Update openclaw.mdx

fix: beta release
fix: remove the process env from the tests and fix the plugin manifest
2026-04-22 20:49:05 +05:30 · 2026-04-22 20:45:05 +05:30 · 2026-04-22 20:38:45 +05:30
363 changed files with 1514 additions and 29684 deletions
@@ -12,7 +12,7 @@
      "name": "mem0",
      "source": "./mem0-plugin",
      "description": "Mem0 memory layer for AI applications. Add persistent memory, personalization, and semantic search to Claude workflows.",
-      "version": "0.1.2"
+      "version": "0.1.0"
    }
  ]
 }
@@ -12,7 +12,7 @@
      "name": "mem0",
      "source": "./mem0-plugin",
      "description": "Mem0 memory layer for AI applications. Add persistent memory, personalization, and semantic search.",
-      "version": "0.1.1"
+      "version": "0.1.0"
    }
  ]
 }
@@ -4,10 +4,6 @@ __pycache__/
 *$py.class
 **/node_modules/

-# Self-hosted server local runtime state
-server/history/
-server/.env
-
 # C extensions
 *.so

@@ -19,8 +15,8 @@ dist/
 downloads/
 eggs/
 .eggs/
-/lib/
-/lib64/
+lib/
+lib64/
 parts/
 sdist/
 var/
@@ -27,7 +27,7 @@ This is a **polyglot monorepo** containing Python and TypeScript packages, CLIs,
 | `server/` | FastAPI REST server for self-hosted Mem0 (Docker: FastAPI + PostgreSQL/pgvector + Neo4j) |
 | `openmemory/` | Self-hosted memory platform — `api/` (FastAPI + Alembic + MCP server) and `ui/` (Next.js 15 + React 19) |
 | `mem0-plugin/` | AI editor plugins (Claude Code, Cursor, Codex) — MCP server connection, lifecycle hooks, skills |
-| `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/` |
+| `skills/` | Claude Code skill definitions — `mem0/`, `mem0-cli/`, `mem0-vercel-ai-sdk/` |
 | `docs/` | Documentation site (Mintlify) |
 | `tests/` | Python SDK tests (pytest) |
 | `evaluation/` | Benchmarking framework — LOCOMO evals, experiment runner, score generation |
@@ -387,9 +387,7 @@ Model Context Protocol support in multiple places:
 ### Plugin & Skills System

 - `mem0-plugin/` provides integrations for Claude Code, Cursor, and Codex via MCP server connections and lifecycle hooks for automatic memory capture.
- `skills/` contains structured skill definitions for AI agents, split into two categories:
-  - **Reference skills** (always-on SDK knowledge): `mem0` (Python + TS SDKs, framework integrations), `mem0-cli` (terminal workflows), `mem0-vercel-ai-sdk` (Vercel AI provider).
-  - **Pipeline skills** (run on demand): `mem0-integrate` wires Mem0 into an existing repo via a TDD pipeline; `mem0-test-integration` verifies what the integrator produced on the same branch. The two are loosely coupled via `.mem0-integration/` artifacts.
+- `skills/` contains structured skill definitions for AI agents, covering SDK usage, CLI workflows, and Vercel AI SDK patterns.

 ### Adding a New Provider

@@ -1313,7 +1313,7 @@ async def delete_memory(memory_id: str):
 - **Documentation**: https://docs.mem0.ai
 - **GitHub Repository**: https://github.com/mem0ai/mem0
 - **Discord Community**: https://mem0.dev/DiG
- **Platform**: https://app.mem0.ai?utm_source=oss&utm_medium=llm
+- **Platform**: https://app.mem0.ai
 - **Research Paper**: https://mem0.ai/research
 - **Examples**: https://github.com/mem0ai/mem0/tree/main/examples

@@ -39,7 +39,7 @@
 </p>

 <p align="center">
-  <a href="https://mem0.ai/research"><strong>📄 Benchmarking Mem0's token-efficient memory algorithm →</strong></a>
+  <a href="https://mem0.ai/research"><strong>📄 Building Production-Ready AI Agents with Scalable Long-Term Memory →</strong></a>
 </p>

 ## New Memory Algorithm (April 2026)
@@ -85,17 +85,18 @@ See the [migration guide](https://docs.mem0.ai/migration/oss-v2-to-v3) for upgra

 ## 🚀 Quickstart Guide <a name="quickstart"></a>

-| | Library | Self-Hosted Server | Cloud Platform |
-|---|---------|-------------------|----------------|
-| **Best for** | Testing, prototyping | Teams running on their own infrastructure | Zero-ops production use |
-| **Setup** | `pip install mem0ai` | `docker compose up` | Sign up at [app.mem0.ai](https://app.mem0.ai?utm_source=oss&utm_medium=readme) |
-| **Dashboard** | -- | [Yes](https://docs.mem0.ai/open-source/setup) | Yes |
-| **Auth & API Keys** | -- | Yes | Yes |
-| **Advanced Features** | -- | Teasers | All included |
+Choose between our hosted platform or self-hosted package:

-Just testing? Use the library. Building for a team? Self-hosted. Want zero ops? Cloud.
+### Hosted Platform

-### Library (pip / npm)
+Get up and running in minutes with automatic updates, analytics, and enterprise security.
+
+1. Sign up on [Mem0 Platform](https://app.mem0.ai)
+2. Embed the memory layer via SDK or API keys
+
+### Self-Hosted (Open Source)
+
+Install the sdk via pip:

 ```bash
 pip install mem0ai
@@ -109,30 +110,10 @@ python -m spacy download en_core_web_sm
 ```

 Install sdk via npm:
-
 ```bash
 npm install mem0ai
 ```

-### Self-Hosted Server
-
-> **Note:** Self-hosted auth is on by default. Upgrading from a pre-auth build? Set `ADMIN_API_KEY`, register an admin through the wizard, or `AUTH_DISABLED=true` for local dev only. See [upgrade notes](https://docs.mem0.ai/open-source/setup#upgrade-notes).
-
-```bash
-# Recommended: one command — start the stack, create an admin, issue the first API key.
-cd server && make bootstrap
-
-# Manual: start the stack and finish setup via the browser wizard.
-cd server && docker compose up -d    # http://localhost:3000
-```
-
-See the [self-hosted docs](https://docs.mem0.ai/open-source/overview) for configuration.
-
-### Cloud Platform
-
-1. Sign up on [Mem0 Platform](https://app.mem0.ai?utm_source=oss&utm_medium=readme)
-2. Embed the memory layer via SDK or API keys
-
 ### CLI

 Manage memories from your terminal:
@@ -147,27 +128,6 @@ mem0 search "What does Alice prefer?" --user-id alice

 See the [CLI documentation](https://docs.mem0.ai/platform/cli) for the full command reference.

-### Agent Skills
-
-Teach your AI coding assistant (Claude Code, Codex, Cursor, Windsurf, OpenCode, OpenClaw, and any tool that supports the skills standard) how to build with Mem0. Two categories:
-
-**Reference skills — always on** (SDK knowledge loaded into the assistant's context):
-
-```bash
-npx skills add https://github.com/mem0ai/mem0 --skill mem0
-npx skills add https://github.com/mem0ai/mem0 --skill mem0-cli
-npx skills add https://github.com/mem0ai/mem0 --skill mem0-vercel-ai-sdk
-```
-
-**Pipeline skills — run on demand** (execute an end-to-end workflow in an existing repo):
-
-```bash
-npx skills add https://github.com/mem0ai/mem0 --skill mem0-integrate
-npx skills add https://github.com/mem0ai/mem0 --skill mem0-test-integration
-```
-
-Use `/mem0-integrate` to wire Mem0 into an existing repo via a test-first pipeline, then `/mem0-test-integration` to verify. See the [skills catalog](./skills/) or [Vibecoding with Mem0](https://docs.mem0.ai/vibecoding) for the full picture.
-
 ### Basic Usage

 Mem0 requires an LLM to function, with `gpt-5-mini` from OpenAI as the default. However, it supports a variety of LLMs; for details, refer to our [Supported LLMs documentation](https://docs.mem0.ai/components/llms/overview).
@@ -96,7 +96,7 @@ export function printError(message: string, hint?: string): void {
 	const resolvedHint =
 		hint ??
 		(message.includes("Authentication failed")
-			? `Run ${brand("mem0 init")} to reconfigure your API key · https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=cli-node`
+			? `Run ${brand("mem0 init")} to reconfigure your API key · https://app.mem0.ai/dashboard/api-keys`
 			: undefined);
 	if (resolvedHint) {
 		console.error(`  ${dim(resolvedHint)}`);
@@ -185,7 +185,7 @@ function promptLine(label: string, defaultValue?: string): Promise<string> {
 async function setupPlatform(config: Mem0Config): Promise<void> {
 	console.log();
 	console.log(
-		`  ${dim("Get your API key at https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=cli-node")}`,
+		`  ${dim("Get your API key at https://app.mem0.ai/dashboard/api-keys")}`,
 	);
 	console.log();

@@ -234,7 +234,7 @@ async function validatePlatform(config: Mem0Config): Promise<void> {
 		} else {
 			printError(
 				`Could not connect: ${status.error ?? "Unknown error"}`,
-				"Visit https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=cli-node to get a new key, or run mem0 init again.",
+				"Visit https://app.mem0.ai/dashboard/api-keys to get a new key, or run mem0 init again.",
 			);
 		}
 	} catch (e) {
@@ -63,7 +63,7 @@ export async function cmdStatus(
 				`  ${dim("Run")} ${brand("mem0 init")} ${dim("to reconfigure your API key")}`,
 			);
 			lines.push(
-				`  ${dim("Get a key at")} ${brand("https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=cli-node")}`,
+				`  ${dim("Get a key at")} ${brand("https://app.mem0.ai/dashboard/api-keys")}`,
 			);
 		}
 	}
@@ -146,7 +146,7 @@ def timed_status(console: Console, message: str):
            if "Authentication failed" in ctx.error_msg:
                _err.print(
                    f"  [{DIM_COLOR}]Run [bold]mem0 init[/bold] to reconfigure your API key"
-                    f" · [bold]https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=cli-python[/bold][/]"
+                    f" · [bold]https://app.mem0.ai/dashboard/api-keys[/bold][/]"
                )
        raise
    else:
@@ -19,13 +19,7 @@ from mem0_cli.branding import (
    print_info,
    print_success,
 )
-from mem0_cli.config import (
-    CONFIG_FILE,
-    DEFAULT_BASE_URL,
-    Mem0Config,
-    load_config,
-    save_config,
-)
+from mem0_cli.config import CONFIG_FILE, DEFAULT_BASE_URL, Mem0Config, load_config, save_config

 console = Console()
 err_console = Console(stderr=True)
@@ -358,9 +352,7 @@ def run_init(
 def _setup_platform(config: Mem0Config) -> None:
    """Platform setup flow."""
    console.print()
-    console.print(
-        f"  [{DIM_COLOR}]Get your API key at https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=cli-python[/]"
-    )
+    console.print(f"  [{DIM_COLOR}]Get your API key at https://app.mem0.ai/dashboard/api-keys[/]")
    console.print()

    console.print(f"  [{BRAND_COLOR}]API Key[/]: ", end="")
@@ -412,7 +404,7 @@ def _validate_platform(config: Mem0Config) -> None:
            print_error(
                err_console,
                f"Could not connect: {status.get('error', 'Unknown error')}",
-                hint="Visit https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=cli-python to get a new key, then run mem0 init again.",
+                hint="Visit https://app.mem0.ai/dashboard/api-keys to get a new key, then run mem0 init again.",
            )
    except Exception as e:
        print_error(err_console, f"Connection test failed: {e}")
@@ -77,7 +77,7 @@ def cmd_status(
                f"  [{DIM_COLOR}]Run [bold]mem0 init[/bold] to reconfigure your API key[/]"
            )
            lines.append(
-                f"  [{DIM_COLOR}]Get a key at [bold]https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=cli-python[/bold][/]"
+                f"  [{DIM_COLOR}]Get a key at [bold]https://app.mem0.ai/dashboard/api-keys[/bold][/]"
            )
    lines.append(f"  [{DIM_COLOR}]Latency:[/]  {_elapsed:.2f}s")

@@ -10,7 +10,7 @@ description: "REST APIs for memory management, search, and entity operations"
 Mem0 provides a comprehensive REST API for integrating advanced memory capabilities into your applications. Create, search, update, and manage memories across users, agents, and custom entities with simple HTTP requests.

 <Info>
-**Quick start:** Get your API key from the <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=api-reference" rel="nofollow">Mem0 Dashboard</a> and make your first memory operation in minutes.
+**Quick start:** Get your API key from the <a href="https://app.mem0.ai/dashboard/api-keys" rel="nofollow">Mem0 Dashboard</a> and make your first memory operation in minutes.
 </Info>

 ---
@@ -87,7 +87,7 @@ All API requests require authentication using Token-based authentication. Includ
 Authorization: Token <your-api-key>
 ```

-Get your API key from the <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=api-reference" rel="nofollow">Mem0 Dashboard</a>.
+Get your API key from the <a href="https://app.mem0.ai/dashboard/api-keys" rel="nofollow">Mem0 Dashboard</a>.

 <Warning>
 **Keep your API key secure.** Never expose it in client-side code or public repositories. Use environment variables and server-side requests only.
@@ -1,18 +1,18 @@
 ---
-title: Add Memories
-description: "Add facts, messages, or metadata to a user memory store with async processing and event tracking via the V3 additive pipeline."
-openapi: post /v3/memories/add/
+title: 'Add Memories'
+description: "Add facts, messages, or metadata to a user memory store with support for async processing and event tracking."
+openapi: post /v1/memories/
 ---

-Extract and store memories from a conversation using the V3 additive pipeline. The endpoint uses single-pass ADD-only extraction — one LLM call, no UPDATE/DELETE. Memories accumulate over time; nothing is overwritten.
+Add new facts, messages, or metadata to a user’s memory store. The Add Memories endpoint accepts either raw text or conversational turns and commits them asynchronously so the memory is ready for later search, retrieval, and graph queries.

 ## Endpoint

 - **Method**: `POST`
- **URL**: `/v3/memories/add/`
+- **URL**: `/v1/memories/`
 - **Content-Type**: `application/json`

-Processing is asynchronous. The response returns an `event_id` you can poll via `GET /v1/event/{event_id}/`.
+Memories are processed asynchronously by default. The response contains queued events you can track while the platform finalizes enrichment.

 ## Required headers

@@ -23,7 +23,7 @@ Processing is asynchronous. The response returns an `event_id` you can poll via

 ## Request body

-Provide conversation messages for Mem0 to extract memories from. At least one entity ID (`user_id`, `agent_id`, `app_id`, or `run_id`) is required so the memory is scoped to a session. Entity IDs are accepted at the top level.
+Provide at least one message or direct memory string. Most callers supply `messages` so Mem0 can infer structured memories as part of ingestion.

 <CodeGroup>
 ```json Basic request
@@ -43,15 +43,12 @@ Provide conversation messages for Mem0 to extract memories from. At least one en

 | Field | Type | Required | Description |
 | --- | --- | --- | --- |
-| `messages` | array | Yes | Conversation turns for Mem0 to extract memories from. Each object should include `role` and `content`. |
-| `user_id` | string | No* | Associates the memory with a user. |
-| `agent_id` | string | No* | Associates the memory with an agent. |
-| `run_id` | string | No* | Associates the memory with a run. |
-| `app_id` | string | No* | Associates the memory with an app. |
+| `user_id` | string | No* | Associates the memory with a user. Provide when you want the memory scoped to a specific identity. |
+| `messages` | array | No* | Conversation turns for Mem0 to infer memories from. Each object should include `role` and `content`. |
 | `metadata` | object | Optional | Custom key/value metadata (e.g., `{"topic": "preferences"}`). |
 | `infer` | boolean (default `true`) | Optional | Set to `false` to skip inference and store the provided text as-is. |

-> \* At least one entity ID (`user_id`, `agent_id`, `app_id`, or `run_id`) is required.
+> \* Provide at least one `messages` entry to describe what you are storing. For scoped memories, include `user_id`. You can also attach `agent_id`, `app_id`, `run_id`, `project_id`, or `org_id` to refine ownership.

 <Tip>
  Need more details? See [all request parameters](#body-messages) below for complete field descriptions, types, and constraints.
@@ -59,15 +56,19 @@ Provide conversation messages for Mem0 to extract memories from. At least one en

 ## Response

-The request is queued for background processing. The response contains an `event_id` for tracking status.
+Successful requests return an array of events queued for processing. Each event includes the generated memory text and an identifier you can persist for auditing.

 <CodeGroup>
 ```json 200 response
-{
-  "message": "Memory processing has been queued for background execution",
-  "status": "PENDING",
-  "event_id": "evt-uuid"
-}
+[
+  {
+    "id": "mem_01JF8ZS4Y0R0SPM13R5R6H32CJ",
+    "event": "ADD",
+    "data": {
+      "memory": "The user moved to Austin in 2025."
+    }
+  }
+]
 ```

 ```json 400 response
@@ -80,7 +81,3 @@ The request is queued for background processing. The response contains an `event
 ```
 </CodeGroup>

-<Info>
-Poll the event status via `GET /v1/event/{event_id}/`. Status will be `SUCCEEDED` or `FAILED` once processing completes.
-</Info>
-
@@ -1,12 +1,10 @@
 ---
 title: "Get Memories"
-description: "Retrieve memories with paginated results and advanced filtering using logical operators like AND, OR, NOT, and comparison queries."
-openapi: post /v3/memories/
+description: "Retrieve memories with advanced filtering using logical operators like AND, OR, NOT, and comparison queries."
+openapi: post /v2/memories/
 ---

-List memories scoped by filters with paginated results. Entity IDs (`user_id`, `agent_id`, `app_id`, `run_id`) **must** be passed inside the `filters` object — top-level entity IDs are rejected with 400.
-
-The `filters` object supports complex logical operations (AND, OR, NOT) and comparison operators:
+The v2 get memories API is powerful and flexible, allowing for more precise memory listing without the need for a search query. It supports complex logical operations (AND, OR, NOT) and comparison operators for advanced filtering capabilities. The comparison operators include:

 - `in`: Matches any of the values specified
 - `gte`: Greater than or equal to
@@ -17,8 +15,6 @@ The `filters` object supports complex logical operations (AND, OR, NOT) and comp
 - `icontains`: Case-insensitive containment check
 - `*`: Wildcard character that matches everything

-Pass `page` and `page_size` as query parameters to paginate through results.
-
 <CodeGroup>
 ```python Code
 memories = client.get_all(
@@ -31,17 +27,12 @@ memories = client.get_all(
                "created_at": {"gte": "2024-07-01", "lte": "2024-07-31"}
            }
        ]
-    },
-    page=1,
-    page_size=50
+    }
 )
 ```

 ```python Output
 {
-    "count": 2,
-    "next": null,
-    "previous": null,
    "results": [
        {
            "id": "f4cbdb08-7062-4f3e-8eb2-9f5c80dfe64c",
@@ -55,13 +46,10 @@ memories = client.get_all(
            "created_at": "2024-07-05T15:30:00Z",
            "updated_at": "2024-07-05T15:30:00Z"
        }
-    ]
+    ],
+    "total": 2
 }
 ```

 </CodeGroup>

-<Info>
-The response is a paginated envelope with `count`, `next`, `previous`, and `results`. Use `page` and `page_size` query params to step through results.
-</Info>
-
@@ -1,14 +1,10 @@
 ---
 title: 'Search Memories'
-description: "Search memories with hybrid retrieval (semantic + BM25 + entity matching) and advanced filtering using logical and comparison operators."
-openapi: post /v3/memories/search/
+description: "Search memories with semantic queries and advanced filtering using logical and comparison operators."
+openapi: post /v2/memories/search/
 ---

-Relevance-ranked hybrid search across stored memories. V3 uses multi-signal retrieval — semantic, BM25 keyword, and entity matching scored in parallel and fused. The returned `score` is a combined `[0, 1]` value.
-
-Entity IDs (`user_id`, `agent_id`, `app_id`, `run_id`) **must** be passed inside the `filters` object — top-level entity IDs are rejected with 400. At least one entity ID is required.
-
-The `filters` object supports complex logical operations (AND, OR, NOT) and comparison operators:
+The v2 search API is powerful and flexible, allowing for more precise memory retrieval. It supports complex logical operations (AND, OR, NOT) and comparison operators for advanced filtering capabilities. The comparison operators include:
 - `in`: Matches any of the values specified
 - `gte`: Greater than or equal to
 - `lte`: Less than or equal to
@@ -18,14 +14,6 @@ The `filters` object supports complex logical operations (AND, OR, NOT) and comp
 - `icontains`: Case-insensitive containment check
 - `*`: Wildcard character that matches everything

-### Search parameter defaults
-
-| Parameter | V1/V2 | V3 |
-| --- | --- | --- |
-| `top_k` | Supported (default 10) | Supported (1-1000, default 10) |
-| `threshold` | No default | Default `0.1` (pass `0.0` to disable) |
-| `rerank` | Default `true` | Default `false` (pass `true` to enable) |
-
 <CodeGroup>
 ```python Platform API Example
 related_memories = client.search(
@@ -45,19 +33,20 @@ related_memories = client.search(

 ```json Output
 {
-  "results": [
+  "memories": [
    {
      "id": "ea925981-272f-40dd-b576-be64e4871429",
      "memory": "Likes to play cricket and plays cricket on weekends.",
      "metadata": {
        "category": "hobbies"
      },
-      "score": 0.82,
+      "score": 0.32116443111457704,
      "created_at": "2024-07-26T10:29:36.630547-07:00",
      "updated_at": null,
-      "categories": ["hobbies"]
+      "user_id": "alice",
+      "agent_id": "sports-agent"
    }
-  ]
+  ],
 }
 ```
 </CodeGroup>
@@ -109,19 +109,6 @@ client.project.update(
 )
 ```

-#### Toggle Memory Decay
-
-`decay` is a per-project boolean that turns on [Memory Decay](/platform/features/memory-decay) — a search-time ranking bias that reinforces recently-accessed memories and gently dampens stale ones. The flag is `false` by default; set it via the same project-update endpoint:
-
-```bash cURL
-curl -X PATCH https://api.mem0.ai/api/v1/orgs/organizations/$ORG_ID/projects/$PROJECT_ID/ \
-  -H "Authorization: Token $MEM0_API_KEY" \
-  -H "Content-Type: application/json" \
-  -d '{"decay": true}'
-```
-
-The current state is returned on every project read (and supports `?fields=decay` for a minimal response). Toggling has no effect on stored memories, only on how v3 search ranks them.
-
 ### Delete Project

 <Warning>
@@ -4,19 +4,6 @@ description: "Major product launches, headline features, and milestones for Mem0
 mode: "wide"
 ---

-<Update label="2026-05-08" description="Memory Decay">
-
-**Memory Decay — Recently-Used Memories Surface Higher, Automatically**
-
-Per-project search-time ranking bias that boosts recently-touched memories and gently dampens stale ones. Off by default; opt in per project via the `decay` field on the project endpoint, or via `client.project.update(decay=True)` in the SDKs (Python `v2.0.2` / TypeScript `v3.0.3`).
-
- **Soft bias, never a filter.** The scaling factor stays in `0.3×–1.5×`. Decay can reorder candidates but never zeros them out — anything that surfaced before decay can still surface after.
- **Reinforcement loop.** Every memory returned in a search has its access history updated, so frequently-used facts naturally float to the top over time.
- **Public score still clamped to `[0, 1]`.** Existing API contract preserved; no client-side changes needed.
- **v3 search only**, fully reversible. See [Memory Decay docs](/platform/features/memory-decay).
-
-</Update>
-
 <Update label="2026-04-14" description="Mem0 SDK v2.0.0 / v3.0.0">

 **New Memory Algorithm — State-of-the-Art Accuracy at ~3-4x Lower Cost**
@@ -4,61 +4,7 @@ description: "Release notes for the OpenClaw plugin and agent harness."
 mode: "wide"
 ---

-<Update label="2026-04-29" description="v1.0.11">
-
-**New Features:**
- **Skills-mode auto-setup:** `enableSkillsConfig()` now runs automatically after onboarding — enables triage, recall (with reranking + keyword search), and dream consolidation with `tools.profile = "full"` and disables the built-in session-memory hook to avoid conflicts
- **Memory runtime capability:** Plugin now exposes `runtime.getMemorySearchManager()` and `resolveMemoryBackendConfig()` on the registered memory capability, enabling OpenClaw gateway to query memory status and backend config directly
- **Dimension-aware collections:** OSS wizard detects embedder dimension changes and creates a new collection (`mem0_<dims>d`) automatically, with a warning about old memories being inaccessible under the new embedder
- **Tool documentation in skills:** Both `memory-triage` and `memory-dream` SKILL.md files now include full tool reference sections listing all available tools with parameters
-
-**Improvements:**
- **Auto-capture and auto-recall default to enabled:** `autoCapture` and `autoRecall` now default to `true` (was `false`). Manifest descriptions updated accordingly. Ignored in skills mode
- **`memory_update` over delete+add:** Skills now prefer `memory_update` for in-place edits — atomic and preserves edit history. Consolidation pattern updated: update best memory, delete redundant ones
- **Search threshold lowered:** Default `searchThreshold` reduced from `0.5` to `0.1` for broader recall. Removed hardcoded `0.6` recall-specific override — all searches now use the configured threshold
- **Embedder dimension propagation:** Vector store config auto-resolves dimensions from embedder config when not explicitly set. Syncs `dimension` and `embeddingModelDims` fields for Qdrant/PGVector compatibility
- **Config file write safety:** `writeFullConfig()` now re-reads and deep-merges the `plugins` section before writing, preserving `installs` and `slots` written by the OpenClaw gateway
- **Additional embedder models:** Added `mxbai-embed-large` (1024), `all-minilm` (384), and `snowflake-arctic-embed` (1024) to known embedder dimensions
-
-**Security:**
- Bumped `protobufjs` to `>=7.5.5` via pnpm overrides (GHSA-xq3m-2v4x-88gg) ([#5012](https://github.com/mem0ai/mem0/pull/5012))
-
-**Fixes:**
- Moved `bootstrapTelemetryFlag()` and removed `ensureInstallRecord()` from module-level side effects — both now run inside `register()` to avoid crashes when loaded outside OpenClaw gateway
- Fixed OSS history DB path resolution: absolute paths no longer passed through `resolvePath()`, preventing double-prefix bugs
- Manifest `providerAuthEnvVars` replaced with spec-compliant `setup.providers` format using `id` + `envVars`
-
-**Dependencies:**
- Bumped `mem0ai` from `3.0.1` to `3.0.2`
- Bumped `pluginApi` and `minGatewayVersion` compat to `>=2026.4.24`
-
-</Update>
-
-<Update label="2026-04-23" description="v1.0.10">
-
-**Security:**
- Telemetry `distinct_id` now uses SHA-256 instead of MD5 — prevents rainbow-table reversal of API key hashes
- User email is now SHA-256 hashed before sending as `distinct_id` — no PII in telemetry payloads
- Declared PostHog telemetry endpoint (`us.i.posthog.com`) in `providerEndpoints`
-
-**Fixes:**
- Fixed version-pinned install records preventing plugin updates. `ensureInstallRecord()` now detects semver-pinned specs (e.g. `@mem0/openclaw-mem0@1.0.7`) and rewrites them to `@latest` or `clawhub:` prefix so `openclaw plugins update` resolves to the newest release
- Fixed `searchThreshold` default inconsistency: standardized to `0.3` across docs, README, and manifest
- `PLUGIN_VERSION` now injected at build time via tsup `define` from `package.json` — no more hardcoded version strings
-
-**Manifest Compliance:**
- Removed non-spec fields: `requiredEnvVars`, `dataLocations`, `privacy`, `setup` (with `externalEndpoints`, `providers`, `requiresRuntime`, `postInstallHint`)
- Replaced `setup.externalEndpoints` with spec-compliant `providerEndpoints` using `endpointClass` + `hosts` format
- Env var declarations now rely solely on `providerAuthEnvVars` (already spec-compliant)
-
-**Docs:**
- Fixed `openclaw plugins update` command: uses plugin ID (`openclaw-mem0`), not npm package name (`@mem0/openclaw-mem0`)
- Added update section to README
- Removed redundant "Key Features" and "Conclusion" sections from integration docs
-
-</Update>
-
-<Update label="2026-04-22" description="v1.0.9">
+<Update label="2026-04-22" description="v1.0.9-beta.0">

 **Security & Compliance:**
 - Added top-level `requiredEnvVars` to plugin manifest, declaring env vars per mode (platform, OSS OpenAI, OSS Anthropic, OSS Ollama). Fixes ClaHub scanner "required env vars: none" mismatch
@@ -4,13 +4,6 @@ description: "Release notes for the Mem0 hosted platform — backend, dashboard,
 mode: "wide"
 ---

-<Update label="2026-05-04" description="">
-
-**New Features:**
- **Memory Decay:** Per-project search-time ranking bias that boosts recently-used memories and gently dampens stale ones. Opt-in via `decay` on the project endpoint; off by default. The scaling factor stays in `0.3×–1.5×`, the public `score` remains clamped to `[0, 1]`, and the bias never filters a candidate out. See [Memory Decay docs](/platform/features/memory-decay).
-
-</Update>
-
 <Update label="2026-04-16" description="">

 **Improvements:**
@@ -7,37 +7,6 @@ mode: "wide"
 <Tabs>
 <Tab title="Python">

-<Update label="2026-05-08" description="v2.0.2">
-
-**Bug Fixes:**
- **Telemetry:** Stitch OSS and platform PostHog identities on `MemoryClient` init so `$identify` events fire and a single user is no longer tracked as two or three disconnected personas ([#5040](https://github.com/mem0ai/mem0/pull/5040))
- **Security:** Harden against SQL injection and prompt injection ([#4997](https://github.com/mem0ai/mem0/pull/4997))
-
-**New Features:**
- **SDK:** Expose `decay` on `project.update` ([#5062](https://github.com/mem0ai/mem0/pull/5062))
-
-**Improvements:**
- **Plugin:** Hand `mem0` search decisions to the agent ([#4992](https://github.com/mem0ai/mem0/pull/4992))
-
-</Update>
-
-<Update label="2026-04-25" description="v2.0.1">
-
-**Bug Fixes:**
- **Client:** Map `user_id`, `agent_id`, `run_id` entity params to filters in `GET /memories` ([#4960](https://github.com/mem0ai/mem0/pull/4960))
- **Memory:** Honor `prompt` param in vector store extraction pipeline ([#4914](https://github.com/mem0ai/mem0/pull/4914))
- **Memory:** Add missing `text_lemmatized` field in `AsyncMemory._create_memory` ([#4886](https://github.com/mem0ai/mem0/pull/4886))
- **Memory:** Merge same-key operator dicts in AND metadata filters ([#4853](https://github.com/mem0ai/mem0/pull/4853))
- **LLMs:** Narrow `_is_reasoning_model` check to not match `gpt-5.x` variants ([#4746](https://github.com/mem0ai/mem0/pull/4746))
- **Vector Stores:** Add `ca_certs` config option for Elasticsearch vector store ([#3993](https://github.com/mem0ai/mem0/pull/3993))
- **Vector Stores:** Add `agent_id` and `run_id` to Elasticsearch/OpenSearch default mappings ([#4906](https://github.com/mem0ai/mem0/pull/4906))
- **Embeddings:** Set FastEmbed `embedding_dims` from model metadata at init ([#4711](https://github.com/mem0ai/mem0/pull/4711))
-
-**Security:**
- Bump vulnerable dependencies to patched versions ([#4835](https://github.com/mem0ai/mem0/pull/4835))
-
-</Update>
-
 <Update label="2026-04-14" description="v2.0.0">

 **Major Release** — Python SDK with V3 memory pipeline, ADD-only extraction, and cleaned-up API surface.
@@ -924,29 +893,6 @@ See the [OSS v1 to v2 migration guide](https://docs.mem0.ai/migration/oss-v1-to-
 </Tab>

 <Tab title="TypeScript">
-<Update label="2026-05-08" description="v3.0.3">
-
-**Bug Fixes:**
- **Telemetry:** Stitch OSS and platform PostHog identities on `MemoryClient` init so `$identify` events fire and a single user is no longer tracked as two or three disconnected personas ([#5040](https://github.com/mem0ai/mem0/pull/5040))
- **Vector Stores:** Fix inverted vector distance in PGVector implementation ([#4944](https://github.com/mem0ai/mem0/pull/4944))
- **Security:** Harden against SQL injection and prompt injection ([#4997](https://github.com/mem0ai/mem0/pull/4997))
-
-**New Features:**
- **SDK:** Expose `decay` on `project.update` ([#5062](https://github.com/mem0ai/mem0/pull/5062))
-
-</Update>
-
-<Update label="2026-04-25" description="v3.0.2">
-
-**Bug Fixes:**
- **LLMs:** Forward `timeout` config to OpenAI client in JS OSS LLM providers ([#4770](https://github.com/mem0ai/mem0/pull/4770))
-
-**Improvements:**
- **Telemetry:** Harden TS telemetry version injection and require changelog entry on version bump ([#4900](https://github.com/mem0ai/mem0/pull/4900))
- **Docs:** Update memory tool list, CLI usage, and config file reading logic ([#4861](https://github.com/mem0ai/mem0/pull/4861))
-
-</Update>
-
 <Update label="2026-04-20" description="v3.0.1">

 **Bug Fixes:**
@@ -45,7 +45,7 @@ Before you begin, follow these steps to set up the demo application:
   OPENAI_API_KEY=your_openai_api_key
   MEM0_API_KEY=your_mem0_api_key
   ```
-   You can obtain your `MEM0_API_KEY` by signing up at <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=cookbook-companions-quickstart" rel="nofollow">Mem0 API Dashboard</a>.
+   You can obtain your `MEM0_API_KEY` by signing up at <a href="https://app.mem0.ai/dashboard/api-keys" rel="nofollow">Mem0 API Dashboard</a>.

 5. Start the development server:
   ```bash
@@ -38,7 +38,7 @@ client = MemoryClient(api_key="your-api-key")
 ```

 <Note>
-Replace `your-api-key` with your actual Mem0 API key from the <a href="https://app.mem0.ai?utm_source=oss&utm_medium=cookbook-memory-ingestion" rel="nofollow">dashboard</a>. Without proper API authentication, memory operations will fail.
+Replace `your-api-key` with your actual Mem0 API key from the <a href="https://app.mem0.ai" rel="nofollow">dashboard</a>. Without proper API authentication, memory operations will fail.
 </Note>

 ---
@@ -17,7 +17,7 @@ from mem0 import MemoryClient
 client = MemoryClient(api_key="m0-...")
 ```

-Grab an API key from the <a href="https://app.mem0.ai/?utm_source=oss&utm_medium=cookbook-entity-partitioning" rel="nofollow">Mem0 dashboard</a> to get started.
+Grab an API key from the <a href="https://app.mem0.ai/" rel="nofollow">Mem0 dashboard</a> to get started.

 ## Store and Retrieve Scoped Memories

@@ -20,7 +20,7 @@ client = MemoryClient(api_key="your-api-key")
 ```

 <Note>
-Your API key needs export permissions to download memory data. Check your project settings on the <a href="https://app.mem0.ai?utm_source=oss&utm_medium=cookbook-exporting-memories" rel="nofollow">dashboard</a> if export operations fail with authentication errors.
+Your API key needs export permissions to download memory data. Check your project settings on the <a href="https://app.mem0.ai" rel="nofollow">dashboard</a> if export operations fail with authentication errors.
 </Note>

 Let's add some sample memories to work with:
@@ -42,7 +42,7 @@ Create a `.env` file in the root of the project and add the following (you can u

 ```bash
 # Mem0 Configuration
-MEM0_API_KEY= # Mem0 API Key (get from https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=cookbook-eliza-os)
+MEM0_API_KEY= # Mem0 API Key (get from https://app.mem0.ai/dashboard/api-keys)
 MEM0_USER_ID= # Default: eliza-os-user
 MEM0_PROVIDER= # Default: openai
 MEM0_PROVIDER_API_KEY= # API Key for the provider (OpenAI, Anthropic, etc.)
@@ -55,7 +55,7 @@ GEMINI_API_KEY=your-gemini-api-key-here
 ```

 <Note>
-Ensure you have your Mem0 API key from the <a href="https://app.mem0.ai?utm_source=oss&utm_medium=cookbook-gemini-3" rel="nofollow">Mem0 Dashboard</a> and your Gemini API key from the [Google AI Studio](https://ai.studio/app/api-keys).
+Ensure you have your Mem0 API key from the <a href="https://app.mem0.ai" rel="nofollow">Mem0 Dashboard</a> and your Gemini API key from the [Google AI Studio](https://ai.studio/app/api-keys).
 </Note>

 ## Gemini Memory Agent
@@ -41,7 +41,7 @@ Set up your environment variables:
 - `MEM0_API_KEY`: Your Mem0 Platform API key
 - `OPENAI_API_KEY`: Your OpenAI API key

-You can obtain your Mem0 Platform API key from the <a href="https://app.mem0.ai?utm_source=oss&utm_medium=cookbook-llamaindex-multiagent" rel="nofollow">Mem0 Platform</a>.
+You can obtain your Mem0 Platform API key from the <a href="https://app.mem0.ai" rel="nofollow">Mem0 Platform</a>.

 ## Complete Implementation

@@ -357,7 +357,7 @@ Based on our previous session, I remember we covered Vision Language Models and
 ## Help & Resources

 - [LlamaIndex Agent Workflows](https://docs.llamaindex.ai/en/stable/use_cases/agents/)
- <a href="https://app.mem0.ai/?utm_source=oss&utm_medium=cookbook-llamaindex-multiagent" rel="nofollow">Mem0 Platform</a>
+- <a href="https://app.mem0.ai/" rel="nofollow">Mem0 Platform</a>

 ---

@@ -25,7 +25,7 @@ os.environ["OPENAI_API_KEY"] = "<your-openai-api-key>"
 llm = OpenAI(model="gpt-5-mini")
 ```

-Initialize the Mem0 client. You can find your API key <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=cookbook-llamaindex-react" rel="nofollow">here</a>. Read about Mem0 [Open Source](https://docs.mem0.ai/open-source/overview).
+Initialize the Mem0 client. You can find your API key <a href="https://app.mem0.ai/dashboard/api-keys" rel="nofollow">here</a>. Read about Mem0 [Open Source](https://docs.mem0.ai/open-source/overview).
 ```python
 os.environ["MEM0_API_KEY"] = "<your-mem0-api-key>"

@@ -223,7 +223,7 @@ context = Mem0Context(user_id="user123")
 ## Resources

 - [Mem0 Documentation](https://docs.mem0.ai/introduction)
- <a href="https://app.mem0.ai/dashboard?utm_source=oss&utm_medium=cookbook-agents-sdk-tool" rel="nofollow">Mem0 Dashboard</a>
+- <a href="https://app.mem0.ai/dashboard" rel="nofollow">Mem0 Dashboard</a>
 - [API Reference](https://docs.mem0.ai/api-reference)

 ---
@@ -23,7 +23,7 @@ MEM0_API_KEY=your_mem0_api_key
 OPENAI_API_KEY=your_openai_api_key
 ```

-Get your Mem0 API key from the <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=cookbook-openai-tool-calls" rel="nofollow">Mem0 Dashboard</a>.
+Get your Mem0 API key from the <a href="https://app.mem0.ai/dashboard/api-keys" rel="nofollow">Mem0 Dashboard</a>.

 ### Configuration

@@ -303,7 +303,7 @@ run().catch(console.error);
 ## Resources

 - [Mem0 Documentation](https://docs.mem0.ai/introduction)
- <a href="https://app.mem0.ai/dashboard?utm_source=oss&utm_medium=cookbook-openai-tool-calls" rel="nofollow">Mem0 Dashboard</a>
+- <a href="https://app.mem0.ai/dashboard" rel="nofollow">Mem0 Dashboard</a>
 - [API Reference](https://docs.mem0.ai/api-reference)
 - [OpenAI Documentation](https://platform.openai.com/docs)

@@ -83,8 +83,7 @@
                          "platform/advanced-memory-operations",
                          "platform/features/criteria-retrieval",
                          "platform/features/contextual-add",
-                          "platform/features/custom-instructions",
-                          "platform/features/memory-decay"
+                          "platform/features/custom-instructions"
                        ]
                      },
                      {
@@ -154,7 +153,6 @@
                    "icon": "rocket",
                    "pages": [
                      "open-source/overview",
-                      "open-source/setup",
                      "vibecoding",
                      "open-source/python-quickstart",
                      "open-source/node-quickstart"
@@ -580,7 +578,7 @@
    "primary": {
      "type": "button",
      "label": "Your Dashboard",
-      "href": "https://app.mem0.ai?utm_source=oss&utm_medium=docs-nav"
+      "href": "https://app.mem0.ai"
    }
  },
  "footer": {
@@ -610,7 +608,7 @@
        "title": "Try in Playground",
        "description": "Open this example in the interactive Mem0 playground",
        "icon": "play",
-        "href": "https://app.mem0.ai/playground?utm_source=oss&utm_medium=docs-nav"
+        "href": "https://app.mem0.ai/playground"
      }
    ]
  },
@@ -24,7 +24,7 @@ pip install mem0ai agentops python-dotenv
 2. Valid API keys:
   - [AgentOps API Key](https://app.agentops.ai/dashboard/api-keys)
   - OpenAI API Key (for LLM operations)
-   - <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=integration-agentops" rel="nofollow">Mem0 API Key</a> (optional, for cloud operations)
+   - <a href="https://app.mem0.ai/dashboard/api-keys" rel="nofollow">Mem0 API Key</a> (optional, for cloud operations)

 ## Basic Integration Example

@@ -23,7 +23,7 @@ pip install agno mem0ai python-dotenv
 ```

 2. Valid API keys:
-   - <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=integration-agno" rel="nofollow">Mem0 API Key</a>
+   - <a href="https://app.mem0.ai/dashboard/api-keys" rel="nofollow">Mem0 API Key</a>
   - OpenAI API Key (for the agent model)

 ## Quick Integration (Using `Mem0Tools`)
@@ -19,7 +19,7 @@ pip install autogen mem0ai openai python-dotenv

 First, we'll import the necessary libraries and set up our configurations.

-<Note>Remember to get the Mem0 API key from <a href="https://app.mem0.ai?utm_source=oss&utm_medium=integration-autogen" rel="nofollow">Mem0 Platform</a>.</Note>
+<Note>Remember to get the Mem0 API key from <a href="https://app.mem0.ai" rel="nofollow">Mem0 Platform</a>.</Note>

 ```python
 import os
@@ -32,7 +32,7 @@ load_dotenv()

 # Configuration
 # OPENAI_API_KEY = 'sk-xxx'  # Replace with your actual OpenAI API key
-# MEM0_API_KEY = 'your-mem0-key'  # Replace with your actual Mem0 API key from https://app.mem0.ai?utm_source=oss&utm_medium=integration-autogen
+# MEM0_API_KEY = 'your-mem0-key'  # Replace with your actual Mem0 API key from https://app.mem0.ai
 USER_ID = "alice"

 # Set up OpenAI API key
@@ -18,7 +18,7 @@ In this guide, you'll:
 - **Python 3.12+**
 - **[uv](https://docs.astral.sh/uv/)** — Python package manager
 - **Node.js 18+** and **npm** — only needed if using the web console
- A **Mem0 API key** from <a href="https://app.mem0.ai?utm_source=oss&utm_medium=integration-chatdev" rel="nofollow">app.mem0.ai</a>
+- A **Mem0 API key** from <a href="https://app.mem0.ai" rel="nofollow">app.mem0.ai</a>
 - An **OpenAI API key** (or another LLM provider supported by ChatDev)

 ## Setup and Configuration
@@ -39,7 +39,7 @@ cd frontend && npm install && cd ..

 Set up your environment variables in a `.env` file:

-<Note>Get your Mem0 API key from <a href="https://app.mem0.ai?utm_source=oss&utm_medium=integration-chatdev" rel="nofollow">Mem0 Platform</a>.</Note>
+<Note>Get your Mem0 API key from <a href="https://app.mem0.ai" rel="nofollow">Mem0 Platform</a>.</Note>

 ```bash
 MEM0_API_KEY=your-mem0-api-key
@@ -194,7 +194,7 @@ This means retrieval returns memories from **both** the user's scope and the age

 | Field | Required | Description |
 |-------|----------|-------------|
-| `api_key` | Yes | Mem0 API key from <a href="https://app.mem0.ai?utm_source=oss&utm_medium=integration-chatdev" rel="nofollow">app.mem0.ai</a> |
+| `api_key` | Yes | Mem0 API key from <a href="https://app.mem0.ai" rel="nofollow">app.mem0.ai</a> |
 | `user_id` | No | Scope memories to a specific user |
 | `agent_id` | No | Scope memories to a specific agent |

@@ -216,9 +216,9 @@ This means retrieval returns memories from **both** the user's scope and the age

 - **No memories returned on first run** — This is expected. Memories are stored *after* the agent responds, so the first interaction has no prior context. Memories appear starting from the second interaction onward.
 - **`mem0ai` not installed** — If you see `ImportError: mem0ai is required for Mem0Memory`, run `uv add mem0ai` or `pip install mem0ai` to add the dependency.
- **Invalid API key** — A wrong or expired `MEM0_API_KEY` will log errors like `Mem0 search failed` or `Mem0 add failed` but won't crash the agent. Check your key at <a href="https://app.mem0.ai?utm_source=oss&utm_medium=integration-chatdev" rel="nofollow">app.mem0.ai</a>.
+- **Invalid API key** — A wrong or expired `MEM0_API_KEY` will log errors like `Mem0 search failed` or `Mem0 add failed` but won't crash the agent. Check your key at <a href="https://app.mem0.ai" rel="nofollow">app.mem0.ai</a>.
 - **Pipeline headers in memories** — ChatDev automatically strips internal pipeline headers (e.g., `=== INPUT FROM TASK (user) ===`) before sending text to Mem0, so your memories stay clean.
- **Clearing test memories** — To delete memories created during testing, use the Mem0 dashboard at <a href="https://app.mem0.ai?utm_source=oss&utm_medium=integration-chatdev" rel="nofollow">app.mem0.ai</a> or the Python SDK: `MemoryClient().delete_all(user_id="your-test-user")`.
+- **Clearing test memories** — To delete memories created during testing, use the Mem0 dashboard at <a href="https://app.mem0.ai" rel="nofollow">app.mem0.ai</a> or the Python SDK: `MemoryClient().delete_all(user_id="your-test-user")`.

 ## Key Features

@@ -17,8 +17,8 @@ Add persistent memory to [**Claude Code**](https://docs.anthropic.com/en/docs/cl
 Before setting up Mem0 with Claude Code, ensure you have:

 1. A Mem0 Platform account and API key:
-   - <a href="https://app.mem0.ai?utm_source=oss&utm_medium=integration-claude-code" rel="nofollow">Sign up at app.mem0.ai</a>
-   - <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=integration-claude-code" rel="nofollow">Get your API key</a> (starts with `m0-`)
+   - <a href="https://app.mem0.ai" rel="nofollow">Sign up at app.mem0.ai</a>
+   - <a href="https://app.mem0.ai/dashboard/api-keys" rel="nofollow">Get your API key</a> (starts with `m0-`)

 2. Claude Code CLI or Claude Cowork desktop app installed

@@ -17,8 +17,8 @@ Add persistent memory to [**OpenAI Codex**](https://openai.com/index/codex/) wit
 Before setting up Mem0 with Codex, ensure you have:

 1. A Mem0 Platform account and API key:
-   - <a href="https://app.mem0.ai?utm_source=oss&utm_medium=integration-codex" rel="nofollow">Sign up at app.mem0.ai</a>
-   - <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=integration-codex" rel="nofollow">Get your API key</a> (starts with `m0-`)
+   - <a href="https://app.mem0.ai" rel="nofollow">Sign up at app.mem0.ai</a>
+   - <a href="https://app.mem0.ai/dashboard/api-keys" rel="nofollow">Get your API key</a> (starts with `m0-`)

 2. OpenAI Codex access

@@ -30,100 +30,91 @@ export MEM0_API_KEY="m0-your-api-key"

 ## Installation

-### Option A — Direct MCP (Recommended)
+### Option A — Repo Marketplace (Recommended for Teams)

-The fastest way to connect Codex to Mem0 — no downloads, no marketplace. Codex reads MCP servers from `~/.codex/config.toml` as TOML. Add:
+Add a `.agents/plugins/marketplace.json` to your repository root:

-```toml
-[mcp_servers.mem0]
-url = "https://mcp.mem0.ai/mcp"
-bearer_token_env_var = "MEM0_API_KEY"
+```json
+{
+  "name": "mem0-plugins",
+  "interface": {
+    "displayName": "Mem0 Plugins"
+  },
+  "plugins": [
+    {
+      "name": "mem0",
+      "source": {
+        "source": "local",
+        "path": "./plugins/mem0"
+      },
+      "policy": {
+        "installation": "AVAILABLE",
+        "authentication": "ON_INSTALL"
+      },
+      "category": "Productivity"
+    }
+  ]
+}
 ```

-Make sure `MEM0_API_KEY` is exported in the shell you launch Codex from, then restart Codex.
+Then in Codex, browse the repo's plugin directory and install Mem0.

-<Info>
-  Codex's `codex mcp add` CLI only supports stdio MCP servers. Because Mem0's MCP is HTTP/streamable, you configure it by editing `config.toml` directly (or via the **Plugins → Connect to a custom MCP → Streamable HTTP** UI in the Codex app).
-</Info>
+### Option B — Personal Marketplace

-### Option B — Sideload the Plugin (Advanced)
+Add to `~/.agents/plugins/marketplace.json`:

-For the full plugin experience — MCP server **plus** the Mem0 SDK skill, memory protocol skill, and opt-in lifecycle hooks — sideload the plugin from a local clone. The Mem0 repo already ships a marketplace manifest at [`.agents/plugins/marketplace.json`](https://github.com/mem0ai/mem0/blob/main/.agents/plugins/marketplace.json), so there's no JSON to author by hand. This follows the Codex [build-plugins](https://developers.openai.com/codex/plugins/build) local-testing workflow.
-
-<Info>
-  Don't combine Option B with Option A. The plugin manifest declares its MCP server via [`.codex-mcp.json`](https://github.com/mem0ai/mem0/blob/main/mem0-plugin/.codex-mcp.json), so Codex auto-registers the `mem0` MCP server when the plugin loads. Adding the same `[mcp_servers.mem0]` block to `~/.codex/config.toml` will create a duplicate registration.
-</Info>
-
-**Step 1.** Clone the Mem0 repository anywhere on disk:
-
-```bash
-git clone https://github.com/mem0ai/mem0.git ~/codex-plugins/mem0-source
+```json
+{
+  "name": "mem0-plugins",
+  "interface": {
+    "displayName": "Mem0 Plugins"
+  },
+  "plugins": [
+    {
+      "name": "mem0",
+      "source": {
+        "source": "local",
+        "path": "/path/to/mem0-plugin"
+      },
+      "policy": {
+        "installation": "AVAILABLE",
+        "authentication": "ON_INSTALL"
+      },
+      "category": "Productivity"
+    }
+  ]
+}
 ```

-**Step 2.** Register the bundled marketplace with Codex's CLI:
+### Option C — Manual MCP Configuration

-```bash
-codex plugin marketplace add ~/codex-plugins/mem0-source
+Add to your Codex MCP config:
+
+```json
+{
+  "mcpServers": {
+    "mem0": {
+      "type": "http",
+      "url": "https://mcp.mem0.ai/mcp/",
+      "headers": {
+        "Authorization": "Token ${MEM0_API_KEY}"
+      }
+    }
+  }
+}
 ```

-This points Codex at the repo's `.agents/plugins/marketplace.json`. The bundled file uses `path: "./mem0-plugin"`, which Codex resolves relative to the clone root.
-
-<Info>
-  **Why we recommend this over hand-authoring `~/.agents/plugins/marketplace.json`:** Codex requires `source.path` in any marketplace manifest to be **relative** (starting with `./`) and **inside the marketplace root**. The repo's bundled manifest already satisfies this — the marketplace root is the clone directory, and `mem0-plugin/` lives inside it. With a personal `~/.agents/plugins/marketplace.json`, the root is `~/` and the clone has to live under `~/` too. The CLI form sidesteps that constraint.
-</Info>
-
-**Step 3.** Restart Codex, run `/plugins`, browse the `Mem0 Plugins` marketplace, and install **Mem0**.
-
-**Step 4 (optional) — enable lifecycle hooks.** Codex doesn't auto-wire hooks from plugin manifests; it only reads them from `~/.codex/hooks.json` (or `<repo>/.codex/hooks.json`). Run the bundled installer once to merge the Mem0 entries into your global hooks file:
-
-```bash
-python3 ~/codex-plugins/mem0-source/mem0-plugin/scripts/install_codex_hooks.py
-```
-
-Then enable the hooks feature flag in `~/.codex/config.toml`:
-
-```toml
-[features]
-codex_hooks = true
-```
-
-Restart Codex. The installer registers three hooks pointing at scripts inside your clone:
-
-| Event | Behavior |
-|-------|----------|
-| `SessionStart` | Loads prior memories as bootstrap context |
-| `UserPromptSubmit` | Injects relevant memories before each prompt |
-| `Stop` | Reminds the agent to persist learnings at turn end |
-
-Re-running the installer is idempotent. To remove the hooks: `python3 ~/codex-plugins/mem0-source/mem0-plugin/scripts/install_codex_hooks.py --uninstall`.
-
-<Warning>
-  The hooks file stores absolute paths into your clone (e.g. `~/codex-plugins/mem0-source/mem0-plugin/scripts/...`). If you move or delete the clone, the hooks will break silently — re-run the installer from the new location, or run `--uninstall` first.
-</Warning>
-
-### Managing the Plugin
-
-Codex provides CLI commands for managing marketplaces after install:
-
-```bash
-codex plugin marketplace upgrade               # pull latest plugin versions
-codex plugin marketplace remove mem0-plugins   # unregister the marketplace
-```
-
-To pull updates to the plugin source itself, `git pull` inside your clone (`~/codex-plugins/mem0-source`) and then run `codex plugin marketplace upgrade` to refresh Codex's plugin cache. Plugins are cached at `~/.codex/plugins/cache/<marketplace>/<plugin>/<version>/`.
-
 <Info icon="check">
-  After either option, start a new Codex task and ask: *"List my mem0 entities"* or *"Search my memories for hello"*. If the `mem0` tools appear and respond, you're all set.
+  Start a new Codex task and ask: *"List my mem0 entities"* or *"Search my memories for hello"*. If the `mem0` tools appear and respond, you're all set.
 </Info>

 ## What's Included

-| Component | Sideloaded Plugin | Direct MCP |
-|-----------|:-----------------:|:----------:|
+| Component | Plugin Install | MCP Only |
+|-----------|:--------------:|:--------:|
 | MCP Server (9 memory tools) | Yes | Yes |
 | Memory Protocol Skill | Yes | No |
 | Mem0 SDK Skill | Yes | No |
-| Lifecycle Hooks (opt-in) | Yes | No |

 ## Available MCP Tools

@@ -143,7 +134,7 @@ Once installed, the following tools are available in every Codex session:

 ## Memory Protocol Skill

-When the plugin is sideloaded, the memory protocol skill instructs the agent to:
+Codex uses a skill-based approach instead of lifecycle hooks. When installed via the plugin marketplace, the memory protocol skill instructs the agent to:

 ### On Every New Task
 1. Call `search_memories` with a query related to the current task to load relevant context
@@ -208,12 +199,8 @@ You: Add WebSocket support for real-time notification delivery.

 - **"Connection failed"** — Verify `MEM0_API_KEY` is set in your shell: `echo $MEM0_API_KEY`
 - **No tools appearing** — Restart your Codex session after plugin installation
- **Duplicate `mem0` MCP server / "tool collision" errors** — You combined Option A (Direct MCP) with Option B (sideload). The sideloaded plugin auto-registers `mem0` from `.codex-mcp.json`, so remove the `[mcp_servers.mem0]` block from `~/.codex/config.toml`.
- **`plugin/read failed in TUI`** — Codex can't find the plugin directory the marketplace points at. If you used `codex plugin marketplace add <path>`, confirm the path is your clone root and that `<clone>/.agents/plugins/marketplace.json` exists. If you hand-authored `~/.agents/plugins/marketplace.json`, `source.path` must be relative (start with `./`), inside the marketplace root (`~/` for personal installs), and end in `mem0-plugin` — e.g. `"./codex-plugins/mem0-source/mem0-plugin"`.
- **Plugin not found in `/plugins`** — Run `codex plugin marketplace add ~/path/to/clone` again, or confirm the marketplace was registered with `codex plugin marketplace remove mem0-plugins` then re-add.
- **Skills not loading** — Verify the `skills` field in `plugin.json` points to a valid directory containing `SKILL.md` files.
- **Hooks not firing** — Confirm `codex_hooks = true` is in `~/.codex/config.toml` under `[features]`, and that `~/.codex/hooks.json` contains the Mem0 entries (re-run the installer if not). Restart Codex after enabling the flag.
- **Hooks broke after moving the clone** — The installer bakes absolute paths into `~/.codex/hooks.json` pointing at scripts inside your clone. If you moved or renamed the clone directory, run `python3 <new-clone>/mem0-plugin/scripts/install_codex_hooks.py` from the new location — the installer is idempotent and replaces the old entries.
+- **Plugin not found** — Ensure `.agents/plugins/marketplace.json` is at the repository root and `source.path` points to the correct plugin directory
+- **Skills not loading** — Verify the `skills` field in `plugin.json` points to a valid directory containing `SKILL.md` files

 <CardGroup cols={2}>
  <Card title="Mem0 MCP Setup" icon="puzzle-piece" href="/platform/mem0-mcp">
@@ -22,7 +22,7 @@ pip install crewai crewai-tools mem0ai

 Import required modules and set up configurations:

-<Note>Remember to get your API keys from <a href="https://app.mem0.ai?utm_source=oss&utm_medium=integration-crewai" rel="nofollow">Mem0 Platform</a>, [OpenAI](https://platform.openai.com) and [Serper Dev](https://serper.dev) for search capabilities.</Note>
+<Note>Remember to get your API keys from <a href="https://app.mem0.ai" rel="nofollow">Mem0 Platform</a>, [OpenAI](https://platform.openai.com) and [Serper Dev](https://serper.dev) for search capabilities.</Note>

 ```python
 import os
@@ -17,8 +17,8 @@ Add persistent memory to [**Cursor**](https://cursor.com) with the Mem0 plugin.
 Before setting up Mem0 with Cursor, ensure you have:

 1. A Mem0 Platform account and API key:
-   - <a href="https://app.mem0.ai?utm_source=oss&utm_medium=integration-cursor" rel="nofollow">Sign up at app.mem0.ai</a>
-   - <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=integration-cursor" rel="nofollow">Get your API key</a> (starts with `m0-`)
+   - <a href="https://app.mem0.ai" rel="nofollow">Sign up at app.mem0.ai</a>
+   - <a href="https://app.mem0.ai/dashboard/api-keys" rel="nofollow">Get your API key</a> (starts with `m0-`)

 2. Cursor installed ([cursor.com](https://cursor.com))

@@ -38,7 +38,7 @@ npx flowise start

 ### 2. Obtain Your Mem0 API Key

-1. Navigate to the <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=integration-flowise" rel="nofollow">Mem0 API Key dashboard</a>.
+1. Navigate to the <a href="https://app.mem0.ai/dashboard/api-keys" rel="nofollow">Mem0 API Key dashboard</a>.
 2. Generate or copy your existing Mem0 API Key.

 ![Mem0 API Key](https://raw.githubusercontent.com/FlowiseAI/FlowiseDocs/main/en/.gitbook/assets/mem0/api-key.png)
@@ -70,7 +70,7 @@ Test your memory configuration:

 1. Save your Flowise configuration
 2. Run a test chat and store some information
-3. Verify the stored memories in the <a href="https://app.mem0.ai/dashboard/requests?utm_source=oss&utm_medium=integration-flowise" rel="nofollow">Mem0 Dashboard</a>
+3. Verify the stored memories in the <a href="https://app.mem0.ai/dashboard/requests" rel="nofollow">Mem0 Dashboard</a>

 ![Flowise Test Chat](https://raw.githubusercontent.com/FlowiseAI/FlowiseDocs/main/en/.gitbook/assets/mem0/flowise-chat-1.png)

@@ -103,7 +103,7 @@ Available settings include:

 ### Platform Configuration

-Additional settings available in <a href="https://app.mem0.ai/dashboard/project-settings?utm_source=oss&utm_medium=integration-flowise" rel="nofollow">Mem0 Project Settings</a>:
+Additional settings available in <a href="https://app.mem0.ai/dashboard/project-settings" rel="nofollow">Mem0 Project Settings</a>:

 1. **Custom Instructions**: Define memory extraction rules
 2. **Expiration Date**: Set automatic memory cleanup periods
@@ -22,7 +22,7 @@ pip install google-adk mem0ai python-dotenv
 ```

 2. Valid API keys:
-   - <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=integration-google-ai-adk" rel="nofollow">Mem0 API Key</a>
+   - <a href="https://app.mem0.ai/dashboard/api-keys" rel="nofollow">Mem0 API Key</a>
   - Google AI Studio API Key

 ## Basic Integration Example
@@ -52,7 +52,7 @@ hermes memory setup

 Select **mem0** as the provider and enter your Mem0 API key when prompted. The wizard writes your config to `~/.hermes/mem0.json`.

-<Note>Get your API key from <a href="https://app.mem0.ai?utm_source=oss&utm_medium=integration-hermes" rel="nofollow">app.mem0.ai</a>.</Note>
+<Note>Get your API key from <a href="https://app.mem0.ai" rel="nofollow">app.mem0.ai</a>.</Note>

 ### Option 2: Manual Configuration

@@ -16,7 +16,7 @@ Combining Mem0 with Keywords AI allows you to:
 4. Optimize token usage and reduce costs

 <Note>
-You can get your Mem0 API key from the <a href="https://app.mem0.ai/?utm_source=oss&utm_medium=integration-keywords" rel="nofollow">Mem0 dashboard</a>.
+You can get your Mem0 API key from the <a href="https://app.mem0.ai/" rel="nofollow">Mem0 dashboard</a>.
 </Note>

 ## Setup and Configuration
@@ -22,7 +22,7 @@ pip install langchain langchain_openai mem0ai python-dotenv

 Import required modules and set up configurations:

-<Note>Remember to get the Mem0 API key from <a href="https://app.mem0.ai?utm_source=oss&utm_medium=integration-langchain" rel="nofollow">Mem0 Platform</a>.</Note>
+<Note>Remember to get the Mem0 API key from <a href="https://app.mem0.ai" rel="nofollow">Mem0 Platform</a>.</Note>

 ```python
 import os
@@ -23,7 +23,7 @@ pip install langgraph langchain-openai mem0ai python-dotenv

 Import required modules and set up configurations:

-<Note>Remember to get the Mem0 API key from <a href="https://app.mem0.ai?utm_source=oss&utm_medium=integration-langgraph" rel="nofollow">Mem0 Platform</a>.</Note>
+<Note>Remember to get the Mem0 API key from <a href="https://app.mem0.ai" rel="nofollow">Mem0 Platform</a>.</Note>

 ```python
 from typing import Annotated, TypedDict, List
@@ -22,7 +22,7 @@ pip install llama-index-core llama-index-memory-mem0 python-dotenv
 Set your Mem0 Platform API key as an environment variable. You can replace `<your-mem0-api-key>` with your actual API key:

 <Note type="info">
-  You can obtain your Mem0 Platform API key from the <a href="https://app.mem0.ai/login?utm_source=oss&utm_medium=integration-llama-index" rel="nofollow">Mem0 Platform</a>.
+  You can obtain your Mem0 Platform API key from the <a href="https://app.mem0.ai/login" rel="nofollow">Mem0 Platform</a>.
 </Note>

 ```python
@@ -23,7 +23,7 @@ npm install @mastra/core @mastra/mem0 @ai-sdk/openai zod

 Set up your environment variables:

-<Note>Remember to get the Mem0 API key from <a href="https://app.mem0.ai?utm_source=oss&utm_medium=integration-mastra" rel="nofollow">Mem0 Platform</a>.</Note>
+<Note>Remember to get the Mem0 API key from <a href="https://app.mem0.ai" rel="nofollow">Mem0 Platform</a>.</Note>

 ```bash
 MEM0_API_KEY=your-mem0-api-key
@@ -22,7 +22,7 @@ pip install openai-agents mem0ai
 ```

 2. Valid API keys:
-   - <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=integration-openai-agents-sdk" rel="nofollow">Mem0 API Key</a>
+   - <a href="https://app.mem0.ai/dashboard/api-keys" rel="nofollow">Mem0 API Key</a>
   - [OpenAI API Key](https://platform.openai.com/api-keys)

 ## Basic Integration Example
@@ -1,6 +1,6 @@
 ---
 title: OpenClaw
-description: "Add long-term memory to OpenClaw agents using the Mem0 plugin with skills-based memory extraction and recall."
+description: "Add long-term memory to OpenClaw agents using the Mem0 plugin with auto-recall and auto-capture support."
 ---

 Add long-term memory to [OpenClaw](https://github.com/openclaw/openclaw) agents with the `@mem0/openclaw-mem0` plugin. Your agent forgets everything between sessions — this plugin fixes that by automatically watching conversations, extracting what matters, and bringing it back when relevant.
@@ -12,12 +12,11 @@ Add long-term memory to [OpenClaw](https://github.com/openclaw/openclaw) agents
 </Frame>

 The plugin provides:
-1. **Triage** — The agent extracts durable facts from conversations using a structured protocol with importance gates and domain overlays
-2. **Recall** — Before each turn, relevant memories are retrieved with reranking and injected into context
-3. **Dream** — Periodic memory consolidation: merges duplicates, resolves conflicts, prunes stale entries
-4. **Agent Tools** — Eight tools for explicit memory operations during conversations
+1. **Auto-Recall** — Before the agent responds, memories matching the current message are injected into context
+2. **Auto-Capture** — After the agent responds, the exchange is sent to Mem0 which decides what's worth keeping
+3. **Agent Tools** — Eight tools for explicit memory operations during conversations

-Skills mode, `autoRecall`, and `autoCapture` are all enabled by default during `openclaw mem0 init`.
+Both auto-recall and auto-capture are opt-in (`autoRecall: true`, `autoCapture: true` in config). Once enabled, they run silently with no manual intervention required.

 ## Requirements

@@ -25,12 +24,12 @@ Check your OpenClaw version:

 ```bash
 openclaw --version
-# OpenClaw 2026.4.25 (aa36ee6)
+# OpenClaw 2026.4.15 (041266a)
 ```

 | OpenClaw Version | Plugin Support |
 |------------------|----------------|
-| `>= 2026.4.25`   | Fully supported |
+| `>= 2026.4.15`   | Fully supported |

 ## Installation

@@ -101,9 +100,9 @@ You no longer need manual config editing to get started. Everything happens insi
  </Step>
 </Steps>

-That's it. No API key, no config file editing, no environment variables. The plugin is now active with skills-based memory (triage, recall, and dream) running automatically.
+That's it. No API key, no config file editing, no environment variables. The plugin is now active and auto-capture and auto-recall are running on every turn.

-<Note>The chat flow uses the same underlying config as manual setup — it writes `apiKey`, `userId`, and `skills` config into `openclaw.json` for you. You can still open the file to inspect or override values afterward.</Note>
+<Note>The chat flow uses the same underlying config as manual setup — it writes `apiKey` and `userId` into `openclaw.json` for you. You can still open the file to inspect or override values afterward.</Note>

 #### Option 2: Manual Config

@@ -115,7 +114,7 @@ That's it. No API key, no config file editing, no environment variables. The plu
  </Step>

  <Step title="Get your API key">
-    Get your API key from <a href="https://app.mem0.ai?utm_source=oss&utm_medium=integration-openclaw" rel="nofollow">app.mem0.ai</a>.
+    Get your API key from <a href="https://app.mem0.ai?utm_source=mem0-docs" rel="nofollow">app.mem0.ai</a>.
  </Step>

  <Step title="Select the plugin as your memory backend in `openclaw.json`">
@@ -132,19 +131,7 @@ That's it. No API key, no config file editing, no environment variables. The plu
            "enabled": true,
            "config": {
              "apiKey": "${MEM0_API_KEY}",
-              "userId": "alice",  // any unique identifier you choose for this user
-              "skills": {
-                "triage": { "enabled": true },
-                "recall": {
-                  "enabled": true,
-                  "tokenBudget": 1500,
-                  "rerank": true,
-                  "keywordSearch": true,
-                  "identityAlwaysInclude": true
-                },
-                "dream": { "enabled": true },
-                "domain": "companion"
-              }
+              "userId": "alice"  // any unique identifier you choose for this user
            }
          }
        }
@@ -341,8 +328,8 @@ openclaw mem0 status --json
 |-----|------|---------|-------------|
 | `mode` | `"platform"` \| `"open-source"` | `"platform"` | Which backend to use |
 | `userId` | `string` | OS username | Scope memories per user |
-| `autoRecall` | `boolean` | `true` | Inject memories before each turn. Ignored when `skills` is configured. |
-| `autoCapture` | `boolean` | `true` | Store facts after each turn. Ignored when `skills` is configured. |
+| `autoRecall` | `boolean` | `false` | Inject memories before each turn (opt-in) |
+| `autoCapture` | `boolean` | `false` | Store facts after each turn (opt-in) |
 | `topK` | `number` | `5` | Max memories per recall |
 | `searchThreshold` | `number` | `0.3` | Min similarity (0–1) |

@@ -375,9 +362,11 @@ Everything inside `oss` is optional — defaults use OpenAI embeddings (`text-em
 ### Updating the Plugin

 ```bash
-openclaw plugins update openclaw-mem0
+openclaw plugins update @mem0/openclaw-mem0
 ```

+<Note>Use the npm package name (`@mem0/openclaw-mem0`) for plugin management commands, not the plugin ID (`openclaw-mem0`).</Note>
+
 ### Checking Plugin Status

 ```bash
@@ -422,9 +411,8 @@ If the plugin installs but doesn't work:

 If `openclaw plugins update` fails:

-1. Use the plugin ID: `openclaw plugins update openclaw-mem0`
-2. Update all plugins at once: `openclaw plugins update --all`
-3. If that fails, uninstall and reinstall:
+1. Use the full npm package name: `openclaw plugins update @mem0/openclaw-mem0`
+2. If that fails, uninstall and reinstall:
   ```bash
   openclaw plugins uninstall openclaw-mem0
   openclaw plugins install @mem0/openclaw-mem0
@@ -439,11 +427,9 @@ If `openclaw plugins update` fails:
 | **Platform** | Conversations sent to `api.mem0.ai` for extraction and storage | Mem0 cloud |
 | **Open-source** | Embeddings generated via configured provider (default: OpenAI API). Vectors stored locally. | `~/.mem0/vector_store.db` (SQLite) |

-### Auto-Capture and Auto-Recall
+### Enabling Auto-Capture and Auto-Recall

-Auto-capture and auto-recall are **enabled by default**. When skills mode is configured (the default after `openclaw mem0 init`), these are ignored in favor of the skills-based triage/recall/dream protocol.
-
-To disable either:
+Auto-capture and auto-recall are disabled by default (opt-in). To enable either or both:

 ```json5
 {
@@ -451,8 +437,8 @@ To disable either:
    "entries": {
      "openclaw-mem0": {
        "config": {
-          "autoCapture": false,   // disable automatic fact extraction
-          "autoRecall": false     // disable automatic memory injection
+          "autoCapture": true,   // send conversations to Mem0 for extraction
+          "autoRecall": true     // inject relevant memories into context
        }
      }
    }
@@ -460,7 +446,7 @@ To disable either:
 }
 ```

-The agent can always use memory tools (`memory_add`, `memory_search`, etc.) explicitly regardless of these settings.
+Without these enabled, the agent can still use memory tools (`memory_add`, `memory_search`, etc.) explicitly — only the automatic background behavior is off.

 ### Credential Protection

@@ -490,6 +476,17 @@ export MEM0_TELEMETRY=false

 The plugin injects memory-related instructions into the agent's system context via OpenClaw's `prependSystemContext` mechanism. This includes the memory triage protocol and recalled memories. This is the standard OpenClaw plugin SDK pattern for memory backends — no user-facing prompts are modified.

+## Key Features
+
+1. **Zero Configuration** — Auto-recall and auto-capture work out of the box with no prompting required
+2. **Dual Memory Scopes** — Session-scoped short-term and user-scoped long-term memories
+3. **Flexible Backend** — Use Mem0 Cloud for managed service or self-host with open-source mode
+4. **Rich Tool Suite** — Eight agent tools for explicit memory operations when needed
+
+## Conclusion
+
+The `@mem0/openclaw-mem0` plugin gives OpenClaw agents persistent memory with minimal setup. Whether using Mem0 Cloud or self-hosting, your agents can now remember user preferences, facts, and context across sessions automatically.
+
 <CardGroup cols={2}>
  <Card title="OpenAI Agents SDK" icon="robot" href="/integrations/openai-agents-sdk">
    Build agents with OpenAI's SDK and Mem0
@@ -9,7 +9,7 @@ Mem0 is a self-improving memory layer for LLM applications, enabling personalize

 **Get your API Key**: You'll need a Mem0 API key to use this extension:

-a. Sign up at <a href="https://app.mem0.ai?utm_source=oss&utm_medium=integration-raycast" rel="nofollow">app.mem0.ai</a>
+a. Sign up at <a href="https://app.mem0.ai" rel="nofollow">app.mem0.ai</a>

 b. Navigate to your API Keys page

@@ -29,7 +29,7 @@ npm install @mem0/vercel-ai-provider

 ### Setting Up Mem0

-1. Get your **Mem0 API Key** from the <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=integration-vercel-ai-sdk" rel="nofollow">Mem0 Dashboard</a>.
+1. Get your **Mem0 API Key** from the <a href="https://app.mem0.ai/dashboard/api-keys" rel="nofollow">Mem0 Dashboard</a>.

 2. Initialize the Mem0 Client in your application:

@@ -161,7 +161,6 @@ If the user is on a pre-current major (Python < 2, TS < 3, or Platform `output_f
 - [Open Source Configuration](https://docs.mem0.ai/open-source/configuration) [OSS]: Use when configuring `Memory` - LLM, embedder, vector store, graph store.
 - [Open Source Python Quickstart](https://docs.mem0.ai/open-source/python-quickstart) [OSS]: Use for the first self-hosted Python integration.
 - [Open Source Node.js Quickstart](https://docs.mem0.ai/open-source/node-quickstart) [OSS]: Use for the first self-hosted Node integration.
- [Self-Hosted Setup](https://docs.mem0.ai/open-source/setup) [OSS]: Use when standing up the bundled REST server and dashboard via Docker Compose, including auth, API keys, and the setup wizard.

 ## Core Concepts

@@ -187,7 +186,6 @@ If the user is on a pre-current major (Python < 2, TS < 3, or Platform `output_f
 - [Criteria-Based Retrieval](https://docs.mem0.ai/platform/features/criteria-retrieval) [Platform]: Use when targeting memories by custom criteria, not just semantic similarity.
 - [Contextual Add](https://docs.mem0.ai/platform/features/contextual-add) [Platform]: Use when `add()` should consider the surrounding conversation, not just the latest turn.
 - [Custom Instructions](https://docs.mem0.ai/platform/features/custom-instructions) [Platform]: Use when tailoring what Mem0 extracts and stores on Platform.
- [Memory Decay](https://docs.mem0.ai/platform/features/memory-decay) [Platform]: Use when search results should boost recently-reinforced memories and dampen stale ones — opt-in per project, search-time only, never filters candidates out.
 - [Advanced Memory Operations](https://docs.mem0.ai/platform/advanced-memory-operations) [Platform]: Use when basic CRUD is not enough - batch ops, complex filters, workflows.

 ### Features - Data Management
@@ -28,7 +28,7 @@ Move your Mem0 implementation to managed infrastructure with enterprise features

 ## Plan

-1. **Sign up**: Create an account on <a href="https://app.mem0.ai?utm_source=oss&utm_medium=migration-oss-to-platform" rel="nofollow">Mem0 Platform</a>.
+1. **Sign up**: Create an account on <a href="https://app.mem0.ai" rel="nofollow">Mem0 Platform</a>.
 2. **Get API Key**: Navigate to **Settings > API Keys** and generate a new key.
 3. **Review Usage**: Identify where you instantiate `Memory` and where you call `search` or `get_all`.

@@ -372,7 +372,7 @@ If you encounter issues, you can revert immediately by switching your import bac

 ## Next Steps

- <a href="https://app.mem0.ai?utm_source=oss&utm_medium=migration-oss-to-platform" rel="nofollow">Platform Dashboard</a> - Monitor usage and manage settings.
+- <a href="https://app.mem0.ai" rel="nofollow">Platform Dashboard</a> - Monitor usage and manage settings.
 - [Webhooks Setup](/platform/features/webhooks) - Configure real-time event notifications.
 - [Organizations & Projects](/api-reference/organizations-projects) - Set up multi-tenancy for your team.

@@ -13,10 +13,6 @@ The Mem0 REST API server exposes every OSS memory operation over HTTP. Run it al
  - You plan to explore or debug endpoints through the built-in OpenAPI page at `/docs`.
 </Info>

-<Warning>
-  **First time self-hosting, or upgrading from a pre-1.x build?** Start at [Self-Hosted Setup](/open-source/setup). It walks through the stack, the setup wizard, and the upgrade path for deployments that relied on open endpoints or `ADMIN_API_KEY`. This page covers the API surface and auth modes only.
-</Warning>
-
 <Warning>
  **OSS vs Platform API paths:** The self-hosted OSS server does **not** use the `/v1/` prefix. For example, the endpoint is `POST /memories`, not `POST /v1/memories/`. The [API Reference](/api-reference) documents the hosted platform at `api.mem0.ai` which uses `/v1/` paths — those do not apply to the OSS server.
 </Warning>
@@ -30,7 +26,7 @@ The Mem0 REST API server exposes every OSS memory operation over HTTP. Run it al
 ## Feature

 - **CRUD endpoints:** Create, retrieve, search, update, delete, and reset memories by `user_id`, `agent_id`, or `run_id`.
- **Authentication:** On by default. Dashboard sessions use JWTs; programmatic clients use per-user `X-API-Key` headers. Legacy `ADMIN_API_KEY` is still supported.
+- **API key authentication:** Optionally secure all endpoints with a shared API key via the `X-API-Key` header.
 - **Status health check:** Access base routes to confirm the server is online.
 - **OpenAPI explorer:** Visit `/docs` for interactive testing and schema reference.

@@ -44,75 +40,51 @@ The Mem0 REST API server exposes every OSS memory operation over HTTP. Run it al
  <Tab title="Steps">
 1. Create `server/.env` with your keys:

-    ```bash
-    OPENAI_API_KEY=your-openai-api-key
-    JWT_SECRET=$(openssl rand -base64 48)
-    ```
+```bash
+OPENAI_API_KEY=your-openai-api-key
+```

-2. Bootstrap the stack in one command:
+2. Start the stack:

-    ```bash
-    cd server
-    make bootstrap    # starts Compose, creates an admin, issues the first API key
-    ```
+```bash
+cd server
+docker compose up
+```

-    Or to start the stack only and finish setup via the browser wizard at http://localhost:3000:
-
-    ```bash
-    cd server
-    docker compose up -d
-    ```
-
-3. API is at `http://localhost:8888`. Code edits auto-reload.
+3. Reach the API at `http://localhost:8888`. Edits to the server or library auto-reload.
  </Tab>
 </Tabs>

-<AccordionGroup>
-  <Accordion title="Other install paths">
-    **Run with Docker**
+### Run with Docker

-    <Tabs>
-      <Tab title="Pull image">
+<Tabs>
+  <Tab title="Pull image">
 ```bash
 docker pull mem0/mem0-api-server
 ```
-      </Tab>
-      <Tab title="Build locally">
+  </Tab>
+  <Tab title="Build locally">
 ```bash
 docker build -t mem0-api-server .
 ```
-      </Tab>
-    </Tabs>
+  </Tab>
+</Tabs>

-    1. Create a `.env` file with `OPENAI_API_KEY` and `JWT_SECRET`.
-    2. Run the container:
+1. Create a `.env` file with `OPENAI_API_KEY`.  
+2. Run the container:

-    ```bash
-    docker run -p 8000:8000 --env-file .env mem0-api-server
-    ```
+```bash
+docker run -p 8000:8000 --env-file .env mem0-api-server
+```

-    3. Visit `http://localhost:8000`.
+3. Visit `http://localhost:8000`.

-    **Run directly (no Docker)**
+### Run directly (no Docker)

-    <Warning>
-      This path skips Docker and assumes Postgres is already running and reachable at `POSTGRES_HOST:POSTGRES_PORT`. For a single-command local setup with Postgres included, use Docker Compose above.
-    </Warning>
-
-    ```bash
-    pip install -r requirements.txt
-    uvicorn main:app --reload
-    ```
-  </Accordion>
-</AccordionGroup>
-
-<Note>
-  Compose publishes internal port 8000 as 8888 on the host. Raw Docker and raw uvicorn listen on 8000 unless remapped.
-</Note>
-
-<Note>
-  `JWT_SECRET` is required once auth is enabled — the server returns `500` on auth endpoints if it's unset. Generate one with `openssl rand -base64 48`. See [Self-Hosted Setup](/open-source/setup#configure-the-environment) for the full env var table.
-</Note>
+```bash
+pip install -r requirements.txt
+uvicorn main:app --reload
+```

 <Tip>
  Use a process manager such as `systemd`, Supervisor, or PM2 when deploying the FastAPI server for production resilience.
@@ -126,74 +98,35 @@ docker build -t mem0-api-server .

 ## Authentication

-Auth is on by default. Protected endpoints require either a JWT (from the dashboard login flow) or an `X-API-Key` header. The `/` redirect, `/docs`, and `/openapi.json` routes stay open so you can reach the OpenAPI explorer.
+The server supports optional API key authentication. When the `ADMIN_API_KEY` environment variable is set, every endpoint requires a valid `X-API-Key` header. The `/` redirect, `/docs`, and `/openapi.json` routes remain open so you can always reach the interactive API explorer.

-| Mode | How to send it | When to use it |
-|---|---|---|
-| Bearer JWT | `Authorization: Bearer <access_token>` | Dashboard sessions; tokens come from `POST /auth/login` and refresh via `POST /auth/refresh` |
-| Per-user API key | `X-API-Key: m0sk_...` | Programmatic access scoped to a single dashboard user |
-| Legacy `ADMIN_API_KEY` | `X-API-Key: <env value>` | Back-compat for deployments that set the `ADMIN_API_KEY` env var |
-| `AUTH_DISABLED=true` | — | Local development only; bypasses auth entirely |
+| `ADMIN_API_KEY` value | Behavior |
+|---|---|
+| Not set / empty | All endpoints are open (no auth) |
+| Any non-empty string | Requests must include `X-API-Key: <your-key>` |

-The `/docs` OpenAPI explorer supports both auth modes. Click **Authorize** at the top of the page and paste either `Bearer <access_token>` (JWT) or your `X-API-Key` value. Protected endpoints return `401` until you authorize.
+### Enable authentication

-### Log in and use a JWT
-
-Register the first admin (only works when no user exists yet), then log in:
+Add the key to your `.env` file:

 ```bash
-# First admin only — returns 403 after the first admin is registered
-curl -X POST http://localhost:8888/auth/register \
-  -H "Content-Type: application/json" \
-  -d '{"name": "Admin", "email": "admin@example.com", "password": "strong-password"}'
+ADMIN_API_KEY=your-secret-api-key
 ```

-```bash
-curl -X POST http://localhost:8888/auth/login \
-  -H "Content-Type: application/json" \
-  -d '{"email": "admin@example.com", "password": "your-password"}'
-```
-
-Use the returned `access_token` as a bearer token:
+Then include the header in every request:

 ```bash
-curl -X POST http://localhost:8888/memories \
+curl -X POST http://localhost:8000/memories \
  -H "Content-Type: application/json" \
-  -H "Authorization: Bearer <access_token>" \
+  -H "X-API-Key: your-secret-api-key" \
  -d '{
    "messages": [{"role": "user", "content": "I love pizza."}],
    "user_id": "alice"
  }'
 ```

-When the access token expires, exchange the refresh token at `POST /auth/refresh`.
-
-### Create and use a per-user API key
-
-Create a key from the dashboard **API Keys** page, or call `POST /api-keys` with a JWT. The full `m0sk_...` value is returned **once** at creation time — store it securely.
-
-```bash
-curl -X POST http://localhost:8888/memories \
-  -H "Content-Type: application/json" \
-  -H "X-API-Key: m0sk_your_key_here" \
-  -d '{
-    "messages": [{"role": "user", "content": "I love pizza."}],
-    "user_id": "alice"
-  }'
-```
-
-Per-user keys inherit the creating user's scope. List or revoke them via `GET /api-keys` and `DELETE /api-keys/{id}`.
-
-### Legacy `ADMIN_API_KEY`
-
-Set the `ADMIN_API_KEY` environment variable and send it as `X-API-Key`. The request is treated as admin-level and is not tied to a dashboard user. This mode is kept for back-compat with older self-hosted deployments — prefer JWT or per-user keys for new setups.
-
-```bash
-ADMIN_API_KEY=your-long-admin-key
-```
-
 <Warning>
-  Setting `AUTH_DISABLED=true` makes every protected endpoint open — the server logs a warning at startup when it's enabled. The server also warns when `ADMIN_API_KEY` is shorter than 16 characters. Never enable `AUTH_DISABLED` in production, and always use a long `ADMIN_API_KEY` if you rely on the legacy fallback.
+  The server logs a warning at startup when `ADMIN_API_KEY` is not set. Always set it in production.
 </Warning>

 ---
@@ -203,7 +136,7 @@ ADMIN_API_KEY=your-long-admin-key
 ### Create and search memories via HTTP

 ```bash
-curl -X POST http://localhost:8888/memories \
+curl -X POST http://localhost:8000/memories \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [
@@ -218,7 +151,7 @@ curl -X POST http://localhost:8888/memories \
 </Info>

 ```bash
-curl -X POST http://localhost:8888/search \
+curl -X POST http://localhost:8000/search \
  -H "Content-Type: application/json" \
  -d '{
    "query": "vegetable",
@@ -228,7 +161,7 @@ curl -X POST http://localhost:8888/search \

 ### Explore with OpenAPI docs

-1. Navigate to `http://localhost:8888/docs` (Compose) or `http://localhost:8000/docs` (raw Docker / uvicorn).
+1. Navigate to `http://localhost:8000/docs`.
 2. Pick an endpoint (e.g., `POST /search`).  
 3. Fill in parameters and click **Execute** to try requests in-browser.

@@ -242,13 +175,9 @@ curl -X POST http://localhost:8888/search \

 The OSS REST server exposes the following endpoints. None use the `/v1/` prefix.

-### Memory operations
-
 | Method | Path | Description |
 |--------|------|-------------|
-| `POST` | `/configure` | Set memory configuration. Rejects unbundled providers with a 400 |
-| `GET` | `/configure` | Get the current memory configuration |
-| `GET` | `/configure/providers` | List the LLM and embedder providers bundled in the container |
+| `POST` | `/configure` | Set memory configuration |
 | `POST` | `/memories` | Create memories |
 | `GET` | `/memories` | Get all memories (filter by `user_id`, `agent_id`, or `run_id`) |
 | `GET` | `/memories/{memory_id}` | Get a specific memory |
@@ -259,43 +188,6 @@ The OSS REST server exposes the following endpoints. None use the `/v1/` prefix.
 | `POST` | `/search` | Search memories |
 | `POST` | `/reset` | Reset all memories |

-### Authentication
-
-| Method | Path | Description |
-|--------|------|-------------|
-| `GET` | `/auth/setup-status` | Returns `{needsSetup: bool}`. Open, no auth required |
-| `POST` | `/auth/register` | Register the first admin. Registration closes after the first admin is created; additional accounts are provisioned by the existing admin. |
-| `POST` | `/auth/login` | Exchange email and password for access and refresh JWTs |
-| `POST` | `/auth/refresh` | Exchange a refresh token for a new access token |
-| `GET` | `/auth/me` | Get the current authenticated user (JWT required) |
-| `PATCH` | `/auth/me` | Update the caller's name or email. 409 if the new email is already in use |
-| `POST` | `/auth/change-password` | Change the caller's password. 401 if the current password is wrong; new password must be at least 8 characters |
-
-### API keys
-
-All `/api-keys` endpoints require a JWT.
-
-| Method | Path | Description |
-|--------|------|-------------|
-| `GET` | `/api-keys` | List the caller's API keys |
-| `POST` | `/api-keys` | Create a new key; the full `m0sk_...` value is returned once |
-| `DELETE` | `/api-keys/{id}` | Revoke an API key |
-
-### Request logs
-
-| Method | Path | Description |
-|--------|------|-------------|
-| `GET` | `/requests?limit=N` | Recent API call log (JWT or admin key) |
-
-### Entities
-
-| Method | Path | Description |
-|--------|------|-------------|
-| `GET` | `/entities` | Distinct `user_id` / `agent_id` / `run_id` values with memory counts |
-| `DELETE` | `/entities/{entity_type}/{entity_id}` | Cascade-delete all memories for an entity; `entity_type` is `user`, `agent`, or `run` |
-
-The `/auth/*`, `/api-keys`, `/requests`, and `/entities` routes are new to the self-hosted server and primarily back the dashboard, but you can call them directly from your own tooling.
-
 ---

 ## Verify the feature is working
@@ -309,7 +201,7 @@ The `/auth/*`, `/api-keys`, `/requests`, and `/entities` routes are new to the s

 ## Best practices

-1. **Keep auth on:** Auth is enabled by default. Never set `AUTH_DISABLED=true` in production. If you rely on `ADMIN_API_KEY`, use a long value (16+ chars) or prefer per-user API keys.
+1. **Enable authentication:** Set `ADMIN_API_KEY` to secure all endpoints, or use an API gateway for more advanced schemes.
 2. **Use HTTPS:** Terminate TLS at your load balancer or reverse proxy.
 3. **Monitor uptime:** Track request rates, latency, and error codes per endpoint.
 4. **Version configs:** Keep environment files and Docker Compose definitions in source control.
@@ -15,15 +15,12 @@ Mem0 Open Source delivers the same adaptive memory engine as the platform, but p
 - **Extendable codebase**: Fork the repo, add providers, and ship custom automations.

 <Info>
-  Two ways to run Mem0 OSS: as a **library** inside your app (Python or Node), or as a **self-hosted server** with a dashboard, per-user API keys, and a request audit log.
+  Begin with the <Link href="/open-source/python-quickstart">Python quickstart</Link> (or the Node.js variant) to clone the repo, configure dependencies, and validate memory reads/writes locally.
 </Info>

 ## Choose your path

-<CardGroup cols={3}>
-  <Card title="Self-hosted setup" icon="rocket-launch" href="/open-source/setup">
-    Run `make bootstrap` to launch the server + dashboard, create an admin, and issue your first API key.
-  </Card>
+<CardGroup cols={2}>
  <Card title="Python Quickstart" icon="python" href="/open-source/python-quickstart">
    Bootstrap CLI and verify add/search loop.
  </Card>
@@ -44,6 +41,15 @@ Mem0 Open Source delivers the same adaptive memory engine as the platform, but p
  </Card>
 </CardGroup>

+<CardGroup cols={2}>
+  <Card title="Deploy with Docker Compose" icon="server" href="/open-source/features/rest-api">
+    Reference deployment with REST endpoints.
+  </Card>
+  <Card title="Use the REST API" icon="code" href="/open-source/features/rest-api">
+    Async add/search flows and automation.
+  </Card>
+</CardGroup>
+
 <Tip>
  Need a managed alternative? Compare hosting models in the <Link href="/platform/platform-vs-oss">Platform vs OSS guide</Link> or switch tabs to the Platform documentation.
 </Tip>
@@ -64,28 +70,20 @@ Mem0 Open Source delivers the same adaptive memory engine as the platform, but p
 ## Default components

 <Note>
-  **Library defaults** (when you `import` Mem0 and call `Memory()` directly):
+  Mem0 OSS works out of the box with sensible defaults:
  - LLM: OpenAI `gpt-5-mini` (via `OPENAI_API_KEY`)
  - Embeddings: OpenAI `text-embedding-3-small`
-  - Vector store: Local Qdrant at `/tmp/qdrant`
-  - History store: SQLite at `~/.mem0/history.db`
-  - Reranker: Disabled until configured
+  - Vector store: Local Qdrant instance storing data at `/tmp/qdrant`
+  - History store: SQLite database at `~/.mem0/history.db`
+  - Reranker: Disabled until you configure a provider

  Override any component with <Link href="/open-source/configuration">`Memory.from_config`</Link>.
 </Note>

-<Note>
-  **Self-hosted server defaults** (the `server/` Docker Compose stack):
-  - LLM: OpenAI `gpt-4.1-nano-2025-04-14` (override with `MEM0_DEFAULT_LLM_MODEL`)
-  - Embeddings: OpenAI `text-embedding-3-small` (override with `MEM0_DEFAULT_EMBEDDER_MODEL`)
-  - Vector store: Postgres + pgvector
-  - Bundled providers: `openai`, `anthropic`, `gemini` — switch from the Configuration page
-
-  See <Link href="/open-source/setup#supported-providers">Self-Hosted Setup</Link> for the full provider list and how to extend it.
-</Note>
-
 ## Keep going

+{/* DEBUG: verify CTA targets */}
+
 <CardGroup cols={2}>
  <Card
    title="Review Platform vs OSS"
@@ -100,3 +98,7 @@ Mem0 Open Source delivers the same adaptive memory engine as the platform, but p
    href="/open-source/python-quickstart"
  />
 </CardGroup>
+
+<Tip>
+  Need a managed alternative? Compare hosting models in the <Link href="/platform/platform-vs-oss">Platform vs OSS guide</Link> or switch tabs to the Platform documentation.
+</Tip>
@@ -1,218 +0,0 @@
---
-title: "Self-Hosted Setup"
-description: "Stand up the Mem0 REST server and dashboard in a few minutes — admin account, API keys, and a live audit log included."
-icon: "rocket-launch"
---
-
-The self-hosted bundle ships the REST API and a web dashboard together. Configure your LLM provider and secrets in a `.env` file, start the containers, then choose how to create your admin account: through the browser-based setup wizard, or from the command line.
-
-<Info>
-  **Use this page when…**
-  - You want a self-hosted Mem0 with a dashboard, not just the Python or Node library.
-  - You need per-user API keys and a request audit log for your team.
-  - You're upgrading from a pre-1.x server that relied on `ADMIN_API_KEY` or open endpoints.
-</Info>
-
-<Warning>
-  **Upgrading from 1.x?** Auth is now on by default. Deployments that ran with an empty `ADMIN_API_KEY` will return `401` on every protected endpoint until you either set `ADMIN_API_KEY`, register an admin through the wizard, or set `AUTH_DISABLED=true` for local development. See [Upgrade notes](#upgrade-notes) below.
-</Warning>
-
---
-
-## Prerequisites
-
- Docker and Docker Compose (the reference path).
- An `OPENAI_API_KEY` (or equivalent — the server reads the same component config as the library).
- A free port `8888` for the API and `3000` for the dashboard.
-
---
-
-## Configure the environment
-
-Copy `server/.env.example` to `server/.env` and fill in the required values. The server refuses to start if `JWT_SECRET` is unset once auth is enabled.
-
-| Variable | Required | Purpose |
-|---|---|---|
-| `OPENAI_API_KEY` | Yes | Default LLM and embedder provider. |
-| `JWT_SECRET` | Yes | Signs access and refresh tokens. Use a long random value. A missing secret causes auth endpoints to return `500`. |
-| `ADMIN_API_KEY` | Optional | Legacy shared admin key. Kept for back-compat; prefer per-user keys for new setups. |
-| `AUTH_DISABLED` | Optional | `true` turns off auth for local development only. Never enable in production. |
-| `DASHBOARD_URL` | Optional | Origin the API accepts for CORS. Defaults to `http://localhost:3000`. Set this when you front the dashboard on a custom domain. |
-| `POSTGRES_*` | Optional | Override the bundled Postgres / pgvector connection. |
-
-<Tip>
-  Generate a `JWT_SECRET` with `openssl rand -base64 48` or `python -c "import secrets; print(secrets.token_urlsafe(48))"`.
-</Tip>
-
---
-
-## Start the stack
-
-Pick the path that fits your workflow.
-
-### Browser-first (setup wizard)
-
-```bash
-cd server
-make up
-```
-
-This starts the containers and runs database migrations. The REST API listens on `http://localhost:8888` and the dashboard on `http://localhost:3000`.
-
-Open `http://localhost:3000` — since no admin account exists yet, the dashboard redirects to the one-time setup wizard at `/setup`. See [Run the setup wizard](#run-the-setup-wizard) below.
-
-### Agent-first (command line)
-
-First, set `OPENAI_API_KEY` (or `ANTHROPIC_API_KEY` / `GOOGLE_API_KEY`) in `server/.env`. `make bootstrap` does not prompt for it, and the runtime test will fail without a valid provider key.
-
-```bash
-cd server
-make bootstrap
-```
-
-`make bootstrap` starts the same containers, then automatically creates the admin account and generates the first API key via the CLI. The admin credentials and API key are printed to your terminal — no browser required.
-
-You can override the generated credentials:
-
-```bash
-make bootstrap EMAIL=admin@company.com PASSWORD='strong-password' NAME='Admin'
-```
-
-Because `make bootstrap` already creates the admin, the setup wizard is skipped. Opening `http://localhost:3000` takes you straight to the login page.
-
-<Tip>
-  For machine-readable output (useful in CI), run `OUTPUT=json make seed` after `make up`.
-</Tip>
-
---
-
-## Run the setup wizard
-
-<Info>
-  This section applies to the **browser-first** path (`make up`). If you used `make bootstrap`, the admin and API key were already created — skip ahead to [What the dashboard gives you](#what-the-dashboard-gives-you).
-</Info>
-
-On a fresh install the dashboard redirects to `/setup`. Each step submits on Enter.
-
-**1. Create the admin account.** Name, email, password. This account becomes the first admin. Registration closes after the first admin is created; additional accounts are provisioned by the existing admin.
-
-**2. Review the effective config.** Read-only display of the LLM and embedder the server is running with, sourced from your environment. If anything is wrong here, stop the stack, fix the `.env`, and restart — the dashboard intentionally does not let you change provider secrets at runtime.
-
-**3. Generate your first API key.** The full `m0sk_...` value is shown **once**. Copy it immediately — the server only stores the prefix and a bcrypt hash.
-
-**4. Tell us your use case.** Pick a preset or describe your use case in a few words. Mem0 generates custom instructions that tell the memory system what to prioritize. You can edit the instructions before saving, or skip this step entirely.
-
-**5. Test the key.** A ready-to-paste `curl` exercises `POST /memories` against your new key. Click "Run Test" to fire it from the browser. Success lands you in the dashboard at `/dashboard/requests`, where you'll see the test call in the live audit log.
-
---
-
-## What the dashboard gives you
-
-| Page | What it does |
-|---|---|
-| **Requests** | Default landing page. Live audit log of every API call, with status, latency, and auth mode. |
-| **Memories** | Browse and search the memories your server has stored. |
-| **Entities** | Distinct `user_id` / `agent_id` / `run_id` values with memory counts and cascade-delete. |
-| **API Keys** | Issue per-user keys, label them, and revoke. |
-| **Configuration** | Runtime override for LLM and embedder. Changes persist to the app database and reapply on restart, layered over the values from your `.env`. |
-| **Settings** | Account and session controls. |
-
-For the underlying endpoints (including `/auth/*`, `/api-keys`, `/requests`, `/entities`), see the [REST API reference](/open-source/features/rest-api).
-
---
-
-## Supported providers
-
-The shipped container bundles the Python packages for:
-
- **LLMs** — `openai`, `anthropic`, `gemini`
- **Embedders** — `openai`, `gemini`
-
-The Configuration page and `POST /configure` only accept providers from these lists. Anything else returns a 400 up front instead of failing at the first memory write.
-
-**To add another provider**, for example to run embeddings locally with `sentence-transformers`:
-
-1. Add the package to `server/requirements.txt` (e.g. `sentence-transformers>=2.0`).
-2. Extend `BUNDLED_LLM_PROVIDERS` or `BUNDLED_EMBEDDER_PROVIDERS` in `server/main.py`.
-3. Rebuild the image (`make up` or `docker compose build`).
-
-Heavy providers (`sentence-transformers` pulls in PyTorch, ~2 GB) are intentionally kept out of the default image.
-
---
-
-## Upgrade notes
-
-### Upgrading from a pre-auth build
-
-Previous self-hosted builds allowed open access when `ADMIN_API_KEY` was unset. This build enables auth by default. After pulling the new image, pick **one**:
-
-1. **Fastest, zero client changes** — set `ADMIN_API_KEY` to a long random value (16+ characters). Existing clients that send `X-API-Key: <your-key>` keep working unchanged.
-2. **Recommended for teams** — visit `http://<host>:3000`, run the setup wizard, and switch clients to per-user API keys. You get the audit log and revocation for free.
-3. **Local development only** — set `AUTH_DISABLED=true`. The server logs a warning on every boot. Never use this in production.
-
-The server prints an unmissable startup banner when it detects the "upgraded but not configured" state so you know exactly which option to pick.
-
-### Other changes in this release
-
- Dashboard ships as a second container in the reference Compose stack, wired to the API over the internal Docker network.
- New tables: `users`, `api_keys`, `request_logs`. Alembic handles the migration automatically on first boot.
-
-If `alembic upgrade head` fails on first boot, see the [Troubleshooting](#troubleshooting) section below.
-
---
-
-## Troubleshooting
-
-<AccordionGroup>
-  <Accordion title="Port 3000 or 8888 is already in use">
-    Find the owning process on either port:
-    ```bash
-    lsof -iTCP:3000 -sTCP:LISTEN
-    lsof -iTCP:8888 -sTCP:LISTEN
-    ```
-    Kill it (`kill <PID>`) or change the host port in `server/docker-compose.yaml`.
-  </Accordion>
-
-  <Accordion title="JWT_SECRET is required">
-    The server refuses to start without one. Generate a secret and add it to `server/.env`:
-    ```bash
-    echo "JWT_SECRET=$(openssl rand -base64 48)" >> server/.env
-    ```
-    `AUTH_DISABLED=true` is valid for local dev only, never production.
-  </Accordion>
-
-  <Accordion title=".env changes aren't applied after editing">
-    `docker compose restart` does not re-read `env_file`. To pick up changes:
-    ```bash
-    cd server && docker compose up -d --force-recreate mem0
-    # or
-    cd server && make up
-    ```
-  </Accordion>
-
-  <Accordion title="Provider returns 401 (bad API key)">
-    Provider credential errors surface as `502 Upstream provider error.`. Check `docker compose logs mem0` for the full trace, then fix the key on the Configuration page and hit **Save**.
-  </Accordion>
-
-  <Accordion title="Alembic migrations fail on startup">
-    Inspect the logs:
-    ```bash
-    docker compose logs mem0 | grep -i alembic
-    ```
-    If the database is unrecoverable, reset the volume (**this destroys all memories and users**):
-    ```bash
-    docker compose down -v
-    ```
-  </Accordion>
-</AccordionGroup>
-
---
-
-<CardGroup cols={2}>
-  <Card title="REST API reference" icon="code" href="/open-source/features/rest-api">
-    Endpoint tables, auth modes, and example requests.
-  </Card>
-  <Card title="Configure components" icon="sliders" href="/open-source/configuration">
-    Swap LLMs, embedders, vector stores, and rerankers.
-  </Card>
-</CardGroup>
@@ -53,7 +53,7 @@ For detailed per-client instructions, see the [Mem0 MCP Quickstart](/platform/me

 ## Available tools

-The MCP server exposes 11 memory tools to your AI client:
+The MCP server exposes 9 memory tools to your AI client:

 | Tool | Purpose |
 |------|---------|
@@ -66,8 +66,6 @@ The MCP server exposes 11 memory tools to your AI client:
 | `delete_entities` | Remove user/agent/app entities |
 | `get_memory` | Retrieve single memory by ID |
 | `list_entities` | View stored entities |
-| `list_events` | List memory operation events with filters and pagination |
-| `get_event_status` | Check the status of an async memory operation by `event_id` |

 ## How it works

@@ -1,191 +0,0 @@
---
-title: Memory Decay
-description: "Boost recently-used memories and gently dampen stale ones at search time, without filtering anything out."
---
-
-# Memory Decay
-
-Older memories drift in relevance at different speeds. A user's coffee order matters every morning; a one-off project name from last quarter rarely matters again. Memory Decay makes that intuition explicit at search time: every time a memory is returned in a search it gets a small reinforcement, and memories that haven't been touched in a while have their ranking score gently dampened.
-
-It is **a soft ranking bias, never a filter.** Decay never zeroes a candidate out — at worst it scales its score by `0.3×`. Anything that would have surfaced without decay can still surface with decay on, just with a different ranking among similarly-scored results.
-
-<Info>
-  **Use Memory Decay when…**
-  - Search results are crowded with old facts the user no longer cares about.
-  - You want recently-used memories to drift to the top automatically — without writing custom scoring logic.
-  - You want this preference applied per project so cohorts can be compared side-by-side.
-</Info>
-
-<Warning>
-  Memory Decay is **opt-in per project** and **off by default**. Search behavior is bit-identical to today until you turn it on. The toggle applies to v3 search only.
-</Warning>
-
-## How it works
-
-Every memory carries a small piece of bookkeeping: when was it last retrieved, and how often. Memory Decay turns that history into a *scaling factor* in the range `0.3×` to `1.5×` and multiplies it into the ranking score at search time.
-
-| Memory state | Scaling factor | Ranking effect |
-|---|---|---|
-| Just accessed | ≈ **1.5×** | Strong boost |
-| Touched today | 1.2 – 1.4× | Mild boost |
-| Idle for a few days | 0.6 – 1.0× | Mild dampening |
-| Idle for weeks | 0.4 – 0.6× | Stronger dampening |
-| Idle for many months / years | ≈ **0.3×** | Floor — never lower |
-
-The bounds matter: `0.3` is the floor and `1.5` is the ceiling, so decay can meaningfully reorder candidates without ever dominating the underlying relevance score.
-
-At search time the pipeline:
-
-1. Widens the candidate pool (`top_k × 3`, with a floor of 50) so reordering has room.
-2. Multiplies each candidate's score by its scaling factor.
-3. Sorts on the unclamped product so the full `0.3×–1.5×` range can rearrange candidates.
-4. Returns the public `score` clamped to `[0, 1]` so the API contract is preserved.
-5. Truncates to the `top_k` you requested.
-6. Records a fire-and-forget reinforcement against each returned memory — its access history grows by one, capped at the most recent 20 touches.
-
-Memories created before decay was enabled don't yet have an access history. They use a sensible fallback: their `updated_at` is treated as a single past touch, so the same scale above applies based on how stale that update is — a recently-updated legacy memory enters near the neutral band, a long-stale one sits closer to the floor. Once surfaced in a search after decay is on, they accumulate access history naturally and behave like any other memory.
-
-## Configure access
-
- Set `MEM0_API_KEY` in your environment, or pass it to the SDK constructor.
- Initialize the client with the organization and project you want to scope to.
-
-The toggle lives on the project. You enable decay by patching the project's `decay` field; everything else — your `add` calls, your `search` calls, your application code — stays exactly the same.
-
-## Enable decay for a project
-
-### 1. Turn the flag on
-
-The toggle is exposed on the standard project-update endpoint, the same place where `multilingual` and `custom_categories` live.
-
-<CodeGroup>
-```python Python
-client.project.update(decay=True)
-```
-
-```javascript JavaScript
-await client.project.update({ decay: true });
-```
-
-```bash cURL
-curl -X PATCH https://api.mem0.ai/api/v1/orgs/organizations/$ORG_ID/projects/$PROJECT_ID/ \
-  -H "Authorization: Token $MEM0_API_KEY" \
-  -H "Content-Type: application/json" \
-  -d '{"decay": true}'
-```
-
-```json Response
-{ "message": "Updated decay" }
-```
-</CodeGroup>
-
-### 2. Confirm the state
-
-`decay` is returned on every project read. To fetch only this field, use `?fields=decay`.
-
-<CodeGroup>
-```python Python
-response = client.project.get(fields=["decay"])
-print(response["decay"])
-```
-
-```javascript JavaScript
-const response = await client.project.get({ fields: ["decay"] });
-console.log(response.decay);
-```
-
-```bash cURL
-curl "https://api.mem0.ai/api/v1/orgs/organizations/$ORG_ID/projects/$PROJECT_ID/?fields=decay" \
-  -H "Authorization: Token $MEM0_API_KEY"
-```
-
-```json Response
-{ "decay": true }
-```
-</CodeGroup>
-
-### 3. Turn it back off
-
-The toggle is fully reversible. Setting it to `false` immediately restores the pre-decay ranking; nothing about your stored memories is modified or lost.
-
-<CodeGroup>
-```python Python
-client.project.update(decay=False)
-```
-
-```javascript JavaScript
-await client.project.update({ decay: false });
-```
-
-```bash cURL
-curl -X PATCH https://api.mem0.ai/api/v1/orgs/organizations/$ORG_ID/projects/$PROJECT_ID/ \
-  -H "Authorization: Token $MEM0_API_KEY" \
-  -H "Content-Type: application/json" \
-  -d '{"decay": false}'
-```
-</CodeGroup>
-
-<Note>
-  The toggle is idempotent. Re-applying the same value is a no-op, and access history accumulated while decay was on is preserved if you flip it back on later.
-</Note>
-
-## What changes when decay is on
-
- **Search ranking reorders.** A relevant memory you reinforced an hour ago will tend to outrank an equally-relevant memory that was last touched a month ago.
- **The candidate pool over-fetches** to give the scaling factor room to reorder. You still get exactly the `top_k` you requested, but the items returned can come from a deeper slice of the pre-decay ranking than before.
- **The public `score` field stays in `[0, 1]`.** Even when the internal product exceeds 1, the field returned to the client is clamped, so existing assertions and downstream UI logic continue to work.
-
-## What stays the same
-
- **Public API shape** — every endpoint accepts the same parameters and returns the same fields. You don't touch your client code.
- **Threshold semantics on the request side** — your `threshold` is still applied during candidate selection.
- **Memory creation and storage** — every new memory still lands the same way. Decay is a search-time concern.
- **Per-memory data** — categories, metadata, timestamps, embeddings: untouched.
-
-<Warning>
-  Because the scaling factor is applied *after* the threshold filter has already run, an item that passed the request `threshold` can come back with a public `score` slightly below it (a stale candidate dampened by `0.3×`). This is intentional — decay is a soft bias, not a filter. If you require a hard `score >= threshold` invariant on the response, filter client-side after the call.
-</Warning>
-
-## Lifecycle of a memory under decay
-
-| Stage | Scaling factor | Effect |
-|---|---|---|
-| Just added | ≈ 1.5× | Strong boost — fresh facts surface easily. |
-| Reinforced on a recent search | 1.2 – 1.5× | Sustains its boost for the next several searches. |
-| Idle for a few days | 0.6 – 1.0× | Falls back into the neutral band. |
-| Idle for weeks | 0.4 – 0.6× | Mild dampening — can still surface for strong matches. |
-| Pre-decay legacy memory (no access history) | 0.3 – 1.0× | Falls back to `updated_at`: recently-updated entries land near 1.0×, long-stale entries approach the 0.3× floor. |
-
-The reinforcement is bounded: each memory tracks at most the last 20 access timestamps, so the boost stays well-behaved no matter how many times a memory is retrieved.
-
-## FAQ
-
-**Will decay ever drop a result that would otherwise surface?**
-No. The floor is `0.3×` — the scaling factor can dampen a score, never zero it. Threshold filtering happens *before* decay, so any candidate that cleared the threshold is in the pool decay reorders.
-
-**Why is the public score sometimes below my requested threshold?**
-The threshold is applied to the candidate pool pre-decay; the scaling factor then reshapes scores in the `0.3×–1.5×` band. A stale-but-relevant candidate can come back with a final score slightly under your threshold by design — the candidate stays visible but visibly dampened. Filter client-side if you need a hard floor on the response.
-
-**Does decay change how I add memories?**
-No. The `client.add(...)` path is unchanged. Decay is a search-time ranking adjustment.
-
-**What if I had memories before turning decay on?**
-They use a fallback: the memory's `updated_at` is treated as a single historical touch, so the same scaling applies based on how stale that update is — a recently-updated legacy memory enters near the neutral band (~1.0×), a long-stale one closer to the floor (~0.3×). Once retrieved they accumulate access history and behave like any other memory.
-
-**Can I tune how aggressively decay scales scores?**
-Not in this version. The current scaling is calibrated to be conservative — wide enough to meaningfully reorder candidates, narrow enough to never dominate the underlying relevance score. Per-project tuning is on the roadmap.
-
-**Can I see the scaling factor per result?**
-Internal scoring details are persisted on the search Event for support and debugging. They aren't exposed in the public response by design — the response surface stays a single `score` field.
-
-**Does decay interact with reranking?**
-Yes — they layer cleanly. The reranker produces a richer relevance score; decay then biases that score by reinforcement history before final truncation to `top_k`.
-
-## What's next
-
-This release is deliberately the simplest version of decay we could ship — every memory contributes to ranking through its access history alone, so the signal can be evaluated in isolation. On the roadmap:
-
- **Category-aware weighting.** A fact tagged `health` will be able to carry more weight than a passing observation tagged `misc`, so important categories don't get dampened the same way as noise.
- **Auto-tuning per project.** Project-scoped automatic adjustment of how aggressively decay scales scores, based on observed access patterns — replacing the fixed scaling band with one that fits your workload.
-
-Both extensions are forward-compatible — no migration on your side will be needed when they ship.
@@ -7,10 +7,10 @@ estimatedTime: "~2 minutes"

 <Info>
  **Prerequisites**
-  - Mem0 Platform account (<a href="https://app.mem0.ai?utm_source=oss&utm_medium=platform-mem0-mcp" rel="nofollow">Sign up here</a>)
-  - API key (<a href="https://app.mem0.ai/settings/api-keys?utm_source=oss&utm_medium=platform-mem0-mcp" rel="nofollow">Get one from dashboard</a>)
+  - Mem0 Platform account (<a href="https://app.mem0.ai" rel="nofollow">Sign up here</a>)
+  - API key (<a href="https://app.mem0.ai/settings/api-keys" rel="nofollow">Get one from dashboard</a>)
  - Node.js 14+ (for npx)
-  - An MCP-compatible client (Claude, Claude Code, Codex, Cursor, Windsurf, VS Code, OpenCode)
+  - An MCP-compatible client (Claude, Claude Code, Cursor, Windsurf, VS Code, OpenCode)
 </Info>

 ## What is Mem0 MCP?
@@ -46,8 +46,6 @@ The MCP server exposes these memory tools to your AI client:
 | `delete_all_memories` | Bulk delete all memories in scope |
 | `delete_entities` | Delete a user/agent/app/run entity and its memories |
 | `list_entities` | Enumerate users/agents/apps/runs stored in Mem0 |
-| `list_events` | List memory operation events with filters and pagination |
-| `get_event_status` | Check the status of an async memory operation by `event_id` |

 ---

@@ -88,33 +86,6 @@ You can also configure individual clients:
    ```
  </Accordion>

-  <Accordion title="Codex">
-    **Direct MCP (fastest, MCP only).** Codex reads MCP servers from `~/.codex/config.toml` as TOML (not JSON). Add:
-
-    ```toml
-    [mcp_servers.mem0]
-    url = "https://mcp.mem0.ai/mcp"
-    bearer_token_env_var = "MEM0_API_KEY"
-    ```
-
-    Export `MEM0_API_KEY` in the shell you launch Codex from, then restart Codex. `codex mcp add` only supports stdio servers, so HTTP servers must be added via `config.toml` directly — or via the **Plugins → Connect to a custom MCP → Streamable HTTP** UI in the Codex app.
-
-    <Note>
-      Codex uses the server name `mem0` (not `mem0-mcp` like the other clients on this page) so it matches the name the bundled plugin registers if you ever sideload it later.
-    </Note>
-
-    **Sideloaded plugin (full experience).** If you want the memory protocol skill, Mem0 SDK skill, and opt-in lifecycle hooks alongside the MCP server, sideload the plugin from a clone of `mem0ai/mem0`. The repo ships a marketplace manifest at `.agents/plugins/marketplace.json`, so you can register it with one CLI call:
-
-    ```bash
-    git clone https://github.com/mem0ai/mem0.git ~/codex-plugins/mem0-source
-    codex plugin marketplace add ~/codex-plugins/mem0-source
-    ```
-
-    Then run `codex` and `/plugins`, browse the **Mem0 Plugins** marketplace, and install **Mem0**. Don't combine this with the Direct MCP setup above — the sideloaded plugin auto-registers `mem0` via `.codex-mcp.json`, so a manual `[mcp_servers.mem0]` block would create a duplicate.
-
-    See the [Codex integration guide](/integrations/codex) for full details, lifecycle-hook setup, and management commands (`codex plugin marketplace upgrade` / `remove`).
-  </Accordion>
-
  <Accordion title="Cursor">
    ```bash
    npx mcp-add \
@@ -192,7 +163,7 @@ Agent: Updated your project status successfully.
 ```

 <Info icon="check">
-  If you get "Connection failed", ensure you have a valid API key from <a href="https://app.mem0.ai/settings/api-keys?utm_source=oss&utm_medium=platform-mem0-mcp" rel="nofollow">Mem0 Dashboard</a>.
+  If you get "Connection failed", ensure you have a valid API key from <a href="https://app.mem0.ai/settings/api-keys" rel="nofollow">Mem0 Dashboard</a>.
 </Info>

 ---
@@ -200,7 +171,7 @@ Agent: Updated your project status successfully.
 ## Quick Recovery

 - **"Connection refused"** → Check your internet connection and ensure the MCP client is correctly configured
- **"Invalid API key"** → Get a new key from <a href="https://app.mem0.ai/settings/api-keys?utm_source=oss&utm_medium=platform-mem0-mcp" rel="nofollow">Mem0 Dashboard</a>
+- **"Invalid API key"** → Get a new key from <a href="https://app.mem0.ai/settings/api-keys" rel="nofollow">Mem0 Dashboard</a>
 - **"npx command not found"** → Install Node.js from [nodejs.org](https://nodejs.org)

 ---
@@ -60,7 +60,7 @@ Mem0 is the memory engine that keeps conversations contextual so users never rep
  <Card title="Connect Integrations" icon="plug" href="/integrations">
    LangChain, CrewAI, Vercel AI SDK.
  </Card>
-  <Card title="Monitor in the Dashboard" icon="presentation" href="https://app.mem0.ai/login?utm_source=oss&utm_medium=platform-overview">
+  <Card title="Monitor in the Dashboard" icon="presentation" href="https://app.mem0.ai/login">
    Track activity and manage workspaces.
  </Card>
 </CardGroup>
@@ -150,7 +150,7 @@ Mem0 offers two powerful ways to add memory to your AI applications. Choose base
  <Card
    title="Try Platform Free"
    icon="rocket"
-    href="https://app.mem0.ai/login?utm_source=oss&utm_medium=platform-vs-oss"
+    href="https://app.mem0.ai/login"
  >
    Sign up and test the Platform with our free tier. No credit card required.
  </Card>
@@ -9,7 +9,7 @@ Get started with Mem0 Platform's hosted API in under 5 minutes. This guide shows

 ## Prerequisites

- Mem0 Platform account (<a href="https://app.mem0.ai?utm_source=oss&utm_medium=platform-quickstart" rel="nofollow">Sign up here</a>)
+- Mem0 Platform account (<a href="https://app.mem0.ai" rel="nofollow">Sign up here</a>)
 - API key (<a href="https://app.mem0.ai/dashboard/settings?tab=api-keys&subtab=configuration" rel="nofollow">Get one from dashboard</a>)
 - Python 3.10+, Node.js 14+, or cURL

@@ -12,7 +12,7 @@ We follow the llms.txt standard:
 - [llms.txt](https://docs.mem0.ai/llms.txt)

 <CardGroup cols={2}>
-  <Card title="Get an API Key" icon="key" href="https://app.mem0.ai/login?utm_source=oss&utm_medium=vibecoding">
+  <Card title="Get an API Key" icon="key" href="https://app.mem0.ai/login">
    Sign up for Mem0 Platform and start building
  </Card>
  <Card title="Quickstart" icon="rocket" href="/platform/quickstart">
@@ -22,41 +22,19 @@ We follow the llms.txt standard:

 ## Agent Skills

-Mem0 ships two kinds of skills for AI coding assistants. Both work with Claude Code, Codex, Cursor, Windsurf, OpenCode, OpenClaw, and any assistant that supports the skills standard.
-
-### Reference skills — always on
-
-Teach your assistant Mem0's SDK surface so it writes correct code in everyday development:
+Teach your coding assistant how to build with Mem0:

 ```bash
 npx skills add https://github.com/mem0ai/mem0 --skill mem0
-npx skills add https://github.com/mem0ai/mem0 --skill mem0-cli
-npx skills add https://github.com/mem0ai/mem0 --skill mem0-vercel-ai-sdk
 ```

- `mem0` — Python and TypeScript SDKs (Platform + OSS), plus framework integrations (LangChain, CrewAI, OpenAI Agents, LangGraph, LlamaIndex, etc.)
- `mem0-cli` — terminal workflows for the `mem0` CLI (both Node and Python builds)
- `mem0-vercel-ai-sdk` — `@mem0/vercel-ai-provider` and `createMem0`
-
-### Pipeline skills — run on demand
-
-Let your assistant execute an end-to-end workflow in an existing repo. Invoked as slash commands:
-
-```bash
-npx skills add https://github.com/mem0ai/mem0 --skill mem0-integrate
-npx skills add https://github.com/mem0ai/mem0 --skill mem0-test-integration
-```
-
- `/mem0-integrate` — wire Mem0 into an existing repository using a goal-driven, test-first pipeline. Detects the stack, asks whether to use Platform or OSS, writes failing tests first, and keeps the integration additive and feature-flagged.
- `/mem0-test-integration` — verify what `/mem0-integrate` produced. Runs the repo's native test suite and a real end-to-end smoke flow against your API key, then produces a scorecard.
-
-See the [skills index](https://github.com/mem0ai/mem0/tree/main/skills) for the full catalog.
+Works with Claude Code, Cursor, Windsurf, and any assistant that supports skills. Once installed, your assistant understands Mem0's full API, framework integrations, and common patterns.

 ## MCP Server Setup

 Connect Claude, Claude Code, Cursor, Windsurf, VS Code, OpenCode, or any MCP-compatible client to Mem0.

-Get your API key from <a href="https://app.mem0.ai?utm_source=oss&utm_medium=vibecoding" rel="nofollow">app.mem0.ai</a>, then add Mem0 MCP with a single command:
+Get your API key from <a href="https://app.mem0.ai" rel="nofollow">app.mem0.ai</a>, then add Mem0 MCP with a single command:

 ```bash
 npx mcp-add \
@@ -91,7 +91,7 @@ export const Assistant = () => {
            </button>
            <GithubButton url="https://github.com/mem0ai/mem0/tree/main/examples" />

-            <Link href={"https://app.mem0.ai/?utm_source=oss&utm_medium=example-mem0-demo"} target="_blank" className="py-1 ml-2 px-4 font-semibold dark:bg-zinc-100 dark:hover:bg-zinc-200 bg-zinc-800 text-white rounded-full hover:bg-zinc-900 dark:text-[#475569]">
+            <Link href={"https://app.mem0.ai/"} target="_blank" className="py-1 ml-2 px-4 font-semibold dark:bg-zinc-100 dark:hover:bg-zinc-200 bg-zinc-800 text-white rounded-full hover:bg-zinc-900 dark:text-[#475569]">
              Playground
            </Link>
          </div>
@@ -174,7 +174,7 @@ export const Thread: FC<ThreadProps> = ({
                    <GithubButton url="https://github.com/mem0ai/mem0/tree/main/examples" className="w-full rounded-lg h-9 pl-2 text-sm font-semibold bg-zinc-800 dark:border-zinc-800 dark:text-white text-white hover:bg-zinc-900" text="View on Github" />

                    <Link
-                      href={"https://app.mem0.ai/?utm_source=oss&utm_medium=example-mem0-demo"}
+                      href={"https://app.mem0.ai/"}
                      target="_blank"
                      className="py-2 px-4 w-full rounded-lg h-9 pl-3 text-sm font-semibold dark:bg-zinc-800 dark:hover:bg-zinc-700 bg-zinc-800 text-white hover:bg-zinc-900 dark:text-white"
                    >
@@ -2,7 +2,7 @@

 Add persistent long-term memory to your [NemoClaw](https://docs.nvidia.com/nemoclaw/latest/get-started/quickstart.html) OpenClaw agent using the `@mem0/openclaw-mem0` plugin.

-> **Note:** This plugin requires **Mem0 Platform mode** (i.e., a Mem0 API key from [app.mem0.ai](https://app.mem0.ai?utm_source=oss&utm_medium=example-nemoclaw)). Open-source mode is not supported in NemoClaw sandboxes because the sandbox proxy blocks `/v1/embeddings` requests required by the open-source backend. See [Known Limitations](#known-limitations) for details.
+> **Note:** This plugin requires **Mem0 Platform mode** (i.e., a Mem0 API key from [app.mem0.ai](https://app.mem0.ai)). Open-source mode is not supported in NemoClaw sandboxes because the sandbox proxy blocks `/v1/embeddings` requests required by the open-source backend. See [Known Limitations](#known-limitations) for details.

 ## Prerequisites

@@ -15,7 +15,7 @@ Add persistent long-term memory to your [NemoClaw](https://docs.nvidia.com/nemoc
 **Accounts required:**

 - **NVIDIA** — sign up at [build.nvidia.com](https://build.nvidia.com), generate an API key at [build.nvidia.com/settings/api-keys](https://build.nvidia.com/settings/api-keys) (starts with `nvapi-`)
- **Mem0** — sign up at [app.mem0.ai](https://app.mem0.ai?utm_source=oss&utm_medium=example-nemoclaw), generate an API key from the dashboard (starts with `m0-`)
+- **Mem0** — sign up at [app.mem0.ai](https://app.mem0.ai), generate an API key from the dashboard (starts with `m0-`)

 **Supported platforms:** Ubuntu 22.04+, macOS (via Docker), Windows (WSL 2 + Docker)

@@ -269,4 +269,4 @@ Then re-run `nemoclaw onboard`.
 - [Mem0 Documentation](https://docs.mem0.ai)
 - [NemoClaw Documentation](https://docs.nvidia.com/nemoclaw/latest/get-started/quickstart.html)
 - [`@mem0/openclaw-mem0` on npm](https://www.npmjs.com/package/@mem0/openclaw-mem0)
- [Mem0 Dashboard](https://app.mem0.ai?utm_source=oss&utm_medium=example-nemoclaw)
+- [Mem0 Dashboard](https://app.mem0.ai)
@@ -4,7 +4,7 @@ import { zodResponsesFunction } from "openai/helpers/zod";
 import { z } from "zod";

 const mem0Config = {
-    apiKey: process.env.MEM0_API_KEY, // GET THIS API KEY FROM MEM0 (https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=example-openai-inbuilt-tools)
+    apiKey: process.env.MEM0_API_KEY, // GET THIS API KEY FROM MEM0 (https://app.mem0.ai/dashboard/api-keys)
    user_id: "sample-user",
 };

@@ -1,6 +1,6 @@
 {
  "name": "mem0",
-  "version": "0.1.2",
+  "version": "0.1.0",
  "description": "Mem0 memory layer for AI applications. Add persistent memory, personalization, and semantic search to Claude workflows using the Mem0 Platform MCP server.",
  "author": {
    "name": "Mem0",
@@ -1,7 +1,7 @@
 {
  "mcpServers": {
    "mem0": {
-      "url": "https://mcp.mem0.ai/mcp",
+      "url": "https://mcp.mem0.ai/mcp/",
      "bearer_token_env_var": "MEM0_API_KEY"
    }
  }
@@ -1,6 +1,6 @@
 {
  "name": "mem0",
-  "version": "0.1.1",
+  "version": "0.1.0",
  "description": "Mem0 memory layer for AI applications. Add persistent memory, personalization, and semantic search to Codex workflows using the Mem0 Platform MCP server.",
  "author": {
    "name": "Mem0",
@@ -1,6 +1,6 @@
 {
  "name": "mem0",
-  "version": "0.1.1",
+  "version": "0.1.0",
  "description": "Mem0 memory layer for AI applications. Add persistent memory, personalization, and semantic search using the Mem0 Platform MCP server.",
  "author": {
    "name": "Mem0",
@@ -6,8 +6,8 @@ Add persistent memory to your AI workflows. Store, retrieve, and manage memories

 > **You must complete this step before installing the plugin.**

-1. Sign up at [app.mem0.ai](https://app.mem0.ai?utm_source=oss&utm_medium=mem0-plugin-readme) if you haven't already
-2. Go to [app.mem0.ai/dashboard/api-keys](https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=mem0-plugin-readme)
+1. Sign up at [app.mem0.ai](https://app.mem0.ai) if you haven't already
+2. Go to [app.mem0.ai/dashboard/api-keys](https://app.mem0.ai/dashboard/api-keys)
 3. Click **Create API Key** and copy the key (starts with `m0-`)
 4. Add it to your shell profile:

@@ -49,62 +49,62 @@ This installs the full plugin including the MCP server, lifecycle hooks (automat

 ### Codex

-**Option A — Direct MCP** (fastest, MCP only):
+**Option A — Repo marketplace** (recommended for teams):

-Codex reads MCP servers from `~/.codex/config.toml` as TOML. Add:
+Add the plugin marketplace to your repo root (already included in this repository):

-```toml
-[mcp_servers.mem0]
-url = "https://mcp.mem0.ai/mcp"
-bearer_token_env_var = "MEM0_API_KEY"
+```
+.agents/plugins/marketplace.json
 ```

-Export `MEM0_API_KEY` in your shell and restart Codex. `codex mcp add` only supports stdio servers, so HTTP servers like Mem0's must be added via `config.toml` directly (or via the **Plugins → Connect to a custom MCP → Streamable HTTP** UI in the Codex app).
+Then in Codex, browse the repo's plugin directory and install Mem0.

-**Option B — Sideload the plugin** (full experience: MCP + skills + opt-in hooks):
+**Option B — Personal marketplace**:

-Clone the repo and register the bundled marketplace with one CLI call:
+Add to `~/.agents/plugins/marketplace.json`:

-```bash
-git clone https://github.com/mem0ai/mem0.git ~/codex-plugins/mem0-source
-codex plugin marketplace add ~/codex-plugins/mem0-source
+```json
+{
+  "name": "mem0-plugins",
+  "interface": {
+    "displayName": "Mem0 Plugins"
+  },
+  "plugins": [
+    {
+      "name": "mem0",
+      "source": {
+        "source": "local",
+        "path": "/path/to/mem0/mem0-plugin"
+      },
+      "policy": {
+        "installation": "AVAILABLE",
+        "authentication": "ON_INSTALL"
+      },
+      "category": "Productivity"
+    }
+  ]
+}
 ```

-This points Codex at the repo's `.agents/plugins/marketplace.json`, which references `mem0-plugin/` as the local source. Restart Codex, run `/plugins`, and install **Mem0** from the **Mem0 Plugins** marketplace.
+**Option C — Manual MCP configuration**:

-> **Don't combine with Option A.** The plugin manifest auto-registers `mem0` as an MCP server via `mem0-plugin/.codex-mcp.json` — adding a manual `[mcp_servers.mem0]` block would duplicate the registration.
+Add to your Codex MCP config:

-**Optional — enable lifecycle hooks.** Codex doesn't auto-wire hooks from plugin manifests; it only reads `~/.codex/hooks.json` (or `<repo>/.codex/hooks.json`) ([docs](https://developers.openai.com/codex/hooks)). Run the bundled installer once to merge Mem0's entries:
-
-```bash
-python3 ~/codex-plugins/mem0-source/mem0-plugin/scripts/install_codex_hooks.py
+```json
+{
+  "mcpServers": {
+    "mem0": {
+      "type": "http",
+      "url": "https://mcp.mem0.ai/mcp/",
+      "headers": {
+        "Authorization": "Token ${MEM0_API_KEY}"
+      }
+    }
+  }
+}
 ```

-This merges three entries into `~/.codex/hooks.json` with absolute paths pointing into your clone:
-
-| Event | What it does |
-|-------|--------------|
-| `SessionStart` | Loads prior memories as bootstrap context |
-| `UserPromptSubmit` | Injects relevant memories into the prompt |
-| `Stop` | Reminds the agent to persist learnings at turn end |
-
-Re-running the installer is idempotent (replaces the Mem0 entries rather than duplicating) and preserves any other hooks you have. To remove: `python3 .../install_codex_hooks.py --uninstall`. If you move or delete the clone directory, re-run the installer from the new location — the hooks file stores absolute paths.
-
-Codex hooks also require the `codex_hooks` feature flag in `~/.codex/config.toml`:
-
-```toml
-[features]
-codex_hooks = true
-```
-
-The installer prints a reminder if the flag isn't set. Restart Codex after editing the config.
-
-**Managing the plugin:**
-
-```bash
-codex plugin marketplace upgrade               # pull latest plugin versions
-codex plugin marketplace remove mem0-plugins   # unregister the marketplace
-```
+This installs the MCP server and the Mem0 SDK skill. Codex uses the skill-based memory protocol instead of lifecycle hooks.

 ### Cursor

@@ -145,43 +145,17 @@ After installing, confirm the MCP server is connected:

 ## What's included

-| Component | Claude Code / Cowork | Cursor (Marketplace) | Cursor (Deeplink/Manual) | Codex (Sideload) | Codex (Direct MCP) |
-|-----------|:--------------------:|:--------------------:|:------------------------:|:----------------:|:------------------:|
-| MCP Server | Yes | Yes | Yes | Yes | Yes |
-| Lifecycle Hooks | Yes | Yes | No | Opt-in | No |
-| Mem0 SDK Skill | Yes | Yes | No | Yes | No |
-| Memory Protocol Skill | No | No | No | Yes | No |
+| Component | Claude Code / Cowork | Cursor (Marketplace) | Cursor (Deeplink/Manual) | Codex |
+|-----------|:--------------------:|:--------------------:|:------------------------:|:-----:|
+| MCP Server | Yes | Yes | Yes | Yes |
+| Lifecycle Hooks | Yes | Yes | No | No |
+| Mem0 SDK Skill | Yes | Yes | No | Yes |
+| Memory Protocol Skill | No | No | No | Yes |

 - **MCP Server** — Connects to the Mem0 remote MCP server (`mcp.mem0.ai`), providing tools to add, search, update, and delete memories. No local dependencies required.
- **Lifecycle Hooks** — Automatic memory capture at key points. Claude Code and Cursor wire hooks up natively when the plugin is installed (session start, context compaction, task completion, session end). Codex hooks are opt-in via a one-time installer (`scripts/install_codex_hooks.py`) that writes entries into `~/.codex/hooks.json` for `SessionStart`, `UserPromptSubmit`, and `Stop`.
+- **Lifecycle Hooks** — Automatic memory capture at key points: session start, context compaction, task completion, and session end. (Claude Code/Cursor only)
 - **Mem0 SDK Skill** — Guides the AI on how to integrate the Mem0 SDK (Python & TypeScript) into your applications.
- **Memory Protocol Skill** — Codex-specific skill that instructs the agent to retrieve relevant memories at task start, store learnings on completion, and capture session state before context loss. Complements the lifecycle hooks on Codex.
-
-## Updating the plugin
-
-When the plugin updates (new version pulled from the marketplace, or a fresh local install), the MCP server connection in your existing Claude Code / Cursor / Codex session is left holding a stale handle and stops responding. **Restart your client to reconnect:**
-
- **Claude Code:** run `/restart` in the prompt, or close and reopen the CLI.
- **Cursor:** quit and relaunch.
- **Codex:** restart the editor session.
-
-Your `MEM0_API_KEY` doesn't need to be re-entered — the auth header is re-read from your environment on the new session. The plugin's MCP config uses `${MEM0_API_KEY}` interpolation at session start, not at install time, so as long as the env var is set persistently (in your shell profile or `~/.claude/settings.json` `env` block), reconnection is automatic on restart.
-
-If reconnection still fails after a restart, check that `MEM0_API_KEY` is reachable in the new shell (`echo $MEM0_API_KEY`) and confirm you're using a key that starts with `m0-` (from https://app.mem0.ai/dashboard/api-keys, not a legacy token).
-
-## Optional: tune categories for coding workflows
-
-mem0 auto-tags every memory with one or more `categories` from a project-level list. The default list is consumer-oriented (`food`, `hobbies`, `music` …) — useful for chat assistants, less so for code. A one-shot script in this plugin replaces it with a coding-focused taxonomy:
-
-```bash
-# Dry-run first -- prints current vs proposed, no changes:
-python mem0-plugin/scripts/setup_coding_categories.py
-
-# Actually write:
-python mem0-plugin/scripts/setup_coding_categories.py --apply
-```
-
-Requires the `mem0ai` Python SDK (`pip install mem0ai`) and `MEM0_API_KEY` set. New memories will then auto-tag against `architecture_decisions`, `anti_patterns`, `task_learnings`, `tooling_setup`, `bug_fixes`, `coding_conventions`, `user_preferences`. Re-run with a different list any time; `project.update(custom_categories=[...])` always replaces.
+- **Memory Protocol Skill** — Codex-specific skill that instructs the agent to retrieve relevant memories at task start, store learnings on completion, and capture session state before context loss. Replaces lifecycle hooks on platforms that don't support them.

 ## MCP Tools

@@ -1,39 +0,0 @@
-{
-  "hooks": {
-    "SessionStart": [
-      {
-        "matcher": "startup|resume",
-        "hooks": [
-          {
-            "type": "command",
-            "command": "${CODEX_PLUGIN_ROOT}/scripts/on_session_start.sh",
-            "statusMessage": "Loading mem0 context..."
-          }
-        ]
-      }
-    ],
-    "UserPromptSubmit": [
-      {
-        "hooks": [
-          {
-            "type": "command",
-            "command": "${CODEX_PLUGIN_ROOT}/scripts/on_user_prompt.sh",
-            "statusMessage": "Checking memory relevance...",
-            "timeout": 5
-          }
-        ]
-      }
-    ],
-    "Stop": [
-      {
-        "hooks": [
-          {
-            "type": "command",
-            "command": "${CODEX_PLUGIN_ROOT}/scripts/on_stop_codex.sh",
-            "timeout": 10
-          }
-        ]
-      }
-    ]
-  }
-}
@@ -15,6 +15,10 @@
    "preCompact": [
      {
        "command": "${CURSOR_PLUGIN_ROOT}/scripts/on_pre_compact.sh"
+      },
+      {
+        "command": "python3 ${CURSOR_PLUGIN_ROOT}/scripts/on_pre_compact.py",
+        "timeout": 30
      }
    ],
    "stop": [
@@ -30,6 +30,12 @@
            "type": "command",
            "command": "${CLAUDE_PLUGIN_ROOT}/scripts/on_pre_compact.sh",
            "statusMessage": "Preparing pre-compaction summary..."
+          },
+          {
+            "type": "command",
+            "command": "python3 ${CLAUDE_PLUGIN_ROOT}/scripts/on_pre_compact.py",
+            "statusMessage": "Saving session state to mem0...",
+            "timeout": 30
          }
        ]
      }
@@ -51,7 +57,7 @@
          {
            "type": "command",
            "command": "${CLAUDE_PLUGIN_ROOT}/scripts/on_user_prompt.sh",
-            "statusMessage": "Checking memory relevance...",
+            "statusMessage": "Searching mem0 memories...",
            "timeout": 5
          }
        ]
@@ -1,59 +0,0 @@
-"""Resolve mem0 user_id with deterministic priority.
-
-Resolution priority:
-  1. MEM0_USER_ID env var (explicit override)
-  2. ~/.mem0/identity.json cache (pinned to current MEM0_API_KEY fingerprint)
-  3. Derived: "mem0-" + sha256(MEM0_API_KEY)[:12]
-  4. Fallback: $USER, else "default"
-
-Same MEM0_API_KEY across machines yields the same user_id, which fixes
-the "47 user buckets per account" symptom from running on multiple
-laptops with different $USER values.
-"""
-
-from __future__ import annotations
-
-import hashlib
-import json
-import os
-from datetime import datetime, timezone
-
-_CACHE_PATH = os.path.expanduser("~/.mem0/identity.json")
-
-
-def resolve_user_id() -> str:
-    explicit = os.environ.get("MEM0_USER_ID", "").strip()
-    if explicit:
-        return explicit
-
-    api_key = os.environ.get("MEM0_API_KEY", "").strip()
-    if api_key:
-        digest = hashlib.sha256(api_key.encode("utf-8")).hexdigest()
-        fingerprint = digest[:8]
-
-        try:
-            with open(_CACHE_PATH, "r") as f:
-                cached = json.load(f)
-            if cached.get("api_key_fingerprint") == fingerprint and cached.get("user_id"):
-                return cached["user_id"]
-        except (OSError, json.JSONDecodeError):
-            pass
-
-        derived = "mem0-" + digest[:12]
-        try:
-            os.makedirs(os.path.dirname(_CACHE_PATH), exist_ok=True)
-            with open(_CACHE_PATH, "w") as f:
-                json.dump(
-                    {
-                        "user_id": derived,
-                        "source": "api_key",
-                        "api_key_fingerprint": fingerprint,
-                        "resolved_at": datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ"),
-                    },
-                    f,
-                )
-        except OSError:
-            pass
-        return derived
-
-    return os.environ.get("USER") or "default"
@@ -1,57 +0,0 @@
-# Source this file. Sets MEM0_RESOLVED_USER_ID.
-#
-# Resolution priority:
-#   1. MEM0_USER_ID env var (explicit override)
-#   2. ~/.mem0/identity.json cache (pinned to current MEM0_API_KEY fingerprint)
-#   3. Derived: "mem0-" + sha256(MEM0_API_KEY)[:12]
-#   4. Fallback: $USER, else "default"
-#
-# Same MEM0_API_KEY across machines yields the same user_id, which fixes
-# the "47 user buckets per account" symptom from running on multiple
-# laptops with different $USER values.
-
-_mem0_sha256() {
-  if command -v sha256sum >/dev/null 2>&1; then
-    sha256sum | cut -d' ' -f1
-  else
-    shasum -a 256 | cut -d' ' -f1
-  fi
-}
-
-_mem0_resolve_identity() {
-  if [ -n "${MEM0_USER_ID:-}" ]; then
-    printf '%s' "$MEM0_USER_ID"
-    return
-  fi
-
-  local api_key="${MEM0_API_KEY:-}"
-  local cache="$HOME/.mem0/identity.json"
-
-  if [ -n "$api_key" ]; then
-    local digest
-    digest=$(printf '%s' "$api_key" | _mem0_sha256)
-    local fp="${digest:0:8}"
-
-    if [ -f "$cache" ]; then
-      local cached_fp cached_id
-      cached_fp=$(jq -r '.api_key_fingerprint // ""' "$cache" 2>/dev/null)
-      cached_id=$(jq -r '.user_id // ""' "$cache" 2>/dev/null)
-      if [ "$cached_fp" = "$fp" ] && [ -n "$cached_id" ]; then
-        printf '%s' "$cached_id"
-        return
-      fi
-    fi
-
-    local derived="mem0-${digest:0:12}"
-    mkdir -p "$HOME/.mem0" 2>/dev/null && \
-      printf '{"user_id":"%s","source":"api_key","api_key_fingerprint":"%s","resolved_at":"%s"}\n' \
-        "$derived" "$fp" "$(date -u +%FT%TZ)" > "$cache" 2>/dev/null
-    printf '%s' "$derived"
-    return
-  fi
-
-  printf '%s' "${USER:-default}"
-}
-
-MEM0_RESOLVED_USER_ID="$(_mem0_resolve_identity)"
-export MEM0_RESOLVED_USER_ID
@@ -13,10 +13,6 @@

 set -euo pipefail

-if [ -n "${MEM0_DEBUG:-}" ]; then
-  mkdir -p "$HOME/.mem0" && exec 2>>"$HOME/.mem0/hooks.log"
-fi
-
 INPUT=$(cat)

 FILE_PATH=$(echo "$INPUT" | jq -r '.tool_input.file_path // .tool_input.path // ""' 2>/dev/null || echo "")
@@ -26,7 +22,7 @@ if [ -z "$FILE_PATH" ]; then
 fi

 case "$FILE_PATH" in
-  */MEMORY.md|*/.claude/memory/*)
+  */MEMORY.md|*/memory/*.md|*/.claude/*/memory/*)
    echo "BLOCKED: Do not write to $FILE_PATH. Use the mem0 MCP \`add_memory\` tool instead to persist memories. This project uses mem0 for all memory storage." >&2
    exit 2
    ;;
@@ -1,172 +0,0 @@
-#!/usr/bin/env python3
-"""Capture the post-compaction summary into mem0.
-
-PreCompact hooks fire BEFORE the summary is generated, so they can't
-store the actual compact-summary text. This script runs at
-SessionStart with source=compact, reads the transcript, finds the
-most recent entry flagged isCompactSummary=true, and stores it as a
-memory tagged metadata.type=compact_summary.
-
-Input:  JSON on stdin with transcript_path, session_id, source
-Output: stderr logs only (exit 0 always -- must not block)
-
-Spawned in the background by on_session_start.sh; the user-facing
-bootstrap text continues without waiting on the network.
-"""
-
-from __future__ import annotations
-
-import json
-import logging
-import os
-import sys
-import urllib.error
-import urllib.request
-from datetime import date, timedelta
-
-sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
-from _identity import resolve_user_id
-
-log = logging.getLogger("mem0-compact-summary")
-log.setLevel(logging.DEBUG)
-_handler = logging.StreamHandler(sys.stderr)
-_handler.setFormatter(logging.Formatter("[mem0-compact-summary] %(message)s"))
-log.addHandler(_handler)
-
-if os.environ.get("MEM0_DEBUG"):
-    _log_dir = os.path.expanduser("~/.mem0")
-    try:
-        os.makedirs(_log_dir, exist_ok=True)
-        _file_handler = logging.FileHandler(os.path.join(_log_dir, "hooks.log"))
-        _file_handler.setFormatter(logging.Formatter("[mem0-compact-summary] %(asctime)s %(message)s"))
-        log.addHandler(_file_handler)
-    except OSError:
-        pass
-
-API_URL = "https://api.mem0.ai"
-MAX_TAIL_LINES = 2000
-MAX_SUMMARY_CHARS = 50000
-# Compact summaries describe a single session's state -- stale after a quarter.
-COMPACT_SUMMARY_EXPIRY_DAYS = 90
-
-
-def tail_lines(filepath: str, n: int) -> list[str]:
-    try:
-        with open(filepath, "rb") as f:
-            f.seek(0, 2)
-            file_size = f.tell()
-            if file_size == 0:
-                return []
-            chunk_size = min(file_size, n * 4096)
-            f.seek(max(0, file_size - chunk_size))
-            data = f.read().decode("utf-8", errors="replace")
-            return data.splitlines()[-n:]
-    except OSError:
-        return []
-
-
-def find_compact_summary(lines: list[str]) -> str:
-    """Walk transcript backwards, return text content of the most recent
-    entry flagged isCompactSummary=true. Empty string if none found."""
-    for line in reversed(lines):
-        line = line.strip()
-        if not line:
-            continue
-        try:
-            entry = json.loads(line)
-        except json.JSONDecodeError:
-            continue
-        if not entry.get("isCompactSummary"):
-            continue
-
-        message = entry.get("message", {})
-        content = message.get("content", [])
-        if isinstance(content, str):
-            return content[:MAX_SUMMARY_CHARS]
-        if isinstance(content, list):
-            parts = []
-            for block in content:
-                if isinstance(block, str):
-                    parts.append(block)
-                elif isinstance(block, dict) and block.get("type") == "text":
-                    parts.append(block.get("text", ""))
-            return "\n".join(parts).strip()[:MAX_SUMMARY_CHARS]
-    return ""
-
-
-def store_summary(api_key: str, summary: str, user_id: str, session_id: str) -> bool:
-    expires = (date.today() + timedelta(days=COMPACT_SUMMARY_EXPIRY_DAYS)).isoformat()
-    body = {
-        "messages": [{"role": "user", "content": summary}],
-        "user_id": user_id,
-        "metadata": {
-            "type": "compact_summary",
-            "source": "session-start-compact",
-            "session_id": session_id,
-        },
-        "infer": False,
-        "expiration_date": expires,
-    }
-
-    data = json.dumps(body).encode("utf-8")
-    req = urllib.request.Request(
-        f"{API_URL}/v1/memories/",
-        data=data,
-        headers={
-            "Content-Type": "application/json",
-            "Authorization": f"Token {api_key}",
-        },
-        method="POST",
-    )
-    try:
-        with urllib.request.urlopen(req, timeout=15) as resp:
-            if resp.status in (200, 201):
-                log.info("Compact summary stored")
-                return True
-            log.warning("API returned status %d", resp.status)
-            return False
-    except urllib.error.URLError as e:
-        log.warning("API call failed: %s", e)
-        return False
-
-
-def main():
-    api_key = os.environ.get("MEM0_API_KEY", "")
-    if not api_key:
-        log.debug("MEM0_API_KEY not set, skipping capture")
-        return
-
-    try:
-        hook_input = json.loads(sys.stdin.read())
-    except (json.JSONDecodeError, OSError):
-        log.debug("No valid JSON on stdin")
-        return
-
-    transcript_path = hook_input.get("transcript_path", "")
-    if not transcript_path:
-        log.debug("No transcript_path provided")
-        return
-
-    session_id = hook_input.get("session_id", "")
-    user_id = resolve_user_id()
-
-    lines = tail_lines(transcript_path, MAX_TAIL_LINES)
-    if not lines:
-        log.debug("Transcript empty or unreadable: %s", transcript_path)
-        return
-
-    summary = find_compact_summary(lines)
-    if not summary:
-        log.debug("No isCompactSummary entry found")
-        return
-
-    log.info("Capturing compact summary (%d chars)", len(summary))
-    store_summary(api_key, summary, user_id, session_id)
-
-
-if __name__ == "__main__":
-    try:
-        main()
-    except Exception as e:
-        log.error("Unexpected error: %s", e)
-    sys.exit(0)
@@ -1,149 +0,0 @@
-#!/usr/bin/env python3
-"""Install Mem0 lifecycle hooks into ~/.codex/hooks.json.
-
-Codex discovers hooks only at ~/.codex/hooks.json or <repo>/.codex/hooks.json,
-and has no plugin-host mechanism for auto-wiring hooks from an installed
-plugin. This installer reads the template at hooks/codex-hooks.json, rewrites
-the ${CODEX_PLUGIN_ROOT} placeholder to the absolute install path of this
-plugin, then merges the entries into ~/.codex/hooks.json.
-
-Re-running is idempotent: existing Mem0 entries (identified by the plugin
-directory name in the command string) are removed before fresh entries are
-added, so upgrades don't leave duplicates.
-
-Usage:
-  python3 install_codex_hooks.py            # install or update
-  python3 install_codex_hooks.py --uninstall   # remove Mem0 entries
-
-After installing, Codex requires the hooks feature flag in ~/.codex/config.toml:
-
-  [features]
-  codex_hooks = true
-"""
-
-from __future__ import annotations
-
-import argparse
-import json
-import sys
-from pathlib import Path
-
-SCRIPT_DIR = Path(__file__).resolve().parent
-PLUGIN_ROOT = SCRIPT_DIR.parent
-
-CODEX_DIR = Path.home() / ".codex"
-HOOKS_FILE = CODEX_DIR / "hooks.json"
-CONFIG_FILE = CODEX_DIR / "config.toml"
-
-TEMPLATE_FILE = PLUGIN_ROOT / "hooks" / "codex-hooks.json"
-
-# Substring we look for when identifying entries this installer owns.
-# Matches the plugin directory name, which stays stable across install paths.
-OWNER_MARKER = "mem0-plugin"
-
-
-def load_template() -> dict:
-    raw = TEMPLATE_FILE.read_text()
-    raw = raw.replace("${CODEX_PLUGIN_ROOT}", str(PLUGIN_ROOT))
-    return json.loads(raw)
-
-
-def load_existing() -> dict:
-    if not HOOKS_FILE.exists():
-        return {"hooks": {}}
-    try:
-        return json.loads(HOOKS_FILE.read_text())
-    except (json.JSONDecodeError, OSError) as e:
-        print(f"error: failed to read {HOOKS_FILE}: {e}", file=sys.stderr)
-        sys.exit(1)
-
-
-def is_owned_entry(entry: dict) -> bool:
-    for hook in entry.get("hooks", []):
-        if OWNER_MARKER in hook.get("command", ""):
-            return True
-    return False
-
-
-def strip_owned_entries(config: dict) -> dict:
-    hooks = config.get("hooks", {}) or {}
-    for event in list(hooks.keys()):
-        hooks[event] = [e for e in hooks[event] if not is_owned_entry(e)]
-        if not hooks[event]:
-            del hooks[event]
-    config["hooks"] = hooks
-    return config
-
-
-def merge_template(config: dict, template: dict) -> dict:
-    hooks = config.setdefault("hooks", {})
-    for event, entries in template.get("hooks", {}).items():
-        hooks.setdefault(event, []).extend(entries)
-    return config
-
-
-def write_config(config: dict) -> None:
-    CODEX_DIR.mkdir(parents=True, exist_ok=True)
-    HOOKS_FILE.write_text(json.dumps(config, indent=2) + "\n")
-
-
-def feature_flag_enabled() -> bool:
-    if not CONFIG_FILE.exists():
-        return False
-    content = CONFIG_FILE.read_text()
-    for line in content.splitlines():
-        stripped = line.split("#", 1)[0].strip().replace(" ", "")
-        if stripped == "codex_hooks=true":
-            return True
-    return False
-
-
-def print_feature_flag_hint() -> None:
-    print()
-    print("Codex hooks feature flag is not enabled.")
-    print(f"Add this to {CONFIG_FILE}:")
-    print()
-    print("  [features]")
-    print("  codex_hooks = true")
-    print()
-    print("Then restart Codex.")
-
-
-def main() -> int:
-    parser = argparse.ArgumentParser(description="Install or remove Mem0 Codex hooks.")
-    parser.add_argument(
-        "--uninstall",
-        action="store_true",
-        help="Remove Mem0 entries from ~/.codex/hooks.json and exit.",
-    )
-    args = parser.parse_args()
-
-    config = load_existing()
-
-    if args.uninstall:
-        config = strip_owned_entries(config)
-        write_config(config)
-        print(f"Removed Mem0 hooks from {HOOKS_FILE}")
-        return 0
-
-    if not TEMPLATE_FILE.exists():
-        print(f"error: template not found at {TEMPLATE_FILE}", file=sys.stderr)
-        return 1
-
-    template = load_template()
-    config = strip_owned_entries(config)
-    config = merge_template(config, template)
-    write_config(config)
-
-    print(f"Installed Mem0 hooks into {HOOKS_FILE}")
-    print(f"Plugin path: {PLUGIN_ROOT}")
-    print("Events: SessionStart, UserPromptSubmit, Stop")
-
-    if not feature_flag_enabled():
-        print_feature_flag_hint()
-
-    return 0
-
-
-if __name__ == "__main__":
-    sys.exit(main())
@@ -18,12 +18,8 @@ import json
 import logging
 import os
 import sys
-import urllib.error
 import urllib.request
-from datetime import date, timedelta
-
-sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
-from _identity import resolve_user_id
+import urllib.error

 log = logging.getLogger("mem0-capture")
 log.setLevel(logging.DEBUG)
@@ -31,25 +27,11 @@ _handler = logging.StreamHandler(sys.stderr)
 _handler.setFormatter(logging.Formatter("[mem0-capture] %(message)s"))
 log.addHandler(_handler)

-if os.environ.get("MEM0_DEBUG"):
-    _log_dir = os.path.expanduser("~/.mem0")
-    try:
-        os.makedirs(_log_dir, exist_ok=True)
-        _file_handler = logging.FileHandler(os.path.join(_log_dir, "hooks.log"))
-        _file_handler.setFormatter(logging.Formatter("[mem0-capture] %(asctime)s %(message)s"))
-        log.addHandler(_file_handler)
-    except OSError:
-        pass
-
 API_URL = "https://api.mem0.ai"
 MAX_TAIL_LINES = 500
 MAX_USER_MESSAGES = 30
 MAX_BASH_COMMANDS = 20
 MAX_ASSISTANT_TEXT = 10000
-# session_state captures churn fast (active codebase, files in flight). Past
-# ~3 months they're stale noise. Durable facts (decisions, conventions) are
-# stored separately by the agent without an expiration_date.
-SESSION_STATE_EXPIRY_DAYS = 90


 def tail_lines(filepath: str, n: int) -> list[str]:
@@ -167,9 +149,8 @@ def build_content(state: dict, source: str) -> str:
    return "\n".join(parts)


-def store_memory(api_key: str, content: str, user_id: str, source: str, session_id: str = "") -> bool:
+def store_memory(api_key: str, content: str, user_id: str, source: str) -> bool:
    """Store session state as a memory via the Mem0 REST API."""
-    expires = (date.today() + timedelta(days=SESSION_STATE_EXPIRY_DAYS)).isoformat()
    body = {
        "messages": [
            {"role": "user", "content": content}
@@ -178,9 +159,7 @@ def store_memory(api_key: str, content: str, user_id: str, source: str, session_
        "metadata": {
            "type": "session_state",
            "source": source,
-            "session_id": session_id,
        },
-        "expiration_date": expires,
    }

    data = json.dumps(body).encode("utf-8")
@@ -228,8 +207,7 @@ def main():
        log.debug("No transcript_path provided")
        return

-    session_id = hook_input.get("session_id", "")
-    user_id = resolve_user_id()
+    user_id = os.environ.get("MEM0_USER_ID", os.environ.get("USER", "default"))

    lines = tail_lines(transcript_path, MAX_TAIL_LINES)
    if not lines:
@@ -250,7 +228,7 @@ def main():
        len(state["bash_commands"]),
    )

-    store_memory(api_key, content, user_id, source, session_id)
+    store_memory(api_key, content, user_id, source)


 if __name__ == "__main__":
@@ -5,16 +5,12 @@
 # the full context before it gets compressed.
 #
 # Output: Text instructions injected into Claude's context.
-# Claude still has the full conversation and can write an accurate summary,
-# which it stores via add_memory(infer=False) so the platform preserves
-# the structure verbatim instead of running a second extraction pass.
+# Claude still has the full conversation and can write an accurate summary.
+# A companion Python script (on_pre_compact.py) also runs to capture
+# transcript state directly via the Mem0 REST API as a safety net.

 set -euo pipefail

-if [ -n "${MEM0_DEBUG:-}" ]; then
-  mkdir -p "$HOME/.mem0" && exec 2>>"$HOME/.mem0/hooks.log"
-fi
-
 cat <<'EOF'
 ## CRITICAL: Pre-Compaction Session Summary

@@ -22,9 +18,7 @@ Context compaction is about to happen. You are about to lose most of your conver

 ### Step 1: Store session summary

-Call `add_memory` with `infer=False` and a thorough summary covering ALL of the following.
-
-`infer=False` is critical here: you've already done the extraction work yourself using full context. Without it, the platform runs a second LLM pass that loses your structure and pulls fragmented facts. With it, your summary is preserved verbatim.
+Call `add_memory` with a thorough summary covering ALL of the following:

 ```
 ## Session Summary (Pre-Compaction)
@@ -50,19 +44,11 @@ Call `add_memory` with `infer=False` and a thorough summary covering ALL of the
 the post-compaction agent continue without asking redundant questions]
 ```

-Tool call shape:
-```
-add_memory(
-  messages=[{"role":"user","content":"<the summary above>"}],
-  user_id="<the active user_id from the SessionStart bootstrap>",
-  metadata={"type":"session_state","source":"pre-compaction"},
-  infer=False,
-)
-```
+Include metadata: `{"type": "session_state", "source": "pre-compaction"}`

 ### Step 2: Store any unstored learnings

-If there are learnings from this session that you haven't stored yet, store them as separate memories with `infer=False` (same reasoning -- you've already extracted the fact, don't re-extract):
+If there are learnings from this session that you haven't stored yet, store them as separate memories:
 - Failed approaches -> metadata `{"type": "anti_pattern"}`
 - Successful strategies -> metadata `{"type": "task_learning"}`
 - Architecture decisions -> metadata `{"type": "decision"}`
@@ -11,34 +11,9 @@
 # even if jq is missing or stdin is malformed.
 set -uo pipefail

-if [ -n "${MEM0_DEBUG:-}" ]; then
-  mkdir -p "$HOME/.mem0" && exec 2>>"$HOME/.mem0/hooks.log"
-fi
-
-# Skip the bootstrap entirely if no API key is configured -- the agent
-# would otherwise be told to call mem0 MCP tools that will all fail.
-if [ -z "${MEM0_API_KEY:-}" ]; then
-  exit 0
-fi
-
-SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
-# shellcheck source=_identity.sh
-. "$SCRIPT_DIR/_identity.sh"
-
 INPUT=$(cat)
 SOURCE=$(echo "$INPUT" | jq -r '.source // "startup"' 2>/dev/null || echo "startup")

-# Identity line is emitted before every bootstrap variant so the agent
-# uses the same user_id the hooks resolved. Without this, the agent's
-# search_memories/add_memory MCP calls may bind to a different bucket
-# than what the hooks write to.
-echo "## Mem0 Identity"
-echo ""
-echo "Active user_id: \`$MEM0_RESOLVED_USER_ID\`"
-echo ""
-echo "Always include \`{\"user_id\": \"$MEM0_RESOLVED_USER_ID\"}\` (wrapped in an \`AND\` clause) in every \`search_memories\` filter and as \`user_id\` on every \`add_memory\` call. This keeps memories under one bucket regardless of which machine you're on."
-echo ""
-
 if [ "$SOURCE" = "startup" ]; then
  cat <<'EOF'
 ## Mem0 Session Bootstrap
@@ -65,22 +40,14 @@ Continue where you left off.
 EOF

 elif [ "$SOURCE" = "compact" ]; then
-  # Capture the just-generated compact summary in the background.
-  # PreCompact fires too early to see this entry; SessionStart-compact
-  # is the first place isCompactSummary=true is in the transcript.
-  echo "$INPUT" | python3 "$SCRIPT_DIR/capture_compact_summary.py" 2>/dev/null &
-
  cat <<'EOF'
 ## Mem0 Post-Compaction Recovery

-Context was just compacted. The Claude Code-generated compact summary
-is being captured to mem0 in the background as `metadata.type=compact_summary`.
+Context was just compacted. You may have lost important session context.

-1. Call `search_memories` to reload context, layering up to three angles:
-   - `metadata.type=session_state` -- the rich pre-compaction summary you wrote
-   - `metadata.type=compact_summary` -- the platform-generated condensed summary just now
-   - `metadata.type=decision` / `anti_pattern` -- specific facts you stored during the session
-2. Continue working from the recovered context.
+1. Call `search_memories` with queries related to what you were working on to reload relevant knowledge.
+2. Check for any session state memories that were saved before compaction.
+3. Continue working based on the recovered context.
 EOF
 fi

@@ -12,10 +12,6 @@

 set -euo pipefail

-if [ -n "${MEM0_DEBUG:-}" ]; then
-  mkdir -p "$HOME/.mem0" && exec 2>>"$HOME/.mem0/hooks.log"
-fi
-
 SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"

 INPUT=$(cat)
@@ -1,48 +0,0 @@
-#!/usr/bin/env bash
-# Hook: Stop (Codex)
-#
-# Fires when Codex finishes a turn. Reminds the agent to persist any
-# important learnings via the mem0 MCP tools before the turn closes.
-#
-# Input:  JSON on stdin with session_id, turn_id, stop_hook_active,
-#         last_assistant_message, transcript_path, cwd,
-#         hook_event_name, model
-# Output: JSON on stdout (Codex rejects plain text on Stop).
-#         - stop_hook_active=true  -> {"continue": true}  (let the turn end)
-#         - stop_hook_active=false -> {"decision":"block","reason":"..."}
-#           (continue the turn with the reminder as context)
-#
-# We must respect stop_hook_active or we'd loop forever: every "block"
-# reopens the turn, which triggers Stop again when the agent settles.
-
-set -uo pipefail
-
-if [ -n "${MEM0_DEBUG:-}" ]; then
-  mkdir -p "$HOME/.mem0" && exec 2>>"$HOME/.mem0/hooks.log"
-fi
-
-INPUT=$(cat)
-STOP_HOOK_ACTIVE=$(echo "$INPUT" | jq -r '.stop_hook_active // false' 2>/dev/null || echo "false")
-
-if [ "$STOP_HOOK_ACTIVE" = "true" ]; then
-  printf '{"continue":true}\n'
-  exit 0
-fi
-
-REASON=$(cat <<'EOF'
-Before finishing, check if there are important learnings from this interaction that should be persisted using the mem0 `add_memory` tool:
-
-1. Were any significant decisions made? -> Store with metadata `{"type": "decision"}`
-2. Were any new patterns or strategies discovered? -> Store with metadata `{"type": "task_learning"}`
-3. Did any approach fail? -> Store with metadata `{"type": "anti_pattern"}`
-4. Did you learn anything about the user's preferences? -> Store with metadata `{"type": "user_preference"}`
-5. Were there environment/setup discoveries? -> Store with metadata `{"type": "environmental"}`
-
-Memories can be as detailed as needed — include full context, reasoning, code snippets, file paths, and examples. Longer, searchable memories are more valuable than vague one-liners.
-
-If nothing notable happened in this interaction, it's fine to skip. Only store genuinely useful learnings.
-EOF
-)
-
-jq -cn --arg reason "$REASON" '{decision:"block", reason:$reason}'
-exit 0
@@ -9,10 +9,6 @@

 set -euo pipefail

-if [ -n "${MEM0_DEBUG:-}" ]; then
-  mkdir -p "$HOME/.mem0" && exec 2>>"$HOME/.mem0/hooks.log"
-fi
-
 INPUT=$(cat)
 TASK_SUBJECT=$(echo "$INPUT" | jq -r '.task_subject // "unknown task"' 2>/dev/null || echo "unknown task")

@@ -1,72 +1,61 @@
 #!/usr/bin/env bash
 # Hook: UserPromptSubmit
 #
-# Fires on every user message. Instead of pre-searching mem0 with the
-# raw prompt, this injects a decision rubric telling the agent when
-# and how to search itself. The agent has more context than this
-# script does -- let it decide.
+# Fires on every user message. Searches mem0 for relevant memories
+# and injects them into Claude's context before processing.
 #
-# Input:  JSON on stdin (prompt, session_id, cwd, transcript_path)
-# Output: Decision rubric injected into Claude's context (exit 0)
+# Input:  JSON on stdin with prompt, session_id, cwd, transcript_path
+# Output: Matching memories as context text (exit 0)
+#
+# Skips search for very short prompts (< 20 chars) and when
+# MEM0_API_KEY is not set. Uses a 3s timeout to minimize latency.

-# Intentionally omit -e so the script always exits 0 even if jq fails --
-# must never block the user's prompt.
+# Intentionally omit -e so the script always exits 0 even if
+# curl or jq fail — must never block the user's prompt.
 set -uo pipefail

-if [ -n "${MEM0_DEBUG:-}" ]; then
-  mkdir -p "$HOME/.mem0" && exec 2>>"$HOME/.mem0/hooks.log"
-fi
-
 INPUT=$(cat)
 PROMPT=$(echo "$INPUT" | jq -r '.prompt // ""' 2>/dev/null || echo "")

-# Acknowledgements and short replies don't warrant memory context
+# Skip trivial prompts — not worth a network call
 if [ ${#PROMPT} -lt 20 ]; then
  exit 0
 fi

-# No API key means the agent can't search anyway
-if [ -z "${MEM0_API_KEY:-}" ]; then
+API_KEY="${MEM0_API_KEY:-}"
+if [ -z "$API_KEY" ]; then
  exit 0
 fi

-SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
-# shellcheck source=_identity.sh
-. "$SCRIPT_DIR/_identity.sh"
-USER_ID="$MEM0_RESOLVED_USER_ID"
+USER_ID="${MEM0_USER_ID:-${USER:-default}}"

-cat <<EOF
-## Memory check
+# Build request body safely via jq to avoid injection
+BODY=$(jq -n --arg query "$PROMPT" --arg user_id "$USER_ID" \
+  '{query: $query, filters: {user_id: $user_id}, top_k: 5}')

-Before responding, decide whether persistent memory context from mem0 would
-improve your answer. The agent -- not this hook -- owns this decision.
+# Search mem0 for memories relevant to this prompt
+RESPONSE=$(curl -s --max-time 3 \
+  -X POST "https://api.mem0.ai/v2/memories/search/" \
+  -H "Authorization: Token $API_KEY" \
+  -H "Content-Type: application/json" \
+  -d "$BODY" \
+  2>/dev/null || echo "")

-**Search WHEN** the user:
- references past work, decisions, or things "we" built
- asks "how should we...", "best way to...", or any decision-style question
- hits an error, bug, or asks for debugging help
- requests work that touches their stack, tools, conventions, or preferences
- starts a non-trivial task in a known project
+if [ -z "$RESPONSE" ]; then
+  exit 0
+fi

-**Skip WHEN:**
- the prompt is an acknowledgement or continuation
- the user is *stating* new info -- that's a write trigger (\`add_memory\`), not a search
- it's a pure syntax / factual question answerable from general knowledge
- you already searched this scope earlier in the turn
+# Extract memories from response (API returns a flat array)
+MEMORIES=$(echo "$RESPONSE" | jq -r '
+  if type == "array" then . else .results // [] end |
+  if length == 0 then empty else
+  "## Relevant memories from mem0\n\n" +
+  (map(select(.memory != null) | "- " + .memory) | join("\n"))
+  end
+' 2>/dev/null || echo "")

-**If searching, do it well:**
- Run **2-4 parallel** \`search_memories\` calls with different angles, not one
-  query that echoes the user's prompt.
- Phrase queries as **nouns** ("auth module decisions"), not full sentences.
- Filter shape: the root must be a logical operator (\`AND\` / \`OR\` / \`NOT\`)
-  with an array, and metadata uses a **nested** object (not dotted keys).
-  Combine \`user_id\` with one \`metadata.type\` clause per call:
-  - \`{"AND": [{"user_id": "$USER_ID"}, {"metadata": {"type": "decision"}}]}\` -- design / architecture
-  - \`{"AND": [{"user_id": "$USER_ID"}, {"metadata": {"type": "anti_pattern"}}]}\` -- debugging, error handling
-  - \`{"AND": [{"user_id": "$USER_ID"}, {"metadata": {"type": "user_preference"}}]}\` -- tooling, stack, style
-  - \`{"AND": [{"user_id": "$USER_ID"}, {"metadata": {"type": "convention"}}]}\` -- established patterns
- Or scope with just \`{"AND": [{"user_id": "$USER_ID"}]}\` when no metadata filter fits.
- Empty results are normal -- proceed without context.
-EOF
+if [ -n "$MEMORIES" ]; then
+  echo "$MEMORIES"
+fi

 exit 0
@@ -1,142 +0,0 @@
-#!/usr/bin/env python3
-"""Replace mem0's default category taxonomy with one tuned for coding workflows.
-
-mem0 auto-tags every memory with one or more `categories`. By default the list
-is consumer-oriented (food, hobbies, music, ...), which is meaningless for code.
-This script replaces the project's category list with a coding-focused one.
-
-The change is project-level (per the platform docs, per-request overrides are
-not supported on the managed API). Run once per project; future memories will
-be tagged using the new list automatically.
-
-Usage:
-  python setup_coding_categories.py            # dry-run: show current vs proposed, no changes
-  python setup_coding_categories.py --apply    # actually call project.update()
-
-Requires the mem0ai Python SDK and MEM0_API_KEY to be set.
-"""
-
-from __future__ import annotations
-
-import argparse
-import json
-import os
-import sys
-
-CODING_CATEGORIES = [
-    {
-        "architecture_decisions": (
-            "Design choices, system structure, technology selection, trade-offs evaluated, "
-            "and architectural patterns adopted in the project."
-        )
-    },
-    {
-        "anti_patterns": (
-            "Approaches that failed, debugging dead-ends, common mistakes to avoid, "
-            "and lessons learned from things that didn't work."
-        )
-    },
-    {
-        "task_learnings": (
-            "Strategies and approaches that succeeded for specific tasks, including tooling "
-            "tricks, workflow shortcuts, and effective problem-solving patterns."
-        )
-    },
-    {
-        "tooling_setup": (
-            "Development environment, build tools, dependencies, package managers, deploy "
-            "pipelines, and configuration steps for the project."
-        )
-    },
-    {
-        "bug_fixes": (
-            "Specific bug fixes with root cause analysis, the fix applied, and how the bug "
-            "was diagnosed -- useful for recognising similar issues later."
-        )
-    },
-    {
-        "coding_conventions": (
-            "Code style, naming patterns, file organisation, error-handling conventions, "
-            "and team agreements about how code is written in this project."
-        )
-    },
-    {
-        "user_preferences": (
-            "User's stated preferences for tools, libraries, languages, formatting, "
-            "and ways of working."
-        )
-    },
-]
-
-
-def _print_categories(label: str, cats):
-    print(f"=== {label} ===")
-    if cats:
-        print(json.dumps(cats, indent=2))
-    else:
-        print("(none / using mem0 defaults)")
-    print()
-
-
-def main() -> int:
-    ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
-    ap.add_argument(
-        "--apply",
-        action="store_true",
-        help="Actually call project.update(). Without this flag, runs in dry-run mode.",
-    )
-    args = ap.parse_args()
-
-    if not os.environ.get("MEM0_API_KEY"):
-        print("ERROR: MEM0_API_KEY is not set. Export it and try again.", file=sys.stderr)
-        return 1
-
-    try:
-        from mem0 import MemoryClient
-    except ImportError:
-        print(
-            "ERROR: the mem0ai Python SDK is not installed.\n"
-            "Install with: pip install mem0ai\n"
-            "Then re-run this script.",
-            file=sys.stderr,
-        )
-        return 1
-
-    try:
-        client = MemoryClient()
-    except Exception as e:
-        print(
-            f"ERROR initialising MemoryClient: {e}\n"
-            "Most commonly this is an invalid MEM0_API_KEY -- check the key at "
-            "https://app.mem0.ai/dashboard/api-keys",
-            file=sys.stderr,
-        )
-        return 1
-
-    try:
-        current = client.project.get(fields=["custom_categories"])
-        current_cats = current.get("custom_categories") if isinstance(current, dict) else None
-    except Exception as e:
-        print(f"ERROR fetching current categories: {e}", file=sys.stderr)
-        return 1
-
-    _print_categories("Current project categories", current_cats)
-    _print_categories("Proposed coding categories", CODING_CATEGORIES)
-
-    if not args.apply:
-        print("Dry-run only -- no changes made. Re-run with --apply to write.")
-        return 0
-
-    print("Applying coding categories...")
-    try:
-        response = client.project.update(custom_categories=CODING_CATEGORIES)
-    except Exception as e:
-        print(f"ERROR applying update: {e}", file=sys.stderr)
-        return 1
-
-    print("Done.", response if response else "")
-    return 0
-
-
-if __name__ == "__main__":
-    sys.exit(main())
@@ -0,0 +1,62 @@
+---
+name: mem0-codex
+description: >
+  Mem0 persistent memory integration for Codex. Automatically retrieve relevant
+  memories at the start of each task, store key learnings when tasks complete,
+  and capture session state before context is lost. Use the mem0 MCP tools
+  (add_memory, search_memories, get_memories, etc.) for all memory operations.
+---
+
+# Mem0 Memory Protocol for Codex
+
+You have access to persistent memory via the mem0 MCP tools. Follow this protocol to maintain context across sessions.
+
+## On every new task
+
+1. Call `search_memories` with a query related to the current task or project to load relevant context.
+2. Review returned memories to understand what has been learned in prior sessions.
+3. If appropriate, call `get_memories` to browse all stored memories for this user.
+
+## After completing significant work
+
+Extract key learnings and store them using the `add_memory` tool:
+
+- **Decisions made** -> Include metadata `{"type": "decision"}`
+- **Strategies that worked** -> Include metadata `{"type": "task_learning"}`
+- **Failed approaches** -> Include metadata `{"type": "anti_pattern"}`
+- **User preferences observed** -> Include metadata `{"type": "user_preference"}`
+- **Environment/setup discoveries** -> Include metadata `{"type": "environmental"}`
+- **Conventions established** -> Include metadata `{"type": "convention"}`
+
+Memories can be as detailed as needed -- include full context, reasoning, code snippets, file paths, and examples. Longer, searchable memories are more valuable than vague one-liners.
+
+## Before losing context
+
+If context is about to be compacted or the session is ending, store a comprehensive session summary:
+
+```
+## Session Summary
+
+### User's Goal
+[What the user originally asked for]
+
+### What Was Accomplished
+[Numbered list of tasks completed]
+
+### Key Decisions Made
+[Architectural choices, trade-offs discussed]
+
+### Files Created or Modified
+[Important file paths with what changed]
+
+### Current State
+[What is in progress, pending items, next steps]
+```
+
+Include metadata: `{"type": "session_state"}`
+
+## Memory hygiene
+
+- Do NOT write to MEMORY.md or any file-based memory. Use mem0 MCP tools exclusively.
+- Only store genuinely useful learnings. Skip trivial interactions.
+- Use specific, searchable language in memory content.
@@ -1,170 +0,0 @@
---
-name: mem0-mcp
-description: >
-  Mem0 memory protocol for agents using the mem0 MCP tools (Claude Code, Cursor,
-  Codex, and any other MCP-aware runtime). Decide deliberately when memory context
-  would help, run targeted searches with metadata filters when it would, and store
-  key learnings as work completes. Use the mem0 MCP tools (add_memory,
-  search_memories, get_memories, etc.) for all memory operations.
---
-
-# Mem0 MCP Memory Protocol
-
-You have access to persistent memory via the mem0 MCP tools. Follow this protocol to maintain context across sessions.
-
-## On every new task
-
-Decide whether persistent memory context would improve your response, then act accordingly. Don't search by default — search deliberately.
-
-### Decide: search or skip?
-
-**Search WHEN** the user:
- references past work, decisions, or things "we" built
- asks "how should we...", "best way to...", or any decision-style question
- hits an error, bug, or asks for debugging help
- requests work that touches their stack, tools, conventions, or preferences
- starts a non-trivial task in a known project
-
-**Skip WHEN:**
- the prompt is an acknowledgement or continuation ("ok", "thanks", "continue")
- the user is *stating* new info — that's a write trigger (`add_memory`), not a search
- it's a pure syntax / factual question answerable from general knowledge
- you already searched this scope earlier in the turn
-
-Empty results are normal. Proceed without context — they don't mean the system is broken.
-
-### How to search well
-
-When you do search, run **2–4 parallel** `search_memories` calls at different angles instead of one query echoing the user's prompt.
-
-**Query phrasing:**
- Use **nouns**, not sentences. `"auth module decisions"` beats `"what did we decide about auth"`.
- Strip conversational filler. *"remember when we picked Postgres?"* → search `"Postgres choice"`.
- Use entity names, not pronouns. Resolve "that thing" from recent context first.
- Don't search on meta-questions ("what was that?") — use recent context or `get_memories` ordered by `created_at`.
-
-**Metadata filters** match the same `type` values written under "After completing significant work" below.
-
-Two rules from the v2 filter spec:
-
-1. The root **must** be a logical operator (`AND` / `OR` / `NOT`) with an array. A bare `{"user_id": "..."}` won't work.
-2. Metadata uses a **nested** object, not a dotted key. `{"metadata": {"type": "decision"}}`, never `{"metadata.type": "decision"}`. Only top-level metadata keys are filterable.
-
-Combine `user_id` with one metadata clause per call:
-
-| `metadata.type` clause | Use for |
-|--------|---------|
-| `{"metadata": {"type": "decision"}}` | design / architecture / "how should we" questions |
-| `{"metadata": {"type": "anti_pattern"}}` | debugging, error handling, things that failed before |
-| `{"metadata": {"type": "user_preference"}}` | tooling, stack, style — always include for code work |
-| `{"metadata": {"type": "convention"}}` | established patterns in this project |
-
-Full filter (replace `<your_user_id>` with the active user_id from your runtime):
-```python
-filters={"AND": [{"user_id": "<your_user_id>"}, {"metadata": {"type": "decision"}}]}
-```
-
-### Worked example
-
-User asks: *"Refactor the auth module to use JWT."*
-
-Don't:
-```python
-search_memories(query="Refactor the auth module to use JWT")
-# Hits whatever shares words. Misses prior decisions and preferences.
-```
-
-Do (parallel — substitute the active `user_id` for `<your_user_id>`):
-```python
-search_memories(query="auth module decisions",
-                filters={"AND": [{"user_id": "<your_user_id>"}, {"metadata": {"type": "decision"}}]})
-search_memories(query="JWT",
-                filters={"AND": [{"user_id": "<your_user_id>"}]})
-search_memories(query="auth refactor failures",
-                filters={"AND": [{"user_id": "<your_user_id>"}, {"metadata": {"type": "anti_pattern"}}]})
-search_memories(query="auth",
-                filters={"AND": [{"user_id": "<your_user_id>"}, {"metadata": {"type": "user_preference"}}]})
-```
-
-## After completing significant work
-
-Extract key learnings and store them using the `add_memory` tool:
-
- **Decisions made** -> Include metadata `{"type": "decision"}`
- **Strategies that worked** -> Include metadata `{"type": "task_learning"}`
- **Failed approaches** -> Include metadata `{"type": "anti_pattern"}`
- **User preferences observed** -> Include metadata `{"type": "user_preference"}`
- **Environment/setup discoveries** -> Include metadata `{"type": "environmental"}`
- **Conventions established** -> Include metadata `{"type": "convention"}`
-
-> `metadata.type` (which you set explicitly) and `categories` (which the platform auto-tags after the project's custom-category list — see `scripts/setup_coding_categories.py`) are complementary. Always set `metadata.type` for explicit filtering; the platform fills in `categories` on its own. Don't try to set `categories` on `add_memory` calls — per-request overrides aren't supported on the managed API.
-
-### Expiration: high-churn vs durable
-
-Some memory types are state snapshots that go stale fast; others are durable facts that should outlive the session that created them. Mark the difference with `expiration_date` on writes.
-
-| Type | Expiration | Why |
-|---|---|---|
-| `session_state`, `compact_summary` | `expiration_date` ≈ today + 90 days | Describe a single moment of project state. Useless after a quarter; clutter the recall surface. |
-| `decision`, `anti_pattern`, `convention`, `user_preference`, `task_learning`, `environmental` | omit `expiration_date` | Durable facts. A decision made last year is still a decision; same for a convention or a user preference. |
-
-`add_memory` accepts `expiration_date` as a string (`"YYYY-MM-DD"`). The two server-side hooks (`on_pre_compact.py`, `capture_compact_summary.py`) already set this for the types they write. When you write directly via the MCP tool, follow the same rule.
-
-### Recency filter on recall
-
-When the user is asking about *current* state ("where were we", "what's the active task", "the latest decision on X"), filter recall to recent memories so stale snapshots don't surface:
-
-```python
-# Last 90 days only
-{"AND": [{"user_id": "<id>"}, {"metadata": {"type": "session_state"}}, {"created_at": {"gte": "<90 days ago, YYYY-MM-DD>"}}]}
-```
-
-Skip the recency filter when the user is asking about durable facts ("what conventions does this project use", "have we hit this bug before") — those are timeless and recency would hide them.
-
-Memories can be as detailed as needed -- include full context, reasoning, code snippets, file paths, and examples. Longer, searchable memories are more valuable than vague one-liners.
-
-### Use `infer=False` for already-structured content
-
-When you've done the extraction work yourself — pre-compaction summaries, decisions, anti-patterns, conventions you've explicitly identified — pass `infer=False` so the platform stores your text verbatim instead of running a second extraction pass over it.
-
-```python
-add_memory(
-    messages=[{"role": "user", "content": "<your structured fact>"}],
-    user_id="<active user_id>",
-    metadata={"type": "decision"},
-    infer=False,
-)
-```
-
-Stick to one mode per distinct piece of content — don't mix `infer=True` (default) and `infer=False` for the same fact, you'll get duplicates. Default (`infer=True`) is right for raw conversational signal you want extracted; `infer=False` is right for pre-extracted structure.
-
-## Before losing context
-
-If context is about to be compacted or the session is ending, store a comprehensive session summary:
-
-```
-## Session Summary
-
-### User's Goal
-[What the user originally asked for]
-
-### What Was Accomplished
-[Numbered list of tasks completed]
-
-### Key Decisions Made
-[Architectural choices, trade-offs discussed]
-
-### Files Created or Modified
-[Important file paths with what changed]
-
-### Current State
-[What is in progress, pending items, next steps]
-```
-
-Include metadata: `{"type": "session_state"}`
-
-## Memory hygiene
-
- Do NOT write to MEMORY.md or any file-based memory. Use mem0 MCP tools exclusively.
- Only store genuinely useful learnings. Skip trivial interactions.
- Use specific, searchable language in memory content.
@@ -1,6 +1,6 @@
 # Mem0 Skill for Claude

-Add persistent memory to any AI application in minutes using [Mem0 Platform](https://app.mem0.ai?utm_source=oss&utm_medium=mem0-plugin-skill-readme).
+Add persistent memory to any AI application in minutes using [Mem0 Platform](https://app.mem0.ai).

 ## What This Skill Does

@@ -24,7 +24,7 @@ See the [plugin README](../../README.md) for full setup instructions.

 ### Prerequisites

- A Mem0 Platform API key ([Get one here](https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=mem0-plugin-skill-readme))
+- A Mem0 Platform API key ([Get one here](https://app.mem0.ai/dashboard/api-keys))
 - Python 3.10+ or Node.js 18+
 - Set the environment variable:

@@ -63,7 +63,7 @@ skills/mem0/

 ## Links

- [Mem0 Platform Dashboard](https://app.mem0.ai?utm_source=oss&utm_medium=mem0-plugin-skill-readme)
+- [Mem0 Platform Dashboard](https://app.mem0.ai)
 - [Mem0 Documentation](https://docs.mem0.ai)
 - [Mem0 GitHub](https://github.com/mem0ai/mem0)
 - [API Reference](https://docs.mem0.ai/api-reference)
@@ -1,34 +1,25 @@
 ---
 name: mem0
 description: >
-  Mem0 Platform SDK for adding persistent memory to AI applications.
-  TRIGGER when: user mentions "mem0", "MemoryClient", "memory layer",
-  "remember user preferences", "persistent context", "personalization",
-  or needs to add long-term memory to chatbots, agents, or AI apps.
-  Covers Python SDK (mem0ai), TypeScript SDK (mem0ai), and framework integrations
-  (LangChain, CrewAI, OpenAI Agents SDK, Pipecat, LlamaIndex, AutoGen, LangGraph).
-  Also covers the open-source self-hosted Memory class.
-  This is the DEFAULT mem0 skill for ambiguous queries.
-  DO NOT TRIGGER when: user asks about CLI commands, terminal usage, or shell
-  scripts (use mem0-cli), or Vercel AI SDK / @mem0/vercel-ai-provider / createMem0
-  (use mem0-vercel-ai-sdk).
+  Integrate Mem0 Platform into AI applications for persistent memory, personalization, and semantic search.
+  Use this skill when the user mentions "mem0", "memory layer", "remember user preferences",
+  "persistent context", "personalization", or needs to add long-term memory to chatbots, agents,
+  or AI apps. Covers Python and TypeScript SDKs, framework integrations (LangChain, CrewAI,
+  Vercel AI SDK, OpenAI Agents SDK, Pipecat), and the full Platform API. Use even when the user
+  doesn't explicitly say "mem0" but describes needing conversation memory, user context retention,
+  or knowledge retrieval across sessions.
 license: Apache-2.0
 metadata:
  author: mem0ai
-  version: "0.1.1"
+  version: "0.1.0"
  category: ai-memory
  tags: "memory, personalization, ai, python, typescript, vector-search"
-compatibility: Requires Python 3.10+ or Node.js 18+, pip install mem0ai or npm install mem0ai, MEM0_API_KEY env var (Platform), and internet access to api.mem0.ai. Uses Mem0 v3 API.
+compatibility: Requires Python 3.10+ or Node.js 18+, pip install mem0ai or npm install mem0ai, MEM0_API_KEY env var, and internet access to api.mem0.ai
 ---

 # Mem0 Platform Integration

-> **Skill Graph:** This skill is part of the Mem0 skill graph:
-> - **mem0** (this skill) -- Platform Client SDK + OSS (Python + TypeScript)
-> - **[mem0-cli](https://github.com/mem0ai/mem0/tree/main/skills/mem0-cli)** -- Command-line interface
-> - **[mem0-vercel-ai-sdk](https://github.com/mem0ai/mem0/tree/main/skills/mem0-vercel-ai-sdk)** -- Vercel AI SDK provider
-
-Mem0 is a managed memory layer for AI applications. It stores, retrieves, and manages user memories via API — no infrastructure to deploy. For self-hosted usage, see the OSS section in the client references below.
+Mem0 is a managed memory layer for AI applications. It stores, retrieves, and manages user memories via API — no infrastructure to deploy.

 ## Step 1: Install and authenticate

@@ -44,7 +35,7 @@ npm install mem0ai
 export MEM0_API_KEY="m0-your-api-key"
 ```

-Get an API key at: https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=mem0-plugin-skill
+Get an API key at: https://app.mem0.ai/dashboard/api-keys

 ## Step 2: Initialize the client

@@ -77,14 +68,14 @@ client.add(messages, user_id="alice")

 ### Search memories
 ```python
-results = client.search("dietary preferences", filters={"user_id": "alice"})
+results = client.search("dietary preferences", user_id="alice")
 for mem in results.get("results", []):
    print(mem["memory"])
 ```

 ### Get all memories
 ```python
-all_memories = client.get_all(filters={"user_id": "alice"})
+all_memories = client.get_all(user_id="alice")
 ```

 ### Update a memory
@@ -109,12 +100,12 @@ openai = OpenAI()

 def chat(user_input: str, user_id: str) -> str:
    # 1. Retrieve relevant memories
-    memories = mem0.search(user_input, filters={"user_id": user_id})
+    memories = mem0.search(user_input, user_id=user_id)
    context = "\n".join([m["memory"] for m in memories.get("results", [])])

    # 2. Generate response with memory context
    response = openai.chat.completions.create(
-        model="gpt-5-mini",
+        model="gpt-4.1-nano-2025-04-14",
        messages=[
            {"role": "system", "content": f"User context:\n{context}"},
            {"role": "user", "content": user_input},
@@ -132,20 +123,11 @@ def chat(user_input: str, user_id: str) -> str:

 ## Common edge cases

- **Search returns empty:** Memories process asynchronously. Wait 2-3s after `add()` before searching. Also verify `user_id` matches exactly (case-sensitive) and use `filters={"user_id": "..."}` syntax.
+- **Search returns empty:** Memories process asynchronously. Wait 2-3s after `add()` before searching. Also verify `user_id` matches exactly (case-sensitive).
 - **AND filter with user_id + agent_id returns empty:** Entities are stored separately. Use `OR` instead, or query separately.
 - **Duplicate memories:** Don't mix `infer=True` (default) and `infer=False` for the same data. Stick to one mode.
 - **Wrong import:** Always use `from mem0 import MemoryClient` (or `AsyncMemoryClient` for async). Do not use `from mem0 import Memory`.
- **v3 defaults:** `top_k=20`, `threshold=0.1`, `rerank=False`. Adjust as needed for your use case.
-
-## v2 Compatibility
-
-If you're using SDK v2.x, note these differences:
- **Entity IDs:** Pass `user_id` as top-level kwarg to `search()` instead of inside `filters`
- **Defaults:** `top_k=100`, no threshold, `rerank=True`
- **Graph memory:** Available via `enable_graph=True`
-
-See the [migration guide](https://docs.mem0.ai/migration/oss-v2-to-v3) for details.
+- **Immutable memories:** Cannot be updated or deleted once created. Use `client.history(memory_id)` to track changes over time.

 ## Live documentation search

@@ -159,17 +141,7 @@ python ${CLAUDE_SKILL_DIR}/scripts/mem0_doc_search.py --index

 No API key needed — searches docs.mem0.ai directly.

-## Client SDK References
-
-Language-specific deep references (Platform + OSS):
-
-| Language | File |
-|----------|------|
-| Python (MemoryClient + AsyncMemoryClient + Memory OSS) | [client/python.md](client/python.md) |
-| TypeScript/Node.js (MemoryClient + Memory OSS) | [client/node.md](client/node.md) |
-| Python vs TypeScript differences | [client/differences.md](client/differences.md) |
-
-## Platform References
+## References

 Load these on demand for deeper detail:

@@ -180,12 +152,5 @@ Load these on demand for deeper detail:
 | API reference (endpoints, filters, object schema) | [references/api-reference.md](references/api-reference.md) |
 | Architecture (pipeline, lifecycle, scoping, performance) | [references/architecture.md](references/architecture.md) |
 | Platform features (retrieval, graph, categories, MCP, etc.) | [references/features.md](references/features.md) |
-| Framework integrations (LangChain, CrewAI, OpenAI Agents, etc.) | [references/integration-patterns.md](references/integration-patterns.md) |
+| Framework integrations (LangChain, CrewAI, Vercel AI, etc.) | [references/integration-patterns.md](references/integration-patterns.md) |
 | Use cases & examples (real-world patterns with code) | [references/use-cases.md](references/use-cases.md) |
-
-## Related Mem0 Skills
-
-| Skill | When to use | Link |
-|-------|-------------|------|
-| mem0-cli | Terminal commands, scripting, CI/CD, agent tool loops | [GitHub](https://github.com/mem0ai/mem0/tree/main/skills/mem0-cli) |
-| mem0-vercel-ai-sdk | Vercel AI SDK provider with automatic memory | [GitHub](https://github.com/mem0ai/mem0/tree/main/skills/mem0-vercel-ai-sdk) |
@@ -1,129 +0,0 @@
-# Python vs TypeScript SDK Differences
-
-Quick-reference cheatsheet for developers working across both Mem0 SDKs.
-
-## Constructor
-
-| Aspect | Python | TypeScript |
-|--------|--------|------------|
-| Import (Platform) | `from mem0 import MemoryClient` | `import MemoryClient from 'mem0ai'` |
-| Import (OSS) | `from mem0 import Memory` | `import { Memory } from 'mem0ai/oss'` |
-| Constructor | `MemoryClient(api_key="m0-xxx")` | `new MemoryClient({ apiKey: 'm0-xxx' })` |
-| Required param | `api_key` (positional or kwarg) | `apiKey` (in options object) |
-
-Both read from `MEM0_API_KEY` env var if no key provided.
-
-## Method Naming
-
-| Operation | Python | TypeScript |
-|-----------|--------|------------|
-| Add | `add()` | `add()` |
-| Search | `search()` | `search()` |
-| Get | `get()` | `get()` |
-| Get all | `get_all()` | `getAll()` |
-| Update | `update()` | `update()` |
-| Delete | `delete()` | `delete()` |
-| Delete all | `delete_all()` | `deleteAll()` |
-| History | `history()` | `history()` |
-| Batch update | `batch_update()` | `batchUpdate()` |
-| Batch delete | `batch_delete()` | `batchDelete()` |
-| List users | `users()` | `users()` |
-| Delete users | `delete_users()` | `deleteUsers()` |
-| Get project | `project.get()` | `getProject()` |
-| Update project | `project.update()` | `updateProject()` |
-| Create webhook | `create_webhook()` | `createWebhook()` |
-| Get webhooks | `get_webhooks()` | `getWebhooks()` |
-| Update webhook | `update_webhook()` | `updateWebhook()` |
-| Delete webhook | `delete_webhook()` | `deleteWebhook()` |
-| Create export | `create_memory_export()` | `createMemoryExport()` |
-| Get export | `get_memory_export()` | `getMemoryExport()` |
-| Feedback | `feedback()` | `feedback()` |
-
-**Rule:** Python uses `snake_case`, TypeScript uses `camelCase` for method names.
-
-## Parameter Passing
-
-```python
-# Python: kwargs
-client.add(messages, user_id="alice", metadata={"source": "chat"})
-client.search("query", filters={"user_id": "alice"}, top_k=5, rerank=True)
-```
-
-```typescript
-// TypeScript: options object with camelCase for top-level params, snake_case for filter keys
-await client.add(messages, { userId: 'alice', metadata: { source: 'chat' } });
-await client.search('query', { filters: { user_id: 'alice' }, topK: 5, rerank: true });
-```
-
-**v3:** Python uses `snake_case` everywhere. TypeScript uses `camelCase` for top-level params (`userId`, `topK`) but `snake_case` for filter keys (`user_id`, `agent_id`).
-
-## Architectural Differences
-
-| Aspect | Python | TypeScript |
-|--------|--------|------------|
-| HTTP library | httpx | axios |
-| Default timeout | 300s | 60s |
-| Sync support | Yes (`MemoryClient`) | No (all async) |
-| Async support | Yes (`AsyncMemoryClient`) | All methods are async |
-| Project management | `client.project.*` (separate class) | `client.getProject()` / `client.updateProject()` |
-| Context manager | `async with AsyncMemoryClient()` | Not supported |
-
-## Platform Features: Python-only
-
-These methods exist in Python but not TypeScript:
-
-| Method | Description |
-|--------|-------------|
-| `get_summary(filters)` | Get summary of memories |
-| `reset()` | Delete ALL data (users + memories) |
-| `project.create(name)` | Create a new project |
-| `project.delete()` | Delete current project |
-| `project.get_members()` | List project members |
-| `project.add_member(email, role)` | Add member to project |
-| `project.update_member(email, role)` | Change member role |
-| `project.remove_member(email)` | Remove member |
-
-## Platform Features: TypeScript-only
-
-| Method | Description |
-|--------|-------------|
-| `deleteUser(data)` | Convenience method for single entity deletion |
-| `ping()` | Health check endpoint |
-
-## OSS Config Naming
-
-| Python config key | TypeScript config key |
-|-------------------|----------------------|
-| `vector_store` | `vectorStore` |
-| `history_db_path` | `historyDbPath` |
-| `custom_instructions` | `customInstructions` |
-
-## OSS Scope Parameter Naming
-
-| Python | TypeScript |
-|--------|------------|
-| `user_id="alice"` | `userId: 'alice'` |
-| `agent_id="bot"` | `agentId: 'bot'` |
-| `run_id="session"` | `runId: 'session'` |
-
-## Entity ID Passing (v3)
-
-| Method | Python | TypeScript |
-|--------|--------|------------|
-| add() | Top-level: `user_id="alice"` | Top-level: `{ userId: 'alice' }` |
-| search() | In filters: `filters={"user_id": "alice"}` | In filters: `{ filters: { user_id: 'alice' } }` |
-| get_all() | In filters: `filters={"user_id": "alice"}` | In filters: `{ filters: { user_id: 'alice' } }` |
-
-## Common Gotcha
-
-When searching/filtering, both Python and TypeScript use `snake_case` for filter keys. TypeScript only uses `camelCase` for top-level method parameters:
-
-```python
-# Python - snake_case in filters
-results = client.search("query", filters={"user_id": "alice"})
-```
-
-```typescript
-// TypeScript - snake_case in filters, camelCase for top-level params
-const results = await client.search('query', { filters: { user_id: 'alice' }, topK: 20 });
-```
@@ -1,418 +0,0 @@
-# Mem0 Node.js / TypeScript SDK Reference
-
-Complete reference for the `mem0ai` npm package. Covers both the Platform client (managed API) and the Open Source self-hosted variant.
-
---
-
-## Platform Client
-
-### Installation
-
-```bash
-npm install mem0ai
-export MEM0_API_KEY="m0-your-api-key"
-```
-
-### MemoryClient
-
-```typescript
-import MemoryClient from 'mem0ai';
-
-const client = new MemoryClient({ apiKey: 'm0-xxx' });
-```
-
-**Constructor:** `new MemoryClient({ apiKey })`. If `apiKey` is not provided, reads from `MEM0_API_KEY` environment variable.
-
- HTTP library: `axios`
- Timeout: 60 seconds
- Base URL: `https://api.mem0.ai`
- All methods are async (return `Promise`)
-
---
-
-### Memory Methods
-
-#### add(messages, options?)
-
-Store new memories from messages.
-
-```typescript
-const messages = [
-    { role: 'user', content: "I'm a vegetarian and allergic to nuts." },
-    { role: 'assistant', content: "Got it! I'll remember that." },
-];
-await client.add(messages, { userId: 'alice' });
-```
-
-| Parameter | Type | Description |
-|-----------|------|-------------|
-| `messages` | `Message[]` | Array of `{role, content}` objects |
-| `options.userId` | string | User identifier |
-| `options.agentId` | string | Agent identifier |
-| `options.appId` | string | Application identifier |
-| `options.runId` | string | Session identifier |
-| `options.metadata` | object | Custom key-value pairs |
-| `options.infer` | boolean | If false, store raw text (default: true) |
-
-**Returns:** `Promise<any>` -- list of events
-
-#### search(query, options?)
-
-Search memories by semantic similarity.
-
-```typescript
-const results = await client.search('dietary preferences', { filters: { user_id: 'alice' }, topK: 20 });
-for (const mem of results.results) {
-    console.log(mem.memory, mem.score);
-}
-```
-
-| Parameter | Type | Description |
-|-----------|------|-------------|
-| `query` | string | Natural language search query |
-| `options.filters` | object | Filter object with entity IDs (`user_id`, `agent_id`, etc.) and/or `AND`/`OR`/`NOT` conditions |
-| `options.topK` | number | Number of results (default: 20) |
-| `options.rerank` | boolean | Enable semantic reranking (default: false) |
-| `options.threshold` | number | Minimum similarity (default: 0.1) |
-
-**Returns:** `Promise<SearchResult>` -- `{results: [{id, memory, score, ...}]}`
-
-#### get(memoryId)
-
-```typescript
-const memory = await client.get('ea925981-...');
-```
-
-#### getAll(options?)
-
-Retrieve all memories. Requires at least one entity identifier in filters.
-
-```typescript
-const memories = await client.getAll({ filters: { user_id: 'alice' } });
-// With filters
-const filtered = await client.getAll({
-    filters: { AND: [{ user_id: 'alice' }, { categories: { contains: 'health' } }] },
-});
-```
-
-| Parameter | Type | Description |
-|-----------|------|-------------|
-| `options.filters` | object | Filter object with entity IDs (`user_id`, `agent_id`, etc.) and/or `AND`/`OR`/`NOT` conditions |
-| `options.page` | number | Page number |
-| `options.pageSize` | number | Results per page |
-
-#### update(memoryId, data)
-
-```typescript
-await client.update('ea925981-...', { text: 'Updated: vegan since 2024' });
-await client.update('ea925981-...', { text: 'Updated', metadata: { verified: true } });
-```
-
-| Parameter | Type | Description |
-|-----------|------|-------------|
-| `memoryId` | string | Memory ID |
-| `data.text` | string | New content |
-| `data.metadata` | object | New metadata |
-| `data.timestamp` | string | New timestamp |
-
-#### delete(memoryId)
-
-```typescript
-await client.delete('ea925981-...');
-```
-
-#### deleteAll(options?)
-
-```typescript
-await client.deleteAll({ userId: 'alice' });
-```
-
-#### history(memoryId)
-
-```typescript
-const history = await client.history('ea925981-...');
-// Returns: [{previousValue, newValue, action, timestamps}]
-```
-
---
-
-### Batch Methods
-
-#### batchUpdate(memories)
-
-```typescript
-await client.batchUpdate([
-    { memoryId: 'uuid-1', text: 'Updated text' },
-    { memoryId: 'uuid-2', text: 'Another update' },
-]);
-```
-
-#### batchDelete(memories)
-
-```typescript
-await client.batchDelete(['uuid-1', 'uuid-2', 'uuid-3']);
-```
-
---
-
-### User/Entity Management
-
-#### users()
-
-```typescript
-const users = await client.users();
-// Returns: {results: [{type: "user", name: "alice"}, ...]}
-```
-
-#### deleteUser(data) / deleteUsers(data)
-
-```typescript
-await client.deleteUser({ userId: 'alice' });  // Single entity
-await client.deleteUsers({ agentId: 'bot-1' }); // Flexible
-```
-
---
-
-### Project Management
-
-```typescript
-// Get project config
-const config = await client.getProject({ fields: ['customCategories'] });
-
-// Update project settings
-await client.updateProject({
-    customInstructions: 'Extract dietary preferences and health info',
-    customCategories: [{ health: 'Medical and dietary info' }],
-});
-```
-
---
-
-### Webhooks
-
-```typescript
-// List
-const webhooks = await client.getWebhooks({ projectId: 'proj_123' });
-
-// Create
-const webhook = await client.createWebhook({
-    url: 'https://your-app.com/webhook',
-    name: 'Memory Logger',
-    projectId: 'proj_123',
-    eventTypes: ['memory_add', 'memory_update'],
-});
-
-// Update
-await client.updateWebhook({
-    webhookId: 'wh_123',
-    name: 'Updated Logger',
-    url: 'https://new-url.com',
-});
-
-// Delete
-await client.deleteWebhook({ webhookId: 'wh_123' });
-```
-
---
-
-### Feedback
-
-```typescript
-await client.feedback({
-    memoryId: 'mem-123',
-    feedback: 'POSITIVE',
-    feedbackReason: 'Accurately captured preference',
-});
-```
-
---
-
-### Export
-
-```typescript
-const exportReq = await client.createMemoryExport({
-    schema: JSON.stringify({ type: 'object', properties: { name: { type: 'string' } } }),
-    filters: { user_id: 'alice' },
-});
-
-const result = await client.getMemoryExport({ memoryExportId: exportReq.id });
-```
-
---
-
-### TypeScript Types
-
-Key interfaces from `mem0.types.ts`:
-
-```typescript
-interface Message { role: string; content: string; }
-interface Memory { id: string; memory: string; userId: string; categories: string[]; score?: number; /* ... */ }
-interface MemoryOptions { userId?: string; agentId?: string; appId?: string; runId?: string; metadata?: object; /* ... */ }
-interface SearchOptions { filters?: object; topK?: number; rerank?: boolean; threshold?: number; /* ... */ }
-interface MemoryHistory { id: string; memoryId: string; previousValue: string; newValue: string; action: string; /* ... */ }
-interface FeedbackPayload { memoryId: string; feedback: string; feedbackReason?: string; }
-interface WebhookCreatePayload { url: string; name: string; projectId: string; eventTypes: string[]; }
-```
-
---
-
-## Open Source / Self-Hosted
-
-### Installation
-
-```bash
-npm install mem0ai
-```
-
-### Memory Class
-
-```typescript
-import { Memory } from 'mem0ai/oss';
-
-const m = new Memory();  // Uses default config
-```
-
-**Import:** `from 'mem0ai/oss'` (NOT the default export -- that is `MemoryClient` for Platform)
-
-### Configuration
-
-```typescript
-const config = {
-    llm: {
-        provider: 'openai',        // openai, groq, anthropic, google, ollama, lmstudio, mistral, azure
-        config: {
-            model: 'gpt-5-mini',
-            apiKey: 'sk-xxx',
-        },
-    },
-    embedder: {
-        provider: 'openai',        // openai, ollama, lmstudio, google, azure, langchain, anthropic
-        config: {
-            model: 'text-embedding-3-small',
-            apiKey: 'sk-xxx',
-        },
-    },
-    vectorStore: {
-        provider: 'qdrant',        // memory, qdrant, redis, supabase, langchain, azure_ai_search, pgvector
-        config: {
-            collectionName: 'my_memories',
-            host: 'localhost',
-            port: 6333,
-        },
-    },
-    historyDbPath: 'history.db',
-    customInstructions: '...',
-    disableHistory: false,
-};
-
-const m = new Memory(config);
-// Or from dict with validation:
-const m2 = Memory.fromConfig(config);
-```
-
-### Methods
-
-All methods are async (return `Promise`):
-
-#### add(messages, config)
-
-```typescript
-await m.add('I prefer dark mode', { userId: 'alice' });
-await m.add([
-    { role: 'user', content: 'I like hiking' },
-    { role: 'assistant', content: 'Great outdoor activity!' },
-], { userId: 'alice' });
-```
-
-| Parameter | Type | Description |
-|-----------|------|-------------|
-| `messages` | `string \| Message[]` | Content to store |
-| `config.userId` | string | User identifier (at least one scope required) |
-| `config.agentId` | string | Agent identifier |
-| `config.runId` | string | Session identifier |
-| `config.metadata` | object | Custom key-value pairs |
-| `config.filters` | object | Additional filters |
-| `config.infer` | boolean | LLM inference (default: true) |
-
-**Returns:** `Promise<{results: [...], relations?: [...]}>`
-
-#### search(query, config)
-
-```typescript
-const results = await m.search('dietary preferences', { filters: { user_id: 'alice' }, topK: 5 });
-```
-
-| Parameter | Type | Description |
-|-----------|------|-------------|
-| `query` | string | Search query |
-| `config.filters` | object | Filter object with entity IDs (`user_id`, `agent_id`, `run_id`, etc.) |
-| `config.topK` | number | Max results (default: 20) |
-
-#### get(memoryId) / getAll(config) / update(memoryId, data) / delete(memoryId) / deleteAll(config) / history(memoryId)
-
-Same interface patterns. Note: OSS `update` takes a string for data, not an object.
-
-```typescript
-await m.update('mem-id', 'new content');
-```
-
-#### reset()
-
-Clear the entire vector store and history.
-
-```typescript
-await m.reset();
-```
-
---
-
-## Key Differences: Platform vs OSS
-
-| Aspect | Platform (`MemoryClient`) | OSS (`Memory`) |
-|--------|--------------------------|----------------|
-| **Import** | `import MemoryClient from 'mem0ai'` | `import { Memory } from 'mem0ai/oss'` |
-| **Auth** | API key required (`MEM0_API_KEY`) | No API key -- config-based |
-| **Execution** | API calls to `api.mem0.ai` | Local execution |
-| **Infrastructure** | Fully managed | Self-managed vector DB, embedder, LLM |
-| **Param style** | Top-level: `camelCase` (`userId`, `topK`), filter keys: `snake_case` (`user_id`) | Top-level: `camelCase` (`userId`, `topK`), filter keys: `snake_case` (`user_id`) |
-| **Batch ops** | `batchUpdate`, `batchDelete` | Not available |
-| **Webhooks** | Full CRUD | Not available |
-| **Export** | `createMemoryExport` | Not available |
-| **Feedback** | `feedback()` | Not available |
-| **Project mgmt** | `getProject`, `updateProject` | Not available |
-| **User listing** | `users()`, `deleteUser()` | Not available |
-| **History** | Platform-managed | SQLite (configurable) |
-
---
-
-## v2 Compatibility
-
-If you're using SDK v2.x:
-
-**Naming Changes:**
- Top-level params now use camelCase: `topK`, `rerank` (not `top_k`)
- Filter keys use snake_case: `user_id`, `agent_id`
- OSS: `limit` renamed to `topK`
-
-**API Changes:**
-```typescript
-// v2 - top-level entity IDs, snake_case
-await client.search("query", { user_id: "alice", top_k: 20 });
-
-// v3 - filters object with snake_case keys, camelCase top-level params
-await client.search("query", { filters: { user_id: "alice" }, topK: 20 });
-```
-
-**Default Changes:**
-| Param | v2 | v3 |
-|-------|----|----|
-| `topK` | 100 | 20 |
-| `threshold` | none | 0.1 |
-| `rerank` | true | false |
-
-**Removed:**
- `OutputFormat` and `API_VERSION` enums
- `organizationId`, `projectId` from constructor
- `enableGraph`, `asyncMode`, `outputFormat`, `immutable`, `expirationDate`, `filterMemories`, `batchSize`, `forceAddOnly`, `includes`, `excludes`, `keywordSearch`
-
-See the [v2 to v3 migration guide](https://docs.mem0.ai/migration/oss-v2-to-v3) for details.
@@ -1,487 +0,0 @@
-# Mem0 Python SDK Reference
-
-Complete reference for the `mem0ai` Python package. Covers both the Platform client (managed API) and the Open Source self-hosted variant.
-
---
-
-## Platform Client
-
-### Installation
-
-```bash
-pip install mem0ai
-export MEM0_API_KEY="m0-your-api-key"
-```
-
-### MemoryClient (Synchronous)
-
-```python
-from mem0 import MemoryClient
-
-client = MemoryClient(api_key="m0-xxx")
-```
-
-**Constructor:** `MemoryClient(api_key=None)`. If `api_key` is not provided, reads from `MEM0_API_KEY` environment variable. Raises `ValueError` if no key found.
-
- HTTP library: `httpx`
- Timeout: 300 seconds
- Base URL: `https://api.mem0.ai`
-
-### AsyncMemoryClient (Asynchronous)
-
-```python
-from mem0 import AsyncMemoryClient
-
-client = AsyncMemoryClient(api_key="m0-xxx")
-
-# Or use as context manager
-async with AsyncMemoryClient(api_key="m0-xxx") as client:
-    results = await client.search("query", filters={"user_id": "alice"})
-```
-
-Same methods as `MemoryClient`, all `async`/`await`. Supports async context manager.
-
---
-
-### Memory Methods
-
-#### add(messages, **kwargs)
-
-Store new memories from messages.
-
-```python
-messages = [
-    {"role": "user", "content": "I'm a vegetarian and allergic to nuts."},
-    {"role": "assistant", "content": "Got it! I'll remember that."}
-]
-client.add(messages, user_id="alice")
-```
-
-| Parameter | Type | Default | Description |
-|-----------|------|---------|-------------|
-| `messages` | str \| dict \| list[dict] | required | Message content. Strings auto-convert to user messages |
-| `user_id` | str | None | User identifier |
-| `agent_id` | str | None | Agent identifier |
-| `app_id` | str | None | Application identifier |
-| `run_id` | str | None | Session/run identifier |
-| `metadata` | dict | None | Custom key-value pairs |
-| `infer` | bool | True | If False, store raw text without LLM inference |
-| `custom_categories` | list | None | Override project categories |
-| `custom_instructions` | str | None | Override extraction instructions |
-| `timestamp` | int \| float \| str | None | Custom timestamp (Unix epoch or ISO 8601) |
-
-**Returns:** `dict` -- list of events: `[{"id": "...", "event": "ADD", "data": {"memory": "..."}}]`
-
-#### search(query, **kwargs)
-
-Search memories by semantic similarity.
-
-```python
-results = client.search("dietary preferences", filters={"user_id": "alice"})
-for mem in results.get("results", []):
-    print(mem["memory"], mem["score"])
-```
-
-| Parameter | Type | Default | Description |
-|-----------|------|---------|-------------|
-| `query` | str | required | Natural language search query |
-| `filters` | dict | None | Filter object with entity IDs and/or `AND`/`OR`/`NOT` conditions (e.g., `{"user_id": "alice"}`) |
-| `top_k` | int | 10 | Number of results |
-| `rerank` | bool | False | Enable deep semantic reranking (+150-200ms) |
-| `threshold` | float | 0.1 | Minimum similarity score |
-| `fields` | list | None | Specific fields to return |
-| `categories` | list | None | Filter by category |
-
-**Returns:** `dict` -- `{"results": [{id, memory, user_id, categories, score, created_at, ...}]}`
-
-#### get(memory_id)
-
-Retrieve a single memory by ID.
-
-```python
-memory = client.get(memory_id="ea925981-...")
-```
-
-**Returns:** `dict` -- full memory object
-
-#### get_all(**kwargs)
-
-Retrieve all memories with optional filtering. Requires at least one entity identifier.
-
-```python
-memories = client.get_all(filters={"user_id": "alice"})
-# With compound filters
-memories = client.get_all(filters={"AND": [{"user_id": "alice"}, {"categories": {"contains": "health"}}]})
-```
-
-| Parameter | Type | Default | Description |
-|-----------|------|---------|-------------|
-| `filters` | dict | None | Filter object with entity IDs and/or `AND`/`OR`/`NOT` conditions |
-| `top_k` | int | None | Limit results |
-| `page` | int | None | Page number |
-| `page_size` | int | None | Results per page |
-
-**Returns:** `dict` -- `{"results": [...]}`
-
-#### update(memory_id, text=None, metadata=None, timestamp=None)
-
-Update a memory's content, metadata, or timestamp. At least one parameter required.
-
-```python
-client.update("ea925981-...", text="Updated: vegan since 2024")
-client.update("ea925981-...", metadata={"verified": True})
-```
-
-**Returns:** `dict` -- updated memory
-
-#### delete(memory_id)
-
-Permanently delete a single memory.
-
-```python
-client.delete("ea925981-...")
-```
-
-#### delete_all(**kwargs)
-
-Delete all memories matching filters. Irreversible.
-
-```python
-client.delete_all(user_id="alice")
-```
-
-#### history(memory_id)
-
-Get the change history of a memory.
-
-```python
-history = client.history("ea925981-...")
-# Returns: [{previous_value, new_value, action, timestamps}]
-```
-
---
-
-### Batch Methods
-
-#### batch_update(memories)
-
-Update up to 1000 memories in a single request.
-
-```python
-client.batch_update([
-    {"memory_id": "uuid-1", "text": "Updated text"},
-    {"memory_id": "uuid-2", "text": "Another update", "metadata": {"verified": True}},
-])
-```
-
-#### batch_delete(memories)
-
-Delete up to 1000 memories in a single request.
-
-```python
-client.batch_delete([
-    {"memory_id": "uuid-1"},
-    {"memory_id": "uuid-2"},
-])
-```
-
---
-
-### User/Entity Management
-
-#### users()
-
-List all users, agents, and sessions that have memories.
-
-```python
-users = client.users()
-# Returns: {"results": [{"type": "user", "name": "alice"}, ...]}
-```
-
-#### delete_users(user_id=None, agent_id=None, app_id=None, run_id=None)
-
-Delete a specific entity and all its memories.
-
-```python
-client.delete_users(user_id="alice")
-```
-
-#### reset()
-
-Delete ALL users, agents, sessions, and memories. Complete data reset.
-
-```python
-client.reset()
-```
-
---
-
-### Export & Summary
-
-#### create_memory_export(schema, **kwargs)
-
-Create a structured export of memories.
-
-```python
-import json
-
-schema = json.dumps({
-    "type": "object",
-    "properties": {
-        "name": {"type": "string"},
-        "preferences": {"type": "array", "items": {"type": "string"}},
-    }
-})
-export = client.create_memory_export(schema=schema, user_id="alice")
-```
-
-#### get_memory_export(**kwargs)
-
-Retrieve a previously created export.
-
-```python
-result = client.get_memory_export(memory_export_id=export["id"])
-```
-
-#### get_summary(filters=None)
-
-Get a summary of memories.
-
-```python
-summary = client.get_summary(filters={"user_id": "alice"})
-```
-
---
-
-### Feedback
-
-#### feedback(memory_id, feedback=None, feedback_reason=None)
-
-Provide quality feedback on a memory.
-
-```python
-client.feedback(
-    memory_id="mem-123",
-    feedback="POSITIVE",  # POSITIVE | NEGATIVE | VERY_NEGATIVE | None (clear)
-    feedback_reason="Accurately captured preference"
-)
-```
-
---
-
-### Webhooks
-
-```python
-# List
-webhooks = client.get_webhooks(project_id="proj_123")
-
-# Create
-webhook = client.create_webhook(
-    url="https://your-app.com/webhook",
-    name="Memory Logger",
-    project_id="proj_123",
-    event_types=["memory_add", "memory_update"]
-)
-
-# Update
-client.update_webhook(webhook_id=123, name="Updated", url="https://new-url.com")
-
-# Delete
-client.delete_webhook(webhook_id=123)
-```
-
---
-
-### Project Management
-
-Access via `client.project.*`:
-
-```python
-# Get project config
-config = client.project.get(fields=["custom_categories", "custom_instructions"])
-
-# Update project settings
-client.project.update(
-    custom_instructions="Extract dietary preferences and health info",
-    custom_categories=[{"health": "Medical and dietary info"}],
-    multilingual=True,
-)
-
-# Create/delete project
-client.project.create(name="My Project", description="...")
-client.project.delete()
-
-# Member management
-members = client.project.get_members()
-client.project.add_member(email="user@example.com", role="READER")  # READER or OWNER
-client.project.update_member(email="user@example.com", role="OWNER")
-client.project.remove_member(email="user@example.com")
-```
-
---
-
-## Open Source / Self-Hosted
-
-### Installation
-
-```bash
-pip install mem0ai
-```
-
-### Memory Class
-
-```python
-from mem0 import Memory
-
-m = Memory()  # Uses default config (OpenAI embedder + in-memory vector store)
-```
-
-**Import:** `from mem0 import Memory` (NOT `MemoryClient` -- that is the Platform client)
-
-### Configuration
-
-```python
-config = {
-    "llm": {
-        "provider": "openai",        # openai, groq, azure, ollama, lmstudio, google, anthropic, mistral
-        "config": {
-            "model": "gpt-5-mini",
-            "api_key": "sk-xxx",
-        }
-    },
-    "embedder": {
-        "provider": "openai",        # openai, ollama, azure, lmstudio, google, huggingface
-        "config": {
-            "model": "text-embedding-3-small",
-            "api_key": "sk-xxx",
-        }
-    },
-    "vector_store": {
-        "provider": "qdrant",        # faiss, qdrant, pgvector, redis, supabase, azure_ai_search, memory
-        "config": {
-            "collection_name": "my_memories",
-            "host": "localhost",
-            "port": 6333,
-        }
-    },
-    "history_db_path": "history.db",              # SQLite path for change history
-    "custom_instructions": "...",                  # Custom LLM prompt for extraction
-}
-
-m = Memory.from_config(config)
-```
-
-### Context Manager
-
-```python
-with Memory(config) as m:
-    m.add("I prefer dark mode", user_id="alice")
-    results = m.search("preferences", filters={"user_id": "alice"})
-# SQLite connections released automatically
-```
-
-### Methods
-
-All methods mirror the Platform client but run locally:
-
-#### add(messages, *, user_id, agent_id, run_id, metadata, infer=True)
-
-```python
-m.add("I'm a vegetarian", user_id="alice")
-m.add([
-    {"role": "user", "content": "I like hiking"},
-    {"role": "assistant", "content": "Great outdoor activity!"}
-], user_id="alice")
-```
-
-At least one of `user_id`, `agent_id`, `run_id` required.
-
-**Returns:** `{"results": [...], "relations": [...]}`
-
-#### search(query, *, filters=None, top_k=20, threshold=0.1, rerank=False)
-
-```python
-results = m.search("dietary preferences", filters={"user_id": "alice"}, top_k=5)
-```
-
-Entity IDs (`user_id`, `agent_id`, `run_id`) must be passed inside the `filters` dict.
-
-Supports filter operators: `eq`, `ne`, `in`, `nin`, `gt`, `gte`, `lt`, `lte`, `contains`, `not_contains`.
-
-#### get(memory_id) / get_all(**kwargs) / update(memory_id, data, metadata=None) / delete(memory_id) / delete_all(**kwargs) / history(memory_id)
-
-Same interface as Platform client.
-
-#### reset()
-
-Clear the entire vector store collection and history database. Recreates the vector store.
-
-```python
-m.reset()
-```
-
-#### close()
-
-Release SQLite connections. Called automatically when using context manager.
-
-### AsyncMemory
-
-```python
-from mem0 import AsyncMemory
-
-m = AsyncMemory(config)
-await m.add("text", user_id="alice")
-results = await m.search("query", filters={"user_id": "alice"})
-```
-
---
-
-## Key Differences: Platform vs OSS
-
-| Aspect | Platform (`MemoryClient`) | OSS (`Memory`) |
-|--------|--------------------------|----------------|
-| **Import** | `from mem0 import MemoryClient` | `from mem0 import Memory` |
-| **Auth** | API key required (`MEM0_API_KEY`) | No API key -- config-based |
-| **Execution** | API calls to `api.mem0.ai` | Local execution |
-| **Infrastructure** | Fully managed | Self-managed vector DB, embedder, LLM |
-| **Entity filtering** | `filters={"user_id": "..."}` | `filters={"user_id": "..."}` |
-| **Batch ops** | `batch_update`, `batch_delete` | Not available |
-| **Webhooks** | Full CRUD | Not available |
-| **Export** | `create_memory_export`, `get_memory_export` | Not available |
-| **Feedback** | `feedback()` | Not available |
-| **Project mgmt** | `client.project.*` | Not available |
-| **User listing** | `users()`, `delete_users()` | Not available |
-| **Custom prompts** | Via project settings | Direct config (`custom_instructions`) |
-| **History** | Platform-managed | SQLite (configurable) |
-| **Async** | `AsyncMemoryClient` | `AsyncMemory` |
-
---
-
-## v2 Compatibility
-
-If you're using SDK v2.x or the v2 API:
-
-**API Changes:**
- **Entity IDs in search/get_all:** Pass `user_id`, `agent_id` as top-level kwargs instead of inside `filters`
-  ```python
-  # v2
-  results = client.search("query", user_id="alice")
-  # v3
-  results = client.search("query", filters={"user_id": "alice"})
-  ```
- **add() returns:** v2 returns ADD, UPDATE, DELETE events; v3 returns ADD only
-
-**Default Changes:**
-| Param | v2 | v3 |
-|-------|----|----|
-| `top_k` | 100 | 20 |
-| `threshold` | None | 0.1 |
-| `rerank` | True | False |
-
-**Removed Parameters:**
- Constructor: `org_id`, `project_id`
- add(): `async_mode`, `output_format`, `enable_graph`, `immutable`, `expiration_date`, `filter_memories`, `batch_size`, `force_add_only`, `includes`, `excludes`, `keyword_search`
- search()/get_all(): `enable_graph`
- Config: `enable_graph`, `graph_store`, `custom_fact_extraction_prompt` (renamed to `custom_instructions`)
-
-See the [v2 to v3 migration guide](https://docs.mem0.ai/migration/oss-v2-to-v3) for full details.
@@ -8,15 +8,13 @@ All endpoints require: `Authorization: Token <MEM0_API_KEY>`

 | Operation | Method | URL |
 |-----------|--------|-----|
-| Add Memories | `POST` | `/v3/memories/add/` |
-| Search Memories | `POST` | `/v3/memories/search/` |
-| Get All Memories | `POST` | `/v3/memories/` |
+| Add Memories | `POST` | `/v1/memories/` |
+| Search Memories | `POST` | `/v2/memories/search/` |
+| Get All Memories | `POST` | `/v2/memories/` |
 | Get Single Memory | `GET` | `/v1/memories/{memory_id}/` |
 | Update Memory | `PUT` | `/v1/memories/{memory_id}/` |
 | Delete Memory | `DELETE` | `/v1/memories/{memory_id}/` |

-Note: v1/v2 endpoints still work (backward compatible).
-
 ## Memory Object Structure

 | Field | Type | Description |
@@ -29,6 +27,8 @@ Note: v1/v2 endpoints still work (backward compatible).
 | `run_id` | string (nullable) | Run/session identifier |
 | `metadata` | object | Custom key-value pairs |
 | `categories` | array of strings | Auto-assigned category tags |
+| `immutable` | boolean | If true, prevents modification |
+| `expiration_date` | datetime (nullable) | Auto-expiry date |
 | `hash` | string | Content hash |
 | `created_at` | datetime | Creation timestamp |
 | `updated_at` | datetime | Last modification timestamp |
@@ -50,9 +50,10 @@ Memories can be scoped to different levels:

 ## Processing Model

- Memories are processed **asynchronously** (v3 default)
- Add responses return queued `ADD` events only (v3 is ADD-only, no UPDATE/DELETE)
- Poll status via `GET /v1/event/{event_id}/`
+- Memories are processed **asynchronously by default** (`async_mode=true`)
+- Add responses return queued events (`ADD`, `UPDATE`, `DELETE`) for tracking
+- Set `async_mode=false` for synchronous processing when needed
+- Graph metadata is processed asynchronously -- use `get_all()` for complete graph data

 ## Filter System

@@ -105,17 +106,19 @@ Root must be `AND`, `OR`, or `NOT`. Simple shorthand `{"user_id": "alice"}` also

 ## Response Formats

-### Add Response (v3)
+### Add Response

 ```json
-{
-  "message": "Memory processing has been queued for background execution",
-  "status": "PENDING",
-  "event_id": "evt-uuid"
-}
+[
+  {
+    "id": "mem_01JF8ZS4Y0R0SPM13R5R6H32CJ",
+    "event": "ADD",
+    "data": { "memory": "The user moved to Austin in 2025." }
+  }
+]
 ```

-v3 is ADD-only. No UPDATE or DELETE events.
+Event types: `ADD`, `UPDATE`, `DELETE`. A single add can trigger multiple events.

 ### Search Response

@@ -134,17 +137,4 @@ v3 is ADD-only. No UPDATE or DELETE events.
 }
 ```

-In v3, `score` is a combined multi-signal relevance score.
-
-### Get All Response (v3)
-
-```json
-{
-  "count": 123,
-  "next": "https://api.mem0.ai/v3/memories/?page=2&page_size=50",
-  "previous": null,
-  "results": [...]
-}
-```
-
-v3 returns paginated envelope. Use `page` and `page_size` query params.
+With `enable_graph=true`, includes additional `relations` array with entity relationships.
@@ -25,9 +25,9 @@ User Input → Retrieve relevant memories → Enrich LLM prompt → Generate res

 Mem0 handles the complexity of extraction, deduplication, conflict resolution, and semantic retrieval so your application only needs to call `search()` and `add()`.

-**Storage architecture:**
+**Dual storage architecture:**
 - **Vector store**: Embeddings for semantic similarity search
- **Entity store**: Automatic entity linking for relationship-aware retrieval
+- **Graph store** (optional): Entity nodes and relationship edges for structured knowledge

 ---

@@ -40,32 +40,41 @@ Messages In
    │
    ▼
 ┌─────────────────────┐
-│  1. EXTRACTION       │  Single LLM call extracts all distinct new facts
+│  1. EXTRACTION       │  LLM analyzes messages, extracts key facts
 │     (infer=True)     │  If infer=False, stores raw text as-is
 └─────────┬───────────┘
          │
          ▼
 ┌─────────────────────┐
-│  2. DEDUPLICATION    │  Hash-based dedup (MD5 prevents exact duplicates)
-│                      │  No UPDATE/DELETE - v3 is ADD-only
+│  2. CONFLICT         │  Checks existing memories for duplicates
+│     RESOLUTION       │  Latest truth wins (newer overrides older)
+│                      │  Only runs when infer=True
 └─────────┬───────────┘
          │
          ▼
 ┌─────────────────────┐
-│  3. STORAGE          │  Batch embed → vector store
-│                      │  Entity extraction → entity store
+│  3. STORAGE          │  Generates embeddings → vector store
+│                      │  Optional: entity extraction → graph store
+│                      │  Indexes metadata, categories, timestamps
 └─────────┬───────────┘
          │
          ▼
    Memory Object
+    (id, memory, categories, structured_attributes)
 ```

-### Processing (v3)
+### Processing modes

-v3 processes memories asynchronously by default:
- API returns immediately: `{"status": "PENDING", "event_id": "evt-..."}`
- Poll status via `GET /v1/event/{event_id}/`
+**Async (default, `async_mode=True`):**
+- API returns immediately: `{"status": "PENDING", "event_id": "..."}`
+- Processing happens in background
 - Use webhooks for completion notifications
+- Best for: high-throughput, non-blocking workflows
+
+**Sync (`async_mode=False`):**
+- API waits for full processing
+- Returns complete memory object with `id`, `event`, `memory`
+- Best for: real-time access immediately after add

 ### Extraction modes

@@ -84,7 +93,7 @@ v3 processes memories asynchronously by default:

 ---

-## Retrieval Pipeline (v3)
+## Retrieval Pipeline

 ### What happens when you call `client.search()`

@@ -93,37 +102,45 @@ Query In
    │
    ▼
 ┌─────────────────────┐
-│  1. PREPROCESSING    │  Lemmatize keywords, extract entities
+│  1. QUERY EMBEDDING  │  Convert query to vector representation
 └─────────┬───────────┘
          │
          ▼
 ┌─────────────────────┐
-│  2. PARALLEL SCORING │  Semantic search (vector similarity)
-│                      │  BM25 keyword search (term matching)
-│                      │  Entity matching (entity graph boost)
+│  2. VECTOR SEARCH    │  Cosine similarity across stored embeddings
+│                      │  Scoped by filters (user_id, agent_id, etc.)
 └─────────┬───────────┘
          │
-          ▼
+          ▼  (optional enhancements)
 ┌─────────────────────┐
-│  3. SCORE FUSION     │  Combine signals into single score
-│                      │  Optional: rerank=True for deep reordering
+│  3a. KEYWORD SEARCH  │  Expands results with specific terms (+10ms)
+│  3b. RERANKING       │  Deep semantic reordering (+150-200ms)
+│  3c. FILTER MEMORIES │  Precision filtering, removes low-relevance (+200-300ms)
+└─────────┬───────────┘
+          │
+          ▼  (if enable_graph=True)
+┌─────────────────────┐
+│  4. GRAPH LOOKUP     │  Finds entity relationships
+│                      │  Appends relations WITHOUT reranking vector results
 └─────────┬───────────┘
          │
          ▼
-    Results (combined score per memory)
+    Results + Relations
 ```

-### v3 Search Defaults
+### Retrieval enhancement combinations

-| Parameter | Default | Notes |
-|-----------|---------|-------|
-| `top_k` | 20 | Was 100 in v2 |
-| `threshold` | 0.1 | Was None in v2 |
-| `rerank` | False | Was True in v2 |
+| Configuration | Latency | Best for |
+|--------------|---------|----------|
+| Base search only | ~100ms | Simple lookups |
+| `keyword_search=True` | ~110ms | Entity-heavy queries, broad coverage |
+| `rerank=True` | ~250-300ms | User-facing results, top-N precision |
+| `keyword_search=True` + `rerank=True` | ~310ms | Balanced (recommended for most apps) |
+| `rerank=True` + `filter_memories=True` | ~400-500ms | Safety-critical, production systems |

 ### Implicit null scoping

-When you search with `filters={"user_id": "alice"}` only, Mem0 returns memories where `agent_id`, `app_id`, and `run_id` are all null. This prevents cross-scope leakage by default.
+When you search with `user_id="alice"` only, Mem0 returns memories where `agent_id`, `app_id`, and `run_id` are all null. This prevents cross-scope leakage by default.

 To include memories with non-null fields, use explicit filters:
 ```python
@@ -133,23 +150,53 @@ filters={"OR": [{"user_id": "alice"}]}

 ---

-## Memory Lifecycle (v3)
+## Memory Lifecycle

-v3 uses ADD-only extraction. Memories accumulate over time rather than being consolidated.
+```
+CREATE ──→ ACTIVE ──→ UPDATE ──→ ACTIVE
+  │           │                     │
+  │           ▼                     ▼
+  │       EXPIRED              EXPIRED
+  │      (still stored,       (still stored,
+  │       not retrieved)       not retrieved)
+  │           │                     │
+  ▼           ▼                     ▼
+DELETE    DELETE               DELETE
+(permanent)
+```

 ### Creation
- `client.add(messages, user_id="...")`
- Single-pass extraction → deduplication → storage
- Returns `{"event_id": "...", "status": "PENDING"}`
+- Triggered by `client.add(messages, user_id="...")`
+- Messages processed through extraction → conflict resolution → storage
+- Gets unique UUID, `created_at` timestamp
+- Optional: custom `timestamp`, `expiration_date`, `metadata`, `immutable`

 ### Updates
- `client.update(memory_id, text="...")` replaces text
- Batch: `client.batch_update([...])`
+- `client.update(memory_id, text="...")` replaces text and reindexes
+- `client.batch_update([...])` for up to 1000 memories at once
+- Immutable memories (`immutable=True`) cannot be updated — must delete and re-add
+
+### Deduplication
+- Automatic during `add()` with `infer=True`
+- Conflict resolution merges duplicate facts
+- Latest truth wins when contradictions detected
+- Prevents memory bloat from repeated information
+
+### Expiration
+- Optional `expiration_date` parameter (ISO 8601 or `YYYY-MM-DD`)
+- After expiration: memory NOT returned in searches but remains in storage
+- Useful for time-sensitive info (events, temporary preferences, session state)

 ### Deletion
- Single: `client.delete(memory_id)`
- Batch: `client.batch_delete([...])`
- Bulk: `client.delete_all(filters={"user_id": "alice"})`
+- Single: `client.delete(memory_id)` — permanent, no recovery
+- Batch: `client.batch_delete([memory_ids])` — up to 1000
+- Bulk: `client.delete_all(user_id="alice")` — all memories for entity
+- `delete_all()` without filters raises error to prevent accidental data loss
+
+### History tracking
+- `client.history(memory_id)` returns version timeline
+- Shows all changes: `{previous_value, new_value, action, timestamps}`
+- Useful for audit trails and debugging

 ---

@@ -167,6 +214,8 @@ v3 uses ADD-only extraction. Memories accumulate over time rather than being con
  "categories": ["health", "preferences"],
  "created_at": "2025-03-12T12:34:56Z",
  "updated_at": "2025-03-12T12:34:56Z",
+  "expiration_date": null,
+  "immutable": false,
  "structured_attributes": {
    "day": 12, "month": 3, "year": 2025,
    "hour": 12, "minute": 34,
@@ -190,6 +239,8 @@ v3 uses ADD-only extraction. Memories accumulate over time rather than being con
 | `categories` | array | Auto-assigned or custom category tags |
 | `created_at` | datetime | Creation timestamp |
 | `updated_at` | datetime | Last modification timestamp |
+| `expiration_date` | datetime | Auto-expiry date (stops retrieval, data persists) |
+| `immutable` | boolean | If true, prevents modification |
 | `structured_attributes` | object | Temporal breakdown for time-based queries |
 | `score` | float | Semantic similarity (search results only, 0-1) |

@@ -271,7 +322,7 @@ Mem0 supports three layers of memory, from shortest to longest lived:
 ```python
 def chat(user_input: str, user_id: str, session_id: str) -> str:
    # 1. Retrieve user memories (long-term preferences)
-    user_mems = mem0.search(user_input, filters={"user_id": user_id})
+    user_mems = mem0.search(user_input, user_id=user_id)

    # 2. Retrieve session memories (current task context)
    session_mems = mem0.search(user_input, filters={
@@ -299,13 +350,18 @@ def chat(user_input: str, user_id: str, session_id: str) -> str:

 | Operation | Typical Latency |
 |-----------|----------------|
-| Hybrid search (v3 default) | ~100-150ms |
+| Base vector search | ~100ms |
+| + keyword_search | +10ms |
 | + reranking | +150-200ms |
-| Add (async) | < 50ms response |
+| + filter_memories | +200-300ms |
+| Add (async, default) | < 50ms response, background processing |
+| Add (sync) | 500ms-2s depending on extraction complexity |
+| Graph operations | Slight overhead for large stores |

 ### Processing

- **Async (default):** Returns immediately, processes in background
+- **Async mode (default):** Returns immediately, processes in background
+- **Sync mode:** Waits for full extraction + storage pipeline
 - **Batch operations:** Up to 1000 memories per batch_update/batch_delete
 - **Webhooks:** Real-time notifications when async processing completes

@@ -5,7 +5,7 @@ Additional platform capabilities beyond core CRUD operations.
 ## Table of Contents

 - [Advanced Retrieval](#advanced-retrieval)
- [Entity Linking](#entity-linking)
+- [Graph Memory](#graph-memory)
 - [Custom Categories](#custom-categories)
 - [Custom Instructions](#custom-instructions)
 - [Criteria Retrieval](#criteria-retrieval)
@@ -18,58 +18,124 @@ Additional platform capabilities beyond core CRUD operations.

 ## Advanced Retrieval

-### Hybrid Search (v3 Default)
+Three enhancement options for tuning search precision, recall, and latency.

-v3 uses multi-signal hybrid search combining:
- **Semantic search** (vector similarity)
- **BM25 keyword search** (normalized term matching)
- **Entity matching** (entity graph boost)
+### Keyword Search (`keyword_search=True`)

-This is automatic — no configuration needed.
+Expands results to include memories with specific terms, names, and technical keywords.
+
+- Latency: +10ms
+- Recall: Significantly increased
+- Best for: entity-heavy queries, comprehensive coverage

 ### Reranking (`rerank=True`)

 Deep semantic reordering of results — most relevant first.

 - Latency: +150-200ms
- Default: `False` (was `True` in v2)
+- Accuracy: Significantly improved
 - Best for: user-facing results, top-N precision

+### Filter Memories (`filter_memories=True`)
+
+Precision filtering — removes low-relevance results entirely.
+
+- Latency: +200-300ms
+- Precision: Maximized
+- Best for: safety-critical applications, production systems
+
+### Recommended Combinations
+
 **Python:**
 ```python
-results = client.search(query, filters={"user_id": "user123"}, rerank=True)
+# Fast & broad
+results = client.search(query, keyword_search=True, user_id="user123")
+
+# Balanced (recommended for most apps)
+results = client.search(query, keyword_search=True, rerank=True, user_id="user123")
+
+# High precision (critical apps)
+results = client.search(query, rerank=True, filter_memories=True, user_id="user123")
 ```

 **TypeScript:**
 ```typescript
 const results = await client.search(query, {
-    filters: { user_id: 'user123' },
+    user_id: 'user123',
+    keyword_search: true,
    rerank: true,
 });
 ```

 ---

-## Entity Linking
+## Graph Memory

-v3 replaces graph memory with built-in entity linking. Entities (proper nouns, quoted text, compound noun phrases) are automatically extracted and linked across memories.
+Entity-level knowledge graph that creates relationships between memories.

 ### How It Works

-1. **Extraction**: During `add()`, entities are automatically extracted from memory text
-2. **Storage**: Entities are stored in a parallel collection (`{collection}_entities`)
-3. **Retrieval**: During `search()`, query entities are matched and used to boost relevant memories
+1. **Extraction**: LLM analyzes conversation and identifies entities and relationships
+2. **Storage**: Embeddings go to vector store; entity nodes and edges go to graph store
+3. **Retrieval**: Vector search returns semantic matches; graph relations are appended to results

-Entity linking is automatic — no configuration required. The boost is folded into the combined `score` on each result.
+Graph relations **augment** vector results without reordering them. Vector similarity always determines hit sequence.

-### v2 Migration Note
+### Enabling Graph Memory

-If you were using `enable_graph=True` in v2:
- Remove `enable_graph` from all API calls
- Remove `graph_store` from OSS configuration
- Entity relationships are now consumed through retrieval ranking, not exposed as a separate `relations` array
+**Per request:**
+```python
+client.add(messages, user_id="alice", enable_graph=True)
+client.search("query", user_id="alice", enable_graph=True)
+client.get_all(filters={"AND": [{"user_id": "alice"}]}, enable_graph=True)
+```

-See the [v2 to v3 migration guide](https://docs.mem0.ai/migration/oss-v2-to-v3) for details.
+**Project-level (default for all operations):**
+```python
+client.project.update(enable_graph=True)
+```
+
+```javascript
+await client.updateProject({ enable_graph: true });
+```
+
+### Relation Structure
+
+Each relation in the response contains:
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `source` | string | Source entity name |
+| `source_type` | string | Source entity type (e.g., "Person") |
+| `relationship` | string | Relationship label (e.g., "lives_in") |
+| `target` | string | Target entity name |
+| `target_type` | string | Target entity type (e.g., "City") |
+| `score` | number | Confidence score |
+
+**Example:**
+```json
+{
+  "relations": [
+    {
+      "source": "Joseph",
+      "source_type": "Person",
+      "relationship": "lives_in",
+      "target": "Seattle",
+      "target_type": "City",
+      "score": 0.92
+    }
+  ]
+}
+```
+
+### Technical Notes
+
+- Graph Memory adds processing time; see docs for current plan availability
+- Works optimally with rich conversation histories containing entity relationships
+- Best suited for long-running assistants tracking evolving information
+- Graph writes and reads toggle independently per request
+- Multi-agent context supported via `user_id`, `agent_id`, `run_id` scoping
+- Add operations are asynchronous; graph metadata may not be immediately available

 ---

@@ -94,7 +160,7 @@ client.project.update(custom_categories=new_categories)
 ```

 ```javascript
-await client.updateProject({ customCategories: newCategories });
+await client.updateProject({ custom_categories: new_categories });
 ```

 **Retrieve active categories:**
@@ -119,7 +185,7 @@ client.project.update(custom_instructions="Your guidelines here...")
 ```

 ```javascript
-await client.updateProject({ customInstructions: "Your guidelines here..." });
+await client.updateProject({ custom_instructions: "Your guidelines here..." });
 ```

 ### Template Structure
@@ -163,7 +229,7 @@ client.project.update(retrieval_criteria=retrieval_criteria)

 ```typescript
 await client.updateProject({
-    retrievalCriteria: [
+    retrieval_criteria: [
        { name: 'joy', description: 'Positive emotions', weight: 3 },
        { name: 'urgency', description: 'Time-sensitive items', weight: 4 },
    ],
@@ -215,7 +281,7 @@ for item in feedback_data:
 ```typescript
 await client.feedback('mem-123', {
    feedback: 'POSITIVE',
-    feedbackReason: 'Accurately captured dietary preference',
+    feedback_reason: 'Accurately captured dietary preference',
 });
 ```

@@ -283,18 +349,23 @@ Use the `name` field in messages to identify speakers. Mem0 maps names to entity

 ## MCP Integration

-Model Context Protocol integration enables AI clients (Claude, Claude Code, Cursor, Windsurf, VS Code, OpenCode) to manage Mem0 memory autonomously.
+Model Context Protocol integration enables AI clients (Claude Desktop, Cursor, custom agents) to manage Mem0 memory autonomously.

-### Setup
+### Configuration

-Add Mem0 MCP to your clients with a single command:
-
-```bash
-npx mcp-add \
-  --name mem0-mcp \
-  --type http \
-  --url "https://mcp.mem0.ai/mcp" \
-  --clients "claude,claude code,cursor,windsurf,vscode,opencode"
+```json
+{
+  "mcpServers": {
+    "mem0": {
+      "command": "uvx",
+      "args": ["mem0-mcp-server"],
+      "env": {
+        "MEM0_API_KEY": "m0-your-api-key",
+        "MEM0_DEFAULT_USER_ID": "your-user-id"
+      }
+    }
+  }
+}
 ```

 ### Available MCP Tools
@@ -306,7 +377,7 @@ The MCP server exposes 9 memory tools that AI agents can use autonomously:

 ### How It Works

-1. Add Mem0 MCP to your AI client using the setup command above
+1. Configure the MCP server in your AI client
 2. The agent autonomously decides when to store/retrieve memories
 3. No manual API calls needed — the agent manages memory as part of its reasoning

--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
KartikandGitHub	a07788c719	Update openclaw.mdx	2026-04-22 20:49:05 +05:30
kartik-mem0	0a82a8376c	fix: beta release	2026-04-22 20:45:05 +05:30
kartik-mem0	c95c660cbd	fix: remove the process env from the tests and fix the plugin manifest	2026-04-22 20:38:45 +05:30