formulate a precise, concise answer based solely on the evidence in the memories
-    6. Double-check that your answer directly addresses the question asked
-    7. Ensure your final answer is specific and avoids vague time references
-
-    Memories for user {{speaker_1_user_id}}:
-
-    {{speaker_1_memories}}
-
-    Memories for user {{speaker_2_user_id}}:
-
-    {{speaker_2_memories}}
-
-    Question: {{question}}
-
-    Answer:
-    """
-
-
-ANSWER_PROMPT_ZEP = """
-    You are an intelligent memory assistant tasked with retrieving accurate information from conversation memories.
-
-    # CONTEXT:
-    You have access to memories from a conversation. These memories contain
-    timestamped information that may be relevant to answering the question.
-
-    # INSTRUCTIONS:
-    1. Carefully analyze all provided memories
-    2. Pay special attention to the timestamps to determine the answer
-    3. If the question asks about a specific event or fact, look for direct evidence in the memories
-    4. If the memories contain contradictory information, prioritize the most recent memory
-    5. If there is a question about time references (like "last year", "two months ago", etc.), 
-       calculate the actual date based on the memory timestamp. For example, if a memory from 
-       4 May 2022 mentions "went to India last year," then the trip occurred in 2021.
-    6. Always convert relative time references to specific dates, months, or years. For example, 
-       convert "last year" to "2022" or "two months ago" to "March 2023" based on the memory 
-       timestamp. Ignore the reference while answering the question.
-    7. Focus only on the content of the memories. Do not confuse character 
-       names mentioned in memories with the actual users who created those memories.
-    8. The answer should be less than 5-6 words.
-
-    # APPROACH (Think step by step):
-    1. First, examine all memories that contain information related to the question
-    2. Examine the timestamps and content of these memories carefully
-    3. Look for explicit mentions of dates, times, locations, or events that answer the question
-    4. If the answer requires calculation (e.g., converting relative time references), show your work
-    5. Formulate a precise, concise answer based solely on the evidence in the memories
-    6. Double-check that your answer directly addresses the question asked
-    7. Ensure your final answer is specific and avoids vague time references
-
-    Memories:
-
-    {{memories}}
-
-    Question: {{question}}
-    Answer:
-    """
@@ -1,75 +0,0 @@
-import argparse
-import os
-
-from src.langmem import LangMemManager
-from src.memzero.add import MemoryADD
-from src.memzero.search import MemorySearch
-from src.openai.predict import OpenAIPredict
-from src.rag import RAGManager
-from src.utils import METHODS, TECHNIQUES
-from src.zep.add import ZepAdd
-from src.zep.search import ZepSearch
-
-
-class Experiment:
-    def __init__(self, technique_type, chunk_size):
-        self.technique_type = technique_type
-        self.chunk_size = chunk_size
-
-    def run(self):
-        print(f"Running experiment with technique: {self.technique_type}, chunk size: {self.chunk_size}")
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Run memory experiments")
-    parser.add_argument("--technique_type", choices=TECHNIQUES, default="mem0", help="Memory technique to use")
-    parser.add_argument("--method", choices=METHODS, default="add", help="Method to use")
-    parser.add_argument("--chunk_size", type=int, default=1000, help="Chunk size for processing")
-    parser.add_argument("--output_folder", type=str, default="results/", help="Output path for results")
-    parser.add_argument("--top_k", type=int, default=30, help="Number of top memories to retrieve")
-    parser.add_argument("--filter_memories", action="store_true", default=False, help="Whether to filter memories")
-    parser.add_argument("--is_graph", action="store_true", default=False, help="Whether to use graph-based search")
-    parser.add_argument("--num_chunks", type=int, default=1, help="Number of chunks to process")
-
-    args = parser.parse_args()
-
-    # Add your experiment logic here
-    print(f"Running experiments with technique: {args.technique_type}, chunk size: {args.chunk_size}")
-
-    if args.technique_type == "mem0":
-        if args.method == "add":
-            memory_manager = MemoryADD(data_path="dataset/locomo10.json", is_graph=args.is_graph)
-            memory_manager.process_all_conversations()
-        elif args.method == "search":
-            output_file_path = os.path.join(
-                args.output_folder,
-                f"mem0_results_top_{args.top_k}_filter_{args.filter_memories}_graph_{args.is_graph}.json",
-            )
-            memory_searcher = MemorySearch(output_file_path, args.top_k, args.filter_memories, args.is_graph)
-            memory_searcher.process_data_file("dataset/locomo10.json")
-    elif args.technique_type == "rag":
-        output_file_path = os.path.join(args.output_folder, f"rag_results_{args.chunk_size}_k{args.num_chunks}.json")
-        rag_manager = RAGManager(data_path="dataset/locomo10_rag.json", chunk_size=args.chunk_size, k=args.num_chunks)
-        rag_manager.process_all_conversations(output_file_path)
-    elif args.technique_type == "langmem":
-        output_file_path = os.path.join(args.output_folder, "langmem_results.json")
-        langmem_manager = LangMemManager(dataset_path="dataset/locomo10_rag.json")
-        langmem_manager.process_all_conversations(output_file_path)
-    elif args.technique_type == "zep":
-        if args.method == "add":
-            zep_manager = ZepAdd(data_path="dataset/locomo10.json")
-            zep_manager.process_all_conversations("1")
-        elif args.method == "search":
-            output_file_path = os.path.join(args.output_folder, "zep_search_results.json")
-            zep_manager = ZepSearch()
-            zep_manager.process_data_file("dataset/locomo10.json", "1", output_file_path)
-    elif args.technique_type == "openai":
-        output_file_path = os.path.join(args.output_folder, "openai_results.json")
-        openai_manager = OpenAIPredict()
-        openai_manager.process_data_file("dataset/locomo10.json", output_file_path)
-    else:
-        raise ValueError(f"Invalid technique type: {args.technique_type}")
-
-
-if __name__ == "__main__":
-    main()
@@ -1,185 +0,0 @@
-import json
-import multiprocessing as mp
-import os
-import time
-from collections import defaultdict
-
-from dotenv import load_dotenv
-from jinja2 import Template
-from langgraph.checkpoint.memory import MemorySaver
-from langgraph.prebuilt import create_react_agent
-from langgraph.store.memory import InMemoryStore
-from langgraph.utils.config import get_store
-from langmem import create_manage_memory_tool, create_search_memory_tool
-from openai import OpenAI
-from prompts import ANSWER_PROMPT
-from tqdm import tqdm
-
-load_dotenv()
-
-client = OpenAI()
-
-ANSWER_PROMPT_TEMPLATE = Template(ANSWER_PROMPT)
-
-
-def get_answer(question, speaker_1_user_id, speaker_1_memories, speaker_2_user_id, speaker_2_memories):
-    prompt = ANSWER_PROMPT_TEMPLATE.render(
-        question=question,
-        speaker_1_user_id=speaker_1_user_id,
-        speaker_1_memories=speaker_1_memories,
-        speaker_2_user_id=speaker_2_user_id,
-        speaker_2_memories=speaker_2_memories,
-    )
-
-    t1 = time.time()
-    response = client.chat.completions.create(
-        model=os.getenv("MODEL"), messages=[{"role": "system", "content": prompt}], temperature=0.0
-    )
-    t2 = time.time()
-    return response.choices[0].message.content, t2 - t1
-
-
-def prompt(state):
-    """Prepare the messages for the LLM."""
-    store = get_store()
-    memories = store.search(
-        ("memories",),
-        query=state["messages"][-1].content,
-    )
-    system_msg = f"""You are a helpful assistant.
-
-## Memories
-<memories>
-{memories}
-</memories>
-"""
-    return [{"role": "system", "content": system_msg}, *state["messages"]]
-
-
-class LangMem:
-    def __init__(
-        self,
-    ):
-        self.store = InMemoryStore(
-            index={
-                "dims": 1536,
-                "embed": f"openai:{os.getenv('EMBEDDING_MODEL')}",
-            }
-        )
-        self.checkpointer = MemorySaver()  # Checkpoint graph state
-
-        self.agent = create_react_agent(
-            f"openai:{os.getenv('MODEL')}",
-            prompt=prompt,
-            tools=[
-                create_manage_memory_tool(namespace=("memories",)),
-                create_search_memory_tool(namespace=("memories",)),
-            ],
-            store=self.store,
-            checkpointer=self.checkpointer,
-        )
-
-    def add_memory(self, message, config):
-        return self.agent.invoke({"messages": [{"role": "user", "content": message}]}, config=config)
-
-    def search_memory(self, query, config):
-        try:
-            t1 = time.time()
-            response = self.agent.invoke({"messages": [{"role": "user", "content": query}]}, config=config)
-            t2 = time.time()
-            return response["messages"][-1].content, t2 - t1
-        except Exception as e:
-            print(f"Error in search_memory: {e}")
-            return "", t2 - t1
-
-
-class LangMemManager:
-    def __init__(self, dataset_path):
-        self.dataset_path = dataset_path
-        with open(self.dataset_path, "r") as f:
-            self.data = json.load(f)
-
-    def process_all_conversations(self, output_file_path):
-        OUTPUT = defaultdict(list)
-
-        # Process conversations in parallel with multiple workers
-        def process_conversation(key_value_pair):
-            key, value = key_value_pair
-            result = defaultdict(list)
-
-            chat_history = value["conversation"]
-            questions = value["question"]
-
-            agent1 = LangMem()
-            agent2 = LangMem()
-            config = {"configurable": {"thread_id": f"thread-{key}"}}
-            speakers = set()
-
-            # Identify speakers
-            for conv in chat_history:
-                speakers.add(conv["speaker"])
-
-            if len(speakers) != 2:
-                raise ValueError(f"Expected 2 speakers, got {len(speakers)}")
-
-            speaker1 = list(speakers)[0]
-            speaker2 = list(speakers)[1]
-
-            # Add memories for each message
-            for conv in tqdm(chat_history, desc=f"Processing messages {key}", leave=False):
-                message = f"{conv['timestamp']} | {conv['speaker']}: {conv['text']}"
-                if conv["speaker"] == speaker1:
-                    agent1.add_memory(message, config)
-                elif conv["speaker"] == speaker2:
-                    agent2.add_memory(message, config)
-                else:
-                    raise ValueError(f"Expected speaker1 or speaker2, got {conv['speaker']}")
-
-            # Process questions
-            for q in tqdm(questions, desc=f"Processing questions {key}", leave=False):
-                category = q["category"]
-
-                if int(category) == 5:
-                    continue
-
-                answer = q["answer"]
-                question = q["question"]
-                response1, speaker1_memory_time = agent1.search_memory(question, config)
-                response2, speaker2_memory_time = agent2.search_memory(question, config)
-
-                generated_answer, response_time = get_answer(question, speaker1, response1, speaker2, response2)
-
-                result[key].append(
-                    {
-                        "question": question,
-                        "answer": answer,
-                        "response1": response1,
-                        "response2": response2,
-                        "category": category,
-                        "speaker1_memory_time": speaker1_memory_time,
-                        "speaker2_memory_time": speaker2_memory_time,
-                        "response_time": response_time,
-                        "response": generated_answer,
-                    }
-                )
-
-            return result
-
-        # Use multiprocessing to process conversations in parallel
-        with mp.Pool(processes=10) as pool:
-            results = list(
-                tqdm(
-                    pool.imap(process_conversation, list(self.data.items())),
-                    total=len(self.data),
-                    desc="Processing conversations",
-                )
-            )
-
-        # Combine results from all workers
-        for result in results:
-            for key, items in result.items():
-                OUTPUT[key].extend(items)
-
-        # Save final results
-        with open(output_file_path, "w") as f:
-            json.dump(OUTPUT, f, indent=4)
@@ -1,141 +0,0 @@
-import json
-import os
-import threading
-import time
-from concurrent.futures import ThreadPoolExecutor
-
-from dotenv import load_dotenv
-from tqdm import tqdm
-
-from mem0 import MemoryClient
-
-load_dotenv()
-
-
-# Update custom instructions
-custom_instructions = """
-Generate personal memories that follow these guidelines:
-
-1. Each memory should be self-contained with complete context, including:
-   - The person's name, do not use "user" while creating memories
-   - Personal details (career aspirations, hobbies, life circumstances)
-   - Emotional states and reactions
-   - Ongoing journeys or future plans
-   - Specific dates when events occurred
-
-2. Include meaningful personal narratives focusing on:
-   - Identity and self-acceptance journeys
-   - Family planning and parenting
-   - Creative outlets and hobbies
-   - Mental health and self-care activities
-   - Career aspirations and education goals
-   - Important life events and milestones
-
-3. Make each memory rich with specific details rather than general statements
-   - Include timeframes (exact dates when possible)
-   - Name specific activities (e.g., "charity race for mental health" rather than just "exercise")
-   - Include emotional context and personal growth elements
-
-4. Extract memories only from user messages, not incorporating assistant responses
-
-5. Format each memory as a paragraph with a clear narrative structure that captures the person's experience, challenges, and aspirations
-"""
-
-
-class MemoryADD:
-    def __init__(self, data_path=None, batch_size=2, is_graph=False):
-        self.mem0_client = MemoryClient(
-            api_key=os.getenv("MEM0_API_KEY"),
-            org_id=os.getenv("MEM0_ORGANIZATION_ID"),
-            project_id=os.getenv("MEM0_PROJECT_ID"),
-        )
-
-        self.mem0_client.update_project(custom_instructions=custom_instructions)
-        self.batch_size = batch_size
-        self.data_path = data_path
-        self.data = None
-        self.is_graph = is_graph
-        if data_path:
-            self.load_data()
-
-    def load_data(self):
-        with open(self.data_path, "r") as f:
-            self.data = json.load(f)
-        return self.data
-
-    def add_memory(self, user_id, message, metadata, retries=3):
-        for attempt in range(retries):
-            try:
-                _ = self.mem0_client.add(
-                    message, user_id=user_id, version="v2", metadata=metadata, enable_graph=self.is_graph
-                )
-                return
-            except Exception as e:
-                if attempt < retries - 1:
-                    time.sleep(1)  # Wait before retrying
-                    continue
-                else:
-                    raise e
-
-    def add_memories_for_speaker(self, speaker, messages, timestamp, desc):
-        for i in tqdm(range(0, len(messages), self.batch_size), desc=desc):
-            batch_messages = messages[i : i + self.batch_size]
-            self.add_memory(speaker, batch_messages, metadata={"timestamp": timestamp})
-
-    def process_conversation(self, item, idx):
-        conversation = item["conversation"]
-        speaker_a = conversation["speaker_a"]
-        speaker_b = conversation["speaker_b"]
-
-        speaker_a_user_id = f"{speaker_a}_{idx}"
-        speaker_b_user_id = f"{speaker_b}_{idx}"
-
-        # delete all memories for the two users
-        self.mem0_client.delete_all(user_id=speaker_a_user_id)
-        self.mem0_client.delete_all(user_id=speaker_b_user_id)
-
-        for key in conversation.keys():
-            if key in ["speaker_a", "speaker_b"] or "date" in key or "timestamp" in key:
-                continue
-
-            date_time_key = key + "_date_time"
-            timestamp = conversation[date_time_key]
-            chats = conversation[key]
-
-            messages = []
-            messages_reverse = []
-            for chat in chats:
-                if chat["speaker"] == speaker_a:
-                    messages.append({"role": "user", "content": f"{speaker_a}: {chat['text']}"})
-                    messages_reverse.append({"role": "assistant", "content": f"{speaker_a}: {chat['text']}"})
-                elif chat["speaker"] == speaker_b:
-                    messages.append({"role": "assistant", "content": f"{speaker_b}: {chat['text']}"})
-                    messages_reverse.append({"role": "user", "content": f"{speaker_b}: {chat['text']}"})
-                else:
-                    raise ValueError(f"Unknown speaker: {chat['speaker']}")
-
-            # add memories for the two users on different threads
-            thread_a = threading.Thread(
-                target=self.add_memories_for_speaker,
-                args=(speaker_a_user_id, messages, timestamp, "Adding Memories for Speaker A"),
-            )
-            thread_b = threading.Thread(
-                target=self.add_memories_for_speaker,
-                args=(speaker_b_user_id, messages_reverse, timestamp, "Adding Memories for Speaker B"),
-            )
-
-            thread_a.start()
-            thread_b.start()
-            thread_a.join()
-            thread_b.join()
-
-        print("Messages added successfully")
-
-    def process_all_conversations(self, max_workers=10):
-        if not self.data:
-            raise ValueError("No data loaded. Please set data_path and call load_data() first.")
-        with ThreadPoolExecutor(max_workers=max_workers) as executor:
-            futures = [executor.submit(self.process_conversation, item, idx) for idx, item in enumerate(self.data)]
-
-            for future in futures:
-                future.result()
@@ -1,215 +0,0 @@
-import json
-import os
-import time
-from collections import defaultdict
-from concurrent.futures import ThreadPoolExecutor
-
-from dotenv import load_dotenv
-from jinja2 import Template
-from openai import OpenAI
-from prompts import ANSWER_PROMPT, ANSWER_PROMPT_GRAPH
-from tqdm import tqdm
-
-from mem0 import MemoryClient
-
-load_dotenv()
-
-
-class MemorySearch:
-    def __init__(self, output_path="results.json", top_k=10, filter_memories=False, is_graph=False):
-        self.mem0_client = MemoryClient(
-            api_key=os.getenv("MEM0_API_KEY"),
-            org_id=os.getenv("MEM0_ORGANIZATION_ID"),
-            project_id=os.getenv("MEM0_PROJECT_ID"),
-        )
-        self.top_k = top_k
-        self.openai_client = OpenAI()
-        self.results = defaultdict(list)
-        self.output_path = output_path
-        self.filter_memories = filter_memories
-        self.is_graph = is_graph
-
-        if self.is_graph:
-            self.ANSWER_PROMPT = ANSWER_PROMPT_GRAPH
-        else:
-            self.ANSWER_PROMPT = ANSWER_PROMPT
-
-    def search_memory(self, user_id, query, max_retries=3, retry_delay=1):
-        start_time = time.time()
-        retries = 0
-        while retries < max_retries:
-            try:
-                if self.is_graph:
-                    print("Searching with graph")
-                    memories = self.mem0_client.search(
-                        query,
-                        user_id=user_id,
-                        top_k=self.top_k,
-                        filter_memories=self.filter_memories,
-                        enable_graph=True,
-                        output_format="v1.1",
-                    )
-                else:
-                    memories = self.mem0_client.search(
-                        query, user_id=user_id, top_k=self.top_k, filter_memories=self.filter_memories
-                    )
-                break
-            except Exception as e:
-                print("Retrying...")
-                retries += 1
-                if retries >= max_retries:
-                    raise e
-                time.sleep(retry_delay)
-
-        end_time = time.time()
-        if not self.is_graph:
-            semantic_memories = [
-                {
-                    "memory": memory["memory"],
-                    "timestamp": memory["metadata"]["timestamp"],
-                    "score": round(memory["score"], 2),
-                }
-                for memory in memories
-            ]
-            graph_memories = None
-        else:
-            semantic_memories = [
-                {
-                    "memory": memory["memory"],
-                    "timestamp": memory["metadata"]["timestamp"],
-                    "score": round(memory["score"], 2),
-                }
-                for memory in memories["results"]
-            ]
-            graph_memories = [
-                {"source": relation["source"], "relationship": relation["relationship"], "target": relation["target"]}
-                for relation in memories["relations"]
-            ]
-        return semantic_memories, graph_memories, end_time - start_time
-
-    def answer_question(self, speaker_1_user_id, speaker_2_user_id, question, answer, category):
-        speaker_1_memories, speaker_1_graph_memories, speaker_1_memory_time = self.search_memory(
-            speaker_1_user_id, question
-        )
-        speaker_2_memories, speaker_2_graph_memories, speaker_2_memory_time = self.search_memory(
-            speaker_2_user_id, question
-        )
-
-        search_1_memory = [f"{item['timestamp']}: {item['memory']}" for item in speaker_1_memories]
-        search_2_memory = [f"{item['timestamp']}: {item['memory']}" for item in speaker_2_memories]
-
-        template = Template(self.ANSWER_PROMPT)
-        answer_prompt = template.render(
-            speaker_1_user_id=speaker_1_user_id.split("_")[0],
-            speaker_2_user_id=speaker_2_user_id.split("_")[0],
-            speaker_1_memories=json.dumps(search_1_memory, indent=4),
-            speaker_2_memories=json.dumps(search_2_memory, indent=4),
-            speaker_1_graph_memories=json.dumps(speaker_1_graph_memories, indent=4),
-            speaker_2_graph_memories=json.dumps(speaker_2_graph_memories, indent=4),
-            question=question,
-        )
-
-        t1 = time.time()
-        response = self.openai_client.chat.completions.create(
-            model=os.getenv("MODEL"), messages=[{"role": "system", "content": answer_prompt}], temperature=0.0
-        )
-        t2 = time.time()
-        response_time = t2 - t1
-        return (
-            response.choices[0].message.content,
-            speaker_1_memories,
-            speaker_2_memories,
-            speaker_1_memory_time,
-            speaker_2_memory_time,
-            speaker_1_graph_memories,
-            speaker_2_graph_memories,
-            response_time,
-        )
-
-    def process_question(self, val, speaker_a_user_id, speaker_b_user_id):
-        question = val.get("question", "")
-        answer = val.get("answer", "")
-        category = val.get("category", -1)
-        evidence = val.get("evidence", [])
-        adversarial_answer = val.get("adversarial_answer", "")
-
-        (
-            response,
-            speaker_1_memories,
-            speaker_2_memories,
-            speaker_1_memory_time,
-            speaker_2_memory_time,
-            speaker_1_graph_memories,
-            speaker_2_graph_memories,
-            response_time,
-        ) = self.answer_question(speaker_a_user_id, speaker_b_user_id, question, answer, category)
-
-        result = {
-            "question": question,
-            "answer": answer,
-            "category": category,
-            "evidence": evidence,
-            "response": response,
-            "adversarial_answer": adversarial_answer,
-            "speaker_1_memories": speaker_1_memories,
-            "speaker_2_memories": speaker_2_memories,
-            "num_speaker_1_memories": len(speaker_1_memories),
-            "num_speaker_2_memories": len(speaker_2_memories),
-            "speaker_1_memory_time": speaker_1_memory_time,
-            "speaker_2_memory_time": speaker_2_memory_time,
-            "speaker_1_graph_memories": speaker_1_graph_memories,
-            "speaker_2_graph_memories": speaker_2_graph_memories,
-            "response_time": response_time,
-        }
-
-        # Save results after each question is processed
-        with open(self.output_path, "w") as f:
-            json.dump(self.results, f, indent=4)
-
-        return result
-
-    def process_data_file(self, file_path):
-        with open(file_path, "r") as f:
-            data = json.load(f)
-
-        for idx, item in tqdm(enumerate(data), total=len(data), desc="Processing conversations"):
-            qa = item["qa"]
-            conversation = item["conversation"]
-            speaker_a = conversation["speaker_a"]
-            speaker_b = conversation["speaker_b"]
-
-            speaker_a_user_id = f"{speaker_a}_{idx}"
-            speaker_b_user_id = f"{speaker_b}_{idx}"
-
-            for question_item in tqdm(
-                qa, total=len(qa), desc=f"Processing questions for conversation {idx}", leave=False
-            ):
-                result = self.process_question(question_item, speaker_a_user_id, speaker_b_user_id)
-                self.results[idx].append(result)
-
-                # Save results after each question is processed
-                with open(self.output_path, "w") as f:
-                    json.dump(self.results, f, indent=4)
-
-        # Final save at the end
-        with open(self.output_path, "w") as f:
-            json.dump(self.results, f, indent=4)
-
-    def process_questions_parallel(self, qa_list, speaker_a_user_id, speaker_b_user_id, max_workers=1):
-        def process_single_question(val):
-            result = self.process_question(val, speaker_a_user_id, speaker_b_user_id)
-            # Save results after each question is processed
-            with open(self.output_path, "w") as f:
-                json.dump(self.results, f, indent=4)
-            return result
-
-        with ThreadPoolExecutor(max_workers=max_workers) as executor:
-            results = list(
-                tqdm(executor.map(process_single_question, qa_list), total=len(qa_list), desc="Answering Questions")
-            )
-
-        # Final save at the end
-        with open(self.output_path, "w") as f:
-            json.dump(self.results, f, indent=4)
-
-        return results
@@ -1,131 +0,0 @@
-import argparse
-import json
-import os
-import time
-from collections import defaultdict
-
-from dotenv import load_dotenv
-from jinja2 import Template
-from openai import OpenAI
-from tqdm import tqdm
-
-load_dotenv()
-
-
-ANSWER_PROMPT = """
-    You are an intelligent memory assistant tasked with retrieving accurate information from conversation memories.
-
-    # CONTEXT:
-    You have access to memories from a conversation. These memories contain
-    timestamped information that may be relevant to answering the question.
-
-    # INSTRUCTIONS:
-    1. Carefully analyze all provided memories
-    2. Pay special attention to the timestamps to determine the answer
-    3. If the question asks about a specific event or fact, look for direct evidence in the memories
-    4. If the memories contain contradictory information, prioritize the most recent memory
-    5. If there is a question about time references (like "last year", "two months ago", etc.), 
-       calculate the actual date based on the memory timestamp. For example, if a memory from 
-       4 May 2022 mentions "went to India last year," then the trip occurred in 2021.
-    6. Always convert relative time references to specific dates, months, or years. For example, 
-       convert "last year" to "2022" or "two months ago" to "March 2023" based on the memory 
-       timestamp. Ignore the reference while answering the question.
-    7. Focus only on the content of the memories. Do not confuse character 
-       names mentioned in memories with the actual users who created those memories.
-    8. The answer should be less than 5-6 words.
-
-    # APPROACH (Think step by step):
-    1. First, examine all memories that contain information related to the question
-    2. Examine the timestamps and content of these memories carefully
-    3. Look for explicit mentions of dates, times, locations, or events that answer the question
-    4. If the answer requires calculation (e.g., converting relative time references), show your work
-    5. Formulate a precise, concise answer based solely on the evidence in the memories
-    6. Double-check that your answer directly addresses the question asked
-    7. Ensure your final answer is specific and avoids vague time references
-
-    Memories:
-
-    {{memories}}
-
-    Question: {{question}}
-    Answer:
-    """
-
-
-class OpenAIPredict:
-    def __init__(self, model="gpt-4o-mini"):
-        self.model = model
-        self.openai_client = OpenAI()
-        self.results = defaultdict(list)
-
-    def search_memory(self, idx):
-        with open(f"memories/{idx}.txt", "r") as file:
-            memories = file.read()
-
-        return memories, 0
-
-    def process_question(self, val, idx):
-        question = val.get("question", "")
-        answer = val.get("answer", "")
-        category = val.get("category", -1)
-        evidence = val.get("evidence", [])
-        adversarial_answer = val.get("adversarial_answer", "")
-
-        response, search_memory_time, response_time, context = self.answer_question(idx, question)
-
-        result = {
-            "question": question,
-            "answer": answer,
-            "category": category,
-            "evidence": evidence,
-            "response": response,
-            "adversarial_answer": adversarial_answer,
-            "search_memory_time": search_memory_time,
-            "response_time": response_time,
-            "context": context,
-        }
-
-        return result
-
-    def answer_question(self, idx, question):
-        memories, search_memory_time = self.search_memory(idx)
-
-        template = Template(ANSWER_PROMPT)
-        answer_prompt = template.render(memories=memories, question=question)
-
-        t1 = time.time()
-        response = self.openai_client.chat.completions.create(
-            model=os.getenv("MODEL"), messages=[{"role": "system", "content": answer_prompt}], temperature=0.0
-        )
-        t2 = time.time()
-        response_time = t2 - t1
-        return response.choices[0].message.content, search_memory_time, response_time, memories
-
-    def process_data_file(self, file_path, output_file_path):
-        with open(file_path, "r") as f:
-            data = json.load(f)
-
-        for idx, item in tqdm(enumerate(data), total=len(data), desc="Processing conversations"):
-            qa = item["qa"]
-
-            for question_item in tqdm(
-                qa, total=len(qa), desc=f"Processing questions for conversation {idx}", leave=False
-            ):
-                result = self.process_question(question_item, idx)
-                self.results[idx].append(result)
-
-                # Save results after each question is processed
-                with open(output_file_path, "w") as f:
-                    json.dump(self.results, f, indent=4)
-
-        # Final save at the end
-        with open(output_file_path, "w") as f:
-            json.dump(self.results, f, indent=4)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--output_file_path", type=str, required=True)
-    args = parser.parse_args()
-    openai_predict = OpenAIPredict()
-    openai_predict.process_data_file("../../dataset/locomo10.json", args.output_file_path)
@@ -1,183 +0,0 @@
-import json
-import os
-import time
-from collections import defaultdict
-
-import numpy as np
-import tiktoken
-from dotenv import load_dotenv
-from jinja2 import Template
-from openai import OpenAI
-from tqdm import tqdm
-
-load_dotenv()
-
-PROMPT = """
-# Question: 
-{{QUESTION}}
-
-# Context: 
-{{CONTEXT}}
-
-# Short answer:
-"""
-
-
-class RAGManager:
-    def __init__(self, data_path="dataset/locomo10_rag.json", chunk_size=500, k=1):
-        self.model = os.getenv("MODEL")
-        self.client = OpenAI()
-        self.data_path = data_path
-        self.chunk_size = chunk_size
-        self.k = k
-
-    def generate_response(self, question, context):
-        template = Template(PROMPT)
-        prompt = template.render(CONTEXT=context, QUESTION=question)
-
-        max_retries = 3
-        retries = 0
-
-        while retries <= max_retries:
-            try:
-                t1 = time.time()
-                response = self.client.chat.completions.create(
-                    model=self.model,
-                    messages=[
-                        {
-                            "role": "system",
-                            "content": "You are a helpful assistant that can answer "
-                            "questions based on the provided context."
-                            "If the question involves timing, use the conversation date for reference."
-                            "Provide the shortest possible answer."
-                            "Use words directly from the conversation when possible."
-                            "Avoid using subjects in your answer.",
-                        },
-                        {"role": "user", "content": prompt},
-                    ],
-                    temperature=0,
-                )
-                t2 = time.time()
-                return response.choices[0].message.content.strip(), t2 - t1
-            except Exception as e:
-                retries += 1
-                if retries > max_retries:
-                    raise e
-                time.sleep(1)  # Wait before retrying
-
-    def clean_chat_history(self, chat_history):
-        cleaned_chat_history = ""
-        for c in chat_history:
-            cleaned_chat_history += f"{c['timestamp']} | {c['speaker']}: {c['text']}\n"
-
-        return cleaned_chat_history
-
-    def calculate_embedding(self, document):
-        response = self.client.embeddings.create(model=os.getenv("EMBEDDING_MODEL"), input=document)
-        return response.data[0].embedding
-
-    def calculate_similarity(self, embedding1, embedding2):
-        return np.dot(embedding1, embedding2) / (np.linalg.norm(embedding1) * np.linalg.norm(embedding2))
-
-    def search(self, query, chunks, embeddings, k=1):
-        """
-        Search for the top-k most similar chunks to the query.
-
-        Args:
-            query: The query string
-            chunks: List of text chunks
-            embeddings: List of embeddings for each chunk
-            k: Number of top chunks to return (default: 1)
-
-        Returns:
-            combined_chunks: The combined text of the top-k chunks
-            search_time: Time taken for the search
-        """
-        t1 = time.time()
-        query_embedding = self.calculate_embedding(query)
-        similarities = [self.calculate_similarity(query_embedding, embedding) for embedding in embeddings]
-
-        # Get indices of top-k most similar chunks
-        if k == 1:
-            # Original behavior - just get the most similar chunk
-            top_indices = [np.argmax(similarities)]
-        else:
-            # Get indices of top-k chunks
-            top_indices = np.argsort(similarities)[-k:][::-1]
-
-        # Combine the top-k chunks
-        combined_chunks = "\n<->\n".join([chunks[i] for i in top_indices])
-
-        t2 = time.time()
-        return combined_chunks, t2 - t1
-
-    def create_chunks(self, chat_history, chunk_size=500):
-        """
-        Create chunks using tiktoken for more accurate token counting
-        """
-        # Get the encoding for the model
-        encoding = tiktoken.encoding_for_model(os.getenv("EMBEDDING_MODEL"))
-
-        documents = self.clean_chat_history(chat_history)
-
-        if chunk_size == -1:
-            return [documents], []
-
-        chunks = []
-
-        # Encode the document
-        tokens = encoding.encode(documents)
-
-        # Split into chunks based on token count
-        for i in range(0, len(tokens), chunk_size):
-            chunk_tokens = tokens[i : i + chunk_size]
-            chunk = encoding.decode(chunk_tokens)
-            chunks.append(chunk)
-
-        embeddings = []
-        for chunk in chunks:
-            embedding = self.calculate_embedding(chunk)
-            embeddings.append(embedding)
-
-        return chunks, embeddings
-
-    def process_all_conversations(self, output_file_path):
-        with open(self.data_path, "r") as f:
-            data = json.load(f)
-
-        FINAL_RESULTS = defaultdict(list)
-        for key, value in tqdm(data.items(), desc="Processing conversations"):
-            chat_history = value["conversation"]
-            questions = value["question"]
-
-            chunks, embeddings = self.create_chunks(chat_history, self.chunk_size)
-
-            for item in tqdm(questions, desc="Answering questions", leave=False):
-                question = item["question"]
-                answer = item.get("answer", "")
-                category = item["category"]
-
-                if self.chunk_size == -1:
-                    context = chunks[0]
-                    search_time = 0
-                else:
-                    context, search_time = self.search(question, chunks, embeddings, k=self.k)
-                response, response_time = self.generate_response(question, context)
-
-                FINAL_RESULTS[key].append(
-                    {
-                        "question": question,
-                        "answer": answer,
-                        "category": category,
-                        "context": context,
-                        "response": response,
-                        "search_time": search_time,
-                        "response_time": response_time,
-                    }
-                )
-                with open(output_file_path, "w+") as f:
-                    json.dump(FINAL_RESULTS, f, indent=4)
-
-        # Save results
-        with open(output_file_path, "w+") as f:
-            json.dump(FINAL_RESULTS, f, indent=4)
@@ -1,3 +0,0 @@
-TECHNIQUES = ["mem0", "rag", "langmem", "zep", "openai"]
-
-METHODS = ["add", "search"]
@@ -1,76 +0,0 @@
-import argparse
-import json
-import os
-
-from dotenv import load_dotenv
-from tqdm import tqdm
-from zep_cloud import Message
-from zep_cloud.client import Zep
-
-load_dotenv()
-
-
-class ZepAdd:
-    def __init__(self, data_path=None):
-        self.zep_client = Zep(api_key=os.getenv("ZEP_API_KEY"))
-        self.data_path = data_path
-        self.data = None
-        if data_path:
-            self.load_data()
-
-    def load_data(self):
-        with open(self.data_path, "r") as f:
-            self.data = json.load(f)
-        return self.data
-
-    def process_conversation(self, run_id, item, idx):
-        conversation = item["conversation"]
-
-        user_id = f"run_id_{run_id}_experiment_user_{idx}"
-        session_id = f"run_id_{run_id}_experiment_session_{idx}"
-
-        # # delete all memories for the two users
-        # self.zep_client.user.delete(user_id=user_id)
-        # self.zep_client.memory.delete(session_id=session_id)
-
-        self.zep_client.user.add(user_id=user_id)
-        self.zep_client.memory.add_session(
-            user_id=user_id,
-            session_id=session_id,
-        )
-
-        print("Starting to add memories... for user", user_id)
-        for key in tqdm(conversation.keys(), desc=f"Processing user {user_id}"):
-            if key in ["speaker_a", "speaker_b"] or "date" in key:
-                continue
-
-            date_time_key = key + "_date_time"
-            timestamp = conversation[date_time_key]
-            chats = conversation[key]
-
-            for chat in tqdm(chats, desc=f"Adding chats for {key}", leave=False):
-                self.zep_client.memory.add(
-                    session_id=session_id,
-                    messages=[
-                        Message(
-                            role=chat["speaker"],
-                            role_type="user",
-                            content=f"{timestamp}: {chat['text']}",
-                        )
-                    ],
-                )
-
-    def process_all_conversations(self, run_id):
-        if not self.data:
-            raise ValueError("No data loaded. Please set data_path and call load_data() first.")
-        for idx, item in tqdm(enumerate(self.data)):
-            if idx == 0:
-                self.process_conversation(run_id, item, idx)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--run_id", type=str, required=True)
-    args = parser.parse_args()
-    zep_add = ZepAdd(data_path="../../dataset/locomo10.json")
-    zep_add.process_all_conversations(args.run_id)
@@ -1,140 +0,0 @@
-import argparse
-import json
-import os
-import time
-from collections import defaultdict
-
-from dotenv import load_dotenv
-from jinja2 import Template
-from openai import OpenAI
-from prompts import ANSWER_PROMPT_ZEP
-from tqdm import tqdm
-from zep_cloud import EntityEdge, EntityNode
-from zep_cloud.client import Zep
-
-load_dotenv()
-
-TEMPLATE = """
-FACTS and ENTITIES represent relevant context to the current conversation.
-
-# These are the most relevant facts and their valid date ranges
-# format: FACT (Date range: from - to)
-
-{facts}
-
-
-# These are the most relevant entities
-# ENTITY_NAME: entity summary
-
-{entities}
-
-"""
-
-
-class ZepSearch:
-    def __init__(self):
-        self.zep_client = Zep(api_key=os.getenv("ZEP_API_KEY"))
-        self.results = defaultdict(list)
-        self.openai_client = OpenAI()
-
-    def format_edge_date_range(self, edge: EntityEdge) -> str:
-        # return f"{datetime(edge.valid_at).strftime('%Y-%m-%d %H:%M:%S') if edge.valid_at else 'date unknown'} - {(edge.invalid_at.strftime('%Y-%m-%d %H:%M:%S') if edge.invalid_at else 'present')}"
-        return f"{edge.valid_at if edge.valid_at else 'date unknown'} - {(edge.invalid_at if edge.invalid_at else 'present')}"
-
-    def compose_search_context(self, edges: list[EntityEdge], nodes: list[EntityNode]) -> str:
-        facts = [f"  - {edge.fact} ({self.format_edge_date_range(edge)})" for edge in edges]
-        entities = [f"  - {node.name}: {node.summary}" for node in nodes]
-        return TEMPLATE.format(facts="\n".join(facts), entities="\n".join(entities))
-
-    def search_memory(self, run_id, idx, query, max_retries=3, retry_delay=1):
-        start_time = time.time()
-        retries = 0
-        while retries < max_retries:
-            try:
-                user_id = f"run_id_{run_id}_experiment_user_{idx}"
-                edges_results = (
-                    self.zep_client.graph.search(
-                        user_id=user_id, reranker="cross_encoder", query=query, scope="edges", limit=20
-                    )
-                ).edges
-                node_results = (
-                    self.zep_client.graph.search(user_id=user_id, reranker="rrf", query=query, scope="nodes", limit=20)
-                ).nodes
-                context = self.compose_search_context(edges_results, node_results)
-                break
-            except Exception as e:
-                print("Retrying...")
-                retries += 1
-                if retries >= max_retries:
-                    raise e
-                time.sleep(retry_delay)
-
-        end_time = time.time()
-
-        return context, end_time - start_time
-
-    def process_question(self, run_id, val, idx):
-        question = val.get("question", "")
-        answer = val.get("answer", "")
-        category = val.get("category", -1)
-        evidence = val.get("evidence", [])
-        adversarial_answer = val.get("adversarial_answer", "")
-
-        response, search_memory_time, response_time, context = self.answer_question(run_id, idx, question)
-
-        result = {
-            "question": question,
-            "answer": answer,
-            "category": category,
-            "evidence": evidence,
-            "response": response,
-            "adversarial_answer": adversarial_answer,
-            "search_memory_time": search_memory_time,
-            "response_time": response_time,
-            "context": context,
-        }
-
-        return result
-
-    def answer_question(self, run_id, idx, question):
-        context, search_memory_time = self.search_memory(run_id, idx, question)
-
-        template = Template(ANSWER_PROMPT_ZEP)
-        answer_prompt = template.render(memories=context, question=question)
-
-        t1 = time.time()
-        response = self.openai_client.chat.completions.create(
-            model=os.getenv("MODEL"), messages=[{"role": "system", "content": answer_prompt}], temperature=0.0
-        )
-        t2 = time.time()
-        response_time = t2 - t1
-        return response.choices[0].message.content, search_memory_time, response_time, context
-
-    def process_data_file(self, file_path, run_id, output_file_path):
-        with open(file_path, "r") as f:
-            data = json.load(f)
-
-        for idx, item in tqdm(enumerate(data), total=len(data), desc="Processing conversations"):
-            qa = item["qa"]
-
-            for question_item in tqdm(
-                qa, total=len(qa), desc=f"Processing questions for conversation {idx}", leave=False
-            ):
-                result = self.process_question(run_id, question_item, idx)
-                self.results[idx].append(result)
-
-                # Save results after each question is processed
-                with open(output_file_path, "w") as f:
-                    json.dump(self.results, f, indent=4)
-
-        # Final save at the end
-        with open(output_file_path, "w") as f:
-            json.dump(self.results, f, indent=4)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--run_id", type=str, required=True)
-    args = parser.parse_args()
-    zep_search = ZepSearch()
-    zep_search.process_data_file("../../dataset/locomo10.json", args.run_id, "results/zep_search_results.json")
@@ -42,7 +42,7 @@ import {
  isSubagentSession,
 } from "./isolation.ts";
 import {
-  loadTriagePrompt,
+  loadCompactTriagePrompt,
  loadDreamPrompt,
  isSkillsMode,
 } from "./skill-loader.ts";
@@ -464,7 +464,7 @@ function registerHooks(
          "openclaw-mem0: skills-mode skipping recall for system/bootstrap prompt",
        );
        // Still inject the protocol, just skip recall search
-        const systemContext = loadTriagePrompt(cfg.skills ?? {});
+        const systemContext = loadCompactTriagePrompt(cfg.skills ?? {});
        return { prependSystemContext: systemContext };
      }

@@ -474,7 +474,7 @@ function registerHooks(
      const userId = _effectiveUserId(isSubagent ? undefined : sessionId);

      // Static protocol goes in prependSystemContext (cacheable across turns)
-      let systemContext = loadTriagePrompt(cfg.skills ?? {});
+      let systemContext = loadCompactTriagePrompt(cfg.skills ?? {});
      if (isSubagent) {
        systemContext =
          "You are a subagent — use these memories for context but do not assume you are this user. Do NOT store new memories.\n\n" +
@@ -69,7 +69,8 @@
      "langsmith@<0.6.0": "^0.6.0",
      "picomatch@<2.3.2": "^2.3.2",
      "@qdrant/js-client-rest": "^1.18.0",
-      "uuid@<11.1.1": ">=11.1.1"
+      "uuid@<11.1.1": ">=11.1.1",
+      "esbuild": ">=0.28.1"
    }
  }
 }
@@ -11,6 +11,7 @@ overrides:
  picomatch@<2.3.2: ^2.3.2
  '@qdrant/js-client-rest': ^1.18.0
  uuid@<11.1.1: '>=11.1.1'
+  esbuild: '>=0.28.1'

 importers:

@@ -40,10 +41,10 @@ importers:
        version: 5.9.3
      vite:
        specifier: ^8.0.5
-        version: 8.0.16(@types/node@22.19.20)(esbuild@0.27.7)
+        version: 8.0.16(@types/node@22.19.20)(esbuild@0.28.1)
      vitest:
        specifier: ^4.1.7
-        version: 4.1.8(@types/node@22.19.20)(@vitest/coverage-v8@4.1.8)(vite@8.0.16(@types/node@22.19.20)(esbuild@0.27.7))
+        version: 4.1.8(@types/node@22.19.20)(@vitest/coverage-v8@4.1.8)(vite@8.0.16(@types/node@22.19.20)(esbuild@0.28.1))

 packages:

@@ -149,158 +150,158 @@ packages:
  '@emnapi/wasi-threads@1.2.1':
    resolution: {integrity: sha512-uTII7OYF+/Mes/MrcIOYp5yOtSMLBWSIoLPpcgwipoiKbli6k322tcoFsxoIIxPDqW01SQGAgko4EzZi2BNv2w==}

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+  '@esbuild/aix-ppc64@0.28.1':
+    resolution: {integrity: sha512-Svl7tq8k/08+p6CXPpRjQ1fKX+1odH/BQbb48fV6fj3CWHhsoIOoY87w1oHXm0qEpkIK3ZfVgp0hed3XBXzXMQ==}
    engines: {node: '>=18'}
    cpu: [ppc64]
    os: [aix]

-  '@esbuild/android-arm64@0.27.7':
-    resolution: {integrity: sha512-62dPZHpIXzvChfvfLJow3q5dDtiNMkwiRzPylSCfriLvZeq0a1bWChrGx/BbUbPwOrsWKMn8idSllklzBy+dgQ==}
+  '@esbuild/android-arm64@0.28.1':
+    resolution: {integrity: sha512-34EGEbCIAgosYz6goLcopX6Mo7NyGv9tfwEM2/7Ce2VcVRk568iSvniGWcUXIy7wEDR1wzolcxcriFVrWYcwBg==}
    engines: {node: '>=18'}
    cpu: [arm64]
    os: [android]

-  '@esbuild/android-arm@0.27.7':
-    resolution: {integrity: sha512-jbPXvB4Yj2yBV7HUfE2KHe4GJX51QplCN1pGbYjvsyCZbQmies29EoJbkEc+vYuU5o45AfQn37vZlyXy4YJ8RQ==}
+  '@esbuild/android-arm@0.28.1':
+    resolution: {integrity: sha512-0k2F129Xdio1TdJfzJ8sy1Q47vUD2NnwdhiAf7drUN1EBTfPf4hsFCtmMgu/6m8JSzsBrlmVjudMBQqOfG8usQ==}
    engines: {node: '>=18'}
    cpu: [arm]
    os: [android]

-  '@esbuild/android-x64@0.27.7':
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+  '@esbuild/android-x64@0.28.1':
+    resolution: {integrity: sha512-dbwY7ltSMDWsRatcRpCnES4F+im88OCUgGZjy52shC7GqHRE/cYlxNbB4Z4UpJswpcc4Qxd2oE/ufM0p61IKng==}
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    cpu: [arm64]
    os: [darwin]

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-    resolution: {integrity: sha512-OfatkLojr6U+WN5EDYuoQhtM+1xco+/6FSzJJnuWiUw5eVcicbyK3dq5EeV/QHT1uy6GoDhGbFpprUiHUYggrw==}
+  '@esbuild/netbsd-x64@0.28.1':
+    resolution: {integrity: sha512-aeL6lAnN89Hz43Mlh1G8ARasbuoYvSITDEx0tHh5b7jJnHcssqgjy9Yx430GDpmCa6OyrKoS0aNRjKundRizGg==}
    engines: {node: '>=18'}
    cpu: [x64]
    os: [netbsd]

-  '@esbuild/openbsd-arm64@0.27.7':
-    resolution: {integrity: sha512-AFuojMQTxAz75Fo8idVcqoQWEHIXFRbOc1TrVcFSgCZtQfSdc1RXgB3tjOn/krRHENUB4j00bfGjyl2mJrU37A==}
+  '@esbuild/openbsd-arm64@0.28.1':
+    resolution: {integrity: sha512-MEFJe5C3R8pwXdZ5Y21oo6m7ePiS0d9pWucn99O/wvyJZChoIQKrQDxKrGeW8F5+T0okTHesAmDeiHDTIq0V/Q==}
    engines: {node: '>=18'}
    cpu: [arm64]
    os: [openbsd]

-  '@esbuild/openbsd-x64@0.27.7':
-    resolution: {integrity: sha512-+A1NJmfM8WNDv5CLVQYJ5PshuRm/4cI6WMZRg1by1GwPIQPCTs1GLEUHwiiQGT5zDdyLiRM/l1G0Pv54gvtKIg==}
+  '@esbuild/openbsd-x64@0.28.1':
+    resolution: {integrity: sha512-i/ZLIOafE0Z8cI/XANJAixoJL/uRAoS2xOA3rb0xN+KK0K177cMAsQYkzHtBrtMXAKuAc7HGgcWiZ/sRC1Nxgw==}
    engines: {node: '>=18'}
    cpu: [x64]
    os: [openbsd]

-  '@esbuild/openharmony-arm64@0.27.7':
-    resolution: {integrity: sha512-+KrvYb/C8zA9CU/g0sR6w2RBw7IGc5J2BPnc3dYc5VJxHCSF1yNMxTV5LQ7GuKteQXZtspjFbiuW5/dOj7H4Yw==}
+  '@esbuild/openharmony-arm64@0.28.1':
+    resolution: {integrity: sha512-ge+Z7EXFNt2BO1oAMsVpiQ8EwndV9i1xXerAeTIK7AtPs3bKFXQM7nlRxDSIUIMeueR1CNXxqztLzdNeReKBJg==}
    engines: {node: '>=18'}
    cpu: [arm64]
    os: [openharmony]

-  '@esbuild/sunos-x64@0.27.7':
-    resolution: {integrity: sha512-ikktIhFBzQNt/QDyOL580ti9+5mL/YZeUPKU2ivGtGjdTYoqz6jObj6nOMfhASpS4GU4Q/Clh1QtxWAvcYKamA==}
+  '@esbuild/sunos-x64@0.28.1':
+    resolution: {integrity: sha512-BEjgtECkL3vY+SaSQ6nzVfiALUeFxpawyp8Jmf5PtYhf1Ug40N1h/hxlhts+f1FvSvarEigdxS3BlSMI2PJLcQ==}
    engines: {node: '>=18'}
    cpu: [x64]
    os: [sunos]

-  '@esbuild/win32-arm64@0.27.7':
-    resolution: {integrity: sha512-7yRhbHvPqSpRUV7Q20VuDwbjW5kIMwTHpptuUzV+AA46kiPze5Z7qgt6CLCK3pWFrHeNfDd1VKgyP4O+ng17CA==}
+  '@esbuild/win32-arm64@0.28.1':
+    resolution: {integrity: sha512-lCv9eK/H6ZJWbE7bh2nw54CZ9M2nupBxJcTsdk/QQnWkdSjKGuxmmH8/GWrlT1eMmZfn4dGcCjRte397WqfQXA==}
    engines: {node: '>=18'}
    cpu: [arm64]
    os: [win32]

-  '@esbuild/win32-ia32@0.27.7':
-    resolution: {integrity: sha512-SmwKXe6VHIyZYbBLJrhOoCJRB/Z1tckzmgTLfFYOfpMAx63BJEaL9ExI8x7v0oAO3Zh6D/Oi1gVxEYr5oUCFhw==}
+  '@esbuild/win32-ia32@0.28.1':
+    resolution: {integrity: sha512-zvb/mB2bSCoJOpoCBgYKKpX6YM6mJBlBUVUtVj41DlZJVEB6/0CKlRYxP5wWl1C1ILiCoAU5wZZ4q1P3qeS6Eg==}
    engines: {node: '>=18'}
    cpu: [ia32]
    os: [win32]

-  '@esbuild/win32-x64@0.27.7':
-    resolution: {integrity: sha512-56hiAJPhwQ1R4i+21FVF7V8kSD5zZTdHcVuRFMW0hn753vVfQN8xlx4uOPT4xoGH0Z/oVATuR82AiqSTDIpaHg==}
+  '@esbuild/win32-x64@0.28.1':
+    resolution: {integrity: sha512-bm4Mowrv+GXMlpWX++EcXw/iLyd1o3+bJkC2DkWXYVvgZCqD/bSj9ctZeAMC3cIxgjRVR2Dufaiu4YPxr5gW1A==}
    engines: {node: '>=18'}
    cpu: [x64]
    os: [win32]
@@ -879,7 +880,7 @@ packages:
    resolution: {integrity: sha512-3WrrOuZiyaaZPWiEt4G3+IffISVC9HYlWueJEBWED4ZH4aIAC2PnkdnuRrR94M+w6yGWn4AglWtJtBI8YqvgoA==}
    engines: {node: ^12.20.0 || ^14.13.1 || >=16.0.0}
    peerDependencies:
-      esbuild: '>=0.18'
+      esbuild: '>=0.28.1'

  cac@6.7.14:
    resolution: {integrity: sha512-b6Ilus+c3RrdDk+JhLKUAQfzzgLEPy6wcXqS7f/xe1EETvsDP6GORG7SFuOs6cID5YkqchW/LXZbX5bc8j7ZcQ==}
@@ -1035,8 +1036,8 @@ packages:
    resolution: {integrity: sha512-j6vWzfrGVfyXxge+O0x5sh6cvxAog0a/4Rdd2K36zCMV5eJ+/+tOAngRO8cODMNWbVRdVlmGZQL2YS3yR8bIUA==}
    engines: {node: '>= 0.4'}

-  esbuild@0.27.7:
-    resolution: {integrity: sha512-IxpibTjyVnmrIQo5aqNpCgoACA/dTKLTlhMHihVHhdkxKyPO1uBBthumT0rdHmcsk9uMonIWS0m4FljWzILh3w==}
+  esbuild@0.28.1:
+    resolution: {integrity: sha512-HrJrvZv5ayxBzPfwphOoNzkzOIIlifzk0KJrGK2c8R4+LKpMtpYLQeUdjnwjWv/LZlkH2laZk+4w78pi99D4Vw==}
    engines: {node: '>=18'}
    hasBin: true

@@ -2016,7 +2017,7 @@ packages:
    peerDependencies:
      '@types/node': ^20.19.0 || >=22.12.0
      '@vitejs/devtools': ^0.1.18
-      esbuild: ^0.27.0 || ^0.28.0
+      esbuild: '>=0.28.1'
      jiti: '>=1.21.0'
      less: ^4.0.0
      sass: ^1.70.0
@@ -2323,82 +2324,82 @@ snapshots:
      tslib: 2.8.1
    optional: true

-  '@esbuild/aix-ppc64@0.27.7':
+  '@esbuild/aix-ppc64@0.28.1':
    optional: true

-  '@esbuild/android-arm64@0.27.7':
+  '@esbuild/android-arm64@0.28.1':
    optional: true

-  '@esbuild/android-arm@0.27.7':
+  '@esbuild/android-arm@0.28.1':
    optional: true

-  '@esbuild/android-x64@0.27.7':
+  '@esbuild/android-x64@0.28.1':
    optional: true

-  '@esbuild/darwin-arm64@0.27.7':
+  '@esbuild/darwin-arm64@0.28.1':
    optional: true

-  '@esbuild/darwin-x64@0.27.7':
+  '@esbuild/darwin-x64@0.28.1':
    optional: true

-  '@esbuild/freebsd-arm64@0.27.7':
+  '@esbuild/freebsd-arm64@0.28.1':
    optional: true

-  '@esbuild/freebsd-x64@0.27.7':
+  '@esbuild/freebsd-x64@0.28.1':
    optional: true

-  '@esbuild/linux-arm64@0.27.7':
+  '@esbuild/linux-arm64@0.28.1':
    optional: true

-  '@esbuild/linux-arm@0.27.7':
+  '@esbuild/linux-arm@0.28.1':
    optional: true

-  '@esbuild/linux-ia32@0.27.7':
+  '@esbuild/linux-ia32@0.28.1':
    optional: true

-  '@esbuild/linux-loong64@0.27.7':
+  '@esbuild/linux-loong64@0.28.1':
    optional: true

-  '@esbuild/linux-mips64el@0.27.7':
+  '@esbuild/linux-mips64el@0.28.1':
    optional: true

-  '@esbuild/linux-ppc64@0.27.7':
+  '@esbuild/linux-ppc64@0.28.1':
    optional: true

-  '@esbuild/linux-riscv64@0.27.7':
+  '@esbuild/linux-riscv64@0.28.1':
    optional: true

-  '@esbuild/linux-s390x@0.27.7':
+  '@esbuild/linux-s390x@0.28.1':
    optional: true

-  '@esbuild/linux-x64@0.27.7':
+  '@esbuild/linux-x64@0.28.1':
    optional: true

-  '@esbuild/netbsd-arm64@0.27.7':
+  '@esbuild/netbsd-arm64@0.28.1':
    optional: true

-  '@esbuild/netbsd-x64@0.27.7':
+  '@esbuild/netbsd-x64@0.28.1':
    optional: true

-  '@esbuild/openbsd-arm64@0.27.7':
+  '@esbuild/openbsd-arm64@0.28.1':
    optional: true

-  '@esbuild/openbsd-x64@0.27.7':
+  '@esbuild/openbsd-x64@0.28.1':
    optional: true

-  '@esbuild/openharmony-arm64@0.27.7':
+  '@esbuild/openharmony-arm64@0.28.1':
    optional: true

-  '@esbuild/sunos-x64@0.27.7':
+  '@esbuild/sunos-x64@0.28.1':
    optional: true

-  '@esbuild/win32-arm64@0.27.7':
+  '@esbuild/win32-arm64@0.28.1':
    optional: true

-  '@esbuild/win32-ia32@0.27.7':
+  '@esbuild/win32-ia32@0.28.1':
    optional: true

-  '@esbuild/win32-x64@0.27.7':
+  '@esbuild/win32-x64@0.28.1':
    optional: true

  '@google/genai@1.52.0':
@@ -2779,7 +2780,7 @@ snapshots:
      obug: 2.1.2
      std-env: 4.1.0
      tinyrainbow: 3.1.0
-      vitest: 4.1.8(@types/node@22.19.20)(@vitest/coverage-v8@4.1.8)(vite@8.0.16(@types/node@22.19.20)(esbuild@0.27.7))
+      vitest: 4.1.8(@types/node@22.19.20)(@vitest/coverage-v8@4.1.8)(vite@8.0.16(@types/node@22.19.20)(esbuild@0.28.1))

  '@vitest/expect@4.1.8':
    dependencies:
@@ -2790,13 +2791,13 @@ snapshots:
      chai: 6.2.2
      tinyrainbow: 3.1.0

-  '@vitest/mocker@4.1.8(vite@8.0.16(@types/node@22.19.20)(esbuild@0.27.7))':
+  '@vitest/mocker@4.1.8(vite@8.0.16(@types/node@22.19.20)(esbuild@0.28.1))':
    dependencies:
      '@vitest/spy': 4.1.8
      estree-walker: 3.0.3
      magic-string: 0.30.21
    optionalDependencies:
-      vite: 8.0.16(@types/node@22.19.20)(esbuild@0.27.7)
+      vite: 8.0.16(@types/node@22.19.20)(esbuild@0.28.1)

  '@vitest/pretty-format@4.1.8':
    dependencies:
@@ -2914,9 +2915,9 @@ snapshots:
    dependencies:
      run-applescript: 7.1.0

-  bundle-require@5.1.0(esbuild@0.27.7):
+  bundle-require@5.1.0(esbuild@0.28.1):
    dependencies:
-      esbuild: 0.27.7
+      esbuild: 0.28.1
      load-tsconfig: 0.2.5

  cac@6.7.14: {}
@@ -3050,34 +3051,34 @@ snapshots:
      has-tostringtag: 1.0.2
      hasown: 2.0.4

-  esbuild@0.27.7:
+  esbuild@0.28.1:
    optionalDependencies:
-      '@esbuild/aix-ppc64': 0.27.7
-      '@esbuild/android-arm': 0.27.7
-      '@esbuild/android-arm64': 0.27.7
-      '@esbuild/android-x64': 0.27.7
-      '@esbuild/darwin-arm64': 0.27.7
-      '@esbuild/darwin-x64': 0.27.7
-      '@esbuild/freebsd-arm64': 0.27.7
-      '@esbuild/freebsd-x64': 0.27.7
-      '@esbuild/linux-arm': 0.27.7
-      '@esbuild/linux-arm64': 0.27.7
-      '@esbuild/linux-ia32': 0.27.7
-      '@esbuild/linux-loong64': 0.27.7
-      '@esbuild/linux-mips64el': 0.27.7
-      '@esbuild/linux-ppc64': 0.27.7
-      '@esbuild/linux-riscv64': 0.27.7
-      '@esbuild/linux-s390x': 0.27.7
-      '@esbuild/linux-x64': 0.27.7
-      '@esbuild/netbsd-arm64': 0.27.7
-      '@esbuild/netbsd-x64': 0.27.7
-      '@esbuild/openbsd-arm64': 0.27.7
-      '@esbuild/openbsd-x64': 0.27.7
-      '@esbuild/openharmony-arm64': 0.27.7
-      '@esbuild/sunos-x64': 0.27.7
-      '@esbuild/win32-arm64': 0.27.7
-      '@esbuild/win32-ia32': 0.27.7
-      '@esbuild/win32-x64': 0.27.7
+      '@esbuild/aix-ppc64': 0.28.1
+      '@esbuild/android-arm': 0.28.1
+      '@esbuild/android-arm64': 0.28.1
+      '@esbuild/android-x64': 0.28.1
+      '@esbuild/darwin-arm64': 0.28.1
+      '@esbuild/darwin-x64': 0.28.1
+      '@esbuild/freebsd-arm64': 0.28.1
+      '@esbuild/freebsd-x64': 0.28.1
+      '@esbuild/linux-arm': 0.28.1
+      '@esbuild/linux-arm64': 0.28.1
+      '@esbuild/linux-ia32': 0.28.1
+      '@esbuild/linux-loong64': 0.28.1
+      '@esbuild/linux-mips64el': 0.28.1
+      '@esbuild/linux-ppc64': 0.28.1
+      '@esbuild/linux-riscv64': 0.28.1
+      '@esbuild/linux-s390x': 0.28.1
+      '@esbuild/linux-x64': 0.28.1
+      '@esbuild/netbsd-arm64': 0.28.1
+      '@esbuild/netbsd-x64': 0.28.1
+      '@esbuild/openbsd-arm64': 0.28.1
+      '@esbuild/openbsd-x64': 0.28.1
+      '@esbuild/openharmony-arm64': 0.28.1
+      '@esbuild/sunos-x64': 0.28.1
+      '@esbuild/win32-arm64': 0.28.1
+      '@esbuild/win32-ia32': 0.28.1
+      '@esbuild/win32-x64': 0.28.1

  escape-string-regexp@2.0.0: {}

@@ -4021,12 +4022,12 @@ snapshots:

  tsup@8.5.1(postcss@8.5.15)(typescript@5.9.3):
    dependencies:
-      bundle-require: 5.1.0(esbuild@0.27.7)
+      bundle-require: 5.1.0(esbuild@0.28.1)
      cac: 6.7.14
      chokidar: 4.0.3
      consola: 3.4.2
      debug: 4.4.3
-      esbuild: 0.27.7
+      esbuild: 0.28.1
      fix-dts-default-cjs-exports: 1.0.1
      joycon: 3.1.1
      picocolors: 1.1.1
@@ -4069,7 +4070,7 @@ snapshots:

  uuid@14.0.0: {}

-  vite@8.0.16(@types/node@22.19.20)(esbuild@0.27.7):
+  vite@8.0.16(@types/node@22.19.20)(esbuild@0.28.1):
    dependencies:
      lightningcss: 1.32.0
      picomatch: 4.0.4
@@ -4078,13 +4079,13 @@ snapshots:
      tinyglobby: 0.2.17
    optionalDependencies:
      '@types/node': 22.19.20
-      esbuild: 0.27.7
+      esbuild: 0.28.1
      fsevents: 2.3.3

-  vitest@4.1.8(@types/node@22.19.20)(@vitest/coverage-v8@4.1.8)(vite@8.0.16(@types/node@22.19.20)(esbuild@0.27.7)):
+  vitest@4.1.8(@types/node@22.19.20)(@vitest/coverage-v8@4.1.8)(vite@8.0.16(@types/node@22.19.20)(esbuild@0.28.1)):
    dependencies:
      '@vitest/expect': 4.1.8
-      '@vitest/mocker': 4.1.8(vite@8.0.16(@types/node@22.19.20)(esbuild@0.27.7))
+      '@vitest/mocker': 4.1.8(vite@8.0.16(@types/node@22.19.20)(esbuild@0.28.1))
      '@vitest/pretty-format': 4.1.8
      '@vitest/runner': 4.1.8
      '@vitest/snapshot': 4.1.8
@@ -4101,7 +4102,7 @@ snapshots:
      tinyexec: 1.2.4
      tinyglobby: 0.2.17
      tinyrainbow: 3.1.0
-      vite: 8.0.16(@types/node@22.19.20)(esbuild@0.27.7)
+      vite: 8.0.16(@types/node@22.19.20)(esbuild@0.28.1)
      why-is-node-running: 2.3.0
    optionalDependencies:
      '@types/node': 22.19.20
@@ -19,3 +19,4 @@ overrides:
  "picomatch@<2.3.2": "^2.3.2"
  "@qdrant/js-client-rest": "^1.18.0"
  "uuid@<11.1.1": ">=11.1.1"
+  "esbuild": ">=0.28.1"
@@ -2,7 +2,12 @@
 * Tests for path traversal prevention in skill-loader.
 */
 import { describe, it, expect } from "vitest";
-import { safePath, loadSkill } from "./skill-loader.ts";
+import {
+  safePath,
+  loadSkill,
+  loadTriagePrompt,
+  loadCompactTriagePrompt,
+} from "./skill-loader.ts";

 // ---------------------------------------------------------------------------
 // safePath — path containment
@@ -68,3 +73,71 @@ describe("loadSkill path traversal", () => {
    expect(result?.prompt).toBeTruthy();
  });
 });
+
+describe("loadCompactTriagePrompt", () => {
+  it("keeps the core triage instructions without inlining the full skill body", () => {
+    const prompt = loadCompactTriagePrompt();
+
+    expect(prompt).toContain("Use `memory_add` tool for ALL user facts");
+    expect(prompt).toContain("Batch facts by CATEGORY");
+    expect(prompt).toContain("ALWAYS rewrite the query");
+    expect(prompt).not.toContain("## Worked Examples");
+    expect(prompt).not.toContain("### memory_search");
+    expect(prompt).not.toContain("Conference requires at least 4 breakout rooms");
+    expect(prompt.length).toBeLessThan(3500);
+  });
+
+  it("keeps the always-recall guidance aligned with the full triage prompt", () => {
+    const config = { recall: { strategy: "always" as const } };
+    const expected =
+      "Automatic recall runs for both long-term and session memory. Use manual searches only when you need more specific context.";
+
+    expect(loadCompactTriagePrompt(config)).toContain(expected);
+    expect(loadTriagePrompt(config)).toContain(expected);
+  });
+
+  it("keeps the manual-recall guidance in compact mode", () => {
+    const prompt = loadCompactTriagePrompt({
+      recall: { strategy: "manual" },
+    });
+
+    expect(prompt).toContain("No automatic recall happens in manual mode.");
+  });
+
+  it("includes short config summaries and truncates oversized custom rules", () => {
+    const prompt = loadCompactTriagePrompt({
+      categories: {
+        travel: { importance: 0.7, ttl: "30d" },
+      },
+      customRules: {
+        include: [
+          "Always remember workshop venue requirements.",
+          "Keep track of recurring conference planning constraints.",
+          "Capture every catering preference, transit note, and presenter dependency in detail.",
+        ],
+        exclude: [
+          "Never store one-off demo logs, temporary ETA chatter, or transitory checklist updates.",
+          "Skip verbose retrospectives unless the user explicitly says the lesson should persist.",
+        ],
+      },
+    });
+
+    expect(prompt).toContain("travel (importance 0.7, expires: 30d)");
+    expect(prompt).toContain("include rule(s) and 2 exclude rule(s) are configured");
+    expect(prompt).toContain("Prompt kept compact, full rule text omitted");
+    expect(prompt).toContain('Preview: include "Always remember workshop venue requirements."');
+  });
+
+  it("omits default credential patterns in compact mode unless custom patterns are configured", () => {
+    const defaultPrompt = loadCompactTriagePrompt();
+    const customPrompt = loadCompactTriagePrompt({
+      triage: {
+        credentialPatterns: ["mem0-secret=", "Bearer "],
+      },
+    });
+
+    expect(defaultPrompt).not.toContain("Credential patterns to scan");
+    expect(customPrompt).toContain("Credential patterns to scan");
+    expect(customPrompt).toContain("mem0-secret=");
+  });
+});
@@ -191,8 +191,16 @@ function renderCategoriesBlock(
  return lines.join("\n");
 }

-function renderTriageKnobs(config: SkillsConfig): string {
+function renderTriageKnobs(
+  config: SkillsConfig,
+  options: {
+    includeCredentialPatterns?: boolean;
+    includeDefaultCredentialPatterns?: boolean;
+  } = {},
+): string {
  const lines: string[] = [];
+  const includeCredentialPatterns = options.includeCredentialPatterns ?? true;
+  const includeDefaultCredentialPatterns = options.includeDefaultCredentialPatterns ?? true;

  if (config.triage?.importanceThreshold !== undefined) {
    lines.push(
@@ -200,13 +208,82 @@ function renderTriageKnobs(config: SkillsConfig): string {
    );
  }

-  const patterns = resolveCredentialPatterns(config);
-  lines.push(`- Credential patterns to scan: ${patterns.map((p) => `\`${p}\``).join(", ")}`);
+  const hasCustomCredentialPatterns = config.triage?.credentialPatterns !== undefined;
+  if (includeCredentialPatterns && (includeDefaultCredentialPatterns || hasCustomCredentialPatterns)) {
+    const patterns = resolveCredentialPatterns(config);
+    lines.push(`- Credential patterns to scan: ${patterns.map((p) => `\`${p}\``).join(", ")}`);
+  }

  if (lines.length === 0) return "";
  return "\n## Active Configuration Overrides\n\n" + lines.join("\n");
 }

+const COMPACT_CUSTOM_RULE_CHAR_BUDGET = 280;
+const COMPACT_CUSTOM_RULE_PREVIEW_LIMIT = 2;
+
+function formatCategoryConfig(
+  name: string,
+  cat: CategoryConfig,
+): string {
+  const ttlLabel = cat.ttl ? `expires: ${cat.ttl}` : "permanent";
+  const immLabel = cat.immutable ? ", immutable" : "";
+  return `${name} (importance ${cat.importance}, ${ttlLabel}${immLabel})`;
+}
+
+function renderCompactCategories(config: SkillsConfig): string[] {
+  if (!config.categories || Object.keys(config.categories).length === 0) {
+    return [];
+  }
+
+  const mergedCategories = resolveCategories(config);
+  return [
+    "## Active Category Overrides",
+    "",
+    ...Object.entries(config.categories).map(([name]) =>
+      `- ${formatCategoryConfig(name, mergedCategories[name]!)}`,
+    ),
+  ];
+}
+
+function renderCompactCustomRules(config: SkillsConfig): string[] {
+  const includeRules = config.customRules?.include ?? [];
+  const excludeRules = config.customRules?.exclude ?? [];
+  if (includeRules.length === 0 && excludeRules.length === 0) {
+    return [];
+  }
+
+  const formatRuleList = (label: string, rules: string[]) =>
+    `${label}: ${rules.map((rule) => `"${rule}"`).join("; ")}`;
+
+  const lines: string[] = [];
+  if (includeRules.length > 0) {
+    lines.push(formatRuleList("Include", includeRules));
+  }
+  if (excludeRules.length > 0) {
+    lines.push(formatRuleList("Exclude", excludeRules));
+  }
+
+  const combined = lines.join(" ");
+  if (combined.length <= COMPACT_CUSTOM_RULE_CHAR_BUDGET) {
+    return ["## Active Custom Rules", "", ...lines.map((line) => `- ${line}`)];
+  }
+
+  const preview = [
+    ...includeRules.slice(0, COMPACT_CUSTOM_RULE_PREVIEW_LIMIT).map((rule) => `include "${rule}"`),
+    ...excludeRules.slice(0, COMPACT_CUSTOM_RULE_PREVIEW_LIMIT).map((rule) => `exclude "${rule}"`),
+  ];
+
+  return [
+    "## Active Custom Rules",
+    "",
+    `- ${includeRules.length} include rule(s) and ${excludeRules.length} exclude rule(s) are configured.`,
+    `- Prompt kept compact, full rule text omitted because it exceeded ${COMPACT_CUSTOM_RULE_CHAR_BUDGET} characters.`,
+    ...(preview.length > 0
+      ? [`- Preview: ${preview.join("; ")}`]
+      : []),
+  ];
+}
+
 // ============================================================================
 // TTL Helpers
 // ============================================================================
@@ -358,6 +435,11 @@ export function loadTriagePrompt(config: SkillsConfig = {}): string {
          "- Before updating a memory, search to find the existing version.",
        );
        parts.push("");
+      } else if (strategy === "always") {
+        parts.push(
+          "Automatic recall runs for both long-term and session memory. Use manual searches only when you need more specific context.",
+        );
+        parts.push("");
      }

      parts.push(
@@ -453,6 +535,122 @@ export function loadTriagePrompt(config: SkillsConfig = {}): string {
  return parts.join("\n");
 }

+/**
+ * Build a compact memory system prompt for skills mode turns.
+ *
+ * This keeps the required storage and search protocol, plus short config
+ * summaries, without inlining the full memory-triage skill body every turn.
+ * If the skill file cannot be read, delegate to loadTriagePrompt(), which has
+ * its own minimal inline fallback.
+ */
+export function loadCompactTriagePrompt(config: SkillsConfig = {}): string {
+  if (!readSkillFile("memory-triage")) {
+    return loadTriagePrompt(config);
+  }
+
+  const parts: string[] = [];
+  parts.push("<memory-system>");
+  parts.push(
+    "IMPORTANT: Use `memory_add` tool for ALL user facts. NEVER write user info to workspace files (USER.md, memory/).",
+  );
+  parts.push(
+    "After every response, evaluate whether a new agent would benefit from remembering this days later. Most turns should produce zero memory operations.",
+  );
+  parts.push(
+    "Only store durable, self-contained, third-person facts: identity, preferences with rationale, standing rules, decisions, projects, configurations, technical context, and relationships.",
+  );
+  parts.push(
+    "Never store credentials, tokens, webhook secrets, or raw tool output. If a secret was configured, store only that the credential was configured.",
+  );
+  parts.push(
+    "When a recalled fact materially changes, search for the existing memory and update it in place. Skip cosmetic rewording.",
+  );
+  parts.push("");
+  parts.push("## Tool Usage");
+  parts.push("");
+  parts.push(
+    "Batch facts by CATEGORY. All facts in one memory_add call must share the same category because category determines retention policy (TTL, immutability). If a turn has facts in different categories, make one call per category.",
+  );
+  parts.push(
+    'Format: memory_add(facts: ["User is Alex, backend engineer at Stripe, PST timezone"], category: "identity")',
+  );
+  parts.push(
+    'Mixed categories: memory_add(..., category: "identity") and memory_add(..., category: "decision") in separate calls.',
+  );
+  parts.push(
+    "Categories: identity, configuration, rule, preference, decision, technical, relationship, project.",
+  );
+
+  const categoryLines = renderCompactCategories(config);
+  if (categoryLines.length > 0) {
+    parts.push("");
+    parts.push(...categoryLines);
+  }
+
+  const knobLines = renderTriageKnobs(config, {
+    includeDefaultCredentialPatterns: false,
+  })
+    .split("\n")
+    .filter(Boolean);
+  if (knobLines.length > 0) {
+    parts.push("");
+    parts.push(...knobLines);
+  }
+
+  const customRuleLines = renderCompactCustomRules(config);
+  if (customRuleLines.length > 0) {
+    parts.push("");
+    parts.push(...customRuleLines);
+  }
+
+  if (config.recall?.enabled !== false) {
+    const strategy = config.recall?.strategy ?? "smart";
+    parts.push("");
+    parts.push("## Searching Memory");
+    parts.push("");
+
+    if (strategy === "manual") {
+      parts.push(
+        "No automatic recall happens in manual mode. Use memory_search proactively at conversation start, when context is missing, when topics shift, and before updating a memory.",
+      );
+    } else if (strategy === "always") {
+      parts.push(
+        "Automatic recall runs for both long-term and session memory. Use manual searches only when you need more specific context.",
+      );
+    } else {
+      parts.push(
+        "Automatic recall runs for long-term memory. Use manual searches when the injected context is not enough.",
+      );
+    }
+
+    parts.push(
+      "When calling memory_search, ALWAYS rewrite the query. NEVER pass the user's raw message.",
+    );
+    parts.push(
+      "Convert the request into 3-6 factual keywords that match stored memory language: user, decided, prefers, rule, configured, based in, plus the concrete nouns and names from the request.",
+    );
+    parts.push(
+      'WRONG: memory_search("Who was that nutritionist my wife recommended?")',
+    );
+    parts.push(
+      'RIGHT: memory_search("nutritionist wife recommended relationship")',
+    );
+    parts.push('WRONG: memory_search("What timezone am I in?")');
+    parts.push('RIGHT: memory_search("user timezone location based")');
+    // Intentionally omitted from the compact path: ENTITY SCOPING and SEARCH SCOPE.
+    // loadTriagePrompt() keeps the full explanatory sections for the non-compact path.
+    parts.push(
+      'Scope: use "long-term" for durable user context, "session" for this conversation, and "all" only when you truly need both.',
+    );
+    parts.push(
+      "When the request implies a time range or category, add filters or categories instead of broadening the query text.",
+    );
+  }
+
+  parts.push("</memory-system>");
+  return parts.join("\n");
+}
+
 /**
 * Load the dream skill prompt for consolidation sessions.
 */
@@ -71,7 +71,8 @@
  },
  "pnpm": {
    "overrides": {
-      "uuid@<11.1.1": ">=11.1.1"
+      "uuid@<11.1.1": ">=11.1.1",
+      "esbuild": ">=0.28.1"
    }
  }
 }
@@ -6,6 +6,7 @@ settings:

 overrides:
  uuid@<11.1.1: '>=11.1.1'
+  esbuild: '>=0.28.1'

 importers:

@@ -13,7 +14,7 @@ importers:
    dependencies:
      mem0ai:
        specifier: ^3.0.7
-        version: 3.0.7(@anthropic-ai/sdk@0.91.1(zod@3.25.76))(@azure/identity@4.13.1)(@azure/search-documents@12.2.0)(@cloudflare/workers-types@4.20260610.1)(@google/genai@1.52.0)(@langchain/core@1.1.48(openai@6.26.0(ws@8.21.0)(zod@3.25.76))(ws@8.21.0))(@mistralai/mistralai@2.2.1)(@qdrant/js-client-rest@1.18.0(typescript@6.0.3))(@supabase/supabase-js@2.108.1)(@types/jest@29.5.14)(@types/pg@8.11.0)(better-sqlite3@12.10.0)(cloudflare@4.5.0)(compromise@14.15.1)(groq-sdk@0.3.0)(natural@8.1.1)(ollama@0.5.18)(pg@8.21.0)(redis@5.12.1)(ws@8.21.0)
+        version: 3.0.7(@anthropic-ai/sdk@0.91.1(zod@3.25.76))(@azure/identity@4.13.1)(@azure/search-documents@12.2.0)(@cloudflare/workers-types@4.20260610.1)(@google/genai@1.52.0)(@langchain/core@1.1.48(openai@6.26.0(ws@8.21.0)(zod@3.25.76))(ws@8.21.0))(@mistralai/mistralai@2.2.1)(@qdrant/js-client-rest@1.18.0(typescript@6.0.3))(@supabase/supabase-js@2.108.1)(@types/jest@29.5.14)(@types/pg@8.11.0)(better-sqlite3@12.10.0)(cloudflare@4.5.0)(compromise@14.15.1)(groq-sdk@0.3.0)(natural@8.1.1)(ollama@0.5.18)(pg@8.11.3)(redis@5.12.1)(ws@8.21.0)
    devDependencies:
      '@earendil-works/pi-ai':
        specifier: ^0.79.0
@@ -35,7 +36,7 @@ importers:
        version: 6.0.3
      vitest:
        specifier: ^4.1.7
-        version: 4.1.8(@types/node@25.9.2)(vite@8.0.16(@types/node@25.9.2)(esbuild@0.27.7)(jiti@2.7.0)(yaml@2.9.0))
+        version: 4.1.8(@types/node@25.9.2)(vite@8.0.16(@types/node@25.9.2)(esbuild@0.28.1)(jiti@2.7.0)(yaml@2.9.0))

 packages:

@@ -253,158 +254,158 @@ packages:
  '@emnapi/wasi-threads@1.2.1':
    resolution: {integrity: sha512-uTII7OYF+/Mes/MrcIOYp5yOtSMLBWSIoLPpcgwipoiKbli6k322tcoFsxoIIxPDqW01SQGAgko4EzZi2BNv2w==}

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-    resolution: {integrity: sha512-EKX3Qwmhz1eMdEJokhALr0YiD0lhQNwDqkPYyPhiSwKrh7/4KRjQc04sZ8db+5DVVnZ1LmbNDI1uAMPEUBnQPg==}
+  '@esbuild/aix-ppc64@0.28.1':
+    resolution: {integrity: sha512-Svl7tq8k/08+p6CXPpRjQ1fKX+1odH/BQbb48fV6fj3CWHhsoIOoY87w1oHXm0qEpkIK3ZfVgp0hed3XBXzXMQ==}
    engines: {node: '>=18'}
    cpu: [ppc64]
    os: [aix]

-  '@esbuild/android-arm64@0.27.7':
-    resolution: {integrity: sha512-62dPZHpIXzvChfvfLJow3q5dDtiNMkwiRzPylSCfriLvZeq0a1bWChrGx/BbUbPwOrsWKMn8idSllklzBy+dgQ==}
+  '@esbuild/android-arm64@0.28.1':
+    resolution: {integrity: sha512-34EGEbCIAgosYz6goLcopX6Mo7NyGv9tfwEM2/7Ce2VcVRk568iSvniGWcUXIy7wEDR1wzolcxcriFVrWYcwBg==}
    engines: {node: '>=18'}
    cpu: [arm64]
    os: [android]

-  '@esbuild/android-arm@0.27.7':
-    resolution: {integrity: sha512-jbPXvB4Yj2yBV7HUfE2KHe4GJX51QplCN1pGbYjvsyCZbQmies29EoJbkEc+vYuU5o45AfQn37vZlyXy4YJ8RQ==}
+  '@esbuild/android-arm@0.28.1':
+    resolution: {integrity: sha512-0k2F129Xdio1TdJfzJ8sy1Q47vUD2NnwdhiAf7drUN1EBTfPf4hsFCtmMgu/6m8JSzsBrlmVjudMBQqOfG8usQ==}
    engines: {node: '>=18'}
    cpu: [arm]
    os: [android]

-  '@esbuild/android-x64@0.27.7':
-    resolution: {integrity: sha512-x5VpMODneVDb70PYV2VQOmIUUiBtY3D3mPBG8NxVk5CogneYhkR7MmM3yR/uMdITLrC1ml/NV1rj4bMJuy9MCg==}
+  '@esbuild/android-x64@0.28.1':
+    resolution: {integrity: sha512-dbwY7ltSMDWsRatcRpCnES4F+im88OCUgGZjy52shC7GqHRE/cYlxNbB4Z4UpJswpcc4Qxd2oE/ufM0p61IKng==}
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+  '@esbuild/darwin-arm64@0.28.1':
+    resolution: {integrity: sha512-TZbWkQY7kvTAXbXUT7uVACR5cMHsDiSz9z7ZKAX/RTq/WJEk3QyRr0wZpNhBDX+/0CtdqUIJlOiodQcta6tY3Q==}
    engines: {node: '>=18'}
    cpu: [arm64]
    os: [darwin]

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+  '@esbuild/darwin-x64@0.28.1':
+    resolution: {integrity: sha512-zfdzgK9ACBNZLI/CyHTOx81SyNbM6YXn7rxSgX97VjyiPl9W1i4Ka4fgKECEoFCKGpvBj5qArWIGgQjOwkgskQ==}
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    cpu: [arm64]
    os: [openbsd]

-  '@esbuild/openbsd-x64@0.27.7':
-    resolution: {integrity: sha512-+A1NJmfM8WNDv5CLVQYJ5PshuRm/4cI6WMZRg1by1GwPIQPCTs1GLEUHwiiQGT5zDdyLiRM/l1G0Pv54gvtKIg==}
+  '@esbuild/openbsd-x64@0.28.1':
+    resolution: {integrity: sha512-i/ZLIOafE0Z8cI/XANJAixoJL/uRAoS2xOA3rb0xN+KK0K177cMAsQYkzHtBrtMXAKuAc7HGgcWiZ/sRC1Nxgw==}
    engines: {node: '>=18'}
    cpu: [x64]
    os: [openbsd]

-  '@esbuild/openharmony-arm64@0.27.7':
-    resolution: {integrity: sha512-+KrvYb/C8zA9CU/g0sR6w2RBw7IGc5J2BPnc3dYc5VJxHCSF1yNMxTV5LQ7GuKteQXZtspjFbiuW5/dOj7H4Yw==}
+  '@esbuild/openharmony-arm64@0.28.1':
+    resolution: {integrity: sha512-ge+Z7EXFNt2BO1oAMsVpiQ8EwndV9i1xXerAeTIK7AtPs3bKFXQM7nlRxDSIUIMeueR1CNXxqztLzdNeReKBJg==}
    engines: {node: '>=18'}
    cpu: [arm64]
    os: [openharmony]

-  '@esbuild/sunos-x64@0.27.7':
-    resolution: {integrity: sha512-ikktIhFBzQNt/QDyOL580ti9+5mL/YZeUPKU2ivGtGjdTYoqz6jObj6nOMfhASpS4GU4Q/Clh1QtxWAvcYKamA==}
+  '@esbuild/sunos-x64@0.28.1':
+    resolution: {integrity: sha512-BEjgtECkL3vY+SaSQ6nzVfiALUeFxpawyp8Jmf5PtYhf1Ug40N1h/hxlhts+f1FvSvarEigdxS3BlSMI2PJLcQ==}
    engines: {node: '>=18'}
    cpu: [x64]
    os: [sunos]

-  '@esbuild/win32-arm64@0.27.7':
-    resolution: {integrity: sha512-7yRhbHvPqSpRUV7Q20VuDwbjW5kIMwTHpptuUzV+AA46kiPze5Z7qgt6CLCK3pWFrHeNfDd1VKgyP4O+ng17CA==}
+  '@esbuild/win32-arm64@0.28.1':
+    resolution: {integrity: sha512-lCv9eK/H6ZJWbE7bh2nw54CZ9M2nupBxJcTsdk/QQnWkdSjKGuxmmH8/GWrlT1eMmZfn4dGcCjRte397WqfQXA==}
    engines: {node: '>=18'}
    cpu: [arm64]
    os: [win32]

-  '@esbuild/win32-ia32@0.27.7':
-    resolution: {integrity: sha512-SmwKXe6VHIyZYbBLJrhOoCJRB/Z1tckzmgTLfFYOfpMAx63BJEaL9ExI8x7v0oAO3Zh6D/Oi1gVxEYr5oUCFhw==}
+  '@esbuild/win32-ia32@0.28.1':
+    resolution: {integrity: sha512-zvb/mB2bSCoJOpoCBgYKKpX6YM6mJBlBUVUtVj41DlZJVEB6/0CKlRYxP5wWl1C1ILiCoAU5wZZ4q1P3qeS6Eg==}
    engines: {node: '>=18'}
    cpu: [ia32]
    os: [win32]

-  '@esbuild/win32-x64@0.27.7':
-    resolution: {integrity: sha512-56hiAJPhwQ1R4i+21FVF7V8kSD5zZTdHcVuRFMW0hn753vVfQN8xlx4uOPT4xoGH0Z/oVATuR82AiqSTDIpaHg==}
+  '@esbuild/win32-x64@0.28.1':
+    resolution: {integrity: sha512-bm4Mowrv+GXMlpWX++EcXw/iLyd1o3+bJkC2DkWXYVvgZCqD/bSj9ctZeAMC3cIxgjRVR2Dufaiu4YPxr5gW1A==}
    engines: {node: '>=18'}
    cpu: [x64]
    os: [win32]
@@ -469,30 +470,35 @@ packages:
    engines: {node: '>= 10'}
    cpu: [arm64]
    os: [linux]
+    libc: [glibc]

  '@mariozechner/clipboard-linux-arm64-musl@0.3.9':
    resolution: {integrity: sha512-AGuJdgKsmJdm4Pych7kv3sqe591ERRaAHW3xjLooiFzn8J+PxUyof++7YZrB5Y5tpnTO+K18Og3taj2NpluCRQ==}
    engines: {node: '>= 10'}
    cpu: [arm64]
    os: [linux]
+    libc: [musl]

  '@mariozechner/clipboard-linux-riscv64-gnu@0.3.9':
    resolution: {integrity: sha512-DXBEAiuMpk7dhS1a9NzNxVAFi1vaKoPu7rQNgY8LIDLGrK3lnIp3nT10DUum+PKVJoJppIP+NAA8IZe4DMNDPw==}
    engines: {node: '>= 10'}
    cpu: [riscv64]
    os: [linux]
+    libc: [glibc]

  '@mariozechner/clipboard-linux-x64-gnu@0.3.9':
    resolution: {integrity: sha512-WORrMLd6EpElEME7JRKfSaY34nW1P5LbdgK5YNCS1ncG2LqmITsSMEJ8nh2mpvxb3TxqbOOKgY7k9eMJYlW9Mw==}
    engines: {node: '>= 10'}
    cpu: [x64]
    os: [linux]
+    libc: [glibc]

  '@mariozechner/clipboard-linux-x64-musl@0.3.9':
    resolution: {integrity: sha512-/DHn+1DrfL6oRaPPWXaOKvonFFrni666fxd+zFqiQEfvBH0tsHVWjq9iqBk0oDp0qaPA72lIMy5BptxISBEhZQ==}
    engines: {node: '>= 10'}
    cpu: [x64]
    os: [linux]
+    libc: [musl]

  '@mariozechner/clipboard-win32-arm64-msvc@0.3.9':
    resolution: {integrity: sha512-O5FHD3ErkMwMhNzAfu3ggy0ug4z7btZuoQgwwxlzPrwV2bxlD6WDpqBY4NCgICAgZdDKdp+loUEKVAVt8aYnhQ==}
@@ -639,36 +645,42 @@ packages:
    engines: {node: ^20.19.0 || >=22.12.0}
    cpu: [arm64]
    os: [linux]
+    libc: [glibc]

  '@rolldown/binding-linux-arm64-musl@1.0.3':
    resolution: {integrity: sha512-VWkUHwWriDciit80wleYwKILoR/KMvxh/IdwS/paX+ZgpuRpCrKLUdadJbc0NpBEiyhpYawsJ73j9aCvOH+f7Q==}
    engines: {node: ^20.19.0 || >=22.12.0}
    cpu: [arm64]
    os: [linux]
+    libc: [musl]

  '@rolldown/binding-linux-ppc64-gnu@1.0.3':
    resolution: {integrity: sha512-5f1laC0SlIR0yDbFCd8acUhvJIag6N3zC5P7oUPN6wX0aOma+uKJ0wBDH5aq7I1PVI2ttTlhJwzwRIBnLiSGEg==}
    engines: {node: ^20.19.0 || >=22.12.0}
    cpu: [ppc64]
    os: [linux]
+    libc: [glibc]

  '@rolldown/binding-linux-s390x-gnu@1.0.3':
    resolution: {integrity: sha512-Iq4ko0r4XsgbrF/LunNgHtAGLRRVE2kXonAXQ/MV0mC6jQpMOhW1SvtZja2EhC/kd05++bP78dsqBeIQyYJ6Yg==}
    engines: {node: ^20.19.0 || >=22.12.0}
    cpu: [s390x]
    os: [linux]
+    libc: [glibc]

  '@rolldown/binding-linux-x64-gnu@1.0.3':
    resolution: {integrity: sha512-B8m6tD5+/N5FeNQFbKlLA/2yVq9ycQP1SeedyEYYKWBNR3ZQbkvIUcNnDNM03lO1l5F2roiiFJGgvoLLyZXtSg==}
    engines: {node: ^20.19.0 || >=22.12.0}
    cpu: [x64]
    os: [linux]
+    libc: [glibc]

  '@rolldown/binding-linux-x64-musl@1.0.3':
    resolution: {integrity: sha512-pSdpdUJHkuCxun9LE7jvgUB9qsRgaiyNNCX7m/AvHTcq67AiT/Yhoxvw5zPfhrM8k/BfP8ce/hMOpthKDpEUow==}
    engines: {node: ^20.19.0 || >=22.12.0}
    cpu: [x64]
    os: [linux]
+    libc: [musl]

  '@rolldown/binding-openharmony-arm64@1.0.3':
    resolution: {integrity: sha512-OXXS3RKJgX2uLwM+gYyuH5omcH8fL1LJs96pZGgtetVCahON57+d4SJHzTgZiOjxgGkSnpXpOsWuPDGAKAigEg==}
@@ -730,66 +742,79 @@ packages:
    resolution: {integrity: sha512-Q8CBCCQtDFrYtXoeUXSrnFXKOnyUhx6bz+SkL6A0E7V8kAiCJ5pamq1WtbfpVGhR5TSpXY6ak3avmDc5fHTyJA==}
    cpu: [arm]
    os: [linux]
+    libc: [glibc]

  '@rollup/rollup-linux-arm-musleabihf@4.61.1':
    resolution: {integrity: sha512-nwnhk1581l0FBVellGcVCAT0Oi06onEA3WB53sf01VO3I0UPBkMH9sXONYME2K0ovXcNayJfNtHfm6mpJElatQ==}
    cpu: [arm]
    os: [linux]
+    libc: [musl]

  '@rollup/rollup-linux-arm64-gnu@4.61.1':
    resolution: {integrity: sha512-x5Xr49hwt3hdW75UOZm3395YwwzPyauktslv29KpWL/T+vVAzoT3azLcTWv0eMciBNrx+DYjH4paehHoLpPvpg==}
    cpu: [arm64]
    os: [linux]
+    libc: [glibc]

  '@rollup/rollup-linux-arm64-musl@4.61.1':
    resolution: {integrity: sha512-unMS3H73DpaoPyyEVPjGKleM/s0mkmsauTENpw4INQY8y4+IuLNjkueQ5QCtC0D3N38Y38yhAU8OoZ20S2Tm6w==}
    cpu: [arm64]
    os: [linux]
+    libc: [musl]

  '@rollup/rollup-linux-loong64-gnu@4.61.1':
    resolution: {integrity: sha512-zNZzGRnAhwjFEYmvphJRV5XaQGjs62cCmeYYHUT//NbvEnHauw+I85nGG+SiVg5ld4GX8D1IbKIX+ozITQnhMQ==}
    cpu: [loong64]
    os: [linux]
+    libc: [glibc]

  '@rollup/rollup-linux-loong64-musl@4.61.1':
    resolution: {integrity: sha512-LdpWGL8X209B2SIvWjqlc8VZgM6PKfontSerGepuldQmHYrAOtnMCXeJkxXGbC+PPZVOuu5czJo7fNV6aeW8rQ==}
    cpu: [loong64]
    os: [linux]
+    libc: [musl]

  '@rollup/rollup-linux-ppc64-gnu@4.61.1':
    resolution: {integrity: sha512-EC5kTtNaNGOmbMGqar8dvJy6y/hg99GAwjfBz++pxZhQATXGcRjd6c5en5wcbru0vkRmiMGsQKdMJOOf6sza4g==}
    cpu: [ppc64]
    os: [linux]
+    libc: [glibc]

  '@rollup/rollup-linux-ppc64-musl@4.61.1':
    resolution: {integrity: sha512-8hiwp6D4acEcNK78I4rP0/XtS1sknWIAMJBPdR4l6zUtyTm5KiTDr5bXmWt4foY7nAN7AThDHgkLIEZOWKbzWw==}
    cpu: [ppc64]
    os: [linux]
+    libc: [musl]

  '@rollup/rollup-linux-riscv64-gnu@4.61.1':
    resolution: {integrity: sha512-10dh/h/BqA7DuMPWSxkR8uks18FRwnwOEqr5zOTEl+NOwP/OMzKX8OFR/Of9xxDA7D5qef1Nzar5WDD2kCCr1g==}
    cpu: [riscv64]
    os: [linux]
+    libc: [glibc]

  '@rollup/rollup-linux-riscv64-musl@4.61.1':
    resolution: {integrity: sha512-YKJ5lg35DP17gcAOggnihe+APw9HLyj1Xn7gsmGumBJAUDa6NGXNixJzmkWLhcK9TOuuyQjdamzvJefkO7qHZQ==}
    cpu: [riscv64]
    os: [linux]
+    libc: [musl]

  '@rollup/rollup-linux-s390x-gnu@4.61.1':
    resolution: {integrity: sha512-Mlil5G2Jj6a7B3LWGctg+XPL9vdXYuzCtNXfxOQ0nPjc2m6ueUktocPGH9bnAM0bNRKb/bAWTujUU7IJQdQA+g==}
    cpu: [s390x]
    os: [linux]
+    libc: [glibc]

  '@rollup/rollup-linux-x64-gnu@4.61.1':
    resolution: {integrity: sha512-bVWIOIk6pV01p4CdUbPP7CJ/434z+OooYjDuFcR+44N35YvKUC66G8MGnvcWx5mWKW3g61J+t74l3Kj15Kwn2Q==}
    cpu: [x64]
    os: [linux]
+    libc: [glibc]

  '@rollup/rollup-linux-x64-musl@4.61.1':
    resolution: {integrity: sha512-qy5pBvZbqNFheBz61R1rzsezjm0J7O2oNGoWtGoY89SZYLUfxAJTBAqDChqAIdB4rCiIbi9nF7yZ83GnNiLwSw==}
    cpu: [x64]
    os: [linux]
+    libc: [musl]

  '@rollup/rollup-openbsd-x64@4.61.1':
    resolution: {integrity: sha512-E83TXjI4zm0+5f2qO+UOudaCYIhYwpJ5jq6YCZNIZ+6CbfhKrkAGezeiASBL9ElxAxFsRS9ZhESv8mfnj6TKeg==}
@@ -1080,6 +1105,10 @@ packages:
  buffer-equal-constant-time@1.0.1:
    resolution: {integrity: sha512-zRpUiDwd/xk6ADqPMATG8vc9VPrkck7T07OIx0gnjmJAnHnTVXNQG3vfvWNuiZIkwu9KrKdA1iJKfsfTVxE6NA==}

+  buffer-writer@2.0.0:
+    resolution: {integrity: sha512-a7ZpuTZU1TRtnwyCNW3I5dc0wWNC3VR9S++Ewyk2HHZdrO3CQJqSpd+95Us590V6AL7JqUAH2IwZ/398PmNFgw==}
+    engines: {node: '>=4'}
+
  buffer@5.7.1:
    resolution: {integrity: sha512-EHcyIPBQ4BSGlvjB16k5KgAJ27CIsHY/2JBmCRReo48y9rQ3MaUzWX3KVlBa4U7MyX02HdVj0K7C3WaB3ju7FQ==}

@@ -1091,7 +1120,7 @@ packages:
    resolution: {integrity: sha512-3WrrOuZiyaaZPWiEt4G3+IffISVC9HYlWueJEBWED4ZH4aIAC2PnkdnuRrR94M+w6yGWn4AglWtJtBI8YqvgoA==}
    engines: {node: ^12.20.0 || ^14.13.1 || >=16.0.0}
    peerDependencies:
-      esbuild: '>=0.18'
+      esbuild: '>=0.28.1'

  cac@6.7.14:
    resolution: {integrity: sha512-b6Ilus+c3RrdDk+JhLKUAQfzzgLEPy6wcXqS7f/xe1EETvsDP6GORG7SFuOs6cID5YkqchW/LXZbX5bc8j7ZcQ==}
@@ -1259,8 +1288,8 @@ packages:
    resolution: {integrity: sha512-j6vWzfrGVfyXxge+O0x5sh6cvxAog0a/4Rdd2K36zCMV5eJ+/+tOAngRO8cODMNWbVRdVlmGZQL2YS3yR8bIUA==}
    engines: {node: '>= 0.4'}

-  esbuild@0.27.7:
-    resolution: {integrity: sha512-IxpibTjyVnmrIQo5aqNpCgoACA/dTKLTlhMHihVHhdkxKyPO1uBBthumT0rdHmcsk9uMonIWS0m4FljWzILh3w==}
+  esbuild@0.28.1:
+    resolution: {integrity: sha512-HrJrvZv5ayxBzPfwphOoNzkzOIIlifzk0KJrGK2c8R4+LKpMtpYLQeUdjnwjWv/LZlkH2laZk+4w78pi99D4Vw==}
    engines: {node: '>=18'}
    hasBin: true

@@ -1600,24 +1629,28 @@ packages:
    engines: {node: '>= 12.0.0'}
    cpu: [arm64]
    os: [linux]
+    libc: [glibc]

  lightningcss-linux-arm64-musl@1.32.0:
    resolution: {integrity: sha512-UpQkoenr4UJEzgVIYpI80lDFvRmPVg6oqboNHfoH4CQIfNA+HOrZ7Mo7KZP02dC6LjghPQJeBsvXhJod/wnIBg==}
    engines: {node: '>= 12.0.0'}
    cpu: [arm64]
    os: [linux]
+    libc: [musl]

  lightningcss-linux-x64-gnu@1.32.0:
    resolution: {integrity: sha512-V7Qr52IhZmdKPVr+Vtw8o+WLsQJYCTd8loIfpDaMRWGUZfBOYEJeyJIkqGIDMZPwPx24pUMfwSxxI8phr/MbOA==}
    engines: {node: '>= 12.0.0'}
    cpu: [x64]
    os: [linux]
+    libc: [glibc]

  lightningcss-linux-x64-musl@1.32.0:
    resolution: {integrity: sha512-bYcLp+Vb0awsiXg/80uCRezCYHNg1/l3mt0gzHnWV9XP1W5sKa5/TCdGWaR/zBM2PeF/HbsQv/j2URNOiVuxWg==}
    engines: {node: '>= 12.0.0'}
    cpu: [x64]
    os: [linux]
+    libc: [musl]

  lightningcss-win32-arm64-msvc@1.32.0:
    resolution: {integrity: sha512-8SbC8BR40pS6baCM8sbtYDSwEVQd4JlFTOlaD3gWGHfThTcABnNDBda6eTZeqbofalIJhFx0qKzgHJmcPTnGdw==}
@@ -1901,6 +1934,9 @@ packages:
    resolution: {integrity: sha512-rhIwUycgwwKcP9yTOOFK/AKsAopjjCakVqLHePO3CC6Mir1Z99xT+R63jZxAT5lFZLa2inS5h+ZS2GvR99/FBg==}
    engines: {node: '>=8'}

+  packet-reader@1.0.0:
+    resolution: {integrity: sha512-HAKu/fG3HpHFO0AA8WE8q2g+gBJaZ9MG7fcKk+IJPLTGAD6Psw4443l+9DGRbOIh3/aXr7Phy0TjilYivJo5XQ==}
+
  partial-json@0.1.7:
    resolution: {integrity: sha512-Njv/59hHaokb/hRUjce3Hdv12wd60MtM9Z5Olmn+nehe0QDAsRtRbJPvJ0Z91TusF0SuZRIvnM+S4l6EIP8leA==}

@@ -1949,6 +1985,15 @@ packages:
    resolution: {integrity: sha512-o2XFanIMy/3+mThw69O8d4n1E5zsLhdO+OPqswezu7Z5ekP4hYDqlDjlmOpYMbzY2Br0ufCwJLdDIXeNVwcWFg==}
    engines: {node: '>=10'}

+  pg@8.11.3:
+    resolution: {integrity: sha512-+9iuvG8QfaaUrrph+kpF24cXkH1YOOUeArRNYIxq1viYHZagBxrTno7cecY1Fa44tJeZvaoG+Djpkc3JwehN5g==}
+    engines: {node: '>= 8.0.0'}
+    peerDependencies:
+      pg-native: '>=3.0.1'
+    peerDependenciesMeta:
+      pg-native:
+        optional: true
+
  pg@8.21.0:
    resolution: {integrity: sha512-AUP1EYJuHraQGsVoCQVIcM7TEJVGtDzxWtGFZd8rds9d+CCXlU5Js1rYgfLNvxy9iJrpHjGrRjoi/3BT9fRyiA==}
    engines: {node: '>= 16.0.0'}
@@ -2330,7 +2375,7 @@ packages:
    peerDependencies:
      '@types/node': ^20.19.0 || >=22.12.0
      '@vitejs/devtools': ^0.1.18
-      esbuild: ^0.27.0 || ^0.28.0
+      esbuild: '>=0.28.1'
      jiti: '>=1.21.0'
      less: ^4.0.0
      sass: ^1.70.0
@@ -2925,82 +2970,82 @@ snapshots:
      tslib: 2.8.1
    optional: true

-  '@esbuild/aix-ppc64@0.27.7':
+  '@esbuild/aix-ppc64@0.28.1':
    optional: true

-  '@esbuild/android-arm64@0.27.7':
+  '@esbuild/android-arm64@0.28.1':
    optional: true

-  '@esbuild/android-arm@0.27.7':
+  '@esbuild/android-arm@0.28.1':
    optional: true

-  '@esbuild/android-x64@0.27.7':
+  '@esbuild/android-x64@0.28.1':
    optional: true

-  '@esbuild/darwin-arm64@0.27.7':
+  '@esbuild/darwin-arm64@0.28.1':
    optional: true

-  '@esbuild/darwin-x64@0.27.7':
+  '@esbuild/darwin-x64@0.28.1':
    optional: true

-  '@esbuild/freebsd-arm64@0.27.7':
+  '@esbuild/freebsd-arm64@0.28.1':
    optional: true

-  '@esbuild/freebsd-x64@0.27.7':
+  '@esbuild/freebsd-x64@0.28.1':
    optional: true

-  '@esbuild/linux-arm64@0.27.7':
+  '@esbuild/linux-arm64@0.28.1':
    optional: true

-  '@esbuild/linux-arm@0.27.7':
+  '@esbuild/linux-arm@0.28.1':
    optional: true

-  '@esbuild/linux-ia32@0.27.7':
+  '@esbuild/linux-ia32@0.28.1':
    optional: true

-  '@esbuild/linux-loong64@0.27.7':
+  '@esbuild/linux-loong64@0.28.1':
    optional: true

-  '@esbuild/linux-mips64el@0.27.7':
+  '@esbuild/linux-mips64el@0.28.1':
    optional: true

-  '@esbuild/linux-ppc64@0.27.7':
+  '@esbuild/linux-ppc64@0.28.1':
    optional: true

-  '@esbuild/linux-riscv64@0.27.7':
+  '@esbuild/linux-riscv64@0.28.1':
    optional: true

-  '@esbuild/linux-s390x@0.27.7':
+  '@esbuild/linux-s390x@0.28.1':
    optional: true

-  '@esbuild/linux-x64@0.27.7':
+  '@esbuild/linux-x64@0.28.1':
    optional: true

-  '@esbuild/netbsd-arm64@0.27.7':
+  '@esbuild/netbsd-arm64@0.28.1':
    optional: true

-  '@esbuild/netbsd-x64@0.27.7':
+  '@esbuild/netbsd-x64@0.28.1':
    optional: true

-  '@esbuild/openbsd-arm64@0.27.7':
+  '@esbuild/openbsd-arm64@0.28.1':
    optional: true

-  '@esbuild/openbsd-x64@0.27.7':
+  '@esbuild/openbsd-x64@0.28.1':
    optional: true

-  '@esbuild/openharmony-arm64@0.27.7':
+  '@esbuild/openharmony-arm64@0.28.1':
    optional: true

-  '@esbuild/sunos-x64@0.27.7':
+  '@esbuild/sunos-x64@0.28.1':
    optional: true

-  '@esbuild/win32-arm64@0.27.7':
+  '@esbuild/win32-arm64@0.28.1':
    optional: true

-  '@esbuild/win32-ia32@0.27.7':
+  '@esbuild/win32-ia32@0.28.1':
    optional: true

-  '@esbuild/win32-x64@0.27.7':
+  '@esbuild/win32-x64@0.28.1':
    optional: true

  '@google/genai@1.52.0':
@@ -3478,13 +3523,13 @@ snapshots:
      chai: 6.2.2
      tinyrainbow: 3.1.0

-  '@vitest/mocker@4.1.8(vite@8.0.16(@types/node@25.9.2)(esbuild@0.27.7)(jiti@2.7.0)(yaml@2.9.0))':
+  '@vitest/mocker@4.1.8(vite@8.0.16(@types/node@25.9.2)(esbuild@0.28.1)(jiti@2.7.0)(yaml@2.9.0))':
    dependencies:
      '@vitest/spy': 4.1.8
      estree-walker: 3.0.3
      magic-string: 0.30.21
    optionalDependencies:
-      vite: 8.0.16(@types/node@25.9.2)(esbuild@0.27.7)(jiti@2.7.0)(yaml@2.9.0)
+      vite: 8.0.16(@types/node@25.9.2)(esbuild@0.28.1)(jiti@2.7.0)(yaml@2.9.0)

  '@vitest/pretty-format@4.1.8':
    dependencies:
@@ -3597,6 +3642,8 @@ snapshots:

  buffer-equal-constant-time@1.0.1: {}

+  buffer-writer@2.0.0: {}
+
  buffer@5.7.1:
    dependencies:
      base64-js: 1.5.1
@@ -3606,9 +3653,9 @@ snapshots:
    dependencies:
      run-applescript: 7.1.0

-  bundle-require@5.1.0(esbuild@0.27.7):
+  bundle-require@5.1.0(esbuild@0.28.1):
    dependencies:
-      esbuild: 0.27.7
+      esbuild: 0.28.1
      load-tsconfig: 0.2.5

  cac@6.7.14: {}
@@ -3752,34 +3799,34 @@ snapshots:
      has-tostringtag: 1.0.2
      hasown: 2.0.4

-  esbuild@0.27.7:
+  esbuild@0.28.1:
    optionalDependencies:
-      '@esbuild/aix-ppc64': 0.27.7
-      '@esbuild/android-arm': 0.27.7
-      '@esbuild/android-arm64': 0.27.7
-      '@esbuild/android-x64': 0.27.7
-      '@esbuild/darwin-arm64': 0.27.7
-      '@esbuild/darwin-x64': 0.27.7
-      '@esbuild/freebsd-arm64': 0.27.7
-      '@esbuild/freebsd-x64': 0.27.7
-      '@esbuild/linux-arm': 0.27.7
-      '@esbuild/linux-arm64': 0.27.7
-      '@esbuild/linux-ia32': 0.27.7
-      '@esbuild/linux-loong64': 0.27.7
-      '@esbuild/linux-mips64el': 0.27.7
-      '@esbuild/linux-ppc64': 0.27.7
-      '@esbuild/linux-riscv64': 0.27.7
-      '@esbuild/linux-s390x': 0.27.7
-      '@esbuild/linux-x64': 0.27.7
-      '@esbuild/netbsd-arm64': 0.27.7
-      '@esbuild/netbsd-x64': 0.27.7
-      '@esbuild/openbsd-arm64': 0.27.7
-      '@esbuild/openbsd-x64': 0.27.7
-      '@esbuild/openharmony-arm64': 0.27.7
-      '@esbuild/sunos-x64': 0.27.7
-      '@esbuild/win32-arm64': 0.27.7
-      '@esbuild/win32-ia32': 0.27.7
-      '@esbuild/win32-x64': 0.27.7
+      '@esbuild/aix-ppc64': 0.28.1
+      '@esbuild/android-arm': 0.28.1
+      '@esbuild/android-arm64': 0.28.1
+      '@esbuild/android-x64': 0.28.1
+      '@esbuild/darwin-arm64': 0.28.1
+      '@esbuild/darwin-x64': 0.28.1
+      '@esbuild/freebsd-arm64': 0.28.1
+      '@esbuild/freebsd-x64': 0.28.1
+      '@esbuild/linux-arm': 0.28.1
+      '@esbuild/linux-arm64': 0.28.1
+      '@esbuild/linux-ia32': 0.28.1
+      '@esbuild/linux-loong64': 0.28.1
+      '@esbuild/linux-mips64el': 0.28.1
+      '@esbuild/linux-ppc64': 0.28.1
+      '@esbuild/linux-riscv64': 0.28.1
+      '@esbuild/linux-s390x': 0.28.1
+      '@esbuild/linux-x64': 0.28.1
+      '@esbuild/netbsd-arm64': 0.28.1
+      '@esbuild/netbsd-x64': 0.28.1
+      '@esbuild/openbsd-arm64': 0.28.1
+      '@esbuild/openbsd-x64': 0.28.1
+      '@esbuild/openharmony-arm64': 0.28.1
+      '@esbuild/sunos-x64': 0.28.1
+      '@esbuild/win32-arm64': 0.28.1
+      '@esbuild/win32-ia32': 0.28.1
+      '@esbuild/win32-x64': 0.28.1

  escape-string-regexp@2.0.0: {}

@@ -4190,7 +4237,7 @@ snapshots:
      crypt: 0.0.2
      is-buffer: 1.1.6

-  mem0ai@3.0.7(@anthropic-ai/sdk@0.91.1(zod@3.25.76))(@azure/identity@4.13.1)(@azure/search-documents@12.2.0)(@cloudflare/workers-types@4.20260610.1)(@google/genai@1.52.0)(@langchain/core@1.1.48(openai@6.26.0(ws@8.21.0)(zod@3.25.76))(ws@8.21.0))(@mistralai/mistralai@2.2.1)(@qdrant/js-client-rest@1.18.0(typescript@6.0.3))(@supabase/supabase-js@2.108.1)(@types/jest@29.5.14)(@types/pg@8.11.0)(better-sqlite3@12.10.0)(cloudflare@4.5.0)(compromise@14.15.1)(groq-sdk@0.3.0)(natural@8.1.1)(ollama@0.5.18)(pg@8.21.0)(redis@5.12.1)(ws@8.21.0):
+  mem0ai@3.0.7(@anthropic-ai/sdk@0.91.1(zod@3.25.76))(@azure/identity@4.13.1)(@azure/search-documents@12.2.0)(@cloudflare/workers-types@4.20260610.1)(@google/genai@1.52.0)(@langchain/core@1.1.48(openai@6.26.0(ws@8.21.0)(zod@3.25.76))(ws@8.21.0))(@mistralai/mistralai@2.2.1)(@qdrant/js-client-rest@1.18.0(typescript@6.0.3))(@supabase/supabase-js@2.108.1)(@types/jest@29.5.14)(@types/pg@8.11.0)(better-sqlite3@12.10.0)(cloudflare@4.5.0)(compromise@14.15.1)(groq-sdk@0.3.0)(natural@8.1.1)(ollama@0.5.18)(pg@8.11.3)(redis@5.12.1)(ws@8.21.0):
    dependencies:
      '@anthropic-ai/sdk': 0.91.1(zod@3.25.76)
      '@azure/identity': 4.13.1
@@ -4211,7 +4258,7 @@ snapshots:
      natural: 8.1.1
      ollama: 0.5.18
      openai: 4.104.0(ws@8.21.0)(zod@3.25.76)
-      pg: 8.21.0
+      pg: 8.11.3
      redis: 5.12.1
      uuid: 14.0.0
      zod: 3.25.76
@@ -4402,6 +4449,8 @@ snapshots:
    dependencies:
      p-finally: 1.0.0

+  packet-reader@1.0.0: {}
+
  partial-json@0.1.7: {}

  path-expression-matcher@1.5.0: {}
@@ -4424,6 +4473,10 @@ snapshots:

  pg-numeric@1.0.2: {}

+  pg-pool@3.14.0(pg@8.11.3):
+    dependencies:
+      pg: 8.11.3
+
  pg-pool@3.14.0(pg@8.21.0):
    dependencies:
      pg: 8.21.0
@@ -4448,6 +4501,18 @@ snapshots:
      postgres-interval: 3.0.0
      postgres-range: 1.1.4

+  pg@8.11.3:
+    dependencies:
+      buffer-writer: 2.0.0
+      packet-reader: 1.0.0
+      pg-connection-string: 2.13.0
+      pg-pool: 3.14.0(pg@8.11.3)
+      pg-protocol: 1.14.0
+      pg-types: 2.2.0
+      pgpass: 1.0.5
+    optionalDependencies:
+      pg-cloudflare: 1.4.0
+
  pg@8.21.0:
    dependencies:
      pg-connection-string: 2.13.0
@@ -4783,12 +4848,12 @@ snapshots:

  tsup@8.5.1(jiti@2.7.0)(postcss@8.5.15)(typescript@6.0.3)(yaml@2.9.0):
    dependencies:
-      bundle-require: 5.1.0(esbuild@0.27.7)
+      bundle-require: 5.1.0(esbuild@0.28.1)
      cac: 6.7.14
      chokidar: 4.0.3
      consola: 3.4.2
      debug: 4.4.3
-      esbuild: 0.27.7
+      esbuild: 0.28.1
      fix-dts-default-cjs-exports: 1.0.1
      joycon: 3.1.1
      picocolors: 1.1.1
@@ -4837,7 +4902,7 @@ snapshots:

  uuid@14.0.0: {}

-  vite@8.0.16(@types/node@25.9.2)(esbuild@0.27.7)(jiti@2.7.0)(yaml@2.9.0):
+  vite@8.0.16(@types/node@25.9.2)(esbuild@0.28.1)(jiti@2.7.0)(yaml@2.9.0):
    dependencies:
      lightningcss: 1.32.0
      picomatch: 4.0.4
@@ -4846,15 +4911,15 @@ snapshots:
      tinyglobby: 0.2.17
    optionalDependencies:
      '@types/node': 25.9.2
-      esbuild: 0.27.7
+      esbuild: 0.28.1
      fsevents: 2.3.3
      jiti: 2.7.0
      yaml: 2.9.0

-  vitest@4.1.8(@types/node@25.9.2)(vite@8.0.16(@types/node@25.9.2)(esbuild@0.27.7)(jiti@2.7.0)(yaml@2.9.0)):
+  vitest@4.1.8(@types/node@25.9.2)(vite@8.0.16(@types/node@25.9.2)(esbuild@0.28.1)(jiti@2.7.0)(yaml@2.9.0)):
    dependencies:
      '@vitest/expect': 4.1.8
-      '@vitest/mocker': 4.1.8(vite@8.0.16(@types/node@25.9.2)(esbuild@0.27.7)(jiti@2.7.0)(yaml@2.9.0))
+      '@vitest/mocker': 4.1.8(vite@8.0.16(@types/node@25.9.2)(esbuild@0.28.1)(jiti@2.7.0)(yaml@2.9.0))
      '@vitest/pretty-format': 4.1.8
      '@vitest/runner': 4.1.8
      '@vitest/snapshot': 4.1.8
@@ -4871,7 +4936,7 @@ snapshots:
      tinyexec: 1.2.4
      tinyglobby: 0.2.17
      tinyrainbow: 3.1.0
-      vite: 8.0.16(@types/node@25.9.2)(esbuild@0.27.7)(jiti@2.7.0)(yaml@2.9.0)
+      vite: 8.0.16(@types/node@25.9.2)(esbuild@0.28.1)(jiti@2.7.0)(yaml@2.9.0)
      why-is-node-running: 2.3.0
    optionalDependencies:
      '@types/node': 25.9.2
@@ -3,3 +3,4 @@ packages:

 overrides:
  "uuid@<11.1.1": ">=11.1.1"
+  "esbuild": ">=0.28.1"
@@ -84,7 +84,8 @@
      "minimatch@>=5.0.0 <5.1.8": "^5.1.8",
      "minimatch@>=9.0.0 <9.0.7": "^9.0.7",
      "picomatch@<2.3.2": "^2.3.2",
-      "rollup@>=4.0.0 <4.59.0": "^4.59.0"
+      "rollup@>=4.0.0 <4.59.0": "^4.59.0",
+      "esbuild": ">=0.28.1"
    }
  }
 }
@@ -11,6 +11,7 @@ overrides:
  minimatch@>=9.0.0 <9.0.7: ^9.0.7
  picomatch@<2.3.2: ^2.3.2
  rollup@>=4.0.0 <4.59.0: ^4.59.0
+  esbuild: '>=0.28.1'

 importers:

@@ -67,7 +68,7 @@ importers:
        version: 3.1.14
      ts-jest:
        specifier: ^29.4.11
-        version: 29.4.11(@babel/core@7.29.7)(@jest/transform@29.7.0)(@jest/types@29.6.3)(babel-jest@29.7.0(@babel/core@7.29.7))(esbuild@0.27.7)(jest-util@29.7.0)(jest@29.7.0(@types/node@18.19.130)(ts-node@10.9.2(@types/node@18.19.130)(typescript@5.9.3)))(typescript@5.9.3)
+        version: 29.4.11(@babel/core@7.29.7)(@jest/transform@29.7.0)(@jest/types@29.6.3)(babel-jest@29.7.0(@babel/core@7.29.7))(esbuild@0.28.1)(jest-util@29.7.0)(jest@29.7.0(@types/node@18.19.130)(ts-node@10.9.2(@types/node@18.19.130)(typescript@5.9.3)))(typescript@5.9.3)
      ts-node:
        specifier: ^10.9.2
        version: 10.9.2(@types/node@18.19.130)(typescript@5.9.3)
@@ -303,158 +304,158 @@ packages:
    resolution: {integrity: sha512-0dEVyRLM/lG4gp1R/Ik5bfPl/1wX00xFwd5KcNH602tzBa09oF7pbTKETEhR1GjZ75K6OJnYFu8II2dyMhONMw==}
    engines: {node: '>=16'}

-  '@esbuild/aix-ppc64@0.27.7':
-    resolution: {integrity: sha512-EKX3Qwmhz1eMdEJokhALr0YiD0lhQNwDqkPYyPhiSwKrh7/4KRjQc04sZ8db+5DVVnZ1LmbNDI1uAMPEUBnQPg==}
+  '@esbuild/aix-ppc64@0.28.1':
+    resolution: {integrity: sha512-Svl7tq8k/08+p6CXPpRjQ1fKX+1odH/BQbb48fV6fj3CWHhsoIOoY87w1oHXm0qEpkIK3ZfVgp0hed3XBXzXMQ==}
    engines: {node: '>=18'}
    cpu: [ppc64]
    os: [aix]

-  '@esbuild/android-arm64@0.27.7':
-    resolution: {integrity: sha512-62dPZHpIXzvChfvfLJow3q5dDtiNMkwiRzPylSCfriLvZeq0a1bWChrGx/BbUbPwOrsWKMn8idSllklzBy+dgQ==}
+  '@esbuild/android-arm64@0.28.1':
+    resolution: {integrity: sha512-34EGEbCIAgosYz6goLcopX6Mo7NyGv9tfwEM2/7Ce2VcVRk568iSvniGWcUXIy7wEDR1wzolcxcriFVrWYcwBg==}
    engines: {node: '>=18'}
    cpu: [arm64]
    os: [android]

-  '@esbuild/android-arm@0.27.7':
-    resolution: {integrity: sha512-jbPXvB4Yj2yBV7HUfE2KHe4GJX51QplCN1pGbYjvsyCZbQmies29EoJbkEc+vYuU5o45AfQn37vZlyXy4YJ8RQ==}
+  '@esbuild/android-arm@0.28.1':
+    resolution: {integrity: sha512-0k2F129Xdio1TdJfzJ8sy1Q47vUD2NnwdhiAf7drUN1EBTfPf4hsFCtmMgu/6m8JSzsBrlmVjudMBQqOfG8usQ==}
    engines: {node: '>=18'}
    cpu: [arm]
    os: [android]

-  '@esbuild/android-x64@0.27.7':
-    resolution: {integrity: sha512-x5VpMODneVDb70PYV2VQOmIUUiBtY3D3mPBG8NxVk5CogneYhkR7MmM3yR/uMdITLrC1ml/NV1rj4bMJuy9MCg==}
+  '@esbuild/android-x64@0.28.1':
+    resolution: {integrity: sha512-dbwY7ltSMDWsRatcRpCnES4F+im88OCUgGZjy52shC7GqHRE/cYlxNbB4Z4UpJswpcc4Qxd2oE/ufM0p61IKng==}
    engines: {node: '>=18'}
    cpu: [x64]
    os: [android]

-  '@esbuild/darwin-arm64@0.27.7':
-    resolution: {integrity: sha512-5lckdqeuBPlKUwvoCXIgI2D9/ABmPq3Rdp7IfL70393YgaASt7tbju3Ac+ePVi3KDH6N2RqePfHnXkaDtY9fkw==}
+  '@esbuild/darwin-arm64@0.28.1':
+    resolution: {integrity: sha512-TZbWkQY7kvTAXbXUT7uVACR5cMHsDiSz9z7ZKAX/RTq/WJEk3QyRr0wZpNhBDX+/0CtdqUIJlOiodQcta6tY3Q==}
    engines: {node: '>=18'}
    cpu: [arm64]
    os: [darwin]

-  '@esbuild/darwin-x64@0.27.7':
-    resolution: {integrity: sha512-rYnXrKcXuT7Z+WL5K980jVFdvVKhCHhUwid+dDYQpH+qu+TefcomiMAJpIiC2EM3Rjtq0sO3StMV/+3w3MyyqQ==}
+  '@esbuild/darwin-x64@0.28.1':
+    resolution: {integrity: sha512-zfdzgK9ACBNZLI/CyHTOx81SyNbM6YXn7rxSgX97VjyiPl9W1i4Ka4fgKECEoFCKGpvBj5qArWIGgQjOwkgskQ==}
    engines: {node: '>=18'}
    cpu: [x64]
    os: [darwin]

-  '@esbuild/freebsd-arm64@0.27.7':
-    resolution: {integrity: sha512-B48PqeCsEgOtzME2GbNM2roU29AMTuOIN91dsMO30t+Ydis3z/3Ngoj5hhnsOSSwNzS+6JppqWsuhTp6E82l2w==}
+  '@esbuild/freebsd-arm64@0.28.1':
+    resolution: {integrity: sha512-wG2EA8ENdEI0qhkSZMjfqrdY+ziCYCPMmtZjjIwOmXFjmyzEHn+UUxk5of+SYsjtfs3VpnlC7QLzSI5hY/rOAw==}
    engines: {node: '>=18'}
    cpu: [arm64]
    os: [freebsd]

-  '@esbuild/freebsd-x64@0.27.7':
-    resolution: {integrity: sha512-jOBDK5XEjA4m5IJK3bpAQF9/Lelu/Z9ZcdhTRLf4cajlB+8VEhFFRjWgfy3M1O4rO2GQ/b2dLwCUGpiF/eATNQ==}
+  '@esbuild/freebsd-x64@0.28.1':
+    resolution: {integrity: sha512-i7dZ9vQgnvSCzi/rYCXNgtF/U+eKZNJBzu3eTQbRgHnM7tNSizLOkRFAl3qzVc/Op/u5YkHHa4pf/3DOYHthLQ==}
    engines: {node: '>=18'}
    cpu: [x64]
    os: [freebsd]

-  '@esbuild/linux-arm64@0.27.7':
-    resolution: {integrity: sha512-RZPHBoxXuNnPQO9rvjh5jdkRmVizktkT7TCDkDmQ0W2SwHInKCAV95GRuvdSvA7w4VMwfCjUiPwDi0ZO6Nfe9A==}
+  '@esbuild/linux-arm64@0.28.1':
+    resolution: {integrity: sha512-yHs+0uc8+nvEAfAfxrWQKK5peSNzBc4PegcMO0EJ2hT71uA7vB8Ihg2e77R2P7SG5uYjPbHlLLmve4LLLRCf0g==}
    engines: {node: '>=18'}
    cpu: [arm64]
    os: [linux]

-  '@esbuild/linux-arm@0.27.7':
-    resolution: {integrity: sha512-RkT/YXYBTSULo3+af8Ib0ykH8u2MBh57o7q/DAs3lTJlyVQkgQvlrPTnjIzzRPQyavxtPtfg0EopvDyIt0j1rA==}
+  '@esbuild/linux-arm@0.28.1':
+    resolution: {integrity: sha512-qVXBOHQS+d5Y722GwJzJUtOLlX7km3CraOaGormF1pDtPd2C/l1SHRPgjLunLGe51Sh5YYWKMFDyV4SxgMQYTQ==}
    engines: {node: '>=18'}
    cpu: [arm]
    os: [linux]

-  '@esbuild/linux-ia32@0.27.7':
-    resolution: {integrity: sha512-GA48aKNkyQDbd3KtkplYWT102C5sn/EZTY4XROkxONgruHPU72l+gW+FfF8tf2cFjeHaRbWpOYa/uRBz/Xq1Pg==}
+  '@esbuild/linux-ia32@0.28.1':
+    resolution: {integrity: sha512-d1z4ZuP0ajrfz/FhGT4vv278rX8KnPPJx8i5+AtK7TYbx9Le9F1hyzurZpkEyjkGa9dUGhQow4C1NmeGvqxN2w==}
    engines: {node: '>=18'}
    cpu: [ia32]
    os: [linux]

-  '@esbuild/linux-loong64@0.27.7':
-    resolution: {integrity: sha512-a4POruNM2oWsD4WKvBSEKGIiWQF8fZOAsycHOt6JBpZ+JN2n2JH9WAv56SOyu9X5IqAjqSIPTaJkqN8F7XOQ5Q==}
+  '@esbuild/linux-loong64@0.28.1':
+    resolution: {integrity: sha512-M5sRjUVZrkm1OAPR3dlOYzNmN+loZKGVi1VUQGrwuqLcbR6qeAz+famMhjASeH3YVKvZz+zT1jlh/keC3Rj/lg==}
    engines: {node: '>=18'}
    cpu: [loong64]
    os: [linux]

-  '@esbuild/linux-mips64el@0.27.7':
-    resolution: {integrity: sha512-KabT5I6StirGfIz0FMgl1I+R1H73Gp0ofL9A3nG3i/cYFJzKHhouBV5VWK1CSgKvVaG4q1RNpCTR2LuTVB3fIw==}
+  '@esbuild/linux-mips64el@0.28.1':
+    resolution: {integrity: sha512-mRObBZeHh2OxcBFPWE/FjylkRgZdYuiTR3vaTozquCGOH14iP9oN4x4Ge81CoIDYQrXmIxpFumJBu5MtZpnQJQ==}
    engines: {node: '>=18'}
    cpu: [mips64el]
    os: [linux]

-  '@esbuild/linux-ppc64@0.27.7':
-    resolution: {integrity: sha512-gRsL4x6wsGHGRqhtI+ifpN/vpOFTQtnbsupUF5R5YTAg+y/lKelYR1hXbnBdzDjGbMYjVJLJTd2OFmMewAgwlQ==}
+  '@esbuild/linux-ppc64@0.28.1':
+    resolution: {integrity: sha512-slScBsMAb3GFDcdrCgLwZtPYRoH2H/youv10QiZyRjmsP48fznoveWytSgCI/R0ZcUgpc0ZhIUEx6LHts8yrfQ==}
    engines: {node: '>=18'}
    cpu: [ppc64]
    os: [linux]

-  '@esbuild/linux-riscv64@0.27.7':
-    resolution: {integrity: sha512-hL25LbxO1QOngGzu2U5xeXtxXcW+/GvMN3ejANqXkxZ/opySAZMrc+9LY/WyjAan41unrR3YrmtTsUpwT66InQ==}
+  '@esbuild/linux-riscv64@0.28.1':
+    resolution: {integrity: sha512-kw0owk1o0GFETUJyW0jc0G4Yzs0BHZn0JDZ8JRT088vjJYX777BAs1fDGxAC+q831qOs2DTC96mNsG2opdfyyQ==}
    engines: {node: '>=18'}
    cpu: [riscv64]
    os: [linux]

-  '@esbuild/linux-s390x@0.27.7':
-    resolution: {integrity: sha512-2k8go8Ycu1Kb46vEelhu1vqEP+UeRVj2zY1pSuPdgvbd5ykAw82Lrro28vXUrRmzEsUV0NzCf54yARIK8r0fdw==}
+  '@esbuild/linux-s390x@0.28.1':
+    resolution: {integrity: sha512-/lAIjX8aYFRByhh6L5rYtPEDRqa9de/4V/juOXcta5frjvzXO4/sqEtyytse0g3zZFuWu5cDN0MkLz2qRDD2Ag==}
    engines: {node: '>=18'}
    cpu: [s390x]
    os: [linux]

-  '@esbuild/linux-x64@0.27.7':
-    resolution: {integrity: sha512-hzznmADPt+OmsYzw1EE33ccA+HPdIqiCRq7cQeL1Jlq2gb1+OyWBkMCrYGBJ+sxVzve2ZJEVeePbLM2iEIZSxA==}
+  '@esbuild/linux-x64@0.28.1':
+    resolution: {integrity: sha512-u/anNYF2mmVOEDwLtnQ1wOr3EZ9sTNGLWrsYGYwHWzGA3Si84IOkHXlbWTD1NB+9/1lcnweYKO54uhxZydNzfA==}
    engines: {node: '>=18'}
    cpu: [x64]
    os: [linux]

-  '@esbuild/netbsd-arm64@0.27.7':
-    resolution: {integrity: sha512-b6pqtrQdigZBwZxAn1UpazEisvwaIDvdbMbmrly7cDTMFnw/+3lVxxCTGOrkPVnsYIosJJXAsILG9XcQS+Yu6w==}
+  '@esbuild/netbsd-arm64@0.28.1':
+    resolution: {integrity: sha512-oks0DYbLwWMmaakTsCb+zL4E+aHRVLom9IJZOAthMQEPiQmydXHkziYEsGYRx0uNV/IjEKGAV941JzH02pflqw==}
    engines: {node: '>=18'}
    cpu: [arm64]
    os: [netbsd]

-  '@esbuild/netbsd-x64@0.27.7':
-    resolution: {integrity: sha512-OfatkLojr6U+WN5EDYuoQhtM+1xco+/6FSzJJnuWiUw5eVcicbyK3dq5EeV/QHT1uy6GoDhGbFpprUiHUYggrw==}
+  '@esbuild/netbsd-x64@0.28.1':
+    resolution: {integrity: sha512-aeL6lAnN89Hz43Mlh1G8ARasbuoYvSITDEx0tHh5b7jJnHcssqgjy9Yx430GDpmCa6OyrKoS0aNRjKundRizGg==}
    engines: {node: '>=18'}
    cpu: [x64]
    os: [netbsd]

-  '@esbuild/openbsd-arm64@0.27.7':
-    resolution: {integrity: sha512-AFuojMQTxAz75Fo8idVcqoQWEHIXFRbOc1TrVcFSgCZtQfSdc1RXgB3tjOn/krRHENUB4j00bfGjyl2mJrU37A==}
+  '@esbuild/openbsd-arm64@0.28.1':
+    resolution: {integrity: sha512-MEFJe5C3R8pwXdZ5Y21oo6m7ePiS0d9pWucn99O/wvyJZChoIQKrQDxKrGeW8F5+T0okTHesAmDeiHDTIq0V/Q==}
    engines: {node: '>=18'}
    cpu: [arm64]
    os: [openbsd]

-  '@esbuild/openbsd-x64@0.27.7':
-    resolution: {integrity: sha512-+A1NJmfM8WNDv5CLVQYJ5PshuRm/4cI6WMZRg1by1GwPIQPCTs1GLEUHwiiQGT5zDdyLiRM/l1G0Pv54gvtKIg==}
+  '@esbuild/openbsd-x64@0.28.1':
+    resolution: {integrity: sha512-i/ZLIOafE0Z8cI/XANJAixoJL/uRAoS2xOA3rb0xN+KK0K177cMAsQYkzHtBrtMXAKuAc7HGgcWiZ/sRC1Nxgw==}
    engines: {node: '>=18'}
    cpu: [x64]
    os: [openbsd]

-  '@esbuild/openharmony-arm64@0.27.7':
-    resolution: {integrity: sha512-+KrvYb/C8zA9CU/g0sR6w2RBw7IGc5J2BPnc3dYc5VJxHCSF1yNMxTV5LQ7GuKteQXZtspjFbiuW5/dOj7H4Yw==}
+  '@esbuild/openharmony-arm64@0.28.1':
+    resolution: {integrity: sha512-ge+Z7EXFNt2BO1oAMsVpiQ8EwndV9i1xXerAeTIK7AtPs3bKFXQM7nlRxDSIUIMeueR1CNXxqztLzdNeReKBJg==}
    engines: {node: '>=18'}
    cpu: [arm64]
    os: [openharmony]

-  '@esbuild/sunos-x64@0.27.7':
-    resolution: {integrity: sha512-ikktIhFBzQNt/QDyOL580ti9+5mL/YZeUPKU2ivGtGjdTYoqz6jObj6nOMfhASpS4GU4Q/Clh1QtxWAvcYKamA==}
+  '@esbuild/sunos-x64@0.28.1':
+    resolution: {integrity: sha512-BEjgtECkL3vY+SaSQ6nzVfiALUeFxpawyp8Jmf5PtYhf1Ug40N1h/hxlhts+f1FvSvarEigdxS3BlSMI2PJLcQ==}
    engines: {node: '>=18'}
    cpu: [x64]
    os: [sunos]

-  '@esbuild/win32-arm64@0.27.7':
-    resolution: {integrity: sha512-7yRhbHvPqSpRUV7Q20VuDwbjW5kIMwTHpptuUzV+AA46kiPze5Z7qgt6CLCK3pWFrHeNfDd1VKgyP4O+ng17CA==}
+  '@esbuild/win32-arm64@0.28.1':
+    resolution: {integrity: sha512-lCv9eK/H6ZJWbE7bh2nw54CZ9M2nupBxJcTsdk/QQnWkdSjKGuxmmH8/GWrlT1eMmZfn4dGcCjRte397WqfQXA==}
    engines: {node: '>=18'}
    cpu: [arm64]
    os: [win32]

-  '@esbuild/win32-ia32@0.27.7':
-    resolution: {integrity: sha512-SmwKXe6VHIyZYbBLJrhOoCJRB/Z1tckzmgTLfFYOfpMAx63BJEaL9ExI8x7v0oAO3Zh6D/Oi1gVxEYr5oUCFhw==}
+  '@esbuild/win32-ia32@0.28.1':
+    resolution: {integrity: sha512-zvb/mB2bSCoJOpoCBgYKKpX6YM6mJBlBUVUtVj41DlZJVEB6/0CKlRYxP5wWl1C1ILiCoAU5wZZ4q1P3qeS6Eg==}
    engines: {node: '>=18'}
    cpu: [ia32]
    os: [win32]

-  '@esbuild/win32-x64@0.27.7':
-    resolution: {integrity: sha512-56hiAJPhwQ1R4i+21FVF7V8kSD5zZTdHcVuRFMW0hn753vVfQN8xlx4uOPT4xoGH0Z/oVATuR82AiqSTDIpaHg==}
+  '@esbuild/win32-x64@0.28.1':
+    resolution: {integrity: sha512-bm4Mowrv+GXMlpWX++EcXw/iLyd1o3+bJkC2DkWXYVvgZCqD/bSj9ctZeAMC3cIxgjRVR2Dufaiu4YPxr5gW1A==}
    engines: {node: '>=18'}
    cpu: [x64]
    os: [win32]
@@ -866,7 +867,7 @@ packages:
    resolution: {integrity: sha512-3WrrOuZiyaaZPWiEt4G3+IffISVC9HYlWueJEBWED4ZH4aIAC2PnkdnuRrR94M+w6yGWn4AglWtJtBI8YqvgoA==}
    engines: {node: ^12.20.0 || ^14.13.1 || >=16.0.0}
    peerDependencies:
-      esbuild: '>=0.18'
+      esbuild: '>=0.28.1'

  cac@6.7.14:
    resolution: {integrity: sha512-b6Ilus+c3RrdDk+JhLKUAQfzzgLEPy6wcXqS7f/xe1EETvsDP6GORG7SFuOs6cID5YkqchW/LXZbX5bc8j7ZcQ==}
@@ -1011,8 +1012,8 @@ packages:
    resolution: {integrity: sha512-Zf5H2Kxt2xjTvbJvP2ZWLEICxA6j+hAmMzIlypy4xcBg1vKVnx89Wy0GbS+kf5cwCVFFzdCFh2XSCFNULS6csw==}
    engines: {node: '>= 0.4'}

-  esbuild@0.27.7:
-    resolution: {integrity: sha512-IxpibTjyVnmrIQo5aqNpCgoACA/dTKLTlhMHihVHhdkxKyPO1uBBthumT0rdHmcsk9uMonIWS0m4FljWzILh3w==}
+  esbuild@0.28.1:
+    resolution: {integrity: sha512-HrJrvZv5ayxBzPfwphOoNzkzOIIlifzk0KJrGK2c8R4+LKpMtpYLQeUdjnwjWv/LZlkH2laZk+4w78pi99D4Vw==}
    engines: {node: '>=18'}
    hasBin: true

@@ -2142,82 +2143,82 @@ snapshots:
    dependencies:
      '@edge-runtime/primitives': 4.1.0

-  '@esbuild/aix-ppc64@0.27.7':
+  '@esbuild/aix-ppc64@0.28.1':
    optional: true

-  '@esbuild/android-arm64@0.27.7':
+  '@esbuild/android-arm64@0.28.1':
    optional: true

-  '@esbuild/android-arm@0.27.7':
+  '@esbuild/android-arm@0.28.1':
    optional: true

-  '@esbuild/android-x64@0.27.7':
+  '@esbuild/android-x64@0.28.1':
    optional: true

-  '@esbuild/darwin-arm64@0.27.7':
+  '@esbuild/darwin-arm64@0.28.1':
    optional: true

-  '@esbuild/darwin-x64@0.27.7':
+  '@esbuild/darwin-x64@0.28.1':
    optional: true

-  '@esbuild/freebsd-arm64@0.27.7':
+  '@esbuild/freebsd-arm64@0.28.1':
    optional: true

-  '@esbuild/freebsd-x64@0.27.7':
+  '@esbuild/freebsd-x64@0.28.1':
    optional: true

-  '@esbuild/linux-arm64@0.27.7':
+  '@esbuild/linux-arm64@0.28.1':
    optional: true

-  '@esbuild/linux-arm@0.27.7':
+  '@esbuild/linux-arm@0.28.1':
    optional: true

-  '@esbuild/linux-ia32@0.27.7':
+  '@esbuild/linux-ia32@0.28.1':
    optional: true

-  '@esbuild/linux-loong64@0.27.7':
+  '@esbuild/linux-loong64@0.28.1':
    optional: true

-  '@esbuild/linux-mips64el@0.27.7':
+  '@esbuild/linux-mips64el@0.28.1':
    optional: true

-  '@esbuild/linux-ppc64@0.27.7':
+  '@esbuild/linux-ppc64@0.28.1':
    optional: true

-  '@esbuild/linux-riscv64@0.27.7':
+  '@esbuild/linux-riscv64@0.28.1':
    optional: true

-  '@esbuild/linux-s390x@0.27.7':
+  '@esbuild/linux-s390x@0.28.1':
    optional: true

-  '@esbuild/linux-x64@0.27.7':
+  '@esbuild/linux-x64@0.28.1':
    optional: true

-  '@esbuild/netbsd-arm64@0.27.7':
+  '@esbuild/netbsd-arm64@0.28.1':
    optional: true

-  '@esbuild/netbsd-x64@0.27.7':
+  '@esbuild/netbsd-x64@0.28.1':
    optional: true

-  '@esbuild/openbsd-arm64@0.27.7':
+  '@esbuild/openbsd-arm64@0.28.1':
    optional: true

-  '@esbuild/openbsd-x64@0.27.7':
+  '@esbuild/openbsd-x64@0.28.1':
    optional: true

-  '@esbuild/openharmony-arm64@0.27.7':
+  '@esbuild/openharmony-arm64@0.28.1':
    optional: true

-  '@esbuild/sunos-x64@0.27.7':
+  '@esbuild/sunos-x64@0.28.1':
    optional: true

-  '@esbuild/win32-arm64@0.27.7':
+  '@esbuild/win32-arm64@0.28.1':
    optional: true

-  '@esbuild/win32-ia32@0.27.7':
+  '@esbuild/win32-ia32@0.28.1':
    optional: true

-  '@esbuild/win32-x64@0.27.7':
+  '@esbuild/win32-x64@0.28.1':
    optional: true

  '@istanbuljs/load-nyc-config@1.1.0':
@@ -2702,9 +2703,9 @@ snapshots:

  buffer-from@1.1.2: {}

-  bundle-require@5.1.0(esbuild@0.27.7):
+  bundle-require@5.1.0(esbuild@0.28.1):
    dependencies:
-      esbuild: 0.27.7
+      esbuild: 0.28.1
      load-tsconfig: 0.2.5

  cac@6.7.14: {}
@@ -2823,34 +2824,34 @@ snapshots:

  es-errors@1.3.0: {}

-  esbuild@0.27.7:
+  esbuild@0.28.1:
    optionalDependencies:
-      '@esbuild/aix-ppc64': 0.27.7
-      '@esbuild/android-arm': 0.27.7
-      '@esbuild/android-arm64': 0.27.7
-      '@esbuild/android-x64': 0.27.7
-      '@esbuild/darwin-arm64': 0.27.7
-      '@esbuild/darwin-x64': 0.27.7
-      '@esbuild/freebsd-arm64': 0.27.7
-      '@esbuild/freebsd-x64': 0.27.7
-      '@esbuild/linux-arm': 0.27.7
-      '@esbuild/linux-arm64': 0.27.7
-      '@esbuild/linux-ia32': 0.27.7
-      '@esbuild/linux-loong64': 0.27.7
-      '@esbuild/linux-mips64el': 0.27.7
-      '@esbuild/linux-ppc64': 0.27.7
-      '@esbuild/linux-riscv64': 0.27.7
-      '@esbuild/linux-s390x': 0.27.7
-      '@esbuild/linux-x64': 0.27.7
-      '@esbuild/netbsd-arm64': 0.27.7
-      '@esbuild/netbsd-x64': 0.27.7
-      '@esbuild/openbsd-arm64': 0.27.7
-      '@esbuild/openbsd-x64': 0.27.7
-      '@esbuild/openharmony-arm64': 0.27.7
-      '@esbuild/sunos-x64': 0.27.7
-      '@esbuild/win32-arm64': 0.27.7
-      '@esbuild/win32-ia32': 0.27.7
-      '@esbuild/win32-x64': 0.27.7
+      '@esbuild/aix-ppc64': 0.28.1
+      '@esbuild/android-arm': 0.28.1
+      '@esbuild/android-arm64': 0.28.1
+      '@esbuild/android-x64': 0.28.1
+      '@esbuild/darwin-arm64': 0.28.1
+      '@esbuild/darwin-x64': 0.28.1
+      '@esbuild/freebsd-arm64': 0.28.1
+      '@esbuild/freebsd-x64': 0.28.1
+      '@esbuild/linux-arm': 0.28.1
+      '@esbuild/linux-arm64': 0.28.1
+      '@esbuild/linux-ia32': 0.28.1
+      '@esbuild/linux-loong64': 0.28.1
+      '@esbuild/linux-mips64el': 0.28.1
+      '@esbuild/linux-ppc64': 0.28.1
+      '@esbuild/linux-riscv64': 0.28.1
+      '@esbuild/linux-s390x': 0.28.1
+      '@esbuild/linux-x64': 0.28.1
+      '@esbuild/netbsd-arm64': 0.28.1
+      '@esbuild/netbsd-x64': 0.28.1
+      '@esbuild/openbsd-arm64': 0.28.1
+      '@esbuild/openbsd-x64': 0.28.1
+      '@esbuild/openharmony-arm64': 0.28.1
+      '@esbuild/sunos-x64': 0.28.1
+      '@esbuild/win32-arm64': 0.28.1
+      '@esbuild/win32-ia32': 0.28.1
+      '@esbuild/win32-x64': 0.28.1

  escalade@3.2.0: {}

@@ -3711,7 +3712,7 @@ snapshots:

  ts-interface-checker@0.1.13: {}

-  ts-jest@29.4.11(@babel/core@7.29.7)(@jest/transform@29.7.0)(@jest/types@29.6.3)(babel-jest@29.7.0(@babel/core@7.29.7))(esbuild@0.27.7)(jest-util@29.7.0)(jest@29.7.0(@types/node@18.19.130)(ts-node@10.9.2(@types/node@18.19.130)(typescript@5.9.3)))(typescript@5.9.3):
+  ts-jest@29.4.11(@babel/core@7.29.7)(@jest/transform@29.7.0)(@jest/types@29.6.3)(babel-jest@29.7.0(@babel/core@7.29.7))(esbuild@0.28.1)(jest-util@29.7.0)(jest@29.7.0(@types/node@18.19.130)(ts-node@10.9.2(@types/node@18.19.130)(typescript@5.9.3)))(typescript@5.9.3):
    dependencies:
      bs-logger: 0.2.6
      fast-json-stable-stringify: 2.1.0
@@ -3729,7 +3730,7 @@ snapshots:
      '@jest/transform': 29.7.0
      '@jest/types': 29.6.3
      babel-jest: 29.7.0(@babel/core@7.29.7)
-      esbuild: 0.27.7
+      esbuild: 0.28.1
      jest-util: 29.7.0

  ts-node@10.9.2(@types/node@18.19.130)(typescript@5.9.3):
@@ -3752,12 +3753,12 @@ snapshots:

  tsup@8.5.1(typescript@5.9.3):
    dependencies:
-      bundle-require: 5.1.0(esbuild@0.27.7)
+      bundle-require: 5.1.0(esbuild@0.28.1)
      cac: 6.7.14
      chokidar: 4.0.3
      consola: 3.4.2
      debug: 4.4.3(supports-color@5.5.0)
-      esbuild: 0.27.7
+      esbuild: 0.28.1
      fix-dts-default-cjs-exports: 1.0.1
      joycon: 3.1.1
      picocolors: 1.1.1
@@ -12,3 +12,4 @@ overrides:
  "minimatch@>=9.0.0 <9.0.7": "^9.0.7"
  "picomatch@<2.3.2": "^2.3.2"
  "rollup@>=4.0.0 <4.59.0": "^4.59.0"
+  "esbuild": ">=0.28.1"
@@ -154,7 +154,8 @@
      "rollup@>=4.0.0 <4.59.0": "^4.59.0",
      "tar-fs@>=2.0.0 <2.1.4": "^2.1.4",
      "glob@>=10.2.0 <10.5.0": "^10.5.0",
-      "@modelcontextprotocol/sdk": "^1.25.4"
+      "@modelcontextprotocol/sdk": "^1.25.4",
+      "esbuild": ">=0.28.1"
    }
  }
 }
@@ -21,6 +21,7 @@ overrides:
  tar-fs@>=2.0.0 <2.1.4: ^2.1.4
  glob@>=10.2.0 <10.5.0: ^10.5.0
  '@modelcontextprotocol/sdk': ^1.25.4
+  esbuild: '>=0.28.1'

 importers:

@@ -125,7 +126,7 @@ importers:
        version: 5.0.10
      ts-jest:
        specifier: ^29.2.6
-        version: 29.4.11(@babel/core@7.29.7)(@jest/transform@29.7.0)(@jest/types@29.6.3)(babel-jest@29.7.0(@babel/core@7.29.7))(esbuild@0.27.7)(jest-util@29.7.0)(jest@29.7.0(@types/node@22.19.21)(ts-node@10.9.2(@types/node@22.19.21)(typescript@5.5.4)))(typescript@5.5.4)
+        version: 29.4.11(@babel/core@7.29.7)(@jest/transform@29.7.0)(@jest/types@29.6.3)(babel-jest@29.7.0(@babel/core@7.29.7))(esbuild@0.28.1)(jest-util@29.7.0)(jest@29.7.0(@types/node@22.19.21)(ts-node@10.9.2(@types/node@22.19.21)(typescript@5.5.4)))(typescript@5.5.4)
      ts-node:
        specifier: ^10.9.2
        version: 10.9.2(@types/node@22.19.21)(typescript@5.5.4)
@@ -375,158 +376,158 @@ packages:
    resolution: {integrity: sha512-IchNf6dN4tHoMFIn/7OE8LWZ19Y6q/67Bmf6vnGREv8RSbBVb9LPJxEcnwrcwX6ixSvaiGoomAUvu4YSxXrVgw==}
    engines: {node: '>=12'}

-  '@esbuild/aix-ppc64@0.27.7':
-    resolution: {integrity: sha512-EKX3Qwmhz1eMdEJokhALr0YiD0lhQNwDqkPYyPhiSwKrh7/4KRjQc04sZ8db+5DVVnZ1LmbNDI1uAMPEUBnQPg==}
+  '@esbuild/aix-ppc64@0.28.1':
+    resolution: {integrity: sha512-Svl7tq8k/08+p6CXPpRjQ1fKX+1odH/BQbb48fV6fj3CWHhsoIOoY87w1oHXm0qEpkIK3ZfVgp0hed3XBXzXMQ==}
    engines: {node: '>=18'}
    cpu: [ppc64]
    os: [aix]

-  '@esbuild/android-arm64@0.27.7':
-    resolution: {integrity: sha512-62dPZHpIXzvChfvfLJow3q5dDtiNMkwiRzPylSCfriLvZeq0a1bWChrGx/BbUbPwOrsWKMn8idSllklzBy+dgQ==}
+  '@esbuild/android-arm64@0.28.1':
+    resolution: {integrity: sha512-34EGEbCIAgosYz6goLcopX6Mo7NyGv9tfwEM2/7Ce2VcVRk568iSvniGWcUXIy7wEDR1wzolcxcriFVrWYcwBg==}
    engines: {node: '>=18'}
    cpu: [arm64]
    os: [android]

-  '@esbuild/android-arm@0.27.7':
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    os: [linux]

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    cpu: [loong64]
    os: [linux]

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    os: [linux]

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    cpu: [arm64]
    os: [netbsd]

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    os: [openbsd]

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    cpu: [arm64]
    os: [openharmony]

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+  '@esbuild/sunos-x64@0.28.1':
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    engines: {node: '>=18'}
    cpu: [x64]
    os: [sunos]

-  '@esbuild/win32-arm64@0.27.7':
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+  '@esbuild/win32-arm64@0.28.1':
+    resolution: {integrity: sha512-lCv9eK/H6ZJWbE7bh2nw54CZ9M2nupBxJcTsdk/QQnWkdSjKGuxmmH8/GWrlT1eMmZfn4dGcCjRte397WqfQXA==}
    engines: {node: '>=18'}
    cpu: [arm64]
    os: [win32]

-  '@esbuild/win32-ia32@0.27.7':
-    resolution: {integrity: sha512-SmwKXe6VHIyZYbBLJrhOoCJRB/Z1tckzmgTLfFYOfpMAx63BJEaL9ExI8x7v0oAO3Zh6D/Oi1gVxEYr5oUCFhw==}
+  '@esbuild/win32-ia32@0.28.1':
+    resolution: {integrity: sha512-zvb/mB2bSCoJOpoCBgYKKpX6YM6mJBlBUVUtVj41DlZJVEB6/0CKlRYxP5wWl1C1ILiCoAU5wZZ4q1P3qeS6Eg==}
    engines: {node: '>=18'}
    cpu: [ia32]
    os: [win32]

-  '@esbuild/win32-x64@0.27.7':
-    resolution: {integrity: sha512-56hiAJPhwQ1R4i+21FVF7V8kSD5zZTdHcVuRFMW0hn753vVfQN8xlx4uOPT4xoGH0Z/oVATuR82AiqSTDIpaHg==}
+  '@esbuild/win32-x64@0.28.1':
+    resolution: {integrity: sha512-bm4Mowrv+GXMlpWX++EcXw/iLyd1o3+bJkC2DkWXYVvgZCqD/bSj9ctZeAMC3cIxgjRVR2Dufaiu4YPxr5gW1A==}
    engines: {node: '>=18'}
    cpu: [x64]
    os: [win32]
@@ -1206,7 +1207,7 @@ packages:
    resolution: {integrity: sha512-3WrrOuZiyaaZPWiEt4G3+IffISVC9HYlWueJEBWED4ZH4aIAC2PnkdnuRrR94M+w6yGWn4AglWtJtBI8YqvgoA==}
    engines: {node: ^12.20.0 || ^14.13.1 || >=16.0.0}
    peerDependencies:
-      esbuild: '>=0.18'
+      esbuild: '>=0.28.1'

  cac@6.7.14:
    resolution: {integrity: sha512-b6Ilus+c3RrdDk+JhLKUAQfzzgLEPy6wcXqS7f/xe1EETvsDP6GORG7SFuOs6cID5YkqchW/LXZbX5bc8j7ZcQ==}
@@ -1450,8 +1451,8 @@ packages:
    resolution: {integrity: sha512-j6vWzfrGVfyXxge+O0x5sh6cvxAog0a/4Rdd2K36zCMV5eJ+/+tOAngRO8cODMNWbVRdVlmGZQL2YS3yR8bIUA==}
    engines: {node: '>= 0.4'}

-  esbuild@0.27.7:
-    resolution: {integrity: sha512-IxpibTjyVnmrIQo5aqNpCgoACA/dTKLTlhMHihVHhdkxKyPO1uBBthumT0rdHmcsk9uMonIWS0m4FljWzILh3w==}
+  esbuild@0.28.1:
+    resolution: {integrity: sha512-HrJrvZv5ayxBzPfwphOoNzkzOIIlifzk0KJrGK2c8R4+LKpMtpYLQeUdjnwjWv/LZlkH2laZk+4w78pi99D4Vw==}
    engines: {node: '>=18'}
    hasBin: true

@@ -3381,82 +3382,82 @@ snapshots:
    dependencies:
      '@jridgewell/trace-mapping': 0.3.9

-  '@esbuild/aix-ppc64@0.27.7':
+  '@esbuild/aix-ppc64@0.28.1':
    optional: true

-  '@esbuild/android-arm64@0.27.7':
+  '@esbuild/android-arm64@0.28.1':
    optional: true

-  '@esbuild/android-arm@0.27.7':
+  '@esbuild/android-arm@0.28.1':
    optional: true

-  '@esbuild/android-x64@0.27.7':
+  '@esbuild/android-x64@0.28.1':
    optional: true

-  '@esbuild/darwin-arm64@0.27.7':
+  '@esbuild/darwin-arm64@0.28.1':
    optional: true

-  '@esbuild/darwin-x64@0.27.7':
+  '@esbuild/darwin-x64@0.28.1':
    optional: true

-  '@esbuild/freebsd-arm64@0.27.7':
+  '@esbuild/freebsd-arm64@0.28.1':
    optional: true

-  '@esbuild/freebsd-x64@0.27.7':
+  '@esbuild/freebsd-x64@0.28.1':
    optional: true

-  '@esbuild/linux-arm64@0.27.7':
+  '@esbuild/linux-arm64@0.28.1':
    optional: true

-  '@esbuild/linux-arm@0.27.7':
+  '@esbuild/linux-arm@0.28.1':
    optional: true

-  '@esbuild/linux-ia32@0.27.7':
+  '@esbuild/linux-ia32@0.28.1':
    optional: true

-  '@esbuild/linux-loong64@0.27.7':
+  '@esbuild/linux-loong64@0.28.1':
    optional: true

-  '@esbuild/linux-mips64el@0.27.7':
+  '@esbuild/linux-mips64el@0.28.1':
    optional: true

-  '@esbuild/linux-ppc64@0.27.7':
+  '@esbuild/linux-ppc64@0.28.1':
    optional: true

-  '@esbuild/linux-riscv64@0.27.7':
+  '@esbuild/linux-riscv64@0.28.1':
    optional: true

-  '@esbuild/linux-s390x@0.27.7':
+  '@esbuild/linux-s390x@0.28.1':
    optional: true

-  '@esbuild/linux-x64@0.27.7':
+  '@esbuild/linux-x64@0.28.1':
    optional: true

-  '@esbuild/netbsd-arm64@0.27.7':
+  '@esbuild/netbsd-arm64@0.28.1':
    optional: true

-  '@esbuild/netbsd-x64@0.27.7':
+  '@esbuild/netbsd-x64@0.28.1':
    optional: true

-  '@esbuild/openbsd-arm64@0.27.7':
+  '@esbuild/openbsd-arm64@0.28.1':
    optional: true

-  '@esbuild/openbsd-x64@0.27.7':
+  '@esbuild/openbsd-x64@0.28.1':
    optional: true

-  '@esbuild/openharmony-arm64@0.27.7':
+  '@esbuild/openharmony-arm64@0.28.1':
    optional: true

-  '@esbuild/sunos-x64@0.27.7':
+  '@esbuild/sunos-x64@0.28.1':
    optional: true

-  '@esbuild/win32-arm64@0.27.7':
+  '@esbuild/win32-arm64@0.28.1':
    optional: true

-  '@esbuild/win32-ia32@0.27.7':
+  '@esbuild/win32-ia32@0.28.1':
    optional: true

-  '@esbuild/win32-x64@0.27.7':
+  '@esbuild/win32-x64@0.28.1':
    optional: true

  '@google/genai@1.52.0':
@@ -4218,9 +4219,9 @@ snapshots:
    dependencies:
      run-applescript: 7.1.0

-  bundle-require@5.1.0(esbuild@0.27.7):
+  bundle-require@5.1.0(esbuild@0.28.1):
    dependencies:
-      esbuild: 0.27.7
+      esbuild: 0.28.1
      load-tsconfig: 0.2.5

  cac@6.7.14: {}
@@ -4435,34 +4436,34 @@ snapshots:
      has-tostringtag: 1.0.2
      hasown: 2.0.4

-  esbuild@0.27.7:
+  esbuild@0.28.1:
    optionalDependencies:
-      '@esbuild/aix-ppc64': 0.27.7
-      '@esbuild/android-arm': 0.27.7
-      '@esbuild/android-arm64': 0.27.7
-      '@esbuild/android-x64': 0.27.7
-      '@esbuild/darwin-arm64': 0.27.7
-      '@esbuild/darwin-x64': 0.27.7
-      '@esbuild/freebsd-arm64': 0.27.7
-      '@esbuild/freebsd-x64': 0.27.7
-      '@esbuild/linux-arm': 0.27.7
-      '@esbuild/linux-arm64': 0.27.7
-      '@esbuild/linux-ia32': 0.27.7
-      '@esbuild/linux-loong64': 0.27.7
-      '@esbuild/linux-mips64el': 0.27.7
-      '@esbuild/linux-ppc64': 0.27.7
-      '@esbuild/linux-riscv64': 0.27.7
-      '@esbuild/linux-s390x': 0.27.7
-      '@esbuild/linux-x64': 0.27.7
-      '@esbuild/netbsd-arm64': 0.27.7
-      '@esbuild/netbsd-x64': 0.27.7
-      '@esbuild/openbsd-arm64': 0.27.7
-      '@esbuild/openbsd-x64': 0.27.7
-      '@esbuild/openharmony-arm64': 0.27.7
-      '@esbuild/sunos-x64': 0.27.7
-      '@esbuild/win32-arm64': 0.27.7
-      '@esbuild/win32-ia32': 0.27.7
-      '@esbuild/win32-x64': 0.27.7
+      '@esbuild/aix-ppc64': 0.28.1
+      '@esbuild/android-arm': 0.28.1
+      '@esbuild/android-arm64': 0.28.1
+      '@esbuild/android-x64': 0.28.1
+      '@esbuild/darwin-arm64': 0.28.1
+      '@esbuild/darwin-x64': 0.28.1
+      '@esbuild/freebsd-arm64': 0.28.1
+      '@esbuild/freebsd-x64': 0.28.1
+      '@esbuild/linux-arm': 0.28.1
+      '@esbuild/linux-arm64': 0.28.1
+      '@esbuild/linux-ia32': 0.28.1
+      '@esbuild/linux-loong64': 0.28.1
+      '@esbuild/linux-mips64el': 0.28.1
+      '@esbuild/linux-ppc64': 0.28.1
+      '@esbuild/linux-riscv64': 0.28.1
+      '@esbuild/linux-s390x': 0.28.1
+      '@esbuild/linux-x64': 0.28.1
+      '@esbuild/netbsd-arm64': 0.28.1
+      '@esbuild/netbsd-x64': 0.28.1
+      '@esbuild/openbsd-arm64': 0.28.1
+      '@esbuild/openbsd-x64': 0.28.1
+      '@esbuild/openharmony-arm64': 0.28.1
+      '@esbuild/sunos-x64': 0.28.1
+      '@esbuild/win32-arm64': 0.28.1
+      '@esbuild/win32-ia32': 0.28.1
+      '@esbuild/win32-x64': 0.28.1

  escalade@3.2.0: {}

@@ -6016,7 +6017,7 @@ snapshots:

  ts-interface-checker@0.1.13: {}

-  ts-jest@29.4.11(@babel/core@7.29.7)(@jest/transform@29.7.0)(@jest/types@29.6.3)(babel-jest@29.7.0(@babel/core@7.29.7))(esbuild@0.27.7)(jest-util@29.7.0)(jest@29.7.0(@types/node@22.19.21)(ts-node@10.9.2(@types/node@22.19.21)(typescript@5.5.4)))(typescript@5.5.4):
+  ts-jest@29.4.11(@babel/core@7.29.7)(@jest/transform@29.7.0)(@jest/types@29.6.3)(babel-jest@29.7.0(@babel/core@7.29.7))(esbuild@0.28.1)(jest-util@29.7.0)(jest@29.7.0(@types/node@22.19.21)(ts-node@10.9.2(@types/node@22.19.21)(typescript@5.5.4)))(typescript@5.5.4):
    dependencies:
      bs-logger: 0.2.6
      fast-json-stable-stringify: 2.1.0
@@ -6034,7 +6035,7 @@ snapshots:
      '@jest/transform': 29.7.0
      '@jest/types': 29.6.3
      babel-jest: 29.7.0(@babel/core@7.29.7)
-      esbuild: 0.27.7
+      esbuild: 0.28.1
      jest-util: 29.7.0

  ts-node@10.9.2(@types/node@22.19.21)(typescript@5.5.4):
@@ -6059,12 +6060,12 @@ snapshots:

  tsup@8.5.1(typescript@5.5.4):
    dependencies:
-      bundle-require: 5.1.0(esbuild@0.27.7)
+      bundle-require: 5.1.0(esbuild@0.28.1)
      cac: 6.7.14
      chokidar: 4.0.3
      consola: 3.4.2
      debug: 4.4.3(supports-color@5.5.0)
-      esbuild: 0.27.7
+      esbuild: 0.28.1
      fix-dts-default-cjs-exports: 1.0.1
      joycon: 3.1.1
      picocolors: 1.1.1
@@ -22,3 +22,4 @@ overrides:
  "tar-fs@>=2.0.0 <2.1.4": "^2.1.4"
  "glob@>=10.2.0 <10.5.0": "^10.5.0"
  "@modelcontextprotocol/sdk": "^1.25.4"
+  "esbuild": ">=0.28.1"
@@ -112,6 +112,13 @@ export class ConfigManager {
              | undefined) ??
            userConf?.url ??
            defaultConf.baseURL;
+          const llmRaw = userConf as Record<string, unknown> | undefined;
+          const temperature =
+            userConf?.temperature ??
+            (llmRaw?.temperature as number | undefined);
+          const topP = userConf?.topP ?? (llmRaw?.top_p as number | undefined);
+          const maxTokens =
+            userConf?.maxTokens ?? (llmRaw?.max_tokens as number | undefined);

          return {
            baseURL: llmBaseURL,
@@ -125,6 +132,9 @@ export class ConfigManager {
              userConf?.modelProperties !== undefined
                ? userConf.modelProperties
                : defaultConf.modelProperties,
+            temperature,
+            topP,
+            maxTokens,
          };
        })(),
      },
@@ -5,6 +5,9 @@ import { LLMConfig, Message } from "../types";
 export class AnthropicLLM implements LLM {
  private client: Anthropic;
  private model: string;
+  private maxTokens: number;
+  private temperature?: number;
+  private topP?: number;

  constructor(config: LLMConfig) {
    const apiKey = config.apiKey || process.env.ANTHROPIC_API_KEY;
@@ -12,18 +15,24 @@ export class AnthropicLLM implements LLM {
      throw new Error("Anthropic API key is required");
    }
    this.client = new Anthropic({ apiKey });
-    this.model = config.model || "claude-3-sonnet-20240229";
+    this.model = config.model || "claude-sonnet-4-6";
+    // Defaults mirror the Python provider's AnthropicConfig
+    // (max_tokens=2000, temperature=0.1, top_p omitted).
+    this.maxTokens = config.maxTokens ?? 2000;
+    this.temperature = config.temperature ?? 0.1;
+    this.topP = config.topP;
  }

  async generateResponse(
    messages: Message[],
    responseFormat?: { type: string },
-  ): Promise<string> {
+    tools?: any[],
+  ): Promise<string | LLMResponse> {
    // Extract system message if present
    const systemMessage = messages.find((msg) => msg.role === "system");
    const otherMessages = messages.filter((msg) => msg.role !== "system");

-    const response = await this.client.messages.create({
+    const params: Anthropic.MessageCreateParamsNonStreaming = {
      model: this.model,
      messages: otherMessages.map((msg) => ({
        role: msg.role as "user" | "assistant",
@@ -36,8 +45,41 @@ export class AnthropicLLM implements LLM {
        typeof systemMessage?.content === "string"
          ? systemMessage.content
          : undefined,
-      max_tokens: 4096,
-    });
+      max_tokens: this.maxTokens,
+    };
+
+    // Anthropic rejects requests that include both temperature and top_p;
+    // prefer temperature, matching the Python provider's _get_common_params.
+    if (this.temperature !== undefined) {
+      params.temperature = this.temperature;
+    } else if (this.topP !== undefined) {
+      params.top_p = this.topP;
+    }
+
+    if (tools) {
+      params.tools = tools;
+      params.tool_choice = { type: "auto" };
+    }
+
+    const response = await this.client.messages.create(params);
+
+    if (tools) {
+      let content = "";
+      const toolCalls: Array<{ name: string; arguments: string }> = [];
+
+      for (const block of response.content) {
+        if (block.type === "text") {
+          content = block.text;
+        } else if (block.type === "tool_use") {
+          toolCalls.push({
+            name: block.name,
+            arguments: JSON.stringify(block.input),
+          });
+        }
+      }
+
+      return { content, role: "assistant", toolCalls };
+    }

    const firstBlock = response.content[0];
    if (firstBlock.type === "text") {
@@ -49,9 +91,9 @@ export class AnthropicLLM implements LLM {

  async generateChat(messages: Message[]): Promise<LLMResponse> {
    const response = await this.generateResponse(messages);
-    return {
-      content: response,
-      role: "assistant",
-    };
+    if (typeof response === "string") {
+      return { content: response, role: "assistant" };
+    }
+    return response;
  }
 }
@@ -84,6 +84,58 @@ const ENTITY_PARAMS = [
  "runId",
 ];

+const ADD_CONTEXT_SEARCH_MAX_CHARS = 15000;
+const ADD_MAX_EXTRACTED_MEMORIES = 20;
+const ADD_MEMORY_EMBEDDING_MAX_CHARS = 15000;
+const ADD_MAX_ENTITY_LINKS_PER_EVENT = 100;
+const SEARCH_QUERY_MAX_CHARS = 15000;
+
+function tailCapText(text: string, maxChars: number): string {
+  if (text.length <= maxChars) return text;
+  return text.slice(-maxChars);
+}
+
+function capExtractedMemories<T>(memories: T[]): T[] {
+  if (memories.length <= ADD_MAX_EXTRACTED_MEMORIES) return memories;
+  console.warn(
+    `Extracted memory cap applied: extracted=${memories.length} processing=${ADD_MAX_EXTRACTED_MEMORIES}`,
+  );
+  return memories.slice(-ADD_MAX_EXTRACTED_MEMORIES);
+}
+
+function capMemoryEmbeddingTexts(texts: string[]): string[] {
+  const capped = texts.map((text) =>
+    tailCapText(text, ADD_MEMORY_EMBEDDING_MAX_CHARS),
+  );
+  const cappedCount = texts.filter(
+    (text, index) => text !== capped[index],
+  ).length;
+  if (cappedCount > 0) {
+    console.warn(
+      `Memory embedding input cap applied: capped_count=${cappedCount} max_chars=${ADD_MEMORY_EMBEDDING_MAX_CHARS}`,
+    );
+  }
+  return capped;
+}
+
+function capSearchQuery(query: string): string {
+  const capped = tailCapText(query, SEARCH_QUERY_MAX_CHARS);
+  if (capped.length !== query.length) {
+    console.warn(
+      `Search query cap applied: original_chars=${query.length} searched_chars=${capped.length}`,
+    );
+  }
+  return capped;
+}
+
+function capEntityKeys(keys: string[]): string[] {
+  if (keys.length <= ADD_MAX_ENTITY_LINKS_PER_EVENT) return keys;
+  console.warn(
+    `ADD entity-linking cap applied: extracted=${keys.length} processing=${ADD_MAX_ENTITY_LINKS_PER_EVENT}`,
+  );
+  return keys.slice(0, ADD_MAX_ENTITY_LINKS_PER_EVENT);
+}
+
 /**
 * Validates that no top-level entity parameters are passed in config.
 * @throws Error if entity params are found at top level
@@ -734,7 +786,16 @@ export class Memory {
    const parsedMessages = messages.map((m) => m.content).join("\n");

    // Phase 1: Existing memory retrieval
-    const queryEmbedding = await this.embedder.embed(parsedMessages);
+    const searchMessages = tailCapText(
+      parsedMessages,
+      ADD_CONTEXT_SEARCH_MAX_CHARS,
+    );
+    if (searchMessages.length !== parsedMessages.length) {
+      console.warn(
+        `ADD context-search embedding capped: original_chars=${parsedMessages.length} embedded_chars=${searchMessages.length}`,
+      );
+    }
+    const queryEmbedding = await this.embedder.embed(searchMessages);
    const existingResults = await this.vectorStore.search(
      queryEmbedding,
      10,
@@ -805,6 +866,7 @@ export class Memory {
      console.error("Error parsing extraction response:", e);
      extractedMemories = [];
    }
+    extractedMemories = capExtractedMemories(extractedMemories);

    if (extractedMemories.length === 0) {
      // Save messages even if nothing extracted
@@ -826,17 +888,21 @@ export class Memory {
    const memTexts = extractedMemories
      .map((m) => m.text ?? "")
      .filter((t) => t.length > 0);
+    const memEmbeddingTexts = capMemoryEmbeddingTexts(memTexts);
    let embedMap: Record<string, number[]> = {};
    try {
-      const memEmbeddingsList = await this.embedder.embedBatch(memTexts);
+      const memEmbeddingsList =
+        await this.embedder.embedBatch(memEmbeddingTexts);
      for (let i = 0; i < memTexts.length; i++) {
        embedMap[memTexts[i]] = memEmbeddingsList[i];
      }
    } catch {
      // Fallback: embed individually
-      for (const text of memTexts) {
+      for (let i = 0; i < memTexts.length; i++) {
+        const text = memTexts[i];
+        const embeddingText = memEmbeddingTexts[i];
        try {
-          embedMap[text] = await this.embedder.embed(text);
+          embedMap[text] = await this.embedder.embed(embeddingText);
        } catch (e) {
          console.warn(`Failed to embed memory text: ${e}`);
        }
@@ -1007,9 +1073,8 @@ export class Memory {

      const orderedKeys = Object.keys(globalEntities);
      if (orderedKeys.length > 0) {
-        const entityTexts = orderedKeys.map(
-          (k) => globalEntities[k].entityText,
-        );
+        const entityKeys = capEntityKeys(orderedKeys);
+        const entityTexts = entityKeys.map((k) => globalEntities[k].entityText);

        // 7b: Single batch embed for all unique entities
        let entityEmbeddings: (number[] | null)[];
@@ -1029,9 +1094,9 @@ export class Memory {

        // Filter out entities with failed embeddings
        const valid: Array<{ index: number; key: string }> = [];
-        for (let i = 0; i < orderedKeys.length; i++) {
+        for (let i = 0; i < entityKeys.length; i++) {
          if (entityEmbeddings[i] !== null) {
-            valid.push({ index: i, key: orderedKeys[i] });
+            valid.push({ index: i, key: entityKeys[i] });
          }
        }

@@ -1256,6 +1321,7 @@ export class Memory {
    }

    const searchStartMs = Date.now();
+    query = capSearchQuery(query);

    // Step 1: Preprocess query
    const queryLemmatized = lemmatizeForBm25(query);
@@ -1722,20 +1788,13 @@ export class Memory {
      existingEmbeddings[data] || (await this.embedder.embed(data));

    const newMetadata = {
+      ...existingMemory.payload,
      ...metadata,
      data,
      hash: createHash("md5").update(data).digest("hex"),
+      textLemmatized: lemmatizeForBm25(data),
      createdAt: existingMemory.payload.createdAt,
      updatedAt: new Date().toISOString(),
-      ...(existingMemory.payload.user_id && {
-        user_id: existingMemory.payload.user_id,
-      }),
-      ...(existingMemory.payload.agent_id && {
-        agent_id: existingMemory.payload.agent_id,
-      }),
-      ...(existingMemory.payload.run_id && {
-        run_id: existingMemory.payload.run_id,
-      }),
    };

    await this.vectorStore.update(memoryId, embedding, newMetadata);
@@ -49,6 +49,9 @@ export interface LLMConfig {
  model?: string | any;
  modelProperties?: Record<string, any>;
  timeout?: number;
+  temperature?: number;
+  topP?: number;
+  maxTokens?: number;
 }

 export interface MemoryConfig {
@@ -131,6 +134,9 @@ export const MemoryConfigSchema = z.object({
      baseURL: z.string().optional(),
      url: z.string().optional(),
      timeout: z.number().optional(),
+      temperature: z.number().optional(),
+      topP: z.number().optional(),
+      maxTokens: z.number().optional(),
    }),
  }),
  historyDbPath: z.string().optional(),
@@ -0,0 +1,263 @@
+/// <reference types="jest" />
+/**
+ * Anthropic LLM — unit tests (mocked @anthropic-ai/sdk).
+ */
+
+const mockCreate = jest.fn();
+
+jest.mock("@anthropic-ai/sdk", () => {
+  return jest.fn().mockImplementation(() => ({
+    messages: { create: mockCreate },
+  }));
+});
+
+import { AnthropicLLM } from "../src/llms/anthropic";
+
+describe("AnthropicLLM (unit)", () => {
+  beforeEach(() => mockCreate.mockClear());
+
+  it("returns text when no tools are provided and model returns a text block", async () => {
+    mockCreate.mockResolvedValueOnce({
+      content: [{ type: "text", text: '{"facts": ["fact1"]}' }],
+    });
+
+    const llm = new AnthropicLLM({ apiKey: "test-key" });
+    const result = await llm.generateResponse([
+      { role: "user", content: "Hello" },
+    ]);
+
+    expect(mockCreate).toHaveBeenCalledTimes(1);
+    expect(result).toBe('{"facts": ["fact1"]}');
+
+    // No tools → tool_choice must NOT be forwarded
+    const callArgs = mockCreate.mock.calls[0][0];
+    expect(callArgs.tool_choice).toBeUndefined();
+  });
+
+  // Bug #1 regression: bare string "auto" must NOT be sent; object form required
+  it("forwards tool_choice as { type: 'auto' } (not bare string) when tools are provided", async () => {
+    mockCreate.mockResolvedValueOnce({
+      content: [
+        {
+          type: "tool_use",
+          id: "toolu_1",
+          name: "add_graph_memory",
+          input: { source: "Alice", destination: "Bob" },
+        },
+      ],
+    });
+
+    const tools = [
+      {
+        name: "add_graph_memory",
+        description: "Add a graph memory",
+        input_schema: { type: "object", properties: {} },
+      },
+    ];
+
+    const llm = new AnthropicLLM({ apiKey: "test-key" });
+    await llm.generateResponse(
+      [{ role: "user", content: "Alice knows Bob" }],
+      undefined,
+      tools,
+    );
+
+    const callArgs = mockCreate.mock.calls[0][0];
+    // Must be object form, not a bare string
+    expect(callArgs.tool_choice).toEqual({ type: "auto" });
+    expect(callArgs.tool_choice).not.toBe("auto");
+  });
+
+  // Bug #2 regression: must NOT throw on a tool_use block
+  it("does NOT throw when the model returns a tool_use block", async () => {
+    mockCreate.mockResolvedValueOnce({
+      content: [
+        {
+          type: "tool_use",
+          id: "toolu_1",
+          name: "add_graph_memory",
+          input: { source: "Alice", destination: "Bob" },
+        },
+      ],
+    });
+
+    const tools = [
+      {
+        name: "add_graph_memory",
+        description: "Add a graph memory",
+        input_schema: { type: "object", properties: {} },
+      },
+    ];
+
+    const llm = new AnthropicLLM({ apiKey: "test-key" });
+    await expect(
+      llm.generateResponse(
+        [{ role: "user", content: "Alice knows Bob" }],
+        undefined,
+        tools,
+      ),
+    ).resolves.not.toThrow();
+  });
+
+  it("parses tool_use blocks into toolCalls with JSON-stringified arguments", async () => {
+    const inputObj = { source: "Alice", destination: "Bob" };
+    mockCreate.mockResolvedValueOnce({
+      content: [
+        {
+          type: "tool_use",
+          id: "toolu_1",
+          name: "add_graph_memory",
+          input: inputObj,
+        },
+      ],
+    });
+
+    const tools = [
+      {
+        name: "add_graph_memory",
+        description: "Add a graph memory",
+        input_schema: { type: "object", properties: {} },
+      },
+    ];
+
+    const llm = new AnthropicLLM({ apiKey: "test-key" });
+    const result = await llm.generateResponse(
+      [{ role: "user", content: "Alice knows Bob" }],
+      undefined,
+      tools,
+    );
+
+    expect(result).toHaveProperty("toolCalls");
+    const response = result as {
+      content: string;
+      role: string;
+      toolCalls: Array<{ name: string; arguments: string }>;
+    };
+    expect(response.toolCalls).toHaveLength(1);
+    expect(response.toolCalls[0].name).toBe("add_graph_memory");
+    expect(JSON.parse(response.toolCalls[0].arguments)).toEqual(inputObj);
+  });
+
+  it("handles a mixed text + tool_use response", async () => {
+    const inputObj = { source: "Alice", destination: "Bob" };
+    mockCreate.mockResolvedValueOnce({
+      content: [
+        { type: "text", text: "Calling the tool now." },
+        {
+          type: "tool_use",
+          id: "toolu_2",
+          name: "add_graph_memory",
+          input: inputObj,
+        },
+      ],
+    });
+
+    const tools = [
+      {
+        name: "add_graph_memory",
+        description: "Add a graph memory",
+        input_schema: { type: "object", properties: {} },
+      },
+    ];
+
+    const llm = new AnthropicLLM({ apiKey: "test-key" });
+    const result = await llm.generateResponse(
+      [{ role: "user", content: "Alice knows Bob" }],
+      undefined,
+      tools,
+    );
+
+    expect(result).toHaveProperty("toolCalls");
+    const response = result as {
+      content: string;
+      role: string;
+      toolCalls: Array<{ name: string; arguments: string }>;
+    };
+    expect(response.content).toBe("Calling the tool now.");
+    expect(response.role).toBe("assistant");
+    expect(response.toolCalls).toHaveLength(1);
+    expect(response.toolCalls[0].name).toBe("add_graph_memory");
+    expect(JSON.parse(response.toolCalls[0].arguments)).toEqual(inputObj);
+  });
+
+  it("returns a structured response when tools are provided but the model returns only a text block", async () => {
+    mockCreate.mockResolvedValueOnce({
+      content: [{ type: "text", text: "Just a text response" }],
+    });
+
+    const tools = [
+      {
+        name: "noop",
+        description: "No operation",
+        input_schema: { type: "object", properties: {} },
+      },
+    ];
+
+    const llm = new AnthropicLLM({ apiKey: "test-key" });
+    const result = await llm.generateResponse(
+      [{ role: "user", content: "Hello" }],
+      undefined,
+      tools,
+    );
+
+    expect(result).toEqual({
+      content: "Just a text response",
+      role: "assistant",
+      toolCalls: [],
+    });
+  });
+
+  // Parity with the Python provider's AnthropicConfig defaults:
+  // model claude-sonnet-4-6, max_tokens 2000, temperature 0.1, top_p omitted.
+  it("sends Python-parity defaults (model, max_tokens, temperature)", async () => {
+    mockCreate.mockResolvedValueOnce({
+      content: [{ type: "text", text: "ok" }],
+    });
+
+    const llm = new AnthropicLLM({ apiKey: "test-key" });
+    await llm.generateResponse([{ role: "user", content: "Hi" }]);
+
+    const callArgs = mockCreate.mock.calls[0][0];
+    expect(callArgs.model).toBe("claude-sonnet-4-6");
+    expect(callArgs.max_tokens).toBe(2000);
+    expect(callArgs.temperature).toBe(0.1);
+    expect(callArgs.top_p).toBeUndefined();
+  });
+
+  it("forwards maxTokens, temperature, and model from config", async () => {
+    mockCreate.mockResolvedValueOnce({
+      content: [{ type: "text", text: "ok" }],
+    });
+
+    const llm = new AnthropicLLM({
+      apiKey: "test-key",
+      model: "claude-opus-4-8",
+      maxTokens: 1024,
+      temperature: 0.7,
+    });
+    await llm.generateResponse([{ role: "user", content: "Hi" }]);
+
+    const callArgs = mockCreate.mock.calls[0][0];
+    expect(callArgs.model).toBe("claude-opus-4-8");
+    expect(callArgs.max_tokens).toBe(1024);
+    expect(callArgs.temperature).toBe(0.7);
+  });
+
+  // Anthropic rejects requests with both temperature and top_p set.
+  it("never sends both temperature and top_p (prefers temperature)", async () => {
+    mockCreate.mockResolvedValueOnce({
+      content: [{ type: "text", text: "ok" }],
+    });
+
+    const llm = new AnthropicLLM({
+      apiKey: "test-key",
+      temperature: 0.5,
+      topP: 0.9,
+    });
+    await llm.generateResponse([{ role: "user", content: "Hi" }]);
+
+    const callArgs = mockCreate.mock.calls[0][0];
+    expect(callArgs.temperature).toBe(0.5);
+    expect(callArgs.top_p).toBeUndefined();
+  });
+});
@@ -176,3 +176,79 @@ describe("Memory - add()", () => {
    );
  });
 });
+
+describe("Memory - ingestion caps", () => {
+  test("caps add context-search embedding to the recent tail", async () => {
+    const memory = createMemory({
+      vectorStore: {
+        provider: "memory",
+        config: {
+          collectionName: `test-add-context-cap-${Date.now()}`,
+          dimension: 1536,
+          dbPath: ":memory:",
+        },
+      },
+    });
+    const embedSpy = jest.spyOn((memory as any).embedder, "embed");
+    const longText = `old-${"x".repeat(15000)}`;
+
+    await memory.add(longText, { userId: `cap_user_${Date.now()}` });
+
+    expect(embedSpy.mock.calls[0][0]).toBe(longText.slice(-15000));
+    await memory.reset();
+  });
+
+  test("caps extracted memories to the latest 20 before embedding", async () => {
+    const memory = createMemory({
+      vectorStore: {
+        provider: "memory",
+        config: {
+          collectionName: `test-add-memory-cap-${Date.now()}`,
+          dimension: 1536,
+          dbPath: ":memory:",
+        },
+      },
+    });
+    (memory as any).llm.generateResponse = jest.fn().mockResolvedValue(
+      JSON.stringify({
+        memory: Array.from({ length: 25 }, (_, i) => ({
+          id: String(i),
+          text: `memory-${i}`,
+        })),
+      }),
+    );
+    const embedBatchSpy = jest.spyOn((memory as any).embedder, "embedBatch");
+
+    const result: SearchResult = await memory.add("remember these", {
+      userId: `cap_user_${Date.now()}`,
+    });
+
+    expect(result.results).toHaveLength(20);
+    expect(embedBatchSpy.mock.calls[0][0]).toEqual(
+      Array.from({ length: 20 }, (_, i) => `memory-${i + 5}`),
+    );
+    await memory.reset();
+  });
+
+  test("caps search query before embedding", async () => {
+    const memory = createMemory({
+      vectorStore: {
+        provider: "memory",
+        config: {
+          collectionName: `test-search-cap-${Date.now()}`,
+          dimension: 1536,
+          dbPath: ":memory:",
+        },
+      },
+    });
+    const embedSpy = jest.spyOn((memory as any).embedder, "embed");
+    const longQuery = `old-${"q".repeat(15000)}`;
+
+    await memory.search(longQuery, {
+      filters: { user_id: `cap_user_${Date.now()}` },
+    });
+
+    expect(embedSpy.mock.calls[0][0]).toBe(longQuery.slice(-15000));
+    await memory.reset();
+  });
+});
@@ -191,6 +191,21 @@ describe("Memory - update()", () => {
    const after: MemoryItem | null = await memory.get(id);
    expect(after!.hash).not.toBe(before!.hash);
  });
+
+  test("preserves custom metadata fields after update", async () => {
+    const addResult: SearchResult = await memory.add("Original text", {
+      userId,
+      metadata: { category: "hobbies", priority: "high" },
+      infer: false,
+    });
+    const id = addResult.results[0].id;
+    await memory.update(id, "Updated text");
+    const after: MemoryItem | null = await memory.get(id);
+    expect(after!.memory).toBe("Updated text");
+    expect(after!.metadata).toEqual(
+      expect.objectContaining({ category: "hobbies", priority: "high" }),
+    );
+  });
 });

 // ─── delete() ────────────────────────────────────────────
@@ -0,0 +1,64 @@
+import os
+from typing import Optional
+
+from mem0.configs.llms.base import BaseLlmConfig
+
+
+class GeminiConfig(BaseLlmConfig):
+    """
+    Configuration class for Google Gemini LLM.
+
+    Supports both the Gemini Developer API (via API key) and Vertex AI (via GCP credentials).
+    """
+
+    def __init__(
+        self,
+        # Base parameters
+        model: Optional[str] = None,
+        temperature: float = 0.1,
+        api_key: Optional[str] = None,
+        max_tokens: int = 2000,
+        top_p: float = 0.1,
+        top_k: int = 1,
+        enable_vision: bool = False,
+        vision_details: Optional[str] = "auto",
+        http_client_proxies: Optional[dict] = None,
+        # Gemini-specific parameters
+        vertexai: Optional[bool] = None,
+        project: Optional[str] = None,
+        location: Optional[str] = None,
+    ):
+        """
+        Initialize Gemini configuration.
+
+        Args:
+            model: Gemini model to use (e.g., "gemini-2.0-flash"), defaults to None
+            temperature: Controls randomness, defaults to 0.1
+            api_key: Google API key for the Gemini Developer API, defaults to None
+            max_tokens: Maximum tokens to generate, defaults to 2000
+            top_p: Nucleus sampling parameter, defaults to 0.1
+            top_k: Top-k sampling parameter, defaults to 1
+            enable_vision: Enable vision capabilities, defaults to False
+            vision_details: Vision detail level, defaults to "auto"
+            http_client_proxies: HTTP client proxy settings, defaults to None
+            vertexai: Whether to use Vertex AI backend. If None, checks GOOGLE_GENAI_USE_VERTEXAI env var.
+            project: GCP project ID for Vertex AI. If None, checks GOOGLE_CLOUD_PROJECT env var.
+            location: GCP location for Vertex AI. If None, checks GOOGLE_CLOUD_LOCATION env var.
+        """
+        super().__init__(
+            model=model,
+            temperature=temperature,
+            api_key=api_key,
+            max_tokens=max_tokens,
+            top_p=top_p,
+            top_k=top_k,
+            enable_vision=enable_vision,
+            vision_details=vision_details,
+            http_client_proxies=http_client_proxies,
+        )
+
+        if vertexai is None:
+            vertexai = os.getenv("GOOGLE_GENAI_USE_VERTEXAI", "").lower() in ("true", "1", "yes")
+        self.vertexai = vertexai
+        self.project = project or os.getenv("GOOGLE_CLOUD_PROJECT")
+        self.location = location or os.getenv("GOOGLE_CLOUD_LOCATION", "us-central1")
@@ -16,8 +16,8 @@ class HuggingFaceEmbedding(EmbeddingBase):
    def __init__(self, config: Optional[BaseEmbedderConfig] = None):
        super().__init__(config)

-        if config.huggingface_base_url:
-            self.client = OpenAI(base_url=config.huggingface_base_url)
+        if self.config.huggingface_base_url:
+            self.client = OpenAI(base_url=self.config.huggingface_base_url)
            self.config.model = self.config.model or "tei"
        else:
            self.config.model = self.config.model or "multi-qa-MiniLM-L6-cos-v1"
@@ -35,7 +35,7 @@ class AnthropicLLM(LLMBase):
        super().__init__(config)

        if not self.config.model:
-            self.config.model = "claude-3-5-sonnet-20240620"
+            self.config.model = "claude-sonnet-4-6"

        api_key = self.config.api_key or os.getenv("ANTHROPIC_API_KEY")
        self.client = anthropic.Anthropic(api_key=api_key)
@@ -106,7 +106,20 @@ class AnthropicLLM(LLMBase):

        if tools:  # TODO: Remove tools if no issues found with new memory addition logic
            params["tools"] = tools
-            params["tool_choice"] = tool_choice
+            params["tool_choice"] = {"type": tool_choice}

        response = self.client.messages.create(**params)
+        return self._parse_response(response, tools)
+
+    def _parse_response(self, response, tools):
+        if tools:
+            result = {"content": None, "tool_calls": []}
+            for block in response.content:
+                if block.type == "text":
+                    result["content"] = block.text
+                elif block.type == "tool_use":
+                    result["tool_calls"].append(
+                        {"name": block.name, "arguments": block.input}
+                    )
+            return result
        return response.content[0].text
@@ -409,7 +409,7 @@ class AWSBedrockLLM(LLMBase):
            elif self.provider == "cohere":
                return response_json.get("generations", [{"text": ""}])[0].get("text", "")
            elif self.provider == "ai21":
-                return response_json.get("completions", [{"data", {"text": ""}}])[0].get("data", {}).get("text", "")
+                return response_json.get("completions", [{"data": {"text": ""}}])[0].get("data", {}).get("text", "")
            else:
                # Generic parsing - try common response fields
                for field in ["content", "text", "completion", "generation"]:
@@ -72,13 +72,26 @@ class AzureOpenAIStructuredLLM(LLMBase):

        messages[-1]["content"] = user_prompt

+        is_reasoning = self._is_reasoning_model(self.config.model)
        params = {
            "model": self.config.model,
            "messages": messages,
-            "temperature": self.config.temperature,
-            "max_tokens": self.config.max_tokens,
-            "top_p": self.config.top_p,
        }
+        # Reasoning models (o1/o3/GPT-5 series) reject temperature/top_p; only
+        # forward the sampling params for non-reasoning models. Mirrors the
+        # reasoning-aware handling of OpenAIStructuredLLM (#5458).
+        if not is_reasoning:
+            params["temperature"] = self.config.temperature
+            params["top_p"] = self.config.top_p
+        # Reasoning models require max_completion_tokens rather than max_tokens.
+        if is_reasoning or self._uses_max_completion_tokens(self.config.model):
+            params["max_completion_tokens"] = self.config.max_tokens
+        else:
+            params["max_tokens"] = self.config.max_tokens
+        if is_reasoning:
+            reasoning_effort = getattr(self.config, "reasoning_effort", None)
+            if reasoning_effort:
+                params["reasoning_effort"] = reasoning_effort
        if response_format:
            params["response_format"] = response_format
        if tools:
@@ -79,6 +79,25 @@ class LLMBase(ABC):

        return False

+    def _uses_max_completion_tokens(self, model: str) -> bool:
+        """
+        Check if the model expects ``max_completion_tokens`` instead of ``max_tokens``.
+
+        The whole GPT-5 family (gpt-5.4-mini, gpt-5.4-nano, gpt-5.5, ...) rejects the
+        legacy ``max_tokens`` parameter on the Chat Completions API and requires
+        ``max_completion_tokens``. Older models (gpt-4.x, gpt-3.5, etc.) still accept
+        ``max_tokens``.
+
+        Args:
+            model: The model name to check
+
+        Returns:
+            bool: True if the model requires ``max_completion_tokens``
+        """
+        # Strip provider prefixes (e.g. "openai/gpt-5.4-mini" -> "gpt-5.4-mini")
+        base_model = (model or "").lower().rsplit("/", 1)[-1]
+        return base_model.startswith("gpt-5")
+
    def _get_supported_params(self, **kwargs) -> Dict:
        """
        Get parameters that are supported by the current model.
@@ -141,10 +160,15 @@ class LLMBase(ABC):
        """
        params = {
            "temperature": self.config.temperature,
-            "max_tokens": self.config.max_tokens,
            "top_p": self.config.top_p,
        }

+        model = getattr(self.config, "model", "")
+        if self._uses_max_completion_tokens(model):
+            params["max_completion_tokens"] = self.config.max_tokens
+        else:
+            params["max_tokens"] = self.config.max_tokens
+
        # Add provider-specific parameters from kwargs
        params.update(kwargs)

@@ -1,5 +1,5 @@
 import os
-from typing import Dict, List, Optional
+from typing import Dict, List, Optional, Union

 try:
    from google import genai
@@ -8,18 +8,39 @@ except ImportError:
    raise ImportError("The 'google-genai' library is required. Please install it using 'pip install google-genai'.")

 from mem0.configs.llms.base import BaseLlmConfig
+from mem0.configs.llms.gemini import GeminiConfig
 from mem0.llms.base import LLMBase


 class GeminiLLM(LLMBase):
-    def __init__(self, config: Optional[BaseLlmConfig] = None):
+    def __init__(self, config: Optional[Union[BaseLlmConfig, GeminiConfig, Dict]] = None):
+        # Convert to GeminiConfig if needed
+        if config is None:
+            config = GeminiConfig()
+        elif isinstance(config, dict):
+            config = GeminiConfig(**config)
+        elif isinstance(config, BaseLlmConfig) and not isinstance(config, GeminiConfig):
+            config = GeminiConfig(
+                model=config.model,
+                temperature=config.temperature,
+                api_key=config.api_key,
+                max_tokens=config.max_tokens,
+                top_p=config.top_p,
+                top_k=config.top_k,
+                enable_vision=config.enable_vision,
+                vision_details=config.vision_details,
+            )
+
        super().__init__(config)

        if not self.config.model:
            self.config.model = "gemini-2.0-flash"

-        api_key = self.config.api_key or os.getenv("GOOGLE_API_KEY")
-        self.client = genai.Client(api_key=api_key)
+        if self.config.vertexai:
+            self.client = genai.Client(vertexai=True, project=self.config.project, location=self.config.location)
+        else:
+            api_key = self.config.api_key or os.getenv("GOOGLE_API_KEY")
+            self.client = genai.Client(api_key=api_key)

    def _parse_response(self, response, tools):
        """
@@ -157,12 +178,15 @@ class GeminiLLM(LLMBase):
        # Extract system instruction and reformat messages
        system_instruction, contents = self._reformat_messages(messages)

-        # Prepare generation config
-        config_params = {
-            "temperature": self.config.temperature,
-            "max_output_tokens": self.config.max_tokens,
-            "top_p": self.config.top_p,
-        }
+        # Prepare generation config — only include non-None values so the
+        # Gemini SDK uses its own defaults instead of rejecting None.
+        config_params = {}
+        if self.config.temperature is not None:
+            config_params["temperature"] = self.config.temperature
+        if self.config.max_tokens is not None:
+            config_params["max_output_tokens"] = self.config.max_tokens
+        if self.config.top_p is not None:
+            config_params["top_p"] = self.config.top_p

        # Add system instruction to config if present
        if system_instruction:
@@ -57,6 +57,7 @@ class LangchainLLM(LLMBase):
        response_format=None,
        tools: Optional[List[Dict]] = None,
        tool_choice: str = "auto",
+        **kwargs,
    ):
        """
        Generate a response based on the given messages using langchain_community.
@@ -66,6 +67,8 @@ class LangchainLLM(LLMBase):
            response_format (str or object, optional): Format of the response. Not used in Langchain.
            tools (list, optional): List of tools that the model can call.
            tool_choice (str, optional): Tool choice method.
+            **kwargs: Additional model parameters forwarded to the underlying LangChain
+                model's ``invoke`` (matches the ``LLMBase.generate_response`` contract).

        Returns:
            str: The generated response.
@@ -90,5 +93,5 @@ class LangchainLLM(LLMBase):
        if tools:
            langchain_model = langchain_model.bind_tools(tools=tools, tool_choice=tool_choice)

-        response: AIMessage = langchain_model.invoke(langchain_messages)
+        response: AIMessage = langchain_model.invoke(langchain_messages, **kwargs)
        return self._parse_response(response, tools)
@@ -67,16 +67,19 @@ class LiteLLM(LLMBase):
        Returns:
            str: The generated response.
        """
-        if not litellm.supports_function_calling(self.config.model):
+        if tools and not litellm.supports_function_calling(self.config.model):
            raise ValueError(f"Model '{self.config.model}' in litellm does not support function calling.")

        params = {
            "model": self.config.model,
            "messages": messages,
            "temperature": self.config.temperature,
-            "max_tokens": self.config.max_tokens,
            "top_p": self.config.top_p,
        }
+        if self._uses_max_completion_tokens(self.config.model):
+            params["max_completion_tokens"] = self.config.max_tokens
+        else:
+            params["max_tokens"] = self.config.max_tokens
        if response_format:
            params["response_format"] = response_format
        if tools:  # TODO: Remove tools if no issues found with new memory addition logic
@@ -119,11 +119,12 @@ class OllamaLLM(LLMBase):
        # Handle JSON response format by using Ollama's native format parameter
        if response_format and response_format.get("type") == "json_object":
            params["format"] = "json"
-            # Also add JSON format instruction to the last message as a fallback
+            messages = [dict(m) for m in messages]
            if messages and messages[-1]["role"] == "user":
                messages[-1]["content"] += "\n\nPlease respond with valid JSON only."
            else:
                messages.append({"role": "user", "content": "Please respond with valid JSON only."})
+            params["messages"] = messages

        # Add options for Ollama (temperature, num_predict, top_p)
        options = {
@@ -36,11 +36,8 @@ class OpenAIStructuredLLM(LLMBase):
        Returns:
            str: The generated response.
        """
-        params = {
-            "model": self.config.model,
-            "messages": messages,
-            "temperature": self.config.temperature,
-        }
+        params = self._get_supported_params(messages=messages)
+        params["model"] = self.config.model

        if response_format:
            params["response_format"] = response_format
@@ -28,7 +28,7 @@ class SarvamLLM(LLMBase):
            getattr(self.config, "sarvam_base_url", None) or os.getenv("SARVAM_API_BASE") or "https://api.sarvam.ai/v1"
        )

-    def generate_response(self, messages: List[Dict[str, str]], response_format=None) -> str:
+    def generate_response(self, messages: List[Dict[str, str]], response_format=None, **kwargs) -> str:
        """
        Generate a response based on the given messages using Sarvam-M.

@@ -36,6 +36,8 @@ class SarvamLLM(LLMBase):
            messages (list): List of message dicts containing 'role' and 'content'.
            response_format (str or object, optional): Format of the response.
                                                     Currently not used by Sarvam API.
+            **kwargs: Additional provider-specific parameters forwarded to the Sarvam
+                request payload (matches the ``LLMBase.generate_response`` contract).

        Returns:
            str: The generated response.
@@ -72,6 +74,9 @@ class SarvamLLM(LLMBase):
                if param in self.config.model:
                    params[param] = self.config.model[param]

+        # Forward any per-call provider-specific parameters (LLMBase contract).
+        params.update(kwargs)
+
        try:
            response = requests.post(url, headers=headers, json=params, timeout=30)
            response.raise_for_status()
@@ -58,6 +58,7 @@ class TogetherLLM(LLMBase):
        response_format=None,
        tools: Optional[List[Dict]] = None,
        tool_choice: str = "auto",
+        **kwargs,
    ):
        """
        Generate a response based on the given messages using TogetherAI.
@@ -67,6 +68,8 @@ class TogetherLLM(LLMBase):
            response_format (str or object, optional): Format of the response. Defaults to "text".
            tools (list, optional): List of tools that the model can call. Defaults to None.
            tool_choice (str, optional): Tool choice method. Defaults to "auto".
+            **kwargs: Additional provider-specific parameters forwarded to the Together
+                client (matches the ``LLMBase.generate_response`` contract).

        Returns:
            str: The generated response.
@@ -78,6 +81,7 @@ class TogetherLLM(LLMBase):
            "max_tokens": self.config.max_tokens,
            "top_p": self.config.top_p,
        }
+        params.update(kwargs)
        if response_format:
            params["response_format"] = response_format
        if tools:  # TODO: Remove tools if no issues found with new memory addition logic
@@ -80,6 +80,63 @@ warnings.filterwarnings("ignore", category=DeprecationWarning, message=".*swigva
 # Initialize logger early for util functions
 logger = logging.getLogger(__name__)

+ADD_CONTEXT_SEARCH_MAX_CHARS = 15000
+ADD_MAX_EXTRACTED_MEMORIES = 20
+ADD_MEMORY_EMBEDDING_MAX_CHARS = 15000
+ADD_MAX_ENTITY_LINKS_PER_EVENT = 100
+SEARCH_QUERY_MAX_CHARS = 15000
+
+
+def _tail_cap_text(text: str, max_chars: int) -> str:
+    if len(text) <= max_chars:
+        return text
+    return text[-max_chars:]
+
+
+def _cap_extracted_memories(extracted_memories: list) -> list:
+    if len(extracted_memories) <= ADD_MAX_EXTRACTED_MEMORIES:
+        return extracted_memories
+    logger.warning(
+        "Extracted memory cap applied: extracted=%d processing=%d",
+        len(extracted_memories),
+        ADD_MAX_EXTRACTED_MEMORIES,
+    )
+    return extracted_memories[-ADD_MAX_EXTRACTED_MEMORIES:]
+
+
+def _cap_memory_embedding_texts(mem_texts: list[str]) -> list[str]:
+    capped = [_tail_cap_text(t, ADD_MEMORY_EMBEDDING_MAX_CHARS) for t in mem_texts]
+    capped_count = sum(1 for original, capped_text in zip(mem_texts, capped) if len(original) != len(capped_text))
+    if capped_count:
+        logger.warning(
+            "Memory embedding input cap applied: capped_count=%d max_chars=%d",
+            capped_count,
+            ADD_MEMORY_EMBEDDING_MAX_CHARS,
+        )
+    return capped
+
+
+def _cap_search_query(query: str) -> str:
+    capped = _tail_cap_text(query, SEARCH_QUERY_MAX_CHARS)
+    if len(capped) != len(query):
+        logger.warning(
+            "Search query cap applied: original_chars=%d searched_chars=%d",
+            len(query),
+            len(capped),
+        )
+    return capped
+
+
+def _cap_entity_keys(entity_keys: list[str]) -> list[str]:
+    if len(entity_keys) <= ADD_MAX_ENTITY_LINKS_PER_EVENT:
+        return entity_keys
+    logger.warning(
+        "ADD entity-linking cap applied: extracted=%d processing=%d",
+        len(entity_keys),
+        ADD_MAX_ENTITY_LINKS_PER_EVENT,
+    )
+    return entity_keys[:ADD_MAX_ENTITY_LINKS_PER_EVENT]
+

 # Fields that hold runtime auth/connection objects and must be preserved.
 # These are non-serializable objects (e.g. AWSV4SignerAuth, RequestsHttpConnection)
@@ -804,9 +861,16 @@ class Memory(MemoryBase):

        # Phase 1: Existing memory retrieval
        search_filters = {k: v for k, v in filters.items() if k in ("user_id", "agent_id", "run_id") and v}
-        query_embedding = self.embedding_model.embed(parsed_messages, "search")
+        search_messages = _tail_cap_text(parsed_messages, ADD_CONTEXT_SEARCH_MAX_CHARS)
+        if len(search_messages) != len(parsed_messages):
+            logger.warning(
+                "ADD context-search embedding capped: original_chars=%d embedded_chars=%d",
+                len(parsed_messages),
+                len(search_messages),
+            )
+        query_embedding = self.embedding_model.embed(search_messages, "search")
        existing_results = self.vector_store.search(
-            query=parsed_messages,
+            query=search_messages,
            vectors=query_embedding,
            top_k=10,
            filters=search_filters,
@@ -860,6 +924,7 @@ class Memory(MemoryBase):
        except Exception as e:
            logger.error(f"Error parsing extraction response: {e}")
            extracted_memories = []
+        extracted_memories = _cap_extracted_memories(extracted_memories)

        if not extracted_memories:
            # Save messages even if nothing extracted
@@ -868,15 +933,16 @@ class Memory(MemoryBase):

        # Phase 3: Batch embed all extracted memory texts
        mem_texts = [m.get("text", "") for m in extracted_memories if m.get("text")]
+        mem_embedding_texts = _cap_memory_embedding_texts(mem_texts)
        try:
-            mem_embeddings_list = self.embedding_model.embed_batch(mem_texts, "add")
+            mem_embeddings_list = self.embedding_model.embed_batch(mem_embedding_texts, "add")
            embed_map = dict(zip(mem_texts, mem_embeddings_list))
        except Exception:
            # Fallback: embed individually
            embed_map = {}
-            for text in mem_texts:
+            for text, embedding_text in zip(mem_texts, mem_embedding_texts):
                try:
-                    embed_map[text] = self.embedding_model.embed(text, "add")
+                    embed_map[text] = self.embedding_model.embed(embedding_text, "add")
                except Exception as e:
                    logger.warning(f"Failed to embed memory text: {e}")

@@ -978,7 +1044,7 @@ class Memory(MemoryBase):
                        global_entities[key] = [entity_type, entity_text, {memory_id}]

            if global_entities:
-                ordered_keys = list(global_entities.keys())
+                ordered_keys = _cap_entity_keys(list(global_entities.keys()))
                entity_texts = [global_entities[k][1] for k in ordered_keys]

                # 7b: Single batch embed for all unique entities
@@ -1478,6 +1544,7 @@ class Memory(MemoryBase):
        # Guard against None threshold (backward compat)
        if threshold is None:
            threshold = 0.1
+        query = _cap_search_query(query)

        # Step 1: Preprocess query
        query_lemmatized = lemmatize_for_bm25(query)
@@ -1839,7 +1906,9 @@ class Memory(MemoryBase):

        prev_value = existing_memory.payload.get("data")

-        new_metadata = deepcopy(metadata) if metadata is not None else {}
+        new_metadata = deepcopy(existing_memory.payload)
+        if metadata is not None:
+            new_metadata.update(metadata)

        new_metadata["data"] = data
        new_metadata["hash"] = hashlib.md5(data.encode()).hexdigest()
@@ -1847,17 +1916,9 @@ class Memory(MemoryBase):
        new_metadata["created_at"] = existing_memory.payload.get("created_at")
        new_metadata["updated_at"] = datetime.now(timezone.utc).isoformat()

-        # Preserve session identifiers from existing memory only if not provided in new metadata
-        if "user_id" not in new_metadata and "user_id" in existing_memory.payload:
-            new_metadata["user_id"] = existing_memory.payload["user_id"]
-        if "agent_id" not in new_metadata and "agent_id" in existing_memory.payload:
-            new_metadata["agent_id"] = existing_memory.payload["agent_id"]
-        if "run_id" not in new_metadata and "run_id" in existing_memory.payload:
-            new_metadata["run_id"] = existing_memory.payload["run_id"]
+        # actor_id is immutable after creation (issue #4490)
        if "actor_id" in existing_memory.payload:
            new_metadata["actor_id"] = existing_memory.payload["actor_id"]
-        if "role" not in new_metadata and "role" in existing_memory.payload:
-            new_metadata["role"] = existing_memory.payload["role"]

        if data in existing_embeddings:
            embeddings = existing_embeddings[data]
@@ -2316,10 +2377,17 @@ class AsyncMemory(MemoryBase):

        # Phase 1: Existing memory retrieval
        search_filters = {k: v for k, v in effective_filters.items() if k in ("user_id", "agent_id", "run_id") and v}
-        query_embedding = await asyncio.to_thread(self.embedding_model.embed, parsed_messages, "search")
+        search_messages = _tail_cap_text(parsed_messages, ADD_CONTEXT_SEARCH_MAX_CHARS)
+        if len(search_messages) != len(parsed_messages):
+            logger.warning(
+                "ADD context-search embedding capped: original_chars=%d embedded_chars=%d",
+                len(parsed_messages),
+                len(search_messages),
+            )
+        query_embedding = await asyncio.to_thread(self.embedding_model.embed, search_messages, "search")
        existing_results = await asyncio.to_thread(
            self.vector_store.search,
-            query=parsed_messages,
+            query=search_messages,
            vectors=query_embedding,
            top_k=10,
            filters=search_filters,
@@ -2374,6 +2442,7 @@ class AsyncMemory(MemoryBase):
        except Exception as e:
            logger.error(f"Error parsing extraction response (async): {e}")
            extracted_memories = []
+        extracted_memories = _cap_extracted_memories(extracted_memories)

        if not extracted_memories:
            await asyncio.to_thread(self.db.save_messages, messages, session_scope)
@@ -2381,14 +2450,15 @@ class AsyncMemory(MemoryBase):

        # Phase 3: Batch embed all extracted memory texts
        mem_texts = [m.get("text", "") for m in extracted_memories if m.get("text")]
+        mem_embedding_texts = _cap_memory_embedding_texts(mem_texts)
        try:
-            mem_embeddings_list = await asyncio.to_thread(self.embedding_model.embed_batch, mem_texts, "add")
+            mem_embeddings_list = await asyncio.to_thread(self.embedding_model.embed_batch, mem_embedding_texts, "add")
            embed_map = dict(zip(mem_texts, mem_embeddings_list))
        except Exception:
            embed_map = {}
-            for text in mem_texts:
+            for text, embedding_text in zip(mem_texts, mem_embedding_texts):
                try:
-                    embed_map[text] = await asyncio.to_thread(self.embedding_model.embed, text, "add")
+                    embed_map[text] = await asyncio.to_thread(self.embedding_model.embed, embedding_text, "add")
                except Exception as e:
                    logger.warning(f"Failed to embed memory text (async): {e}")

@@ -2491,7 +2561,7 @@ class AsyncMemory(MemoryBase):
                        global_entities[key] = [entity_type, entity_text, {memory_id}]

            if global_entities:
-                ordered_keys = list(global_entities.keys())
+                ordered_keys = _cap_entity_keys(list(global_entities.keys()))
                entity_texts = [global_entities[k][1] for k in ordered_keys]

                # 7b: Batch embed entities
@@ -2996,6 +3066,7 @@ class AsyncMemory(MemoryBase):
    async def _search_vector_store(self, query, filters, limit, threshold=0.1, explain=False):
        if threshold is None:
            threshold = 0.1
+        query = _cap_search_query(query)

        # Step 1: Preprocess query (CPU-bound)
        query_lemmatized = await asyncio.to_thread(lemmatize_for_bm25, query)
@@ -3241,9 +3312,15 @@ class AsyncMemory(MemoryBase):
        for memory in memories[0]:
            delete_tasks.append(self._delete_memory(memory.id))

-        await asyncio.gather(*delete_tasks)
+        results = await asyncio.gather(*delete_tasks, return_exceptions=True)

-        logger.info(f"Deleted {len(memories[0])} memories")
+        errors = [r for r in results if isinstance(r, BaseException)]
+        if errors:
+            logger.warning("Failed to delete %d out of %d memories", len(errors), len(results))
+            for err in errors:
+                logger.warning("Delete error: %s", err)
+
+        logger.info(f"Deleted {len(results) - len(errors)} memories")

        decay_usage_notice = detect_decay_usage_from_delete_all(len(memories[0]))
        if decay_usage_notice:
@@ -3371,7 +3448,9 @@ class AsyncMemory(MemoryBase):

        prev_value = existing_memory.payload.get("data")

-        new_metadata = deepcopy(metadata) if metadata is not None else {}
+        new_metadata = deepcopy(existing_memory.payload)
+        if metadata is not None:
+            new_metadata.update(metadata)

        new_metadata["data"] = data
        new_metadata["hash"] = hashlib.md5(data.encode()).hexdigest()
@@ -3379,18 +3458,9 @@ class AsyncMemory(MemoryBase):
        new_metadata["created_at"] = existing_memory.payload.get("created_at")
        new_metadata["updated_at"] = datetime.now(timezone.utc).isoformat()

-        # Preserve session identifiers from existing memory only if not provided in new metadata
-        if "user_id" not in new_metadata and "user_id" in existing_memory.payload:
-            new_metadata["user_id"] = existing_memory.payload["user_id"]
-        if "agent_id" not in new_metadata and "agent_id" in existing_memory.payload:
-            new_metadata["agent_id"] = existing_memory.payload["agent_id"]
-        if "run_id" not in new_metadata and "run_id" in existing_memory.payload:
-            new_metadata["run_id"] = existing_memory.payload["run_id"]
-
+        # actor_id is immutable after creation (issue #4490)
        if "actor_id" in existing_memory.payload:
            new_metadata["actor_id"] = existing_memory.payload["actor_id"]
-        if "role" not in new_metadata and "role" in existing_memory.payload:
-            new_metadata["role"] = existing_memory.payload["role"]

        if data in existing_embeddings:
            embeddings = existing_embeddings[data]
@@ -3482,6 +3552,13 @@ class AsyncMemory(MemoryBase):
            self.config.vector_store.provider, self.config.vector_store.config
        )

+        if self._entity_store is not None:
+            try:
+                await asyncio.to_thread(self._entity_store.reset)
+            except Exception as e:
+                logger.warning(f"Failed to reset entity store: {e}")
+            self._entity_store = None
+
        capture_event("mem0.reset", self, {"sync_type": "async"})
        await display_first_run_notice_async(self, "async", "reset")

@@ -61,12 +61,18 @@ def ensure_json_instruction(system_prompt, user_prompt):
 def parse_messages(messages):
    response = ""
    for msg in messages:
-        if msg["role"] == "system":
-            response += f"system: {msg['content']}\n"
-        if msg["role"] == "user":
-            response += f"user: {msg['content']}\n"
-        if msg["role"] == "assistant":
-            response += f"assistant: {msg['content']}\n"
+        role = msg.get("role")
+        content = msg.get("content")
+        # Skip messages without textual content (e.g. assistant tool-call
+        # messages that carry `tool_calls` but no `content` key).
+        if content is None:
+            continue
+        if role == "system":
+            response += f"system: {content}\n"
+        elif role == "user":
+            response += f"user: {content}\n"
+        elif role == "assistant":
+            response += f"assistant: {content}\n"
    return response


@@ -173,12 +179,19 @@ def parse_vision_messages(messages, llm=None, vision_details="auto"):
    """
    returned_messages = []
    for msg in messages:
-        if msg["role"] == "system":
+        role = msg.get("role")
+        content = msg.get("content")
+        if role == "system":
            returned_messages.append(msg)
            continue

+        # Skip messages without content (e.g. assistant tool-call messages
+        # that carry `tool_calls` but no `content` key).
+        if content is None:
+            continue
+
        # Handle message content
-        if isinstance(msg["content"], list):
+        if isinstance(content, list):
            if llm is None:
                text_parts = [
                    part["text"] for part in msg["content"]
@@ -186,17 +199,17 @@ def parse_vision_messages(messages, llm=None, vision_details="auto"):
                ]
                if not text_parts:
                    continue
-                returned_messages.append({"role": msg["role"], "content": " ".join(text_parts)})
+                returned_messages.append({"role": role, "content": " ".join(text_parts)})
            else:
                description = get_image_description(msg, llm, vision_details)
-                returned_messages.append({"role": msg["role"], "content": description})
-        elif isinstance(msg["content"], dict) and msg["content"].get("type") == "image_url":
+                returned_messages.append({"role": role, "content": description})
+        elif isinstance(content, dict) and content.get("type") == "image_url":
            if llm is None:
                continue
-            image_url = msg["content"]["image_url"]["url"]
+            image_url = content["image_url"]["url"]
            try:
                description = get_image_description(image_url, llm, vision_details)
-                returned_messages.append({"role": msg["role"], "content": description})
+                returned_messages.append({"role": role, "content": description})
            except Exception:
                raise Exception(f"Error while downloading {image_url}.")
        else:
@@ -82,4 +82,5 @@ class CohereReranker(BaseReranker):
            # Fallback to original order if reranking fails
            for doc in documents:
                doc['rerank_score'] = 0.0
-            return documents[:top_k] if top_k else documents
+            final_top_k = top_k or self.config.top_k
+            return documents[:final_top_k] if final_top_k else documents
@@ -93,4 +93,5 @@ class ZeroEntropyReranker(BaseReranker):
            # Fallback to original order if reranking fails
            for doc in documents:
                doc['rerank_score'] = 0.0
-            return documents[:top_k] if top_k else documents
+            final_top_k = top_k or self.config.top_k
+            return documents[:final_top_k] if final_top_k else documents
@@ -7,6 +7,7 @@ from mem0.configs.llms.aws_bedrock import AWSBedrockConfig
 from mem0.configs.llms.azure import AzureOpenAIConfig
 from mem0.configs.llms.base import BaseLlmConfig
 from mem0.configs.llms.deepseek import DeepSeekConfig
+from mem0.configs.llms.gemini import GeminiConfig
 from mem0.configs.llms.lmstudio import LMStudioConfig
 from mem0.configs.llms.minimax import MinimaxConfig
 from mem0.configs.llms.ollama import OllamaConfig
@@ -46,7 +47,7 @@ class LlmFactory:
        "openai_structured": ("mem0.llms.openai_structured.OpenAIStructuredLLM", OpenAIConfig),
        "anthropic": ("mem0.llms.anthropic.AnthropicLLM", AnthropicConfig),
        "azure_openai_structured": ("mem0.llms.azure_openai_structured.AzureOpenAIStructuredLLM", AzureOpenAIConfig),
-        "gemini": ("mem0.llms.gemini.GeminiLLM", BaseLlmConfig),
+        "gemini": ("mem0.llms.gemini.GeminiLLM", GeminiConfig),
        "deepseek": ("mem0.llms.deepseek.DeepSeekLLM", DeepSeekConfig),
        "minimax": ("mem0.llms.minimax.MiniMaxLLM", MinimaxConfig),
        "xai": ("mem0.llms.xai.XAILLM", XAIConfig),
@@ -187,7 +187,7 @@ class ChromaDB(VectorStoreBase):
        """
        self.collection.update(ids=vector_id, embeddings=vector, metadatas=payload)

-    def get(self, vector_id: str) -> OutputData:
+    def get(self, vector_id: str) -> Optional[OutputData]:
        """
        Retrieve a vector by ID.

@@ -195,10 +195,11 @@ class ChromaDB(VectorStoreBase):
            vector_id (str): ID of the vector to retrieve.

        Returns:
-            OutputData: Retrieved vector.
+            Optional[OutputData]: Retrieved vector, or None if the ID is not found.
        """
        result = self.collection.get(ids=[vector_id])
-        return self._parse_output(result)[0]
+        parsed = self._parse_output(result)
+        return parsed[0] if parsed else None

    def list_cols(self) -> List[chromadb.Collection]:
        """
@@ -290,7 +290,7 @@ class MilvusDB(VectorStoreBase):
        schema = {"id": vector_id, "vectors": vector, "metadata": payload, "text": text}
        self.client.upsert(collection_name=self.collection_name, data=schema)

-    def get(self, vector_id):
+    def get(self, vector_id) -> Optional[OutputData]:
        """
        Retrieve a vector by ID.

@@ -298,9 +298,11 @@ class MilvusDB(VectorStoreBase):
            vector_id (str): ID of the vector to retrieve.

        Returns:
-            OutputData: Retrieved vector.
+            Optional[OutputData]: Retrieved vector, or None if the ID is not found.
        """
        result = self.client.get(collection_name=self.collection_name, ids=vector_id)
+        if not result:
+            return None
        output = OutputData(
            id=result[0].get("id", None),
            score=None,
@@ -391,7 +391,7 @@ class MongoDB(VectorStoreBase):
        """Reset the index by deleting and recreating it."""
        logger.warning(f"Resetting index {self.collection_name}...")
        self.delete_col()
-        self.collection = self.create_col(self.collection_name)
+        self.collection = self.create_col()

    def __del__(self) -> None:
        """Close the database connection when the object is deleted."""
@@ -230,7 +230,7 @@ class PineconeDB(VectorStoreBase):
        if filter_dict:
            query_params["filter"] = filter_dict

-        if self.hybrid_search and self.sparse_encoder and "text" in filters:
+        if self.hybrid_search and self.sparse_encoder and filters and "text" in filters:
            query_text = filters.get("text")
            if query_text:
                sparse_vector = self.sparse_encoder.encode_queries(query_text)
@@ -142,8 +142,11 @@ class RedisDB(VectorStoreBase):
        self.index.load(data, id_field="memory_id")

    def search(self, query: str, vectors: list, top_k: int = 5, filters: dict = None):
-        conditions = [Tag(key) == value for key, value in filters.items() if value is not None]
-        filter = reduce(lambda x, y: x & y, conditions)
+        filter = None
+        if filters:
+            conditions = [Tag(key) == value for key, value in filters.items() if value is not None]
+            if conditions:
+                filter = reduce(lambda x, y: x & y, conditions)

        v = VectorQuery(
            vector=np.array(vectors, dtype=np.float32).tobytes(),
@@ -312,11 +315,14 @@ class RedisDB(VectorStoreBase):
        """
        List all recent created memories from the vector store.
        """
-        conditions = [Tag(key) == value for key, value in filters.items() if value is not None]
-        filter = reduce(lambda x, y: x & y, conditions)
-        query = Query(str(filter)).sort_by("created_at", asc=False)
+        filter = None
+        if filters:
+            conditions = [Tag(key) == value for key, value in filters.items() if value is not None]
+            if conditions:
+                filter = reduce(lambda x, y: x & y, conditions)
+        query = Query(str(filter) if filter is not None else "*").sort_by("created_at", asc=False)
        if top_k is not None:
-            query = Query(str(filter)).sort_by("created_at", asc=False).paging(0, top_k)
+            query = query.paging(0, top_k)

        results = self.index.search(query)
        return [
@@ -176,7 +176,7 @@ class Supabase(VectorStoreBase):
        """
        result = self.collection.fetch([(vector_id,)])
        if not result:
-            return []
+            return None

        record = result[0]
        return OutputData(id=str(record.id), score=None, payload=record.metadata)
@@ -305,7 +305,7 @@ class Weaviate(VectorStoreBase):
                existing_payload: Mapping[str, str] = existing_data
                collection.data.update(uuid=vector_id, properties=existing_payload, vector=vector)

-    def get(self, vector_id):
+    def get(self, vector_id) -> Optional[OutputData]:
        """
        Retrieve a vector by ID.

@@ -313,7 +313,7 @@ class Weaviate(VectorStoreBase):
            vector_id: ID of the vector to retrieve.

        Returns:
-            dict: Retrieved vector and metadata.
+            Optional[OutputData]: Retrieved vector, or None if the ID is not found.
        """
        vector_id = get_valid_uuid(vector_id)
        collection = self.client.collections.get(str(self.collection_name))
@@ -322,9 +322,8 @@ class Weaviate(VectorStoreBase):
            uuid=vector_id,
            return_properties=["hash", "created_at", "updated_at", "user_id", "agent_id", "run_id", "data", "category"],
        )
-        # results = {}
-        # print("reponse",response)
-        # for obj in response.objects:
+        if response is None:
+            return None
        payload = response.properties.copy()
        payload["id"] = str(response.uuid).split("'")[0]
        results = OutputData(
@@ -389,4 +388,4 @@ class Weaviate(VectorStoreBase):
        """Reset the index by deleting and recreating it."""
        logger.warning(f"Resetting index {self.collection_name}...")
        self.delete_col()
-        self.create_col()
+        self.create_col(self.embedding_model_dims)
@@ -27,7 +27,7 @@ dependencies = [
 nlp = [
    "spacy>=3.7.0",
 ]
-vector_stores = [
+vector-stores = [
    "vecs>=0.4.0",
    "chromadb>=0.4.24",
    "cassandra-driver>=3.29.0",
@@ -103,7 +103,7 @@ only-include = ["mem0"]
 python = "3.10"
 features = [
  "test",
-  "vector_stores",
+  "vector-stores",
  "llms",
  "extras",
 ]
@@ -112,7 +112,7 @@ features = [
 python = "3.11"
 features = [
  "test",
-  "vector_stores",
+  "vector-stores",
  "llms",
  "extras",
 ]
@@ -121,7 +121,7 @@ features = [
 python = "3.12"
 features = [
  "test",
-  "vector_stores",
+  "vector-stores",
  "llms",
  "extras",
 ]
@@ -424,3 +424,31 @@ class TestMiniMaxProvider:
            assert msg["role"] != "system"
        # user message must be present
        assert kwargs["messages"][0]["role"] == "user"
+
+
+# ---------------------------------------------------------------------------
+# _parse_response — legacy InvokeModel provider-specific parsing
+# ---------------------------------------------------------------------------
+
+class TestParseResponseLegacy:
+    def test_ai21_missing_completions_returns_empty(self, mock_boto3):
+        """When AI21 response lacks 'completions', the fallback default must
+        be a valid dict (not a set literal), returning empty string."""
+        llm = _make_llm("ai21.j2-mid-v1", mock_boto3)
+        import io
+        import json
+        body = io.BytesIO(json.dumps({"not_completions": True}).encode())
+        response = {"body": body}
+        result = llm._parse_response(response, tools=None)
+        assert result == ""
+
+    def test_ai21_normal_response(self, mock_boto3):
+        llm = _make_llm("ai21.j2-mid-v1", mock_boto3)
+        import io
+        import json
+        body = io.BytesIO(json.dumps({
+            "completions": [{"data": {"text": "hello from ai21"}}]
+        }).encode())
+        response = {"body": body}
+        result = llm._parse_response(response, tools=None)
+        assert result == "hello from ai21"
@@ -29,6 +29,7 @@ class DummyConfig:
        max_tokens=256,
        top_p=1.0,
        http_client=None,
+        reasoning_effort=None,
    ):
        self.model = model
        self.azure_kwargs = azure_kwargs or DummyAzureKwargs()
@@ -36,6 +37,7 @@ class DummyConfig:
        self.max_tokens = max_tokens
        self.top_p = top_p
        self.http_client = http_client
+        self.reasoning_effort = reasoning_effort


@mock.patch("mem0.llms.azure_openai_structured.AzureOpenAI")
@@ -119,6 +121,44 @@ def test_generate_response_without_tools(mock_azure_openai):
    assert response == "Hello there!"


+@mock.patch("mem0.llms.azure_openai_structured.AzureOpenAI")
+def test_generate_response_gpt5_uses_max_completion_tokens(mock_azure_openai):
+    mock_client = Mock()
+    mock_azure_openai.return_value = mock_client
+
+    config = DummyConfig(model="gpt-5.4-mini", azure_kwargs=DummyAzureKwargs(api_key="real-key"))
+    llm = AzureOpenAIStructuredLLM(config)
+
+    mock_response = Mock()
+    mock_response.choices = [Mock(message=Mock(content="Hi"))]
+    mock_client.chat.completions.create.return_value = mock_response
+
+    llm.generate_response([{"role": "user", "content": "Hi"}])
+
+    _, kwargs = mock_client.chat.completions.create.call_args
+    assert kwargs["max_completion_tokens"] == 256
+    assert "max_tokens" not in kwargs
+
+
+@mock.patch("mem0.llms.azure_openai_structured.AzureOpenAI")
+def test_generate_response_legacy_model_uses_max_tokens(mock_azure_openai):
+    mock_client = Mock()
+    mock_azure_openai.return_value = mock_client
+
+    config = DummyConfig(model="gpt-4.1", azure_kwargs=DummyAzureKwargs(api_key="real-key"))
+    llm = AzureOpenAIStructuredLLM(config)
+
+    mock_response = Mock()
+    mock_response.choices = [Mock(message=Mock(content="Hi"))]
+    mock_client.chat.completions.create.return_value = mock_response
+
+    llm.generate_response([{"role": "user", "content": "Hi"}])
+
+    _, kwargs = mock_client.chat.completions.create.call_args
+    assert kwargs["max_tokens"] == 256
+    assert "max_completion_tokens" not in kwargs
+
+
@mock.patch("mem0.llms.azure_openai_structured.AzureOpenAI")
 def test_generate_response_with_tools(mock_azure_openai):
    mock_client = Mock()
@@ -171,3 +211,56 @@ def test_generate_response_with_tools_no_tool_calls(mock_azure_openai):

    assert response["content"] == "No tools needed."
    assert response["tool_calls"] == []
+
+
+@mock.patch("mem0.llms.azure_openai_structured.AzureOpenAI")
+def test_reasoning_model_drops_sampling_params(mock_azure_openai):
+    """Reasoning models (o1/o3/GPT-5) reject temperature/max_tokens/top_p."""
+    mock_client = Mock()
+    mock_azure_openai.return_value = mock_client
+
+    config = DummyConfig(
+        model="o3-mini",
+        azure_kwargs=DummyAzureKwargs(api_key="real-key"),
+        reasoning_effort="low",
+    )
+    llm = AzureOpenAIStructuredLLM(config)
+
+    mock_response = Mock()
+    mock_response.choices = [Mock(message=Mock(content="ok"))]
+    mock_client.chat.completions.create.return_value = mock_response
+
+    llm.generate_response([{"role": "user", "content": "Hi"}])
+
+    call_kwargs = mock_client.chat.completions.create.call_args[1]
+    assert "temperature" not in call_kwargs  # reasoning models reject these
+    assert "max_tokens" not in call_kwargs
+    assert "top_p" not in call_kwargs
+    assert call_kwargs["reasoning_effort"] == "low"
+    assert call_kwargs["model"] == "o3-mini"
+
+
+@mock.patch("mem0.llms.azure_openai_structured.AzureOpenAI")
+def test_regular_model_sends_sampling_params(mock_azure_openai):
+    """Regular models still receive the standard sampling params."""
+    mock_client = Mock()
+    mock_azure_openai.return_value = mock_client
+
+    config = DummyConfig(
+        model="gpt-4o",
+        azure_kwargs=DummyAzureKwargs(api_key="real-key"),
+        temperature=0.3,
+    )
+    llm = AzureOpenAIStructuredLLM(config)
+
+    mock_response = Mock()
+    mock_response.choices = [Mock(message=Mock(content="ok"))]
+    mock_client.chat.completions.create.return_value = mock_response
+
+    llm.generate_response([{"role": "user", "content": "Hi"}])
+
+    call_kwargs = mock_client.chat.completions.create.call_args[1]
+    assert call_kwargs["temperature"] == 0.3
+    assert "max_tokens" in call_kwargs  # standard sampling params still forwarded
+    assert "top_p" in call_kwargs
+    assert call_kwargs["model"] == "gpt-4o"
@@ -4,6 +4,7 @@ import pytest
 from google.genai import types

 from mem0.configs.llms.base import BaseLlmConfig
+from mem0.configs.llms.gemini import GeminiConfig
 from mem0.llms.gemini import GeminiLLM


@@ -187,3 +188,113 @@ def test_parse_response_empty_parts_with_tools(mock_gemini_client: Mock):

    result = llm._parse_response(mock_response, tools=[{"function": {"name": "test"}}])
    assert result == {"content": None, "tool_calls": []}
+
+
+def test_none_config_values_omitted_from_generation_config(mock_gemini_client: Mock):
+    """When temperature/max_tokens/top_p are None, they must not be passed
+    to GenerateContentConfig (verified via model_fields_set)."""
+    config = BaseLlmConfig(model="gemini-2.0-flash", temperature=None, max_tokens=None, top_p=None)
+    llm = GeminiLLM(config)
+
+    mock_part = Mock(text="ok")
+    mock_content = Mock(parts=[mock_part])
+    mock_candidate = Mock(content=mock_content)
+    mock_response = Mock(candidates=[mock_candidate])
+    mock_gemini_client.models.generate_content.return_value = mock_response
+
+    llm.generate_response([{"role": "user", "content": "hi"}])
+
+    config_arg = mock_gemini_client.models.generate_content.call_args.kwargs["config"]
+    assert "temperature" not in config_arg.model_fields_set
+    assert "max_output_tokens" not in config_arg.model_fields_set
+    assert "top_p" not in config_arg.model_fields_set
+
+
+def test_explicit_config_values_passed_to_generation_config(mock_gemini_client: Mock):
+    """When temperature/max_tokens/top_p are explicitly set, they must appear."""
+    config = BaseLlmConfig(model="gemini-2.0-flash", temperature=0.5, max_tokens=200, top_p=0.9)
+    llm = GeminiLLM(config)
+
+    mock_part = Mock(text="ok")
+    mock_content = Mock(parts=[mock_part])
+    mock_candidate = Mock(content=mock_content)
+    mock_response = Mock(candidates=[mock_candidate])
+    mock_gemini_client.models.generate_content.return_value = mock_response
+
+    llm.generate_response([{"role": "user", "content": "hi"}])
+
+    config_arg = mock_gemini_client.models.generate_content.call_args.kwargs["config"]
+    assert "temperature" in config_arg.model_fields_set
+    assert config_arg.temperature == 0.5
+    assert "max_output_tokens" in config_arg.model_fields_set
+    assert config_arg.max_output_tokens == 200
+    assert "top_p" in config_arg.model_fields_set
+    assert config_arg.top_p == 0.9
+
+
+# --- Vertex AI backend initialization (issue #3990, PR #4030) ---
+
+
+def test_init_default_path_uses_api_key(monkeypatch):
+    """Default (non-Vertex) path stays backward compatible: client built with api_key, never vertexai."""
+    monkeypatch.delenv("GOOGLE_GENAI_USE_VERTEXAI", raising=False)
+    monkeypatch.setenv("GOOGLE_API_KEY", "test-key")
+    with patch("mem0.llms.gemini.genai.Client") as mock_client_class:
+        GeminiLLM(GeminiConfig(model="gemini-2.0-flash"))
+    mock_client_class.assert_called_once_with(api_key="test-key")
+
+
+def test_init_backward_compat_with_base_config(monkeypatch):
+    """A legacy BaseLlmConfig still works and uses the API-key path (no missing-attr error)."""
+    monkeypatch.delenv("GOOGLE_GENAI_USE_VERTEXAI", raising=False)
+    monkeypatch.setenv("GOOGLE_API_KEY", "legacy-key")
+    with patch("mem0.llms.gemini.genai.Client") as mock_client_class:
+        GeminiLLM(BaseLlmConfig(model="gemini-2.0-flash"))
+    mock_client_class.assert_called_once_with(api_key="legacy-key")
+
+
+def test_init_vertexai_via_explicit_config(monkeypatch):
+    """vertexai=True in GeminiConfig routes to the Vertex AI client with project/location, no api_key."""
+    monkeypatch.delenv("GOOGLE_GENAI_USE_VERTEXAI", raising=False)
+    with patch("mem0.llms.gemini.genai.Client") as mock_client_class:
+        GeminiLLM(GeminiConfig(vertexai=True, project="my-project", location="europe-west1"))
+    mock_client_class.assert_called_once_with(vertexai=True, project="my-project", location="europe-west1")
+
+
+def test_init_vertexai_via_dict_config(monkeypatch):
+    """The factory hands GeminiLLM a dict; the vertexai key still routes to the Vertex client."""
+    monkeypatch.delenv("GOOGLE_GENAI_USE_VERTEXAI", raising=False)
+    with patch("mem0.llms.gemini.genai.Client") as mock_client_class:
+        GeminiLLM({"vertexai": True, "project": "p", "location": "us-central1"})
+    mock_client_class.assert_called_once_with(vertexai=True, project="p", location="us-central1")
+
+
+def test_init_vertexai_via_env_vars(monkeypatch):
+    """GOOGLE_GENAI_USE_VERTEXAI + project/location env vars enable Vertex (the exact ask in issue #3990)."""
+    monkeypatch.setenv("GOOGLE_GENAI_USE_VERTEXAI", "true")
+    monkeypatch.setenv("GOOGLE_CLOUD_PROJECT", "env-project")
+    monkeypatch.setenv("GOOGLE_CLOUD_LOCATION", "asia-south1")
+    with patch("mem0.llms.gemini.genai.Client") as mock_client_class:
+        GeminiLLM(GeminiConfig())
+    mock_client_class.assert_called_once_with(vertexai=True, project="env-project", location="asia-south1")
+
+
+def test_init_vertexai_location_defaults_to_us_central1(monkeypatch):
+    """When Vertex is on but no location is supplied, it defaults to us-central1."""
+    monkeypatch.delenv("GOOGLE_CLOUD_LOCATION", raising=False)
+    monkeypatch.setenv("GOOGLE_GENAI_USE_VERTEXAI", "true")
+    with patch("mem0.llms.gemini.genai.Client") as mock_client_class:
+        GeminiLLM(GeminiConfig(project="p"))
+    mock_client_class.assert_called_once_with(vertexai=True, project="p", location="us-central1")
+
+
+def test_init_base_config_respects_vertexai_env(monkeypatch):
+    """GOOGLE_GENAI_USE_VERTEXAI is the authoritative global switch (Google's own
+    convention): a legacy BaseLlmConfig is routed to Vertex when the env var is set,
+    even if it carries an api_key. This pins the precedence as intentional."""
+    monkeypatch.setenv("GOOGLE_GENAI_USE_VERTEXAI", "true")
+    monkeypatch.setenv("GOOGLE_CLOUD_PROJECT", "env-project")
+    monkeypatch.setenv("GOOGLE_CLOUD_LOCATION", "us-west1")
+    with patch("mem0.llms.gemini.genai.Client") as mock_client_class:
+        GeminiLLM(BaseLlmConfig(model="gemini-2.0-flash", api_key="ignored-key"))
+    mock_client_class.assert_called_once_with(vertexai=True, project="env-project", location="us-west1")
@@ -115,6 +115,20 @@ def test_generate_response_with_tools(mock_langchain_model):
    assert response["tool_calls"][0]["arguments"] == {"data": "Today is a sunny day."}


+def test_generate_response_forwards_extra_kwargs(mock_langchain_model):
+    """Per the LLMBase contract, extra model kwargs must be accepted and forwarded to
+    the underlying LangChain model's ``invoke``."""
+    config = BaseLlmConfig(model=mock_langchain_model, temperature=0.7, max_tokens=100, api_key="test-api-key")
+    llm = LangchainLLM(config)
+    messages = [{"role": "user", "content": "Hello"}]
+
+    response = llm.generate_response(messages, frequency_penalty=0.5)
+
+    mock_langchain_model.invoke.assert_called_once()
+    assert mock_langchain_model.invoke.call_args.kwargs["frequency_penalty"] == 0.5
+    assert response == "This is a test response"
+
+
 def test_invalid_model():
    """Test that LangchainLLM raises an error with an invalid model."""
    config = BaseLlmConfig(model="not-a-valid-model-instance", temperature=0.7, max_tokens=100, api_key="test-api-key")
@@ -19,8 +19,26 @@ def test_generate_response_with_unsupported_model(mock_litellm):

    mock_litellm.supports_function_calling.return_value = False

+    tools = [{"type": "function", "function": {"name": "test", "parameters": {}}}]
+
    with pytest.raises(ValueError, match="Model 'unsupported-model' in litellm does not support function calling."):
-        llm.generate_response(messages)
+        llm.generate_response(messages, tools=tools)
+
+
+def test_generate_response_with_unsupported_model_no_tools(mock_litellm):
+    config = BaseLlmConfig(model="unsupported-model", temperature=0.7, max_tokens=100, top_p=1)
+    llm = litellm.LiteLLM(config)
+    messages = [{"role": "user", "content": "Hello"}]
+
+    mock_response = Mock()
+    mock_response.choices = [Mock(message=Mock(content="hi"))]
+    mock_litellm.completion.return_value = mock_response
+    mock_litellm.supports_function_calling.return_value = False
+
+    response = llm.generate_response(messages)
+
+    assert response == "hi"
+    mock_litellm.supports_function_calling.assert_not_called()


 def test_generate_response_without_tools(mock_litellm):
@@ -44,6 +62,40 @@ def test_generate_response_without_tools(mock_litellm):
    assert response == "I'm doing well, thank you for asking!"


+def test_generate_response_gpt5_uses_max_completion_tokens(mock_litellm):
+    config = BaseLlmConfig(model="gpt-5.4-mini", temperature=0.7, max_tokens=100, top_p=1)
+    llm = litellm.LiteLLM(config)
+    messages = [{"role": "user", "content": "Hello"}]
+
+    mock_response = Mock()
+    mock_response.choices = [Mock(message=Mock(content="Hi"))]
+    mock_litellm.completion.return_value = mock_response
+    mock_litellm.supports_function_calling.return_value = True
+
+    llm.generate_response(messages)
+
+    _, kwargs = mock_litellm.completion.call_args
+    assert kwargs["max_completion_tokens"] == 100
+    assert "max_tokens" not in kwargs
+
+
+def test_generate_response_legacy_model_uses_max_tokens(mock_litellm):
+    config = BaseLlmConfig(model="gpt-4.1", temperature=0.7, max_tokens=100, top_p=1)
+    llm = litellm.LiteLLM(config)
+    messages = [{"role": "user", "content": "Hello"}]
+
+    mock_response = Mock()
+    mock_response.choices = [Mock(message=Mock(content="Hi"))]
+    mock_litellm.completion.return_value = mock_response
+    mock_litellm.supports_function_calling.return_value = True
+
+    llm.generate_response(messages)
+
+    _, kwargs = mock_litellm.completion.call_args
+    assert kwargs["max_tokens"] == 100
+    assert "max_completion_tokens" not in kwargs
+
+
 def test_generate_response_with_tools(mock_litellm):
    config = BaseLlmConfig(model="gpt-4.1-nano-2025-04-14", temperature=0.7, max_tokens=100, top_p=1)
    llm = litellm.LiteLLM(config)
@@ -375,6 +375,45 @@ def test_is_reasoning_model_override_generates_correct_params(mock_openai_client
    assert "temperature" not in call_kwargs


+def test_gpt5_uses_max_completion_tokens(mock_openai_client):
+    """gpt-5.x (non-reasoning) must send max_completion_tokens, not max_tokens.
+
+    The GPT-5 family rejects the legacy max_tokens param on Chat Completions and
+    requires max_completion_tokens. Regression test for
+    https://github.com/mem0ai/mem0/issues/5054
+    """
+    config = OpenAIConfig(model="gpt-5.4-mini", temperature=0.7, max_tokens=100, top_p=1.0)
+    llm = OpenAILLM(config)
+    messages = [{"role": "user", "content": "Hello"}]
+
+    mock_response = Mock()
+    mock_response.choices = [Mock(message=Mock(content="ok"))]
+    mock_openai_client.chat.completions.create.return_value = mock_response
+
+    llm.generate_response(messages)
+
+    call_kwargs = mock_openai_client.chat.completions.create.call_args[1]
+    assert call_kwargs.get("max_completion_tokens") == 100
+    assert "max_tokens" not in call_kwargs
+
+
+def test_gpt4_uses_max_tokens(mock_openai_client):
+    """Older models (gpt-4.x) keep using max_tokens — guards against regressions."""
+    config = OpenAIConfig(model="gpt-4.1-nano-2025-04-14", temperature=0.7, max_tokens=100, top_p=1.0)
+    llm = OpenAILLM(config)
+    messages = [{"role": "user", "content": "Hello"}]
+
+    mock_response = Mock()
+    mock_response.choices = [Mock(message=Mock(content="ok"))]
+    mock_openai_client.chat.completions.create.return_value = mock_response
+
+    llm.generate_response(messages)
+
+    call_kwargs = mock_openai_client.chat.completions.create.call_args[1]
+    assert call_kwargs.get("max_tokens") == 100
+    assert "max_completion_tokens" not in call_kwargs
+
+
 def test_callback_with_tools(mock_openai_client):
    mock_callback = Mock()
    config = OpenAIConfig(model="gpt-4.1-nano-2025-04-14", response_callback=mock_callback)
@@ -0,0 +1,51 @@
+from unittest.mock import Mock, patch
+
+import pytest
+
+from mem0.configs.llms.openai import OpenAIConfig
+from mem0.llms.openai_structured import OpenAIStructuredLLM
+
+
+@pytest.fixture
+def mock_openai_client():
+    with patch("mem0.llms.openai_structured.OpenAI") as mock_openai:
+        mock_client = Mock()
+        mock_openai.return_value = mock_client
+        yield mock_client
+
+
+def _mock_parse(mock_client, content="ok"):
+    mock_response = Mock()
+    mock_response.choices = [Mock(message=Mock(content=content))]
+    mock_client.beta.chat.completions.parse.return_value = mock_response
+
+
+def test_reasoning_model_drops_temperature(mock_openai_client):
+    """Reasoning models reject `temperature`; structured output must not send it."""
+    config = OpenAIConfig(model="o3-mini", reasoning_effort="low")
+    llm = OpenAIStructuredLLM(config)
+    _mock_parse(mock_openai_client)
+
+    llm.generate_response([{"role": "user", "content": "Hello"}])
+
+    call_kwargs = mock_openai_client.beta.chat.completions.parse.call_args[1]
+    assert "temperature" not in call_kwargs  # reasoning models don't accept temperature
+    assert "max_tokens" not in call_kwargs  # also dropped for reasoning models
+    assert "top_p" not in call_kwargs  # also dropped for reasoning models
+    assert call_kwargs["reasoning_effort"] == "low"
+    assert call_kwargs["model"] == "o3-mini"
+
+
+def test_regular_model_sends_sampling_params(mock_openai_client):
+    """Regular models still receive the standard sampling params."""
+    config = OpenAIConfig(model="gpt-4o", temperature=0.3)
+    llm = OpenAIStructuredLLM(config)
+    _mock_parse(mock_openai_client)
+
+    llm.generate_response([{"role": "user", "content": "Hello"}])
+
+    call_kwargs = mock_openai_client.beta.chat.completions.parse.call_args[1]
+    assert call_kwargs["temperature"] == 0.3
+    assert "max_tokens" in call_kwargs  # standard sampling params still forwarded
+    assert "top_p" in call_kwargs
+    assert call_kwargs["model"] == "gpt-4o"
@@ -0,0 +1,46 @@
+from unittest.mock import Mock, patch
+
+import pytest
+
+from mem0.configs.llms.base import BaseLlmConfig
+from mem0.llms.sarvam import SarvamLLM
+
+
+@pytest.fixture
+def sarvam_llm():
+    config = BaseLlmConfig(model="sarvam-m", temperature=0.7, max_tokens=100, top_p=1.0, api_key="test-api-key")
+    return SarvamLLM(config)
+
+
+def _mock_post(content="Hello there!"):
+    mock_response = Mock()
+    mock_response.raise_for_status.return_value = None
+    mock_response.json.return_value = {"choices": [{"message": {"content": content}}]}
+    return mock_response
+
+
+def test_generate_response_returns_content(sarvam_llm):
+    with patch("mem0.llms.sarvam.requests.post", return_value=_mock_post("Hi!")) as mock_post:
+        messages = [
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": "Hello, how are you?"},
+        ]
+        response = sarvam_llm.generate_response(messages)
+
+    assert response == "Hi!"
+    sent_payload = mock_post.call_args.kwargs["json"]
+    assert sent_payload["model"] == "sarvam-m"
+    assert sent_payload["messages"] == messages
+    assert sent_payload["temperature"] == 0.7
+
+
+def test_generate_response_forwards_extra_kwargs(sarvam_llm):
+    """Per the LLMBase contract, extra provider-specific kwargs must be accepted and
+    forwarded into the Sarvam request payload."""
+    with patch("mem0.llms.sarvam.requests.post", return_value=_mock_post("Hi!")) as mock_post:
+        messages = [{"role": "user", "content": "Hello"}]
+        response = sarvam_llm.generate_response(messages, frequency_penalty=0.5)
+
+    assert response == "Hi!"
+    sent_payload = mock_post.call_args.kwargs["json"]
+    assert sent_payload["frequency_penalty"] == 0.5
@@ -84,3 +84,21 @@ def test_generate_response_with_tools(mock_together_client):
    assert len(response["tool_calls"]) == 1
    assert response["tool_calls"][0]["name"] == "add_memory"
    assert response["tool_calls"][0]["arguments"] == {"data": "Today is a sunny day."}
+
+
+def test_generate_response_forwards_extra_kwargs(mock_together_client):
+    """Per the LLMBase contract, extra provider-specific kwargs must be accepted and
+    forwarded to the Together client (matching openai/deepseek/vllm/xai behavior)."""
+    config = BaseLlmConfig(model="mistralai/Mixtral-8x7B-Instruct-v0.1", temperature=0.7, max_tokens=100, top_p=1.0)
+    llm = TogetherLLM(config)
+    messages = [{"role": "user", "content": "Hello"}]
+
+    mock_response = Mock()
+    mock_response.choices = [Mock(message=Mock(content="Hi"))]
+    mock_together_client.chat.completions.create.return_value = mock_response
+
+    response = llm.generate_response(messages, frequency_penalty=0.5)
+
+    assert response == "Hi"
+    call_kwargs = mock_together_client.chat.completions.create.call_args.kwargs
+    assert call_kwargs["frequency_penalty"] == 0.5
@@ -0,0 +1,69 @@
+import logging
+from unittest.mock import Mock
+
+from mem0.memory.main import (
+    ADD_CONTEXT_SEARCH_MAX_CHARS,
+    ADD_MAX_ENTITY_LINKS_PER_EVENT,
+    ADD_MAX_EXTRACTED_MEMORIES,
+    ADD_MEMORY_EMBEDDING_MAX_CHARS,
+    Memory,
+    SEARCH_QUERY_MAX_CHARS,
+    _cap_entity_keys,
+    _cap_extracted_memories,
+    _cap_memory_embedding_texts,
+    _tail_cap_text,
+)
+
+
+def test_tail_cap_text_keeps_recent_suffix():
+    text = "old-" + ("x" * ADD_CONTEXT_SEARCH_MAX_CHARS)
+
+    capped = _tail_cap_text(text, ADD_CONTEXT_SEARCH_MAX_CHARS)
+
+    assert capped == text[-ADD_CONTEXT_SEARCH_MAX_CHARS:]
+
+
+def test_cap_extracted_memories_keeps_last_memories(caplog):
+    memories = [{"text": f"memory-{i}"} for i in range(ADD_MAX_EXTRACTED_MEMORIES + 3)]
+
+    with caplog.at_level(logging.WARNING):
+        capped = _cap_extracted_memories(memories)
+
+    assert capped == memories[-ADD_MAX_EXTRACTED_MEMORIES:]
+    assert any("Extracted memory cap applied" in record.message for record in caplog.records)
+
+
+def test_cap_memory_embedding_texts_keeps_original_text_untouched(caplog):
+    original = "old-" + ("z" * ADD_MEMORY_EMBEDDING_MAX_CHARS)
+
+    with caplog.at_level(logging.WARNING):
+        capped = _cap_memory_embedding_texts([original])
+
+    assert capped == [original[-ADD_MEMORY_EMBEDDING_MAX_CHARS:]]
+    assert original.startswith("old-")
+    assert any("Memory embedding input cap applied" in record.message for record in caplog.records)
+
+
+def test_cap_entity_keys_keeps_first_100_entities(caplog):
+    keys = [f"entity-{i}" for i in range(ADD_MAX_ENTITY_LINKS_PER_EVENT + 3)]
+
+    with caplog.at_level(logging.WARNING):
+        capped = _cap_entity_keys(keys)
+
+    assert capped == keys[:ADD_MAX_ENTITY_LINKS_PER_EVENT]
+    assert any("ADD entity-linking cap applied" in record.message for record in caplog.records)
+
+
+def test_search_vector_store_caps_query_before_embedding():
+    memory = object.__new__(Memory)
+    memory.embedding_model = Mock()
+    memory.embedding_model.embed.return_value = [0.1, 0.2, 0.3]
+    memory.vector_store = Mock()
+    memory.vector_store.search.return_value = []
+    memory.vector_store.keyword_search.return_value = None
+    memory._compute_entity_boosts = Mock(return_value={})
+    query = "old-" + ("q" * SEARCH_QUERY_MAX_CHARS)
+
+    Memory._search_vector_store(memory, query, filters={}, limit=5)
+
+    memory.embedding_model.embed.assert_called_once_with(query[-SEARCH_QUERY_MAX_CHARS:], "search")
@@ -1,10 +1,50 @@
 import pytest
 from unittest.mock import Mock

-from mem0.memory.utils import parse_vision_messages, remove_spaces_from_entities, sanitize_relationship_for_cypher
+from mem0.memory.utils import (
+    parse_messages,
+    parse_vision_messages,
+    remove_spaces_from_entities,
+    sanitize_relationship_for_cypher,
+)
+
+
+class TestParseMessages:
+    def test_skips_message_without_content_key(self):
+        # Reproduces #5067: a FunctionCalling assistant message carries
+        # `tool_calls` but no `content` key -> used to raise KeyError.
+        messages = [
+            {"role": "user", "content": "hi"},
+            {"role": "assistant", "tool_calls": [{"id": "1", "function": {"name": "search"}}]},
+            {"role": "assistant", "content": "done"},
+        ]
+        result = parse_messages(messages)
+        assert result == "user: hi\nassistant: done\n"
+
+    def test_skips_explicit_none_content(self):
+        messages = [{"role": "assistant", "content": None}, {"role": "user", "content": "ok"}]
+        assert parse_messages(messages) == "user: ok\n"
+
+    def test_plain_roles_pass_through(self):
+        messages = [
+            {"role": "system", "content": "sys"},
+            {"role": "user", "content": "u"},
+            {"role": "assistant", "content": "a"},
+        ]
+        assert parse_messages(messages) == "system: sys\nuser: u\nassistant: a\n"


 class TestParseVisionMessages:
+    def test_skips_message_without_content_key(self):
+        # Reproduces #5067 for the vision parser path.
+        messages = [
+            {"role": "user", "content": "hi"},
+            {"role": "assistant", "tool_calls": [{"id": "1", "function": {"name": "search"}}]},
+        ]
+        result = parse_vision_messages(messages, llm=None)
+        assert len(result) == 1
+        assert result[0] == {"role": "user", "content": "hi"}
+
    def test_multimodal_list_without_llm_extracts_text(self):
        messages = [
            {"role": "user", "content": [
@@ -0,0 +1,109 @@
+"""Regression tests for the reranker fallback path honoring ``config.top_k``.
+
+When the underlying rerank call fails, the reranker falls back to returning the
+documents in their original order. That fallback must still respect the
+configured ``top_k`` limit, exactly like the success path does. The HuggingFace
+and SentenceTransformer rerankers already behave this way; these tests pin the
+same contract for the Cohere and ZeroEntropy rerankers.
+"""
+
+import sys
+from types import ModuleType
+from unittest.mock import MagicMock
+
+import pytest
+
+from mem0.configs.rerankers.cohere import CohereRerankerConfig
+from mem0.configs.rerankers.zero_entropy import ZeroEntropyRerankerConfig
+
+
+@pytest.fixture
+def mock_cohere(monkeypatch):
+    """Provide a fake ``cohere`` module so CohereReranker imports/constructs."""
+    fake_cohere = ModuleType("cohere")
+    fake_client = MagicMock()
+    fake_cohere.Client = MagicMock(return_value=fake_client)
+    monkeypatch.setitem(sys.modules, "cohere", fake_cohere)
+
+    import mem0.reranker.cohere_reranker as cohere_reranker
+
+    monkeypatch.setattr(cohere_reranker, "cohere", fake_cohere, raising=False)
+    monkeypatch.setattr(cohere_reranker, "COHERE_AVAILABLE", True, raising=False)
+    return cohere_reranker, fake_client
+
+
+@pytest.fixture
+def mock_zero_entropy(monkeypatch):
+    """Provide a fake ``zeroentropy`` module so ZeroEntropyReranker imports."""
+    fake_module = ModuleType("zeroentropy")
+    fake_client = MagicMock()
+    fake_module.ZeroEntropy = MagicMock(return_value=fake_client)
+    monkeypatch.setitem(sys.modules, "zeroentropy", fake_module)
+
+    import mem0.reranker.zero_entropy_reranker as zero_entropy_reranker
+
+    monkeypatch.setattr(zero_entropy_reranker, "ZeroEntropy", fake_module.ZeroEntropy, raising=False)
+    monkeypatch.setattr(zero_entropy_reranker, "ZERO_ENTROPY_AVAILABLE", True, raising=False)
+    return zero_entropy_reranker, fake_client
+
+
+def _docs(n):
+    return [{"memory": f"doc{i}"} for i in range(n)]
+
+
+class TestCohereFallbackTopK:
+    def test_fallback_respects_config_top_k(self, mock_cohere):
+        module, fake_client = mock_cohere
+        fake_client.rerank.side_effect = RuntimeError("API error")
+
+        reranker = module.CohereReranker(CohereRerankerConfig(api_key="test-key", top_k=2))
+        result = reranker.rerank("query", _docs(5))
+
+        assert len(result) == 2
+
+    def test_fallback_per_call_top_k_overrides_config(self, mock_cohere):
+        module, fake_client = mock_cohere
+        fake_client.rerank.side_effect = RuntimeError("API error")
+
+        reranker = module.CohereReranker(CohereRerankerConfig(api_key="test-key", top_k=4))
+        result = reranker.rerank("query", _docs(5), top_k=1)
+
+        assert len(result) == 1
+
+    def test_fallback_returns_all_when_no_top_k(self, mock_cohere):
+        module, fake_client = mock_cohere
+        fake_client.rerank.side_effect = RuntimeError("API error")
+
+        reranker = module.CohereReranker(CohereRerankerConfig(api_key="test-key"))
+        result = reranker.rerank("query", _docs(5))
+
+        assert len(result) == 5
+
+
+class TestZeroEntropyFallbackTopK:
+    def test_fallback_respects_config_top_k(self, mock_zero_entropy):
+        module, fake_client = mock_zero_entropy
+        fake_client.models.rerank.side_effect = RuntimeError("API error")
+
+        reranker = module.ZeroEntropyReranker(ZeroEntropyRerankerConfig(api_key="test-key", top_k=2))
+        result = reranker.rerank("query", _docs(5))
+
+        assert len(result) == 2
+
+    def test_fallback_per_call_top_k_overrides_config(self, mock_zero_entropy):
+        module, fake_client = mock_zero_entropy
+        fake_client.models.rerank.side_effect = RuntimeError("API error")
+
+        reranker = module.ZeroEntropyReranker(ZeroEntropyRerankerConfig(api_key="test-key", top_k=4))
+        result = reranker.rerank("query", _docs(5), top_k=1)
+
+        assert len(result) == 1
+
+    def test_fallback_returns_all_when_no_top_k(self, mock_zero_entropy):
+        module, fake_client = mock_zero_entropy
+        fake_client.models.rerank.side_effect = RuntimeError("API error")
+
+        reranker = module.ZeroEntropyReranker(ZeroEntropyRerankerConfig(api_key="test-key"))
+        result = reranker.rerank("query", _docs(5))
+
+        assert len(result) == 5
@@ -762,6 +762,55 @@ async def test_async_delete_memory_history_has_timestamps(mock_sqlite, mock_llm_
    datetime.fromisoformat(call_kwargs["updated_at"])  # verify valid ISO timestamp


+@pytest.mark.asyncio
+@patch('mem0.utils.factory.EmbedderFactory.create')
+@patch('mem0.utils.factory.VectorStoreFactory.create')
+@patch('mem0.utils.factory.LlmFactory.create')
+@patch('mem0.memory.main.SQLiteManager')
+async def test_async_delete_all_continues_on_partial_failure(mock_sqlite, mock_llm_factory, mock_vector_factory, mock_embedder_factory):
+    """async delete_all must not abort when a single memory fails to delete.
+
+    Without return_exceptions=True, asyncio.gather raises on the first error
+    and cancels remaining tasks, leaving a partial deletion.
+    """
+    mock_embedder_factory.return_value = MagicMock()
+    mock_vector_store = MagicMock()
+    mock_vector_factory.return_value = mock_vector_store
+    mock_llm_factory.return_value = MagicMock()
+    mock_sqlite.return_value = MagicMock()
+
+    from mem0.memory.main import AsyncMemory
+    config = MemoryConfig()
+    memory = AsyncMemory(config)
+
+    mem1 = MagicMock()
+    mem1.id = "mem-1"
+    mem1.payload = {"data": "one", "created_at": "2024-01-01T00:00:00+00:00", "actor_id": None, "role": None}
+    mem2 = MagicMock()
+    mem2.id = "mem-2"
+    mem2.payload = {"data": "two", "created_at": "2024-01-01T00:00:00+00:00", "actor_id": None, "role": None}
+    mem3 = MagicMock()
+    mem3.id = "mem-3"
+    mem3.payload = {"data": "three", "created_at": "2024-01-01T00:00:00+00:00", "actor_id": None, "role": None}
+
+    mock_vector_store.list.return_value = ([mem1, mem2, mem3],)
+
+    def _get_side_effect(vector_id):
+        if vector_id == "mem-2":
+            raise RuntimeError("simulated store failure")
+        return {
+            "mem-1": mem1,
+            "mem-3": mem3,
+        }.get(vector_id)
+
+    mock_vector_store.get.side_effect = _get_side_effect
+
+    result = await memory.delete_all(user_id="test-user")
+
+    assert result == {"message": "Memories deleted successfully!"}
+    assert mock_vector_store.delete.call_count == 2
+
+
@patch('mem0.utils.factory.EmbedderFactory.create')
@patch('mem0.utils.factory.VectorStoreFactory.create')
@patch('mem0.utils.factory.LlmFactory.create')
@@ -1056,3 +1105,154 @@ class TestHybridSearchWarning:
            Memory(config)

        assert not any("does not support keyword search" in r.message for r in caplog.records)
+
+
+class TestPreserveCustomMetadata:
+
+    @patch('mem0.utils.factory.EmbedderFactory.create')
+    @patch('mem0.utils.factory.VectorStoreFactory.create')
+    @patch('mem0.utils.factory.LlmFactory.create')
+    @patch('mem0.memory.storage.SQLiteManager')
+    def test_update_preserves_custom_metadata(self, mock_sqlite, mock_llm_factory, mock_vector_factory, mock_embedder_factory):
+        mock_embedder_factory.return_value = MagicMock()
+        mock_vector_store = MagicMock()
+        mock_vector_factory.return_value = mock_vector_store
+        mock_llm_factory.return_value = MagicMock()
+        mock_sqlite.return_value = MagicMock()
+
+        existing_payload = {
+            "data": "I love playing tennis",
+            "hash": "abc123",
+            "created_at": "2026-01-01T00:00:00+00:00",
+            "updated_at": "2026-01-01T00:00:00+00:00",
+            "user_id": "user_1",
+            "category": "hobbies",
+            "priority": "high",
+            "source": "chat",
+        }
+        mock_vector_store.get.return_value = MockVectorMemory("mem-1", existing_payload)
+
+        embedder = MagicMock()
+        embedder.embed.return_value = [0.1, 0.2, 0.3]
+        mock_embedder_factory.return_value = embedder
+
+        config = MemoryConfig()
+        memory = Memory(config)
+
+        memory._update_memory("mem-1", "I love playing tennis and swimming", {"I love playing tennis and swimming": [0.1, 0.2, 0.3]})
+
+        call_args = mock_vector_store.update.call_args
+        payload = call_args.kwargs.get("payload") or call_args[1].get("payload")
+        assert payload["category"] == "hobbies"
+        assert payload["priority"] == "high"
+        assert payload["source"] == "chat"
+        assert payload["data"] == "I love playing tennis and swimming"
+        assert payload["user_id"] == "user_1"
+
+    @patch('mem0.utils.factory.EmbedderFactory.create')
+    @patch('mem0.utils.factory.VectorStoreFactory.create')
+    @patch('mem0.utils.factory.LlmFactory.create')
+    @patch('mem0.memory.storage.SQLiteManager')
+    def test_update_with_new_metadata_overrides_existing(self, mock_sqlite, mock_llm_factory, mock_vector_factory, mock_embedder_factory):
+        mock_embedder_factory.return_value = MagicMock()
+        mock_vector_store = MagicMock()
+        mock_vector_factory.return_value = mock_vector_store
+        mock_llm_factory.return_value = MagicMock()
+        mock_sqlite.return_value = MagicMock()
+
+        existing_payload = {
+            "data": "I love playing tennis",
+            "hash": "abc123",
+            "created_at": "2026-01-01T00:00:00+00:00",
+            "updated_at": "2026-01-01T00:00:00+00:00",
+            "user_id": "user_1",
+            "category": "hobbies",
+            "priority": "high",
+        }
+        mock_vector_store.get.return_value = MockVectorMemory("mem-1", existing_payload)
+
+        config = MemoryConfig()
+        memory = Memory(config)
+
+        memory._update_memory(
+            "mem-1", "Updated text",
+            {"Updated text": [0.1, 0.2, 0.3]},
+            metadata={"priority": "low", "new_field": "value"},
+        )
+
+        call_args = mock_vector_store.update.call_args
+        payload = call_args.kwargs.get("payload") or call_args[1].get("payload")
+        assert payload["category"] == "hobbies"
+        assert payload["priority"] == "low"
+        assert payload["new_field"] == "value"
+        assert payload["data"] == "Updated text"
+
+    @patch('mem0.utils.factory.EmbedderFactory.create')
+    @patch('mem0.utils.factory.VectorStoreFactory.create')
+    @patch('mem0.utils.factory.LlmFactory.create')
+    @patch('mem0.memory.storage.SQLiteManager')
+    def test_update_preserves_actor_id_from_original(self, mock_sqlite, mock_llm_factory, mock_vector_factory, mock_embedder_factory):
+        mock_embedder_factory.return_value = MagicMock()
+        mock_vector_store = MagicMock()
+        mock_vector_factory.return_value = mock_vector_store
+        mock_llm_factory.return_value = MagicMock()
+        mock_sqlite.return_value = MagicMock()
+
+        existing_payload = {
+            "data": "I am player #1",
+            "hash": "abc123",
+            "created_at": "2026-01-01T00:00:00+00:00",
+            "updated_at": "2026-01-01T00:00:00+00:00",
+            "user_id": "team",
+            "actor_id": "Alice",
+        }
+        mock_vector_store.get.return_value = MockVectorMemory("mem-1", existing_payload)
+
+        config = MemoryConfig()
+        memory = Memory(config)
+
+        memory._update_memory(
+            "mem-1", "Player #1 is great",
+            {"Player #1 is great": [0.1, 0.2, 0.3]},
+            metadata={"user_id": "team", "actor_id": "Bob"},
+        )
+
+        call_args = mock_vector_store.update.call_args
+        payload = call_args.kwargs.get("payload") or call_args[1].get("payload")
+        assert payload["actor_id"] == "Alice"
+
+    @pytest.mark.asyncio
+    @patch('mem0.utils.factory.EmbedderFactory.create')
+    @patch('mem0.utils.factory.VectorStoreFactory.create')
+    @patch('mem0.utils.factory.LlmFactory.create')
+    @patch('mem0.memory.storage.SQLiteManager')
+    async def test_async_update_preserves_custom_metadata(self, mock_sqlite, mock_llm_factory, mock_vector_factory, mock_embedder_factory):
+        mock_embedder_factory.return_value = MagicMock()
+        mock_vector_store = MagicMock()
+        mock_vector_factory.return_value = mock_vector_store
+        mock_llm_factory.return_value = MagicMock()
+        mock_sqlite.return_value = MagicMock()
+
+        existing_payload = {
+            "data": "I love playing tennis",
+            "hash": "abc123",
+            "created_at": "2026-01-01T00:00:00+00:00",
+            "updated_at": "2026-01-01T00:00:00+00:00",
+            "user_id": "user_1",
+            "category": "hobbies",
+            "source": "chat",
+        }
+        mock_vector_store.get.return_value = MockVectorMemory("mem-1", existing_payload)
+
+        from mem0.memory.main import AsyncMemory
+        config = MemoryConfig()
+        memory = AsyncMemory(config)
+
+        await memory._update_memory("mem-1", "I love swimming", {"I love swimming": [0.1, 0.2, 0.3]})
+
+        call_args = mock_vector_store.update.call_args
+        payload = call_args.kwargs.get("payload") or call_args[1].get("payload")
+        assert payload["category"] == "hobbies"
+        assert payload["source"] == "chat"
+        assert payload["data"] == "I love swimming"
+        assert payload["user_id"] == "user_1"
@@ -154,6 +154,16 @@ def test_get_vector(chromadb_instance):
    assert result.payload == {"name": "vector1"}


+def test_get_missing_vector_returns_none(chromadb_instance):
+    # Chroma returns empty lists for an unknown id; get() must return None
+    # rather than raising IndexError (parity with qdrant/pgvector/faiss).
+    chromadb_instance.collection.get.return_value = {"ids": [], "metadatas": []}
+
+    result = chromadb_instance.get(vector_id="does-not-exist")
+
+    assert result is None
+
+
 def test_list_vectors(chromadb_instance):
    mock_result = {
        "ids": [["id1", "id2"]],
@@ -189,11 +189,17 @@ class TestMilvusDB:
        ]
        
        result = milvus_db.get(vector_id)
-        
+
        assert result.id == vector_id
        assert result.payload == {"user_id": "alice"}
        assert result.score is None

+    def test_get_missing_returns_none(self, milvus_db, mock_milvus_client):
+        """get() must return None (not raise IndexError) for an unknown id."""
+        mock_milvus_client.get.return_value = []
+
+        assert milvus_db.get("missing") is None
+
    def test_list_with_filters(self, milvus_db, mock_milvus_client):
        """Test listing memories with filters."""
        mock_milvus_client.query.return_value = [
@@ -71,3 +71,45 @@ def test_update_with_vector_includes_embedding():
    )
    expected_bytes = np.array(vector, dtype=np.float32).tobytes()
    assert data_dict["embedding"] == expected_bytes
+
+
+def test_search_with_none_filters_does_not_crash():
+    """search() with the base-class default filters=None must run an unfiltered
+    query, not raise. Regression: filters.items() on None raised AttributeError
+    and reduce() over an empty list raised TypeError. keyword_search() in the
+    same file already guards this; search() did not."""
+    db, mock_index = _make_redis_db()
+    mock_index.query.return_value = []
+
+    assert db.search("query", [0.1, 0.2, 0.3, 0.4], top_k=5, filters=None) == []
+    mock_index.query.assert_called_once()
+
+
+def test_search_with_empty_or_all_none_filters_does_not_crash():
+    db, mock_index = _make_redis_db()
+    mock_index.query.return_value = []
+
+    assert db.search("query", [0.1, 0.2, 0.3, 0.4], filters={}) == []
+    assert db.search("query", [0.1, 0.2, 0.3, 0.4], filters={"user_id": None}) == []
+
+
+def test_list_with_none_filters_matches_all():
+    """list() with no filters must issue a match-all ('*') query, not raise."""
+    db, mock_index = _make_redis_db()
+    mock_index.search.return_value = MagicMock(docs=[])
+
+    db.list(filters=None)
+
+    query = mock_index.search.call_args[0][0]
+    assert query.query_string() == "*"
+
+
+def test_list_with_filter_builds_query():
+    """A real filter is still translated into a tag query (no regression)."""
+    db, mock_index = _make_redis_db()
+    mock_index.search.return_value = MagicMock(docs=[])
+
+    db.list(filters={"user_id": "alice"})
+
+    query = mock_index.search.call_args[0][0]
+    assert query.query_string() == "@user_id:{alice}"
@@ -111,6 +111,13 @@ def test_get_vector(supabase_instance, mock_collection):
    assert result.payload == {"name": "vector1"}


+def test_get_missing_returns_none(supabase_instance, mock_collection):
+    # An unknown id yields an empty fetch; get() must return None (not []).
+    mock_collection.fetch.return_value = []
+
+    assert supabase_instance.get(vector_id="missing") is None
+
+
 def test_list_vectors(supabase_instance, mock_collection):
    mock_query_results = [("id1", 0.9, {}), ("id2", 0.8, {})]
    mock_fetch_results = [("id1", [0.1, 0.2, 0.3], {"name": "vector1"}), ("id2", [0.4, 0.5, 0.6], {"name": "vector2"})]
@@ -4,9 +4,7 @@ import uuid
 from unittest.mock import MagicMock, patch

 import dotenv
-import httpx
 import weaviate
-from weaviate.exceptions import UnexpectedStatusCodeException

 from mem0.vector_stores.weaviate import Weaviate

@@ -112,11 +110,13 @@ class TestWeaviateDB(unittest.TestCase):
        assert result.payload == expected_payload

    def test_get_not_found(self):
-        mock_response = httpx.Response(status_code=404, json={"error": "Not found"})
+        # fetch_object_by_id returns None for an unknown id; get() must return
+        # None rather than raising AttributeError on response.properties.
+        self.client_mock.collections.get.return_value.query.fetch_object_by_id.return_value = None

-        self.client_mock.collections.get.return_value.data.get_by_id.side_effect = UnexpectedStatusCodeException(
-            "Not found", mock_response
-        )
+        result = self.weaviate_db.get(vector_id=str(uuid.uuid4()))
+
+        assert result is None

    def test_search(self):
        mock_objects = [{"uuid": "id1", "properties": {"key1": "value1"}, "metadata": {"distance": 0.2}}]
Author	SHA1	Message	Date
Soumil Rathi	ebc82245a5	fix(oss): cap v3 add ingestion fanout	2026-06-16 17:05:20 -07:00
Aayush Soni GitHub kartik-mem0	d772f9a961	feat: support Gemini via Vertex AI as LLM provider (#4030 ) Co-authored-by: kartik-mem0 <kartik.labhshetwar@mem0.ai>	2026-06-16 16:59:06 +05:30
Yash Raj Pandey GitHub kartik-mem0	7c841a2bce	fix(redis): do not crash on empty or None filters in search and list (#5446 ) Co-authored-by: kartik-mem0 <kartik.labhshetwar@mem0.ai>	2026-06-16 16:10:57 +05:30
Hrushikesh YadavandGitHub	6a6dfb4935	fix(huggingface): use self.config instead of raw config parameter (#5538 )	2026-06-16 15:51:27 +05:30
Hrushikesh YadavandGitHub	8b370def80	fix: AsyncMemory.reset() does not reset entity store (#5535 )	2026-06-16 15:50:30 +05:30
Hrushikesh YadavandGitHub	d46464282c	fix(pinecone): hybrid search crashes when filters is None (#5533 )	2026-06-16 15:49:26 +05:30
Hrushikesh YadavandGitHub	bb4a239cb1	fix(mongodb): reset() passes wrong argument to create_col() (#5532 )	2026-06-16 15:48:45 +05:30
Hrushikesh YadavandGitHub	e30f0d91fe	fix(weaviate): reset() crashes with missing vector_size argument (#5531 )	2026-06-16 15:45:47 +05:30
Hrushikesh YadavandGitHub	9f34e858c7	fix(ollama): json format mutates caller's messages list in-place (#5539 )	2026-06-16 15:37:22 +05:30
BartokandGitHub	94bbc13de0	fix(memory): skip messages without a content key in message parsers (#5575 )	2026-06-16 15:29:53 +05:30
Hrushikesh YadavandGitHub	a2f01a8fcc	fix: async delete_all aborts on first error, leaving partial deletion (#5529 )	2026-06-16 11:59:33 +05:30
Hrushikesh YadavandGitHub	30d172e826	fix: omit None config values from Gemini GenerateContentConfig (#5528 )	2026-06-16 11:54:42 +05:30
ly-wang19 GitHub ly-wang19	bb69b036b5	fix(vector_stores): return None from get() for missing IDs (milvus/weaviate/supabase) (#5562 ) Co-authored-by: ly-wang19 <ly-wang19@users.noreply.github.com>	2026-06-16 11:52:59 +05:30
Hrushikesh Yadav GitHub kartik-mem0	b55c51e004	fix(anthropic): tool_choice format and tool response parsing (#5537 ) Co-authored-by: kartik-mem0 <kartik.labhshetwar@mem0.ai>	2026-06-15 17:28:45 +05:30
Harsh Vardhan Gupta GitHub Claude Sonnet 4.6	4492e75d04	fix(deps): bump esbuild >=0.28.1 across all npm packages (#5563 ) Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-06-15 17:04:11 +05:30
Hrushikesh YadavandGitHub	4d949022f2	fix: preserve custom metadata fields during memory update (#5480 )	2026-06-15 16:29:44 +05:30
ly-wang19 GitHub ly-wang19	3ef034a9e4	fix(vector_stores): return None from ChromaDB.get() for missing IDs (#5561 ) Co-authored-by: ly-wang19 <ly-wang19@users.noreply.github.com>	2026-06-15 16:11:03 +05:30
Yash SinghandGitHub	b90e3c0b76	fix(reranker): respect config.top_k in Cohere and ZeroEntropy fallback paths (#5560 )	2026-06-15 16:10:00 +05:30
ly-wang19 GitHub ly-wang19	a8eeddde64	fix(llms): honor reasoning-model params in AzureOpenAIStructuredLLM (#5548 ) Co-authored-by: ly-wang19 <ly-wang19@users.noreply.github.com>	2026-06-15 16:07:19 +05:30
Hrushikesh YadavandGitHub	09a9e34382	fix(litellm): function-calling check blocks all calls on non-tool models (#5536 )	2026-06-15 15:59:46 +05:30
anish GitHub kartik-mem0	66c4394b40	fix(pyproject): rename vector_stores extra to vector-stores for PEP 503/508 compliance (#4934 ) Co-authored-by: kartik-mem0 <kartik.labhshetwar@mem0.ai>	2026-06-15 12:38:23 +05:30
ly-wang19 GitHub ly-wang19 kartik-mem0	32575a65fc	fix(llms): honor reasoning-model params in OpenAIStructuredLLM (#5458 ) Co-authored-by: ly-wang19 <ly-wang19@users.noreply.github.com> Co-authored-by: kartik-mem0 <kartik.labhshetwar@mem0.ai>	2026-06-15 12:36:15 +05:30
Yash SinghandGitHub	66901d7393	fix(llms): accept and forward **kwargs in Together/LangChain/Sarvam providers (#5556 )	2026-06-15 12:23:04 +05:30
Hrushikesh YadavandGitHub	a1eefc31bc	fix(bedrock): use dict literal instead of set in AI21 response parse default (#5527 )	2026-06-15 12:09:34 +05:30
Davide LeopardiandGitHub	de471799d1	fix(llms): send max_completion_tokens for the GPT-5 family across providers (#5547 )	2026-06-15 12:04:19 +05:30
Rod BoevandGitHub	3951ad4705	fix(openclaw): reduce skills-mode triage prompt footprint (#5502 )	2026-06-15 11:18:39 +05:30
KartikandGitHub	9315e3036f	chore: retire in-repo evaluation/ in favor of mem0ai/memory-benchmarks (#5520 )	2026-06-14 00:43:02 +05:30