docs(api-reference): address reviewer comments on search defaults table and retrieval_criteria shape

- search-memories.mdx: collapse V1/V2/V3 comparison table to a single "Default" column showing current (v3) values only (threshold 0.1, rerank false, top_k 10) - organizations-projects.mdx: replace bogus {role, content} retrieval_criteria examples with the correct {name, description, weight} shape, mirroring the canonical criteria-retrieval.mdx examples; update prose to name the three required fields
docs(api-reference): use literal em-dash instead of \u2014 escape in openapi.json
2026-06-26 16:48:19 +05:30 · 2026-06-26 16:03:50 +05:30 · 2026-06-25 14:02:51 +05:30 · 2026-06-25 12:01:24 +05:30 · 2026-06-24 22:19:40 -07:00 · 2026-06-25 09:06:11 +05:30
34 changed files with 1402 additions and 473 deletions
@@ -12,7 +12,7 @@
      "name": "mem0",
      "source": "./integrations/mem0-plugin",
      "description": "Mem0 memory layer for AI applications. Add persistent memory, personalization, and semantic search to Claude workflows.",
-      "version": "0.2.10"
+      "version": "0.2.11"
    }
  ]
 }
@@ -12,7 +12,7 @@
      "name": "mem0",
      "source": "./integrations/mem0-plugin",
      "description": "Mem0 memory layer for AI applications. Add persistent memory, personalization, and semantic search.",
-      "version": "0.2.10"
+      "version": "0.2.11"
    }
  ]
 }
@@ -4,4 +4,4 @@ description: "Submit an export job to create a structured memory export using a
 openapi: post /v1/exports/
 ---

-Submit a job to create a structured export of memories using a customizable Pydantic schema. This process may take some time to complete, especially if you're exporting a large number of memories. You can tailor the export by applying various filters (e.g., `user_id`, `agent_id`, `run_id`, or `session_id`) and by modifying the Pydantic schema to ensure the final data matches your exact needs.
+Submit a job to create a structured export of memories using a customizable Pydantic schema. This process may take some time to complete, especially if you're exporting a large number of memories. You can tailor the export by applying various filters (e.g., `user_id`, `agent_id`, `app_id`, or `run_id`) and by modifying the Pydantic schema to ensure the final data matches your exact needs.
@@ -4,4 +4,4 @@ description: "Retrieve the latest structured memory export after submitting an e
 openapi: post /v1/exports/get
 ---

-Retrieve the latest structured memory export after submitting an export job. You can filter the export by `user_id`, `run_id`, `session_id`, or `app_id` to get the most recent export matching your filters.
+Retrieve the latest structured memory export after submitting an export job. You can filter the export by `user_id`, `agent_id`, `app_id`, `run_id`, `created_at`, or `updated_at` to get the most recent export matching your filters.
@@ -20,11 +20,11 @@ The `filters` object supports complex logical operations (AND, OR, NOT) and comp

 ### Search parameter defaults

-| Parameter | V1/V2 | V3 |
-| --- | --- | --- |
-| `top_k` | Supported (default 10) | Supported (1-1000, default 10) |
-| `threshold` | No default | Default `0.1` (pass `0.0` to disable) |
-| `rerank` | Default `true` | Default `false` (pass `true` to enable) |
+| Parameter | Default |
+| --- | --- |
+| `top_k` | `10` (range 1–1000) |
+| `threshold` | `0.1` (pass `0.0` to disable) |
+| `rerank` | `false` (pass `true` to enable) |

 <CodeGroup>
 ```python Platform API Example
@@ -14,7 +14,7 @@ Organizations and projects are **optional** features. You can use Mem0 without t

 ## Key Capabilities

- **Multi-org/project Support**: Specify organization and project when initializing the Mem0 client to attribute API usage appropriately
+- **Multi-org/project Support**: Organization and project are resolved automatically from your API key via `/v1/ping/` — no org or project params are accepted by `MemoryClient.__init__`. Use a project-specific API key to target a particular project.
 - **Member Management**: Control access to data through organization and project membership
 - **Access Control**: Only members can access memories and data within their organization/project scope
 - **Team Isolation**: Maintain data separation between different teams and projects for secure collaboration
@@ -79,7 +79,7 @@ new_project = client.project.create(

 ### Update Project Settings

-Modify project configuration including custom instructions, categories, and language preferences:
+Modify project configuration including custom instructions, categories, language preferences, retrieval criteria, and memory decay:

 ```python
 # Update project with custom categories
@@ -98,6 +98,17 @@ client.project.update(
 # Use the input language for memory storage and retrieval
 client.project.update(multilingual=True)

+# Set retrieval criteria to control which memories are surfaced in search
+client.project.update(
+    retrieval_criteria=[
+        {"name": "relevance", "description": "How directly relevant this memory is to the current topic or user query", "weight": 3},
+        {"name": "access_frequency", "description": "How often this memory has been accessed or surfaced recently", "weight": 1}
+    ]
+)
+
+# Enable Memory Decay (boosts recently-accessed memories at search time)
+client.project.update(decay=True)
+
 # Update multiple settings at once
 client.project.update(
    custom_instructions="...",
@@ -109,6 +120,34 @@ client.project.update(
 )
 ```

+#### Set Retrieval Criteria
+
+`retrieval_criteria` is a per-project list of dictionaries (`List[Dict]`) that shapes how memories are ranked and filtered during search. Each dictionary has three fields: `name` (identifier), `description` (interpreted by the LLM to score each memory), and `weight` (relative influence on the final score). Use this to focus retrieval on intent-aligned or signal-specific memories:
+
+```python
+client.project.update(
+    retrieval_criteria=[
+        {
+            "name": "joy",
+            "description": "Measure the intensity of positive emotions such as happiness, excitement, or amusement expressed in the memory. A higher score reflects greater joy.",
+            "weight": 3
+        },
+        {
+            "name": "curiosity",
+            "description": "Assess the extent to which the memory reflects inquisitiveness or interest in exploring new information. A higher score reflects stronger curiosity.",
+            "weight": 2
+        },
+        {
+            "name": "access_frequency",
+            "description": "How often this memory has been accessed or surfaced recently.",
+            "weight": 1
+        }
+    ]
+)
+```
+
+Pass an empty list to clear all criteria and restore default retrieval behaviour.
+
 #### Toggle Memory Decay

 `decay` is a per-project boolean that turns on [Memory Decay](/platform/features/memory-decay) — a search-time ranking bias that reinforces recently-accessed memories and gently dampens stale ones. The flag is `false` by default; set it via the same project-update endpoint:
@@ -7,6 +7,13 @@ mode: "wide"
 <Tabs>
 <Tab title="Python">

+<Update label="2026-06-24" description="v2.0.9">
+
+**Bug Fixes:**
+- **Memory (OSS):** Improve entity extraction precision by avoiding sentence-start common noun noise, preserving useful topic phrases, and exact-deduplicating entity links before semantic matching ([#5829](https://github.com/mem0ai/mem0/pull/5829))
+
+</Update>
+
 <Update label="2026-06-24" description="v2.0.8">

 **New Features:**
@@ -1063,6 +1070,13 @@ See the [OSS v1 to v2 migration guide](https://docs.mem0.ai/migration/oss-v1-to-

 <Tab title="TypeScript">

+<Update label="2026-06-24" description="v3.0.11">
+
+**Bug Fixes:**
+- **Memory (OSS):** Align entity extraction with Python by reducing generic entity noise, preserving useful topic phrases, and exact-deduplicating entity links before semantic matching ([#5829](https://github.com/mem0ai/mem0/pull/5829))
+
+</Update>
+
 <Update label="2026-06-24" description="v3.0.10">

 **Bug Fixes:**
@@ -7,7 +7,8 @@ To use DeepSeek LLM models, you have to set the `DEEPSEEK_API_KEY` environment v

 ## Usage

-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory

@@ -36,6 +37,32 @@ messages = [
 m.add(messages, user_id="alice", metadata={"category": "movies"})
 ```

+```typescript TypeScript
+import { Memory } from 'mem0ai/oss';
+
+const config = {
+  llm: {
+    provider: 'deepseek',
+    config: {
+      apiKey: process.env.DEEPSEEK_API_KEY || '',
+      model: 'deepseek-chat',
+      temperature: 0.2,
+      maxTokens: 2000,
+      top_p: 1.0,
+    },
+  },
+};
+const memory = new Memory(config);
+const 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 thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+];
+await memory.add(messages, { userId: 'alice', metadata: { category: 'movies' } });
+``` 
+</CodeGroup>
+
 You can also configure the API base URL in the config:

 ```python
@@ -345,7 +345,7 @@ When evaluating memory systems, keep these considerations in mind:
  <Card title="Research" icon="flask" href="https://mem0.ai/research">
    Published research papers and technical reports
  </Card>
-  <Card title="Blog Post" icon="newspaper" href="https://mem0.ai/blog/new-algorithm">
+  <Card title="Blog Post" icon="newspaper" href="https://mem0.ai/blog/the-token-efficient-memory-algorithm-now-has-temporal-reasoning">
    Detailed writeup of the new algorithm design and results
  </Card>
  <Card title="Platform Migration" icon="arrow-right" href="/migration/platform-v2-to-v3">
@@ -65,6 +65,7 @@ The plugin uses the same shell scripts as Claude Code, Cursor, and Codex — hoo
 | **User prompt** | `UserPromptSubmit` | Searches relevant memories before each message |
 | **Pre-tool** | `PreToolUse` | Blocks MEMORY.md writes, enforces `user_id`/`app_id` on mem0 tools |
 | **Post-tool** | `PostToolUse` | Tracks stats, scans bash errors for related memories |
+| **Stop** | `Stop` | Stores a session summary when the session ends |

 ## Troubleshooting

@@ -64,7 +64,7 @@ Add the Mem0 MCP server directly with a single command:
 npx mcp-add \
  --name mem0-mcp \
  --type http \
-  --url "https://mcp.mem0.ai/mcp" \
+  --url "https://mcp.mem0.ai/mcp/" \
  --clients "claude code"
 ```

@@ -142,7 +142,8 @@ When installed via the plugin marketplace, Mem0 hooks into Claude Code's lifecyc
 | **User prompt** | `UserPromptSubmit` | Searches relevant memories before each message; skips short prompts |
 | **Pre-tool** | `PreToolUse` | Blocks MEMORY.md writes, enforces `user_id`/`app_id` on mem0 tool calls |
 | **Post-tool** | `PostToolUse` | Tracks stats, scans bash errors for related memories |
-| **Pre-compact** | `PreCompact` | Stores a session summary before context compaction |
+| **Stop** | `Stop` | Stores a session summary when the session ends |
+| **Pre-compact** | `PreCompact` | Stores a summary before the context is compacted |

 ## Example Workflow

@@ -41,7 +41,17 @@ Install the full plugin including MCP server, lifecycle hooks, and SDK skill.
   codex plugin marketplace add mem0ai/mem0
   ```

-2. Restart Codex, open the Plugin Directory, browse the **Mem0 Plugins** marketplace, and install **Mem0**.
+2. Install the plugin:
+
+   ```bash
+   codex plugin add mem0@mem0-plugins
+   ```
+
+   Or, in the app: restart Codex, open the Plugin Directory, browse the **Mem0 Plugins** marketplace, and install **Mem0**.
+
+<Note>
+  Step 1 is required for the app UI. Mem0 isn't in OpenAI's curated directory yet, so **without `codex plugin marketplace add`, Mem0 won't appear in the Codex app's Plugin Directory** — searching for it returns nothing. Adding the marketplace surfaces it (under **Created by you**) and makes it installable.
+</Note>

 <Info>
  Do not combine with Option B. The plugin manifest auto-registers the `mem0` MCP server, so adding both will create a duplicate registration.
@@ -49,27 +59,30 @@ Install the full plugin including MCP server, lifecycle hooks, and SDK skill.

 ### Option B — Direct MCP

-The fastest way to connect Codex to Mem0 — no plugin, no marketplace. Add to `~/.codex/config.toml`:
+The fastest way to connect Codex to Mem0 — no plugin, no marketplace. Add the MCP server with a single command:
+
+```bash
+codex mcp add mem0 --url https://mcp.mem0.ai/mcp/ --bearer-token-env-var MEM0_API_KEY
+```
+
+Or add it manually to `~/.codex/config.toml`:

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

 Make sure `MEM0_API_KEY` is exported in the shell you launch Codex from, then restart Codex.

-<Info>
-  Codex's `codex mcp add` CLI only supports stdio MCP servers. Because Mem0's MCP is HTTP/streamable, you configure it by editing `config.toml` directly (or via the **Plugins → Connect to a custom MCP → Streamable HTTP** UI in the Codex app).
-</Info>
-
 This gives you the MCP tools but not the lifecycle hooks or SDK skill.

 ### Managing the Plugin

 ```bash
 codex plugin marketplace upgrade               # pull latest plugin versions
-codex plugin marketplace remove mem0-plugins   # unregister the marketplace
+codex plugin remove mem0@mem0-plugins          # uninstall the plugin (keeps the marketplace)
+codex plugin marketplace remove mem0-plugins   # unregister the marketplace entirely
 ```

 To update, run `codex plugin marketplace upgrade` to pull the latest from the Mem0 repo.
@@ -112,7 +125,8 @@ When installed via the plugin marketplace, Mem0 hooks into Codex's lifecycle to
 | **User prompt** | `UserPromptSubmit` | Searches relevant memories before each message |
 | **Pre-tool** | `PreToolUse` | Blocks MEMORY.md writes, enforces `user_id`/`app_id` on mem0 tool calls |
 | **Post-tool** | `PostToolUse` | Tracks stats, scans bash errors for related memories |
-| **Pre-compact** | `PreCompact` | Stores a session summary before context compaction |
+| **Stop** | `Stop` | Stores a session summary when the session ends |
+| **Pre-compact** | `PreCompact` | Stores a summary before the context is compacted |

 ## Example Workflow

@@ -47,7 +47,7 @@ The fastest way to get started. Click the link below to install the Mem0 MCP ser
 npx mcp-add \
  --name mem0-mcp \
  --type http \
-  --url "https://mcp.mem0.ai/mcp" \
+  --url "https://mcp.mem0.ai/mcp/" \
  --clients "cursor"
 ```

@@ -110,7 +110,8 @@ When installed via the Cursor Marketplace, Mem0 hooks into Cursor's lifecycle:
 | **User prompt** | `beforeSubmitPrompt` | Searches relevant memories before each message; skips short prompts |
 | **Pre-tool (2 handlers)** | `preToolUse` | Blocks MEMORY.md writes, enforces `user_id`/`app_id` on mem0 tool calls |
 | **Post-tool (2 handlers)** | `postToolUse` | Tracks stats, scans bash errors for related memories |
-| **Pre-compact** | `preCompact` | Stores a session summary before context compaction |
+| **Stop** | `stop` | Stores a session summary when the session ends |
+| **Pre-compact** | `preCompact` | Stores a summary before the context is compacted |

 ## Example Workflow

@@ -100,12 +100,12 @@ This section:
 Initialize both the ElevenLabs and Mem0 clients:

 ```python
-    # Initialize ElevenLabs client
-    client = ElevenLabs(api_key=API_KEY)
+# Initialize ElevenLabs client
+client = ElevenLabs(api_key=API_KEY)

-    # Initialize memory client and tools
-    client_tools = ClientTools()
-    mem0_client = AsyncMemoryClient()
+# Initialize memory client and tools
+client_tools = ClientTools()
+mem0_client = AsyncMemoryClient()
 ```

 Here we:
@@ -118,36 +118,36 @@ Here we:
 Define the two key memory functions that will be registered as tools:

 ```python
-    # Define memory-related functions for the agent
-    async def add_memories(parameters):
-        """Add a message to the memory store"""
-        message = parameters.get("message")
-        await mem0_client.add(
-            messages=message,
-            user_id=USER_ID
-        )
-        return "Memory added successfully"
+# Define memory-related functions for the agent
+async def add_memories(parameters):
+    """Add a message to the memory store"""
+    message = parameters.get("message")
+    await mem0_client.add(
+        messages=message,
+        user_id=USER_ID
+    )
+    return "Memory added successfully"

-    async def retrieve_memories(parameters):
-        """Retrieve relevant memories based on the input message"""
-        message = parameters.get("message")
+async def retrieve_memories(parameters):
+    """Retrieve relevant memories based on the input message"""
+    message = parameters.get("message")

-        # For Platform API, user_id goes in filters
-        filters = {"user_id": USER_ID}
+    # For Platform API, user_id goes in filters
+    filters = {"user_id": USER_ID}

-        # Search for relevant memories using the message as a query
-        results = await mem0_client.search(
-            query=message,
-            filters=filters
-        )
+    # Search for relevant memories using the message as a query
+    results = await mem0_client.search(
+        query=message,
+        filters=filters
+    )

-        # Extract and join the memory texts
-        memories = ' '.join([result["memory"] for result in results.get('results', [])])
-        print("[ Memories ]", memories)
+    # Extract and join the memory texts
+    memories = ' '.join([result["memory"] for result in results.get('results', [])])
+    print("[ Memories ]", memories)

-        if memories:
-            return memories
-        return "No memories found"
+    if memories:
+        return memories
+    return "No memories found"
 ```

 These functions:
@@ -171,9 +171,9 @@ These functions:
 Register the memory functions with the ElevenLabs ClientTools system:

 ```python
-    # Register the memory functions as tools for the agent
-    client_tools.register("addMemories", add_memories, is_async=True)
-    client_tools.register("retrieveMemories", retrieve_memories, is_async=True)
+# Register the memory functions as tools for the agent
+client_tools.register("addMemories", add_memories, is_async=True)
+client_tools.register("retrieveMemories", retrieve_memories, is_async=True)
 ```

 This allows the ElevenLabs agent to:
@@ -186,19 +186,19 @@ This allows the ElevenLabs agent to:
 Configure the conversation with ElevenLabs:

 ```python
-    # Initialize the conversation
-    conversation = Conversation(
-        client,
-        AGENT_ID,
-        # Assume auth is required when API_KEY is set
-        requires_auth=bool(API_KEY),
-        audio_interface=DefaultAudioInterface(),
-        client_tools=client_tools,
-        callback_agent_response=lambda response: print(f"Agent: {response}"),
-        callback_agent_response_correction=lambda original, corrected: print(f"Agent: {original} -> {corrected}"),
-        callback_user_transcript=lambda transcript: print(f"User: {transcript}"),
-        # callback_latency_measurement=lambda latency: print(f"Latency: {latency}ms"),
-    )
+# Initialize the conversation
+conversation = Conversation(
+    client,
+    AGENT_ID,
+    # Assume auth is required when API_KEY is set
+    requires_auth=bool(API_KEY),
+    audio_interface=DefaultAudioInterface(),
+    client_tools=client_tools,
+    callback_agent_response=lambda response: print(f"Agent: {response}"),
+    callback_agent_response_correction=lambda original, corrected: print(f"Agent: {original} -> {corrected}"),
+    callback_user_transcript=lambda transcript: print(f"User: {transcript}"),
+    # callback_latency_measurement=lambda latency: print(f"Latency: {latency}ms"),
+)
 ```

 This sets up the conversation with:
@@ -217,16 +217,16 @@ This sets up the conversation with:
 Start and manage the conversation:

 ```python
-    # Start the conversation
-    print(f"Starting conversation with user_id: {USER_ID}")
-    conversation.start_session()
+# Start the conversation
+print(f"Starting conversation with user_id: {USER_ID}")
+conversation.start_session()

-    # Handle Ctrl+C to gracefully end the session
-    signal.signal(signal.SIGINT, lambda sig, frame: conversation.end_session())
+# Handle Ctrl+C to gracefully end the session
+signal.signal(signal.SIGINT, lambda sig, frame: conversation.end_session())

-    # Wait for the conversation to end and get the conversation ID
-    conversation_id = conversation.wait_for_session_end()
-    print(f"Conversation ID: {conversation_id}")
+# Wait for the conversation to end and get the conversation ID
+conversation_id = conversation.wait_for_session_end()
+print(f"Conversation ID: {conversation_id}")


 if __name__ == '__main__':
@@ -445,4 +445,3 @@ By integrating ElevenLabs Conversational AI with Mem0, you can create voice agen
    Create voice-first AI applications
  </Card>
 </CardGroup>
-
@@ -62,7 +62,8 @@ def add(
    filters: dict = None,
    output_format: str = None,  # ❌ REMOVED
    version: str = None         # ❌ REMOVED
-) -> Union[List[dict], dict]
+) -> Union[List[dict], dict]:
+    ...
 ```

 #### v1.0.0  Signature
@@ -76,7 +77,8 @@ def add(
    metadata: dict = None,
    filters: dict = None,
    infer: bool = True          # ✅ NEW: Control memory inference
-) -> dict  # Always returns dict with "results" key
+) -> dict:  # Always returns dict with "results" key
+    ...
 ```

 #### Changes Summary
@@ -147,7 +149,8 @@ def search(
    filters: dict = None,       # Basic key-value only
    output_format: str = None,  # ❌ REMOVED
    version: str = None         # ❌ REMOVED
-) -> Union[List[dict], dict]
+) -> Union[List[dict], dict]:
+    ...
 ```

 #### v1.0.0  Signature
@@ -161,7 +164,8 @@ def search(
    limit: int = 100,
    filters: dict = None,       # ✅ ENHANCED: Advanced operators
    rerank: bool = True         # ✅ NEW: Reranking support
-) -> dict  # Always returns dict with "results" key
+) -> dict:  # Always returns dict with "results" key
+    ...
 ```

 #### Enhanced Filtering
@@ -216,7 +220,8 @@ def get_all(
    filters: dict = None,
    output_format: str = None,  # ❌ REMOVED
    version: str = None         # ❌ REMOVED
-) -> Union[List[dict], dict]
+) -> Union[List[dict], dict]:
+    ...
 ```

 #### v1.0.0  Signature
@@ -227,7 +232,8 @@ def get_all(
    agent_id: str = None,
    run_id: str = None,
    filters: dict = None        # ✅ ENHANCED: Advanced operators
-) -> dict  # Always returns dict with "results" key
+) -> dict:  # Always returns dict with "results" key
+    ...
 ```

 ### update() Method
@@ -239,7 +245,8 @@ def update(
    self,
    memory_id: str,
    data: str
-) -> dict
+) -> dict:
+    ...
 ```

 ### delete() Method
@@ -250,7 +257,8 @@ def update(
 def delete(
    self,
    memory_id: str
-) -> dict
+) -> dict:
+    ...
 ```

 ### delete_all() Method
@@ -344,7 +352,7 @@ config = {
 ### New Configuration Options

 #### Reranker Configuration
-```python
+```text
 # Cohere reranker
 "reranker": {
    "provider": "cohere",
@@ -563,4 +571,4 @@ results = m.search(

 <Info>
 Use this reference to systematically update your codebase. Test each change thoroughly before deploying to production.
-</Info>
+</Info>
@@ -4861,8 +4861,7 @@
 										"items": {
 											"type": "object",
 											"required": [
-												"memory_id",
-												"text"
+												"memory_id"
 											],
 											"properties": {
 												"memory_id": {
@@ -4873,6 +4872,11 @@
 												"text": {
 													"type": "string",
 													"description": "The new text content for the memory"
+												},
+												"metadata": {
+													"type": "object",
+													"additionalProperties": true,
+													"description": "Updated metadata to associate with the memory."
 												}
 											}
 										},
@@ -4948,18 +4952,27 @@
 							"schema": {
 								"type": "object",
 								"properties": {
-									"memory_ids": {
+									"memories": {
 										"type": "array",
 										"items": {
-											"type": "string",
-											"format": "uuid"
+											"type": "object",
+											"properties": {
+												"memory_id": {
+													"type": "string",
+													"format": "uuid",
+													"description": "The unique identifier of the memory to delete."
+												}
+											},
+											"required": [
+												"memory_id"
+											]
 										},
 										"maxItems": 1000,
-										"description": "Array of memory IDs to delete."
+										"description": "Array of memory objects to delete."
 									}
 								},
 								"required": [
-									"memory_ids"
+									"memories"
 								]
 							}
 						}
@@ -6256,4 +6269,4 @@
 		}
 	},
 	"x-original-swagger-version": "2.0"
-}
+}
@@ -117,7 +117,7 @@ results_without_criteria = client.search(
 ### Compare Results

 ### Search Results (with Criteria)
-```python
+```text
 [
    {"memory": "User feels refreshed and ready to take on anything on a beautiful sunny day", "score": 0.666, ...},
    {"memory": "User finally has time to draw something after a long time", "score": 0.616, ...},
@@ -128,7 +128,7 @@ results_without_criteria = client.search(
 ```

 ### Search Results (without Criteria)
-```python
+```text
 [
    {"memory": "User is happy today", "score": 0.607, ...},
    {"memory": "User feels refreshed and ready to take on anything on a beautiful sunny day", "score": 0.512, ...},
@@ -190,7 +190,7 @@ messages = [
 client.add(messages, user_id='alice')
 ```

-```python Memories with categories
+```text Memories with categories
 # Following categories will be created for the memories added
 Sometimes draws and sketches in free time (hobbies)
 Is quite athletic (sports)
@@ -108,10 +108,10 @@ print(response)

 ```javascript JavaScript
 // Basic Export request
-const filters = {"user_id": "alice"};
+const basicFilters = {"user_id": "alice"};
 const response = await client.createMemoryExport({
    schema: json_schema,
-    filters: filters
+    filters: basicFilters
 });

 // Export with custom instructions and additional filters
@@ -124,16 +124,16 @@ const export_instructions = `
 `;

 // For create operation, using only user_id filter as requested
-const filters = {
+const exportFilters = {
    "AND": [
        {"user_id": "alex"},
        {"created_at": {"gte": "2024-01-01"}}
    ]
-}
+};

 const responseWithInstructions = await client.createMemoryExport({
    schema: json_schema,
-    filters: filters,
+    filters: exportFilters,
    exportInstructions: export_instructions
 });

@@ -1,6 +1,6 @@
 {
  "name": "mem0",
-  "version": "0.2.10",
+  "version": "0.2.11",
  "description": "Persistent memory for Claude Code. Remembers decisions, patterns, and preferences across sessions.",
  "author": {
    "name": "Mem0",
@@ -1,6 +1,6 @@
 {
  "name": "mem0",
-  "version": "0.2.10",
+  "version": "0.2.11",
  "description": "Persistent memory for Codex. Remembers decisions, patterns, and preferences across sessions.",
  "author": {
    "name": "Mem0",
@@ -1,6 +1,6 @@
 {
  "name": "mem0",
-  "version": "0.2.10",
+  "version": "0.2.11",
  "description": "Mem0 memory layer for AI applications. Add persistent memory, personalization, and semantic search using the Mem0 Platform MCP server.",
  "author": {
    "name": "Mem0",
@@ -2,6 +2,18 @@

 All notable changes to the Mem0 plugin will be documented in this file.

+## 0.2.11 — Session-summary metadata fix + rerank auto-injected context by default
+
+> Versions: Claude Code / Cursor / Codex `0.2.11`; Antigravity `0.1.3`. All four editors share `scripts/`, so the fix below applies to every editor.
+
+### Fixed
+
+- **`files_touched` was double-JSON-encoded in session summaries (`scripts/capture_session_summary.py`):** the Stop-hook summary set `metadata["files_touched"] = json.dumps(files[:20])` — a pre-serialized JSON string — and then serialized the whole request body again with `json.dumps(body)`. The stored memory therefore carried an escaped string blob (`"[\"mem0/memory/main.py\", \"src/client/index.ts\"]"`) instead of a real array, so file paths surfaced as backslash- and slash-heavy escaped text when those memories were returned by `search_memories`/`get_memories` and shown in Claude Code, Cursor, Codex, and Antigravity. The fix stores the list directly (`metadata["files_touched"] = files[:20]`) so the body is encoded exactly once. New `tests/test_capture_session_summary.py` asserts the posted body contains a JSON array and no escaped-string artifact.
+
+### Changed
+
+- **Auto-injected memory context is now reranked by default (`scripts/_search.py`, `scripts/file_context.py`, `scripts/on_bash_output.sh`, `scripts/on_user_prompt.sh`):** the REST search endpoint does not rerank when `rerank` is omitted, so hook-injected context (file-context, bash-error lookup, session-resume prefetch) was ordered by raw vector similarity and the single most relevant memory could fall outside the injected `top_k` window. A new `should_rerank()` helper turns reranking on for every auto-injection path; the extra ~150–200 ms stays within the hook's curl budget. Opt out with `MEM0_RERANK=0` (also accepts `false`/`no`/`off`). (#5690)
+
 ## 0.2.10 — Accurate per-editor telemetry attribution

 ### Fixed
@@ -1,7 +1,7 @@
 {
  "id": "mem0",
  "name": "mem0",
-  "version": "0.1.2",
+  "version": "0.1.3",
  "description": "Persistent semantic memory for Antigravity agents. Cross-session, user-level recall via the Mem0 Platform MCP server. 16 slash commands, lifecycle hooks for auto-capture and metadata enforcement.",
  "author": { "name": "Mem0", "email": "support@mem0.ai" },
  "publisher": "mem0ai",
@@ -173,7 +173,7 @@ def store_summary(
    if branch:
        metadata["branch"] = branch
    if files:
-        metadata["files_touched"] = json.dumps(files[:20])
+        metadata["files_touched"] = files[:20]

    body = {
        "messages": [{"role": "user", "content": summary_prompt}],
@@ -0,0 +1,79 @@
+"""Regression tests for capture_session_summary.py request body construction.
+
+Guards against the double-JSON-encoding bug where ``files_touched`` was stored
+as a pre-serialized JSON string and then encoded a second time with the rest of
+the request body — surfacing as escaped, slash-heavy blobs in the memories shown
+inside Claude Code / Cursor / Codex / Antigravity (all four editors share this
+script).
+"""
+
+from __future__ import annotations
+
+import json
+
+
+class _FakeResp:
+    status = 200
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, *_):
+        return False
+
+
+def _capture_request_body(monkeypatch):
+    """Patch urlopen so store_summary posts nowhere; capture the request body."""
+    import capture_session_summary as css
+
+    captured: dict = {}
+
+    def fake_urlopen(req, timeout=0):
+        captured["raw"] = req.data.decode("utf-8")
+        captured["body"] = json.loads(captured["raw"])
+        return _FakeResp()
+
+    monkeypatch.setattr(css.urllib.request, "urlopen", fake_urlopen)
+    return captured, css
+
+
+def test_files_touched_is_json_array_not_double_encoded(monkeypatch):
+    """files_touched must be a real JSON array, encoded exactly once."""
+    captured, css = _capture_request_body(monkeypatch)
+    files = ["mem0/memory/main.py", "src/client/index.ts"]
+
+    css.store_summary(
+        api_key="test-key",
+        summary_prompt="did some work",
+        user_id="u1",
+        session_id="s1",
+        project_id="p1",
+        branch="main",
+        files=files,
+    )
+
+    files_touched = captured["body"]["metadata"]["files_touched"]
+    assert isinstance(files_touched, list), (
+        "files_touched must be a JSON array, not a double-encoded string; "
+        f"got {type(files_touched).__name__}: {files_touched!r}"
+    )
+    assert files_touched == files
+    # The file paths must not appear as an escaped JSON string inside the body.
+    assert '\\"' not in captured["raw"]
+
+
+def test_files_touched_omitted_when_no_files(monkeypatch):
+    """No files touched -> no files_touched key (unchanged behaviour)."""
+    captured, css = _capture_request_body(monkeypatch)
+
+    css.store_summary(
+        api_key="test-key",
+        summary_prompt="did some work",
+        user_id="u1",
+        session_id="s1",
+        project_id="p1",
+        branch="main",
+        files=[],
+    )
+
+    assert "files_touched" not in captured["body"]["metadata"]
@@ -1,6 +1,6 @@
 {
  "name": "mem0ai",
-  "version": "3.0.10",
+  "version": "3.0.11",
  "description": "The Memory Layer For Your AI Apps",
  "main": "./dist/index.js",
  "module": "./dist/index.mjs",
@@ -296,6 +296,44 @@ export class Memory {
    return filters;
  }

+  private _normalizeEntityText(value: string): string {
+    return value.trim().toLowerCase().replace(/\s+/g, " ");
+  }
+
+  private async _existingEntitiesByText(
+    entityStore: VectorStore,
+    filters: Record<string, any>,
+  ): Promise<Map<string, { id: string; payload: Record<string, any> }>> {
+    const rowsByText = new Map<
+      string,
+      { id: string; payload: Record<string, any> }
+    >();
+    let rows: Array<{ id: string; payload: Record<string, any> }> = [];
+    try {
+      const listed = await entityStore.list(filters, 10000);
+      rows = (
+        Array.isArray(listed) && Array.isArray(listed[0])
+          ? listed[0]
+          : (listed as any)
+      ) as Array<{ id: string; payload: Record<string, any> }>;
+    } catch (e) {
+      console.debug(
+        `Exact entity lookup failed, falling back to semantic dedup: ${e}`,
+      );
+      return rowsByText;
+    }
+
+    for (const row of rows) {
+      const text = row.payload?.data;
+      if (typeof text !== "string") continue;
+      const key = this._normalizeEntityText(text);
+      if (key && !rowsByText.has(key)) {
+        rowsByText.set(key, row);
+      }
+    }
+    return rowsByText;
+  }
+
  /**
   * Remove `memoryId` from every entity record scoped to `filters`.
   * If an entity's `linkedMemoryIds` becomes empty after removal, the
@@ -393,6 +431,10 @@ export class Memory {
      if (entities.length === 0) return;

      const entityStore = await this.getEntityStore();
+      const exactMatches = await this._existingEntitiesByText(
+        entityStore,
+        filters,
+      );

      for (const entity of entities) {
        try {
@@ -409,12 +451,21 @@ export class Memory {
            score?: number;
            payload: Record<string, any>;
          }> = [];
-          try {
-            matches = await entityStore.search(entityVec, 1, filters);
-          } catch {}
+          const exactMatch = exactMatches.get(
+            this._normalizeEntityText(entity.text),
+          );
+          if (!exactMatch) {
+            try {
+              matches = await entityStore.search(entityVec, 1, filters);
+            } catch {}
+          }

-          if (matches.length > 0 && (matches[0].score ?? 0) >= 0.95) {
-            const match = matches[0];
+          const semanticMatch =
+            matches.length > 0 && (matches[0].score ?? 0) >= 0.95
+              ? matches[0]
+              : undefined;
+          const match = exactMatch ?? semanticMatch;
+          if (match) {
            const payload = match.payload || {};
            const linked = new Set<string>(
              Array.isArray(payload.linkedMemoryIds)
@@ -1062,6 +1113,10 @@ export class Memory {

        if (valid.length > 0) {
          const entityStore = await this.getEntityStore();
+          const exactMatches = await this._existingEntitiesByText(
+            entityStore,
+            filters,
+          );

          // 7c: Search for existing entities one by one (no batch search)
          const toInsertVectors: number[][] = [];
@@ -1077,13 +1132,20 @@ export class Memory {
              score?: number;
              payload: Record<string, any>;
            }> = [];
-            try {
-              matches = await entityStore.search(entityVec, 1, filters);
-            } catch {}
+            const exactMatch = exactMatches.get(key);
+            if (!exactMatch) {
+              try {
+                matches = await entityStore.search(entityVec, 1, filters);
+              } catch {}
+            }

-            if (matches.length > 0 && (matches[0].score ?? 0) >= 0.95) {
+            const semanticMatch =
+              matches.length > 0 && (matches[0].score ?? 0) >= 0.95
+                ? matches[0]
+                : undefined;
+            const match = exactMatch ?? semanticMatch;
+            if (match) {
              // Update existing entity
-              const match = matches[0];
              const payload = match.payload || {};
              const linked = new Set<string>(payload.linkedMemoryIds ?? []);
              for (const mid of memoryIds) linked.add(mid);
@@ -4,8 +4,8 @@
 * Extracts four types of entities from text:
 * - PROPER:   Capitalized multi-word sequences (person names, places, brands)
 * - QUOTED:   Text in single or double quotes (titles, specific terms)
- * - COMPOUND: Multi-word noun phrases with specific modifiers (e.g., "machine learning")
- * - NOUN:     Single nouns from circumstantial compound patterns
+ * - TOPIC:    Multi-word noun/topic phrases with specific modifiers
+ * - IDENTIFIER: Dotted technical identifiers such as person.properties.email
 *
 * Uses the `compromise` npm package for NLP-based extraction when available.
 * Falls back to regex-only extraction if `compromise` is not installed.
@@ -196,6 +196,25 @@ const NON_SPECIFIC_ADJ: Set<string> = new Set([
  "final",
  "initial",
  "side",
+  "top",
+]);
+
+/** Leading words that frame a topic but are not part of the topic itself. */
+const TOPIC_PREFIX_WORDS: Set<string> = new Set([
+  "a",
+  "an",
+  "the",
+  "my",
+  "your",
+  "our",
+  "their",
+  "his",
+  "her",
+  "its",
+  "this",
+  "that",
+  "these",
+  "those",
 ]);

 /** Generic tail words to strip from compound entities. */
@@ -267,6 +286,25 @@ const GENERIC_CAPS: Set<string> = new Set([
  "disadvantages",
 ]);

+/** Generic role/title words that should not become single-token entities. */
+const GENERIC_SINGLE_ENTITY_TERMS: Set<string> = new Set([
+  "user",
+  "assistant",
+  "agent",
+  "customer",
+  "client",
+  "person",
+  "people",
+  "human",
+  "memory",
+  "message",
+  "conversation",
+  "chat",
+  "session",
+  "system",
+  "top",
+]);
+
 /** Markdown/formatting markers to skip during extraction. */
 const FORMATTING_MARKERS: Set<string> = new Set([
  "*",
@@ -287,7 +325,7 @@ const FORMATTING_MARKERS: Set<string> = new Set([
 // ---------------------------------------------------------------------------

 export interface ExtractedEntity {
-  type: "PROPER" | "QUOTED" | "COMPOUND" | "NOUN";
+  type: "PROPER" | "QUOTED" | "TOPIC" | "IDENTIFIER";
  text: string;
 }

@@ -338,32 +376,96 @@ function stripGenericEnding(words: string[]): string[] {
  return words;
 }

-/**
- * Determine if a token position is at the start of a sentence.
- * Simple heuristic: index 0, or preceded by sentence-ending punctuation
- * or formatting markers.
- */
-function isSentenceStart(
-  tokens: string[],
-  idx: number,
-  rawText: string,
-): boolean {
-  if (idx === 0) {
-    return true;
+function stripTopicPrefix(words: string[]): string[] {
+  let start = 0;
+  while (
+    start < words.length &&
+    TOPIC_PREFIX_WORDS.has(words[start].toLowerCase())
+  ) {
+    start++;
  }
-  const prev = tokens[idx - 1];
-  if (/[.!?:]$/.test(prev)) {
+  return words.slice(start);
+}
+
+function cleanToken(token: string): string {
+  return token.replace(/^[^\w.]+|[^\w.]+$/g, "");
+}
+
+function tokenize(text: string): string[] {
+  return (
+    text.match(
+      /[A-Za-z_][\w-]*(?:\.[A-Za-z_][\w-]*)*|\d[\d,]*(?:\.\d+)?|[,:;.!?&]/g,
+    ) ?? []
+  );
+}
+
+function isCapitalized(token: string): boolean {
+  return /^[A-Z]/.test(token) && /[A-Za-z]/.test(token);
+}
+
+function hasInternalCapOrDigit(token: string): boolean {
+  return (
+    /\d/.test(token) ||
+    /[A-Z]/.test(token.slice(1)) ||
+    /^[A-Z]{2,}$/.test(token)
+  );
+}
+
+function isBadSingleNameToken(token: string): boolean {
+  const lower = token.toLowerCase();
+  return GENERIC_SINGLE_ENTITY_TERMS.has(lower) || GENERIC_CAPS.has(lower);
+}
+
+function looksLikeMetricCount(token: string): boolean {
+  return /^\d[\d,]*(?:\.\d+)?$/.test(token);
+}
+
+function isMetricListContext(tokens: string[], idx: number): boolean {
+  const prev = idx > 0 ? tokens[idx - 1] : "";
+  const next = idx + 1 < tokens.length ? tokens[idx + 1] : "";
+  return [":", ",", ";"].includes(prev) || [",", ";"].includes(next);
+}
+
+function isSentenceStart(tokens: string[], idx: number): boolean {
+  if (idx === 0) return true;
+  return (
+    [".", "!", "?", ":"].includes(tokens[idx - 1]) ||
+    FORMATTING_MARKERS.has(tokens[idx - 1])
+  );
+}
+
+function isListItemNameToken(tokens: string[], idx: number): boolean {
+  const token = cleanToken(tokens[idx]);
+  if (!isCapitalized(token) || isBadSingleNameToken(token)) return false;
+  const next = idx + 1 < tokens.length ? cleanToken(tokens[idx + 1]) : "";
+  if (!looksLikeMetricCount(next)) return false;
+  return (
+    isMetricListContext(tokens, idx) || isMetricListContext(tokens, idx + 1)
+  );
+}
+
+function isNameToken(tokens: string[], idx: number): boolean {
+  const token = cleanToken(tokens[idx]);
+  if (!token || !isCapitalized(token) || isBadSingleNameToken(token))
+    return false;
+  if (hasInternalCapOrDigit(token) || isListItemNameToken(tokens, idx))
    return true;
-  }
-  if (FORMATTING_MARKERS.has(prev)) {
-    return true;
-  }
-  // Check for newline before this token in the raw text
-  const tokenStart = rawText.indexOf(tokens[idx]);
-  if (tokenStart > 0 && rawText.charAt(tokenStart - 1) === "\n") {
-    return true;
-  }
-  return false;
+  return !isSentenceStart(tokens, idx);
+}
+
+function cleanEntityText(text: string): string {
+  return text
+    .replace(/^\*+\s*|\s*\*+$/g, "")
+    .replace(/\s*:+$/g, "")
+    .replace(/^\d+\s*\.\s*/, "")
+    .replace(/\s+\d[\d,]*(?:\.\d+)?$/g, "")
+    .replace(/[.,;!?]+$/, "")
+    .trim()
+    .replace(/\s+/g, " ");
+}
+
+function isCoordinatedNameTopic(text: string): boolean {
+  return /\b[A-Z][\w-]+\s+and\s+[A-Z][\w-]+\b/.test(text);
 }

 // ---------------------------------------------------------------------------
@@ -397,86 +499,79 @@ function extractQuoted(text: string): ExtractedEntity[] {
 }

 /**
- * Extract proper noun sequences using capitalization heuristics.
- * Finds sequences of capitalized words that are not at sentence starts.
+ * Extract dotted technical identifiers such as person.properties.email.
+ */
+function extractIdentifiers(text: string): ExtractedEntity[] {
+  const entities: ExtractedEntity[] = [];
+  const identifierRe = /\b[A-Za-z_][\w-]*(?:\.[A-Za-z_][\w-]*)+\b/g;
+  let match: RegExpExecArray | null;
+  while ((match = identifierRe.exec(text)) !== null) {
+    entities.push({ type: "IDENTIFIER", text: match[0] });
+  }
+  return entities;
+}
+
+/**
+ * Extract proper names using capitalization and list-context heuristics.
 */
 function extractProper(text: string): ExtractedEntity[] {
  const entities: ExtractedEntity[] = [];
-  // Tokenize on whitespace, preserving order
-  const tokens = text.split(/\s+/).filter(Boolean);
-  const functionWords = new Set([
-    "'s",
-    "of",
-    "the",
-    "in",
-    "and",
-    "for",
-    "at",
-    "is",
-  ]);
+  const tokens = tokenize(text);
+  const innerConnectors = new Set(["of", "the", "in", "for", "at"]);

  let i = 0;
  while (i < tokens.length) {
-    const tok = tokens[i];
-    // Skip formatting markers
-    if (FORMATTING_MARKERS.has(tok)) {
+    const token = cleanToken(tokens[i]);
+    const next = i + 1 < tokens.length ? tokens[i + 1] : "";
+    const afterNext = i + 2 < tokens.length ? cleanToken(tokens[i + 2]) : "";
+    if (
+      token &&
+      next === "&" &&
+      afterNext &&
+      isCapitalized(token) &&
+      isCapitalized(afterNext) &&
+      !isBadSingleNameToken(token) &&
+      !isBadSingleNameToken(afterNext)
+    ) {
+      entities.push({
+        type: "PROPER",
+        text: cleanEntityText(`${token} & ${afterNext}`),
+      });
+      i += 3;
+      continue;
+    }
+
+    if (!isNameToken(tokens, i)) {
      i++;
      continue;
    }

-    const isLabel = i + 1 < tokens.length && tokens[i + 1] === ":";
-    const isCap =
-      tok.length > 0 &&
-      tok.charAt(0) === tok.charAt(0).toUpperCase() &&
-      /[A-Z]/.test(tok.charAt(0));
-
-    if (isCap && !isLabel) {
-      const seq: Array<{ token: string; idx: number }> = [
-        { token: tok, idx: i },
-      ];
-      let j = i + 1;
-      while (j < tokens.length) {
-        const t = tokens[j];
-        const tIsCap =
-          t.length > 0 &&
-          t.charAt(0) === t.charAt(0).toUpperCase() &&
-          /[A-Z]/.test(t.charAt(0));
-        if (tIsCap || functionWords.has(t.toLowerCase())) {
-          seq.push({ token: t, idx: j });
-          j++;
-        } else {
-          break;
-        }
+    const span = [cleanToken(tokens[i])];
+    let j = i + 1;
+    while (j < tokens.length) {
+      const current = cleanToken(tokens[j]);
+      if (isNameToken(tokens, j)) {
+        span.push(current);
+        j++;
+        continue;
      }
-
-      // Strip trailing function words
-      while (
-        seq.length > 0 &&
-        functionWords.has(seq[seq.length - 1].token.toLowerCase())
+      if (
+        innerConnectors.has(current.toLowerCase()) &&
+        j + 1 < tokens.length &&
+        isNameToken(tokens, j + 1)
      ) {
-        seq.pop();
+        span.push(current, cleanToken(tokens[j + 1]));
+        j += 2;
+        continue;
      }
-
-      if (seq.length > 0) {
-        // Check for at least one mid-sentence capitalized word
-        const hasMidCap = seq.some(({ token, idx: tokenIdx }) => {
-          const isCapWord =
-            /[A-Z]/.test(token.charAt(0)) &&
-            !functionWords.has(token.toLowerCase());
-          return isCapWord && !isSentenceStart(tokens, tokenIdx, text);
-        });
-
-        if (hasMidCap) {
-          const phrase = seq.map((s) => s.token).join(" ");
-          if (phrase.length > 2) {
-            entities.push({ type: "PROPER", text: phrase });
-          }
-        }
-      }
-      i = j;
-    } else {
-      i++;
+      break;
    }
+
+    const phrase = cleanEntityText(span.join(" "));
+    if (phrase.length > 2) {
+      entities.push({ type: "PROPER", text: phrase });
+    }
+    i = Math.max(j, i + 1);
  }

  return entities;
@@ -484,7 +579,7 @@ function extractProper(text: string): ExtractedEntity[] {

 /**
 * Extract compound noun phrases using the `compromise` NLP library.
- * Returns COMPOUND and NOUN entities derived from noun chunks.
+ * Returns TOPIC entities derived from noun chunks.
 */
 function extractCompoundsWithNlp(text: string): ExtractedEntity[] {
  if (!nlp) {
@@ -524,12 +619,12 @@ function extractCompoundsWithNlp(text: string): ExtractedEntity[] {
    const filtered = words.filter(
      (w) => !NON_SPECIFIC_ADJ.has(w.toLowerCase()),
    );
-    const cleaned = stripGenericEnding(filtered);
+    const cleaned = stripGenericEnding(stripTopicPrefix(filtered));

    if (cleaned.length >= 2) {
-      const phrase = cleaned.join(" ");
+      const phrase = cleanEntityText(cleaned.join(" "));
      if (phrase.length > 3) {
-        entities.push({ type: "COMPOUND", text: phrase });
+        entities.push({ type: "TOPIC", text: phrase });
      }
    }
  }
@@ -547,7 +642,7 @@ function extractCompoundsRegex(text: string): ExtractedEntity[] {
  // Multi-word sequences with at least one non-trivial word
  // Match sequences like "machine learning", "New York", "data science"
  const compoundRe =
-    /\b([A-Z][a-z]+(?:\s+(?:of|and|the|for|in)\s+)?[A-Z][a-z]+(?:\s+[A-Z][a-z]+)*)\b/g;
+    /\b([A-Z][a-z]+(?:\s+(?:of|the|for|in)\s+)?[A-Z][a-z]+(?:\s+[A-Z][a-z]+)*)\b/g;
  let match: RegExpExecArray | null;
  while ((match = compoundRe.exec(text)) !== null) {
    const phrase = match[1].trim();
@@ -558,9 +653,12 @@ function extractCompoundsRegex(text: string): ExtractedEntity[] {
        const filtered = words.filter(
          (w) => !NON_SPECIFIC_ADJ.has(w.toLowerCase()),
        );
-        const cleaned = stripGenericEnding(filtered);
+        const cleaned = stripGenericEnding(stripTopicPrefix(filtered));
        if (cleaned.length >= 2) {
-          entities.push({ type: "COMPOUND", text: cleaned.join(" ") });
+          entities.push({
+            type: "TOPIC",
+            text: cleanEntityText(cleaned.join(" ")),
+          });
        }
      }
    }
@@ -590,9 +688,12 @@ function extractCompoundsRegex(text: string): ExtractedEntity[] {
          const filtered = words.filter(
            (w) => !NON_SPECIFIC_ADJ.has(w.toLowerCase()),
          );
-          const cleaned = stripGenericEnding(filtered);
+          const cleaned = stripGenericEnding(stripTopicPrefix(filtered));
          if (cleaned.length >= 2) {
-            entities.push({ type: "COMPOUND", text: cleaned.join(" ") });
+            entities.push({
+              type: "TOPIC",
+              text: cleanEntityText(cleaned.join(" ")),
+            });
          }
        }
      }
@@ -614,9 +715,9 @@ function extractCompoundsRegex(text: string): ExtractedEntity[] {
 *
 * Entity types (in priority order for deduplication):
 *   PROPER   - Capitalized multi-word sequences not at sentence start
- *   COMPOUND - Multi-word noun phrases with specific modifiers
+ *   IDENTIFIER - Dotted technical identifiers
 *   QUOTED   - Text in single or double quotes (min 3 chars)
- *   NOUN     - Single nouns from circumstantial patterns
+ *   TOPIC    - Multi-word noun/topic phrases with specific modifiers
 *
 * @param text - Input text to extract entities from.
 * @returns Deduplicated list of extracted entities.
@@ -630,7 +731,10 @@ export function extractEntities(text: string): ExtractedEntity[] {
  // 2. PROPER entities (capitalization heuristics)
  raw.push(...extractProper(text));

-  // 3. COMPOUND entities (NLP or regex fallback)
+  // 3. IDENTIFIER entities
+  raw.push(...extractIdentifiers(text));
+
+  // 4. TOPIC entities (NLP or regex fallback)
  if (nlp) {
    raw.push(...extractCompoundsWithNlp(text));
  } else {
@@ -654,19 +758,17 @@ export function extractEntities(text: string): ExtractedEntity[] {
  const cleaned: ExtractedEntity[] = [];
  for (const entity of deduped) {
    let txt = entity.text.trim();
-    // Strip leading/trailing asterisks
-    txt = txt.replace(/^\*+\s*|\s*\*+$/g, "");
-    // Strip trailing colons
-    txt = txt.replace(/\s*:+$/, "");
-    // Strip leading numbered list markers
-    txt = txt.replace(/^\d+\s*\.\s*/, "");
-    // Strip trailing sentence punctuation (".", ",", ";", "!", "?") — otherwise
-    // "Paris." and "Paris" produce different embeddings and break entity dedup.
-    txt = txt.replace(/[.,;!?]+$/, "").trim();
+    txt = cleanEntityText(txt);

    if (!txt || txt.length <= 2 || hasArtifacts(txt)) {
      continue;
    }
+    if (
+      entity.type === "TOPIC" &&
+      (/^\d/.test(txt) || isCoordinatedNameTopic(txt))
+    ) {
+      continue;
+    }

    // Filter generic single-word PROPER nouns
    if (
@@ -680,12 +782,12 @@ export function extractEntities(text: string): ExtractedEntity[] {
    cleaned.push({ type: entity.type, text: txt });
  }

-  // Keep best type per entity (PROPER > COMPOUND > QUOTED > NOUN)
+  // Keep best type per entity (PROPER > IDENTIFIER > QUOTED > TOPIC)
  const typePriority: Record<string, number> = {
    PROPER: 0,
-    COMPOUND: 1,
+    IDENTIFIER: 1,
    QUOTED: 2,
-    NOUN: 3,
+    TOPIC: 3,
  };
  const best = new Map<string, ExtractedEntity>();
  for (const entity of cleaned) {
@@ -700,14 +802,17 @@ export function extractEntities(text: string): ExtractedEntity[] {
  }
  const bestEntities = Array.from(best.values());

-  // Remove entities that are substrings of longer entities
-  const allLower = bestEntities.map((e) => e.text.toLowerCase());
+  // Remove entities that are token substrings of longer entities.
  return bestEntities.filter(
    (entity) =>
-      !allLower.some(
+      !bestEntities.some(
        (other) =>
-          entity.text.toLowerCase() !== other &&
-          other.includes(entity.text.toLowerCase()),
+          entity.text.toLowerCase() !== other.text.toLowerCase() &&
+          (typePriority[entity.type] ?? 99) >=
+            (typePriority[other.type] ?? 99) &&
+          new RegExp(
+            `(^|\\s)${entity.text.toLowerCase().replace(/[.*+?^${}()|[\]\\]/g, "\\$&")}(\\s|$)`,
+          ).test(other.text.toLowerCase()),
      ),
  );
 }
@@ -0,0 +1,45 @@
+import { extractEntities } from "../src/utils/entity_extraction";
+
+describe("extractEntities", () => {
+  it("handles product lists, coordinated names, and identifiers", () => {
+    const text =
+      "User reported top inbound integration pages: OpenClaw 25,443, " +
+      "Claude Code 8,916, Codex 2,573, Dify 656. " +
+      "User compared Cartesia and Deepgram. " +
+      "The email field for Mem0 lives at person.properties.email. " +
+      "The qwen endpoint uses person.properties.email. " +
+      "Johnson & Johnson was mentioned. " +
+      "Glasses around my window. " +
+      "On 2026-05-27 there were 90 days of stats.";
+
+    const entityTexts = new Set(
+      extractEntities(text).map((entity) => entity.text),
+    );
+    const normalized = new Set(
+      [...entityTexts].map((entityText) => entityText.toLowerCase()),
+    );
+
+    for (const expected of [
+      "OpenClaw",
+      "Claude Code",
+      "Codex",
+      "Dify",
+      "Cartesia",
+      "Deepgram",
+      "Mem0",
+    ]) {
+      expect(entityTexts.has(expected)).toBe(true);
+    }
+    expect(entityTexts.has("person.properties.email")).toBe(true);
+    expect(entityTexts.has("qwen endpoint")).toBe(true);
+    expect(entityTexts.has("Johnson & Johnson")).toBe(true);
+    expect(entityTexts.has("Johnson")).toBe(false);
+    expect(normalized.has("top")).toBe(false);
+    expect(normalized.has("glasses")).toBe(false);
+    expect(entityTexts.has("Cartesia and Deepgram")).toBe(false);
+    expect(entityTexts.has("Claude Code 8,916")).toBe(false);
+    for (const rejected of ["8,916", "2,573", "656", "2026-05-27", "90"]) {
+      expect(entityTexts.has(rejected)).toBe(false);
+    }
+  });
+});
@@ -81,6 +81,14 @@ warnings.filterwarnings("ignore", category=DeprecationWarning, message=".*swigva
 logger = logging.getLogger(__name__)


+def _vector_store_list_rows(listed):
+    if isinstance(listed, (list, tuple)) and listed and isinstance(listed[0], list):
+        return listed[0]
+    if isinstance(listed, (list, tuple)):
+        return listed
+    return []
+
+
 # Fields that hold runtime auth/connection objects and must be preserved.
 # These are non-serializable objects (e.g. AWSV4SignerAuth, RequestsHttpConnection)
 # needed by clients like OpenSearch — not sensitive strings to redact.
@@ -499,22 +507,49 @@ class Memory(MemoryBase):
            )
        return self._entity_store

+    @staticmethod
+    def _normalize_entity_text(value: str) -> str:
+        return " ".join(value.strip().lower().split())
+
+    def _existing_entities_by_text(self, filters):
+        """Return existing entity rows keyed by normalized payload data."""
+        try:
+            listed = self.entity_store.list(filters=filters, top_k=10000)
+        except Exception as e:
+            logger.debug(f"Exact entity lookup failed, falling back to semantic dedup: {e}")
+            return {}
+
+        rows_by_text = {}
+        for row in _vector_store_list_rows(listed):
+            payload = getattr(row, "payload", None) or {}
+            text = payload.get("data")
+            if not isinstance(text, str):
+                continue
+            normalized = self._normalize_entity_text(text)
+            if normalized and normalized not in rows_by_text:
+                rows_by_text[normalized] = row
+        return rows_by_text
+
    def _upsert_entity(self, entity_text, entity_type, memory_id, filters):
        """Upsert an entity into the entity store, linking it to a memory."""
        try:
            entity_embedding = self.embedding_model.embed(entity_text, "add")
            search_filters = {k: v for k, v in filters.items() if k in ("user_id", "agent_id", "run_id") and v}
+            exact_match = self._existing_entities_by_text(search_filters).get(self._normalize_entity_text(entity_text))

-            existing = self.entity_store.search(
-                query=entity_text,
-                vectors=entity_embedding,
-                top_k=1,
-                filters=search_filters,
-            )
+            existing = []
+            if exact_match is None:
+                existing = self.entity_store.search(
+                    query=entity_text,
+                    vectors=entity_embedding,
+                    top_k=1,
+                    filters=search_filters,
+                )

-            if existing and existing[0].score >= 0.95:
+            semantic_match = existing[0] if existing and existing[0].score >= 0.95 else None
+            match = exact_match or semantic_match
+            if match:
                # Update existing entity's linked_memory_ids
-                match = existing[0]
                payload = match.payload or {}
                linked_ids = payload.get("linked_memory_ids", [])
                if memory_id not in linked_ids:
@@ -609,7 +644,7 @@ class Memory(MemoryBase):
                return
            seen = set()
            for entity_type, entity_text in entities:
-                key = entity_text.strip().lower()
+                key = self._normalize_entity_text(entity_text)
                if not key or key in seen:
                    continue
                seen.add(key)
@@ -971,7 +1006,7 @@ class Memory(MemoryBase):
            for idx, (memory_id, text, embedding, payload) in enumerate(records):
                entities = all_entities[idx] if idx < len(all_entities) else []
                for entity_type, entity_text in entities:
-                    key = entity_text.strip().lower()
+                    key = self._normalize_entity_text(entity_text)
                    if key in global_entities:
                        global_entities[key][2].add(memory_id)
                    else:
@@ -1009,6 +1044,7 @@ class Memory(MemoryBase):
                if valid:
                    valid_indices, valid_keys = zip(*valid)
                    valid_vectors = [entity_embeddings[i] for i in valid_indices]
+                    exact_matches = self._existing_entities_by_text(search_filters)

                    # 7c: Batch search for existing entities
                    valid_texts = [global_entities[k][1] for k in valid_keys]
@@ -1024,10 +1060,12 @@ class Memory(MemoryBase):
                    for j, key in enumerate(valid_keys):
                        entity_type, entity_text, memory_ids = global_entities[key]
                        matches = existing_matches[j] if j < len(existing_matches) else []
+                        exact_match = exact_matches.get(key)

-                        if matches and matches[0].score >= 0.95:
+                        semantic_match = matches[0] if matches and matches[0].score >= 0.95 else None
+                        match = exact_match or semantic_match
+                        if match:
                            # Update existing entity
-                            match = matches[0]
                            payload = match.payload or {}
                            linked = set(payload.get("linked_memory_ids", []))
                            linked |= memory_ids
@@ -1603,7 +1641,7 @@ class Memory(MemoryBase):
        seen = set()
        deduped = []
        for entity_type, entity_text in query_entities[:8]:
-            key = entity_text.strip().lower()
+            key = self._normalize_entity_text(entity_text)
            if key and key not in seen:
                seen.add(key)
                deduped.append((entity_type, entity_text))
@@ -2045,22 +2083,51 @@ class AsyncMemory(MemoryBase):
            )
        return self._entity_store

+    @staticmethod
+    def _normalize_entity_text(value: str) -> str:
+        return " ".join(value.strip().lower().split())
+
+    def _existing_entities_by_text(self, filters):
+        """Return existing entity rows keyed by normalized payload data."""
+        try:
+            listed = self.entity_store.list(filters=filters, top_k=10000)
+        except Exception as e:
+            logger.debug(f"Exact entity lookup failed, falling back to semantic dedup: {e}")
+            return {}
+
+        rows_by_text = {}
+        for row in _vector_store_list_rows(listed):
+            payload = getattr(row, "payload", None) or {}
+            text = payload.get("data")
+            if not isinstance(text, str):
+                continue
+            normalized = self._normalize_entity_text(text)
+            if normalized and normalized not in rows_by_text:
+                rows_by_text[normalized] = row
+        return rows_by_text
+
    async def _upsert_entity_async(self, entity_text, entity_type, memory_id, filters):
        """Async variant of `_upsert_entity` — per-entity search-then-update-or-insert."""
        try:
            entity_embedding = await asyncio.to_thread(self.embedding_model.embed, entity_text, "add")
            search_filters = {k: v for k, v in filters.items() if k in ("user_id", "agent_id", "run_id") and v}
+            exact_match = (
+                await asyncio.to_thread(self._existing_entities_by_text, search_filters)
+            ).get(self._normalize_entity_text(entity_text))

-            existing = await asyncio.to_thread(
-                self.entity_store.search,
-                query=entity_text,
-                vectors=entity_embedding,
-                top_k=1,
-                filters=search_filters,
-            )
+            existing = []
+            if exact_match is None:
+                existing = await asyncio.to_thread(
+                    self.entity_store.search,
+                    query=entity_text,
+                    vectors=entity_embedding,
+                    top_k=1,
+                    filters=search_filters,
+                )

-            if existing and existing[0].score >= 0.95:
-                match = existing[0]
+            semantic_match = existing[0] if existing and existing[0].score >= 0.95 else None
+            match = exact_match or semantic_match
+            if match:
                payload = match.payload or {}
                linked_ids = payload.get("linked_memory_ids", [])
                if memory_id not in linked_ids:
@@ -2163,7 +2230,7 @@ class AsyncMemory(MemoryBase):
                return
            seen = set()
            for entity_type, entity_text in entities:
-                key = entity_text.strip().lower()
+                key = self._normalize_entity_text(entity_text)
                if not key or key in seen:
                    continue
                seen.add(key)
@@ -2512,7 +2579,7 @@ class AsyncMemory(MemoryBase):
            for idx, (memory_id, text, embedding, payload) in enumerate(records):
                entities = all_entities[idx] if idx < len(all_entities) else []
                for entity_type, entity_text in entities:
-                    key = entity_text.strip().lower()
+                    key = self._normalize_entity_text(entity_text)
                    if key in global_entities:
                        global_entities[key][2].add(memory_id)
                    else:
@@ -2547,6 +2614,7 @@ class AsyncMemory(MemoryBase):
                if valid:
                    valid_indices, valid_keys = zip(*valid)
                    valid_vectors = [entity_embeddings[i] for i in valid_indices]
+                    exact_matches = await asyncio.to_thread(self._existing_entities_by_text, search_filters)

                    # 7c: Batch search for existing entities
                    valid_texts = [global_entities[k][1] for k in valid_keys]
@@ -2563,9 +2631,11 @@ class AsyncMemory(MemoryBase):
                    for j, key in enumerate(valid_keys):
                        entity_type, entity_text, memory_ids = global_entities[key]
                        matches = existing_matches[j] if j < len(existing_matches) else []
+                        exact_match = exact_matches.get(key)

-                        if matches and matches[0].score >= 0.95:
-                            match = matches[0]
+                        semantic_match = matches[0] if matches and matches[0].score >= 0.95 else None
+                        match = exact_match or semantic_match
+                        if match:
                            payload = match.payload or {}
                            linked = set(payload.get("linked_memory_ids", []))
                            linked |= memory_ids
@@ -3137,7 +3207,7 @@ class AsyncMemory(MemoryBase):
        seen = set()
        deduped = []
        for entity_type, entity_text in query_entities[:8]:
-            key = entity_text.strip().lower()
+            key = self._normalize_entity_text(entity_text)
            if key and key not in seen:
                seen.add(key)
                deduped.append((entity_type, entity_text))
@@ -1,82 +1,352 @@
 """
 Entity extraction from text using spaCy NLP.

-Extracts four types of entities from text:
+Extracts three types of entities from a spaCy-processed document:
 - **Proper nouns**: Capitalized multi-word sequences (person names, places, brands)
 - **Quoted text**: Text in single or double quotes (titles, specific terms)
 - **Noun compounds**: Multi-word noun phrases with specific modifiers (e.g., "machine learning")
- **Noun fallback**: Single nouns from circumstantial compound patterns

 Public API:
-    extract_entities(text: str) -> List[Tuple[str, str]]
+    ``extract_entities(text)`` accepts a string and owns spaCy model loading.
+    ``extract_entities_batch(texts)`` uses ``nlp.pipe`` for batched extraction.

-Internal:
-    _extract_entities_from_doc(doc) -> List[Tuple[str, str]]
+Returns:
+    List of ``(entity_type, entity_text)`` tuples where entity_type is one of
+    PROPER, QUOTED, TOPIC, or IDENTIFIER. Returns ``[]`` if spaCy is unavailable.
 """

 from __future__ import annotations

-import logging
+from dataclasses import dataclass
 import re
-from typing import List, Tuple

-logger = logging.getLogger(__name__)
+
+@dataclass(frozen=True)
+class _EntityCandidate:
+    entity_type: str
+    text: str
+    source: str
+    start: int
+    end: int
+    confidence: float
+    priority: int
+

 # Words that are too generic to be useful as entity heads
 _GENERIC_HEADS = {
-    "thing", "stuff", "way", "time", "experience", "situation", "case",
-    "fact", "matter", "issue", "idea", "thought", "feeling", "place",
-    "area", "part", "kind", "type", "sort", "lot", "bit", "day", "year",
-    "week", "month", "moment", "instance", "example", "technique",
-    "method", "approach", "process", "step", "tool", "result", "outcome",
-    "goal", "task", "item", "topic", "scale", "size", "level", "degree",
-    "amount", "number", "style", "look", "color", "colour", "shape",
-    "form", "piece", "section", "side", "end", "edge", "surface", "point",
+    "thing",
+    "stuff",
+    "way",
+    "time",
+    "experience",
+    "situation",
+    "case",
+    "fact",
+    "matter",
+    "issue",
+    "idea",
+    "thought",
+    "feeling",
+    "place",
+    "area",
+    "part",
+    "kind",
+    "type",
+    "sort",
+    "lot",
+    "bit",
+    "day",
+    "year",
+    "week",
+    "month",
+    "moment",
+    "instance",
+    "example",
+    "technique",
+    "method",
+    "approach",
+    "process",
+    "step",
+    "tool",
+    "result",
+    "outcome",
+    "goal",
+    "task",
+    "item",
+    "topic",
+    "scale",
+    "size",
+    "level",
+    "degree",
+    "amount",
+    "number",
+    "style",
+    "look",
+    "color",
+    "colour",
+    "shape",
+    "form",
+    "piece",
+    "section",
+    "side",
+    "end",
+    "edge",
+    "surface",
+    "point",
+}
+
+# Entity labels emitted by spaCy that are usually safe to treat as named
+# entities. Numeric and temporal labels are intentionally excluded.
+_ACCEPTED_NER_LABELS = {
+    "PERSON",
+    "ORG",
+    "GPE",
+    "LOC",
+    "FAC",
+    "PRODUCT",
+    "WORK_OF_ART",
+    "EVENT",
+    "NORP",
+    "LAW",
+    "LANGUAGE",
+}
+
+_REJECTED_NER_LABELS = {
+    "DATE",
+    "TIME",
+    "CARDINAL",
+    "ORDINAL",
+    "QUANTITY",
+    "MONEY",
+    "PERCENT",
+}
+
+# Generic role words and title-cased English words that should not become
+# single-token named entities just because spaCy tagged them as PROPN.
+_GENERIC_SINGLE_ENTITY_TERMS = {
+    "user",
+    "assistant",
+    "agent",
+    "customer",
+    "client",
+    "person",
+    "people",
+    "human",
+    "memory",
+    "message",
+    "conversation",
+    "chat",
+    "session",
+    "system",
+    "top",
 }

 # Modifiers that describe circumstance, not content
 _CIRCUMSTANTIAL_MODS = {
-    "solo", "individual", "team", "group", "joint", "collaborative",
-    "first", "last", "next", "previous", "final", "initial", "main", "side",
+    "solo",
+    "individual",
+    "team",
+    "group",
+    "joint",
+    "collaborative",
+    "first",
+    "last",
+    "next",
+    "previous",
+    "final",
+    "initial",
+    "main",
+    "side",
+    "top",
 }

 # Adjectives too vague to make a compound entity specific
 _NON_SPECIFIC_ADJ = {
-    "many", "few", "several", "some", "any", "all", "most", "more",
-    "less", "much", "little", "enough", "various", "numerous", "multiple",
-    "countless", "great", "good", "bad", "nice", "terrible", "awful",
-    "awesome", "amazing", "wonderful", "horrible", "excellent", "poor",
-    "best", "worst", "fine", "okay", "new", "old", "recent", "past",
-    "future", "current", "previous", "next", "last", "first", "latest",
-    "early", "late", "former", "modern", "ancient", "big", "small",
-    "large", "tiny", "huge", "enormous", "long", "short", "tall", "high",
-    "low", "wide", "narrow", "thick", "thin", "deep", "shallow",
-    "similar", "different", "same", "other", "another", "such", "certain",
-    "important", "main", "major", "minor", "key", "primary", "real",
-    "actual", "true", "whole", "entire", "full", "complete", "total",
-    "basic", "simple", "interesting", "boring", "exciting", "special",
-    "particular", "general", "common", "unique", "rare", "typical",
-    "usual", "normal", "regular", "possible", "likely", "potential",
-    "available", "necessary", "only", "solo", "individual", "team",
-    "group", "joint", "collaborative", "final", "initial", "side",
+    "many",
+    "few",
+    "several",
+    "some",
+    "any",
+    "all",
+    "most",
+    "more",
+    "less",
+    "much",
+    "little",
+    "enough",
+    "various",
+    "numerous",
+    "multiple",
+    "countless",
+    "great",
+    "good",
+    "bad",
+    "nice",
+    "terrible",
+    "awful",
+    "awesome",
+    "amazing",
+    "wonderful",
+    "horrible",
+    "excellent",
+    "poor",
+    "best",
+    "worst",
+    "fine",
+    "okay",
+    "new",
+    "old",
+    "recent",
+    "past",
+    "future",
+    "current",
+    "previous",
+    "next",
+    "last",
+    "first",
+    "latest",
+    "early",
+    "late",
+    "former",
+    "modern",
+    "ancient",
+    "big",
+    "small",
+    "large",
+    "tiny",
+    "huge",
+    "enormous",
+    "long",
+    "short",
+    "tall",
+    "high",
+    "low",
+    "wide",
+    "narrow",
+    "thick",
+    "thin",
+    "deep",
+    "shallow",
+    "similar",
+    "different",
+    "same",
+    "other",
+    "another",
+    "such",
+    "certain",
+    "important",
+    "main",
+    "major",
+    "minor",
+    "key",
+    "primary",
+    "real",
+    "actual",
+    "true",
+    "whole",
+    "entire",
+    "full",
+    "complete",
+    "total",
+    "basic",
+    "simple",
+    "interesting",
+    "boring",
+    "exciting",
+    "special",
+    "particular",
+    "general",
+    "common",
+    "unique",
+    "rare",
+    "typical",
+    "usual",
+    "normal",
+    "regular",
+    "possible",
+    "likely",
+    "potential",
+    "available",
+    "necessary",
+    "only",
+    "solo",
+    "individual",
+    "team",
+    "group",
+    "joint",
+    "collaborative",
+    "final",
+    "initial",
+    "side",
 }

 # Generic tail words to strip from compound entities
 _GENERIC_ENDINGS = {
-    "work", "works", "job", "jobs", "task", "tasks", "stuff", "things",
-    "thing", "info", "information", "details", "data", "content",
-    "material", "materials", "activities", "activity", "efforts", "effort",
-    "options", "option", "choices", "choice", "results", "result",
-    "output", "outputs", "products", "product", "items", "item",
+    "work",
+    "works",
+    "job",
+    "jobs",
+    "task",
+    "tasks",
+    "stuff",
+    "things",
+    "thing",
+    "info",
+    "information",
+    "details",
+    "data",
+    "content",
+    "material",
+    "materials",
+    "activities",
+    "activity",
+    "efforts",
+    "effort",
+    "options",
+    "option",
+    "choices",
+    "choice",
+    "results",
+    "result",
+    "output",
+    "outputs",
+    "products",
+    "product",
+    "items",
+    "item",
 }

 # Capitalized single words that are too generic to be proper nouns
 _GENERIC_CAPS = {
-    "works", "items", "things", "stuff", "resources", "options", "tips",
-    "ideas", "steps", "ways", "methods", "tools", "features", "benefits",
-    "examples", "details", "notes", "instructions", "guidelines",
-    "recommendations", "suggestions", "overview", "summary", "conclusion",
-    "introduction", "pros", "cons", "advantages", "disadvantages",
+    "works",
+    "items",
+    "things",
+    "stuff",
+    "resources",
+    "options",
+    "tips",
+    "ideas",
+    "steps",
+    "ways",
+    "methods",
+    "tools",
+    "features",
+    "benefits",
+    "examples",
+    "details",
+    "notes",
+    "instructions",
+    "guidelines",
+    "recommendations",
+    "suggestions",
+    "overview",
+    "summary",
+    "conclusion",
+    "introduction",
+    "pros",
+    "cons",
+    "advantages",
+    "disadvantages",
 }

 # Markdown/formatting markers to skip during extraction
@@ -120,121 +390,208 @@ def _has_artifacts(txt: str) -> bool:
    )


-def extract_entities(text: str) -> List[Tuple[str, str]]:
-    """Extract named entities, quoted text, and noun compounds from text.
-
-    This is the public API that accepts a string. It loads the spaCy model
-    internally and delegates to _extract_entities_from_doc().
-
-    Args:
-        text: Input text to extract entities from.
-
-    Returns:
-        Deduplicated list of (entity_type, entity_text) tuples.
-        Entity types: PROPER, QUOTED, COMPOUND, NOUN.
-        Returns empty list if spaCy is unavailable.
-    """
-    from mem0.utils.spacy_models import get_nlp_full
-
-    nlp = get_nlp_full()
-    if nlp is None:
-        return []
-
-    doc = nlp(text)
-    return _extract_entities_from_doc(doc)
+def _clean_text(txt: str) -> str:
+    txt = re.sub(r"^\*+\s*|\s*\*+$", "", txt.strip())
+    txt = re.sub(r"\s*:+$", "", txt)
+    txt = re.sub(r"^\d+\s*\.\s*", "", txt)
+    return " ".join(txt.split())


-def extract_entities_batch(texts: List[str], batch_size: int = 32) -> List[List[Tuple[str, str]]]:
-    """Extract entities from multiple texts using spaCy's nlp.pipe() for batched NER.
-
-    Uses spaCy's efficient batch processing pipeline instead of calling
-    nlp() individually per text. Significantly faster for multiple texts.
-
-    Args:
-        texts: List of input texts to extract entities from.
-        batch_size: Number of texts to process in each spaCy batch.
-
-    Returns:
-        List of entity lists, one per input text. Each entity list contains
-        (entity_type, entity_text) tuples. Returns list of empty lists if
-        spaCy is unavailable.
-    """
-    if not texts:
-        return []
-
-    from mem0.utils.spacy_models import get_nlp_full
-
-    nlp = get_nlp_full()
-    if nlp is None:
-        return [[] for _ in texts]
-
-    results = []
-    for doc in nlp.pipe(texts, batch_size=batch_size):
-        results.append(_extract_entities_from_doc(doc))
-    return results
+def _norm_text(txt: str) -> str:
+    return " ".join(txt.lower().split())


-def _extract_entities_from_doc(doc) -> List[Tuple[str, str]]:
-    """Extract entities from a spaCy Doc object.
+def _looks_like_technical_identifier(text: str) -> bool:
+    return bool(re.fullmatch(r"[A-Za-z_][\w-]*(?:\.[A-Za-z_][\w-]*)+", text))

-    Ported from platform's shared.core.utils.entity_extraction.extract_entities().
-    """
-    entities: List[Tuple[str, str]] = []
-    text = doc.text
+
+def _has_internal_cap_or_digit(text: str) -> bool:
+    return any(ch.isdigit() for ch in text) or any(ch.isupper() for ch in text[1:])
+
+
+def _looks_like_metric_count_token(tok) -> bool:
+    return tok.pos_ == "NUM" and bool(re.fullmatch(r"\d[\d,]*(?:\.\d+)?", tok.text))
+
+
+def _is_metric_list_context(tokens: list, idx: int) -> bool:
+    prev_text = tokens[idx - 1].text if idx > 0 else ""
+    next_text = tokens[idx + 1].text if idx + 1 < len(tokens) else ""
+    return prev_text in {":", ",", ";"} or next_text in {",", ";"}
+
+
+def _strip_trailing_metric_counts(span_tokens: list, all_tokens: list) -> list:
+    while len(span_tokens) > 1 and _looks_like_metric_count_token(span_tokens[-1]):
+        tok = span_tokens[-1]
+        if "," not in tok.text and not _is_metric_list_context(all_tokens, tok.i):
+            break
+        span_tokens = span_tokens[:-1]
+    return span_tokens
+
+
+def _is_list_item_name_token(tokens: list, idx: int) -> bool:
+    tok = tokens[idx]
+    if not tok.text or tok.text in _FORMATTING_MARKERS or not tok.text[0].isupper():
+        return False
+    if not any(ch.isalpha() for ch in tok.text) or _is_bad_single_name_token(tok):
+        return False
+    next_tok = tokens[idx + 1] if idx + 1 < len(tokens) else None
+    if not next_tok or not _looks_like_metric_count_token(next_tok):
+        return False
+    return _is_metric_list_context(tokens, idx) or _is_metric_list_context(tokens, idx + 1)
+
+
+def _is_name_like_token(tok, tokens: list | None = None, idx: int | None = None) -> bool:
+    if not tok.text or tok.text in _FORMATTING_MARKERS:
+        return False
+    if not tok.text[0].isupper():
+        return False
+    if not any(ch.isalpha() for ch in tok.text):
+        return False
+    if _is_bad_single_name_token(tok):
+        return False
+    if tok.pos_ == "PROPN" or tok.tag_ in {"NNP", "NNPS"}:
+        return True
+    if tokens is not None and idx is not None and _is_list_item_name_token(tokens, idx):
+        return True
+    if _has_internal_cap_or_digit(tok.text):
+        return True
+    return (
+        tokens is not None
+        and idx is not None
+        and tok.pos_ == "NOUN"
+        and tok.dep_ not in {"compound", "amod"}
+        and not _is_sentence_start(tokens, idx)
+    )
+
+
+def _is_bad_single_name_token(tok) -> bool:
+    lower = tok.text.lower()
+    return lower in _GENERIC_SINGLE_ENTITY_TERMS or lower in _GENERIC_CAPS or tok.is_stop
+
+
+def _add_candidate(
+    candidates: list[_EntityCandidate],
+    entity_type: str,
+    text: str,
+    source: str,
+    start: int,
+    end: int,
+    confidence: float,
+    priority: int,
+) -> None:
+    cleaned = _clean_text(text)
+    if not cleaned or len(cleaned) <= 2 or _has_artifacts(cleaned):
+        return
+    candidates.append(
+        _EntityCandidate(
+            entity_type=entity_type,
+            text=cleaned,
+            source=source,
+            start=start,
+            end=end,
+            confidence=confidence,
+            priority=priority,
+        )
+    )
+
+
+def _add_ner_candidates(doc, candidates: list[_EntityCandidate]) -> None:
    tokens = list(doc)
+    for ent in doc.ents:
+        if ent.label_ in _REJECTED_NER_LABELS or ent.label_ not in _ACCEPTED_NER_LABELS:
+            continue
+        ent_tokens = _strip_trailing_metric_counts(list(ent), tokens)
+        if not ent_tokens:
+            continue
+        if any(tok.pos_ == "CCONJ" and tok.text.lower() == "and" for tok in ent_tokens):
+            continue
+        if len(ent_tokens) == 1 and _is_bad_single_name_token(ent_tokens[0]):
+            continue
+        if (
+            len(ent_tokens) == 1
+            and ent_tokens[0].dep_ in {"compound", "amod"}
+            and ent_tokens[0].head.pos_ in {"NOUN", "PROPN"}
+        ):
+            continue
+        _add_candidate(
+            candidates,
+            "PROPER",
+            "".join(tok.text_with_ws for tok in ent_tokens).strip(),
+            "spacy_ner",
+            ent_tokens[0].i,
+            ent_tokens[-1].i + 1,
+            0.95,
+            0,
+        )

-    # === PROPER NOUN SEQUENCES ===
+
+def _add_technical_identifier_candidates(tokens: list, candidates: list[_EntityCandidate]) -> None:
+    for tok in tokens:
+        if _looks_like_technical_identifier(tok.text):
+            _add_candidate(
+                candidates,
+                "IDENTIFIER",
+                tok.text,
+                "technical_identifier",
+                tok.i,
+                tok.i + 1,
+                0.9,
+                1,
+            )
+
+
+def _add_proper_name_candidates(tokens: list, candidates: list[_EntityCandidate]) -> None:
+    allowed_inner_connectors = {"of", "the", "for", "at", "in"}
    i = 0
    while i < len(tokens):
        tok = tokens[i]
-        if tok.text in _FORMATTING_MARKERS:
+        if not _is_name_like_token(tok, tokens, i):
            i += 1
            continue
-        is_cap = tok.text and tok.text[0].isupper()
-        is_label = i + 1 < len(tokens) and tokens[i + 1].text == ":"

-        if is_cap and not is_label and tok.pos_ in {"PROPN", "NOUN", "ADJ"}:
-            seq = [(tok, i)]
-            j = i + 1
-            while j < len(tokens):
-                t = tokens[j]
-                if (t.text and t.text[0].isupper()) or t.text.lower() in {
-                    "'s", "of", "the", "in", "and", "for", "at", "is",
-                }:
-                    seq.append((t, j))
-                    j += 1
-                else:
-                    break
-            # Strip trailing function words
-            while seq and seq[-1][0].text.lower() in {"of", "the", "in", "and", "for", "at", "is", "'s"}:
-                seq.pop()
-            if seq:
-                has_mid_cap = any(
-                    not _is_sentence_start(tokens, idx)
-                    for (t, idx) in seq
-                    if t.text[0].isupper() and t.text.lower() not in {"'s", "of", "the", "in", "and", "for", "at", "is"}
-                )
-                if has_mid_cap:
-                    phrase = "".join(t.text_with_ws for (t, idx) in seq).strip()
-                    if len(phrase) > 2:
-                        entities.append(("PROPER", phrase))
-            i = j
-        else:
-            i += 1
+        span_tokens = [tok]
+        j = i + 1
+        while j < len(tokens):
+            current = tokens[j]
+            if _is_name_like_token(current, tokens, j):
+                span_tokens.append(current)
+                j += 1
+                continue
+            if (
+                current.text.lower() in allowed_inner_connectors
+                and j + 1 < len(tokens)
+                and _is_name_like_token(tokens[j + 1], tokens, j + 1)
+            ):
+                span_tokens.extend([current, tokens[j + 1]])
+                j += 2
+                continue
+            break

-    # === QUOTED TEXT ===
+        name_tokens = [
+            t
+            for t in span_tokens
+            if _is_name_like_token(t, tokens, t.i) or (0 <= t.i < len(tokens) and _is_list_item_name_token(tokens, t.i))
+        ]
+        if len(name_tokens) > 1 or not _is_bad_single_name_token(name_tokens[0]):
+            text = "".join(t.text_with_ws for t in span_tokens).strip()
+            _add_candidate(candidates, "PROPER", text, "proper_name_span", i, j, 0.8, 2)
+        i = max(j, i + 1)
+
+
+def _add_quoted_candidates(text: str, candidates: list[_EntityCandidate]) -> None:
    for m in re.finditer(r'"([^"]+)"', text):
        if len(m.group(1).strip()) > 2:
-            entities.append(("QUOTED", m.group(1).strip()))
+            _add_candidate(candidates, "QUOTED", m.group(1).strip(), "quoted", -1, -1, 0.75, 3)
    for m in re.finditer(r"(?:^|[\s\(\[{,;])'([^']+)'(?=[\s\.,;:!?\)\]]|$)", text):
        if len(m.group(1).strip()) > 2:
-            entities.append(("QUOTED", m.group(1).strip()))
+            _add_candidate(candidates, "QUOTED", m.group(1).strip(), "quoted", -1, -1, 0.75, 3)

-    # === NOUN-NOUN COMPOUNDS ===
+
+def _add_topic_phrase_candidates(doc, candidates: list[_EntityCandidate]) -> None:
    for chunk in doc.noun_chunks:
        chunk_tokens = list(chunk)
-        split_indices: list = []
-        poss_splits: list = []
+        split_indices: list[int] = []
+        poss_splits: list[int] = []
        for idx, tok in enumerate(chunk_tokens):
            if tok.dep_ == "case" and tok.text in {"'s", "\u2019s", "'"}:
                split_indices.append(idx)
@@ -243,14 +600,14 @@ def _extract_entities_from_doc(doc) -> List[Tuple[str, str]]:
                split_indices.append(idx)

        if split_indices:
-            groups: list = []
+            groups: list[list] = []
            prev = 0
            for split_idx in split_indices:
                if split_idx > prev:
                    groups.append(chunk_tokens[prev:split_idx])
                if split_idx in poss_splits:
                    next_split = next((s for s in split_indices if s > split_idx), None)
-                    owned = chunk_tokens[split_idx + 1: next_split if next_split else len(chunk_tokens)]
+                    owned = chunk_tokens[split_idx + 1 : next_split if next_split else len(chunk_tokens)]
                    if owned:
                        first_content = next((t for t in owned if t.pos_ not in {"PUNCT", "PART"}), None)
                        if not (first_content and first_content.text and first_content.text[0].isupper()):
@@ -272,7 +629,8 @@ def _extract_entities_from_doc(doc) -> List[Tuple[str, str]]:
            content = [
                t
                for t in group
-                if t.pos_ not in {"DET", "PRON", "PUNCT", "PART", "ADP", "SCONJ", "NUM"} and (t.pos_ == "ADJ" or not t.is_stop)
+                if t.pos_ not in {"DET", "PRON", "PUNCT", "PART", "ADP", "SCONJ", "NUM"}
+                and (t.pos_ == "ADJ" or not t.is_stop)
            ]
            if not content:
                continue
@@ -286,78 +644,129 @@ def _extract_entities_from_doc(doc) -> List[Tuple[str, str]]:
            if compound_toks:
                is_circ = any(t.lemma_.lower() in _CIRCUMSTANTIAL_MODS for t in compound_toks)
                if is_circ:
-                    val = head.lemma_ if head.pos_ == "NOUN" else head.text
+                    val = head.text
                    if len(val) > 2:
-                        entities.append(("NOUN", val))
+                        _add_candidate(
+                            candidates,
+                            "TOPIC",
+                            val,
+                            "topic_phrase",
+                            head.i,
+                            head.i + 1,
+                            0.45,
+                            4,
+                        )
                else:
                    filtered = _strip_generic_ending(
                        [t for t in content if not (t.pos_ == "ADJ" and t.lemma_.lower() in _NON_SPECIFIC_ADJ)]
                    )
                    if filtered:
-                        phrase = _lemmatize_compound(filtered)
+                        phrase = " ".join(t.text for t in filtered)
                        if len(phrase) > 3 and " " in phrase:
-                            entities.append(("COMPOUND", phrase))
+                            _add_candidate(
+                                candidates,
+                                "TOPIC",
+                                phrase,
+                                "topic_phrase",
+                                filtered[0].i,
+                                filtered[-1].i + 1,
+                                0.45,
+                                4,
+                            )
            elif len(content) > 1 and has_spec_adj:
                filtered = _strip_generic_ending(
-                    [t for t in content if not ((t.pos_ == "ADJ" or t.dep_ == "amod") and t.lemma_.lower() in _NON_SPECIFIC_ADJ)]
+                    [
+                        t
+                        for t in content
+                        if not ((t.pos_ == "ADJ" or t.dep_ == "amod") and t.lemma_.lower() in _NON_SPECIFIC_ADJ)
+                    ]
                )
                if filtered:
-                    phrase = _lemmatize_compound(filtered)
+                    phrase = " ".join(t.text for t in filtered)
                    if len(phrase) > 3 and " " in phrase:
-                        entities.append(("COMPOUND", phrase))
+                        _add_candidate(
+                            candidates,
+                            "TOPIC",
+                            phrase,
+                            "topic_phrase",
+                            filtered[0].i,
+                            filtered[-1].i + 1,
+                            0.45,
+                            4,
+                        )

-    # === FALLBACK: Mis-tagged VERB heads ===
-    processed = {e[1].lower() for e in entities if e[0] == "COMPOUND"}
-    generic_verb_heads = _GENERIC_HEADS | {"find", "buy", "purchase", "sale", "deal", "trip", "visit"}

-    def collect_compounds(head):
-        return [t for t in doc if t.head == head and t.dep_ == "compound"]
+def _spans_overlap(a: _EntityCandidate, b: _EntityCandidate) -> bool:
+    if a.start < 0 or b.start < 0:
+        return False
+    return a.start < b.end and b.start < a.end

-    for tok in doc:
-        if tok.pos_ == "VERB" and tok.dep_ in {"pobj", "dobj", "nsubj"}:
-            comps = sorted(collect_compounds(tok), key=lambda t: t.i)
-            if comps:
-                phrase_toks = comps if tok.lemma_.lower() in generic_verb_heads else comps + [tok]
-                phrase = " ".join(t.text for t in phrase_toks)
-                if phrase.lower() not in processed and len(phrase) > 3 and " " in phrase:
-                    entities.append(("COMPOUND", phrase))
-                    processed.add(phrase.lower())

-    # === DEDUPLICATION & CLEANUP ===
-    seen: set = set()
-    deduped = []
-    for t, e in entities:
-        k = e.lower().strip()
-        if k not in seen and len(k) > 2:
-            seen.add(k)
-            deduped.append((t, e))
+def _resolve_candidates(candidates: list[_EntityCandidate]) -> list[tuple[str, str]]:
+    deduped_by_text: dict[str, _EntityCandidate] = {}
+    for candidate in candidates:
+        key = _norm_text(candidate.text)
+        current = deduped_by_text.get(key)
+        if current is None or (candidate.priority, -candidate.confidence) < (current.priority, -current.confidence):
+            deduped_by_text[key] = candidate

-    cleaned: List[Tuple[str, str]] = []
-    for etype, etext in deduped:
-        txt = re.sub(r"^\*+\s*|\s*\*+$", "", etext.strip())
-        txt = re.sub(r"\s*:+$", "", txt)
-        txt = re.sub(r"^\d+\s*\.\s*", "", txt)
-        if not txt or len(txt) <= 2 or _has_artifacts(txt):
+    ordered = sorted(
+        deduped_by_text.values(),
+        key=lambda c: (c.priority, -c.confidence, -(c.end - c.start), c.start),
+    )
+    accepted: list[_EntityCandidate] = []
+    for candidate in ordered:
+        if any(
+            _spans_overlap(candidate, existing)
+            and not (candidate.entity_type == "TOPIC" and " " in candidate.text and existing.entity_type == "PROPER")
+            for existing in accepted
+        ):
            continue
-        if etype == "PROPER" and " " not in txt and txt.lower() in _GENERIC_CAPS:
-            continue
-        cleaned.append((etype, txt))
+        accepted.append(candidate)

-    # Keep best type per entity (PROPER > COMPOUND > QUOTED > NOUN)
-    type_pri = {"PROPER": 0, "COMPOUND": 1, "QUOTED": 2, "NOUN": 3, "VERB": 4}
-    best: dict = {}
-    for t, e in cleaned:
-        k = e.lower()
-        if k not in best or type_pri.get(t, 99) < type_pri.get(best[k][0], 99):
-            best[k] = (t, e)
-    deduped = list(best.values())
+    accepted.sort(key=lambda c: (c.start if c.start >= 0 else 10**9, c.end, c.priority))
+    return [(candidate.entity_type, candidate.text) for candidate in accepted]

-    # Remove entities that are whole-word substrings of longer entities.
-    # Word-boundary anchoring avoids dropping distinct entities that only share a
-    # leading substring (e.g. "Sam" must survive alongside "Samsung").
-    all_lower = [e[1].lower() for e in deduped]
-    return [
-        (t, e)
-        for t, e in deduped
-        if not any(e.lower() != o and re.search(rf"\b{re.escape(e.lower())}\b", o) for o in all_lower)
-    ]
+
+def _extract_entities_from_doc(doc) -> list[tuple[str, str]]:
+    """Extract typed entity candidates from a spaCy Doc.
+
+    Args:
+        doc: A spaCy ``Doc`` object (from ``nlp(text)``).
+
+    Returns:
+        Deduplicated list of ``(entity_type, entity_text)`` tuples.
+        Entity types include PROPER, QUOTED, TOPIC, and IDENTIFIER.
+    """
+    tokens = list(doc)
+    candidates: list[_EntityCandidate] = []
+    _add_ner_candidates(doc, candidates)
+    _add_technical_identifier_candidates(tokens, candidates)
+    _add_proper_name_candidates(tokens, candidates)
+    _add_quoted_candidates(doc.text, candidates)
+    _add_topic_phrase_candidates(doc, candidates)
+    return _resolve_candidates(candidates)
+
+
+def extract_entities(text: str) -> list[tuple[str, str]]:
+    """Extract typed entity candidates from text."""
+    from mem0.utils.spacy_models import get_nlp_full
+
+    nlp = get_nlp_full()
+    if nlp is None:
+        return []
+    return _extract_entities_from_doc(nlp(text))
+
+
+def extract_entities_batch(texts: list[str], batch_size: int = 32) -> list[list[tuple[str, str]]]:
+    """Extract typed entity candidates from multiple texts."""
+    if not texts:
+        return []
+
+    from mem0.utils.spacy_models import get_nlp_full
+
+    nlp = get_nlp_full()
+    if nlp is None:
+        return [[] for _ in texts]
+
+    return [_extract_entities_from_doc(doc) for doc in nlp.pipe(texts, batch_size=batch_size)]
@@ -4,7 +4,7 @@ build-backend = "hatchling.build"

 [project]
 name = "mem0ai"
-version = "2.0.8"
+version = "2.0.9"
 description = "Long-term memory for AI Agents"
 authors = [
    { name = "Mem0", email = "support@mem0.ai" }
@@ -6,6 +6,7 @@ def _ensure_spacy():
    """Skip tests if spaCy model is not available."""
    try:
        import spacy
+
        spacy.load("en_core_web_sm")
    except Exception:
        pytest.skip("spaCy en_core_web_sm model not available")
@@ -33,8 +34,9 @@ class TestExtractEntities:

        entities = extract_entities("The machine learning engineer built a neural network")
        entity_texts = [e[1].lower() for e in entities]
-        has_compound = any("machine" in t and "learning" in t for t in entity_texts) or \
-                       any("neural" in t and "network" in t for t in entity_texts)
+        has_compound = any("machine" in t and "learning" in t for t in entity_texts) or any(
+            "neural" in t and "network" in t for t in entity_texts
+        )
        assert has_compound, f"Expected compound nouns, got {entities}"

    def test_empty_string(self):
@@ -76,9 +78,37 @@ class TestExtractEntities:
        for entity in entities:
            assert isinstance(entity, tuple)
            assert len(entity) == 2
-            assert entity[0] in ("PROPER", "QUOTED", "COMPOUND", "NOUN")
+            assert entity[0] in ("PROPER", "QUOTED", "TOPIC", "IDENTIFIER")
            assert isinstance(entity[1], str)

+    def test_handles_names_lists_and_identifiers(self):
+        from mem0.utils.entity_extraction import extract_entities
+
+        text = (
+            "User reported top inbound integration pages: OpenClaw 25,443, "
+            "Claude Code 8,916, Codex 2,573, Dify 656. "
+            "User compared Cartesia and Deepgram. "
+            "The email field for Mem0 lives at person.properties.email. "
+            "The qwen endpoint uses person.properties.email. "
+            "Johnson & Johnson was mentioned. "
+            "Glasses around my window. "
+            "On 2026-05-27 there were 90 days of stats."
+        )
+
+        entities = extract_entities(text)
+        entity_texts = {entity_text for _, entity_text in entities}
+        normalized = {entity_text.lower() for entity_text in entity_texts}
+
+        assert {"OpenClaw", "Claude Code", "Codex", "Dify", "Cartesia", "Deepgram", "Mem0"}.issubset(entity_texts)
+        assert "person.properties.email" in entity_texts
+        assert "qwen endpoint" in entity_texts
+        assert "Johnson & Johnson" in entity_texts
+        assert "top" not in normalized
+        assert "glasses" not in normalized
+        assert "Cartesia and Deepgram" not in entity_texts
+        assert "Claude Code 8,916" not in entity_texts
+        assert not {"8,916", "2,573", "656", "2026-05-27", "90"}.intersection(entity_texts)
+

 class TestExtractEntitiesBatch:
    def test_batch_processing(self):
Author	SHA1	Message	Date
kartik-mem0	a4633aa065	docs(api-reference): address reviewer comments on search defaults table and retrieval_criteria shape - search-memories.mdx: collapse V1/V2/V3 comparison table to a single "Default" column showing current (v3) values only (threshold 0.1, rerank false, top_k 10) - organizations-projects.mdx: replace bogus {role, content} retrieval_criteria examples with the correct {name, description, weight} shape, mirroring the canonical criteria-retrieval.mdx examples; update prose to name the three required fields	2026-06-26 16:48:19 +05:30
kartik-mem0	711f866e59	docs(api-reference): use literal em-dash instead of \u2014 escape in openapi.json	2026-06-26 16:03:50 +05:30
kartik-mem0	46757cb1fb	docs(api-reference): document project.update() retrieval_criteria and decay params	2026-06-25 14:02:51 +05:30
kartik-mem0	bbfe0f9bb2	docs(api-reference): align API docs and openapi schema with v3 spec - create-memory-export.mdx: replace `session_id` filter with `app_id` (spec: /v1/exports/ filters = user_id, agent_id, app_id, run_id) - get-memory-export.mdx: replace stale filter list (session_id removed) with correct fields: user_id, agent_id, app_id, run_id, created_at, updated_at - search-memories.mdx: fix comparison table — rerank V1/V2 default was `true`, spec shows `false`; threshold V1 is absent (not "No default"), V2 default is 0.3 (not absent) - organizations-projects.mdx: rewrite Key Capabilities bullet — org/project are NOT init params; they resolve automatically from the API key via /v1/ping/ - openapi.json DELETE /v1/batch/: fix request schema from {memory_ids:[uuid]} to {memories:[{memory_id:uuid}]} to match Python client.batch_delete() - openapi.json PUT /v1/batch/: make `text` optional (remove from required), add optional `metadata` property — matches Python client.batch_update() docstring	2026-06-25 12:01:24 +05:30
Taranjeet Singh	ac296f7534	docs: make example code fences copy-safe (#5833 )	2026-06-24 22:19:40 -07:00
Kartik	ac8f862ff7	fix(mem0-plugin): store files_touched as a list to stop double JSON-encoding (#5806 )	2026-06-25 09:06:11 +05:30
soumil-rathi	b33fa5427c	fix(memory): align entity extraction precision (#5829 ) Making the entity extraction function cleaner Co-authored-by: Soumil Rathi <soumilrathi@gmail.com>	2026-06-24 15:48:37 -07:00
Ashutosh Kasudhan	5d573dd2ae	docs: added typescript support to deepseek provider (#5723 )	2026-06-24 20:55:14 +05:30