chore: doc fix

docs/components/vectordbs/dbs/turbopuffer.mdx

@@ -1,79 +0,0 @@
----
-title: "Turbopuffer"
-description: "Use Turbopuffer as a serverless vector database in Mem0 for low-latency search at scale with native metadata filtering."
----
-[Turbopuffer](https://turbopuffer.com) is a serverless vector database optimized for low-latency search at scale. It offers cost-effective vector storage with native metadata filtering.
-
-### Usage
-
-```python
-import os
-from mem0 import Memory
-
-os.environ["OPENAI_API_KEY"] = "sk-xx"
-os.environ["TURBOPUFFER_API_KEY"] = "tpuf_xxxxxxxxxxxx"
-
-config = {
-    "vector_store": {
-        "provider": "turbopuffer",
-        "config": {
-            "collection_name": "movie_preferences",
-            "embedding_model_dims": 1536,
-            "region": "gcp-us-central1",
-        }
-    }
-}
-
-m = Memory.from_config(config)
-
-messages = [
-    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
-    {"role": "user", "content": "I'm not a big fan of thrillers but I love sci-fi."},
-    {"role": "assistant", "content": "Got it! I'll suggest sci-fi movies instead."}
-]
-
-m.add(messages, user_id="alice", metadata={"category": "movies"})
-
-# Search memories
-results = m.search(query="sci-fi recommendations", user_id="alice")
-```
-
-### Config
-
-Here are the parameters available for configuring Turbopuffer:
-
-| Parameter | Description | Default Value |
-| --- | --- | --- |
-| `collection_name` | Name of the namespace/collection | `mem0` |
-| `embedding_model_dims` | Dimensions of the embedding model (must match your chosen embedding model) | `1536` |
-| `api_key` | Turbopuffer API key | Environment variable: `TURBOPUFFER_API_KEY` |
-| `region` | Turbopuffer region | `gcp-us-central1` |
-| `distance_metric` | Distance metric for vector similarity (`cosine_distance` or `euclidean_squared`) | `cosine_distance` |
-| `batch_size` | Batch size for bulk operations | `100` |
-| `extra_params` | Additional parameters for the Turbopuffer client | `None` |
-
-### Regions
-
-| Region | Location |
-| --- | --- |
-| `gcp-us-central1` | Iowa, USA (Default) |
-| `aws-us-west-2` | Oregon, USA |
-
-### Config Example
-
-```python
-config = {
-    "vector_store": {
-        "provider": "turbopuffer",
-        "config": {
-            "collection_name": "my_memories",
-            "embedding_model_dims": 1536,
-            "api_key": "tpuf_xxxxxxxxxxxx",
-            "region": "aws-us-west-2",
-            "distance_metric": "cosine_distance",
-            "batch_size": 200,
-        }
-    }
-}
-```

docs/components/vectordbs/overview.mdx

@@ -33,7 +33,6 @@ See the list of supported vector databases below.
   <Card title="LangChain" href="/components/vectordbs/dbs/langchain"></Card>
   <Card title="Amazon S3 Vectors" href="/components/vectordbs/dbs/s3_vectors"></Card>
   <Card title="Databricks" href="/components/vectordbs/dbs/databricks"></Card>
-  <Card title="Turbopuffer" href="/components/vectordbs/dbs/turbopuffer"></Card>
 </CardGroup>
 
 ## Usage

docs/docs.json

@@ -231,8 +231,7 @@
                               "components/vectordbs/dbs/cassandra",
                               "components/vectordbs/dbs/s3_vectors",
                               "components/vectordbs/dbs/databricks",
-                              "components/vectordbs/dbs/neptune_analytics",
+                              "components/vectordbs/dbs/neptune_analytics"
-                              "components/vectordbs/dbs/turbopuffer"
                             ]
                           }
                         ]

docs/open-source/features/graph-memory.mdx

@@ -35,7 +35,7 @@ graph LR
 Mem0’s extraction LLM identifies entities, relationships, and timestamps from the conversation payload you send to `memory.add`.
 </Step>
 <Step title="Store vectors and edges together">
-Embeddings land in your configured vector database while nodes and edges flow into a graph backend (Neo4j, Memgraph, Neptune, Kuzu, or Apache AGE).
+Embeddings land in your configured vector database while nodes and edges flow into a Bolt-compatible graph backend (Neo4j, Memgraph, Neptune, or Kuzu).
 </Step>
 <Step title="Expose graph context at search time">
 `memory.search` performs vector similarity (optionally reranked by your configured reranker) and returns the results list. Graph Memory runs in parallel and adds related entities in the `relations` array—it does not reorder the vector hits automatically.
@@ -264,7 +264,7 @@ Monitor graph growth, especially on free tiers, by periodically cleaning dormant
 
 ## Decision Points
 
-- Select the graph store that fits your deployment (managed Aura vs. self-hosted Neo4j vs. AWS Neptune vs. local Kuzu vs. Apache AGE on PostgreSQL).
+- Select the graph store that fits your deployment (managed Aura vs. self-hosted Neo4j vs. AWS Neptune vs. local Kuzu).
 - Decide when to enable graph writes per request; routine conversations may stay vector-only to save latency.
 - Set a policy for pruning stale relationships so your graph stays fast and affordable.
 
@@ -381,42 +381,6 @@ config = {
 
 Kuzu will clear its state when using `:memory:` once the process exits. See the [Kuzu documentation](https://kuzudb.com/docs/) for advanced settings.
   </Accordion>
-  <Accordion title="Apache AGE (PostgreSQL extension)">
-    [Apache AGE](https://age.apache.org/) adds graph database capabilities to PostgreSQL, letting you run Cypher queries alongside SQL on the same server. Start AGE via Docker, then configure Mem0:
-
-```bash
-docker run --name age-postgres \
-  -e POSTGRES_DB=mem0_db \
-  -e POSTGRES_USER=mem0_user \
-  -e POSTGRES_PASSWORD=mem0_pass \
-  -p 5432:5432 \
-  -d apache/age
-```
-
-```python
-from mem0 import Memory
-
-config = {
-    "graph_store": {
-        "provider": "apache_age",
-        "config": {
-            "host": "localhost",
-            "port": 5432,
-            "database": "mem0_db",
-            "username": "mem0_user",
-            "password": "mem0_pass",
-            "graph_name": "mem0_graph",
-        },
-    },
-}
-
-m = Memory.from_config(config_dict=config)
-```
-
-Apache AGE does not have a built-in vector index, so similarity search is computed client-side. This works well for moderate graph sizes; for very large graphs consider pairing AGE with pgvector for the vector store.
-
-Reference: [Apache AGE documentation](https://age.apache.org/age-manual/master/index.html).
-  </Accordion>
 </AccordionGroup>
 
 <CardGroup cols={2}>

docs/open-source/features/rest-api.mdx

@@ -14,7 +14,7 @@ The Mem0 REST API server exposes every OSS memory operation over HTTP. Run it al
 </Info>
 
 <Warning>
-  Enable API key authentication (see below) and HTTPS before exposing the server to anything beyond your internal network.
+  Add your own authentication and HTTPS before exposing the server to anything beyond your internal network. The default image does not include auth.
 </Warning>
 
 ---
@@ -22,7 +22,6 @@ 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`.
-- **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.
 
@@ -92,41 +91,6 @@ uvicorn main:app --reload
 
 ---
 
-## Authentication
-
-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.
-
-| `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>` |
-
-### Enable authentication
-
-Add the key to your `.env` file:
-
-```bash
-ADMIN_API_KEY=your-secret-api-key
-```
-
-Then include the header in every request:
-
-```bash
-curl -X POST http://localhost:8000/memories \
-  -H "Content-Type: application/json" \
-  -H "X-API-Key: your-secret-api-key" \
-  -d '{
-    "messages": [{"role": "user", "content": "I love pizza."}],
-    "user_id": "alice"
-  }'
-```
-
-<Warning>
-  The server logs a warning at startup when `ADMIN_API_KEY` is not set. Always set it in production.
-</Warning>
-
----
-
 ## See it in action
 
 ### Create and search memories via HTTP
@@ -173,7 +137,7 @@ curl "http://localhost:8000/memories/search?user_id=alice&query=vegetable"
 
 ## Best practices
 
-1. **Enable authentication:** Set `ADMIN_API_KEY` to secure all endpoints, or use an API gateway for more advanced schemes.
+1. **Add authentication:** Protect endpoints with API gateways, proxies, or custom FastAPI middleware.
 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.

docs/openmemory/integrations.mdx

@@ -47,7 +47,6 @@ Replace `<client-name>` with the desired client name and `<user-id>` with the va
 | Witsy       | `npx install-mcp http://localhost:8765/mcp/witsy/sse/<user-id> --client witsy` |
 | Enconvo     | `npx install-mcp http://localhost:8765/mcp/enconvo/sse/<user-id> --client enconvo` |
 | Augment     | `npx install-mcp http://localhost:8765/mcp/augment/sse/<user-id> --client augment` |
-| Claude Code | `claude mcp add -s user -t sse OpenMemory http://localhost:8765/mcp/openmemory/sse/<user-id>` |
 
 ### What This Does
 

docs/openmemory/quickstart.mdx

@@ -183,11 +183,6 @@ You can configure the MCP client using the following command (replace `username`
 npx @openmemory/install local "http://localhost:8765/mcp/cursor/sse/username" --client cursor
 ```
 
-**For Claude Code (CLI):**
-```bash
-claude mcp add -s user -t sse OpenMemory "http://localhost:8765/mcp/openmemory/sse/username"
-```
-
 The OpenMemory dashboard will be available at http://localhost:3000. From here, you can view and manage your memories and check connection status with your MCP clients.
 
 Once set up, OpenMemory runs locally on your machine, ensuring all your AI memories remain private and secure while being accessible across any compatible MCP client.

docs/templates/cookbook_template.mdx

@@ -166,13 +166,13 @@ Call out the most common mistake or edge case for this layer.
     title="[Related cookbook / deep dive]"
     description="[Why this pairs well with the current guide]"
     icon="arrow-right"
-    href="#related-link"
+    href="/[related-link]"
   />
   <Card
     title="[Next cookbook in journey]"
     description="[Set expectation for the next step]"
     icon="rocket"
-    href="#next-link"
+    href="/[next-link]"
   />
 </CardGroup>
 ```

docs/templates/migration_guide_template.mdx

@@ -145,13 +145,13 @@ npm install mem0ai@[version]
     title="[Deep dive reference]"
     description="[Why this reference matters post-migration]"
     icon="book"
-    href="#reference-link"
+    href="/[reference-link]"
   />
   <Card
     title="[Applied example or next step]"
     description="[What readers can build now]"
     icon="rocket"
-    href="#example-link"
+    href="/[example-link]"
   />
 </CardGroup>
 ```

examples/nemoclaw/install-mem0-plugin.sh

@@ -1,419 +0,0 @@
-#!/usr/bin/env bash
-# ─────────────────────────────────────────────────────────────────────────────
-# install-mem0-plugin.sh
-#
-# Installs and configures the @mem0/openclaw-mem0 plugin for an existing
-# NemoClaw sandbox. Assumes NemoClaw is already installed and onboarded.
-#
-# Usage:
-#   chmod +x install-mem0-plugin.sh && ./install-mem0-plugin.sh
-#
-# Requirements:
-#   - NemoClaw installed and onboarded (sandbox in Ready state)
-#   - Mem0 API key (from app.mem0.ai)
-# ─────────────────────────────────────────────────────────────────────────────
-
-set -euo pipefail
-
-# ── Colors and formatting ────────────────────────────────────────────────────
-
-RED='\033[0;31m'
-GREEN='\033[0;32m'
-YELLOW='\033[1;33m'
-BLUE='\033[0;34m'
-CYAN='\033[0;36m'
-BOLD='\033[1m'
-DIM='\033[2m'
-NC='\033[0m'
-
-info()    { echo -e "${BLUE}[INFO]${NC}  $*"; }
-success() { echo -e "${GREEN}[OK]${NC}    $*"; }
-warn()    { echo -e "${YELLOW}[WARN]${NC}  $*"; }
-error()   { echo -e "${RED}[ERROR]${NC} $*"; }
-step()    { echo -e "\n${BOLD}${CYAN}── $* ──${NC}\n"; }
-ask()     { echo -en "${BOLD}$*${NC}"; }
-
-die() {
-  error "$*"
-  exit 1
-}
-
-# ── Defaults ─────────────────────────────────────────────────────────────────
-
-MEM0_USER_ID="${MEM0_USER_ID:-default}"
-PLUGIN_PKG="@mem0/openclaw-mem0"
-CONTAINER_NAME="nemoclaw-dev"
-
-# ── Detect platform ──────────────────────────────────────────────────────────
-
-OS_TYPE="$(uname -s)"
-IS_MACOS=false
-IS_LINUX=false
-
-case "$OS_TYPE" in
-  Darwin) IS_MACOS=true ;;
-  Linux)  IS_LINUX=true ;;
-  *)      die "Unsupported OS: $OS_TYPE" ;;
-esac
-
-# ── Helper functions ─────────────────────────────────────────────────────────
-
-check_command() {
-  command -v "$1" &>/dev/null
-}
-
-ensure_nvm() {
-  export NVM_DIR="${NVM_DIR:-$HOME/.nvm}"
-  if [[ -s "$NVM_DIR/nvm.sh" ]]; then
-    source "$NVM_DIR/nvm.sh"
-  fi
-}
-
-ensure_path() {
-  for p in "$HOME/.local/bin" "$HOME/.nvm/versions/node/"*/bin; do
-    if [[ -d "$p" ]] && [[ ":$PATH:" != *":$p:"* ]]; then
-      export PATH="$p:$PATH"
-    fi
-  done
-}
-
-# Wrapper to run a command natively (Linux) or in the container (macOS)
-run_cmd() {
-  if $IS_MACOS; then
-    docker exec "$CONTAINER_NAME" bash -c "export NVM_DIR=/root/.nvm && source /root/.nvm/nvm.sh && $*"
-  else
-    eval "$@"
-  fi
-}
-
-# ── Banner ───────────────────────────────────────────────────────────────────
-
-echo ""
-echo -e "${BOLD}${CYAN}╔══════════════════════════════════════════════════════════════╗${NC}"
-echo -e "${BOLD}${CYAN}║         Mem0 Plugin Installer for NemoClaw                   ║${NC}"
-echo -e "${BOLD}${CYAN}║         Long-term memory for your OpenClaw agent             ║${NC}"
-echo -e "${BOLD}${CYAN}╚══════════════════════════════════════════════════════════════╝${NC}"
-echo ""
-
-if $IS_MACOS; then
-  info "Platform: macOS (using Docker container '$CONTAINER_NAME')"
-else
-  info "Platform: Linux"
-fi
-
-# ═════════════════════════════════════════════════════════════════════════════
-# PRE-CHECK: Verify NemoClaw is installed and sandbox is ready
-# ═════════════════════════════════════════════════════════════════════════════
-
-step "Pre-check: Verifying NemoClaw setup"
-
-if $IS_LINUX; then
-  ensure_nvm
-  ensure_path
-fi
-
-# Verify nemoclaw/openshell are available
-if $IS_MACOS; then
-  if ! docker ps --format '{{.Names}}' | grep -q "^${CONTAINER_NAME}$"; then
-    die "Docker container '$CONTAINER_NAME' is not running. Start it with: docker start $CONTAINER_NAME"
-  fi
-  if ! run_cmd "command -v nemoclaw" &>/dev/null; then
-    die "NemoClaw is not installed in container '$CONTAINER_NAME'. Run the full setup script first."
-  fi
-else
-  if ! check_command openshell; then
-    die "openshell not found. Is NemoClaw installed and onboarded? Try: source ~/.bashrc"
-  fi
-fi
-
-# Detect sandbox
-SANDBOX_NAME=$(run_cmd "openshell sandbox list 2>/dev/null" | awk 'NR>1 && $1!="" {print $1; exit}' || true)
-
-if [[ -z "$SANDBOX_NAME" ]]; then
-  die "No sandbox found. Run 'nemoclaw onboard' first."
-fi
-
-# Verify sandbox is ready
-SANDBOX_PHASE=$(run_cmd "openshell sandbox get '$SANDBOX_NAME' 2>/dev/null" | grep -i "phase" | awk '{print $NF}' || true)
-if [[ "$SANDBOX_PHASE" != "Ready" ]]; then
-  warn "Sandbox '$SANDBOX_NAME' is not in Ready state (current: ${SANDBOX_PHASE:-unknown})."
-  warn "Waiting up to 2 minutes..."
-  WAIT_OK=false
-  for i in $(seq 1 24); do
-    SANDBOX_PHASE=$(run_cmd "openshell sandbox get '$SANDBOX_NAME' 2>/dev/null" | grep -i "phase" | awk '{print $NF}' || true)
-    if [[ "$SANDBOX_PHASE" == "Ready" ]]; then
-      WAIT_OK=true
-      break
-    fi
-    sleep 5
-  done
-  if ! $WAIT_OK; then
-    die "Sandbox '$SANDBOX_NAME' did not become Ready. Run: openshell sandbox list"
-  fi
-fi
-
-success "NemoClaw installed"
-success "Sandbox '$SANDBOX_NAME' is ready"
-
-# ═════════════════════════════════════════════════════════════════════════════
-# STEP 1: Install Mem0 Plugin
-# ═════════════════════════════════════════════════════════════════════════════
-
-step "Step 1: Installing Mem0 plugin ($PLUGIN_PKG)"
-
-# Helper: run a command inside the sandbox non-interactively via piped stdin
-sandbox_exec() {
-  local cmd="$1"
-  if $IS_MACOS; then
-    printf '%s\nexit\n' "$cmd" | docker exec -i "$CONTAINER_NAME" bash -c \
-      "export NVM_DIR=/root/.nvm && source /root/.nvm/nvm.sh && openshell sandbox connect '$SANDBOX_NAME'" 2>&1
-  else
-    printf '%s\nexit\n' "$cmd" | openshell sandbox connect "$SANDBOX_NAME" 2>&1
-  fi
-}
-
-# Check if plugin is already installed
-PLUGIN_EXISTS=false
-if run_cmd "openshell sandbox download '$SANDBOX_NAME' /sandbox/.openclaw/extensions/openclaw-mem0/package.json /tmp/_mem0_plugin_check.json" &>/dev/null; then
-  if [[ -f /tmp/_mem0_plugin_check.json ]] || run_cmd "test -f /tmp/_mem0_plugin_check.json" &>/dev/null; then
-    PLUGIN_EXISTS=true
-  fi
-fi
-rm -f /tmp/_mem0_plugin_check.json 2>/dev/null || true
-run_cmd "rm -f /tmp/_mem0_plugin_check.json" 2>/dev/null || true
-
-if $PLUGIN_EXISTS; then
-  success "Mem0 plugin already installed in sandbox"
-else
-  info "Downloading $PLUGIN_PKG..."
-
-  # Download and build outside the sandbox (on host or in container)
-  run_cmd "cd /tmp && rm -rf openclaw-mem0-full mem0-openclaw-mem0-*.tgz openclaw-mem0-full.tgz"
-
-  if ! run_cmd "cd /tmp && npm pack '$PLUGIN_PKG' 2>/dev/null"; then
-    die "Failed to download $PLUGIN_PKG from npm. Check your internet connection."
-  fi
-
-  success "Downloaded plugin"
-
-  info "Installing plugin dependencies..."
-  run_cmd "mkdir -p /tmp/openclaw-mem0-full && cd /tmp/openclaw-mem0-full && tar xzf /tmp/mem0-openclaw-mem0-*.tgz --strip-components=1 && npm install --omit=dev 2>&1 | tail -3"
-
-  success "Dependencies installed"
-
-  info "Uploading plugin to sandbox..."
-  run_cmd "cd /tmp && tar czf openclaw-mem0-full.tgz -C openclaw-mem0-full ."
-
-  if ! run_cmd "openshell sandbox upload '$SANDBOX_NAME' /tmp/openclaw-mem0-full.tgz /sandbox/openclaw-mem0-full.tgz 2>&1"; then
-    die "Failed to upload plugin to sandbox. Check: openshell sandbox list"
-  fi
-
-  success "Plugin uploaded"
-
-  info "Extracting plugin inside sandbox..."
-  sandbox_exec "mkdir -p ~/.openclaw/extensions/openclaw-mem0 && tar xzf /sandbox/openclaw-mem0-full.tgz/openclaw-mem0-full.tgz -C ~/.openclaw/extensions/openclaw-mem0 2>/dev/null || tar xzf /sandbox/openclaw-mem0-full.tgz -C ~/.openclaw/extensions/openclaw-mem0 2>/dev/null && echo EXTRACT_OK" >/dev/null 2>&1 || true
-
-  # Verify
-  VERIFY_OK=false
-  if run_cmd "openshell sandbox download '$SANDBOX_NAME' /sandbox/.openclaw/extensions/openclaw-mem0/package.json /tmp/_mem0_verify.json" &>/dev/null; then
-    VERIFY_OK=true
-  fi
-  rm -f /tmp/_mem0_verify.json 2>/dev/null || true
-  run_cmd "rm -f /tmp/_mem0_verify.json" 2>/dev/null || true
-  if $VERIFY_OK; then
-    success "Plugin extracted inside sandbox"
-  else
-    warn "Could not verify plugin extraction. You may need to extract manually."
-    if $IS_MACOS; then
-      echo "  docker exec -it $CONTAINER_NAME bash"
-      echo "  export NVM_DIR=/root/.nvm && source /root/.nvm/nvm.sh"
-    fi
-    echo "  nemoclaw $SANDBOX_NAME connect"
-    echo "  mkdir -p ~/.openclaw/extensions/openclaw-mem0"
-    echo "  tar xzf /sandbox/openclaw-mem0-full.tgz/openclaw-mem0-full.tgz -C ~/.openclaw/extensions/openclaw-mem0"
-  fi
-
-  # Clean up
-  run_cmd "rm -rf /tmp/openclaw-mem0-full /tmp/mem0-openclaw-mem0-*.tgz /tmp/openclaw-mem0-full.tgz" 2>/dev/null || true
-fi
-
-# ═════════════════════════════════════════════════════════════════════════════
-# STEP 2: Update Network Policy
-# ═════════════════════════════════════════════════════════════════════════════
-
-step "Step 2: Updating network policy to allow api.mem0.ai and telemetry"
-
-# Find baseline policy
-BASELINE_POLICY=$(run_cmd "find / -path '*/nemoclaw-blueprint/policies/openclaw-sandbox.yaml' 2>/dev/null | head -1" || true)
-
-if [[ -z "$BASELINE_POLICY" ]]; then
-  die "Cannot find NemoClaw baseline policy file. Is NemoClaw installed?"
-fi
-
-info "Baseline policy: $BASELINE_POLICY"
-
-# Check if mem0_api already exists
-HAS_MEM0=$(run_cmd "grep -c mem0_api '$BASELINE_POLICY' 2>/dev/null" || echo "0")
-
-if [[ "$HAS_MEM0" != "0" ]]; then
-  success "mem0_api already in baseline policy"
-else
-  info "Adding api.mem0.ai to network policy..."
-fi
-
-# Create custom policy with mem0_api + telemetry
-run_cmd "node -e \"
-const fs = require('fs');
-let c = fs.readFileSync('$BASELINE_POLICY', 'utf8');
-const mem0Block = '\\n  mem0_api:\\n    name: mem0_api\\n    endpoints:\\n      - host: api.mem0.ai\\n        port: 443\\n        access: full\\n    binaries:\\n      - { path: /usr/local/bin/node }\\n      - { path: /usr/local/bin/openclaw }\\n';
-const telemetryBlock = '\\n  mem0_telemetry:\\n    name: mem0_telemetry\\n    endpoints:\\n      - host: us.i.posthog.com\\n        port: 443\\n        access: full\\n    binaries:\\n      - { path: /usr/local/bin/node }\\n      - { path: /usr/local/bin/openclaw }\\n';
-if (!c.includes('mem0_api')) {
-  if (c.includes('# ── Messaging')) {
-    c = c.replace('  # ── Messaging', mem0Block + '\\n  # ── Messaging');
-  } else {
-    c += mem0Block;
-  }
-}
-if (!c.includes('mem0_telemetry')) {
-  if (c.includes('mem0_api:')) {
-    c = c.replace('  mem0_api:', telemetryBlock + '\\n  mem0_api:');
-  } else {
-    c += telemetryBlock;
-  }
-}
-fs.writeFileSync('/tmp/nemoclaw-mem0-policy.yaml', c);
-console.log('ok');
-\""
-
-success "Custom policy file created"
-
-# Apply the policy
-info "Applying network policy..."
-if ! run_cmd "openshell policy set '$SANDBOX_NAME' --policy /tmp/nemoclaw-mem0-policy.yaml --wait 2>&1"; then
-  error "Failed to apply network policy."
-  echo ""
-  echo "  If you see 'sandbox not found', re-run: nemoclaw onboard"
-  echo "  Then re-run this script."
-  echo ""
-  die "Network policy update failed."
-fi
-
-success "Network policy applied — api.mem0.ai and telemetry allowed"
-
-# ═════════════════════════════════════════════════════════════════════════════
-# STEP 3: Configure Plugin
-# ═════════════════════════════════════════════════════════════════════════════
-
-step "Step 3: Configuring Mem0 plugin"
-
-echo ""
-echo -e "  ${DIM}Get your Mem0 API key from: https://app.mem0.ai${NC}"
-echo -e "  ${DIM}The key starts with 'm0-'${NC}"
-echo ""
-ask "Enter your Mem0 API key: "
-read -r MEM0_API_KEY
-
-if [[ -z "$MEM0_API_KEY" ]]; then
-  die "Mem0 API key is required."
-fi
-
-if [[ ! "$MEM0_API_KEY" =~ ^m0- ]]; then
-  warn "Key doesn't start with 'm0-'. Make sure this is correct."
-fi
-
-echo ""
-echo -e "  ${DIM}The user ID scopes all memories. Pick any unique identifier.${NC}"
-echo -e "  ${DIM}Examples: alice, user_123, your-email@example.com${NC}"
-echo ""
-ask "Enter user ID [$MEM0_USER_ID]: "
-read -r custom_user_id
-MEM0_USER_ID="${custom_user_id:-$MEM0_USER_ID}"
-
-info "Configuring plugin inside sandbox..."
-
-CONFIG_SCRIPT="openclaw config set plugins.slots.memory openclaw-mem0 2>&1 | tail -1 && \
-openclaw config set plugins.entries.openclaw-mem0.enabled true 2>&1 | tail -1 && \
-openclaw config set plugins.entries.openclaw-mem0.config.apiKey '$MEM0_API_KEY' 2>&1 | tail -1 && \
-openclaw config set plugins.entries.openclaw-mem0.config.userId '$MEM0_USER_ID' 2>&1 | tail -1 && \
-echo SETUP_DONE"
-
-CONFIG_OUTPUT=$(sandbox_exec "$CONFIG_SCRIPT" || true)
-
-if echo "$CONFIG_OUTPUT" | grep -q "SETUP_DONE"; then
-  success "Plugin configured (mode: platform, user: $MEM0_USER_ID)"
-else
-  warn "Could not verify config. You may need to configure manually:"
-  echo ""
-  if $IS_MACOS; then
-    echo "  docker exec -it $CONTAINER_NAME bash"
-    echo "  export NVM_DIR=/root/.nvm && source /root/.nvm/nvm.sh"
-  fi
-  echo "  nemoclaw $SANDBOX_NAME connect"
-  echo "  openclaw config set plugins.slots.memory openclaw-mem0"
-  echo "  openclaw config set plugins.entries.openclaw-mem0.enabled true"
-  echo "  openclaw config set plugins.entries.openclaw-mem0.config.apiKey \"$MEM0_API_KEY\""
-  echo "  openclaw config set plugins.entries.openclaw-mem0.config.userId \"$MEM0_USER_ID\""
-  echo ""
-fi
-
-# ═════════════════════════════════════════════════════════════════════════════
-# Done
-# ═════════════════════════════════════════════════════════════════════════════
-
-step "Verification"
-
-echo ""
-echo -e "${BOLD}${GREEN}╔══════════════════════════════════════════════════════════════╗${NC}"
-echo -e "${BOLD}${GREEN}║                    Setup Complete!                           ║${NC}"
-echo -e "${BOLD}${GREEN}╚══════════════════════════════════════════════════════════════╝${NC}"
-echo ""
-echo -e "  ${BOLD}Sandbox:${NC}   $SANDBOX_NAME"
-echo -e "  ${BOLD}Plugin:${NC}    @mem0/openclaw-mem0 (platform mode)"
-echo -e "  ${BOLD}User ID:${NC}   $MEM0_USER_ID"
-if $IS_MACOS; then
-  echo -e "  ${BOLD}Container:${NC} $CONTAINER_NAME"
-fi
-echo ""
-echo -e "  ${BOLD}${CYAN}Next steps:${NC}"
-echo ""
-
-if $IS_MACOS; then
-  echo -e "  1. Open a shell in the container:"
-  echo ""
-  echo -e "     ${DIM}docker exec -it $CONTAINER_NAME bash${NC}"
-  echo -e "     ${DIM}export NVM_DIR=/root/.nvm && source /root/.nvm/nvm.sh${NC}"
-  echo ""
-  echo -e "  2. Connect to the sandbox and start the gateway:"
-  echo ""
-  echo -e "     ${DIM}nemoclaw $SANDBOX_NAME connect${NC}"
-  echo -e "     ${DIM}nemoclaw-start${NC}"
-else
-  echo -e "  1. Connect to the sandbox and start the gateway:"
-  echo ""
-  echo -e "     ${DIM}source ~/.bashrc${NC}"
-  echo -e "     ${DIM}nemoclaw $SANDBOX_NAME connect${NC}"
-  echo -e "     ${DIM}nemoclaw-start${NC}"
-fi
-echo ""
-echo -e "  Then verify the plugin loaded (look for 'openclaw-mem0: registered'):"
-echo ""
-echo -e "     ${DIM}openclaw plugins list${NC}"
-echo ""
-echo -e "  Test auto-capture (storing memories):"
-echo ""
-echo -e "     ${DIM}openclaw agent --agent main --local -m \"My name is Alice\" --session-id test1${NC}"
-echo ""
-echo -e "  Test auto-recall (new session, memories should appear):"
-echo ""
-echo -e "     ${DIM}openclaw agent --agent main --local -m \"What do you know about me?\" --session-id test2${NC}"
-echo ""
-echo -e "  Or use the interactive TUI:"
-echo ""
-echo -e "     ${DIM}openclaw tui${NC}"
-echo ""
-echo -e "  ${YELLOW}Note:${NC} You may see 'Telemetry event capture failed' errors."
-echo -e "  These are harmless and do not affect memory functionality."
-echo ""
-echo -e "  ${BOLD}Documentation:${NC} https://docs.mem0.ai"
-echo -e "  ${BOLD}Plugin source:${NC} https://www.npmjs.com/package/@mem0/openclaw-mem0"
-echo ""

examples/nemoclaw/quickstart.md

@@ -1,272 +0,0 @@
-# Mem0 Plugin for NemoClaw — Quickstart
-
-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)). 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
-
-| Resource | Recommended | Minimum |
-|----------|-------------|---------|
-| CPU      | 4+ vCPU     | 2 vCPU  |
-| RAM      | 16 GB       | 8 GB    |
-| Disk     | 40 GB free  | 20 GB free |
-
-**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), generate an API key from the dashboard (starts with `m0-`)
-
-**Supported platforms:** Ubuntu 22.04+, macOS (via Docker), Windows (WSL 2 + Docker)
-
----
-
-## Choose Your Path
-
-### Option A: Full Setup (NemoClaw + Mem0 Plugin)
-
-Use this if you **don't have NemoClaw installed yet**. The script handles everything: Docker, Node.js, NemoClaw installation, onboarding, Mem0 plugin installation, network policy, and configuration.
-
-```bash
-# Download
-curl -fsSL https://raw.githubusercontent.com/mem0ai/mem0/main/examples/nemoclaw/setup-mem0-nemoclaw.sh -o setup-mem0-nemoclaw.sh
-
-# Run
-chmod +x setup-mem0-nemoclaw.sh
-./setup-mem0-nemoclaw.sh
-```
-
-The script runs through 7 phases:
-
-| Phase | What it does | User input |
-|-------|-------------|------------|
-| 1 | Prerequisites (Docker, RAM, disk) | None (automatic) |
-| 2 | Install NemoClaw | None (automatic) |
-| 3 | NemoClaw onboarding (sandbox + k3s) | Sandbox name, NVIDIA API key |
-| 4 | Install Mem0 plugin into sandbox | None (automatic) |
-| 5 | Update network policy for `api.mem0.ai` | None (automatic) |
-| 6 | Configure plugin | Mem0 API key, user ID |
-| 7 | Verification | None |
-
-### Option B: Plugin Only (NemoClaw Already Installed)
-
-Use this if you **already have NemoClaw installed and onboarded** with a sandbox in `Ready` state.
-
-```bash
-# Download
-curl -fsSL https://raw.githubusercontent.com/mem0ai/mem0/main/examples/nemoclaw/install-mem0-plugin.sh -o install-mem0-plugin.sh
-
-# Run
-chmod +x install-mem0-plugin.sh
-./install-mem0-plugin.sh
-```
-
-The script auto-detects your sandbox and runs 3 steps:
-
-| Step | What it does | User input |
-|------|-------------|------------|
-| 1 | Install Mem0 plugin into sandbox | None (automatic) |
-| 2 | Update network policy for `api.mem0.ai` | None (automatic) |
-| 3 | Configure plugin | Mem0 API key, user ID |
-
----
-
-## Verify the Installation
-
-After either script completes, connect to the sandbox and start the gateway:
-
-```bash
-source ~/.bashrc
-nemoclaw <sandbox-name> connect
-nemoclaw-start
-```
-
-Look for this line in the startup output:
-
-```
-openclaw-mem0: registered (mode: platform, user: your-user-id, graph: false, autoRecall: true, autoCapture: true)
-```
-
-You can also verify with:
-
-```bash
-openclaw plugins list
-```
-
-The `Memory (Mem0)` plugin should show status **loaded**.
-
-## Test the Integration
-
-All test commands run **inside the sandbox**.
-
-### Test 1: Auto-capture (storing memories)
-
-```bash
-openclaw agent --agent main --local \
-  -m "My name is Alice and I work on distributed systems" \
-  --session-id test1
-```
-
-Look for: `openclaw-mem0: auto-captured 1 memories`
-
-### Test 2: Auto-recall (retrieving memories across sessions)
-
-Start a **new session** (different `--session-id`):
-
-```bash
-openclaw agent --agent main --local \
-  -m "What do you know about me?" \
-  --session-id test2
-```
-
-Look for: `openclaw-mem0: injecting 1 memories into context (1 long-term, 0 session)`
-
-The agent should respond with information from the previous session ("Alice", "distributed systems").
-
-### Test 3: Interactive TUI
-
-```bash
-openclaw tui
-```
-
-Send messages and the plugin will automatically capture and recall memories in the background.
-
----
-
-## Plugin Configuration Reference
-
-All options are set inside the sandbox via:
-
-```bash
-openclaw config set plugins.entries.openclaw-mem0.config.<key> <value>
-```
-
-| Key | Type | Default | Description |
-|-----|------|---------|-------------|
-| `mode` | `"platform"` \| `"open-source"` | `"platform"` | Backend mode |
-| `apiKey` | string | — | Mem0 API key (starts with `m0-`) |
-| `userId` | string | `"default"` | Unique identifier for the user |
-| `autoRecall` | boolean | `true` | Inject memories before each agent turn |
-| `autoCapture` | boolean | `true` | Store facts after each agent turn |
-| `topK` | number | `5` | Max memories per recall |
-| `searchThreshold` | number | `0.3` | Min similarity score (0-1) |
-| `orgId` | string | — | Mem0 organization ID |
-| `projectId` | string | — | Mem0 project ID |
-| `enableGraph` | boolean | `false` | Enable entity graph for relationships |
-| `customInstructions` | string | — | Rules for what Mem0 should store/exclude |
-
-## Agent Memory Tools
-
-Once the plugin is active, the agent can use these tools during conversations:
-
-| Tool | Description |
-|------|-------------|
-| `memory_search` | Search memories by natural language |
-| `memory_list` | List all stored memories for a user |
-| `memory_store` | Explicitly save a fact |
-| `memory_get` | Retrieve a memory by ID |
-| `memory_forget` | Delete by ID or by query |
-
-## CLI Commands
-
-Run these inside the sandbox:
-
-```bash
-# Search all memories (long-term + session)
-openclaw mem0 search "what languages does the user know"
-
-# Search only long-term memories
-openclaw mem0 search "user preferences" --scope long-term
-
-# Search only session memories
-openclaw mem0 search "current task" --scope session
-
-# Memory stats
-openclaw mem0 stats
-```
-
----
-
-## Troubleshooting
-
-For detailed troubleshooting steps, see the [troubleshooting guide](troubleshooting-guide.pdf) included in this directory.
-
-### Common Issues
-
-#### `npm tar TAR_ENTRY_ERROR ENOENT` during NemoClaw installation
-
-This is a known npm bug where concurrent tar extraction races cause `ENOENT` errors on deeply nested packages. The `setup-mem0-nemoclaw.sh` script works around this by cloning the NemoClaw repo and installing with `--maxsockets=1` to serialize downloads. If you installed NemoClaw manually and hit this error:
-
-```bash
-git clone --depth 1 https://github.com/NVIDIA/NemoClaw.git ~/.nemoclaw-src
-cd ~/.nemoclaw-src
-npm install --maxsockets=1
-npm link
-```
-
-#### `sandbox not found` during onboarding step 7
-
-The gateway restarted during onboarding and lost the sandbox state. Re-run `nemoclaw onboard`. When prompted that the sandbox already exists, choose `y` to recreate it.
-
-#### `npm error 403 Forbidden` when installing plugin inside sandbox
-
-The OpenShell gateway's TLS proxy blocks scoped npm packages (the `%2f` in the URL). Use the manual installation method (Method 2 in the scripts) which downloads outside the sandbox and uploads the tarball.
-
-#### `capture failed: Connection error` or `recall failed: Connection error`
-
-The network policy is not applied or missing the `mem0_api` entry. Re-run the plugin install script or manually apply the policy:
-
-```bash
-openshell policy set <sandbox-name> --policy /tmp/nemoclaw-mem0-policy.yaml --wait
-```
-
-#### `Telemetry event capture failed: TypeError: fetch failed`
-
-This is harmless. The Mem0 SDK's telemetry endpoint (`us.i.posthog.com`) is blocked by the sandbox proxy. It does not affect memory functionality.
-
-#### Plugin shows `disabled` in `openclaw plugins list`
-
-The memory slot is not set to `openclaw-mem0`. Inside the sandbox, run:
-
-```bash
-openclaw config set plugins.slots.memory openclaw-mem0
-```
-
-#### `K8s namespace not ready` on Ubuntu 24.04
-
-Ubuntu 24.04 defaults to cgroup v2 which causes k3s (used by NemoClaw) to fail. Apply the cgroup fix:
-
-```bash
-sudo python3 -c "
-import json, os
-p = '/etc/docker/daemon.json'
-c = json.load(open(p)) if os.path.exists(p) else {}
-c['default-cgroupns-mode'] = 'host'
-json.dump(c, open(p, 'w'), indent=2)
-"
-sudo systemctl restart docker
-```
-
-Then re-run `nemoclaw onboard`.
-
-### Known Limitations
-
-**Open-source mode is not supported in NemoClaw sandboxes.** The Mem0 open-source mode requires calling `/v1/embeddings` on an external LLM provider. NemoClaw's sandbox proxy intercepts all OpenAI-compatible API requests but only allows `/v1/chat/completions` through. Use **platform mode** instead.
-
----
-
-## Files in This Directory
-
-| File | Description |
-|------|-------------|
-| `setup-mem0-nemoclaw.sh` | Full setup script (NemoClaw + Mem0 plugin) |
-| `install-mem0-plugin.sh` | Plugin-only install script (NemoClaw already set up) |
-| `troubleshooting-guide.pdf` | Detailed setup and troubleshooting guide |
-| `quickstart.md` | This file |
-
-## Links
-
-- [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)

examples/nemoclaw/setup-mem0-nemoclaw.sh

@@ -1,864 +0,0 @@
-#!/usr/bin/env bash
-# ─────────────────────────────────────────────────────────────────────────────
-# setup-mem0-nemoclaw.sh
-#
-# One-script setup for NemoClaw + Mem0 OpenClaw plugin.
-# Supports Ubuntu servers (native) and macOS (via Docker container).
-#
-# Usage:
-#   curl -fsSL https://raw.githubusercontent.com/mem0ai/mem0/main/scripts/setup-mem0-nemoclaw.sh | bash
-#   # or
-#   chmod +x setup-mem0-nemoclaw.sh && ./setup-mem0-nemoclaw.sh
-#
-# Requirements:
-#   - Ubuntu 22.04+ OR macOS with Docker Desktop OR Windows with WSL 2 + Docker Desktop
-#   - 8 GB RAM minimum (16 GB recommended)
-#   - 40 GB free disk (20 GB minimum)
-#   - NVIDIA API key (from build.nvidia.com)
-#   - Mem0 API key (from app.mem0.ai)
-# ─────────────────────────────────────────────────────────────────────────────
-
-set -euo pipefail
-
-# ── Colors and formatting ────────────────────────────────────────────────────
-
-RED='\033[0;31m'
-GREEN='\033[0;32m'
-YELLOW='\033[1;33m'
-BLUE='\033[0;34m'
-CYAN='\033[0;36m'
-BOLD='\033[1m'
-DIM='\033[2m'
-NC='\033[0m'
-
-info()    { echo -e "${BLUE}[INFO]${NC}  $*"; }
-success() { echo -e "${GREEN}[OK]${NC}    $*"; }
-warn()    { echo -e "${YELLOW}[WARN]${NC}  $*"; }
-error()   { echo -e "${RED}[ERROR]${NC} $*"; }
-step()    { echo -e "\n${BOLD}${CYAN}── $* ──${NC}\n"; }
-ask()     { echo -en "${BOLD}$*${NC}"; }
-
-die() {
-  error "$*"
-  exit 1
-}
-
-# ── Defaults ─────────────────────────────────────────────────────────────────
-
-SANDBOX_NAME="${SANDBOX_NAME:-my-assistant}"
-MEM0_USER_ID="${MEM0_USER_ID:-default}"
-MIN_RAM_MB=6000
-MIN_DISK_MB=15000
-PLUGIN_PKG="@mem0/openclaw-mem0"
-CONTAINER_NAME="nemoclaw-dev"
-
-# ── Detect platform ─────────────────────────────────────────────────────────
-
-OS_TYPE="$(uname -s)"
-IS_MACOS=false
-IS_LINUX=false
-IS_WSL=false
-
-case "$OS_TYPE" in
-  Darwin) IS_MACOS=true ;;
-  Linux)
-    IS_LINUX=true
-    # Detect WSL (Windows Subsystem for Linux)
-    if grep -qi "microsoft\|wsl" /proc/version 2>/dev/null; then
-      IS_WSL=true
-    fi
-    ;;
-  MINGW*|MSYS*|CYGWIN*)
-    error "This script cannot run in Git Bash, MSYS2, or Cygwin."
-    echo ""
-    echo "  NemoClaw requires a full Linux environment. On Windows, use WSL 2:"
-    echo ""
-    echo "  1. Open PowerShell as Administrator and run:"
-    echo "     wsl --install -d Ubuntu-24.04"
-    echo ""
-    echo "  2. Restart your computer when prompted"
-    echo ""
-    echo "  3. Open 'Ubuntu' from the Start menu (this opens a WSL 2 shell)"
-    echo ""
-    echo "  4. Install Docker Desktop for Windows:"
-    echo "     https://www.docker.com/products/docker-desktop/"
-    echo "     Enable 'Use the WSL 2 based engine' in Docker Desktop settings"
-    echo "     Enable 'Ubuntu-24.04' under Resources → WSL Integration"
-    echo ""
-    echo "  5. In the Ubuntu WSL 2 shell, re-run this script:"
-    echo "     curl -fsSL https://raw.githubusercontent.com/mem0ai/mem0/main/scripts/setup-mem0-nemoclaw.sh | bash"
-    echo ""
-    die "Please use WSL 2 instead."
-    ;;
-  *)
-    die "Unsupported OS: $OS_TYPE. This script supports Linux (Ubuntu), macOS, and Windows (WSL 2)."
-    ;;
-esac
-
-# ── Helper functions ─────────────────────────────────────────────────────────
-
-check_command() {
-  command -v "$1" &>/dev/null
-}
-
-get_ram_mb() {
-  if $IS_MACOS; then
-    sysctl -n hw.memsize 2>/dev/null | awk '{printf "%.0f", $1/1024/1024}' || echo "0"
-  else
-    free -m 2>/dev/null | awk '/^Mem:/ {print $2}' || echo "0"
-  fi
-}
-
-get_disk_mb() {
-  if $IS_MACOS; then
-    df -m / 2>/dev/null | awk 'NR==2 {print $4}' || echo "0"
-  else
-    df -m / 2>/dev/null | awk 'NR==2 {print $4}' || echo "0"
-  fi
-}
-
-ensure_nvm() {
-  export NVM_DIR="${NVM_DIR:-$HOME/.nvm}"
-  if [[ -s "$NVM_DIR/nvm.sh" ]]; then
-    source "$NVM_DIR/nvm.sh"
-  fi
-}
-
-ensure_path() {
-  for p in "$HOME/.local/bin" "$HOME/.nvm/versions/node/"*/bin; do
-    if [[ -d "$p" ]] && [[ ":$PATH:" != *":$p:"* ]]; then
-      export PATH="$p:$PATH"
-    fi
-  done
-}
-
-wait_for_sandbox() {
-  local name="$1"
-  local timeout=120
-  local elapsed=0
-  while (( elapsed < timeout )); do
-    local phase
-    phase=$($RUN_CMD openshell sandbox get "$name" 2>/dev/null | grep -i "phase" | awk '{print $NF}' || true)
-    if [[ "$phase" == "Ready" ]]; then
-      return 0
-    fi
-    sleep 5
-    elapsed=$((elapsed + 5))
-  done
-  return 1
-}
-
-# ── macOS: run command inside the container ──────────────────────────────────
-# On macOS, NemoClaw runs inside a Docker container. All nemoclaw/openshell
-# commands must be exec'd into the container. On Linux, they run natively.
-
-setup_run_cmd() {
-  if $IS_MACOS; then
-    RUN_CMD="docker exec $CONTAINER_NAME bash -c 'export NVM_DIR=/root/.nvm && source /root/.nvm/nvm.sh && "
-    RUN_CMD_SUFFIX="'"
-    RUN_CMD_IT="docker exec -it $CONTAINER_NAME bash -c 'export NVM_DIR=/root/.nvm && source /root/.nvm/nvm.sh && "
-    RUN_CMD_IT_SUFFIX="'"
-  else
-    RUN_CMD=""
-    RUN_CMD_SUFFIX=""
-    RUN_CMD_IT=""
-    RUN_CMD_IT_SUFFIX=""
-  fi
-}
-
-# Wrapper to run a command natively (Linux) or in the container (macOS)
-run_cmd() {
-  if $IS_MACOS; then
-    docker exec "$CONTAINER_NAME" bash -c "export NVM_DIR=/root/.nvm && source /root/.nvm/nvm.sh && $*"
-  else
-    eval "$@"
-  fi
-}
-
-run_cmd_it() {
-  if $IS_MACOS; then
-    docker exec -it "$CONTAINER_NAME" bash -c "export NVM_DIR=/root/.nvm && source /root/.nvm/nvm.sh && $*"
-  else
-    eval "$@"
-  fi
-}
-
-# ── Banner ───────────────────────────────────────────────────────────────────
-
-echo ""
-echo -e "${BOLD}${CYAN}╔══════════════════════════════════════════════════════════════╗${NC}"
-echo -e "${BOLD}${CYAN}║         Mem0 Plugin Setup for NemoClaw                       ║${NC}"
-echo -e "${BOLD}${CYAN}║         Long-term memory for your OpenClaw agent             ║${NC}"
-echo -e "${BOLD}${CYAN}╚══════════════════════════════════════════════════════════════╝${NC}"
-echo ""
-
-if $IS_MACOS; then
-  info "Platform: macOS (will use Docker container)"
-elif $IS_WSL; then
-  info "Platform: Windows (WSL 2)"
-else
-  info "Platform: Linux"
-fi
-
-# ═════════════════════════════════════════════════════════════════════════════
-# PHASE 1: Prerequisites
-# ═════════════════════════════════════════════════════════════════════════════
-
-step "Phase 1: Checking prerequisites"
-
-# ── OS check ─────────────────────────────────────────────────────────────────
-
-if $IS_LINUX; then
-  if [[ -f /etc/os-release ]]; then
-    source /etc/os-release
-    if [[ "$ID" != "ubuntu" ]]; then
-      warn "Detected OS: $PRETTY_NAME (not Ubuntu). This script is tested on Ubuntu 22.04+."
-      ask "Continue anyway? [y/N]: "
-      read -r cont
-      [[ "$cont" =~ ^[Yy]$ ]] || exit 0
-    else
-      success "OS: $PRETTY_NAME"
-    fi
-  else
-    warn "Cannot detect Linux distribution. Proceeding anyway."
-  fi
-elif $IS_MACOS; then
-  MACOS_VERSION=$(sw_vers -productVersion 2>/dev/null || echo "unknown")
-  success "OS: macOS $MACOS_VERSION"
-fi
-
-# ── RAM check ────────────────────────────────────────────────────────────────
-
-RAM_MB=$(get_ram_mb)
-if (( RAM_MB < MIN_RAM_MB )); then
-  error "Insufficient RAM: ${RAM_MB} MB available, ${MIN_RAM_MB} MB required."
-  echo ""
-  echo "  NemoClaw needs at least 8 GB RAM (16 GB recommended)."
-  if $IS_WSL; then
-    echo ""
-    echo "  WSL 2 may have limited memory. Increase it:"
-    echo "    1. Create/edit %USERPROFILE%\\.wslconfig in Windows"
-    echo "    2. Add:"
-    echo "       [wsl2]"
-    echo "       memory=8GB"
-    echo "    3. Restart WSL:  wsl --shutdown  (from PowerShell)"
-  elif $IS_LINUX; then
-    echo ""
-    echo "  If running on AWS EC2:"
-    echo "    1. Stop the instance"
-    echo "    2. Change instance type to t3.large (8 GB) or t3.xlarge (16 GB)"
-    echo "    3. Start the instance and re-run this script"
-  fi
-  echo ""
-  die "Aborting due to insufficient RAM."
-else
-  success "RAM: ${RAM_MB} MB available"
-fi
-
-# ── Disk check ───────────────────────────────────────────────────────────────
-
-DISK_MB=$(get_disk_mb)
-if (( DISK_MB < MIN_DISK_MB )); then
-  error "Insufficient disk space: ${DISK_MB} MB available, ${MIN_DISK_MB} MB required."
-  echo ""
-  echo "  NemoClaw needs at least 20 GB free disk (40 GB recommended)."
-  echo ""
-  echo "  Quick fix:"
-  echo "    docker system prune -a -f    # Remove unused Docker data"
-  if $IS_LINUX; then
-    echo ""
-    echo "  If running on AWS EC2, expand the EBS volume:"
-    echo "    1. Go to AWS Console → EC2 → Volumes"
-    echo "    2. Select the volume, Actions → Modify Volume → increase size"
-    echo "    3. Then run:"
-    echo "       sudo growpart /dev/xvda 1"
-    echo "       sudo resize2fs /dev/xvda1"
-  fi
-  echo ""
-  die "Aborting due to insufficient disk space."
-else
-  success "Disk: ${DISK_MB} MB available"
-fi
-
-# ── Docker check ─────────────────────────────────────────────────────────────
-
-if ! check_command docker; then
-  if $IS_MACOS; then
-    error "Docker not found."
-    echo ""
-    echo "  Install Docker Desktop for macOS:"
-    echo "    https://www.docker.com/products/docker-desktop/"
-    echo ""
-    echo "  After installing, start Docker Desktop and re-run this script."
-    echo ""
-    die "Docker Desktop is required on macOS."
-  elif $IS_WSL; then
-    error "Docker not found inside WSL."
-    echo ""
-    echo "  Docker Desktop must be installed on Windows with WSL 2 integration enabled:"
-    echo ""
-    echo "  1. Install Docker Desktop for Windows:"
-    echo "     https://www.docker.com/products/docker-desktop/"
-    echo ""
-    echo "  2. Open Docker Desktop → Settings → General:"
-    echo "     ✓ Enable 'Use the WSL 2 based engine'"
-    echo ""
-    echo "  3. Open Docker Desktop → Settings → Resources → WSL Integration:"
-    echo "     ✓ Enable integration with your Ubuntu distribution"
-    echo ""
-    echo "  4. Click 'Apply & restart', then re-run this script in WSL."
-    echo ""
-    die "Docker Desktop WSL 2 integration is required."
-  else
-    info "Docker not found. Installing Docker..."
-    sudo apt-get update -qq
-    sudo apt-get install -y -qq docker.io >/dev/null 2>&1
-    sudo systemctl enable --now docker
-    sudo usermod -aG docker "$USER"
-    success "Docker installed"
-  fi
-fi
-
-if $IS_LINUX; then
-  # Ensure Docker daemon is running
-  if ! sudo docker info &>/dev/null; then
-    info "Starting Docker daemon..."
-    sudo systemctl start docker
-    sleep 2
-  fi
-
-  # Handle Docker group permissions
-  if ! docker info &>/dev/null; then
-    if id -nG "$USER" | grep -qw docker || grep -q "^docker:.*\b${USER}\b" /etc/group; then
-      info "Activating docker group for current session..."
-      exec sg docker -c "$0 $*"
-    else
-      info "Adding $USER to docker group..."
-      sudo usermod -aG docker "$USER"
-      info "Activating docker group for current session..."
-      exec sg docker -c "$0 $*"
-    fi
-  fi
-else
-  # macOS: just verify Docker is responding
-  if ! docker info &>/dev/null; then
-    error "Docker is not running."
-    echo ""
-    echo "  Start Docker Desktop and wait for it to be ready, then re-run this script."
-    echo ""
-    die "Docker Desktop is not running."
-  fi
-fi
-
-success "Docker: running ($(docker --version | awk '{print $3}' | tr -d ','))"
-
-# ── Linux-only: cgroup v2 fix for Ubuntu 24.04 ──────────────────────────────
-
-if $IS_LINUX && [[ "${VERSION_ID:-}" == "24.04" ]]; then
-  DAEMON_JSON="/etc/docker/daemon.json"
-  NEEDS_CGROUP_FIX=false
-
-  if [[ ! -f "$DAEMON_JSON" ]]; then
-    NEEDS_CGROUP_FIX=true
-  elif ! grep -q '"default-cgroupns-mode"' "$DAEMON_JSON" 2>/dev/null; then
-    NEEDS_CGROUP_FIX=true
-  fi
-
-  if $NEEDS_CGROUP_FIX; then
-    warn "Ubuntu 24.04 detected — applying cgroup v2 fix for Docker."
-    warn "This prevents 'K8s namespace not ready' errors during onboarding."
-    sudo python3 -c "
-import json, os
-p = '$DAEMON_JSON'
-c = json.load(open(p)) if os.path.exists(p) else {}
-c['default-cgroupns-mode'] = 'host'
-json.dump(c, open(p, 'w'), indent=2)
-"
-    sudo systemctl restart docker
-    success "Docker cgroup v2 fix applied"
-  else
-    success "Docker cgroup v2: already configured"
-  fi
-fi
-
-# ── Linux-only: Swap check ──────────────────────────────────────────────────
-
-if $IS_LINUX; then
-  SWAP_MB=$(free -m 2>/dev/null | awk '/^Swap:/ {print $2}' || echo "0")
-  if (( RAM_MB < 12000 && SWAP_MB < 2000 )); then
-    warn "Low RAM (${RAM_MB} MB) and low swap. Adding 4 GB swap to prevent OOM kills."
-    if [[ ! -f /swapfile ]]; then
-      sudo fallocate -l 4G /swapfile
-      sudo chmod 600 /swapfile
-      sudo mkswap /swapfile >/dev/null
-      sudo swapon /swapfile
-      success "4 GB swap enabled"
-    else
-      success "Swap file already exists"
-    fi
-  fi
-fi
-
-# ── macOS-only: Create Docker container ──────────────────────────────────────
-
-if $IS_MACOS; then
-  step "Phase 1b: Setting up NemoClaw Docker container"
-
-  if docker ps --format '{{.Names}}' | grep -q "^${CONTAINER_NAME}$"; then
-    success "Container '$CONTAINER_NAME' is already running"
-  elif docker ps -a --format '{{.Names}}' | grep -q "^${CONTAINER_NAME}$"; then
-    info "Starting existing container '$CONTAINER_NAME'..."
-    docker start "$CONTAINER_NAME"
-    success "Container started"
-  else
-    info "Creating Ubuntu container '$CONTAINER_NAME' for NemoClaw..."
-    echo -e "  ${DIM}This container runs NemoClaw with --network host and Docker socket access.${NC}"
-
-    docker run -d \
-      --name "$CONTAINER_NAME" \
-      --privileged \
-      --network host \
-      -v /var/run/docker.sock:/var/run/docker.sock \
-      ubuntu:24.04 sleep infinity
-
-    success "Container '$CONTAINER_NAME' created"
-
-    info "Installing dependencies inside container..."
-    docker exec "$CONTAINER_NAME" bash -c "apt-get update -qq && apt-get install -y -qq curl git docker.io >/dev/null 2>&1"
-    success "Dependencies installed"
-  fi
-
-  # Check if NemoClaw is installed in the container
-  NEMO_IN_CONTAINER=$(docker exec "$CONTAINER_NAME" bash -c "export NVM_DIR=/root/.nvm && source /root/.nvm/nvm.sh 2>/dev/null && command -v nemoclaw" 2>/dev/null || true)
-  if [[ -z "$NEMO_IN_CONTAINER" ]]; then
-    info "Installing NemoClaw inside container (this takes a few minutes)..."
-    # The NVIDIA installer triggers npm tar race conditions. Workaround: clone
-    # and install with --maxsockets=1 to serialize downloads.
-    docker exec -it "$CONTAINER_NAME" bash -c "
-      export NVM_DIR=/root/.nvm &&
-      if [ ! -s \"\$NVM_DIR/nvm.sh\" ]; then
-        curl -fsSL https://raw.githubusercontent.com/nvm-sh/nvm/v0.40.3/install.sh | bash &&
-        source \"\$NVM_DIR/nvm.sh\" &&
-        nvm install 22
-      else
-        source \"\$NVM_DIR/nvm.sh\"
-      fi &&
-      git clone --depth 1 https://github.com/NVIDIA/NemoClaw.git /root/.nemoclaw-src &&
-      cd /root/.nemoclaw-src &&
-      npm install --maxsockets=1 &&
-      npm link
-    "
-    success "NemoClaw installed in container"
-  else
-    success "NemoClaw already installed in container"
-  fi
-fi
-
-# ═════════════════════════════════════════════════════════════════════════════
-# PHASE 2: Install NemoClaw (Linux-only, macOS handled above)
-# ═════════════════════════════════════════════════════════════════════════════
-
-if $IS_LINUX; then
-  step "Phase 2: Installing NemoClaw"
-
-  ensure_nvm
-  ensure_path
-
-  if check_command nemoclaw; then
-    success "NemoClaw already installed: $(nemoclaw --version 2>/dev/null || echo 'found')"
-  else
-    info "Installing NemoClaw (this takes a few minutes)..."
-
-    # Install Node.js via nvm if not available
-    if ! check_command node; then
-      info "Node.js not found — installing via nvm..."
-      export NVM_DIR="${NVM_DIR:-$HOME/.nvm}"
-      curl -fsSL https://raw.githubusercontent.com/nvm-sh/nvm/v0.40.3/install.sh | bash
-      source "$NVM_DIR/nvm.sh"
-      nvm install 22
-      ensure_nvm
-      ensure_path
-    fi
-
-    # The NVIDIA installer (curl | bash) triggers npm tar race conditions
-    # causing ENOENT errors on deeply nested packages. Workaround: clone the
-    # repo and install with --maxsockets=1 to serialize downloads.
-    NEMOCLAW_DIR="$HOME/.nemoclaw-src"
-    rm -rf "$NEMOCLAW_DIR"
-    info "Cloning NemoClaw from GitHub..."
-    git clone --depth 1 https://github.com/NVIDIA/NemoClaw.git "$NEMOCLAW_DIR"
-    cd "$NEMOCLAW_DIR"
-    info "Installing dependencies (serialized to avoid tar race)..."
-    npm install --maxsockets=1
-    npm link
-    cd - >/dev/null
-
-    ensure_nvm
-    ensure_path
-    source "$HOME/.bashrc" 2>/dev/null || true
-    hash -r 2>/dev/null || true
-
-    if ! check_command nemoclaw; then
-      # npm link may place the binary outside the current PATH; find and add it
-      NEMOCLAW_BIN=$(find "$HOME/.nvm" -name nemoclaw \( -type f -o -type l \) -path "*/bin/*" 2>/dev/null | head -1)
-      if [[ -n "$NEMOCLAW_BIN" ]]; then
-        export PATH="$(dirname "$NEMOCLAW_BIN"):$PATH"
-      fi
-    fi
-
-    if ! check_command nemoclaw; then
-      die "NemoClaw installation failed. Try: source ~/.bashrc && nemoclaw --help"
-    fi
-
-    success "NemoClaw installed"
-  fi
-
-  ensure_path
-  if ! check_command openshell; then
-    warn "openshell not on PATH yet — it will be installed during onboarding."
-  fi
-fi
-
-# ═════════════════════════════════════════════════════════════════════════════
-# PHASE 3: NemoClaw Onboarding
-# ═════════════════════════════════════════════════════════════════════════════
-
-step "Phase 3: NemoClaw Onboarding"
-
-# Check if a sandbox already exists
-EXISTING_SANDBOX=""
-if $IS_MACOS; then
-  EXISTING_SANDBOX=$(run_cmd "openshell sandbox list 2>/dev/null" | awk 'NR>1 && $1!="" {print $1; exit}' || true)
-else
-  ensure_path
-  if check_command openshell; then
-    EXISTING_SANDBOX=$(openshell sandbox list 2>/dev/null | awk 'NR>1 && $1!="" {print $1; exit}' || true)
-  fi
-fi
-
-if [[ -n "$EXISTING_SANDBOX" ]]; then
-  success "Sandbox already exists: $EXISTING_SANDBOX"
-  SANDBOX_NAME="$EXISTING_SANDBOX"
-  ask "Use existing sandbox '$SANDBOX_NAME'? [Y/n]: "
-  read -r use_existing
-  if [[ "$use_existing" =~ ^[Nn]$ ]]; then
-    ask "Enter sandbox name [my-assistant]: "
-    read -r custom_name
-    SANDBOX_NAME="${custom_name:-my-assistant}"
-    info "Running NemoClaw onboarding..."
-    echo -e "  ${DIM}You'll need your NVIDIA API key (nvapi-...) from build.nvidia.com${NC}"
-    echo ""
-    run_cmd_it "nemoclaw onboard"
-    if $IS_LINUX; then ensure_path; fi
-  fi
-else
-  info "No existing sandbox found. Running NemoClaw onboarding..."
-  echo ""
-  echo -e "  ${DIM}The onboarding wizard will guide you through 7 steps:${NC}"
-  echo -e "  ${DIM}  1. Preflight checks (automatic)${NC}"
-  echo -e "  ${DIM}  2. Start gateway (automatic, takes 1-2 min)${NC}"
-  echo -e "  ${DIM}  3. Sandbox name — enter a name (e.g. my-assistant)${NC}"
-  echo -e "  ${DIM}  4. NVIDIA API key — paste your nvapi-... key${NC}"
-  echo -e "  ${DIM}  5. Inference provider (automatic)${NC}"
-  echo -e "  ${DIM}  6. OpenClaw setup (automatic)${NC}"
-  echo -e "  ${DIM}  7. Policy presets — type Y to apply pypi and npm${NC}"
-  echo ""
-  ask "Press Enter to start onboarding..."
-  read -r
-
-  run_cmd_it "nemoclaw onboard"
-
-  if $IS_LINUX; then
-    ensure_nvm
-    ensure_path
-  fi
-
-  # Detect sandbox name
-  SANDBOX_NAME=$(run_cmd "openshell sandbox list 2>/dev/null" | awk 'NR>1 && $1!="" {print $1; exit}' || echo "$SANDBOX_NAME")
-fi
-
-# Verify sandbox exists
-SANDBOX_PHASE=$(run_cmd "openshell sandbox get '$SANDBOX_NAME' 2>/dev/null" | grep -i "phase" | awk '{print $NF}' || true)
-if [[ "$SANDBOX_PHASE" != "Ready" ]]; then
-  warn "Sandbox '$SANDBOX_NAME' is not in Ready state (current: ${SANDBOX_PHASE:-unknown})."
-  warn "Waiting up to 2 minutes..."
-  WAIT_OK=false
-  for i in $(seq 1 24); do
-    SANDBOX_PHASE=$(run_cmd "openshell sandbox get '$SANDBOX_NAME' 2>/dev/null" | grep -i "phase" | awk '{print $NF}' || true)
-    if [[ "$SANDBOX_PHASE" == "Ready" ]]; then
-      WAIT_OK=true
-      break
-    fi
-    sleep 5
-  done
-  if ! $WAIT_OK; then
-    die "Sandbox '$SANDBOX_NAME' did not become Ready. Run: openshell sandbox list"
-  fi
-fi
-
-success "Sandbox '$SANDBOX_NAME' is ready"
-
-# ═════════════════════════════════════════════════════════════════════════════
-# PHASE 4: Install Mem0 Plugin
-# ═════════════════════════════════════════════════════════════════════════════
-
-step "Phase 4: Installing Mem0 plugin ($PLUGIN_PKG)"
-
-# Helper: run a command inside the sandbox non-interactively via piped stdin
-sandbox_exec() {
-  local cmd="$1"
-  if $IS_MACOS; then
-    printf '%s\nexit\n' "$cmd" | docker exec -i "$CONTAINER_NAME" bash -c \
-      "export NVM_DIR=/root/.nvm && source /root/.nvm/nvm.sh && openshell sandbox connect '$SANDBOX_NAME'" 2>&1
-  else
-    printf '%s\nexit\n' "$cmd" | openshell sandbox connect "$SANDBOX_NAME" 2>&1
-  fi
-}
-
-# Check if plugin is already installed by trying to download a known file from the sandbox.
-# We avoid sandbox_exec for the check because piped stdin to openshell sandbox connect
-# produces shell prompt noise that causes false positives with grep.
-PLUGIN_EXISTS=false
-if run_cmd "openshell sandbox download '$SANDBOX_NAME' /sandbox/.openclaw/extensions/openclaw-mem0/package.json /tmp/_mem0_plugin_check.json" &>/dev/null; then
-  if [[ -f /tmp/_mem0_plugin_check.json ]] || run_cmd "test -f /tmp/_mem0_plugin_check.json" &>/dev/null; then
-    PLUGIN_EXISTS=true
-  fi
-fi
-rm -f /tmp/_mem0_plugin_check.json 2>/dev/null || true
-run_cmd "rm -f /tmp/_mem0_plugin_check.json" 2>/dev/null || true
-
-if $PLUGIN_EXISTS; then
-  success "Mem0 plugin already installed in sandbox"
-else
-  info "Downloading $PLUGIN_PKG..."
-
-  # Download and build outside the sandbox (on host or in container)
-  run_cmd "cd /tmp && rm -rf openclaw-mem0-full mem0-openclaw-mem0-*.tgz openclaw-mem0-full.tgz"
-
-  if ! run_cmd "cd /tmp && npm pack '$PLUGIN_PKG' 2>/dev/null"; then
-    die "Failed to download $PLUGIN_PKG from npm. Check your internet connection."
-  fi
-
-  success "Downloaded plugin"
-
-  info "Installing plugin dependencies..."
-  run_cmd "mkdir -p /tmp/openclaw-mem0-full && cd /tmp/openclaw-mem0-full && tar xzf /tmp/mem0-openclaw-mem0-*.tgz --strip-components=1 && npm install --omit=dev 2>&1 | tail -3"
-
-  success "Dependencies installed"
-
-  info "Uploading plugin to sandbox..."
-  run_cmd "cd /tmp && tar czf openclaw-mem0-full.tgz -C openclaw-mem0-full ."
-
-  if ! run_cmd "openshell sandbox upload '$SANDBOX_NAME' /tmp/openclaw-mem0-full.tgz /sandbox/openclaw-mem0-full.tgz 2>&1"; then
-    die "Failed to upload plugin to sandbox. Check: openshell sandbox list"
-  fi
-
-  success "Plugin uploaded"
-
-  info "Extracting plugin inside sandbox..."
-  sandbox_exec "mkdir -p ~/.openclaw/extensions/openclaw-mem0 && tar xzf /sandbox/openclaw-mem0-full.tgz/openclaw-mem0-full.tgz -C ~/.openclaw/extensions/openclaw-mem0 2>/dev/null || tar xzf /sandbox/openclaw-mem0-full.tgz -C ~/.openclaw/extensions/openclaw-mem0 2>/dev/null && echo EXTRACT_OK" >/dev/null 2>&1 || true
-
-  # Verify
-  VERIFY_OK=false
-  if run_cmd "openshell sandbox download '$SANDBOX_NAME' /sandbox/.openclaw/extensions/openclaw-mem0/package.json /tmp/_mem0_verify.json" &>/dev/null; then
-    VERIFY_OK=true
-  fi
-  rm -f /tmp/_mem0_verify.json 2>/dev/null || true
-  run_cmd "rm -f /tmp/_mem0_verify.json" 2>/dev/null || true
-  if $VERIFY_OK; then
-    success "Plugin extracted inside sandbox"
-  else
-    warn "Could not verify plugin extraction. You may need to extract manually."
-    if $IS_MACOS; then
-      echo "  docker exec -it $CONTAINER_NAME bash"
-      echo "  export NVM_DIR=/root/.nvm && source /root/.nvm/nvm.sh"
-    fi
-    echo "  nemoclaw $SANDBOX_NAME connect"
-    echo "  mkdir -p ~/.openclaw/extensions/openclaw-mem0"
-    echo "  tar xzf /sandbox/openclaw-mem0-full.tgz/openclaw-mem0-full.tgz -C ~/.openclaw/extensions/openclaw-mem0"
-  fi
-
-  # Clean up
-  run_cmd "rm -rf /tmp/openclaw-mem0-full /tmp/mem0-openclaw-mem0-*.tgz /tmp/openclaw-mem0-full.tgz" 2>/dev/null || true
-fi
-
-# ═════════════════════════════════════════════════════════════════════════════
-# PHASE 5: Update Network Policy
-# ═════════════════════════════════════════════════════════════════════════════
-
-step "Phase 5: Updating network policy to allow api.mem0.ai and telemetry"
-
-# Find baseline policy
-BASELINE_POLICY=$(run_cmd "find / -path '*/nemoclaw-blueprint/policies/openclaw-sandbox.yaml' 2>/dev/null | head -1" || true)
-
-if [[ -z "$BASELINE_POLICY" ]]; then
-  die "Cannot find NemoClaw baseline policy file. Is NemoClaw installed?"
-fi
-
-info "Baseline policy: $BASELINE_POLICY"
-
-# Check if mem0_api already exists
-HAS_MEM0=$(run_cmd "grep -c mem0_api '$BASELINE_POLICY' 2>/dev/null" || echo "0")
-
-if [[ "$HAS_MEM0" != "0" ]]; then
-  success "mem0_api already in baseline policy"
-else
-  info "Adding api.mem0.ai to network policy..."
-fi
-
-# Create custom policy with mem0_api + telemetry — use node for reliable cross-platform YAML editing
-run_cmd "node -e \"
-const fs = require('fs');
-let c = fs.readFileSync('$BASELINE_POLICY', 'utf8');
-const mem0Block = '\\n  mem0_api:\\n    name: mem0_api\\n    endpoints:\\n      - host: api.mem0.ai\\n        port: 443\\n        access: full\\n    binaries:\\n      - { path: /usr/local/bin/node }\\n      - { path: /usr/local/bin/openclaw }\\n';
-const telemetryBlock = '\\n  mem0_telemetry:\\n    name: mem0_telemetry\\n    endpoints:\\n      - host: us.i.posthog.com\\n        port: 443\\n        access: full\\n    binaries:\\n      - { path: /usr/local/bin/node }\\n      - { path: /usr/local/bin/openclaw }\\n';
-if (!c.includes('mem0_api')) {
-  if (c.includes('# ── Messaging')) {
-    c = c.replace('  # ── Messaging', mem0Block + '\\n  # ── Messaging');
-  } else {
-    c += mem0Block;
-  }
-}
-if (!c.includes('mem0_telemetry')) {
-  if (c.includes('mem0_api:')) {
-    c = c.replace('  mem0_api:', telemetryBlock + '\\n  mem0_api:');
-  } else {
-    c += telemetryBlock;
-  }
-}
-fs.writeFileSync('/tmp/nemoclaw-mem0-policy.yaml', c);
-console.log('ok');
-\""
-
-success "Custom policy file created"
-
-# Apply the policy
-info "Applying network policy..."
-if ! run_cmd "openshell policy set '$SANDBOX_NAME' --policy /tmp/nemoclaw-mem0-policy.yaml --wait 2>&1"; then
-  error "Failed to apply network policy."
-  echo ""
-  echo "  If you see 'sandbox not found', re-run: nemoclaw onboard"
-  echo "  Then re-run this script."
-  echo ""
-  die "Network policy update failed."
-fi
-
-success "Network policy applied — api.mem0.ai and telemetry allowed"
-
-# ═════════════════════════════════════════════════════════════════════════════
-# PHASE 6: Configure Plugin
-# ═════════════════════════════════════════════════════════════════════════════
-
-step "Phase 6: Configuring Mem0 plugin"
-
-echo ""
-echo -e "  ${DIM}Get your Mem0 API key from: https://app.mem0.ai${NC}"
-echo -e "  ${DIM}The key starts with 'm0-'${NC}"
-echo ""
-ask "Enter your Mem0 API key: "
-read -r MEM0_API_KEY
-
-if [[ -z "$MEM0_API_KEY" ]]; then
-  die "Mem0 API key is required."
-fi
-
-if [[ ! "$MEM0_API_KEY" =~ ^m0- ]]; then
-  warn "Key doesn't start with 'm0-'. Make sure this is correct."
-fi
-
-echo ""
-echo -e "  ${DIM}The user ID scopes all memories. Pick any unique identifier.${NC}"
-echo -e "  ${DIM}Examples: alice, user_123, your-email@example.com${NC}"
-echo ""
-ask "Enter user ID [$MEM0_USER_ID]: "
-read -r custom_user_id
-MEM0_USER_ID="${custom_user_id:-$MEM0_USER_ID}"
-
-info "Configuring plugin inside sandbox..."
-
-CONFIG_SCRIPT="openclaw config set plugins.slots.memory openclaw-mem0 2>&1 | tail -1 && \
-openclaw config set plugins.entries.openclaw-mem0.enabled true 2>&1 | tail -1 && \
-openclaw config set plugins.entries.openclaw-mem0.config.apiKey '$MEM0_API_KEY' 2>&1 | tail -1 && \
-openclaw config set plugins.entries.openclaw-mem0.config.userId '$MEM0_USER_ID' 2>&1 | tail -1 && \
-echo SETUP_DONE"
-
-CONFIG_OUTPUT=$(sandbox_exec "$CONFIG_SCRIPT" || true)
-
-if echo "$CONFIG_OUTPUT" | grep -q "SETUP_DONE"; then
-  success "Plugin configured (mode: platform, user: $MEM0_USER_ID)"
-else
-  warn "Could not verify config. You may need to configure manually:"
-  echo ""
-  if $IS_MACOS; then
-    echo "  docker exec -it $CONTAINER_NAME bash"
-    echo "  export NVM_DIR=/root/.nvm && source /root/.nvm/nvm.sh"
-  fi
-  echo "  nemoclaw $SANDBOX_NAME connect"
-  echo "  openclaw config set plugins.slots.memory openclaw-mem0"
-  echo "  openclaw config set plugins.entries.openclaw-mem0.enabled true"
-  echo "  openclaw config set plugins.entries.openclaw-mem0.config.apiKey \"$MEM0_API_KEY\""
-  echo "  openclaw config set plugins.entries.openclaw-mem0.config.userId \"$MEM0_USER_ID\""
-  echo ""
-fi
-
-# ═════════════════════════════════════════════════════════════════════════════
-# PHASE 7: Verify & Test
-# ═════════════════════════════════════════════════════════════════════════════
-
-step "Phase 7: Verification"
-
-echo ""
-echo -e "${BOLD}${GREEN}╔══════════════════════════════════════════════════════════════╗${NC}"
-echo -e "${BOLD}${GREEN}║                    Setup Complete!                           ║${NC}"
-echo -e "${BOLD}${GREEN}╚══════════════════════════════════════════════════════════════╝${NC}"
-echo ""
-echo -e "  ${BOLD}Sandbox:${NC}   $SANDBOX_NAME"
-echo -e "  ${BOLD}Plugin:${NC}    @mem0/openclaw-mem0 (platform mode)"
-echo -e "  ${BOLD}User ID:${NC}   $MEM0_USER_ID"
-if $IS_MACOS; then
-  echo -e "  ${BOLD}Container:${NC} $CONTAINER_NAME"
-fi
-echo ""
-echo -e "  ${BOLD}${CYAN}Next steps:${NC}"
-echo ""
-
-if $IS_MACOS; then
-  echo -e "  1. Open a shell in the container:"
-  echo ""
-  echo -e "     ${DIM}docker exec -it $CONTAINER_NAME bash${NC}"
-  echo -e "     ${DIM}export NVM_DIR=/root/.nvm && source /root/.nvm/nvm.sh${NC}"
-  echo ""
-  echo -e "  2. Connect to the sandbox and start the gateway:"
-  echo ""
-  echo -e "     ${DIM}nemoclaw $SANDBOX_NAME connect${NC}"
-  echo -e "     ${DIM}nemoclaw-start${NC}"
-else
-  echo -e "  1. Connect to the sandbox and start the gateway:"
-  echo ""
-  echo -e "     ${DIM}source ~/.bashrc${NC}"
-  echo -e "     ${DIM}nemoclaw $SANDBOX_NAME connect${NC}"
-  echo -e "     ${DIM}nemoclaw-start${NC}"
-fi
-echo ""
-echo -e "  Then verify the plugin loaded (look for 'openclaw-mem0: registered'):"
-echo ""
-echo -e "     ${DIM}openclaw plugins list${NC}"
-echo ""
-echo -e "  Test auto-capture (storing memories):"
-echo ""
-echo -e "     ${DIM}openclaw agent --agent main --local -m \"My name is Alice\" --session-id test1${NC}"
-echo ""
-echo -e "  Test auto-recall (new session, memories should appear):"
-echo ""
-echo -e "     ${DIM}openclaw agent --agent main --local -m \"What do you know about me?\" --session-id test2${NC}"
-echo ""
-echo -e "  Or use the interactive TUI:"
-echo ""
-echo -e "     ${DIM}openclaw tui${NC}"
-echo ""
-echo -e "  ${YELLOW}Note:${NC} You may see 'Telemetry event capture failed' errors."
-echo -e "  These are harmless and do not affect memory functionality."
-echo ""
-echo -e "  ${BOLD}Documentation:${NC} https://docs.mem0.ai"
-echo -e "  ${BOLD}Plugin source:${NC} https://www.npmjs.com/package/@mem0/openclaw-mem0"
-echo ""

mem0-ts/src/oss/src/config/defaults.ts

@@ -34,6 +34,12 @@ export const DEFAULT_MEMORY_CONFIG: MemoryConfig = {
       username: process.env.NEO4J_USERNAME || "neo4j",
       password: process.env.NEO4J_PASSWORD || "password",
     },
+    llm: {
+      provider: "openai",
+      config: {
+        model: "gpt-4-turbo-preview",
+      },
+    },
   },
   historyStore: {
     provider: "sqlite",

mem0-ts/src/oss/src/index.ts

@@ -26,5 +26,4 @@ export * from "./vector_stores/supabase";
 export * from "./vector_stores/langchain";
 export * from "./vector_stores/vectorize";
 export * from "./vector_stores/azure_ai_search";
-export * from "./vector_stores/pgvector";
 export * from "./utils/factory";

mem0-ts/src/oss/src/memory/graph_memory.ts

@@ -80,18 +80,18 @@ export class MemoryGraph {
     );
 
     this.llmProvider = "openai";
-    let llmConfig = this.config.llm.config;
-
     if (this.config.llm?.provider) {
       this.llmProvider = this.config.llm.provider;
     }
     if (this.config.graphStore?.llm?.provider) {
       this.llmProvider = this.config.graphStore.llm.provider;
-      llmConfig = this.config.graphStore.llm.config ?? llmConfig;
     }
 
-    this.llm = LLMFactory.create(this.llmProvider, llmConfig);
+    this.llm = LLMFactory.create(this.llmProvider, this.config.llm.config);
-    this.structuredLlm = LLMFactory.create(this.llmProvider, llmConfig);
+    this.structuredLlm = LLMFactory.create(
+      this.llmProvider,
+      this.config.llm.config,
+    );
     this.threshold = 0.7;
   }
 

mem0-ts/src/oss/src/prompts/index.ts

@@ -31,7 +31,6 @@ export const MemoryUpdateSchema = z.object({
         old_memory: z
           .string()
           .optional()
-          .nullable()
           .describe(
             "The previous content of the memory item if the event was UPDATE.",
           ),

mem0-ts/src/oss/src/utils/factory.ts

@@ -34,7 +34,6 @@ import { LangchainLLM } from "../llms/langchain";
 import { LangchainEmbedder } from "../embeddings/langchain";
 import { LangchainVectorStore } from "../vector_stores/langchain";
 import { AzureAISearch } from "../vector_stores/azure_ai_search";
-import { PGVector } from "../vector_stores/pgvector";
 
 export class EmbedderFactory {
   static create(provider: string, config: EmbeddingConfig): Embedder {
@@ -105,8 +104,6 @@ export class VectorStoreFactory {
         return new VectorizeDB(config as any);
       case "azure-ai-search":
         return new AzureAISearch(config as any);
-      case "pgvector":
-        return new PGVector(config as any);
       default:
         throw new Error(`Unsupported vector store provider: ${provider}`);
     }

mem0-ts/src/oss/src/vector_stores/redis.ts

@@ -522,7 +522,7 @@ export class RedisDB implements VectorStore {
 
       return {
         id: vectorId,
-        payload: toCamelCase(payload),
+        payload,
       };
     } catch (error) {
       console.error("Error getting vector:", error);

mem0-ts/src/oss/tests/config-manager.test.ts

@@ -349,78 +349,6 @@ describe("ConfigManager", () => {
   });
 });
 
-// ─────────────────────────────────────────────────────────────────────────
-// Graph store LLM config propagation (issue #3425)
-// ─────────────────────────────────────────────────────────────────────────
-describe("mergeConfig - graph store LLM config (issue #3425)", () => {
-  const baseEmbedder = {
-    provider: "openai",
-    config: { apiKey: "test-key" },
-  };
-  const baseVectorStore = {
-    provider: "memory",
-    config: { collectionName: "test" },
-  };
-  const graphStoreNeo4j = {
-    provider: "neo4j",
-    config: {
-      url: "neo4j://localhost:7687",
-      username: "neo4j",
-      password: "password",
-    },
-  };
-
-  it("should NOT have a default graphStore.llm — root llm should be the fallback", () => {
-    const config = ConfigManager.mergeConfig({
-      embedder: baseEmbedder,
-      vectorStore: baseVectorStore,
-      llm: {
-        provider: "anthropic",
-        config: { model: "claude-sonnet-4-20250514" },
-      },
-      graphStore: graphStoreNeo4j,
-    });
-
-    // graphStore should NOT have its own llm after merge
-    expect(config.graphStore?.llm).toBeUndefined();
-    // root llm should be anthropic
-    expect(config.llm.provider).toBe("anthropic");
-    expect(config.llm.config.model).toBe("claude-sonnet-4-20250514");
-  });
-
-  it("should preserve explicit graphStore.llm when user provides it", () => {
-    const config = ConfigManager.mergeConfig({
-      embedder: baseEmbedder,
-      vectorStore: baseVectorStore,
-      llm: {
-        provider: "anthropic",
-        config: { model: "claude-sonnet-4-20250514" },
-      },
-      graphStore: {
-        ...graphStoreNeo4j,
-        llm: { provider: "openai", config: { model: "gpt-4o" } },
-      },
-    });
-
-    // graphStore should have its own llm
-    expect(config.graphStore?.llm?.provider).toBe("openai");
-    expect(config.graphStore?.llm?.config).toEqual({ model: "gpt-4o" });
-    // root llm should still be anthropic
-    expect(config.llm.provider).toBe("anthropic");
-  });
-
-  it("should not have graphStore.llm when user does not provide one", () => {
-    const config = ConfigManager.mergeConfig({
-      embedder: baseEmbedder,
-      vectorStore: baseVectorStore,
-      llm: { provider: "openai", config: { model: "gpt-4o" } },
-    });
-
-    // Default graphStore should not have llm
-    expect(config.graphStore?.llm).toBeUndefined();
-  });
-});
-
 // ─────────────────────────────────────────────────────────────────────────
 // Memory class – LM Studio end-to-end flow (mocked factories)
 // ─────────────────────────────────────────────────────────────────────────

mem0-ts/src/oss/tests/graph-memory-parsing.test.ts

@@ -511,137 +511,6 @@ describe("Prompt construction — all json_object sites include 'json'", () => {
 // 5. Edge cases – malformed entity fields in _removeSpacesFromEntities
 // ═══════════════════════════════════════════════════════════════════════════
 
-// ═══════════════════════════════════════════════════════════════════════════
-// 5a. LLM config propagation — graph store uses correct provider & config
-//     Regression test for https://github.com/mem0ai/mem0/issues/3425
-// ═══════════════════════════════════════════════════════════════════════════
-
-describe("LLM config propagation to graph store (issue #3425)", () => {
-  const { LLMFactory } = require("../src/utils/factory");
-
-  beforeEach(() => {
-    (LLMFactory.create as jest.Mock).mockClear();
-  });
-
-  it("uses root llm config when no graphStore.llm is provided", () => {
-    const config = {
-      graphStore: {
-        config: {
-          url: "bolt://localhost:7687",
-          username: "neo4j",
-          password: "test",
-        },
-      },
-      embedder: { provider: "openai", config: {} },
-      llm: {
-        provider: "anthropic",
-        config: { model: "claude-sonnet-4-20250514", apiKey: "sk-ant-test" },
-      },
-    } as any;
-
-    new MemoryGraph(config);
-
-    expect(LLMFactory.create).toHaveBeenCalledWith("anthropic", {
-      model: "claude-sonnet-4-20250514",
-      apiKey: "sk-ant-test",
-    });
-    // Both llm and structuredLlm should use the same config
-    expect(LLMFactory.create).toHaveBeenCalledTimes(2);
-    expect(LLMFactory.create).toHaveBeenNthCalledWith(1, "anthropic", {
-      model: "claude-sonnet-4-20250514",
-      apiKey: "sk-ant-test",
-    });
-    expect(LLMFactory.create).toHaveBeenNthCalledWith(2, "anthropic", {
-      model: "claude-sonnet-4-20250514",
-      apiKey: "sk-ant-test",
-    });
-  });
-
-  it("uses graphStore.llm config when provided, overriding root llm", () => {
-    const config = {
-      graphStore: {
-        config: {
-          url: "bolt://localhost:7687",
-          username: "neo4j",
-          password: "test",
-        },
-        llm: {
-          provider: "openai",
-          config: { model: "gpt-4o", apiKey: "sk-openai-test" },
-        },
-      },
-      embedder: { provider: "openai", config: {} },
-      llm: {
-        provider: "anthropic",
-        config: { model: "claude-sonnet-4-20250514", apiKey: "sk-ant-test" },
-      },
-    } as any;
-
-    new MemoryGraph(config);
-
-    // Should use graphStore.llm, NOT root llm
-    expect(LLMFactory.create).toHaveBeenNthCalledWith(1, "openai", {
-      model: "gpt-4o",
-      apiKey: "sk-openai-test",
-    });
-    expect(LLMFactory.create).toHaveBeenNthCalledWith(2, "openai", {
-      model: "gpt-4o",
-      apiKey: "sk-openai-test",
-    });
-  });
-
-  it("falls back to root llm config when graphStore.llm.config is undefined", () => {
-    // Note: in practice, Zod schema requires config when graphStore.llm is
-    // present. This tests the defensive fallback in MemoryGraph itself.
-    const config = {
-      graphStore: {
-        config: {
-          url: "bolt://localhost:7687",
-          username: "neo4j",
-          password: "test",
-        },
-        llm: {
-          provider: "openai",
-          // config explicitly undefined
-          config: undefined,
-        },
-      },
-      embedder: { provider: "openai", config: {} },
-      llm: {
-        provider: "anthropic",
-        config: { model: "claude-sonnet-4-20250514" },
-      },
-    } as any;
-
-    new MemoryGraph(config);
-
-    // Provider from graphStore.llm, but config falls back to root llm.config
-    expect(LLMFactory.create).toHaveBeenNthCalledWith(1, "openai", {
-      model: "claude-sonnet-4-20250514",
-    });
-  });
-
-  it("defaults to openai when neither root nor graphStore llm provider is set", () => {
-    const config = {
-      graphStore: {
-        config: {
-          url: "bolt://localhost:7687",
-          username: "neo4j",
-          password: "test",
-        },
-      },
-      embedder: { provider: "openai", config: {} },
-      llm: { config: { model: "gpt-4" } },
-    } as any;
-
-    new MemoryGraph(config);
-
-    expect(LLMFactory.create).toHaveBeenNthCalledWith(1, "openai", {
-      model: "gpt-4",
-    });
-  });
-});
-
 describe("_removeSpacesFromEntities (via _establishNodesRelationsFromData)", () => {
   it("normalises spaces and case in entity source/relationship/destination", async () => {
     mockGenerateResponse.mockResolvedValueOnce({

mem0/configs/vector_stores/turbopuffer.py

@@ -1,45 +0,0 @@
-import os
-from typing import Any, Dict, Optional
-
-from pydantic import BaseModel, ConfigDict, Field, model_validator
-
-
-class TurbopufferConfig(BaseModel):
-    collection_name: str = Field("mem0", description="Name of the namespace/collection")
-    embedding_model_dims: int = Field(1536, description="Dimensions of the embedding model")
-    api_key: Optional[str] = Field(None, description="API key for Turbopuffer")
-    region: str = Field("gcp-us-central1", description="Turbopuffer region (e.g., 'gcp-us-central1', 'aws-us-west-2')")
-    distance_metric: str = Field(
-        "cosine_distance",
-        description="Distance metric for vector similarity ('cosine_distance' or 'euclidean_squared')",
-    )
-    batch_size: int = Field(100, description="Batch size for bulk operations")
-    extra_params: Optional[Dict[str, Any]] = Field(
-        None,
-        description="Additional parameters for Turbopuffer client",
-    )
-
-    @model_validator(mode="before")
-    @classmethod
-    def check_api_key(cls, values: Dict[str, Any]) -> Dict[str, Any]:
-        api_key = values.get("api_key")
-        if not api_key and "TURBOPUFFER_API_KEY" not in os.environ:
-            raise ValueError(
-                "Either 'api_key' must be provided or TURBOPUFFER_API_KEY environment variable must be set."
-            )
-        return values
-
-    @model_validator(mode="before")
-    @classmethod
-    def validate_extra_fields(cls, values: Dict[str, Any]) -> Dict[str, Any]:
-        allowed_fields = set(cls.model_fields.keys())
-        input_fields = set(values.keys())
-        extra_fields = input_fields - allowed_fields
-        if extra_fields:
-            raise ValueError(
-                f"Extra fields not allowed: {', '.join(extra_fields)}. "
-                f"Please input only the following fields: {', '.join(allowed_fields)}"
-            )
-        return values
-
-    model_config = ConfigDict(arbitrary_types_allowed=True)

mem0/graphs/configs.py

@@ -77,32 +77,12 @@ class KuzuConfig(BaseModel):
     db: Optional[str] = Field(":memory:", description="Path to a Kuzu database file")
 
 
-class ApacheAgeConfig(BaseModel):
-    host: Optional[str] = Field("localhost", description="PostgreSQL server hostname")
-    port: Optional[int] = Field(5432, description="PostgreSQL server port")
-    database: Optional[str] = Field(None, description="PostgreSQL database name")
-    username: Optional[str] = Field(None, description="PostgreSQL username")
-    password: Optional[str] = Field(None, description="PostgreSQL password")
-    graph_name: Optional[str] = Field("mem0_graph", description="Name of the Apache AGE graph")
-
-    @model_validator(mode="before")
-    def check_required_fields(cls, values):
-        database, username, password = (
-            values.get("database"),
-            values.get("username"),
-            values.get("password"),
-        )
-        if not database or not username or not password:
-            raise ValueError("Please provide 'database', 'username' and 'password'.")
-        return values
-
-
 class GraphStoreConfig(BaseModel):
     provider: str = Field(
-        description="Provider of the data store (e.g., 'neo4j', 'memgraph', 'neptune', 'kuzu', 'apache_age')",
+        description="Provider of the data store (e.g., 'neo4j', 'memgraph', 'neptune', 'kuzu')",
         default="neo4j",
     )
-    config: Union[Neo4jConfig, MemgraphConfig, NeptuneConfig, KuzuConfig, ApacheAgeConfig] = Field(
+    config: Union[Neo4jConfig, MemgraphConfig, NeptuneConfig, KuzuConfig] = Field(
         description="Configuration for the specific data store", default=None
     )
     llm: Optional[LlmConfig] = Field(description="LLM configuration for querying the graph store", default=None)
@@ -130,7 +110,5 @@ class GraphStoreConfig(BaseModel):
             return NeptuneConfig(**v.model_dump())
         elif provider == "kuzu":
             return KuzuConfig(**v.model_dump())
-        elif provider == "apache_age":
-            return ApacheAgeConfig(**v.model_dump())
         else:
             raise ValueError(f"Unsupported graph store provider: {provider}")

mem0/llms/azure_openai_structured.py

@@ -1,4 +1,3 @@
-import json
 import os
 from typing import Dict, List, Optional
 
@@ -7,7 +6,6 @@ from openai import AzureOpenAI
 
 from mem0.configs.llms.base import BaseLlmConfig
 from mem0.llms.base import LLMBase
-from mem0.memory.utils import extract_json
 
 SCOPE = "https://cognitiveservices.azure.com/.default"
 
@@ -85,35 +83,9 @@ class AzureOpenAIStructuredLLM(LLMBase):
             params["tools"] = tools
             params["tool_choice"] = tool_choice
 
+        if tools:
+            params["tools"] = tools
+            params["tool_choice"] = tool_choice
+
         response = self.client.chat.completions.create(**params)
         return self._parse_response(response, tools)
-
-    def _parse_response(self, response, tools):
-        """
-        Process the response based on whether tools are used or not.
-
-        Args:
-            response: The raw response from API.
-            tools: The list of tools provided in the request.
-
-        Returns:
-            str or dict: The processed response.
-        """
-        if tools:
-            processed_response = {
-                "content": response.choices[0].message.content,
-                "tool_calls": [],
-            }
-
-            if response.choices[0].message.tool_calls:
-                for tool_call in response.choices[0].message.tool_calls:
-                    processed_response["tool_calls"].append(
-                        {
-                            "name": tool_call.function.name,
-                            "arguments": json.loads(extract_json(tool_call.function.arguments)),
-                        }
-                    )
-
-            return processed_response
-        else:
-            return response.choices[0].message.content

mem0/llms/gemini.py

@@ -32,25 +32,22 @@ class GeminiLLM(LLMBase):
         Returns:
             str or dict: The processed response.
         """
-        # Get parts safely — content can be None when Gemini blocks the response
-        candidate = response.candidates[0] if response.candidates else None
-        parts = candidate.content.parts if candidate and candidate.content else None
-
         if tools:
             processed_response = {
                 "content": None,
                 "tool_calls": [],
             }
 
-            if parts:
+            # Extract content from the first candidate
-                # Extract content from the first candidate
+            if response.candidates and response.candidates[0].content.parts:
-                for part in parts:
+                for part in response.candidates[0].content.parts:
                     if hasattr(part, "text") and part.text:
                         processed_response["content"] = part.text
                         break
 
-                # Extract function calls
+            # Extract function calls
-                for part in parts:
+            if response.candidates and response.candidates[0].content.parts:
+                for part in response.candidates[0].content.parts:
                     if hasattr(part, "function_call") and part.function_call:
                         fn = part.function_call
                         processed_response["tool_calls"].append(
@@ -62,8 +59,8 @@ class GeminiLLM(LLMBase):
 
             return processed_response
         else:
-            if parts:
+            if response.candidates and response.candidates[0].content.parts:
-                for part in parts:
+                for part in response.candidates[0].content.parts:
                     if hasattr(part, "text") and part.text:
                         return part.text
             return ""

mem0/memory/apache_age_memory.py

@@ -1,573 +0,0 @@
-import json
-import logging
-import time
-
-from mem0.memory.utils import format_entities, sanitize_relationship_for_cypher
-
-try:
-    import age
-except ImportError:
-    raise ImportError("apache-age-python is not installed. Please install it using pip install apache-age-python")
-
-try:
-    from rank_bm25 import BM25Okapi
-except ImportError:
-    raise ImportError("rank_bm25 is not installed. Please install it using pip install rank-bm25")
-
-from mem0.graphs.tools import (
-    DELETE_MEMORY_STRUCT_TOOL_GRAPH,
-    DELETE_MEMORY_TOOL_GRAPH,
-    EXTRACT_ENTITIES_STRUCT_TOOL,
-    EXTRACT_ENTITIES_TOOL,
-    RELATIONS_STRUCT_TOOL,
-    RELATIONS_TOOL,
-)
-from mem0.graphs.utils import EXTRACT_RELATIONS_PROMPT, get_delete_messages
-from mem0.utils.factory import EmbedderFactory, LlmFactory
-
-logger = logging.getLogger(__name__)
-
-
-def _cosine_similarity(vec1, vec2):
-    """Compute cosine similarity between two vectors without numpy."""
-    dot = sum(a * b for a, b in zip(vec1, vec2))
-    norm1 = sum(a * a for a in vec1) ** 0.5
-    norm2 = sum(b * b for b in vec2) ** 0.5
-    if norm1 == 0 or norm2 == 0:
-        return 0.0
-    return dot / (norm1 * norm2)
-
-
-def _get_similar_nodes(nodes, query_embedding, filters, threshold):
-    """Find nodes above the similarity threshold from a fetched node list.
-
-    Shared by ``_find_similar_node`` and ``_search_graph_db`` to avoid
-    duplicating the client-side cosine similarity logic.
-    """
-    matches = []
-    for node in nodes:
-        props = node if isinstance(node, dict) else {}
-        stored_emb = props.get("embedding")
-        if not stored_emb:
-            continue
-        if isinstance(stored_emb, str):
-            stored_emb = json.loads(stored_emb)
-
-        if filters.get("agent_id") and props.get("agent_id") != filters["agent_id"]:
-            continue
-        if filters.get("run_id") and props.get("run_id") != filters["run_id"]:
-            continue
-
-        sim = _cosine_similarity(query_embedding, stored_emb)
-        if sim >= threshold:
-            matches.append({"name": props.get("name"), "similarity": sim, "props": props})
-
-    matches.sort(key=lambda x: x["similarity"], reverse=True)
-    return matches
-
-
-class MemoryGraph:
-    def __init__(self, config):
-        self.config = config
-
-        graph_cfg = self.config.graph_store.config
-        self.graph_name = graph_cfg.graph_name
-
-        # Connect using the Apache AGE Python driver (psycopg2-based)
-        self.ag = age.connect(
-            graph=self.graph_name,
-            host=graph_cfg.host,
-            port=graph_cfg.port,
-            dbname=graph_cfg.database,
-            user=graph_cfg.username,
-            password=graph_cfg.password,
-        )
-
-        self.embedding_model = EmbedderFactory.create(
-            self.config.embedder.provider, self.config.embedder.config, self.config.vector_store.config
-        )
-
-        # Default to openai if no specific provider is configured
-        self.llm_provider = "openai"
-        if self.config.llm and self.config.llm.provider:
-            self.llm_provider = self.config.llm.provider
-        if self.config.graph_store and self.config.graph_store.llm and self.config.graph_store.llm.provider:
-            self.llm_provider = self.config.graph_store.llm.provider
-
-        # Get LLM config with proper null checks
-        llm_config = None
-        if self.config.graph_store and self.config.graph_store.llm and hasattr(self.config.graph_store.llm, "config"):
-            llm_config = self.config.graph_store.llm.config
-        elif hasattr(self.config.llm, "config"):
-            llm_config = self.config.llm.config
-        self.llm = LlmFactory.create(self.llm_provider, llm_config)
-        self.user_id = None
-        self.threshold = self.config.graph_store.threshold if hasattr(self.config.graph_store, "threshold") else 0.7
-
-    # -- helpers ---------------------------------------------------------------
-
-    def _exec_cypher(self, cypher_stmt, cols=None, params=None):
-        """Execute a Cypher query via the AGE driver and return results.
-
-        Uses ``ag.execCypher`` which delegates to psycopg2's safe parameter
-        substitution (``%s`` placeholders).  The *cols* argument specifies the
-        column names in the ``AS (…)`` clause — when ``None`` the driver
-        defaults to a single ``v agtype`` column.
-
-        When *cols* are provided, returns a list of dicts keyed by column name.
-        When *cols* is ``None``, returns vertex/edge property dicts or raw values.
-        """
-        cursor = self.ag.execCypher(cypher_stmt, cols=cols, params=params)
-        rows = cursor.fetchall()
-        if not rows:
-            return []
-
-        col_names = [desc[0] for desc in cursor.description] if cursor.description else None
-        results = []
-        for row in rows:
-            if col_names and len(col_names) > 1:
-                record = {}
-                for i, col_name in enumerate(col_names):
-                    val = row[i]
-                    if hasattr(val, "properties"):
-                        record[col_name] = val.properties
-                    else:
-                        record[col_name] = val
-                results.append(record)
-            else:
-                val = row[0] if len(row) == 1 else row
-                if hasattr(val, "properties"):
-                    results.append(val.properties)
-                else:
-                    results.append(val)
-        return results
-
-    def _fetch_user_nodes_with_embeddings(self, user_id):
-        """Fetch all nodes with embeddings for a given user_id."""
-        return self._exec_cypher(
-            "MATCH (n {user_id: %s}) WHERE n.embedding IS NOT NULL RETURN n",
-            params=(user_id,),
-        )
-
-    def _find_similar_node(self, embedding, filters, threshold=0.9):
-        """Find the most similar existing node by cosine similarity.
-
-        Apache AGE does not have a built-in vector index, so we fetch all
-        node embeddings matching the filters and compute cosine similarity
-        on the client side.  This is adequate for moderate graph sizes; for
-        very large graphs consider pairing AGE with pgvector.
-        """
-        nodes = self._fetch_user_nodes_with_embeddings(filters["user_id"])
-        matches = _get_similar_nodes(nodes, embedding, filters, threshold)
-        return matches[0]["props"] if matches else None
-
-    def _merge_node(self, user_id, name, embedding, agent_id=None, run_id=None):
-        """Create a node if it doesn't exist, or update mentions if it does.
-
-        Apache AGE does not support ``ON CREATE SET`` / ``ON MATCH SET``, so
-        we use ``MERGE … SET`` which always applies the SET clause.  Embeddings
-        and optional filter properties are set in a single query.
-        """
-        set_parts = [
-            "n.embedding = %s",
-            "n.mentions = coalesce(n.mentions, 0) + 1",
-            "n.created = coalesce(n.created, %s)",
-        ]
-        params = [user_id, name, json.dumps(embedding), int(time.time() * 1000)]
-
-        if agent_id:
-            set_parts.append("n.agent_id = %s")
-            params.append(agent_id)
-        if run_id:
-            set_parts.append("n.run_id = %s")
-            params.append(run_id)
-
-        set_clause = ", ".join(set_parts)
-        self._exec_cypher(
-            f"MERGE (n {{user_id: %s, name: %s}}) SET {set_clause}",
-            params=tuple(params),
-        )
-
-    def close(self):
-        """Close the underlying database connection."""
-        if self.ag:
-            self.ag.close()
-
-    # -- public API ------------------------------------------------------------
-
-    def add(self, data, filters):
-        """
-        Adds data to the graph.
-
-        Args:
-            data (str): The data to add to the graph.
-            filters (dict): A dictionary containing filters to be applied during the addition.
-        """
-        entity_type_map = self._retrieve_nodes_from_data(data, filters)
-        to_be_added = self._establish_nodes_relations_from_data(data, filters, entity_type_map)
-        search_output = self._search_graph_db(node_list=list(entity_type_map.keys()), filters=filters)
-        to_be_deleted = self._get_delete_entities_from_search_output(search_output, data, filters)
-
-        deleted_entities = self._delete_entities(to_be_deleted, filters)
-        added_entities = self._add_entities(to_be_added, filters, entity_type_map)
-
-        return {"deleted_entities": deleted_entities, "added_entities": added_entities}
-
-    def search(self, query, filters, limit=100):
-        """
-        Search for memories and related graph data.
-
-        Args:
-            query (str): Query to search for.
-            filters (dict): A dictionary containing filters to be applied during the search.
-            limit (int): The maximum number of nodes and relationships to retrieve. Defaults to 100.
-
-        Returns:
-            list: A list of dicts with keys "source", "relationship", "destination".
-        """
-        entity_type_map = self._retrieve_nodes_from_data(query, filters)
-        search_output = self._search_graph_db(node_list=list(entity_type_map.keys()), filters=filters)
-
-        if not search_output:
-            return []
-
-        search_outputs_sequence = [
-            [item["source"], item["relationship"], item["destination"]] for item in search_output
-        ]
-        bm25 = BM25Okapi(search_outputs_sequence)
-
-        tokenized_query = query.split(" ")
-        reranked_results = bm25.get_top_n(tokenized_query, search_outputs_sequence, n=5)
-
-        search_results = []
-        for item in reranked_results:
-            search_results.append({"source": item[0], "relationship": item[1], "destination": item[2]})
-
-        logger.info(f"Returned {len(search_results)} search results")
-        return search_results
-
-    def delete_all(self, filters):
-        """Delete all nodes and relationships for a user or specific agent."""
-        where_parts = ["n.user_id = %s"]
-        params = [filters["user_id"]]
-        if filters.get("agent_id"):
-            where_parts.append("n.agent_id = %s")
-            params.append(filters["agent_id"])
-        if filters.get("run_id"):
-            where_parts.append("n.run_id = %s")
-            params.append(filters["run_id"])
-        where_clause = " AND ".join(where_parts)
-
-        self._exec_cypher(
-            f"MATCH (n) WHERE {where_clause} DETACH DELETE n",
-            params=tuple(params),
-        )
-        self.ag.commit()
-
-    def get_all(self, filters, limit=100):
-        """
-        Retrieves all nodes and relationships from the graph database based on optional filtering criteria.
-
-        Args:
-            filters (dict): A dictionary containing filters to be applied during the retrieval.
-            limit (int): The maximum number of nodes and relationships to retrieve. Defaults to 100.
-        Returns:
-            list: A list of dictionaries, each containing:
-                - 'source': The source node name.
-                - 'relationship': The relationship type.
-                - 'target': The target node name.
-        """
-        where_parts = ["n.user_id = %s", "m.user_id = %s"]
-        params = [filters["user_id"], filters["user_id"]]
-        if filters.get("agent_id"):
-            where_parts.extend(["n.agent_id = %s", "m.agent_id = %s"])
-            params.extend([filters["agent_id"], filters["agent_id"]])
-        if filters.get("run_id"):
-            where_parts.extend(["n.run_id = %s", "m.run_id = %s"])
-            params.extend([filters["run_id"], filters["run_id"]])
-        where_clause = " AND ".join(where_parts)
-        params.append(limit)
-
-        results = self._exec_cypher(
-            f"MATCH (n)-[r]->(m) WHERE {where_clause} "
-            f"RETURN n.name, type(r), m.name LIMIT %s",
-            cols=["source", "relationship", "target"],
-            params=tuple(params),
-        )
-
-        final_results = []
-        for result in results:
-            final_results.append(
-                {
-                    "source": result["source"],
-                    "relationship": result["relationship"],
-                    "target": result["target"],
-                }
-            )
-
-        logger.info(f"Retrieved {len(final_results)} relationships")
-        return final_results
-
-    # -- LLM-driven extraction -------------------------------------------------
-
-    def _retrieve_nodes_from_data(self, data, filters):
-        """Extracts all the entities mentioned in the query."""
-        _tools = [EXTRACT_ENTITIES_TOOL]
-        if self.llm_provider in ["azure_openai_structured", "openai_structured"]:
-            _tools = [EXTRACT_ENTITIES_STRUCT_TOOL]
-        search_results = self.llm.generate_response(
-            messages=[
-                {
-                    "role": "system",
-                    "content": f"You are a smart assistant who understands entities and their types in a given text. If user message contains self reference such as 'I', 'me', 'my' etc. then use {filters['user_id']} as the source entity. Extract all the entities from the text. ***DO NOT*** answer the question itself if the given text is a question.",
-                },
-                {"role": "user", "content": data},
-            ],
-            tools=_tools,
-        )
-
-        entity_type_map = {}
-
-        try:
-            for tool_call in search_results["tool_calls"]:
-                if tool_call["name"] != "extract_entities":
-                    continue
-                for item in tool_call.get("arguments", {}).get("entities", []):
-                    if "entity" in item and "entity_type" in item:
-                        entity_type_map[item["entity"]] = item["entity_type"]
-        except Exception as e:
-            logger.exception(
-                f"Error in search tool: {e}, llm_provider={self.llm_provider}, search_results={search_results}"
-            )
-
-        entity_type_map = {k.lower().replace(" ", "_"): v.lower().replace(" ", "_") for k, v in entity_type_map.items()}
-        logger.debug(f"Entity type map: {entity_type_map}\n search_results={search_results}")
-        return entity_type_map
-
-    def _establish_nodes_relations_from_data(self, data, filters, entity_type_map):
-        """Establish relations among the extracted nodes."""
-
-        user_identity = f"user_id: {filters['user_id']}"
-        if filters.get("agent_id"):
-            user_identity += f", agent_id: {filters['agent_id']}"
-        if filters.get("run_id"):
-            user_identity += f", run_id: {filters['run_id']}"
-
-        if self.config.graph_store.custom_prompt:
-            system_content = EXTRACT_RELATIONS_PROMPT.replace("USER_ID", user_identity)
-            system_content = system_content.replace("CUSTOM_PROMPT", f"4. {self.config.graph_store.custom_prompt}")
-            messages = [
-                {"role": "system", "content": system_content},
-                {"role": "user", "content": data},
-            ]
-        else:
-            system_content = EXTRACT_RELATIONS_PROMPT.replace("USER_ID", user_identity)
-            messages = [
-                {"role": "system", "content": system_content},
-                {"role": "user", "content": f"List of entities: {list(entity_type_map.keys())}. \n\nText: {data}"},
-            ]
-
-        _tools = [RELATIONS_TOOL]
-        if self.llm_provider in ["azure_openai_structured", "openai_structured"]:
-            _tools = [RELATIONS_STRUCT_TOOL]
-
-        extracted_entities = self.llm.generate_response(
-            messages=messages,
-            tools=_tools,
-        )
-
-        entities = []
-        if extracted_entities and extracted_entities.get("tool_calls"):
-            entities = extracted_entities["tool_calls"][0].get("arguments", {}).get("entities", [])
-
-        entities = self._remove_spaces_from_entities(entities)
-        logger.debug(f"Extracted entities: {entities}")
-        return entities
-
-    # -- graph DB operations ---------------------------------------------------
-
-    def _search_graph_db(self, node_list, filters, limit=100):
-        """Search similar nodes and their respective incoming and outgoing relations."""
-        result_relations = []
-
-        for node in node_list:
-            n_embedding = self.embedding_model.embed(node)
-
-            nodes = self._fetch_user_nodes_with_embeddings(filters["user_id"])
-            similar_nodes = _get_similar_nodes(nodes, n_embedding, filters, self.threshold)
-
-            # Build WHERE clause for relationship target filtering
-            rel_where_parts = ["m.user_id = %s"]
-            rel_params_suffix = [filters["user_id"]]
-            if filters.get("agent_id"):
-                rel_where_parts.append("m.agent_id = %s")
-                rel_params_suffix.append(filters["agent_id"])
-            if filters.get("run_id"):
-                rel_where_parts.append("m.run_id = %s")
-                rel_params_suffix.append(filters["run_id"])
-            rel_where = " AND ".join(rel_where_parts)
-
-            # For each similar node, fetch its relationships
-            for sn in similar_nodes[:limit]:
-                node_name = sn["name"]
-                similarity = sn["similarity"]
-
-                out_params = (filters["user_id"], node_name) + tuple(rel_params_suffix)
-                out_results = self._exec_cypher(
-                    f"MATCH (n {{user_id: %s, name: %s}})-[r]->(m) "
-                    f"WHERE {rel_where} "
-                    f"RETURN n.name, type(r), m.name",
-                    cols=["source", "relationship", "destination"],
-                    params=out_params,
-                )
-
-                in_params = (filters["user_id"], node_name) + tuple(rel_params_suffix)
-                in_results = self._exec_cypher(
-                    f"MATCH (n {{user_id: %s, name: %s}})<-[r]-(m) "
-                    f"WHERE {rel_where} "
-                    f"RETURN m.name, type(r), n.name",
-                    cols=["source", "relationship", "destination"],
-                    params=in_params,
-                )
-
-                for rel in out_results + in_results:
-                    rel["similarity"] = similarity
-                    result_relations.append(rel)
-
-        return result_relations
-
-    def _get_delete_entities_from_search_output(self, search_output, data, filters):
-        """Get the entities to be deleted from the search output."""
-        search_output_string = format_entities(search_output)
-
-        user_identity = f"user_id: {filters['user_id']}"
-        if filters.get("agent_id"):
-            user_identity += f", agent_id: {filters['agent_id']}"
-        if filters.get("run_id"):
-            user_identity += f", run_id: {filters['run_id']}"
-
-        system_prompt, user_prompt = get_delete_messages(search_output_string, data, user_identity)
-
-        _tools = [DELETE_MEMORY_TOOL_GRAPH]
-        if self.llm_provider in ["azure_openai_structured", "openai_structured"]:
-            _tools = [DELETE_MEMORY_STRUCT_TOOL_GRAPH]
-
-        memory_updates = self.llm.generate_response(
-            messages=[
-                {"role": "system", "content": system_prompt},
-                {"role": "user", "content": user_prompt},
-            ],
-            tools=_tools,
-        )
-
-        to_be_deleted = []
-        for item in memory_updates.get("tool_calls", []):
-            if item.get("name") == "delete_graph_memory":
-                to_be_deleted.append(item.get("arguments"))
-        to_be_deleted = self._remove_spaces_from_entities(to_be_deleted)
-        logger.debug(f"Deleted relationships: {to_be_deleted}")
-        return to_be_deleted
-
-    def _delete_entities(self, to_be_deleted, filters):
-        """Delete the entities from the graph."""
-        user_id = filters["user_id"]
-        agent_id = filters.get("agent_id")
-        run_id = filters.get("run_id")
-        results = []
-
-        try:
-            for item in to_be_deleted:
-                source = item["source"]
-                destination = item["destination"]
-                relationship = item["relationship"]
-
-                where_parts = [
-                    "n.user_id = %s", "n.name = %s",
-                    "m.user_id = %s", "m.name = %s",
-                ]
-                params = [user_id, source, user_id, destination]
-                if agent_id:
-                    where_parts.extend(["n.agent_id = %s", "m.agent_id = %s"])
-                    params.extend([agent_id, agent_id])
-                if run_id:
-                    where_parts.extend(["n.run_id = %s", "m.run_id = %s"])
-                    params.extend([run_id, run_id])
-                where_clause = " AND ".join(where_parts)
-
-                result = self._exec_cypher(
-                    f"MATCH (n)-[r:{relationship}]->(m) "
-                    f"WHERE {where_clause} "
-                    f"DELETE r "
-                    f"RETURN n.name, type(r), m.name",
-                    cols=["source", "relationship", "target"],
-                    params=tuple(params),
-                )
-                results.append(result)
-
-            self.ag.commit()
-        except Exception:
-            self.ag.rollback()
-            raise
-
-        return results
-
-    def _add_entities(self, to_be_added, filters, entity_type_map):
-        """Add new entities to the graph. Merge nodes if they already exist.
-
-        Apache AGE does not support ``ON CREATE SET`` / ``ON MATCH SET``, so we
-        use ``MERGE … SET`` which always applies.  The ``coalesce`` pattern
-        ensures ``created`` is only set on the first merge.
-        """
-        user_id = filters["user_id"]
-        agent_id = filters.get("agent_id")
-        run_id = filters.get("run_id")
-        results = []
-
-        try:
-            for item in to_be_added:
-                source = item["source"]
-                destination = item["destination"]
-                relationship = item["relationship"]
-
-                source_embedding = self.embedding_model.embed(source)
-                dest_embedding = self.embedding_model.embed(destination)
-
-                source_match = self._find_similar_node(source_embedding, filters, threshold=self.threshold)
-                dest_match = self._find_similar_node(dest_embedding, filters, threshold=self.threshold)
-
-                effective_source = source_match["name"] if source_match else source
-                effective_dest = dest_match["name"] if dest_match else destination
-
-                # Merge source and destination nodes
-                self._merge_node(user_id, effective_source, source_embedding, agent_id, run_id)
-                self._merge_node(user_id, effective_dest, dest_embedding, agent_id, run_id)
-
-                # Merge relationship
-                result = self._exec_cypher(
-                    f"MATCH (s {{user_id: %s, name: %s}}), (d {{user_id: %s, name: %s}}) "
-                    f"MERGE (s)-[r:{relationship}]->(d) "
-                    f"RETURN s.name, type(r), d.name",
-                    cols=["source", "relationship", "target"],
-                    params=(user_id, effective_source, user_id, effective_dest),
-                )
-                results.append(result)
-
-            self.ag.commit()
-        except Exception:
-            self.ag.rollback()
-            raise
-
-        return results
-
-    def _remove_spaces_from_entities(self, entity_list):
-        for item in entity_list:
-            item["source"] = item["source"].lower().replace(" ", "_")
-            item["relationship"] = sanitize_relationship_for_cypher(item["relationship"].lower().replace(" ", "_"))
-            item["destination"] = item["destination"].lower().replace(" ", "_")
-        return entity_list
-
-    def reset(self):
-        """Reset the graph by clearing all nodes and relationships."""
-        logger.warning("Clearing graph...")
-        self._exec_cypher("MATCH (n) DETACH DELETE n")
-        self.ag.commit()

mem0/memory/graph_memory.py

@@ -174,7 +174,6 @@ class MemoryGraph:
 
         query = f"""
         MATCH (n {self.node_label} {{{node_props_str}}})-[r]->(m {self.node_label} {{{node_props_str}}})
-        WHERE r.valid IS NULL OR r.valid = true
         RETURN n.name AS source, type(r) AS relationship, m.name AS target
         LIMIT $limit
         """
@@ -292,12 +291,10 @@ class MemoryGraph:
             CALL {{
                 WITH n
                 MATCH (n)-[r]->(m {self.node_label} {{{node_props_str}}})
-                WHERE r.valid IS NULL OR r.valid = true
                 RETURN n.name AS source, elementId(n) AS source_id, type(r) AS relationship, elementId(r) AS relation_id, m.name AS destination, elementId(m) AS destination_id
                 UNION
                 WITH n  
                 MATCH (n)<-[r]-(m {self.node_label} {{{node_props_str}}})
-                WHERE r.valid IS NULL OR r.valid = true
                 RETURN m.name AS source, elementId(m) AS source_id, type(r) AS relationship, elementId(r) AS relation_id, n.name AS destination, elementId(n) AS destination_id
             }}
             WITH distinct source, source_id, relationship, relation_id, destination, destination_id, similarity
@@ -395,15 +392,13 @@ class MemoryGraph:
             source_props_str = ", ".join(source_props)
             dest_props_str = ", ".join(dest_props)
 
-            # Soft-delete: mark relationship as invalid instead of removing it,
+            # Delete the specific relationship between nodes
-            # enabling temporal reasoning over historical graph state.
-            # See: https://github.com/mem0ai/mem0/issues/4187
             cypher = f"""
             MATCH (n {self.node_label} {{{source_props_str}}})
             -[r:{relationship}]->
             (m {self.node_label} {{{dest_props_str}}})
-            WHERE r.valid IS NULL OR r.valid = true
+            
-            SET r.valid = false, r.invalidated_at = datetime()
+            DELETE r
             RETURN 
                 n.name AS source,
                 m.name AS target,
@@ -469,16 +464,11 @@ class MemoryGraph:
                 CALL db.create.setNodeVectorProperty(destination, 'embedding', $destination_embedding)
                 WITH source, destination
                 MERGE (source)-[r:{relationship}]->(destination)
-                ON CREATE SET
+                ON CREATE SET 
-                    r.created_at = timestamp(),
+                    r.created = timestamp(),
-                    r.updated_at = timestamp(),
+                    r.mentions = 1
-                    r.mentions = 1,
-                    r.valid = true
                 ON MATCH SET
-                    r.mentions = coalesce(r.mentions, 0) + 1,
+                    r.mentions = coalesce(r.mentions, 0) + 1
-                    r.valid = true,
-                    r.updated_at = timestamp(),
-                    r.invalidated_at = null
                 RETURN source.name AS source, type(r) AS relationship, destination.name AS target
                 """
 
@@ -518,16 +508,11 @@ class MemoryGraph:
                 CALL db.create.setNodeVectorProperty(source, 'embedding', $source_embedding)
                 WITH source, destination
                 MERGE (source)-[r:{relationship}]->(destination)
-                ON CREATE SET
+                ON CREATE SET 
-                    r.created_at = timestamp(),
+                    r.created = timestamp(),
-                    r.updated_at = timestamp(),
+                    r.mentions = 1
-                    r.mentions = 1,
-                    r.valid = true
                 ON MATCH SET
-                    r.mentions = coalesce(r.mentions, 0) + 1,
+                    r.mentions = coalesce(r.mentions, 0) + 1
-                    r.valid = true,
-                    r.updated_at = timestamp(),
-                    r.invalidated_at = null
                 RETURN source.name AS source, type(r) AS relationship, destination.name AS target
                 """
 
@@ -552,16 +537,11 @@ class MemoryGraph:
                 WHERE elementId(destination) = $destination_id
                 SET destination.mentions = coalesce(destination.mentions, 0) + 1
                 MERGE (source)-[r:{relationship}]->(destination)
-                ON CREATE SET
+                ON CREATE SET 
                     r.created_at = timestamp(),
                     r.updated_at = timestamp(),
-                    r.mentions = 1,
+                    r.mentions = 1
-                    r.valid = true
+                ON MATCH SET r.mentions = coalesce(r.mentions, 0) + 1
-                ON MATCH SET
-                    r.mentions = coalesce(r.mentions, 0) + 1,
-                    r.valid = true,
-                    r.updated_at = timestamp(),
-                    r.invalidated_at = null
                 RETURN source.name AS source, type(r) AS relationship, destination.name AS target
                 """
 
@@ -605,18 +585,10 @@ class MemoryGraph:
                 WITH source, destination
                 CALL db.create.setNodeVectorProperty(destination, 'embedding', $dest_embedding)
                 WITH source, destination
-                MERGE (source)-[r:{relationship}]->(destination)
+                MERGE (source)-[rel:{relationship}]->(destination)
-                ON CREATE SET
+                ON CREATE SET rel.created = timestamp(), rel.mentions = 1
-                    r.created_at = timestamp(),
+                ON MATCH SET rel.mentions = coalesce(rel.mentions, 0) + 1
-                    r.updated_at = timestamp(),
+                RETURN source.name AS source, type(rel) AS relationship, destination.name AS target
-                    r.mentions = 1,
-                    r.valid = true
-                ON MATCH SET
-                    r.mentions = coalesce(r.mentions, 0) + 1,
-                    r.valid = true,
-                    r.updated_at = timestamp(),
-                    r.invalidated_at = null
-                RETURN source.name AS source, type(r) AS relationship, destination.name AS target
                 """
 
                 params = {

mem0/memory/main.py

@@ -9,7 +9,7 @@ import uuid
 import warnings
 from copy import deepcopy
 from datetime import datetime, timezone
-from typing import Any, Dict, List, Optional, Union
+from typing import Any, Dict, Optional
 
 from pydantic import ValidationError
 
@@ -479,8 +479,7 @@ class Memory(MemoryBase):
 
                 msg_content = message_dict["content"]
                 msg_embeddings = self.embedding_model.embed(msg_content, "add")
-                # Pass embeddings as a dict so _create_memory can reuse the cached embedding
+                mem_id = self._create_memory(msg_content, msg_embeddings, per_msg_meta)
-                mem_id = self._create_memory(msg_content, {msg_content: msg_embeddings}, per_msg_meta)
 
                 returned_memories.append(
                     {
@@ -609,9 +608,6 @@ class Memory(MemoryBase):
 
                     event_type = resp.get("event")
                     if event_type == "ADD":
-                        # Ensure action_text has an embedding cached to avoid redundant API calls
-                        if action_text not in new_message_embeddings:
-                            new_message_embeddings[action_text] = self.embedding_model.embed(action_text, "add")
                         memory_id = self._create_memory(
                             data=action_text,
                             existing_embeddings=new_message_embeddings,
@@ -619,9 +615,6 @@ class Memory(MemoryBase):
                         )
                         returned_memories.append({"id": memory_id, "memory": action_text, "event": event_type})
                     elif event_type == "UPDATE":
-                        # Ensure action_text has an embedding cached to avoid redundant API calls
-                        if action_text not in new_message_embeddings:
-                            new_message_embeddings[action_text] = self.embedding_model.embed(action_text, "update")
                         self._update_memory(
                             memory_id=temp_uuid_mapping[resp.get("id")],
                             data=action_text,
@@ -651,9 +644,6 @@ class Memory(MemoryBase):
                         if memory_id and (metadata.get("agent_id") or metadata.get("run_id")):
                             # Update only the session identifiers, keep content the same
                             existing_memory = self.vector_store.get(vector_id=memory_id)
-                            if existing_memory is None:
-                                logger.warning(f"Memory {memory_id} not found for session ID update, skipping")
-                                continue
                             updated_metadata = deepcopy(existing_memory.payload)
                             if metadata.get("agent_id"):
                                 updated_metadata["agent_id"] = metadata["agent_id"]
@@ -1160,13 +1150,10 @@ class Memory(MemoryBase):
         capture_event("mem0.history", self, {"memory_id": memory_id, "sync_type": "sync"})
         return self.db.get_history(memory_id)
 
-    def _create_memory(self, data: str, existing_embeddings: Union[Dict[str, List[float]], List[float]], metadata=None):
+    def _create_memory(self, data, existing_embeddings, metadata=None):
         logger.debug(f"Creating memory with {data=}")
-        # existing_embeddings may be a dict (preferred) or a precomputed vector
+        if data in existing_embeddings:
-        if isinstance(existing_embeddings, dict) and data in existing_embeddings:
             embeddings = existing_embeddings[data]
-        elif not isinstance(existing_embeddings, dict):
-            embeddings = existing_embeddings
         else:
             embeddings = self.embedding_model.embed(data, memory_action="add")
         memory_id = str(uuid.uuid4())
@@ -1230,7 +1217,7 @@ class Memory(MemoryBase):
 
         return result
 
-    def _update_memory(self, memory_id, data: str, existing_embeddings: Union[Dict[str, List[float]], List[float]], metadata=None):
+    def _update_memory(self, memory_id, data, existing_embeddings, metadata=None):
         logger.info(f"Updating memory with {data=}")
 
         try:
@@ -1239,9 +1226,6 @@ class Memory(MemoryBase):
             logger.error(f"Error getting memory with ID {memory_id} during update.")
             raise ValueError(f"Error getting memory with ID {memory_id}. Please provide a valid 'memory_id'")
 
-        if existing_memory is None:
-            raise ValueError(f"Memory with id {memory_id} not found. Please provide a valid 'memory_id'")
-
         prev_value = existing_memory.payload.get("data")
 
         new_metadata = deepcopy(metadata) if metadata is not None else {}
@@ -1263,10 +1247,8 @@ class Memory(MemoryBase):
         if "role" not in new_metadata and "role" in existing_memory.payload:
             new_metadata["role"] = existing_memory.payload["role"]
 
-        if isinstance(existing_embeddings, dict) and data in existing_embeddings:
+        if data in existing_embeddings:
             embeddings = existing_embeddings[data]
-        elif not isinstance(existing_embeddings, dict):
-            embeddings = existing_embeddings
         else:
             embeddings = self.embedding_model.embed(data, "update")
 
@@ -1292,8 +1274,6 @@ class Memory(MemoryBase):
     def _delete_memory(self, memory_id):
         logger.info(f"Deleting memory with {memory_id=}")
         existing_memory = self.vector_store.get(vector_id=memory_id)
-        if existing_memory is None:
-            raise ValueError(f"Memory with id {memory_id} not found")
         prev_value = existing_memory.payload.get("data", "")
         self.vector_store.delete(vector_id=memory_id)
         self.db.add_history(
@@ -1535,8 +1515,7 @@ class AsyncMemory(MemoryBase):
 
                 msg_content = message_dict["content"]
                 msg_embeddings = await asyncio.to_thread(self.embedding_model.embed, msg_content, "add")
-                # Pass embeddings as a dict so _create_memory can reuse the cached embedding
+                mem_id = await self._create_memory(msg_content, msg_embeddings, per_msg_meta)
-                mem_id = await self._create_memory(msg_content, {msg_content: msg_embeddings}, per_msg_meta)
 
                 returned_memories.append(
                     {
@@ -1664,11 +1643,6 @@ class AsyncMemory(MemoryBase):
                     event_type = resp.get("event")
 
                     if event_type == "ADD":
-                        # Ensure action_text has an embedding cached to avoid redundant API calls
-                        if action_text not in new_message_embeddings:
-                            new_message_embeddings[action_text] = await asyncio.to_thread(
-                                self.embedding_model.embed, action_text, "add"
-                            )
                         task = asyncio.create_task(
                             self._create_memory(
                                 data=action_text,
@@ -1678,11 +1652,6 @@ class AsyncMemory(MemoryBase):
                         )
                         memory_tasks.append((task, resp, "ADD", None))
                     elif event_type == "UPDATE":
-                        # Ensure action_text has an embedding cached to avoid redundant API calls
-                        if action_text not in new_message_embeddings:
-                            new_message_embeddings[action_text] = await asyncio.to_thread(
-                                self.embedding_model.embed, action_text, "update"
-                            )
                         task = asyncio.create_task(
                             self._update_memory(
                                 memory_id=temp_uuid_mapping[resp["id"]],
@@ -1702,9 +1671,6 @@ class AsyncMemory(MemoryBase):
                             # Create async task to update only the session identifiers
                             async def update_session_ids(mem_id, meta):
                                 existing_memory = await asyncio.to_thread(self.vector_store.get, vector_id=mem_id)
-                                if existing_memory is None:
-                                    logger.warning(f"Memory {mem_id} not found for session ID update, skipping")
-                                    return
                                 updated_metadata = deepcopy(existing_memory.payload)
                                 if meta.get("agent_id"):
                                     updated_metadata["agent_id"] = meta["agent_id"]
@@ -2252,13 +2218,10 @@ class AsyncMemory(MemoryBase):
         capture_event("mem0.history", self, {"memory_id": memory_id, "sync_type": "async"})
         return await asyncio.to_thread(self.db.get_history, memory_id)
 
-    async def _create_memory(self, data: str, existing_embeddings: Union[Dict[str, List[float]], List[float]], metadata=None):
+    async def _create_memory(self, data, existing_embeddings, metadata=None):
         logger.debug(f"Creating memory with {data=}")
-        # existing_embeddings may be a dict (preferred) or a precomputed vector
+        if data in existing_embeddings:
-        if isinstance(existing_embeddings, dict) and data in existing_embeddings:
             embeddings = existing_embeddings[data]
-        elif not isinstance(existing_embeddings, dict):
-            embeddings = existing_embeddings
         else:
             embeddings = await asyncio.to_thread(self.embedding_model.embed, data, memory_action="add")
 
@@ -2341,7 +2304,7 @@ class AsyncMemory(MemoryBase):
 
         return result
 
-    async def _update_memory(self, memory_id, data: str, existing_embeddings: Union[Dict[str, List[float]], List[float]], metadata=None):
+    async def _update_memory(self, memory_id, data, existing_embeddings, metadata=None):
         logger.info(f"Updating memory with {data=}")
 
         try:
@@ -2350,9 +2313,6 @@ class AsyncMemory(MemoryBase):
             logger.error(f"Error getting memory with ID {memory_id} during update.")
             raise ValueError(f"Error getting memory with ID {memory_id}. Please provide a valid 'memory_id'")
 
-        if existing_memory is None:
-            raise ValueError(f"Memory with id {memory_id} not found. Please provide a valid 'memory_id'")
-
         prev_value = existing_memory.payload.get("data")
 
         new_metadata = deepcopy(metadata) if metadata is not None else {}
@@ -2375,10 +2335,8 @@ class AsyncMemory(MemoryBase):
         if "role" not in new_metadata and "role" in existing_memory.payload:
             new_metadata["role"] = existing_memory.payload["role"]
 
-        if isinstance(existing_embeddings, dict) and data in existing_embeddings:
+        if data in existing_embeddings:
             embeddings = existing_embeddings[data]
-        elif not isinstance(existing_embeddings, dict):
-            embeddings = existing_embeddings
         else:
             embeddings = await asyncio.to_thread(self.embedding_model.embed, data, "update")
 
@@ -2406,8 +2364,6 @@ class AsyncMemory(MemoryBase):
     async def _delete_memory(self, memory_id):
         logger.info(f"Deleting memory with {memory_id=}")
         existing_memory = await asyncio.to_thread(self.vector_store.get, vector_id=memory_id)
-        if existing_memory is None:
-            raise ValueError(f"Memory with id {memory_id} not found")
         prev_value = existing_memory.payload.get("data", "")
 
         await asyncio.to_thread(self.vector_store.delete, vector_id=memory_id)

mem0/utils/factory.py

@@ -188,7 +188,6 @@ class VectorStoreFactory:
         "baidu": "mem0.vector_stores.baidu.BaiduDB",
         "cassandra": "mem0.vector_stores.cassandra.CassandraDB",
         "neptune": "mem0.vector_stores.neptune_analytics.NeptuneAnalyticsVector",
-        "turbopuffer": "mem0.vector_stores.turbopuffer.TurbopufferDB",
     }
 
     @classmethod
@@ -219,7 +218,6 @@ class GraphStoreFactory:
         "neptune": "mem0.graphs.neptune.neptunegraph.MemoryGraph",
         "neptunedb": "mem0.graphs.neptune.neptunedb.MemoryGraph",
         "kuzu": "mem0.memory.kuzu_memory.MemoryGraph",
-        "apache_age": "mem0.memory.apache_age_memory.MemoryGraph",
         "default": "mem0.memory.graph_memory.MemoryGraph",
     }
 

mem0/vector_stores/configs.py

@@ -34,7 +34,6 @@ class VectorStoreConfig(BaseModel):
         "faiss": "FAISSConfig",
         "langchain": "LangchainConfig",
         "s3_vectors": "S3VectorsConfig",
-        "turbopuffer": "TurbopufferConfig",
     }
 
     @model_validator(mode="after")

mem0/vector_stores/mongodb.py

@@ -26,7 +26,7 @@ class OutputData(BaseModel):
 
 
 class MongoDB(VectorStoreBase):
-    VECTOR_TYPE = "vector"
+    VECTOR_TYPE = "knnVector"
     SIMILARITY_METRIC = "cosine"
 
     def __init__(self, db_name: str, collection_name: str, embedding_model_dims: int, mongo_uri: str):
@@ -69,16 +69,17 @@ class MongoDB(VectorStoreBase):
             else:
                 search_index_model = SearchIndexModel(
                     name=self.index_name,
-                    type="vectorSearch",
                     definition={
-                        "fields": [
+                        "mappings": {
-                            {
+                            "dynamic": False,
-                                "type": self.VECTOR_TYPE,
+                            "fields": {
-                                "path": "embedding",
+                                "embedding": {
-                                "numDimensions": self.embedding_model_dims,
+                                    "type": self.VECTOR_TYPE,
-                                "similarity": self.SIMILARITY_METRIC,
+                                    "dimensions": self.embedding_model_dims,
-                            }
+                                    "similarity": self.SIMILARITY_METRIC,
-                        ]
+                                }
+                            },
+                        }
                     },
                 )
                 collection.create_search_index(search_index_model)
@@ -140,7 +141,7 @@ class MongoDB(VectorStoreBase):
                     "$vectorSearch": {
                         "index": self.index_name,
                         "limit": limit,
-                        "numCandidates": min(limit * 20, 10000),
+                        "numCandidates": limit,
                         "queryVector": vectors,
                         "path": "embedding",
                     }

mem0/vector_stores/opensearch.py

@@ -113,25 +113,6 @@ class OpenSearchDB(VectorStoreBase):
         if payloads is None:
             payloads = [{} for _ in range(len(vectors))]
 
-        for idx, vec in enumerate(vectors):
-            if vec is None:
-                raise ValueError(
-                    f"Vector at index {idx} is null. "
-                    f"This usually means the embedding model failed to generate an embedding. "
-                    f"Check that your embedding model is configured correctly and returning valid vectors."
-                )
-            if len(vec) == 0:
-                raise ValueError(
-                    f"Vector at index {idx} is empty. "
-                    f"Expected a vector of dimension {self.embedding_model_dims}, got an empty vector."
-                )
-            if len(vec) != self.embedding_model_dims:
-                raise ValueError(
-                    f"Vector at index {idx} has dimension {len(vec)}, "
-                    f"but the index '{self.collection_name}' expects dimension {self.embedding_model_dims}. "
-                    f"Ensure your embedding model's output dimensions match the vector store configuration."
-                )
-
         results = []
         for i, (vec, id_) in enumerate(zip(vectors, ids)):
             body = {
@@ -143,16 +124,14 @@ class OpenSearchDB(VectorStoreBase):
                 self.client.index(index=self.collection_name, body=body)
                 # Force refresh to make documents immediately searchable for tests
                 self.client.indices.refresh(index=self.collection_name)
-
+                
-                results.append(
+                results.append(OutputData(
-                    OutputData(
+                    id=id_,
-                        id=id_,
+                    score=1.0,  # No score for inserts
-                        score=1.0,  # No score for inserts
+                    payload=payloads[i]
-                        payload=payloads[i],
+                ))
-                    )
-                )
             except Exception as e:
-                logger.error(f"Error inserting vector {id_}: {e}", exc_info=True)
+                logger.error(f"Error inserting vector {id_}: {e}")
                 raise
 
         return results
@@ -200,7 +179,7 @@ class OpenSearchDB(VectorStoreBase):
             ]
             return results
         except Exception as e:
-            logger.error(f"Error during search: {e}", exc_info=True)
+            logger.error(f"Error during search: {e}")
             return []
 
     def delete(self, vector_id: str) -> None:
@@ -221,15 +200,6 @@ class OpenSearchDB(VectorStoreBase):
 
     def update(self, vector_id: str, vector: Optional[List[float]] = None, payload: Optional[Dict] = None) -> None:
         """Update a vector and its payload using the custom 'id' field."""
-        if vector is not None:
-            if len(vector) == 0:
-                raise ValueError("Cannot update with an empty vector.")
-            if len(vector) != self.embedding_model_dims:
-                raise ValueError(
-                    f"Update vector has dimension {len(vector)}, "
-                    f"but the index '{self.collection_name}' expects dimension {self.embedding_model_dims}. "
-                    f"Ensure your embedding model's output dimensions match the vector store configuration."
-                )
 
         # First, find the document by custom ID
         search_query = {"query": {"term": {"id": vector_id}}}
@@ -252,9 +222,8 @@ class OpenSearchDB(VectorStoreBase):
         if doc:
             try:
                 response = self.client.update(index=self.collection_name, id=opensearch_id, body={"doc": doc})
-            except Exception as e:
+            except Exception:
-                logger.error(f"Error updating vector {vector_id}: {e}", exc_info=True)
+                pass
-                raise
 
     def get(self, vector_id: str) -> Optional[OutputData]:
         """Retrieve a vector by ID."""
@@ -269,7 +238,7 @@ class OpenSearchDB(VectorStoreBase):
 
             return OutputData(id=hits[0]["_source"].get("id"), score=1.0, payload=hits[0]["_source"].get("payload", {}))
         except Exception as e:
-            logger.error(f"Error retrieving vector {vector_id}: {str(e)}", exc_info=True)
+            logger.error(f"Error retrieving vector {vector_id}: {str(e)}")
             return None
 
     def list_cols(self) -> List[str]:
@@ -312,8 +281,9 @@ class OpenSearchDB(VectorStoreBase):
             ]
             return [results]  # VectorStore expects tuple/list format
         except Exception as e:
-            logger.error(f"Error listing vectors: {e}", exc_info=True)
+            logger.error(f"Error listing vectors: {e}")
             return []
+        
 
     def reset(self):
         """Reset the index by deleting and recreating it."""

mem0/vector_stores/qdrant.py

@@ -1,16 +1,12 @@
 import logging
 import os
 import shutil
-from typing import Optional
 
 from qdrant_client import QdrantClient
 from qdrant_client.models import (
     Distance,
     FieldCondition,
     Filter,
-    MatchAny,
-    MatchExcept,
-    MatchText,
     MatchValue,
     PointIdsList,
     PointStruct,
@@ -142,148 +138,26 @@ class Qdrant(VectorStoreBase):
         ]
         self.client.upsert(collection_name=self.collection_name, points=points)
 
-    def _build_field_condition(self, key: str, value) -> Optional[FieldCondition]:
+    def _create_filter(self, filters: dict) -> Filter:
-        """
-        Build a single FieldCondition from a key-value filter pair.
-
-        Supports the enhanced filter syntax documented at
-        https://docs.mem0.ai/open-source/features/metadata-filtering
-
-        Args:
-            key (str): The payload field name.
-            value: A scalar for simple equality, or a dict with one operator key.
-
-        Returns:
-            Optional[FieldCondition]: The Qdrant field condition, or None if the
-            value is the wildcard '*' (match any / field exists — skip filter).
-        """
-        if not isinstance(value, dict):
-            if value == "*":
-                # Wildcard: match any value. Qdrant has no direct "field exists"
-                # condition via FieldCondition, so we skip this filter (match all).
-                return None
-            if isinstance(value, list):
-                # List shorthand: {"field": ["a", "b"]} treated as in-operator.
-                return FieldCondition(key=key, match=MatchAny(any=value))
-            # Simple equality: {"field": "value"}
-            return FieldCondition(key=key, match=MatchValue(value=value))
-
-        ops = set(value.keys())
-        range_ops = {"gt", "gte", "lt", "lte"}
-        non_range_ops = ops - range_ops
-
-        if ops & range_ops:
-            if non_range_ops:
-                raise ValueError(
-                    f"Cannot mix range operators ({ops & range_ops}) with "
-                    f"non-range operators ({non_range_ops}) for field '{key}'. "
-                    f"Use AND to combine them as separate conditions."
-                )
-            range_kwargs = {op: value[op] for op in range_ops if op in value}
-            return FieldCondition(key=key, range=Range(**range_kwargs))
-        elif "eq" in value:
-            return FieldCondition(key=key, match=MatchValue(value=value["eq"]))
-        elif "ne" in value:
-            return FieldCondition(key=key, match=MatchExcept(**{"except": [value["ne"]]}))
-        elif "in" in value:
-            return FieldCondition(key=key, match=MatchAny(any=value["in"]))
-        elif "nin" in value:
-            return FieldCondition(key=key, match=MatchExcept(**{"except": value["nin"]}))
-        elif "contains" in value or "icontains" in value:
-            # MatchText: with a full-text index, tokenized matching (all words must appear).
-            # Without a full-text index, exact substring match.
-            op = "icontains" if "icontains" in value else "contains"
-            text = value[op]
-            if op == "icontains":
-                logger.debug(
-                    "icontains on field '%s': Qdrant MatchText case sensitivity depends on "
-                    "full-text index configuration. Without a full-text index this behaves "
-                    "as a case-sensitive substring match (same as 'contains').",
-                    key,
-                )
-            return FieldCondition(key=key, match=MatchText(text=text))
-        else:
-            supported = {"eq", "ne", "gt", "gte", "lt", "lte", "in", "nin", "contains", "icontains"}
-            raise ValueError(
-                f"Unsupported filter operator(s) for field '{key}': {ops}. "
-                f"Supported operators: {supported}"
-            )
-
-    def _create_filter(self, filters: dict) -> Optional[Filter]:
         """
         Create a Filter object from the provided filters.
 
-        Supports the enhanced filter syntax with comparison operators (eq, ne,
-        gt, gte, lt, lte), list operators (in, nin), string operators (contains,
-        icontains), and logical operators (AND, OR, NOT).
-
         Args:
             filters (dict): Filters to apply.
 
         Returns:
-            Filter: The created Filter object, or None if filters is empty.
+            Filter: The created Filter object.
         """
         if not filters:
             return None
-
+            
-        # Normalize $or/$not/$and → OR/NOT/AND and deduplicate.
+        conditions = []
-        # Memory._process_metadata_filters() renames OR→$or and NOT→$not,
-        # but effective_filters retains the original OR/NOT keys from
-        # deepcopy(input_filters).  Without dedup the same sub-conditions
-        # would be evaluated twice.
-        key_map = {"$or": "OR", "$not": "NOT", "$and": "AND"}
-        normalized = {}
         for key, value in filters.items():
-            norm_key = key_map.get(key, key)
+            if isinstance(value, dict) and "gte" in value and "lte" in value:
-            if norm_key not in normalized:
+                conditions.append(FieldCondition(key=key, range=Range(gte=value["gte"], lte=value["lte"])))
-                normalized[norm_key] = value
-
-        must = []
-        should = []
-        must_not = []
-
-        for key, value in normalized.items():
-            if key in ("AND", "OR", "NOT"):
-                if not isinstance(value, list):
-                    raise ValueError(
-                        f"{key} filter value must be a list of filter dicts, "
-                        f"got {type(value).__name__}"
-                    )
-                for i, item in enumerate(value):
-                    if not isinstance(item, dict):
-                        raise ValueError(
-                            f"{key} filter list item at index {i} must be a dict, "
-                            f"got {type(item).__name__}: {item!r}"
-                        )
-
-            if key == "AND":
-                for sub in value:
-                    built = self._create_filter(sub)
-                    if built:
-                        must.append(built)
-            elif key == "OR":
-                for sub in value:
-                    built = self._create_filter(sub)
-                    if built:
-                        should.append(built)
-            elif key == "NOT":
-                for sub in value:
-                    built = self._create_filter(sub)
-                    if built:
-                        must_not.append(built)
             else:
-                condition = self._build_field_condition(key, value)
+                conditions.append(FieldCondition(key=key, match=MatchValue(value=value)))
-                if condition is not None:
+        return Filter(must=conditions) if conditions else None
-                    must.append(condition)
-
-        if not any([must, should, must_not]):
-            return None
-
-        return Filter(
-            must=must or None,
-            should=should or None,
-            must_not=must_not or None,
-        )
 
     def search(self, query: str, vectors: list, limit: int = 5, filters: dict = None) -> list:
         """

mem0/vector_stores/turbopuffer.py

@@ -1,337 +0,0 @@
-import logging
-import os
-from typing import Any, Dict, List, Optional, Union
-
-try:
-    from turbopuffer import Turbopuffer as TurbopufferClient
-except ImportError:
-    raise ImportError(
-        "Turbopuffer requires extra dependencies. Install with `pip install turbopuffer`"
-    ) from None
-
-from pydantic import BaseModel
-
-from mem0.vector_stores.base import VectorStoreBase
-
-logger = logging.getLogger(__name__)
-
-
-class OutputData(BaseModel):
-    id: Optional[str]
-    score: Optional[float]
-    payload: Optional[Dict]
-
-
-class TurbopufferDB(VectorStoreBase):
-    def __init__(
-        self,
-        collection_name: str,
-        embedding_model_dims: int,
-        api_key: Optional[str] = None,
-        region: str = "gcp-us-central1",
-        distance_metric: str = "cosine_distance",
-        batch_size: int = 100,
-        extra_params: Optional[Dict[str, Any]] = None,
-    ):
-        """
-        Initialize the Turbopuffer vector store.
-
-        Args:
-            collection_name (str): Name of the namespace/collection.
-            embedding_model_dims (int): Dimensions of the embedding model.
-            api_key (str, optional): API key for Turbopuffer. Defaults to None.
-            region (str, optional): Turbopuffer region. Defaults to "gcp-us-central1".
-            distance_metric (str, optional): Distance metric for vector similarity.
-                Options: "cosine_distance" or "euclidean_squared". Defaults to "cosine_distance".
-            batch_size (int, optional): Batch size for operations. Defaults to 100.
-            extra_params (Dict, optional): Additional parameters for Turbopuffer client. Defaults to None.
-        """
-        api_key = api_key or os.environ.get("TURBOPUFFER_API_KEY")
-        if not api_key:
-            raise ValueError(
-                "Turbopuffer API key must be provided either as a parameter or via TURBOPUFFER_API_KEY environment variable"
-            )
-
-        params = extra_params or {}
-        params["region"] = region
-
-        self.client = TurbopufferClient(api_key=api_key, **params)
-        self.collection_name = collection_name
-        self.embedding_model_dims = embedding_model_dims
-        self.distance_metric = distance_metric
-        self.batch_size = batch_size
-
-        self.namespace = self.client.namespace(self.collection_name)
-
-    def create_col(self, name=None, vector_size=None, distance=None):
-        """
-        Create a new namespace in Turbopuffer.
-        Namespaces are created implicitly on first upsert, so this is a no-op.
-        """
-        pass
-
-    def insert(
-        self,
-        vectors: List[List[float]],
-        payloads: Optional[List[Dict]] = None,
-        ids: Optional[List[Union[str, int]]] = None,
-    ):
-        """
-        Insert vectors into the namespace.
-
-        Args:
-            vectors (list): List of vectors to insert.
-            payloads (list, optional): List of payloads corresponding to vectors. Defaults to None.
-            ids (list, optional): List of IDs corresponding to vectors. Defaults to None.
-        """
-        logger.info(f"Inserting {len(vectors)} vectors into namespace {self.collection_name}")
-
-        if ids is None:
-            ids = [str(i) for i in range(len(vectors))]
-
-        for i in range(0, len(vectors), self.batch_size):
-            batch_end = i + self.batch_size
-            rows = []
-            for j in range(i, min(batch_end, len(vectors))):
-                row = {}
-                if payloads and payloads[j]:
-                    row.update(payloads[j])
-                row["id"] = str(ids[j])
-                row["vector"] = vectors[j]
-                rows.append(row)
-
-            self.namespace.write(
-                upsert_rows=rows,
-                distance_metric=self.distance_metric,
-            )
-
-    def _parse_output(self, rows) -> List[OutputData]:
-        """
-        Parse the output data from Turbopuffer query results.
-
-        Args:
-            rows: List of Row objects from Turbopuffer query.
-
-        Returns:
-            List[OutputData]: Parsed output data.
-        """
-        results = []
-        for row in rows:
-            row_dict = row.model_dump()
-            row_id = str(row_dict.pop("id"))
-            dist = row_dict.pop("$dist", None)
-            row_dict.pop("vector", None)
-
-            score = 1 - dist if dist is not None else None
-
-            results.append(OutputData(
-                id=row_id,
-                score=score,
-                payload=row_dict,
-            ))
-        return results
-
-    def _convert_filters(self, filters: Optional[Dict]):
-        """
-        Convert mem0 filters to Turbopuffer filter format.
-
-        Turbopuffer filters use tuple format: ("And", (("field", "Op", value), ...))
-        """
-        if not filters:
-            return None
-
-        conditions = []
-        for key, value in filters.items():
-            if isinstance(value, dict):
-                if "gte" in value:
-                    conditions.append((key, "Gte", value["gte"]))
-                if "lte" in value:
-                    conditions.append((key, "Lte", value["lte"]))
-            else:
-                conditions.append((key, "Eq", value))
-
-        if not conditions:
-            return None
-        if len(conditions) == 1:
-            return conditions[0]
-        return ("And", tuple(conditions))
-
-    def search(
-        self, query: str, vectors: List[float], limit: int = 5, filters: Optional[Dict] = None
-    ) -> List[OutputData]:
-        """
-        Search for similar vectors.
-
-        Args:
-            query (str): Query text (unused in vector search, kept for interface consistency).
-            vectors (list): Query vector to search with.
-            limit (int, optional): Number of results to return. Defaults to 5.
-            filters (dict, optional): Filters to apply to the search. Defaults to None.
-
-        Returns:
-            list: Search results.
-        """
-        query_params = {
-            "rank_by": ("vector", "ANN", vectors),
-            "top_k": limit,
-            "include_attributes": True,
-        }
-
-        tpuf_filters = self._convert_filters(filters)
-        if tpuf_filters is not None:
-            query_params["filters"] = tpuf_filters
-
-        response = self.namespace.query(**query_params)
-        return self._parse_output(response.rows or [])
-
-    def delete(self, vector_id: Union[str, int]):
-        """
-        Delete a vector by ID.
-
-        Args:
-            vector_id (Union[str, int]): ID of the vector to delete.
-        """
-        self.namespace.write(deletes=[str(vector_id)])
-
-    def update(
-        self,
-        vector_id: Union[str, int],
-        vector: Optional[List[float]] = None,
-        payload: Optional[Dict] = None,
-    ):
-        """
-        Update a vector and its payload.
-
-        Args:
-            vector_id (Union[str, int]): ID of the vector to update.
-            vector (list, optional): Updated vector. Defaults to None.
-            payload (dict, optional): Updated payload. Defaults to None.
-        """
-        if vector is not None:
-            row = {}
-            if payload:
-                row.update(payload)
-            row["id"] = str(vector_id)
-            row["vector"] = vector
-            self.namespace.write(
-                upsert_rows=[row],
-                distance_metric=self.distance_metric,
-            )
-        elif payload is not None:
-            row = dict(payload)
-            row["id"] = str(vector_id)
-            self.namespace.write(patch_rows=[row])
-
-    def get(self, vector_id: Union[str, int]) -> Optional[OutputData]:
-        """
-        Retrieve a vector by ID.
-
-        Args:
-            vector_id (Union[str, int]): ID of the vector to retrieve.
-
-        Returns:
-            OutputData: Retrieved vector data, or None if not found.
-        """
-        try:
-            response = self.namespace.query(
-                top_k=1,
-                rank_by=("vector", "ANN", [0.0] * self.embedding_model_dims),
-                filters=("id", "Eq", str(vector_id)),
-                include_attributes=True,
-            )
-            rows = response.rows or []
-            if rows:
-                return self._parse_output(rows)[0]
-            return None
-        except Exception as e:
-            logger.error(f"Error retrieving vector {vector_id}: {e}")
-            return None
-
-    def list_cols(self) -> list:
-        """
-        List all namespaces.
-
-        Returns:
-            list: List of namespace summaries.
-        """
-        try:
-            result = []
-            for ns in self.client.namespaces():
-                result.append(ns)
-            return result
-        except Exception as e:
-            logger.error(f"Error listing namespaces: {e}")
-            return []
-
-    def delete_col(self):
-        """Delete the entire namespace."""
-        try:
-            self.namespace.delete_all()
-            logger.info(f"Namespace {self.collection_name} deleted successfully")
-        except Exception as e:
-            logger.error(f"Error deleting namespace {self.collection_name}: {e}")
-
-    def col_info(self) -> Dict:
-        """
-        Get information about the namespace.
-
-        Returns:
-            dict: Namespace metadata.
-        """
-        try:
-            metadata = self.namespace.metadata()
-            return {
-                "name": self.collection_name,
-                "approx_row_count": metadata.approx_row_count,
-                "approx_logical_bytes": metadata.approx_logical_bytes,
-                "created_at": str(metadata.created_at),
-                "updated_at": str(metadata.updated_at),
-            }
-        except Exception:
-            return {"name": self.collection_name}
-
-    def list(self, filters: Optional[Dict] = None, limit: int = 100) -> list:
-        """
-        List vectors in the namespace with optional filtering.
-
-        Args:
-            filters (dict, optional): Filters to apply. Defaults to None.
-            limit (int, optional): Number of vectors to return. Defaults to 100.
-
-        Returns:
-            list: Wrapped list of OutputData objects ([[results]]).
-        """
-        query_params = {
-            "rank_by": ("vector", "ANN", [0.0] * self.embedding_model_dims),
-            "top_k": limit,
-            "include_attributes": True,
-        }
-
-        tpuf_filters = self._convert_filters(filters)
-        if tpuf_filters is not None:
-            query_params["filters"] = tpuf_filters
-
-        try:
-            response = self.namespace.query(**query_params)
-            results = self._parse_output(response.rows or [])
-        except Exception as e:
-            logger.error(f"Error listing vectors: {e}")
-            results = []
-        return [results]
-
-    def count(self) -> int:
-        """
-        Get approximate count of vectors in the namespace.
-
-        Returns:
-            int: Approximate number of vectors.
-        """
-        try:
-            metadata = self.namespace.metadata()
-            return metadata.approx_row_count
-        except Exception:
-            return 0
-
-    def reset(self):
-        """Reset the namespace by deleting all vectors."""
-        self.delete_col()

openmemory/api/app/mcp_server.py

@@ -184,7 +184,7 @@ async def search_memory(query: str) -> str:
             for h in hits:
                 # All vector db search functions return OutputData class
                 id, score, payload = h.id, h.score, h.payload
-                if allowed and (h.id is None or h.id not in allowed):
+                if allowed and h.id is None or h.id not in allowed: 
                     continue
                 
                 results.append({

pyproject.toml

@@ -31,7 +31,6 @@ graph = [
     "neo4j>=5.23.1",
     "rank-bm25>=0.2.2",
     "kuzu>=0.11.0",
-    "apache-age-python>=0.0.6",
 ]
 vector_stores = [
     "vecs>=0.4.0",

server/.env.example

@@ -10,6 +10,3 @@ POSTGRES_DB=
 POSTGRES_USER=
 POSTGRES_PASSWORD=
 POSTGRES_COLLECTION_NAME=
-
-# Optional: set to enable API key authentication on all endpoints
-ADMIN_API_KEY=

server/main.py

@@ -1,12 +1,10 @@
 import logging
 import os
-import secrets
 from typing import Any, Dict, List, Optional
 
 from dotenv import load_dotenv
-from fastapi import Depends, FastAPI, HTTPException
+from fastapi import FastAPI, HTTPException
 from fastapi.responses import JSONResponse, RedirectResponse
-from fastapi.security import APIKeyHeader
 from pydantic import BaseModel, Field
 
 from mem0 import Memory
@@ -16,22 +14,6 @@ logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(
 # Load environment variables
 load_dotenv()
 
-ADMIN_API_KEY = os.environ.get("ADMIN_API_KEY", "")
-
-MIN_KEY_LENGTH = 16
-
-if not ADMIN_API_KEY:
-    logging.warning(
-        "ADMIN_API_KEY not set - API endpoints are UNSECURED! "
-        "Set ADMIN_API_KEY environment variable for production use."
-    )
-else:
-    if len(ADMIN_API_KEY) < MIN_KEY_LENGTH:
-        logging.warning(
-            "ADMIN_API_KEY is shorter than %d characters - consider using a longer key for production.",
-            MIN_KEY_LENGTH,
-        )
-    logging.info("API key authentication enabled")
 
 POSTGRES_HOST = os.environ.get("POSTGRES_HOST", "postgres")
 POSTGRES_PORT = os.environ.get("POSTGRES_PORT", "5432")
@@ -78,35 +60,10 @@ MEMORY_INSTANCE = Memory.from_config(DEFAULT_CONFIG)
 
 app = FastAPI(
     title="Mem0 REST APIs",
-    description=(
+    description="A REST API for managing and searching memories for your AI Agents and Apps.",
-        "A REST API for managing and searching memories for your AI Agents and Apps.\n\n"
-        "## Authentication\n"
-        "When the ADMIN_API_KEY environment variable is set, all endpoints require "
-        "the `X-API-Key` header for authentication."
-    ),
     version="1.0.0",
 )
 
-api_key_header = APIKeyHeader(name="X-API-Key", auto_error=False)
-
-
-async def verify_api_key(api_key: Optional[str] = Depends(api_key_header)):
-    """Validate the API key when ADMIN_API_KEY is configured. No-op otherwise."""
-    if ADMIN_API_KEY:
-        if api_key is None:
-            raise HTTPException(
-                status_code=401,
-                detail="X-API-Key header is required.",
-                headers={"WWW-Authenticate": "ApiKey"},
-            )
-        if not secrets.compare_digest(api_key, ADMIN_API_KEY):
-            raise HTTPException(
-                status_code=401,
-                detail="Invalid API key.",
-                headers={"WWW-Authenticate": "ApiKey"},
-            )
-    return api_key
-
 
 class Message(BaseModel):
     role: str = Field(..., description="Role of the message (user or assistant).")
@@ -130,7 +87,7 @@ class SearchRequest(BaseModel):
 
 
 @app.post("/configure", summary="Configure Mem0")
-def set_config(config: Dict[str, Any], _api_key: Optional[str] = Depends(verify_api_key)):
+def set_config(config: Dict[str, Any]):
     """Set memory configuration."""
     global MEMORY_INSTANCE
     MEMORY_INSTANCE = Memory.from_config(config)
@@ -138,7 +95,7 @@ def set_config(config: Dict[str, Any], _api_key: Optional[str] = Depends(verify_
 
 
 @app.post("/memories", summary="Create memories")
-def add_memory(memory_create: MemoryCreate, _api_key: Optional[str] = Depends(verify_api_key)):
+def add_memory(memory_create: MemoryCreate):
     """Store new memories."""
     if not any([memory_create.user_id, memory_create.agent_id, memory_create.run_id]):
         raise HTTPException(status_code=400, detail="At least one identifier (user_id, agent_id, run_id) is required.")
@@ -157,7 +114,6 @@ def get_all_memories(
     user_id: Optional[str] = None,
     run_id: Optional[str] = None,
     agent_id: Optional[str] = None,
-    _api_key: Optional[str] = Depends(verify_api_key),
 ):
     """Retrieve stored memories."""
     if not any([user_id, run_id, agent_id]):
@@ -173,7 +129,7 @@ def get_all_memories(
 
 
 @app.get("/memories/{memory_id}", summary="Get a memory")
-def get_memory(memory_id: str, _api_key: Optional[str] = Depends(verify_api_key)):
+def get_memory(memory_id: str):
     """Retrieve a specific memory by ID."""
     try:
         return MEMORY_INSTANCE.get(memory_id)
@@ -183,7 +139,7 @@ def get_memory(memory_id: str, _api_key: Optional[str] = Depends(verify_api_key)
 
 
 @app.post("/search", summary="Search memories")
-def search_memories(search_req: SearchRequest, _api_key: Optional[str] = Depends(verify_api_key)):
+def search_memories(search_req: SearchRequest):
     """Search for memories based on a query."""
     try:
         params = {k: v for k, v in search_req.model_dump().items() if v is not None and k != "query"}
@@ -194,7 +150,7 @@ def search_memories(search_req: SearchRequest, _api_key: Optional[str] = Depends
 
 
 @app.put("/memories/{memory_id}", summary="Update a memory")
-def update_memory(memory_id: str, updated_memory: Dict[str, Any], _api_key: Optional[str] = Depends(verify_api_key)):
+def update_memory(memory_id: str, updated_memory: Dict[str, Any]):
     """Update an existing memory with new content.
     
     Args:
@@ -212,7 +168,7 @@ def update_memory(memory_id: str, updated_memory: Dict[str, Any], _api_key: Opti
 
 
 @app.get("/memories/{memory_id}/history", summary="Get memory history")
-def memory_history(memory_id: str, _api_key: Optional[str] = Depends(verify_api_key)):
+def memory_history(memory_id: str):
     """Retrieve memory history."""
     try:
         return MEMORY_INSTANCE.history(memory_id=memory_id)
@@ -222,7 +178,7 @@ def memory_history(memory_id: str, _api_key: Optional[str] = Depends(verify_api_
 
 
 @app.delete("/memories/{memory_id}", summary="Delete a memory")
-def delete_memory(memory_id: str, _api_key: Optional[str] = Depends(verify_api_key)):
+def delete_memory(memory_id: str):
     """Delete a specific memory by ID."""
     try:
         MEMORY_INSTANCE.delete(memory_id=memory_id)
@@ -237,7 +193,6 @@ def delete_all_memories(
     user_id: Optional[str] = None,
     run_id: Optional[str] = None,
     agent_id: Optional[str] = None,
-    _api_key: Optional[str] = Depends(verify_api_key),
 ):
     """Delete all memories for a given identifier."""
     if not any([user_id, run_id, agent_id]):
@@ -254,7 +209,7 @@ def delete_all_memories(
 
 
 @app.post("/reset", summary="Reset all memories")
-def reset_memory(_api_key: Optional[str] = Depends(verify_api_key)):
+def reset_memory():
     """Completely reset stored memories."""
     try:
         MEMORY_INSTANCE.reset()

tests/llms/test_azure_openai_structured.py

@@ -1,5 +1,4 @@
 from unittest import mock
-from unittest.mock import Mock
 
 from mem0.llms.azure_openai_structured import SCOPE, AzureOpenAIStructuredLLM
 
@@ -99,75 +98,3 @@ def test_init_with_placeholder_api_key_uses_default_credential(
         args, kwargs = mock_azure_openai.call_args
         assert kwargs["api_key"] is None
         assert kwargs["azure_ad_token_provider"] == "token-provider"
-
-
-@mock.patch("mem0.llms.azure_openai_structured.AzureOpenAI")
-def test_generate_response_without_tools(mock_azure_openai):
-    mock_client = Mock()
-    mock_azure_openai.return_value = mock_client
-
-    config = DummyConfig(model="test-model", azure_kwargs=DummyAzureKwargs(api_key="real-key"))
-    llm = AzureOpenAIStructuredLLM(config)
-
-    mock_response = Mock()
-    mock_response.choices = [Mock(message=Mock(content="Hello there!"))]
-    mock_client.chat.completions.create.return_value = mock_response
-
-    messages = [{"role": "user", "content": "Hi"}]
-    response = llm.generate_response(messages)
-
-    assert response == "Hello there!"
-
-
-@mock.patch("mem0.llms.azure_openai_structured.AzureOpenAI")
-def test_generate_response_with_tools(mock_azure_openai):
-    mock_client = Mock()
-    mock_azure_openai.return_value = mock_client
-
-    config = DummyConfig(model="test-model", azure_kwargs=DummyAzureKwargs(api_key="real-key"))
-    llm = AzureOpenAIStructuredLLM(config)
-
-    mock_tool_call = Mock()
-    mock_tool_call.function.name = "add_memory"
-    mock_tool_call.function.arguments = '{"data": "sunny day"}'
-
-    mock_message = Mock()
-    mock_message.content = "I've added the memory."
-    mock_message.tool_calls = [mock_tool_call]
-
-    mock_response = Mock()
-    mock_response.choices = [Mock(message=mock_message)]
-    mock_client.chat.completions.create.return_value = mock_response
-
-    tools = [{"type": "function", "function": {"name": "add_memory"}}]
-    messages = [{"role": "user", "content": "Remember sunny day"}]
-    response = llm.generate_response(messages, tools=tools)
-
-    assert response["content"] == "I've added the memory."
-    assert len(response["tool_calls"]) == 1
-    assert response["tool_calls"][0]["name"] == "add_memory"
-    assert response["tool_calls"][0]["arguments"] == {"data": "sunny day"}
-
-
-@mock.patch("mem0.llms.azure_openai_structured.AzureOpenAI")
-def test_generate_response_with_tools_no_tool_calls(mock_azure_openai):
-    mock_client = Mock()
-    mock_azure_openai.return_value = mock_client
-
-    config = DummyConfig(model="test-model", azure_kwargs=DummyAzureKwargs(api_key="real-key"))
-    llm = AzureOpenAIStructuredLLM(config)
-
-    mock_message = Mock()
-    mock_message.content = "No tools needed."
-    mock_message.tool_calls = None
-
-    mock_response = Mock()
-    mock_response.choices = [Mock(message=mock_message)]
-    mock_client.chat.completions.create.return_value = mock_response
-
-    tools = [{"type": "function", "function": {"name": "add_memory"}}]
-    messages = [{"role": "user", "content": "Hello"}]
-    response = llm.generate_response(messages, tools=tools)
-
-    assert response["content"] == "No tools needed."
-    assert response["tool_calls"] == []

tests/llms/test_gemini.py

@@ -117,73 +117,3 @@ def test_generate_response_with_tools(mock_gemini_client: Mock):
     assert len(response["tool_calls"]) == 1
     assert response["tool_calls"][0]["name"] == "add_memory"
     assert response["tool_calls"][0]["arguments"] == {"data": "Today is a sunny day."}
-
-
-def test_parse_response_none_content_no_tools(mock_gemini_client: Mock):
-    """Gemini can return content=None when response is blocked by safety filters."""
-    config = BaseLlmConfig(model="gemini-2.0-flash", temperature=0.7, max_tokens=100, top_p=1.0)
-    llm = GeminiLLM(config)
-
-    mock_candidate = Mock(content=None)
-    mock_response = Mock(candidates=[mock_candidate])
-
-    result = llm._parse_response(mock_response, tools=None)
-    assert result == ""
-
-
-def test_parse_response_none_content_with_tools(mock_gemini_client: Mock):
-    """Gemini can return content=None when response is blocked by safety filters (tools path)."""
-    config = BaseLlmConfig(model="gemini-2.0-flash", temperature=0.7, max_tokens=100, top_p=1.0)
-    llm = GeminiLLM(config)
-
-    mock_candidate = Mock(content=None)
-    mock_response = Mock(candidates=[mock_candidate])
-
-    result = llm._parse_response(mock_response, tools=[{"function": {"name": "test"}}])
-    assert result == {"content": None, "tool_calls": []}
-
-
-def test_parse_response_empty_candidates(mock_gemini_client: Mock):
-    """Gemini can return an empty candidates list."""
-    config = BaseLlmConfig(model="gemini-2.0-flash", temperature=0.7, max_tokens=100, top_p=1.0)
-    llm = GeminiLLM(config)
-
-    mock_response = Mock(candidates=[])
-    result = llm._parse_response(mock_response, tools=None)
-    assert result == ""
-
-
-def test_parse_response_none_candidates(mock_gemini_client: Mock):
-    """Gemini can return candidates=None."""
-    config = BaseLlmConfig(model="gemini-2.0-flash", temperature=0.7, max_tokens=100, top_p=1.0)
-    llm = GeminiLLM(config)
-
-    mock_response = Mock(candidates=None)
-    result = llm._parse_response(mock_response, tools=None)
-    assert result == ""
-
-
-def test_parse_response_empty_parts_no_tools(mock_gemini_client: Mock):
-    """Gemini can return content with an empty parts list."""
-    config = BaseLlmConfig(model="gemini-2.0-flash", temperature=0.7, max_tokens=100, top_p=1.0)
-    llm = GeminiLLM(config)
-
-    mock_content = Mock(parts=[])
-    mock_candidate = Mock(content=mock_content)
-    mock_response = Mock(candidates=[mock_candidate])
-
-    result = llm._parse_response(mock_response, tools=None)
-    assert result == ""
-
-
-def test_parse_response_empty_parts_with_tools(mock_gemini_client: Mock):
-    """Gemini can return content with an empty parts list (tools path)."""
-    config = BaseLlmConfig(model="gemini-2.0-flash", temperature=0.7, max_tokens=100, top_p=1.0)
-    llm = GeminiLLM(config)
-
-    mock_content = Mock(parts=[])
-    mock_candidate = Mock(content=mock_content)
-    mock_response = Mock(candidates=[mock_candidate])
-
-    result = llm._parse_response(mock_response, tools=[{"function": {"name": "test"}}])
-    assert result == {"content": None, "tool_calls": []}

tests/memory/test_apache_age_memory.py

@@ -1,226 +0,0 @@
-from unittest.mock import MagicMock, Mock, patch
-
-# age and rank_bm25 are optional deps — mock them so tests run without install
-_age_mock = Mock()
-patch.dict("sys.modules", {
-    "age": _age_mock,
-    "age.models": Mock(),
-    "rank_bm25": Mock(),
-}).start()
-
-from mem0.memory.apache_age_memory import MemoryGraph, _cosine_similarity  # noqa: E402
-
-
-def _make_instance():
-    with patch.object(MemoryGraph, "__init__", return_value=None):
-        instance = MemoryGraph.__new__(MemoryGraph)
-        instance.llm_provider = "openai"
-        instance.llm = MagicMock()
-        instance.embedding_model = MagicMock()
-        instance.config = MagicMock()
-        instance.config.graph_store.custom_prompt = None
-        instance.ag = MagicMock()
-        instance.graph_name = "test_graph"
-        instance.threshold = 0.7
-        return instance
-
-
-class TestCosineSimilarity:
-    """Tests for the _cosine_similarity helper."""
-
-    def test_identical_vectors(self):
-        assert abs(_cosine_similarity([1, 0, 0], [1, 0, 0]) - 1.0) < 1e-6
-
-    def test_orthogonal_vectors(self):
-        assert abs(_cosine_similarity([1, 0, 0], [0, 1, 0])) < 1e-6
-
-    def test_zero_vector(self):
-        assert _cosine_similarity([0, 0, 0], [1, 2, 3]) == 0.0
-
-
-class TestRetrieveNodesFromData:
-    """Tests for _retrieve_nodes_from_data in Apache AGE MemoryGraph."""
-
-    def test_normal_entities_extracted(self):
-        instance = _make_instance()
-        instance.llm.generate_response.return_value = {
-            "tool_calls": [{"name": "extract_entities", "arguments": {"entities": [
-                {"entity": "Alice", "entity_type": "person"},
-                {"entity": "hiking", "entity_type": "activity"},
-            ]}}]
-        }
-        result = instance._retrieve_nodes_from_data("Alice loves hiking", {"user_id": "u1"})
-        assert result == {"alice": "person", "hiking": "activity"}
-
-    def test_malformed_entity_missing_entity_type_is_skipped(self):
-        instance = _make_instance()
-        instance.llm.generate_response.return_value = {
-            "tool_calls": [{"name": "extract_entities", "arguments": {"entities": [
-                {"entity": "matrix multiplication", "entity_type": "task"},
-                {"entity": "task"},
-                {"entity": "ReLU", "entity_type": "task"},
-            ]}}]
-        }
-        result = instance._retrieve_nodes_from_data("some text", {"user_id": "u1"})
-        assert "matrix_multiplication" in result
-        assert "relu" in result
-        assert "task" not in result
-
-    def test_missing_entities_key_returns_empty(self):
-        instance = _make_instance()
-        instance.llm.generate_response.return_value = {
-            "tool_calls": [{"name": "extract_entities", "arguments": {"text": "Hello."}}]
-        }
-        result = instance._retrieve_nodes_from_data("Hello.", {"user_id": "u1"})
-        assert result == {}
-
-    def test_none_tool_calls_returns_empty(self):
-        instance = _make_instance()
-        instance.llm.generate_response.return_value = {"tool_calls": None}
-        result = instance._retrieve_nodes_from_data("hello world", {"user_id": "u1"})
-        assert result == {}
-
-
-class TestEstablishNodesRelationsFromData:
-    """Tests for _establish_nodes_relations_from_data in Apache AGE MemoryGraph."""
-
-    def test_none_response_does_not_crash(self):
-        instance = _make_instance()
-        instance.llm.generate_response.return_value = None
-        result = instance._establish_nodes_relations_from_data(
-            "Hello world", {"user_id": "u1"}, {}
-        )
-        assert result == []
-
-    def test_empty_tool_calls_returns_empty(self):
-        instance = _make_instance()
-        instance.llm.generate_response.return_value = {"tool_calls": []}
-        result = instance._establish_nodes_relations_from_data(
-            "Hello world", {"user_id": "u1"}, {}
-        )
-        assert result == []
-
-    def test_valid_entities_returned(self):
-        instance = _make_instance()
-        instance.llm.generate_response.return_value = {
-            "tool_calls": [{"name": "add_entities", "arguments": {"entities": [
-                {"source": "alice", "relationship": "loves", "destination": "hiking"}
-            ]}}]
-        }
-        result = instance._establish_nodes_relations_from_data(
-            "Alice loves hiking", {"user_id": "u1"}, {"alice": "person"}
-        )
-        assert len(result) == 1
-        assert result[0]["source"] == "alice"
-
-
-class TestRemoveSpacesFromEntities:
-    """Tests for _remove_spaces_from_entities."""
-
-    def test_spaces_and_case(self):
-        instance = _make_instance()
-        entities = [{"source": "Alice Smith", "relationship": "Works At", "destination": "Big Corp"}]
-        result = instance._remove_spaces_from_entities(entities)
-        assert result[0]["source"] == "alice_smith"
-        assert result[0]["relationship"] == "works_at"
-        assert result[0]["destination"] == "big_corp"
-
-
-class TestFindSimilarNode:
-    """Tests for _find_similar_node."""
-
-    def test_returns_none_when_no_nodes(self):
-        instance = _make_instance()
-        instance._exec_cypher = MagicMock(return_value=[])
-        result = instance._find_similar_node([1.0, 0.0], {"user_id": "u1"}, threshold=0.9)
-        assert result is None
-
-    def test_returns_best_match_above_threshold(self):
-        instance = _make_instance()
-        instance._exec_cypher = MagicMock(return_value=[
-            {"name": "alice", "embedding": [1.0, 0.0], "user_id": "u1"},
-            {"name": "bob", "embedding": [0.0, 1.0], "user_id": "u1"},
-        ])
-        result = instance._find_similar_node([1.0, 0.0], {"user_id": "u1"}, threshold=0.9)
-        assert result["name"] == "alice"
-
-    def test_filters_by_agent_id(self):
-        instance = _make_instance()
-        instance._exec_cypher = MagicMock(return_value=[
-            {"name": "alice", "embedding": [1.0, 0.0], "user_id": "u1", "agent_id": "a2"},
-        ])
-        result = instance._find_similar_node(
-            [1.0, 0.0], {"user_id": "u1", "agent_id": "a1"}, threshold=0.9
-        )
-        assert result is None
-
-
-class TestDeleteAll:
-    """Tests for delete_all."""
-
-    def test_calls_exec_cypher_and_commits(self):
-        instance = _make_instance()
-        instance._exec_cypher = MagicMock(return_value=[])
-        instance.delete_all({"user_id": "u1"})
-        instance._exec_cypher.assert_called_once()
-        instance.ag.commit.assert_called_once()
-
-
-class TestGetAll:
-    """Tests for get_all."""
-
-    def test_returns_formatted_results(self):
-        instance = _make_instance()
-        instance._exec_cypher = MagicMock(return_value=[
-            {"source": "alice", "relationship": "KNOWS", "target": "bob"},
-            {"source": "alice", "relationship": "LIKES", "target": "hiking"},
-        ])
-        results = instance.get_all({"user_id": "u1"}, limit=10)
-        assert len(results) == 2
-        assert results[0]["source"] == "alice"
-        assert results[0]["relationship"] == "KNOWS"
-        assert results[0]["target"] == "bob"
-
-    def test_passes_limit_to_cypher(self):
-        """Limit is enforced via LIMIT in the Cypher query, not Python slicing."""
-        instance = _make_instance()
-        instance._exec_cypher = MagicMock(return_value=[
-            {"source": "n0", "relationship": "R", "target": "m0"},
-        ])
-        instance.get_all({"user_id": "u1"}, limit=3)
-        # Verify limit was passed as a parameter to the query
-        cypher_stmt = instance._exec_cypher.call_args[0][0]
-        assert "LIMIT %s" in cypher_stmt
-        params = instance._exec_cypher.call_args[1].get("params") or instance._exec_cypher.call_args[0][2]
-        assert 3 in params
-
-
-class TestAdd:
-    """Tests for the add orchestration method."""
-
-    def test_add_returns_added_and_deleted(self):
-        instance = _make_instance()
-        instance._retrieve_nodes_from_data = MagicMock(return_value={"alice": "person"})
-        instance._establish_nodes_relations_from_data = MagicMock(return_value=[
-            {"source": "alice", "relationship": "knows", "destination": "bob"}
-        ])
-        instance._search_graph_db = MagicMock(return_value=[])
-        instance._get_delete_entities_from_search_output = MagicMock(return_value=[])
-        instance._delete_entities = MagicMock(return_value=[])
-        instance._add_entities = MagicMock(return_value=["added"])
-
-        result = instance.add("Alice knows Bob", {"user_id": "u1"})
-        assert "deleted_entities" in result
-        assert "added_entities" in result
-        assert result["added_entities"] == ["added"]
-
-
-class TestSearch:
-    """Tests for the search method."""
-
-    def test_returns_empty_when_no_search_output(self):
-        instance = _make_instance()
-        instance._retrieve_nodes_from_data = MagicMock(return_value={"alice": "person"})
-        instance._search_graph_db = MagicMock(return_value=[])
-        result = instance.search("Who is Alice?", {"user_id": "u1"})
-        assert result == []

tests/memory/test_graph_memory_soft_delete.py

@@ -1,315 +0,0 @@
-"""Tests for graph memory soft-delete behavior.
-
-Verifies that _delete_entities marks relationships as invalid (soft-delete)
-rather than permanently removing them, and that search/retrieval queries
-exclude soft-deleted relationships by default.
-
-See: https://github.com/mem0ai/mem0/issues/4187
-"""
-
-from unittest.mock import Mock, patch
-
-# Mock optional deps at module level so the import works across all Python
-# versions without triggering transitive C-extension reloads (numpy via
-# qdrant_client). This matches the pattern in test_memgraph_memory.py.
-_neo4j_mock = Mock()
-patch.dict("sys.modules", {
-    "langchain_neo4j": _neo4j_mock,
-    "rank_bm25": Mock(),
-}).start()
-
-from mem0.memory.graph_memory import MemoryGraph  # noqa: E402
-
-
-def _create_graph_memory():
-    """Create a MemoryGraph instance with mocked dependencies."""
-    with patch.object(MemoryGraph, "__init__", lambda self, *a, **kw: None):
-        mg = MemoryGraph.__new__(MemoryGraph)
-        mg.graph = Mock()
-        mg.graph.query = Mock(return_value=[])
-        mg.embedding_model = Mock()
-        mg.embedding_model.embed = Mock(return_value=[0.1] * 128)
-        mg.llm = Mock()
-        mg.node_label = ":Entity"
-        mg.threshold = 0.7
-        mg.llm_provider = "openai"
-    return mg
-
-
-class TestSoftDelete:
-    """Verify _delete_entities uses SET r.valid = false, not DELETE r."""
-
-    def test_delete_entities_sends_soft_delete_cypher(self):
-        mg = _create_graph_memory()
-        mg.graph.query.return_value = [
-            {"source": "Alice", "target": "Bob", "relationship": "KNOWS"}
-        ]
-
-        mg._delete_entities(
-            [{"source": "alice", "destination": "bob", "relationship": "KNOWS"}],
-            {"user_id": "user1"},
-        )
-
-        cypher = mg.graph.query.call_args[0][0]
-        assert "SET r.valid = false" in cypher
-        assert "r.invalidated_at = datetime()" in cypher
-        assert "DELETE r" not in cypher
-
-    def test_delete_entities_only_targets_valid_edges(self):
-        mg = _create_graph_memory()
-        mg._delete_entities(
-            [{"source": "alice", "destination": "bob", "relationship": "KNOWS"}],
-            {"user_id": "user1"},
-        )
-
-        cypher = mg.graph.query.call_args[0][0]
-        assert "r.valid IS NULL OR r.valid = true" in cypher
-
-    def test_delete_entities_is_idempotent(self):
-        mg = _create_graph_memory()
-        item = [{"source": "alice", "destination": "bob", "relationship": "KNOWS"}]
-        filters = {"user_id": "user1"}
-
-        mg.graph.query.return_value = [
-            {"source": "Alice", "target": "Bob", "relationship": "KNOWS"}
-        ]
-        mg._delete_entities(item, filters)
-
-        mg.graph.query.return_value = []
-        mg._delete_entities(item, filters)
-
-        # Both calls should have the same WHERE filter
-        for c in mg.graph.query.call_args_list:
-            assert "r.valid IS NULL OR r.valid = true" in c[0][0]
-
-
-class TestSearchExcludesSoftDeleted:
-    """Verify search and get_all filter out soft-deleted relationships."""
-
-    def test_get_all_filters_soft_deleted(self):
-        mg = _create_graph_memory()
-        mg.get_all(filters={"user_id": "user1"}, limit=10)
-
-        cypher = mg.graph.query.call_args[0][0]
-        assert "r.valid IS NULL OR r.valid = true" in cypher
-
-    def test_search_graph_db_filters_both_directions(self):
-        """_search_graph_db must filter soft-deleted edges in both outgoing and incoming queries."""
-        mg = _create_graph_memory()
-        mg.graph.query.return_value = []
-
-        mg._search_graph_db(node_list=["alice"], filters={"user_id": "user1"})
-
-        cypher = mg.graph.query.call_args[0][0]
-        # The UNION query has two MATCH branches — both must filter
-        occurrences = cypher.count("r.valid IS NULL OR r.valid = true")
-        assert occurrences >= 2, (
-            f"_search_graph_db has {occurrences} valid-filter(s) but needs >= 2 "
-            "(one for outgoing, one for incoming relationships)"
-        )
-
-    def test_delete_all_still_hard_deletes(self):
-        mg = _create_graph_memory()
-        mg.delete_all(filters={"user_id": "user1"})
-
-        cypher = mg.graph.query.call_args[0][0]
-        assert "DETACH DELETE" in cypher
-
-
-class TestMergeResetsValidFlag:
-    """Verify MERGE in _add_entities sets r.valid = true.
-
-    Critical: after soft-delete, a MERGE that matches the existing
-    (invalidated) edge must reset valid=true, or the edge becomes
-    a zombie -- exists but invisible to queries.
-    """
-
-    def _run_add_entities(self, source_found, dest_found):
-        """Helper: call _add_entities with configurable node search results."""
-        mg = _create_graph_memory()
-
-        source_result = (
-            [{"elementId(source_candidate)": "src_id_1"}] if source_found else []
-        )
-        dest_result = (
-            [{"elementId(destination_candidate)": "dst_id_1"}] if dest_found else []
-        )
-
-        mg._search_source_node = Mock(return_value=source_result)
-        mg._search_destination_node = Mock(return_value=dest_result)
-
-        mg._add_entities(
-            [{"source": "alice", "destination": "bob", "relationship": "KNOWS"}],
-            {"user_id": "user1"},
-            entity_type_map={},
-        )
-
-        cypher = mg.graph.query.call_args[0][0]
-        return cypher
-
-    def test_merge_sets_valid_true_when_source_found(self):
-        cypher = self._run_add_entities(source_found=True, dest_found=False)
-        assert "r.valid = true" in cypher
-
-    def test_merge_sets_valid_true_when_dest_found(self):
-        cypher = self._run_add_entities(source_found=False, dest_found=True)
-        assert "r.valid = true" in cypher
-
-    def test_merge_sets_valid_true_when_both_found(self):
-        cypher = self._run_add_entities(source_found=True, dest_found=True)
-        assert "r.valid = true" in cypher
-
-    def test_merge_sets_valid_true_when_neither_found(self):
-        cypher = self._run_add_entities(source_found=False, dest_found=False)
-        assert "r.valid = true" in cypher
-
-    def test_merge_clears_invalidated_at_on_resurrection(self):
-        """When a soft-deleted edge is resurrected via MERGE, invalidated_at must be cleared.
-
-        Without this, a resurrected edge (valid=true) still carries stale
-        invalidated_at metadata, which corrupts temporal reasoning queries.
-        """
-        for label, src, dst in [
-            ("source found", True, False),
-            ("dest found", False, True),
-            ("both found", True, True),
-            ("neither found", False, False),
-        ]:
-            cypher = self._run_add_entities(source_found=src, dest_found=dst)
-            assert "r.invalidated_at = null" in cypher, (
-                f"MERGE path '{label}': ON MATCH SET does not clear r.invalidated_at. "
-                "Resurrected edges will have stale invalidation timestamps."
-            )
-
-
-class TestCypherConsistency:
-    """Verify all MERGE blocks use consistent property names and variable aliases."""
-
-    def _get_merge_cypher(self, source_found, dest_found):
-        mg = _create_graph_memory()
-        mg._search_source_node = Mock(
-            return_value=[{"elementId(source_candidate)": "id1"}] if source_found else []
-        )
-        mg._search_destination_node = Mock(
-            return_value=[{"elementId(destination_candidate)": "id2"}] if dest_found else []
-        )
-        mg._add_entities(
-            [{"source": "alice", "destination": "bob", "relationship": "KNOWS"}],
-            {"user_id": "user1"},
-            entity_type_map={},
-        )
-        return mg.graph.query.call_args[0][0]
-
-    def test_all_blocks_use_created_at_not_created(self):
-        """All MERGE blocks must use r.created_at, not r.created."""
-        for label, src, dst in [
-            ("source found", True, False),
-            ("dest found", False, True),
-            ("both found", True, True),
-            ("neither found", False, False),
-        ]:
-            cypher = self._get_merge_cypher(src, dst)
-            assert "r.created_at" in cypher, (
-                f"MERGE path '{label}': uses r.created instead of r.created_at"
-            )
-
-    def test_all_blocks_use_r_not_rel(self):
-        """All MERGE blocks must use 'r' as the relationship variable, not 'rel'."""
-        for label, src, dst in [
-            ("source found", True, False),
-            ("dest found", False, True),
-            ("both found", True, True),
-            ("neither found", False, False),
-        ]:
-            cypher = self._get_merge_cypher(src, dst)
-            assert "rel." not in cypher, (
-                f"MERGE path '{label}': uses 'rel' variable instead of 'r'"
-            )
-
-    def test_all_blocks_set_updated_at_on_create(self):
-        """All MERGE blocks must set r.updated_at on CREATE for consistent timestamps."""
-        for label, src, dst in [
-            ("source found", True, False),
-            ("dest found", False, True),
-            ("both found", True, True),
-            ("neither found", False, False),
-        ]:
-            cypher = self._get_merge_cypher(src, dst)
-            assert "r.updated_at = timestamp()" in cypher, (
-                f"MERGE path '{label}': missing r.updated_at on CREATE SET"
-            )
-
-
-class TestSoftDeleteWithFilters:
-    """Verify soft-delete works correctly with agent_id and run_id filters."""
-
-    def test_delete_entities_with_agent_id(self):
-        mg = _create_graph_memory()
-        mg._delete_entities(
-            [{"source": "alice", "destination": "bob", "relationship": "KNOWS"}],
-            {"user_id": "user1", "agent_id": "agent1"},
-        )
-
-        cypher = mg.graph.query.call_args[0][0]
-        params = mg.graph.query.call_args[1]["params"]
-        assert "SET r.valid = false" in cypher
-        assert "agent_id: $agent_id" in cypher
-        assert params["agent_id"] == "agent1"
-
-    def test_delete_entities_with_run_id(self):
-        mg = _create_graph_memory()
-        mg._delete_entities(
-            [{"source": "alice", "destination": "bob", "relationship": "KNOWS"}],
-            {"user_id": "user1", "run_id": "run1"},
-        )
-
-        cypher = mg.graph.query.call_args[0][0]
-        params = mg.graph.query.call_args[1]["params"]
-        assert "SET r.valid = false" in cypher
-        assert "run_id: $run_id" in cypher
-        assert params["run_id"] == "run1"
-
-    def test_get_all_with_agent_id_filters_soft_deleted(self):
-        mg = _create_graph_memory()
-        mg.get_all(filters={"user_id": "user1", "agent_id": "agent1"}, limit=10)
-
-        cypher = mg.graph.query.call_args[0][0]
-        assert "r.valid IS NULL OR r.valid = true" in cypher
-        assert "agent_id: $agent_id" in cypher
-
-    def test_merge_with_agent_id_sets_valid_true(self):
-        mg = _create_graph_memory()
-        mg._search_source_node = Mock(
-            return_value=[{"elementId(source_candidate)": "id1"}]
-        )
-        mg._search_destination_node = Mock(return_value=[])
-
-        mg._add_entities(
-            [{"source": "alice", "destination": "bob", "relationship": "KNOWS"}],
-            {"user_id": "user1", "agent_id": "agent1"},
-            entity_type_map={},
-        )
-
-        cypher = mg.graph.query.call_args[0][0]
-        assert "r.valid = true" in cypher
-        assert "agent_id: $agent_id" in cypher
-
-
-class TestResetAndCleanup:
-    """Verify reset and delete_all use hard-delete (DETACH DELETE)."""
-
-    def test_reset_uses_detach_delete(self):
-        mg = _create_graph_memory()
-        mg.reset()
-
-        cypher = mg.graph.query.call_args[0][0]
-        assert "DETACH DELETE" in cypher
-        assert "valid" not in cypher.lower()
-
-    def test_delete_all_does_not_soft_delete(self):
-        mg = _create_graph_memory()
-        mg.delete_all(filters={"user_id": "user1"})
-
-        cypher = mg.graph.query.call_args[0][0]
-        assert "DETACH DELETE" in cypher
-        assert "r.valid = false" not in cypher

tests/test_memory.py

@@ -320,255 +320,3 @@ def test_normalize_facts_mixed():
 
 def test_normalize_facts_filters_empty_strings():
     assert normalize_facts(["", "valid", ""]) == ["valid"]
-
-
-@patch('mem0.utils.factory.EmbedderFactory.create')
-@patch('mem0.utils.factory.VectorStoreFactory.create')
-@patch('mem0.utils.factory.LlmFactory.create')
-@patch('mem0.memory.storage.SQLiteManager')
-def test_delete_nonexistent_memory_raises_error(mock_sqlite, mock_llm_factory, mock_vector_factory, mock_embedder_factory):
-    """
-    Test that delete() raises ValueError when memory_id does not exist
-    and does not attempt to delete from the vector store.
-
-    Issue #3849: memory.delete() fails with AttributeError when memory not found.
-    Should raise a clear ValueError instead.
-    """
-    mock_embedder_factory.return_value = MagicMock()
-    mock_vector_store = MagicMock()
-    mock_vector_factory.return_value = mock_vector_store
-    mock_llm_factory.return_value = MagicMock()
-    mock_sqlite.return_value = MagicMock()
-
-    from mem0.memory.main import Memory as MemoryClass
-    config = MemoryConfig()
-    memory = MemoryClass(config)
-
-    mock_vector_store.get.return_value = None
-
-    with pytest.raises(ValueError, match="Memory with id non-existent-id not found"):
-        memory.delete("non-existent-id")
-
-    mock_vector_store.delete.assert_not_called()
-
-
-@pytest.mark.asyncio
-@patch('mem0.utils.factory.EmbedderFactory.create')
-@patch('mem0.utils.factory.VectorStoreFactory.create')
-@patch('mem0.utils.factory.LlmFactory.create')
-@patch('mem0.memory.storage.SQLiteManager')
-async def test_async_delete_nonexistent_memory_raises_error(mock_sqlite, mock_llm_factory, mock_vector_factory, mock_embedder_factory):
-    """
-    Test that async delete() raises ValueError when memory_id does not exist
-    and does not attempt to delete from the vector store.
-
-    Issue #3849: memory.delete() fails with AttributeError when memory not found.
-    Should raise a clear ValueError instead.
-    """
-    mock_embedder_factory.return_value = MagicMock()
-    mock_vector_store = MagicMock()
-    mock_vector_store.get.return_value = None
-    mock_vector_factory.return_value = mock_vector_store
-    mock_llm_factory.return_value = MagicMock()
-    mock_sqlite.return_value = MagicMock()
-
-    from mem0.memory.main import AsyncMemory
-    config = MemoryConfig()
-    memory = AsyncMemory(config)
-
-    with pytest.raises(ValueError, match="Memory with id non-existent-id not found"):
-        await memory.delete("non-existent-id")
-
-    mock_vector_store.delete.assert_not_called()
-
-
-@patch('mem0.utils.factory.EmbedderFactory.create')
-@patch('mem0.utils.factory.VectorStoreFactory.create')
-@patch('mem0.utils.factory.LlmFactory.create')
-@patch('mem0.memory.storage.SQLiteManager')
-def test_update_nonexistent_memory_raises_error(mock_sqlite, mock_llm_factory, mock_vector_factory, mock_embedder_factory):
-    """
-    Test that _update_memory() raises ValueError when memory_id does not exist.
-
-    Same class of bug as #3849 — vector_store.get() returns None and code
-    accesses .payload without a null check.
-    """
-    mock_embedder_factory.return_value = MagicMock()
-    mock_vector_store = MagicMock()
-    mock_vector_factory.return_value = mock_vector_store
-    mock_llm_factory.return_value = MagicMock()
-    mock_sqlite.return_value = MagicMock()
-
-    from mem0.memory.main import Memory as MemoryClass
-    config = MemoryConfig()
-    memory = MemoryClass(config)
-
-    mock_vector_store.get.return_value = None
-
-    with pytest.raises(ValueError, match="Memory with id non-existent-id not found"):
-        memory._update_memory("non-existent-id", "new data", {"new data": [0.1, 0.2]})
-
-    mock_vector_store.update.assert_not_called()
-
-
-@pytest.mark.asyncio
-@patch('mem0.utils.factory.EmbedderFactory.create')
-@patch('mem0.utils.factory.VectorStoreFactory.create')
-@patch('mem0.utils.factory.LlmFactory.create')
-@patch('mem0.memory.storage.SQLiteManager')
-async def test_async_update_nonexistent_memory_raises_error(mock_sqlite, mock_llm_factory, mock_vector_factory, mock_embedder_factory):
-    """
-    Test that async _update_memory() raises ValueError when memory_id does not exist.
-
-    Same class of bug as #3849 — vector_store.get() returns None and code
-    accesses .payload without a null check.
-    """
-    mock_embedder_factory.return_value = MagicMock()
-    mock_vector_store = MagicMock()
-    mock_vector_store.get.return_value = None
-    mock_vector_factory.return_value = mock_vector_store
-    mock_llm_factory.return_value = MagicMock()
-    mock_sqlite.return_value = MagicMock()
-
-    from mem0.memory.main import AsyncMemory
-    config = MemoryConfig()
-    memory = AsyncMemory(config)
-
-    with pytest.raises(ValueError, match="Memory with id non-existent-id not found"):
-        await memory._update_memory("non-existent-id", "new data", {"new data": [0.1, 0.2]})
-
-    mock_vector_store.update.assert_not_called()
-
-
-@patch('mem0.utils.factory.EmbedderFactory.create')
-@patch('mem0.utils.factory.VectorStoreFactory.create')
-@patch('mem0.utils.factory.LlmFactory.create')
-@patch('mem0.memory.storage.SQLiteManager')
-def test_add_infer_false_embeds_once(mock_sqlite, mock_llm_factory, mock_vector_factory, mock_embedder_factory):
-    """
-    Regression test for issue #3723: adding with infer=False should not trigger duplicate embedding calls.
-
-    Root cause: _create_memory expected a dict for existing_embeddings but received a raw list[float],
-    causing the cache check `data in existing_embeddings` to always fail and trigger a redundant embed.
-    """
-    embedder = MagicMock()
-    embedder.embed.return_value = [0.1, 0.2, 0.3]
-    embedder.config = MagicMock(embedding_dims=3)
-    mock_embedder_factory.return_value = embedder
-
-    mock_vector_store = MagicMock()
-    mock_vector_store.search.return_value = []
-    mock_vector_store.insert.return_value = None
-    mock_vector_store.get.return_value = None
-    telemetry_vector_store = MagicMock()
-    mock_vector_factory.side_effect = [mock_vector_store, telemetry_vector_store]
-
-    mock_llm_factory.return_value = MagicMock()
-    mock_sqlite.return_value = MagicMock()
-
-    from mem0.memory.main import Memory as MemoryClass
-    memory = MemoryClass(MemoryConfig())
-
-    memory.add("foo", user_id="test_user", infer=False)
-
-    assert embedder.embed.call_count == 1
-    mock_vector_store.insert.assert_called_once()
-
-
-@patch('mem0.utils.factory.EmbedderFactory.create')
-@patch('mem0.utils.factory.VectorStoreFactory.create')
-@patch('mem0.utils.factory.LlmFactory.create')
-@patch('mem0.memory.storage.SQLiteManager')
-def test_add_infer_true_caches_embedding_on_llm_rewrite(mock_sqlite, mock_llm_factory, mock_vector_factory, mock_embedder_factory):
-    """
-    Regression test for issue #3723 (infer=True path): when the LLM rewrites a fact during the
-    ADD action, the embedding should be computed once and cached, not computed again inside _create_memory.
-    """
-    embedder = MagicMock()
-    embedder.embed.return_value = [0.1, 0.2, 0.3]
-    embedder.config = MagicMock(embedding_dims=3)
-    mock_embedder_factory.return_value = embedder
-
-    mock_vector_store = MagicMock()
-    mock_vector_store.search.return_value = []
-    mock_vector_store.insert.return_value = None
-    mock_vector_store.get.return_value = None
-    telemetry_vector_store = MagicMock()
-    mock_vector_factory.side_effect = [mock_vector_store, telemetry_vector_store]
-
-    # LLM extracts fact "User likes Python", then ADD action rewrites to "The user enjoys Python"
-    mock_llm = MagicMock()
-    mock_llm.generate_response.side_effect = [
-        json.dumps({"facts": ["User likes Python"]}),
-        json.dumps({"memory": [{"id": "0", "text": "The user enjoys Python", "event": "ADD", "old_memory": None}]}),
-    ]
-    mock_llm_factory.return_value = mock_llm
-
-    mock_sqlite.return_value = MagicMock()
-
-    from mem0.memory.main import Memory as MemoryClass
-    memory = MemoryClass(MemoryConfig())
-
-    memory.add("I like Python", user_id="test_user", infer=True)
-
-    # embed should be called exactly twice:
-    # 1. For the extracted fact "User likes Python" (search)
-    # 2. For the rewritten text "The user enjoys Python" (pre-cached before _create_memory)
-    # It should NOT be called a 3rd time inside _create_memory
-    assert embedder.embed.call_count == 2
-    mock_vector_store.insert.assert_called_once()
-
-
-@patch('mem0.utils.factory.EmbedderFactory.create')
-@patch('mem0.utils.factory.VectorStoreFactory.create')
-@patch('mem0.utils.factory.LlmFactory.create')
-@patch('mem0.memory.storage.SQLiteManager')
-def test_update_infer_true_caches_embedding_on_llm_rewrite(mock_sqlite, mock_llm_factory, mock_vector_factory, mock_embedder_factory):
-    """
-    Regression test for issue #3723 (infer=True UPDATE path): when the LLM rewrites a fact during
-    an UPDATE action, the embedding should be computed once and cached, not computed again inside _update_memory.
-    """
-    embedder = MagicMock()
-    embedder.embed.return_value = [0.1, 0.2, 0.3]
-    embedder.config = MagicMock(embedding_dims=3)
-    mock_embedder_factory.return_value = embedder
-
-    # Existing memory that will be matched for update
-    existing_memory = MockVectorMemory(
-        memory_id="existing-mem-id",
-        payload={
-            "data": "User likes Python",
-            "hash": "abc123",
-            "created_at": "2025-01-01T00:00:00+00:00",
-        },
-    )
-
-    mock_vector_store = MagicMock()
-    mock_vector_store.search.return_value = [existing_memory]
-    mock_vector_store.get.return_value = existing_memory
-    mock_vector_store.insert.return_value = None
-    mock_vector_store.update.return_value = None
-    telemetry_vector_store = MagicMock()
-    mock_vector_factory.side_effect = [mock_vector_store, telemetry_vector_store]
-
-    # LLM extracts fact "User loves Python now", then UPDATE action rewrites to "The user loves Python"
-    mock_llm = MagicMock()
-    mock_llm.generate_response.side_effect = [
-        json.dumps({"facts": ["User loves Python now"]}),
-        json.dumps({"memory": [{"id": "0", "text": "The user loves Python", "event": "UPDATE", "old_memory": "User likes Python"}]}),
-    ]
-    mock_llm_factory.return_value = mock_llm
-
-    mock_sqlite.return_value = MagicMock()
-
-    from mem0.memory.main import Memory as MemoryClass
-    memory = MemoryClass(MemoryConfig())
-
-    memory.add("I love Python now", user_id="test_user", infer=True)
-
-    # embed should be called exactly twice:
-    # 1. For the extracted fact "User loves Python now" (search)
-    # 2. For the rewritten text "The user loves Python" (pre-cached before _update_memory)
-    # It should NOT be called a 3rd time inside _update_memory
-    assert embedder.embed.call_count == 2
-    mock_vector_store.update.assert_called_once()

tests/test_server_auth.py

@@ -1,496 +0,0 @@
-"""Comprehensive E2E tests for REST API server authentication.
-
-Tests the actual server/main.py app through FastAPI's TestClient (full ASGI
-round-trip) covering:
-  - Auth disabled mode (ADMIN_API_KEY unset)
-  - Auth enabled mode (ADMIN_API_KEY set)
-  - Edge cases: empty keys, near-miss keys, timing-safe comparison, header
-    casing, response headers, startup logging, and full CRUD flows through auth.
-"""
-
-import importlib
-import logging
-import os
-from unittest.mock import MagicMock, patch
-
-import pytest
-
-pytest.importorskip("fastapi", reason="fastapi not installed")
-
-from fastapi.testclient import TestClient
-
-
-# ---------------------------------------------------------------------------
-# Fixtures
-# ---------------------------------------------------------------------------
-
-@pytest.fixture
-def _mock_memory():
-    """Patch Memory.from_config so the server imports without a real backend."""
-    mock_instance = MagicMock()
-    # Set up return values so CRUD endpoints return realistic responses
-    mock_instance.get.return_value = {"id": "mem-1", "memory": "test memory", "user_id": "alice"}
-    mock_instance.get_all.return_value = [
-        {"id": "mem-1", "memory": "test memory", "user_id": "alice"},
-    ]
-    mock_instance.add.return_value = {"results": [{"id": "mem-1", "event": "ADD", "memory": "test"}]}
-    mock_instance.search.return_value = [{"id": "mem-1", "memory": "test", "score": 0.9}]
-    mock_instance.update.return_value = {"message": "Memory updated"}
-    mock_instance.history.return_value = [{"id": "mem-1", "old_memory": "a", "new_memory": "b"}]
-    mock_instance.delete.return_value = None
-    mock_instance.delete_all.return_value = {"message": "Memories deleted successfully!"}
-    mock_instance.reset.return_value = None
-
-    with patch.dict(os.environ, {"OPENAI_API_KEY": "fake-key"}):
-        with patch("mem0.Memory.from_config", return_value=mock_instance):
-            yield mock_instance
-
-
-def _load_app(env_overrides: dict):
-    """Reload server/main.py with the given environment and return the FastAPI app."""
-    import server.main as server_main
-
-    with patch.dict(os.environ, env_overrides, clear=False):
-        importlib.reload(server_main)
-    return server_main.app
-
-
-# ---------------------------------------------------------------------------
-# Auth disabled (ADMIN_API_KEY not set)
-# ---------------------------------------------------------------------------
-
-class TestAuthDisabled:
-    """All endpoints should be freely accessible when ADMIN_API_KEY is empty."""
-
-    @pytest.fixture(autouse=True)
-    def _setup(self, _mock_memory):
-        self.app = _load_app({"ADMIN_API_KEY": ""})
-        self.client = TestClient(self.app)
-        self.mock = _mock_memory
-
-    def test_root_redirects_to_docs(self):
-        resp = self.client.get("/", follow_redirects=False)
-        assert resp.status_code == 307
-        assert "/docs" in resp.headers["location"]
-
-    def test_get_memory_without_key(self):
-        resp = self.client.get("/memories/mem-1")
-        assert resp.status_code == 200
-        assert resp.json()["id"] == "mem-1"
-
-    def test_get_all_memories_without_key(self):
-        resp = self.client.get("/memories", params={"user_id": "alice"})
-        assert resp.status_code == 200
-
-    def test_create_memory_without_key(self):
-        resp = self.client.post("/memories", json={
-            "messages": [{"role": "user", "content": "I like pizza"}],
-            "user_id": "alice",
-        })
-        assert resp.status_code == 200
-
-    def test_search_without_key(self):
-        resp = self.client.post("/search", json={"query": "pizza", "user_id": "alice"})
-        assert resp.status_code == 200
-
-    def test_update_memory_without_key(self):
-        resp = self.client.put("/memories/mem-1", json={"data": "updated"})
-        assert resp.status_code == 200
-
-    def test_history_without_key(self):
-        resp = self.client.get("/memories/mem-1/history")
-        assert resp.status_code == 200
-
-    def test_delete_memory_without_key(self):
-        resp = self.client.delete("/memories/mem-1")
-        assert resp.status_code == 200
-
-    def test_delete_all_without_key(self):
-        resp = self.client.delete("/memories", params={"user_id": "alice"})
-        assert resp.status_code == 200
-
-    def test_reset_without_key(self):
-        resp = self.client.post("/reset")
-        assert resp.status_code == 200
-
-    def test_configure_without_key(self):
-        self.mock.from_config = MagicMock()
-        resp = self.client.post("/configure", json={"version": "v1.1"})
-        assert resp.status_code == 200
-
-    def test_supplying_key_still_works_when_auth_disabled(self):
-        """A client that sends X-API-Key should not be penalized when auth is off."""
-        resp = self.client.get(
-            "/memories/mem-1", headers={"X-API-Key": "some-random-key"}
-        )
-        assert resp.status_code == 200
-
-    @pytest.mark.parametrize(
-        "method,path",
-        [
-            ("POST", "/configure"),
-            ("POST", "/memories"),
-            ("GET", "/memories"),
-            ("GET", "/memories/test-id"),
-            ("POST", "/search"),
-            ("PUT", "/memories/test-id"),
-            ("GET", "/memories/test-id/history"),
-            ("DELETE", "/memories/test-id"),
-            ("DELETE", "/memories"),
-            ("POST", "/reset"),
-        ],
-    )
-    def test_no_endpoint_returns_401_when_auth_disabled(self, method, path):
-        resp = self.client.request(method, path)
-        assert resp.status_code != 401, f"{method} {path} should not require auth"
-
-
-# ---------------------------------------------------------------------------
-# Auth enabled (ADMIN_API_KEY set)
-# ---------------------------------------------------------------------------
-
-class TestAuthEnabled:
-    """All protected endpoints must enforce the API key."""
-
-    API_KEY = "test-secret-key-12345"
-
-    @pytest.fixture(autouse=True)
-    def _setup(self, _mock_memory):
-        self.app = _load_app({"ADMIN_API_KEY": self.API_KEY})
-        self.client = TestClient(self.app)
-        self.mock = _mock_memory
-
-    # --- Rejection cases ---
-
-    def test_missing_key_returns_401(self):
-        resp = self.client.get("/memories/mem-1")
-        assert resp.status_code == 401
-
-    def test_missing_key_detail_mentions_header(self):
-        resp = self.client.get("/memories/mem-1")
-        assert "X-API-Key" in resp.json()["detail"]
-
-    def test_wrong_key_returns_401(self):
-        resp = self.client.get("/memories/mem-1", headers={"X-API-Key": "wrong"})
-        assert resp.status_code == 401
-
-    def test_wrong_key_detail_says_invalid(self):
-        resp = self.client.get("/memories/mem-1", headers={"X-API-Key": "wrong"})
-        assert "Invalid" in resp.json()["detail"]
-
-    def test_empty_string_key_returns_401(self):
-        resp = self.client.get("/memories/mem-1", headers={"X-API-Key": ""})
-        assert resp.status_code == 401
-
-    def test_401_includes_www_authenticate_header(self):
-        resp = self.client.get("/memories/mem-1")
-        assert resp.headers.get("www-authenticate") == "ApiKey"
-
-    def test_near_miss_key_rejected(self):
-        """Key that differs by one character should be rejected."""
-        near_miss = self.API_KEY[:-1] + ("6" if self.API_KEY[-1] != "6" else "7")
-        resp = self.client.get("/memories/mem-1", headers={"X-API-Key": near_miss})
-        assert resp.status_code == 401
-
-    def test_key_with_extra_whitespace_rejected(self):
-        resp = self.client.get("/memories/mem-1", headers={"X-API-Key": f" {self.API_KEY} "})
-        assert resp.status_code == 401
-
-    def test_key_prefix_rejected(self):
-        resp = self.client.get("/memories/mem-1", headers={"X-API-Key": self.API_KEY[:5]})
-        assert resp.status_code == 401
-
-    def test_key_with_different_case_rejected(self):
-        resp = self.client.get("/memories/mem-1", headers={"X-API-Key": self.API_KEY.upper()})
-        assert resp.status_code == 401
-
-    @pytest.mark.parametrize(
-        "method,path",
-        [
-            ("POST", "/configure"),
-            ("POST", "/memories"),
-            ("GET", "/memories"),
-            ("GET", "/memories/test-id"),
-            ("POST", "/search"),
-            ("PUT", "/memories/test-id"),
-            ("GET", "/memories/test-id/history"),
-            ("DELETE", "/memories/test-id"),
-            ("DELETE", "/memories"),
-            ("POST", "/reset"),
-        ],
-    )
-    def test_all_endpoints_reject_without_key(self, method, path):
-        resp = self.client.request(method, path)
-        assert resp.status_code == 401, f"{method} {path} should require auth"
-
-    @pytest.mark.parametrize(
-        "method,path",
-        [
-            ("POST", "/configure"),
-            ("POST", "/memories"),
-            ("GET", "/memories"),
-            ("GET", "/memories/test-id"),
-            ("POST", "/search"),
-            ("PUT", "/memories/test-id"),
-            ("GET", "/memories/test-id/history"),
-            ("DELETE", "/memories/test-id"),
-            ("DELETE", "/memories"),
-            ("POST", "/reset"),
-        ],
-    )
-    def test_all_endpoints_reject_wrong_key(self, method, path):
-        resp = self.client.request(method, path, headers={"X-API-Key": "wrong-key"})
-        assert resp.status_code == 401, f"{method} {path} should reject wrong key"
-
-    # --- Acceptance cases ---
-
-    def test_root_does_not_require_key(self):
-        resp = self.client.get("/", follow_redirects=False)
-        assert resp.status_code == 307
-
-    def _authed(self, method, path, **kwargs):
-        headers = kwargs.pop("headers", {})
-        headers["X-API-Key"] = self.API_KEY
-        return self.client.request(method, path, headers=headers, **kwargs)
-
-    def test_get_memory_with_key(self):
-        resp = self._authed("GET", "/memories/mem-1")
-        assert resp.status_code == 200
-        assert resp.json()["id"] == "mem-1"
-
-    def test_get_all_memories_with_key(self):
-        resp = self._authed("GET", "/memories", params={"user_id": "alice"})
-        assert resp.status_code == 200
-
-    def test_create_memory_with_key(self):
-        resp = self._authed("POST", "/memories", json={
-            "messages": [{"role": "user", "content": "I like pizza"}],
-            "user_id": "alice",
-        })
-        assert resp.status_code == 200
-        data = resp.json()
-        assert "results" in data
-
-    def test_search_with_key(self):
-        resp = self._authed("POST", "/search", json={"query": "pizza", "user_id": "alice"})
-        assert resp.status_code == 200
-
-    def test_update_memory_with_key(self):
-        resp = self._authed("PUT", "/memories/mem-1", json={"data": "updated"})
-        assert resp.status_code == 200
-
-    def test_history_with_key(self):
-        resp = self._authed("GET", "/memories/mem-1/history")
-        assert resp.status_code == 200
-
-    def test_delete_memory_with_key(self):
-        resp = self._authed("DELETE", "/memories/mem-1")
-        assert resp.status_code == 200
-
-    def test_delete_all_with_key(self):
-        resp = self._authed("DELETE", "/memories", params={"user_id": "alice"})
-        assert resp.status_code == 200
-
-    def test_reset_with_key(self):
-        resp = self._authed("POST", "/reset")
-        assert resp.status_code == 200
-
-    def test_configure_with_key(self):
-        resp = self._authed("POST", "/configure", json={"version": "v1.1"})
-        assert resp.status_code == 200
-
-
-# ---------------------------------------------------------------------------
-# Full CRUD flow through auth
-# ---------------------------------------------------------------------------
-
-class TestAuthenticatedCRUDFlow:
-    """Verify a complete create → read → search → update → history → delete
-    cycle works end-to-end through the auth layer."""
-
-    API_KEY = "flow-test-key-99"
-
-    @pytest.fixture(autouse=True)
-    def _setup(self, _mock_memory):
-        self.app = _load_app({"ADMIN_API_KEY": self.API_KEY})
-        self.client = TestClient(self.app)
-        self.mock = _mock_memory
-
-    def _authed(self, method, path, **kwargs):
-        headers = kwargs.pop("headers", {})
-        headers["X-API-Key"] = self.API_KEY
-        return self.client.request(method, path, headers=headers, **kwargs)
-
-    def test_full_crud_cycle(self):
-        # 1. Create
-        resp = self._authed("POST", "/memories", json={
-            "messages": [{"role": "user", "content": "I love fresh vegetable pizza"}],
-            "user_id": "alice",
-        })
-        assert resp.status_code == 200
-        self.mock.add.assert_called_once()
-
-        # 2. Read single
-        resp = self._authed("GET", "/memories/mem-1")
-        assert resp.status_code == 200
-        self.mock.get.assert_called_once_with("mem-1")
-
-        # 3. Read all
-        resp = self._authed("GET", "/memories", params={"user_id": "alice"})
-        assert resp.status_code == 200
-        self.mock.get_all.assert_called_once_with(user_id="alice")
-
-        # 4. Search
-        resp = self._authed("POST", "/search", json={"query": "pizza", "user_id": "alice"})
-        assert resp.status_code == 200
-        self.mock.search.assert_called_once()
-
-        # 5. Update
-        resp = self._authed("PUT", "/memories/mem-1", json={"data": "updated content"})
-        assert resp.status_code == 200
-        self.mock.update.assert_called_once()
-
-        # 6. History
-        resp = self._authed("GET", "/memories/mem-1/history")
-        assert resp.status_code == 200
-        self.mock.history.assert_called_once_with(memory_id="mem-1")
-
-        # 7. Delete single
-        resp = self._authed("DELETE", "/memories/mem-1")
-        assert resp.status_code == 200
-        self.mock.delete.assert_called_once_with(memory_id="mem-1")
-
-        # 8. Delete all
-        resp = self._authed("DELETE", "/memories", params={"user_id": "alice"})
-        assert resp.status_code == 200
-        self.mock.delete_all.assert_called_once()
-
-    def test_crud_flow_blocked_without_auth(self):
-        """Same flow should fail at every step without the key."""
-        endpoints = [
-            ("POST", "/memories", {"json": {
-                "messages": [{"role": "user", "content": "test"}], "user_id": "alice"
-            }}),
-            ("GET", "/memories/mem-1", {}),
-            ("GET", "/memories", {"params": {"user_id": "alice"}}),
-            ("POST", "/search", {"json": {"query": "pizza", "user_id": "alice"}}),
-            ("PUT", "/memories/mem-1", {"json": {"data": "x"}}),
-            ("GET", "/memories/mem-1/history", {}),
-            ("DELETE", "/memories/mem-1", {}),
-            ("DELETE", "/memories", {"params": {"user_id": "alice"}}),
-            ("POST", "/reset", {}),
-        ]
-        for method, path, kwargs in endpoints:
-            resp = self.client.request(method, path, **kwargs)
-            assert resp.status_code == 401, f"Unauthenticated {method} {path} should be 401"
-            # Verify the mock was NOT called (auth blocked before reaching handler)
-        self.mock.add.assert_not_called()
-        self.mock.get.assert_not_called()
-        self.mock.search.assert_not_called()
-        self.mock.update.assert_not_called()
-        self.mock.history.assert_not_called()
-        self.mock.delete.assert_not_called()
-        self.mock.delete_all.assert_not_called()
-        self.mock.reset.assert_not_called()
-
-
-# ---------------------------------------------------------------------------
-# Edge cases
-# ---------------------------------------------------------------------------
-
-class TestAuthEdgeCases:
-    """Boundary conditions and unusual inputs."""
-
-    @pytest.fixture(autouse=True)
-    def _setup(self, _mock_memory):
-        self.mock = _mock_memory
-
-    def test_very_long_api_key(self):
-        """Server should handle a very long key without crashing."""
-        long_key = "k" * 4096
-        app = _load_app({"ADMIN_API_KEY": long_key})
-        client = TestClient(app)
-        resp = client.get("/memories/mem-1", headers={"X-API-Key": long_key})
-        assert resp.status_code == 200
-
-    def test_special_characters_in_api_key(self):
-        """Keys with special ASCII characters should work."""
-        special_key = "sk-!@#$%^&*()_+-=[]{}|;:',.<>?/~`"
-        app = _load_app({"ADMIN_API_KEY": special_key})
-        client = TestClient(app)
-
-        resp = client.get("/memories/mem-1", headers={"X-API-Key": special_key})
-        assert resp.status_code == 200
-
-        resp = client.get("/memories/mem-1", headers={"X-API-Key": "wrong"})
-        assert resp.status_code == 401
-
-    def test_key_env_var_not_present_at_all(self):
-        """When the env var is completely absent, auth should be disabled."""
-        import server.main as server_main
-        env = os.environ.copy()
-        env.pop("ADMIN_API_KEY", None)
-        with patch.dict(os.environ, env, clear=True):
-            importlib.reload(server_main)
-        client = TestClient(server_main.app)
-        resp = client.get("/memories/mem-1")
-        assert resp.status_code != 401
-
-    def test_switching_from_enabled_to_disabled(self):
-        """Simulates a server restart with auth toggled off."""
-        # First: auth enabled
-        app1 = _load_app({"ADMIN_API_KEY": "secret"})
-        c1 = TestClient(app1)
-        assert c1.get("/memories/mem-1").status_code == 401
-
-        # Then: auth disabled
-        app2 = _load_app({"ADMIN_API_KEY": ""})
-        c2 = TestClient(app2)
-        assert c2.get("/memories/mem-1").status_code != 401
-
-    def test_openapi_schema_accessible_without_key(self):
-        """The /docs and /openapi.json endpoints should always be reachable."""
-        app = _load_app({"ADMIN_API_KEY": "secret"})
-        client = TestClient(app)
-
-        resp = client.get("/openapi.json")
-        assert resp.status_code == 200
-        schema = resp.json()
-        assert "paths" in schema
-
-        resp = client.get("/docs")
-        assert resp.status_code == 200
-
-    def test_openapi_schema_documents_auth(self):
-        """The OpenAPI schema should mention authentication."""
-        app = _load_app({"ADMIN_API_KEY": "secret"})
-        client = TestClient(app)
-        schema = client.get("/openapi.json").json()
-        assert "Authentication" in schema.get("info", {}).get("description", "")
-
-
-# ---------------------------------------------------------------------------
-# Startup logging
-# ---------------------------------------------------------------------------
-
-class TestStartupLogging:
-    """Verify the server emits the correct log messages at import time."""
-
-    @pytest.fixture(autouse=True)
-    def _setup(self, _mock_memory):
-        pass
-
-    def test_warning_when_auth_disabled(self, caplog):
-        with caplog.at_level(logging.WARNING):
-            _load_app({"ADMIN_API_KEY": ""})
-        assert any("UNSECURED" in r.message for r in caplog.records)
-
-    def test_info_when_auth_enabled(self, caplog):
-        with caplog.at_level(logging.INFO):
-            _load_app({"ADMIN_API_KEY": "a-long-enough-secret-key"})
-        assert any("authentication enabled" in r.message for r in caplog.records)
-
-    def test_warning_when_key_too_short(self, caplog):
-        with caplog.at_level(logging.WARNING):
-            _load_app({"ADMIN_API_KEY": "short"})
-        assert any("shorter than" in r.message for r in caplog.records)

tests/vector_stores/test_mongodb.py

@@ -48,16 +48,17 @@ def test_initalize_create_col(mongo_vector_fixture):
     search_index_model = args[0].document
     assert search_index_model == {
         "name": "test_collection_vector_index",
-        "type": "vectorSearch",
         "definition": {
-            "fields": [
+            "mappings": {
-                {
+                "dynamic": False,
-                    "type": "vector",
+                "fields": {
-                    "path": "embedding",
+                    "embedding": {
-                    "numDimensions": 1536,
+                        "type": "knnVector",
-                    "similarity": "cosine",
+                        "dimensions": 1536,
-                }
+                        "similarity": "cosine",
-            ]
+                    }
+                },
+            }
         },
     }
     assert mongo_vector.collection == mock_collection
@@ -94,7 +95,7 @@ def test_search(mongo_vector_fixture):
                 "$vectorSearch": {
                     "index": "test_collection_vector_index",
                     "limit": 2,
-                    "numCandidates": 40,
+                    "numCandidates": 2,
                     "queryVector": query_vector,
                     "path": "embedding",
                 },

tests/vector_stores/test_opensearch.py

@@ -18,25 +18,25 @@ from mem0.vector_stores.opensearch import OpenSearchDB
 # Mock classes for testing OpenSearch with AWS authentication
 class MockFieldInfo:
     """Mock pydantic field info."""
-
     def __init__(self, default=None):
         self.default = default
 
 
 class MockOpenSearchConfig:
+    
     model_fields = {
-        "collection_name": MockFieldInfo(default="default_collection"),
+        'collection_name': MockFieldInfo(default="default_collection"),
-        "host": MockFieldInfo(default="localhost"),
+        'host': MockFieldInfo(default="localhost"),
-        "port": MockFieldInfo(default=9200),
+        'port': MockFieldInfo(default=9200),
-        "embedding_model_dims": MockFieldInfo(default=1536),
+        'embedding_model_dims': MockFieldInfo(default=1536),
-        "http_auth": MockFieldInfo(default=None),
+        'http_auth': MockFieldInfo(default=None),
-        "auth": MockFieldInfo(default=None),
+        'auth': MockFieldInfo(default=None),
-        "credentials": MockFieldInfo(default=None),
+        'credentials': MockFieldInfo(default=None),
-        "connection_class": MockFieldInfo(default=None),
+        'connection_class': MockFieldInfo(default=None),
-        "use_ssl": MockFieldInfo(default=False),
+        'use_ssl': MockFieldInfo(default=False),
-        "verify_certs": MockFieldInfo(default=False),
+        'verify_certs': MockFieldInfo(default=False),
     }
-
+    
     def __init__(self, collection_name="test_collection", include_auth=True, **kwargs):
         self.collection_name = collection_name
         self.host = kwargs.get("host", "localhost")
@@ -44,7 +44,7 @@ class MockOpenSearchConfig:
         self.embedding_model_dims = kwargs.get("embedding_model_dims", 1536)
         self.use_ssl = kwargs.get("use_ssl", True)
         self.verify_certs = kwargs.get("verify_certs", True)
-
+        
         if any(field in kwargs for field in ["http_auth", "auth", "credentials", "connection_class"]):
             self.http_auth = kwargs.get("http_auth")
             self.auth = kwargs.get("auth")
@@ -63,18 +63,20 @@ class MockOpenSearchConfig:
 
 
 class MockAWSAuth:
+    
     def __init__(self):
         self._lock = threading.Lock()
         self.region = "us-east-1"
-
+    
     def __deepcopy__(self, memo):
         raise TypeError("cannot pickle '_thread.lock' object")
 
 
 class MockConnectionClass:
+    
     def __init__(self):
         self._state = {"connected": False}
-
+    
     def __deepcopy__(self, memo):
         raise TypeError("cannot pickle connection state")
 
@@ -253,104 +255,6 @@ class TestOpenSearchDB(unittest.TestCase):
         self.os_db.delete_col()
         self.client_mock.indices.delete.assert_called_once_with(index="test_collection")
 
-    def test_insert_rejects_null_vectors(self):
-        """Vectors that are None should raise ValueError before hitting OpenSearch."""
-        vectors = [None]
-        payloads = [{"key": "value"}]
-        ids = ["id1"]
-
-        with self.assertRaises(ValueError) as ctx:
-            self.os_db.insert(vectors=vectors, payloads=payloads, ids=ids)
-
-        self.assertIn("null", str(ctx.exception).lower())
-        self.client_mock.index.assert_not_called()
-
-    def test_insert_rejects_empty_vectors(self):
-        """Empty vector lists should raise ValueError."""
-        vectors = [[]]
-        payloads = [{"key": "value"}]
-        ids = ["id1"]
-
-        with self.assertRaises(ValueError) as ctx:
-            self.os_db.insert(vectors=vectors, payloads=payloads, ids=ids)
-
-        self.assertIn("empty", str(ctx.exception).lower())
-        self.client_mock.index.assert_not_called()
-
-    def test_insert_rejects_dimension_mismatch(self):
-        """Vectors with wrong dimensions should raise ValueError with a clear message."""
-        vectors = [[0.1] * 768]
-        payloads = [{"key": "value"}]
-        ids = ["id1"]
-
-        with self.assertRaises(ValueError) as ctx:
-            self.os_db.insert(vectors=vectors, payloads=payloads, ids=ids)
-
-        error_msg = str(ctx.exception)
-        self.assertIn("768", error_msg)
-        self.assertIn("1536", error_msg)
-        self.client_mock.index.assert_not_called()
-
-    def test_update_rejects_empty_vector(self):
-        """Update with an empty vector should raise ValueError."""
-        with self.assertRaises(ValueError) as ctx:
-            self.os_db.update("id1", vector=[], payload={"key": "value"})
-
-        self.assertIn("empty", str(ctx.exception).lower())
-        self.client_mock.search.assert_not_called()
-
-    def test_update_rejects_dimension_mismatch(self):
-        """Update with wrong vector dimensions should raise ValueError."""
-        vector = [0.1] * 768
-        payload = {"key": "value"}
-
-        with self.assertRaises(ValueError) as ctx:
-            self.os_db.update("id1", vector=vector, payload=payload)
-
-        error_msg = str(ctx.exception)
-        self.assertIn("768", error_msg)
-        self.assertIn("1536", error_msg)
-        self.client_mock.search.assert_not_called()
-
-    @patch("mem0.vector_stores.opensearch.logger")
-    def test_update_error_logs_with_exc_info(self, mock_logger):
-        """Update errors should log with exc_info and re-raise."""
-        mock_search_response = {"hits": {"hits": [{"_id": "doc1", "_source": {"id": "id1"}}]}}
-        self.client_mock.search.return_value = mock_search_response
-        self.client_mock.update.side_effect = Exception("Update failed")
-
-        with self.assertRaises(Exception):
-            self.os_db.update("id1", vector=[0.1] * 1536, payload={"key": "value"})
-
-        mock_logger.error.assert_called_once()
-        call_kwargs = mock_logger.error.call_args
-        self.assertTrue(call_kwargs[1].get("exc_info"), "logger.error must be called with exc_info=True")
-
-    @patch("mem0.vector_stores.opensearch.logger")
-    def test_insert_error_logs_with_exc_info(self, mock_logger):
-        """Error logging should include exc_info for full stack trace."""
-        vectors = [[0.1] * 1536]
-        payloads = [{"key": "value"}]
-        ids = ["id1"]
-        self.client_mock.index.side_effect = Exception("Connection refused")
-
-        with self.assertRaises(Exception):
-            self.os_db.insert(vectors=vectors, payloads=payloads, ids=ids)
-
-        mock_logger.error.assert_called_once()
-        call_kwargs = mock_logger.error.call_args
-        self.assertTrue(call_kwargs[1].get("exc_info"), "logger.error must be called with exc_info=True")
-
-    @patch("mem0.vector_stores.opensearch.logger")
-    def test_search_error_logs_with_exc_info(self, mock_logger):
-        """Search error logging should include exc_info for full stack trace."""
-        self.client_mock.search.side_effect = Exception("Search failed")
-        results = self.os_db.search(query="", vectors=[[0.1] * 1536], limit=5)
-        self.assertEqual(results, [])
-        mock_logger.error.assert_called_once()
-        call_kwargs = mock_logger.error.call_args
-        self.assertTrue(call_kwargs[1].get("exc_info"), "logger.error must be called with exc_info=True")
-
     def test_init_with_http_auth(self):
         mock_credentials = MagicMock()
         mock_signer = AWSV4SignerAuth(mock_credentials, "us-east-1", "es")
@@ -378,13 +282,11 @@ class TestOpenSearchDB(unittest.TestCase):
 
 
 # Tests for OpenSearch config deepcopy with AWS authentication (Issue #3464)
-@patch("mem0.utils.factory.EmbedderFactory.create")
+@patch('mem0.utils.factory.EmbedderFactory.create')
-@patch("mem0.utils.factory.VectorStoreFactory.create")
+@patch('mem0.utils.factory.VectorStoreFactory.create')
-@patch("mem0.utils.factory.LlmFactory.create")
+@patch('mem0.utils.factory.LlmFactory.create')
-@patch("mem0.memory.storage.SQLiteManager")
+@patch('mem0.memory.storage.SQLiteManager')
-def test_safe_deepcopy_config_handles_opensearch_auth(
+def test_safe_deepcopy_config_handles_opensearch_auth(mock_sqlite, mock_llm_factory, mock_vector_factory, mock_embedder_factory):
-    mock_sqlite, mock_llm_factory, mock_vector_factory, mock_embedder_factory
-):
     """Test that _safe_deepcopy_config handles OpenSearch configs with AWS auth objects gracefully."""
     mock_embedder_factory.return_value = MagicMock()
     mock_vector_store = MagicMock()
@@ -393,9 +295,9 @@ def test_safe_deepcopy_config_handles_opensearch_auth(
     mock_sqlite.return_value = MagicMock()
 
     from mem0.memory.main import _safe_deepcopy_config
-
+    
     config_with_auth = MockOpenSearchConfig(collection_name="opensearch_test", include_auth=True)
-
+    
     safe_config = _safe_deepcopy_config(config_with_auth)
 
     # Runtime auth objects must be preserved (Issue #3580)
@@ -404,7 +306,7 @@ def test_safe_deepcopy_config_handles_opensearch_auth(
     assert safe_config.connection_class is not None
     # Credentials dict is a sensitive secret and should be redacted
     assert safe_config.credentials is None
-
+    
     assert safe_config.collection_name == "opensearch_test"
     assert safe_config.host == "localhost"
     assert safe_config.port == 9200
@@ -413,10 +315,10 @@ def test_safe_deepcopy_config_handles_opensearch_auth(
     assert safe_config.verify_certs is True
 
 
-@patch("mem0.utils.factory.EmbedderFactory.create")
+@patch('mem0.utils.factory.EmbedderFactory.create')
-@patch("mem0.utils.factory.VectorStoreFactory.create")
+@patch('mem0.utils.factory.VectorStoreFactory.create') 
-@patch("mem0.utils.factory.LlmFactory.create")
+@patch('mem0.utils.factory.LlmFactory.create')
-@patch("mem0.memory.storage.SQLiteManager")
+@patch('mem0.memory.storage.SQLiteManager')
 def test_safe_deepcopy_config_normal_configs(mock_sqlite, mock_llm_factory, mock_vector_factory, mock_embedder_factory):
     """Test that _safe_deepcopy_config handles normal OpenSearch configs without auth."""
     mock_embedder_factory.return_value = MagicMock()
@@ -426,12 +328,12 @@ def test_safe_deepcopy_config_normal_configs(mock_sqlite, mock_llm_factory, mock
     mock_sqlite.return_value = MagicMock()
 
     from mem0.memory.main import _safe_deepcopy_config
-
+    
     config_without_auth = MockOpenSearchConfig(collection_name="normal_test", include_auth=False)
-
+    
     safe_config = _safe_deepcopy_config(config_without_auth)
-
+    
-    assert safe_config.collection_name == "normal_test"
+    assert safe_config.collection_name == "normal_test" 
     assert safe_config.host == "localhost"
     assert safe_config.port == 9200
     assert safe_config.embedding_model_dims == 1536
@@ -439,15 +341,13 @@ def test_safe_deepcopy_config_normal_configs(mock_sqlite, mock_llm_factory, mock
     assert safe_config.verify_certs is True
 
 
-@patch("mem0.utils.factory.EmbedderFactory.create")
+@patch('mem0.utils.factory.EmbedderFactory.create')
-@patch("mem0.utils.factory.VectorStoreFactory.create")
+@patch('mem0.utils.factory.VectorStoreFactory.create')
-@patch("mem0.utils.factory.LlmFactory.create")
+@patch('mem0.utils.factory.LlmFactory.create')
-@patch("mem0.memory.storage.SQLiteManager")
+@patch('mem0.memory.storage.SQLiteManager')
-def test_memory_initialization_opensearch_aws_auth(
+def test_memory_initialization_opensearch_aws_auth(mock_sqlite, mock_llm_factory, mock_vector_factory, mock_embedder_factory):
-    mock_sqlite, mock_llm_factory, mock_vector_factory, mock_embedder_factory
-):
     """Test that Memory initialization works with OpenSearch configs containing AWS auth."""
-
+    
     mock_embedder_factory.return_value = MagicMock()
     mock_vector_store = MagicMock()
     mock_vector_factory.return_value = mock_vector_store

tests/vector_stores/test_qdrant.py

@@ -5,15 +5,9 @@ from unittest.mock import MagicMock
 from qdrant_client import QdrantClient
 from qdrant_client.models import (
     Distance,
-    FieldCondition,
     Filter,
-    MatchAny,
-    MatchExcept,
-    MatchText,
-    MatchValue,
     PointIdsList,
     PointStruct,
-    Range,
     VectorParams,
 )
 
@@ -304,456 +298,3 @@ class TestQdrant(unittest.TestCase):
 
     def tearDown(self):
         del self.qdrant
-
-
-class TestQdrantEnhancedFilters(unittest.TestCase):
-    """Tests for enhanced metadata filtering operators (issue #3975)."""
-
-    def setUp(self):
-        self.client_mock = MagicMock(spec=QdrantClient)
-        self.qdrant = Qdrant(
-            collection_name="test_collection",
-            embedding_model_dims=128,
-            client=self.client_mock,
-        )
-
-    # ------------------------------------------------------------------ #
-    # _build_field_condition                                               #
-    # ------------------------------------------------------------------ #
-
-    def test_simple_equality(self):
-        """Plain value maps to MatchValue."""
-        cond = self.qdrant._build_field_condition("category", "programming")
-        self.assertIsInstance(cond, FieldCondition)
-        self.assertEqual(cond.key, "category")
-        self.assertIsInstance(cond.match, MatchValue)
-        self.assertEqual(cond.match.value, "programming")
-
-    def test_eq_operator(self):
-        """{\"eq\": v} maps to MatchValue."""
-        cond = self.qdrant._build_field_condition("category", {"eq": "programming"})
-        self.assertIsInstance(cond.match, MatchValue)
-        self.assertEqual(cond.match.value, "programming")
-
-    def test_ne_operator(self):
-        """{\"ne\": v} maps to MatchExcept with a single-element list."""
-        cond = self.qdrant._build_field_condition("category", {"ne": "spam"})
-        self.assertIsInstance(cond.match, MatchExcept)
-        self.assertIn("spam", getattr(cond.match, "except_", None) or getattr(cond.match, "except"))
-
-    def test_in_operator(self):
-        """{\"in\": [...]} maps to MatchAny."""
-        cond = self.qdrant._build_field_condition("category", {"in": ["a", "b", "c"]})
-        self.assertIsInstance(cond.match, MatchAny)
-        self.assertEqual(cond.match.any, ["a", "b", "c"])
-
-    def test_nin_operator(self):
-        """{\"nin\": [...]} maps to MatchExcept."""
-        cond = self.qdrant._build_field_condition("status", {"nin": ["deleted", "banned"]})
-        self.assertIsInstance(cond.match, MatchExcept)
-        excluded = getattr(cond.match, "except_", None) or getattr(cond.match, "except")
-        self.assertIn("deleted", excluded)
-        self.assertIn("banned", excluded)
-
-    def test_contains_operator(self):
-        """{\"contains\": x} maps to MatchText."""
-        cond = self.qdrant._build_field_condition("bio", {"contains": "python"})
-        self.assertIsInstance(cond.match, MatchText)
-        self.assertEqual(cond.match.text, "python")
-
-    def test_icontains_operator(self):
-        """{\"icontains\": x} maps to MatchText (same as contains for Qdrant)."""
-        cond = self.qdrant._build_field_condition("bio", {"icontains": "Python"})
-        self.assertIsInstance(cond.match, MatchText)
-        self.assertEqual(cond.match.text, "Python")
-
-    def test_gt_operator(self):
-        """{\"gt\": v} maps to Range with gt only."""
-        cond = self.qdrant._build_field_condition("priority", {"gt": 5})
-        self.assertIsInstance(cond.range, Range)
-        self.assertEqual(cond.range.gt, 5)
-        self.assertIsNone(cond.range.gte)
-        self.assertIsNone(cond.range.lt)
-        self.assertIsNone(cond.range.lte)
-
-    def test_gte_operator(self):
-        """{\"gte\": v} maps to Range with gte only."""
-        cond = self.qdrant._build_field_condition("priority", {"gte": 5})
-        self.assertIsInstance(cond.range, Range)
-        self.assertEqual(cond.range.gte, 5)
-        self.assertIsNone(cond.range.gt)
-
-    def test_lt_operator(self):
-        """{\"lt\": v} maps to Range with lt only."""
-        cond = self.qdrant._build_field_condition("priority", {"lt": 10})
-        self.assertIsInstance(cond.range, Range)
-        self.assertEqual(cond.range.lt, 10)
-
-    def test_lte_operator(self):
-        """{\"lte\": v} maps to Range with lte only."""
-        cond = self.qdrant._build_field_condition("priority", {"lte": 10})
-        self.assertIsInstance(cond.range, Range)
-        self.assertEqual(cond.range.lte, 10)
-
-    def test_range_gte_lte(self):
-        """Combined {\"gte\": x, \"lte\": y} maps to Range with both bounds."""
-        cond = self.qdrant._build_field_condition("priority", {"gte": 5, "lte": 10})
-        self.assertIsInstance(cond.range, Range)
-        self.assertEqual(cond.range.gte, 5)
-        self.assertEqual(cond.range.lte, 10)
-
-    def test_range_gt_lt(self):
-        """Open interval {\"gt\": x, \"lt\": y} maps to Range with both bounds."""
-        cond = self.qdrant._build_field_condition("score", {"gt": 0.5, "lt": 0.9})
-        self.assertIsInstance(cond.range, Range)
-        self.assertEqual(cond.range.gt, 0.5)
-        self.assertEqual(cond.range.lt, 0.9)
-
-    # ------------------------------------------------------------------ #
-    # _create_filter — comparison and list operators                       #
-    # ------------------------------------------------------------------ #
-
-    def test_create_filter_eq(self):
-        result = self.qdrant._create_filter({"category": {"eq": "programming"}})
-        self.assertIsInstance(result, Filter)
-        self.assertEqual(len(result.must), 1)
-        self.assertIsInstance(result.must[0].match, MatchValue)
-
-    def test_create_filter_ne(self):
-        result = self.qdrant._create_filter({"category": {"ne": "spam"}})
-        self.assertIsInstance(result, Filter)
-        self.assertIsInstance(result.must[0].match, MatchExcept)
-
-    def test_create_filter_in(self):
-        result = self.qdrant._create_filter({"category": {"in": ["prog", "data"]}})
-        self.assertIsInstance(result, Filter)
-        self.assertIsInstance(result.must[0].match, MatchAny)
-        self.assertEqual(result.must[0].match.any, ["prog", "data"])
-
-    def test_create_filter_nin(self):
-        result = self.qdrant._create_filter({"status": {"nin": ["deleted", "banned"]}})
-        self.assertIsInstance(result, Filter)
-        self.assertIsInstance(result.must[0].match, MatchExcept)
-
-    def test_create_filter_gt(self):
-        result = self.qdrant._create_filter({"priority": {"gt": 7}})
-        self.assertIsInstance(result, Filter)
-        self.assertIsInstance(result.must[0].range, Range)
-        self.assertEqual(result.must[0].range.gt, 7)
-
-    def test_create_filter_gte_lte_range(self):
-        result = self.qdrant._create_filter({"priority": {"gte": 5, "lte": 9}})
-        self.assertIsInstance(result, Filter)
-        r = result.must[0].range
-        self.assertEqual(r.gte, 5)
-        self.assertEqual(r.lte, 9)
-
-    def test_create_filter_contains(self):
-        result = self.qdrant._create_filter({"bio": {"contains": "python"}})
-        self.assertIsInstance(result, Filter)
-        self.assertIsInstance(result.must[0].match, MatchText)
-
-    # ------------------------------------------------------------------ #
-    # _create_filter — logical operators                                   #
-    # ------------------------------------------------------------------ #
-
-    def test_and_operator(self):
-        """AND populates Filter.must with nested Filter objects."""
-        filters = {"AND": [{"category": "programming"}, {"priority": 10}]}
-        result = self.qdrant._create_filter(filters)
-        self.assertIsInstance(result, Filter)
-        self.assertEqual(len(result.must), 2)
-        # Each item is a Filter that wraps a single FieldCondition
-        for item in result.must:
-            self.assertIsInstance(item, Filter)
-
-    def test_or_operator(self):
-        """OR populates Filter.should with nested Filter objects."""
-        filters = {"OR": [{"category": "programming"}, {"category": "data"}]}
-        result = self.qdrant._create_filter(filters)
-        self.assertIsInstance(result, Filter)
-        self.assertEqual(len(result.should), 2)
-        self.assertIsNone(result.must)
-
-    def test_not_operator(self):
-        """NOT populates Filter.must_not with nested Filter objects."""
-        filters = {"NOT": [{"category": "spam"}]}
-        result = self.qdrant._create_filter(filters)
-        self.assertIsInstance(result, Filter)
-        self.assertEqual(len(result.must_not), 1)
-        self.assertIsNone(result.must)
-        self.assertIsNone(result.should)
-
-    def test_mixed_field_and_and(self):
-        """Top-level field conditions and AND can coexist in must."""
-        filters = {
-            "user_id": "alice",
-            "AND": [{"priority": {"gte": 5}}, {"category": "programming"}],
-        }
-        result = self.qdrant._create_filter(filters)
-        self.assertIsInstance(result, Filter)
-        # user_id FieldCondition + 2 nested AND Filters = 3 items in must
-        self.assertEqual(len(result.must), 3)
-
-    def test_nested_and_or(self):
-        """AND containing an OR sub-condition produces correct nesting."""
-        filters = {
-            "AND": [
-                {"OR": [{"category": "prog"}, {"category": "data"}]},
-                {"priority": {"gt": 3}},
-            ]
-        }
-        result = self.qdrant._create_filter(filters)
-        self.assertIsInstance(result, Filter)
-        self.assertEqual(len(result.must), 2)
-        # First item is a Filter with should (the OR)
-        or_filter = result.must[0]
-        self.assertIsInstance(or_filter, Filter)
-        self.assertEqual(len(or_filter.should), 2)
-
-    # ------------------------------------------------------------------ #
-    # Edge cases                                                           #
-    # ------------------------------------------------------------------ #
-
-    def test_empty_filter_returns_none(self):
-        self.assertIsNone(self.qdrant._create_filter({}))
-        self.assertIsNone(self.qdrant._create_filter(None))
-
-    def test_backward_compat_simple_equality(self):
-        """Plain scalar equality still works as before."""
-        result = self.qdrant._create_filter({"user_id": "alice", "run_id": "r1"})
-        self.assertIsInstance(result, Filter)
-        keys = [c.key for c in result.must]
-        self.assertIn("user_id", keys)
-        self.assertIn("run_id", keys)
-
-    def test_backward_compat_gte_lte_range(self):
-        """Original gte+lte range filter still produces a Range condition."""
-        result = self.qdrant._create_filter({"count": {"gte": 5, "lte": 10}})
-        self.assertIsInstance(result, Filter)
-        self.assertIsInstance(result.must[0].range, Range)
-        self.assertEqual(result.must[0].range.gte, 5)
-        self.assertEqual(result.must[0].range.lte, 10)
-
-    def test_unknown_operator_raises_error(self):
-        """Unknown operator dict should raise ValueError, not ValidationError."""
-        with self.assertRaises(ValueError) as ctx:
-            self.qdrant._build_field_condition("field", {"unknown_op": "foo"})
-        self.assertIn("Unsupported", str(ctx.exception))
-
-    def test_mixed_range_and_non_range_raises_error(self):
-        """Mixing range ops with non-range ops should raise ValueError."""
-        with self.assertRaises(ValueError):
-            self.qdrant._build_field_condition("priority", {"gte": 5, "ne": 10})
-
-    def test_mixed_range_and_eq_raises_error(self):
-        """Mixing range ops with eq should raise ValueError."""
-        with self.assertRaises(ValueError):
-            self.qdrant._build_field_condition("score", {"gt": 0.5, "eq": 1.0})
-
-    def test_wildcard_returns_none(self):
-        """Wildcard '*' should return None (skip filter — match any)."""
-        result = self.qdrant._build_field_condition("category", "*")
-        self.assertIsNone(result)
-
-    def test_create_filter_with_wildcard_skips_it(self):
-        """Wildcard fields should be skipped in the final filter."""
-        result = self.qdrant._create_filter({"category": "*", "user_id": "alice"})
-        self.assertIsInstance(result, Filter)
-        self.assertEqual(len(result.must), 1)
-        self.assertEqual(result.must[0].key, "user_id")
-
-    def test_create_filter_only_wildcard_returns_none(self):
-        """Filter with only wildcard should return None."""
-        result = self.qdrant._create_filter({"category": "*"})
-        self.assertIsNone(result)
-
-    def test_and_with_non_list_raises_error(self):
-        """AND with non-list value should raise ValueError."""
-        with self.assertRaises(ValueError):
-            self.qdrant._create_filter({"AND": "not_a_list"})
-
-    def test_or_with_non_list_raises_error(self):
-        """OR with non-list value should raise ValueError."""
-        with self.assertRaises(ValueError):
-            self.qdrant._create_filter({"OR": {"category": "work"}})
-
-    def test_not_with_non_list_raises_error(self):
-        """NOT with non-list value should raise ValueError."""
-        with self.assertRaises(ValueError):
-            self.qdrant._create_filter({"NOT": "invalid"})
-
-    def test_empty_and_list(self):
-        """AND with empty list should return None."""
-        result = self.qdrant._create_filter({"AND": []})
-        self.assertIsNone(result)
-
-    def test_empty_or_list(self):
-        """OR with empty list should return None."""
-        result = self.qdrant._create_filter({"OR": []})
-        self.assertIsNone(result)
-
-    def test_empty_not_list(self):
-        """NOT with empty list should return None."""
-        result = self.qdrant._create_filter({"NOT": []})
-        self.assertIsNone(result)
-
-    def test_deeply_nested_logical(self):
-        """3-level nesting: AND > OR > NOT."""
-        filters = {
-            "AND": [
-                {
-                    "OR": [
-                        {"category": "work"},
-                        {"category": "personal"}
-                    ]
-                },
-                {"priority": {"gte": 5}},
-                {
-                    "NOT": [
-                        {"status": "archived"}
-                    ]
-                }
-            ]
-        }
-        result = self.qdrant._create_filter(filters)
-        self.assertIsInstance(result, Filter)
-        self.assertEqual(len(result.must), 3)
-
-    def test_boolean_equality(self):
-        """Boolean values should work with MatchValue."""
-        cond = self.qdrant._build_field_condition("active", True)
-        self.assertIsInstance(cond.match, MatchValue)
-        self.assertEqual(cond.match.value, True)
-
-    def test_integer_equality(self):
-        """Integer values should work with MatchValue."""
-        cond = self.qdrant._build_field_condition("count", 42)
-        self.assertIsInstance(cond.match, MatchValue)
-        self.assertEqual(cond.match.value, 42)
-
-    def test_eq_with_boolean(self):
-        """eq operator with boolean should work."""
-        cond = self.qdrant._build_field_condition("active", {"eq": False})
-        self.assertIsInstance(cond.match, MatchValue)
-        self.assertEqual(cond.match.value, False)
-
-    def test_in_with_integers(self):
-        """in operator with integer list should use MatchAny."""
-        cond = self.qdrant._build_field_condition("priority", {"in": [1, 2, 3]})
-        self.assertIsInstance(cond.match, MatchAny)
-        self.assertEqual(cond.match.any, [1, 2, 3])
-
-    def test_wildcard_inside_and(self):
-        """Wildcard inside AND should be skipped, other conditions preserved."""
-        result = self.qdrant._create_filter({
-            "AND": [{"category": "*"}, {"user_id": "alice"}]
-        })
-        self.assertIsInstance(result, Filter)
-        # AND produces nested Filters; the wildcard sub-filter returns None and is skipped
-        # Only the user_id sub-filter remains
-        self.assertEqual(len(result.must), 1)
-
-    def test_list_value_treated_as_match_any(self):
-        """List value shorthand should be treated as in-operator (MatchAny)."""
-        cond = self.qdrant._build_field_condition("tags", ["a", "b", "c"])
-        self.assertIsInstance(cond.match, MatchAny)
-        self.assertEqual(cond.match.any, ["a", "b", "c"])
-
-    def test_list_value_in_create_filter(self):
-        """List value shorthand should work through _create_filter too."""
-        result = self.qdrant._create_filter({"tags": ["python", "rust"]})
-        self.assertIsInstance(result, Filter)
-        self.assertEqual(len(result.must), 1)
-        self.assertIsInstance(result.must[0].match, MatchAny)
-
-    def test_non_dict_item_in_and_raises_error(self):
-        """Non-dict item inside AND list should raise clear ValueError."""
-        with self.assertRaises(ValueError) as ctx:
-            self.qdrant._create_filter({"AND": ["not_a_dict", {"field": "val"}]})
-        self.assertIn("index 0", str(ctx.exception))
-        self.assertIn("must be a dict", str(ctx.exception))
-
-    def test_non_dict_item_in_or_raises_error(self):
-        """Non-dict item inside OR list should raise clear ValueError."""
-        with self.assertRaises(ValueError) as ctx:
-            self.qdrant._create_filter({"OR": [{"field": "val"}, 42]})
-        self.assertIn("index 1", str(ctx.exception))
-
-    def test_empty_dict_value_raises_error(self):
-        """Empty dict as filter value should raise ValueError."""
-        with self.assertRaises(ValueError):
-            self.qdrant._build_field_condition("field", {})
-
-    def test_ne_alone_does_not_trigger_mixed_error(self):
-        """ne as sole operator should NOT trigger mixed-operator error (regression guard)."""
-        cond = self.qdrant._build_field_condition("status", {"ne": "deleted"})
-        self.assertIsInstance(cond.match, MatchExcept)
-
-    def test_eq_with_literal_star(self):
-        """eq operator with literal '*' should match the string '*', not wildcard."""
-        cond = self.qdrant._build_field_condition("category", {"eq": "*"})
-        self.assertIsInstance(cond.match, MatchValue)
-        self.assertEqual(cond.match.value, "*")
-
-    # ------------------------------------------------------------------ #
-    # $or / $not normalization (Memory._process_metadata_filters injects  #
-    # these keys alongside the original OR/NOT)                           #
-    # ------------------------------------------------------------------ #
-
-    def test_dollar_or_handled_as_or(self):
-        """$or injected by Memory middleware should be treated as OR."""
-        filters = {
-            "user_id": "alice",
-            "$or": [{"category": "programming"}, {"category": "data"}],
-        }
-        result = self.qdrant._create_filter(filters)
-        self.assertIsInstance(result, Filter)
-        self.assertIsNotNone(result.should)
-        self.assertEqual(len(result.should), 2)
-
-    def test_dollar_not_handled_as_not(self):
-        """$not injected by Memory middleware should be treated as NOT."""
-        filters = {
-            "user_id": "alice",
-            "$not": [{"category": "spam"}],
-        }
-        result = self.qdrant._create_filter(filters)
-        self.assertIsInstance(result, Filter)
-        self.assertIsNotNone(result.must_not)
-        self.assertEqual(len(result.must_not), 1)
-
-    def test_memory_search_or_shape(self):
-        """Simulate exact shape Memory.search() sends for OR filters.
-
-        effective_filters keeps the original OR key (via deepcopy of
-        input_filters) and _process_metadata_filters adds $or with the
-        same content.  _create_filter should deduplicate so only the
-        first occurrence (OR) is used — exactly 2 should entries.
-        """
-        filters = {
-            "OR": [{"category": "programming"}, {"category": "data"}],
-            "user_id": "test_user",
-            "$or": [{"category": "programming"}, {"category": "data"}],
-        }
-        result = self.qdrant._create_filter(filters)
-        self.assertIsInstance(result, Filter)
-        self.assertIsNotNone(result.should)
-        # Deduplicated: OR wins, $or is skipped — exactly 2 entries
-        self.assertEqual(len(result.should), 2)
-
-    def test_memory_search_not_shape(self):
-        """Simulate exact shape Memory.search() sends for NOT filters.
-
-        Same deduplication as OR: NOT wins, $not is skipped.
-        """
-        filters = {
-            "NOT": [{"category": "spam"}],
-            "user_id": "test_user",
-            "$not": [{"category": "spam"}],
-        }
-        result = self.qdrant._create_filter(filters)
-        self.assertIsInstance(result, Filter)
-        self.assertIsNotNone(result.must_not)
-        # Deduplicated: NOT wins, $not is skipped — exactly 1 entry
-        self.assertEqual(len(result.must_not), 1)

tests/vector_stores/test_turbopuffer.py

@@ -1,725 +0,0 @@
-from datetime import datetime
-from unittest.mock import MagicMock, patch
-
-import pytest
-
-pytest.importorskip("turbopuffer", reason="turbopuffer not installed")
-
-from turbopuffer.types import Row
-
-from mem0.vector_stores.turbopuffer import OutputData, TurbopufferDB
-
-
-def _make_row(id, dist=None, vector=None, **attributes):
-    """Helper to create a turbopuffer Row with extra attributes."""
-    data = {"id": id, "vector": vector}
-    if dist is not None:
-        data["$dist"] = dist
-    data.update(attributes)
-    return Row.model_validate(data)
-
-
-@pytest.fixture
-def mock_client():
-    with patch("mem0.vector_stores.turbopuffer.TurbopufferClient") as MockClient:
-        client_instance = MagicMock()
-        namespace_instance = MagicMock()
-        client_instance.namespace.return_value = namespace_instance
-        MockClient.return_value = client_instance
-        yield client_instance, namespace_instance
-
-
-@pytest.fixture
-def db(mock_client):
-    _, _ = mock_client
-    return TurbopufferDB(
-        collection_name="test_ns",
-        embedding_model_dims=4,
-        api_key="tpuf_test_key",
-        region="gcp-us-central1",
-        distance_metric="cosine_distance",
-        batch_size=2,
-    )
-
-
-# ── Initialization ──────────────────────────────────────────────────
-
-
-class TestInit:
-    def test_init_with_api_key(self, mock_client):
-        client_instance, namespace_instance = mock_client
-        db = TurbopufferDB(
-            collection_name="my_ns",
-            embedding_model_dims=128,
-            api_key="tpuf_key",
-            region="gcp-us-central1",
-        )
-        assert db.collection_name == "my_ns"
-        assert db.embedding_model_dims == 128
-        assert db.distance_metric == "cosine_distance"
-        assert db.batch_size == 100
-        client_instance.namespace.assert_called_with("my_ns")
-
-    def test_init_with_env_var(self, mock_client):
-        with patch.dict("os.environ", {"TURBOPUFFER_API_KEY": "tpuf_env_key"}):
-            db = TurbopufferDB(
-                collection_name="test",
-                embedding_model_dims=4,
-                region="gcp-us-central1",
-            )
-            assert db.collection_name == "test"
-
-    def test_init_without_api_key_raises(self, mock_client):
-        with patch.dict("os.environ", {}, clear=True):
-            # Remove TURBOPUFFER_API_KEY if it exists
-            import os
-            os.environ.pop("TURBOPUFFER_API_KEY", None)
-            with pytest.raises(ValueError, match="API key must be provided"):
-                TurbopufferDB(
-                    collection_name="test",
-                    embedding_model_dims=4,
-                    region="gcp-us-central1",
-                )
-
-    def test_init_with_extra_params(self, mock_client):
-        client_instance, _ = mock_client
-        with patch("mem0.vector_stores.turbopuffer.TurbopufferClient") as MockClient:
-            MockClient.return_value = client_instance
-            TurbopufferDB(
-                collection_name="test",
-                embedding_model_dims=4,
-                api_key="key",
-                region="aws-us-west-2",
-                extra_params={"compression": True},
-            )
-            MockClient.assert_called_with(
-                api_key="key",
-                region="aws-us-west-2",
-                compression=True,
-            )
-
-    def test_init_default_region(self, mock_client):
-        client_instance, _ = mock_client
-        with patch("mem0.vector_stores.turbopuffer.TurbopufferClient") as MockClient:
-            MockClient.return_value = client_instance
-            TurbopufferDB(
-                collection_name="test",
-                embedding_model_dims=4,
-                api_key="key",
-            )
-            MockClient.assert_called_with(
-                api_key="key",
-                region="gcp-us-central1",
-            )
-
-
-# ── create_col ───────────────────────────────────────────────────────
-
-
-class TestCreateCol:
-    def test_create_col_is_noop(self, db):
-        # Should not raise or call anything
-        result = db.create_col()
-        assert result is None
-
-    def test_create_col_with_args_is_noop(self, db):
-        result = db.create_col(name="x", vector_size=128, distance="cosine")
-        assert result is None
-
-
-# ── insert ───────────────────────────────────────────────────────────
-
-
-class TestInsert:
-    def test_insert_with_ids_and_payloads(self, db):
-        vectors = [[0.1, 0.2, 0.3, 0.4], [0.5, 0.6, 0.7, 0.8]]
-        payloads = [{"data": "hello", "user_id": "u1"}, {"data": "world", "user_id": "u2"}]
-        ids = ["id1", "id2"]
-
-        db.insert(vectors, payloads, ids)
-
-        db.namespace.write.assert_called_once()
-        call_kwargs = db.namespace.write.call_args[1]
-        rows = call_kwargs["upsert_rows"]
-        assert len(rows) == 2
-        assert rows[0]["id"] == "id1"
-        assert rows[0]["vector"] == [0.1, 0.2, 0.3, 0.4]
-        assert rows[0]["data"] == "hello"
-        assert rows[0]["user_id"] == "u1"
-        assert rows[1]["id"] == "id2"
-        assert call_kwargs["distance_metric"] == "cosine_distance"
-
-    def test_insert_without_ids_generates_ids(self, db):
-        vectors = [[0.1, 0.2, 0.3, 0.4]]
-        db.insert(vectors)
-
-        db.namespace.write.assert_called_once()
-        call_kwargs = db.namespace.write.call_args[1]
-        assert call_kwargs["upsert_rows"][0]["id"] == "0"
-
-    def test_insert_without_payloads(self, db):
-        vectors = [[0.1, 0.2, 0.3, 0.4]]
-        ids = ["id1"]
-        db.insert(vectors, ids=ids)
-
-        call_kwargs = db.namespace.write.call_args[1]
-        row = call_kwargs["upsert_rows"][0]
-        assert row["id"] == "id1"
-        assert row["vector"] == [0.1, 0.2, 0.3, 0.4]
-        assert len(row) == 2  # only id and vector
-
-    def test_insert_payload_does_not_overwrite_id_or_vector(self, db):
-        """Payload with 'id' or 'vector' keys must not overwrite the actual values."""
-        vectors = [[0.1, 0.2, 0.3, 0.4]]
-        payloads = [{"id": "fake_id", "vector": "fake_vector", "data": "hello"}]
-        ids = ["real_id"]
-        db.insert(vectors, payloads, ids)
-
-        call_kwargs = db.namespace.write.call_args[1]
-        row = call_kwargs["upsert_rows"][0]
-        assert row["id"] == "real_id"
-        assert row["vector"] == [0.1, 0.2, 0.3, 0.4]
-        assert row["data"] == "hello"
-
-    def test_insert_batching(self, db):
-        """batch_size=2, so 3 vectors should produce 2 write calls."""
-        vectors = [[0.1] * 4, [0.2] * 4, [0.3] * 4]
-        ids = ["a", "b", "c"]
-        db.insert(vectors, ids=ids)
-
-        assert db.namespace.write.call_count == 2
-        first_call = db.namespace.write.call_args_list[0][1]
-        second_call = db.namespace.write.call_args_list[1][1]
-        assert len(first_call["upsert_rows"]) == 2
-        assert len(second_call["upsert_rows"]) == 1
-
-
-# ── _parse_output ────────────────────────────────────────────────────
-
-
-class TestParseOutput:
-    def test_parse_rows_with_dist_and_attributes(self, db):
-        rows = [
-            _make_row("id1", dist=0.1, data="hello", user_id="u1"),
-            _make_row("id2", dist=0.3, data="world", user_id="u2"),
-        ]
-        results = db._parse_output(rows)
-
-        assert len(results) == 2
-        assert results[0].id == "id1"
-        assert results[0].score == pytest.approx(0.9)
-        assert results[0].payload == {"data": "hello", "user_id": "u1"}
-        assert results[1].id == "id2"
-        assert results[1].score == pytest.approx(0.7)
-        assert results[1].payload == {"data": "world", "user_id": "u2"}
-
-    def test_parse_rows_without_dist(self, db):
-        rows = [_make_row("id1", data="hello")]
-        results = db._parse_output(rows)
-
-        assert results[0].score is None
-        assert results[0].payload == {"data": "hello"}
-
-    def test_parse_rows_strips_vector_and_id(self, db):
-        rows = [_make_row("id1", dist=0.0, vector=[1.0, 2.0, 3.0, 4.0], data="test")]
-        results = db._parse_output(rows)
-
-        assert "vector" not in results[0].payload
-        assert "id" not in results[0].payload
-        assert "$dist" not in results[0].payload
-        assert results[0].id == "id1"
-
-    def test_parse_empty_rows(self, db):
-        assert db._parse_output([]) == []
-
-
-# ── _convert_filters ─────────────────────────────────────────────────
-
-
-class TestConvertFilters:
-    def test_none_filters(self, db):
-        assert db._convert_filters(None) is None
-
-    def test_empty_filters(self, db):
-        assert db._convert_filters({}) is None
-
-    def test_single_eq_filter(self, db):
-        result = db._convert_filters({"user_id": "u1"})
-        assert result == ("user_id", "Eq", "u1")
-
-    def test_multiple_eq_filters(self, db):
-        result = db._convert_filters({"user_id": "u1", "agent_id": "a1"})
-        assert result[0] == "And"
-        conditions = result[1]
-        assert ("user_id", "Eq", "u1") in conditions
-        assert ("agent_id", "Eq", "a1") in conditions
-
-    def test_range_filter_gte_lte(self, db):
-        result = db._convert_filters({"score": {"gte": 0.5, "lte": 1.0}})
-        assert result[0] == "And"
-        conditions = result[1]
-        assert ("score", "Gte", 0.5) in conditions
-        assert ("score", "Lte", 1.0) in conditions
-
-    def test_range_filter_gte_only(self, db):
-        result = db._convert_filters({"score": {"gte": 0.5}})
-        assert result == ("score", "Gte", 0.5)
-
-    def test_range_filter_lte_only(self, db):
-        result = db._convert_filters({"score": {"lte": 1.0}})
-        assert result == ("score", "Lte", 1.0)
-
-    def test_mixed_eq_and_range_filters(self, db):
-        result = db._convert_filters({"user_id": "u1", "score": {"gte": 0.5}})
-        assert result[0] == "And"
-        conditions = result[1]
-        assert ("user_id", "Eq", "u1") in conditions
-        assert ("score", "Gte", 0.5) in conditions
-
-
-# ── search ───────────────────────────────────────────────────────────
-
-
-class TestSearch:
-    def test_search_basic(self, db):
-        mock_response = MagicMock()
-        mock_response.rows = [
-            _make_row("id1", dist=0.1, data="hello"),
-            _make_row("id2", dist=0.2, data="world"),
-        ]
-        db.namespace.query.return_value = mock_response
-
-        results = db.search("test query", [0.1, 0.2, 0.3, 0.4], limit=2)
-
-        db.namespace.query.assert_called_once_with(
-            rank_by=("vector", "ANN", [0.1, 0.2, 0.3, 0.4]),
-            top_k=2,
-            include_attributes=True,
-        )
-        assert len(results) == 2
-        assert results[0].id == "id1"
-        assert results[0].score == pytest.approx(0.9)
-
-    def test_search_with_filters(self, db):
-        mock_response = MagicMock()
-        mock_response.rows = [_make_row("id1", dist=0.1, data="hello")]
-        db.namespace.query.return_value = mock_response
-
-        results = db.search(
-            "query", [0.1, 0.2, 0.3, 0.4], limit=5, filters={"user_id": "u1"}
-        )
-
-        call_kwargs = db.namespace.query.call_args[1]
-        assert call_kwargs["filters"] == ("user_id", "Eq", "u1")
-        assert len(results) == 1
-
-    def test_search_no_results(self, db):
-        mock_response = MagicMock()
-        mock_response.rows = None
-        db.namespace.query.return_value = mock_response
-
-        results = db.search("query", [0.1, 0.2, 0.3, 0.4])
-        assert results == []
-
-    def test_search_empty_rows(self, db):
-        mock_response = MagicMock()
-        mock_response.rows = []
-        db.namespace.query.return_value = mock_response
-
-        results = db.search("query", [0.1, 0.2, 0.3, 0.4])
-        assert results == []
-
-
-# ── delete ───────────────────────────────────────────────────────────
-
-
-class TestDelete:
-    def test_delete_by_string_id(self, db):
-        db.delete("id1")
-        db.namespace.write.assert_called_once_with(deletes=["id1"])
-
-    def test_delete_by_int_id(self, db):
-        db.delete(123)
-        db.namespace.write.assert_called_once_with(deletes=["123"])
-
-
-# ── update ───────────────────────────────────────────────────────────
-
-
-class TestUpdate:
-    def test_update_with_vector_and_payload(self, db):
-        db.update("id1", vector=[0.5, 0.6, 0.7, 0.8], payload={"data": "updated"})
-
-        db.namespace.write.assert_called_once()
-        call_kwargs = db.namespace.write.call_args[1]
-        row = call_kwargs["upsert_rows"][0]
-        assert row["id"] == "id1"
-        assert row["vector"] == [0.5, 0.6, 0.7, 0.8]
-        assert row["data"] == "updated"
-        assert call_kwargs["distance_metric"] == "cosine_distance"
-
-    def test_update_vector_only(self, db):
-        db.update("id1", vector=[0.5, 0.6, 0.7, 0.8])
-
-        call_kwargs = db.namespace.write.call_args[1]
-        row = call_kwargs["upsert_rows"][0]
-        assert row["id"] == "id1"
-        assert row["vector"] == [0.5, 0.6, 0.7, 0.8]
-        assert "data" not in row
-
-    def test_update_payload_only_uses_patch(self, db):
-        """Payload-only updates should use patch_rows, not upsert_rows."""
-        db.update("id1", vector=None, payload={"data": "patched", "user_id": "u1"})
-
-        call_kwargs = db.namespace.write.call_args[1]
-        row = call_kwargs["patch_rows"][0]
-        assert row["id"] == "id1"
-        assert row["data"] == "patched"
-        assert row["user_id"] == "u1"
-        assert "upsert_rows" not in call_kwargs
-
-    def test_update_payload_does_not_overwrite_id(self, db):
-        """Payload with an 'id' key must not overwrite the actual vector ID."""
-        db.update("real_id", vector=None, payload={"id": "fake_id", "data": "test"})
-        call_kwargs = db.namespace.write.call_args[1]
-        row = call_kwargs["patch_rows"][0]
-        assert row["id"] == "real_id"
-
-    def test_update_nothing(self, db):
-        """Neither vector nor payload: should not call write."""
-        db.update("id1")
-        db.namespace.write.assert_not_called()
-
-
-# ── get ──────────────────────────────────────────────────────────────
-
-
-class TestGet:
-    def test_get_found(self, db):
-        mock_response = MagicMock()
-        mock_response.rows = [_make_row("id1", dist=0.0, data="hello", user_id="u1")]
-        db.namespace.query.return_value = mock_response
-
-        result = db.get("id1")
-
-        db.namespace.query.assert_called_once_with(
-            top_k=1,
-            rank_by=("vector", "ANN", [0.0, 0.0, 0.0, 0.0]),
-            filters=("id", "Eq", "id1"),
-            include_attributes=True,
-        )
-        assert result is not None
-        assert result.id == "id1"
-        assert result.payload["data"] == "hello"
-        assert result.payload["user_id"] == "u1"
-
-    def test_get_not_found(self, db):
-        mock_response = MagicMock()
-        mock_response.rows = []
-        db.namespace.query.return_value = mock_response
-
-        result = db.get("nonexistent")
-        assert result is None
-
-    def test_get_none_rows(self, db):
-        mock_response = MagicMock()
-        mock_response.rows = None
-        db.namespace.query.return_value = mock_response
-
-        result = db.get("id1")
-        assert result is None
-
-    def test_get_handles_exception(self, db):
-        db.namespace.query.side_effect = Exception("API error")
-        result = db.get("id1")
-        assert result is None
-
-
-# ── list_cols ────────────────────────────────────────────────────────
-
-
-class TestListCols:
-    def test_list_cols(self, db):
-        ns1 = MagicMock()
-        ns1.id = "ns1"
-        ns2 = MagicMock()
-        ns2.id = "ns2"
-        db.client.namespaces.return_value = [ns1, ns2]
-
-        result = db.list_cols()
-        assert len(result) == 2
-        db.client.namespaces.assert_called_once()
-
-
-# ── delete_col ───────────────────────────────────────────────────────
-
-
-class TestDeleteCol:
-    def test_delete_col(self, db):
-        db.delete_col()
-        db.namespace.delete_all.assert_called_once()
-
-    def test_delete_col_handles_error(self, db):
-        db.namespace.delete_all.side_effect = Exception("API error")
-        # Should not raise
-        db.delete_col()
-
-
-# ── col_info ─────────────────────────────────────────────────────────
-
-
-class TestColInfo:
-    def test_col_info(self, db):
-        mock_metadata = MagicMock()
-        mock_metadata.approx_row_count = 42
-        mock_metadata.approx_logical_bytes = 1024
-        mock_metadata.created_at = datetime(2025, 1, 1)
-        mock_metadata.updated_at = datetime(2025, 6, 1)
-        db.namespace.metadata.return_value = mock_metadata
-
-        info = db.col_info()
-        assert info["name"] == "test_ns"
-        assert info["approx_row_count"] == 42
-        assert info["approx_logical_bytes"] == 1024
-
-    def test_col_info_handles_error(self, db):
-        db.namespace.metadata.side_effect = Exception("not found")
-        info = db.col_info()
-        assert info == {"name": "test_ns"}
-
-
-# ── list ─────────────────────────────────────────────────────────────
-
-
-class TestList:
-    def test_list_returns_wrapped_format(self, db):
-        """list() must return [[results]] for compatibility with main.py."""
-        mock_response = MagicMock()
-        mock_response.rows = [
-            _make_row("id1", dist=0.1, data="hello"),
-            _make_row("id2", dist=0.2, data="world"),
-        ]
-        db.namespace.query.return_value = mock_response
-
-        result = db.list()
-
-        # Must be wrapped: result[0] is the actual list
-        assert isinstance(result, list)
-        assert isinstance(result[0], list)
-        assert len(result[0]) == 2
-        assert result[0][0].id == "id1"
-        assert result[0][1].id == "id2"
-
-    def test_list_with_filters(self, db):
-        mock_response = MagicMock()
-        mock_response.rows = [_make_row("id1", dist=0.1, data="hello")]
-        db.namespace.query.return_value = mock_response
-
-        db.list(filters={"user_id": "u1"}, limit=50)
-
-        call_kwargs = db.namespace.query.call_args[1]
-        assert call_kwargs["filters"] == ("user_id", "Eq", "u1")
-        assert call_kwargs["top_k"] == 50
-
-    def test_list_empty(self, db):
-        mock_response = MagicMock()
-        mock_response.rows = None
-        db.namespace.query.return_value = mock_response
-
-        result = db.list()
-        assert result == [[]]
-
-    def test_list_uses_zero_vector(self, db):
-        mock_response = MagicMock()
-        mock_response.rows = []
-        db.namespace.query.return_value = mock_response
-
-        db.list()
-        call_kwargs = db.namespace.query.call_args[1]
-        assert call_kwargs["rank_by"] == ("vector", "ANN", [0.0, 0.0, 0.0, 0.0])
-
-    def test_list_compatible_with_main_py_get_all(self, db):
-        """Simulate how main.py _get_all_from_vector_store unwraps list()."""
-        mock_response = MagicMock()
-        mock_response.rows = [_make_row("id1", dist=0.1, data="hello")]
-        db.namespace.query.return_value = mock_response
-
-        memories_result = db.list(filters={"user_id": "u1"})
-
-        # Reproduce main.py unwrapping logic
-        first_element = memories_result[0]
-        if isinstance(first_element, (list, tuple)):
-            actual_memories = first_element
-        else:
-            actual_memories = memories_result
-
-        assert isinstance(actual_memories, list)
-        assert len(actual_memories) == 1
-        assert actual_memories[0].id == "id1"
-        assert actual_memories[0].payload["data"] == "hello"
-
-    def test_list_compatible_with_main_py_delete_all(self, db):
-        """Simulate how main.py delete_all uses list()[0]."""
-        mock_response = MagicMock()
-        mock_response.rows = [
-            _make_row("id1", dist=0.1, data="hello"),
-            _make_row("id2", dist=0.2, data="world"),
-        ]
-        db.namespace.query.return_value = mock_response
-
-        result = db.list(filters={"user_id": "u1"})
-        memories = result[0]
-
-        assert isinstance(memories, list)
-        for mem in memories:
-            assert hasattr(mem, "id")
-            assert hasattr(mem, "payload")
-
-
-# ── count ────────────────────────────────────────────────────────────
-
-
-class TestCount:
-    def test_count(self, db):
-        mock_metadata = MagicMock()
-        mock_metadata.approx_row_count = 100
-        db.namespace.metadata.return_value = mock_metadata
-
-        assert db.count() == 100
-
-    def test_count_handles_error(self, db):
-        db.namespace.metadata.side_effect = Exception("error")
-        assert db.count() == 0
-
-
-# ── reset ────────────────────────────────────────────────────────────
-
-
-class TestReset:
-    def test_reset_calls_delete_all(self, db):
-        db.reset()
-        db.namespace.delete_all.assert_called_once()
-
-
-# ── Config ───────────────────────────────────────────────────────────
-
-
-class TestConfig:
-    def test_config_valid(self):
-        with patch.dict("os.environ", {"TURBOPUFFER_API_KEY": "key"}):
-            from mem0.configs.vector_stores.turbopuffer import TurbopufferConfig
-
-            config = TurbopufferConfig()
-            assert config.collection_name == "mem0"
-            assert config.embedding_model_dims == 1536
-            assert config.distance_metric == "cosine_distance"
-            assert config.batch_size == 100
-            assert config.region == "gcp-us-central1"
-
-    def test_config_custom_values(self):
-        from mem0.configs.vector_stores.turbopuffer import TurbopufferConfig
-
-        config = TurbopufferConfig(
-            collection_name="custom",
-            embedding_model_dims=768,
-            api_key="tpuf_key",
-            region="aws-us-west-2",
-            distance_metric="euclidean_squared",
-            batch_size=50,
-        )
-        assert config.collection_name == "custom"
-        assert config.embedding_model_dims == 768
-        assert config.api_key == "tpuf_key"
-        assert config.region == "aws-us-west-2"
-        assert config.distance_metric == "euclidean_squared"
-        assert config.batch_size == 50
-
-    def test_config_rejects_extra_fields(self):
-        from mem0.configs.vector_stores.turbopuffer import TurbopufferConfig
-
-        with pytest.raises(ValueError, match="Extra fields not allowed"):
-            TurbopufferConfig(api_key="key", unknown_field="value")
-
-    def test_config_requires_api_key(self):
-        from mem0.configs.vector_stores.turbopuffer import TurbopufferConfig
-
-        with patch.dict("os.environ", {}, clear=True):
-            import os
-            os.environ.pop("TURBOPUFFER_API_KEY", None)
-            with pytest.raises(ValueError, match="api_key"):
-                TurbopufferConfig()
-
-    def test_config_accepts_env_var(self):
-        from mem0.configs.vector_stores.turbopuffer import TurbopufferConfig
-
-        with patch.dict("os.environ", {"TURBOPUFFER_API_KEY": "env_key"}):
-            config = TurbopufferConfig()
-            assert config.api_key is None  # not set explicitly, but env var is present
-
-
-# ── Factory Registration ─────────────────────────────────────────────
-
-
-class TestFactoryRegistration:
-    def test_vector_store_factory_has_turbopuffer(self):
-        from mem0.utils.factory import VectorStoreFactory
-
-        assert "turbopuffer" in VectorStoreFactory.provider_to_class
-        assert VectorStoreFactory.provider_to_class["turbopuffer"] == "mem0.vector_stores.turbopuffer.TurbopufferDB"
-
-    def test_vector_store_config_has_turbopuffer(self):
-        from mem0.vector_stores.configs import VectorStoreConfig
-
-        config = VectorStoreConfig.__private_attributes__["_provider_configs"].default
-        assert "turbopuffer" in config
-        assert config["turbopuffer"] == "TurbopufferConfig"
-
-    def test_config_validation_pipeline(self):
-        """Test that VectorStoreConfig correctly resolves turbopuffer config."""
-        from mem0.vector_stores.configs import VectorStoreConfig
-
-        with patch.dict("os.environ", {"TURBOPUFFER_API_KEY": "key"}):
-            config = VectorStoreConfig(
-                provider="turbopuffer",
-                config={"collection_name": "test", "region": "gcp-us-central1"},
-            )
-            assert config.config.collection_name == "test"
-            assert config.config.region == "gcp-us-central1"
-            assert config.config.embedding_model_dims == 1536
-
-
-# ── OutputData ───────────────────────────────────────────────────────
-
-
-class TestOutputData:
-    def test_output_data_has_required_fields(self):
-        od = OutputData(id="test", score=0.9, payload={"data": "hello"})
-        assert od.id == "test"
-        assert od.score == 0.9
-        assert od.payload == {"data": "hello"}
-
-    def test_output_data_nullable_fields(self):
-        od = OutputData(id=None, score=None, payload=None)
-        assert od.id is None
-        assert od.score is None
-        assert od.payload is None
-
-    def test_output_data_payload_access_pattern(self):
-        """Test the exact access pattern used by main.py."""
-        od = OutputData(
-            id="mem1",
-            score=0.95,
-            payload={
-                "data": "User likes sci-fi movies",
-                "hash": "abc123",
-                "created_at": "2025-01-01",
-                "updated_at": "2025-06-01",
-                "user_id": "alice",
-                "agent_id": "agent1",
-            },
-        )
-        assert od.payload.get("data", "") == "User likes sci-fi movies"
-        assert od.payload.get("hash") == "abc123"
-        assert od.payload.get("created_at") == "2025-01-01"
-        assert od.payload.get("user_id") == "alice"
-        assert od.payload.get("run_id") is None  # not present, should return None