feat(skills): introduce Mem0 skill graph with dedicated CLI and Vercel AI SDK skills (#4725)
This commit is contained in:
Saket Aryan
+10
-4
@@ -1,13 +1,15 @@
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# Mem0 Skill for Claude
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Add persistent memory to any AI application in minutes using [Mem0 Platform](https://app.mem0.ai).
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Add persistent memory to any AI application in minutes using [Mem0 Platform](https://app.mem0.ai) or the open-source self-hosted SDK.
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> **Part of the Mem0 Skill Graph:** See also [mem0-cli](../mem0-cli/SKILL.md) (terminal) and [mem0-vercel-ai-sdk](../mem0-vercel-ai-sdk/SKILL.md) (Vercel AI SDK).
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## What This Skill Does
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When installed, Claude can:
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- **Set up Mem0** in your Python or TypeScript project
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- **Integrate memory** into your existing AI app (LangChain, CrewAI, Vercel AI, OpenAI Agents, LangGraph, LlamaIndex, etc.)
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- **Set up Mem0** in your Python or TypeScript project (Platform or OSS)
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- **Integrate memory** into your existing AI app (LangChain, CrewAI, OpenAI Agents, LangGraph, LlamaIndex, etc.)
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- **Generate working code** using real API references and tested patterns
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- **Search live docs** on demand for the latest Mem0 documentation
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@@ -61,6 +63,10 @@ skills/mem0/
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├── SKILL.md # Skill definition and instructions
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├── README.md # This file
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├── LICENSE # Apache-2.0
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├── client/ # Language-specific SDK references (Platform + OSS)
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│ ├── python.md # Python SDK (MemoryClient + Memory OSS)
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│ ├── node.md # TypeScript SDK (MemoryClient + Memory OSS)
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│ └── differences.md # Python vs TypeScript comparison
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├── scripts/
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│ └── mem0_doc_search.py # Search live Mem0 docs on demand
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└── references/ # Documentation (loaded on demand)
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@@ -69,7 +75,7 @@ skills/mem0/
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├── api-reference.md # REST endpoints, filters, memory object
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├── architecture.md # Processing pipeline, lifecycle, scoping, performance
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├── features.md # Retrieval, graph, categories, MCP, webhooks, multimodal
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├── integration-patterns.md # LangChain, CrewAI, Vercel AI, LangGraph, LlamaIndex, etc.
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├── integration-patterns.md # LangChain, CrewAI, OpenAI Agents, LangGraph, LlamaIndex, etc.
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└── use-cases.md # 7 real-world patterns with Python + TypeScript code
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```
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+41
-15
@@ -1,25 +1,34 @@
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---
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name: mem0
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description: >
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Integrate Mem0 Platform into AI applications for persistent memory, personalization, and semantic search.
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Use this skill when the user mentions "mem0", "memory layer", "remember user preferences",
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"persistent context", "personalization", or needs to add long-term memory to chatbots, agents,
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or AI apps. Covers Python and TypeScript SDKs, framework integrations (LangChain, CrewAI,
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Vercel AI SDK, OpenAI Agents SDK, Pipecat), and the full Platform API. Use even when the user
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doesn't explicitly say "mem0" but describes needing conversation memory, user context retention,
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or knowledge retrieval across sessions.
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Mem0 Platform SDK for adding persistent memory to AI applications.
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TRIGGER when: user mentions "mem0", "MemoryClient", "memory layer",
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"remember user preferences", "persistent context", "personalization",
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or needs to add long-term memory to chatbots, agents, or AI apps.
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Covers Python SDK (mem0ai), TypeScript SDK (mem0ai), and framework integrations
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(LangChain, CrewAI, OpenAI Agents SDK, Pipecat, LlamaIndex, AutoGen, LangGraph).
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Also covers the open-source self-hosted Memory class.
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This is the DEFAULT mem0 skill for ambiguous queries.
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DO NOT TRIGGER when: user asks about CLI commands, terminal usage, or shell
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scripts (use mem0-cli), or Vercel AI SDK / @mem0/vercel-ai-provider / createMem0
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(use mem0-vercel-ai-sdk).
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license: Apache-2.0
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metadata:
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author: mem0ai
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version: "1.0.0"
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version: "2.0.0"
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category: ai-memory
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tags: "memory, personalization, ai, python, typescript, vector-search"
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compatibility: Requires Python 3.10+ or Node.js 18+, pip install mem0ai or npm install mem0ai, MEM0_API_KEY env var, and internet access to api.mem0.ai
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compatibility: Requires Python 3.10+ or Node.js 18+, pip install mem0ai or npm install mem0ai, MEM0_API_KEY env var (Platform), and internet access to api.mem0.ai
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---
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# Mem0 Platform Integration
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Mem0 is a managed memory layer for AI applications. It stores, retrieves, and manages user memories via API — no infrastructure to deploy.
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> **Skill Graph:** This skill is part of the Mem0 skill graph:
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> - **mem0** (this skill) -- Platform Client SDK + OSS (Python + TypeScript)
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> - **[mem0-cli](../mem0-cli/SKILL.md)** ([GitHub](https://github.com/mem0ai/mem0/tree/main/skills/mem0-cli)) -- Command-line interface
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> - **[mem0-vercel-ai-sdk](../mem0-vercel-ai-sdk/SKILL.md)** ([GitHub](https://github.com/mem0ai/mem0/tree/main/skills/mem0-vercel-ai-sdk)) -- Vercel AI SDK provider
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Mem0 is a managed memory layer for AI applications. It stores, retrieves, and manages user memories via API — no infrastructure to deploy. For self-hosted usage, see the OSS section in the client references below.
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## Step 1: Install and authenticate
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@@ -134,14 +143,24 @@ def chat(user_input: str, user_id: str) -> str:
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For the latest docs beyond what's in the references, use the doc search tool:
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```bash
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python scripts/mem0_doc_search.py --query "topic"
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python scripts/mem0_doc_search.py --page "/platform/features/graph-memory"
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python scripts/mem0_doc_search.py --index
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python ${CLAUDE_SKILL_DIR}/scripts/mem0_doc_search.py --query "topic"
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python ${CLAUDE_SKILL_DIR}/scripts/mem0_doc_search.py --page "/platform/features/graph-memory"
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python ${CLAUDE_SKILL_DIR}/scripts/mem0_doc_search.py --index
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```
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No API key needed — searches docs.mem0.ai directly.
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## References
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## Client SDK References
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Language-specific deep references (Platform + OSS):
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| Language | File |
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|----------|------|
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| Python (MemoryClient + AsyncMemoryClient + Memory OSS) | [client/python.md](client/python.md) |
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| TypeScript/Node.js (MemoryClient + Memory OSS) | [client/node.md](client/node.md) |
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| Python vs TypeScript differences | [client/differences.md](client/differences.md) |
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## Platform References
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Load these on demand for deeper detail:
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@@ -152,5 +171,12 @@ Load these on demand for deeper detail:
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| API reference (endpoints, filters, object schema) | [references/api-reference.md](references/api-reference.md) |
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| Architecture (pipeline, lifecycle, scoping, performance) | [references/architecture.md](references/architecture.md) |
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| Platform features (retrieval, graph, categories, MCP, etc.) | [references/features.md](references/features.md) |
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| Framework integrations (LangChain, CrewAI, Vercel AI, etc.) | [references/integration-patterns.md](references/integration-patterns.md) |
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| Framework integrations (LangChain, CrewAI, OpenAI Agents, etc.) | [references/integration-patterns.md](references/integration-patterns.md) |
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| Use cases & examples (real-world patterns with code) | [references/use-cases.md](references/use-cases.md) |
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## Related Mem0 Skills
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| Skill | When to use | Link |
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|-------|-------------|------|
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| mem0-cli | Terminal commands, scripting, CI/CD, agent tool loops | [local](../mem0-cli/SKILL.md) / [GitHub](https://github.com/mem0ai/mem0/tree/main/skills/mem0-cli) |
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| mem0-vercel-ai-sdk | Vercel AI SDK provider with automatic memory | [local](../mem0-vercel-ai-sdk/SKILL.md) / [GitHub](https://github.com/mem0ai/mem0/tree/main/skills/mem0-vercel-ai-sdk) |
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@@ -0,0 +1,125 @@
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# Python vs TypeScript SDK Differences
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Quick-reference cheatsheet for developers working across both Mem0 SDKs.
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## Constructor
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| Aspect | Python | TypeScript |
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|--------|--------|------------|
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| Import (Platform) | `from mem0 import MemoryClient` | `import MemoryClient from 'mem0ai'` |
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| Import (OSS) | `from mem0 import Memory` | `import { Memory } from 'mem0ai/oss'` |
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| Constructor | `MemoryClient(api_key="m0-xxx")` | `new MemoryClient({ apiKey: 'm0-xxx' })` |
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| Required param | `api_key` (positional or kwarg) | `apiKey` (in options object) |
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Both read from `MEM0_API_KEY` env var if no key provided.
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## Method Naming
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| Operation | Python | TypeScript |
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|-----------|--------|------------|
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| Add | `add()` | `add()` |
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| Search | `search()` | `search()` |
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| Get | `get()` | `get()` |
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| Get all | `get_all()` | `getAll()` |
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| Update | `update()` | `update()` |
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| Delete | `delete()` | `delete()` |
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| Delete all | `delete_all()` | `deleteAll()` |
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| History | `history()` | `history()` |
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| Batch update | `batch_update()` | `batchUpdate()` |
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| Batch delete | `batch_delete()` | `batchDelete()` |
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| List users | `users()` | `users()` |
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| Delete users | `delete_users()` | `deleteUsers()` |
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| Get project | `project.get()` | `getProject()` |
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| Update project | `project.update()` | `updateProject()` |
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| Create webhook | `create_webhook()` | `createWebhook()` |
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| Get webhooks | `get_webhooks()` | `getWebhooks()` |
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| Update webhook | `update_webhook()` | `updateWebhook()` |
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| Delete webhook | `delete_webhook()` | `deleteWebhook()` |
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| Create export | `create_memory_export()` | `createMemoryExport()` |
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| Get export | `get_memory_export()` | `getMemoryExport()` |
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| Feedback | `feedback()` | `feedback()` |
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**Rule:** Python uses `snake_case`, TypeScript uses `camelCase` for method names.
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## Parameter Passing
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```python
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# Python: kwargs
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client.add(messages, user_id="alice", metadata={"source": "chat"})
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client.search("query", user_id="alice", top_k=5, rerank=True)
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```
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```typescript
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// TypeScript: options object
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await client.add(messages, { user_id: 'alice', metadata: { source: 'chat' } });
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await client.search('query', { user_id: 'alice', top_k: 5, rerank: true });
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```
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**Important:** Both use `snake_case` for API parameter names (`user_id`, `agent_id`, `top_k`, etc.). Only method names differ.
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Exception: OSS TypeScript uses `camelCase` for config params (`userId`, `agentId`, `runId`).
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## Architectural Differences
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| Aspect | Python | TypeScript |
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|--------|--------|------------|
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| HTTP library | httpx | axios |
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| Default timeout | 300s | 60s |
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| Sync support | Yes (`MemoryClient`) | No (all async) |
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| Async support | Yes (`AsyncMemoryClient`) | All methods are async |
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| Project management | `client.project.*` (separate class) | `client.getProject()` / `client.updateProject()` |
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| Context manager | `async with AsyncMemoryClient()` | Not supported |
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## Platform Features: Python-only
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These methods exist in Python but not TypeScript:
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| Method | Description |
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|--------|-------------|
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| `get_summary(filters)` | Get summary of memories |
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| `reset()` | Delete ALL data (users + memories) |
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| `project.create(name)` | Create a new project |
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| `project.delete()` | Delete current project |
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| `project.get_members()` | List project members |
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| `project.add_member(email, role)` | Add member to project |
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| `project.update_member(email, role)` | Change member role |
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| `project.remove_member(email)` | Remove member |
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## Platform Features: TypeScript-only
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| Method | Description |
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|--------|-------------|
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| `deleteUser(data)` | Convenience method for single entity deletion |
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| `ping()` | Health check endpoint |
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## OSS Config Naming
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| Python config key | TypeScript config key |
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|-------------------|----------------------|
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| `vector_store` | `vectorStore` |
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| `graph_store` | `graphStore` |
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| `history_db_path` | `historyDbPath` |
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| `custom_fact_extraction_prompt` | `customPrompt` |
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| `enable_graph` | `enableGraph` |
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## OSS Scope Parameter Naming
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| Python | TypeScript |
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|--------|------------|
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| `user_id="alice"` | `userId: 'alice'` |
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| `agent_id="bot"` | `agentId: 'bot'` |
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| `run_id="session"` | `runId: 'session'` |
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## Common Gotcha
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When searching/filtering, **both SDKs use `snake_case`** for filter keys:
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```python
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# Python
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filters = {"AND": [{"user_id": "alice"}, {"categories": {"contains": "health"}}]}
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```
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```typescript
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// TypeScript -- same snake_case in filter objects!
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const filters = { AND: [{ user_id: 'alice' }, { categories: { contains: 'health' } }] };
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```
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@@ -0,0 +1,412 @@
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# Mem0 Node.js / TypeScript SDK Reference
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Complete reference for the `mem0ai` npm package. Covers both the Platform client (managed API) and the Open Source self-hosted variant.
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---
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## Platform Client
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### Installation
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```bash
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npm install mem0ai
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export MEM0_API_KEY="m0-your-api-key"
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```
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### MemoryClient
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```typescript
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import MemoryClient from 'mem0ai';
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const client = new MemoryClient({ apiKey: 'm0-xxx' });
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```
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**Constructor:** `new MemoryClient({ apiKey })`. If `apiKey` is not provided, reads from `MEM0_API_KEY` environment variable.
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- HTTP library: `axios`
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- Timeout: 60 seconds
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- Base URL: `https://api.mem0.ai`
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- All methods are async (return `Promise`)
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---
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### Memory Methods
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#### add(messages, options?)
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Store new memories from messages.
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```typescript
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const messages = [
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{ role: 'user', content: "I'm a vegetarian and allergic to nuts." },
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{ role: 'assistant', content: "Got it! I'll remember that." },
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];
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await client.add(messages, { user_id: 'alice' });
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```
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| Parameter | Type | Description |
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|-----------|------|-------------|
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| `messages` | `Message[]` | Array of `{role, content}` objects |
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| `options.user_id` | string | User identifier |
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| `options.agent_id` | string | Agent identifier |
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| `options.app_id` | string | Application identifier |
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| `options.run_id` | string | Session identifier |
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| `options.metadata` | object | Custom key-value pairs |
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| `options.enable_graph` | boolean | Activate knowledge graph |
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| `options.infer` | boolean | If false, store raw text (default: true) |
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| `options.immutable` | boolean | Prevent future modification |
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| `options.expiration_date` | string | Auto-expiry (`YYYY-MM-DD`) |
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| `options.includes` | string | Preference filter for inclusion |
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| `options.excludes` | string | Preference filter for exclusion |
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**Returns:** `Promise<any>` -- list of events
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#### search(query, options?)
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Search memories by semantic similarity.
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```typescript
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const results = await client.search('dietary preferences', { user_id: 'alice' });
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for (const mem of results.results) {
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console.log(mem.memory, mem.score);
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}
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```
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| Parameter | Type | Description |
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|-----------|------|-------------|
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| `query` | string | Natural language search query |
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| `options.user_id` | string | Filter by user |
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| `options.agent_id` | string | Filter by agent |
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| `options.filters` | object | V2 filter object (`AND`/`OR`/`NOT`) |
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| `options.top_k` | number | Number of results (default: 10) |
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| `options.rerank` | boolean | Enable semantic reranking |
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| `options.threshold` | number | Minimum similarity (default: 0.3) |
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| `options.keyword_search` | boolean | Enable keyword search |
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| `options.enable_graph` | boolean | Include graph relations |
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| `options.filter_memories` | boolean | Precision filtering |
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**Returns:** `Promise<SearchResult>` -- `{results: [{id, memory, score, ...}], relations: [...]}`
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#### get(memoryId)
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```typescript
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const memory = await client.get('ea925981-...');
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```
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#### getAll(options?)
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Retrieve all memories. Requires at least one entity identifier in filters.
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```typescript
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const memories = await client.getAll({ user_id: 'alice' });
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// With filters
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const filtered = await client.getAll({
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filters: { AND: [{ user_id: 'alice' }, { categories: { contains: 'health' } }] },
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});
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```
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| Parameter | Type | Description |
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|-----------|------|-------------|
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| `options.user_id` | string | Filter by user |
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| `options.filters` | object | V2 filter object |
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| `options.page` | number | Page number |
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| `options.page_size` | number | Results per page |
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| `options.enable_graph` | boolean | Include graph relations |
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#### update(memoryId, data)
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```typescript
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await client.update('ea925981-...', { text: 'Updated: vegan since 2024' });
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await client.update('ea925981-...', { text: 'Updated', metadata: { verified: true } });
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```
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| Parameter | Type | Description |
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|-----------|------|-------------|
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| `memoryId` | string | Memory ID |
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| `data.text` | string | New content |
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| `data.metadata` | object | New metadata |
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| `data.timestamp` | string | New timestamp |
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#### delete(memoryId)
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```typescript
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await client.delete('ea925981-...');
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```
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#### deleteAll(options?)
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```typescript
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await client.deleteAll({ user_id: 'alice' });
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```
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#### history(memoryId)
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```typescript
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const history = await client.history('ea925981-...');
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// Returns: [{previous_value, new_value, action, timestamps}]
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```
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---
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### Batch Methods
|
||||
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#### batchUpdate(memories)
|
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```typescript
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await client.batchUpdate([
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{ memoryId: 'uuid-1', text: 'Updated text' },
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{ memoryId: 'uuid-2', text: 'Another update' },
|
||||
]);
|
||||
```
|
||||
|
||||
#### batchDelete(memories)
|
||||
|
||||
```typescript
|
||||
await client.batchDelete(['uuid-1', 'uuid-2', 'uuid-3']);
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### User/Entity Management
|
||||
|
||||
#### users()
|
||||
|
||||
```typescript
|
||||
const users = await client.users();
|
||||
// Returns: {results: [{type: "user", name: "alice"}, ...]}
|
||||
```
|
||||
|
||||
#### deleteUser(data) / deleteUsers(data)
|
||||
|
||||
```typescript
|
||||
await client.deleteUser({ user_id: 'alice' }); // Single entity
|
||||
await client.deleteUsers({ agent_id: 'bot-1' }); // Flexible
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Project Management
|
||||
|
||||
```typescript
|
||||
// Get project config
|
||||
const config = await client.getProject({ fields: ['custom_categories'] });
|
||||
|
||||
// Update project settings
|
||||
await client.updateProject({
|
||||
custom_instructions: 'Extract dietary preferences and health info',
|
||||
custom_categories: [{ health: 'Medical and dietary info' }],
|
||||
enable_graph: true,
|
||||
});
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Webhooks
|
||||
|
||||
```typescript
|
||||
// List
|
||||
const webhooks = await client.getWebhooks({ project_id: 'proj_123' });
|
||||
|
||||
// Create
|
||||
const webhook = await client.createWebhook({
|
||||
url: 'https://your-app.com/webhook',
|
||||
name: 'Memory Logger',
|
||||
project_id: 'proj_123',
|
||||
event_types: ['memory_add', 'memory_update'],
|
||||
});
|
||||
|
||||
// Update
|
||||
await client.updateWebhook({
|
||||
webhook_id: 'wh_123',
|
||||
name: 'Updated Logger',
|
||||
url: 'https://new-url.com',
|
||||
});
|
||||
|
||||
// Delete
|
||||
await client.deleteWebhook({ webhook_id: 'wh_123' });
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Feedback
|
||||
|
||||
```typescript
|
||||
await client.feedback({
|
||||
memory_id: 'mem-123',
|
||||
feedback: 'POSITIVE',
|
||||
feedback_reason: 'Accurately captured preference',
|
||||
});
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Export
|
||||
|
||||
```typescript
|
||||
const exportReq = await client.createMemoryExport({
|
||||
schema: JSON.stringify({ type: 'object', properties: { name: { type: 'string' } } }),
|
||||
filters: { user_id: 'alice' },
|
||||
});
|
||||
|
||||
const result = await client.getMemoryExport({ memory_export_id: exportReq.id });
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### TypeScript Types
|
||||
|
||||
Key interfaces from `mem0.types.ts`:
|
||||
|
||||
```typescript
|
||||
interface Message { role: string; content: string; }
|
||||
interface Memory { id: string; memory: string; user_id: string; categories: string[]; score?: number; /* ... */ }
|
||||
interface MemoryOptions { user_id?: string; agent_id?: string; app_id?: string; run_id?: string; metadata?: object; /* ... */ }
|
||||
interface SearchOptions { user_id?: string; filters?: object; top_k?: number; rerank?: boolean; threshold?: number; /* ... */ }
|
||||
interface MemoryHistory { id: string; memory_id: string; previous_value: string; new_value: string; action: string; /* ... */ }
|
||||
interface FeedbackPayload { memory_id: string; feedback: string; feedback_reason?: string; }
|
||||
interface WebhookCreatePayload { url: string; name: string; project_id: string; event_types: string[]; }
|
||||
enum OutputFormat { v1_0 = 'v1.0', v1_1 = 'v1.1' }
|
||||
enum API_VERSION { v1 = 'v1', v2 = 'v2' }
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Open Source / Self-Hosted
|
||||
|
||||
### Installation
|
||||
|
||||
```bash
|
||||
npm install mem0ai
|
||||
```
|
||||
|
||||
### Memory Class
|
||||
|
||||
```typescript
|
||||
import { Memory } from 'mem0ai/oss';
|
||||
|
||||
const m = new Memory(); // Uses default config
|
||||
```
|
||||
|
||||
**Import:** `from 'mem0ai/oss'` (NOT the default export -- that is `MemoryClient` for Platform)
|
||||
|
||||
### Configuration
|
||||
|
||||
```typescript
|
||||
const config = {
|
||||
llm: {
|
||||
provider: 'openai', // openai, groq, anthropic, google, ollama, lmstudio, mistral, azure
|
||||
config: {
|
||||
model: 'gpt-4o-mini',
|
||||
apiKey: 'sk-xxx',
|
||||
},
|
||||
},
|
||||
embedder: {
|
||||
provider: 'openai', // openai, ollama, lmstudio, google, azure, langchain, anthropic
|
||||
config: {
|
||||
model: 'text-embedding-3-small',
|
||||
apiKey: 'sk-xxx',
|
||||
},
|
||||
},
|
||||
vectorStore: {
|
||||
provider: 'qdrant', // memory, qdrant, redis, supabase, langchain, azure_ai_search, pgvector
|
||||
config: {
|
||||
collectionName: 'my_memories',
|
||||
host: 'localhost',
|
||||
port: 6333,
|
||||
},
|
||||
},
|
||||
graphStore: { // Optional
|
||||
provider: 'neo4j',
|
||||
config: {
|
||||
url: 'neo4j://localhost:7687',
|
||||
username: 'neo4j',
|
||||
password: 'password',
|
||||
},
|
||||
},
|
||||
historyDbPath: 'history.db',
|
||||
customPrompt: '...',
|
||||
enableGraph: false,
|
||||
disableHistory: false,
|
||||
};
|
||||
|
||||
const m = new Memory(config);
|
||||
// Or from dict with validation:
|
||||
const m2 = Memory.fromConfig(config);
|
||||
```
|
||||
|
||||
### Methods
|
||||
|
||||
All methods are async (return `Promise`):
|
||||
|
||||
#### add(messages, config)
|
||||
|
||||
```typescript
|
||||
await m.add('I prefer dark mode', { userId: 'alice' });
|
||||
await m.add([
|
||||
{ role: 'user', content: 'I like hiking' },
|
||||
{ role: 'assistant', content: 'Great outdoor activity!' },
|
||||
], { userId: 'alice' });
|
||||
```
|
||||
|
||||
| Parameter | Type | Description |
|
||||
|-----------|------|-------------|
|
||||
| `messages` | `string \| Message[]` | Content to store |
|
||||
| `config.userId` | string | User identifier (at least one scope required) |
|
||||
| `config.agentId` | string | Agent identifier |
|
||||
| `config.runId` | string | Session identifier |
|
||||
| `config.metadata` | object | Custom key-value pairs |
|
||||
| `config.filters` | object | Additional filters |
|
||||
| `config.infer` | boolean | LLM inference (default: true) |
|
||||
|
||||
**Returns:** `Promise<{results: [...], relations?: [...]}>`
|
||||
|
||||
#### search(query, config)
|
||||
|
||||
```typescript
|
||||
const results = await m.search('dietary preferences', { userId: 'alice', limit: 5 });
|
||||
```
|
||||
|
||||
| Parameter | Type | Description |
|
||||
|-----------|------|-------------|
|
||||
| `query` | string | Search query |
|
||||
| `config.userId` | string | Filter by user |
|
||||
| `config.agentId` | string | Filter by agent |
|
||||
| `config.runId` | string | Filter by run |
|
||||
| `config.limit` | number | Max results (default: 100) |
|
||||
| `config.filters` | object | Advanced filters |
|
||||
|
||||
#### get(memoryId) / getAll(config) / update(memoryId, data) / delete(memoryId) / deleteAll(config) / history(memoryId)
|
||||
|
||||
Same interface patterns. Note: OSS `update` takes a string for data, not an object.
|
||||
|
||||
```typescript
|
||||
await m.update('mem-id', 'new content');
|
||||
```
|
||||
|
||||
#### reset()
|
||||
|
||||
Clear the entire vector store and history.
|
||||
|
||||
```typescript
|
||||
await m.reset();
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Key Differences: Platform vs OSS
|
||||
|
||||
| Aspect | Platform (`MemoryClient`) | OSS (`Memory`) |
|
||||
|--------|--------------------------|----------------|
|
||||
| **Import** | `import MemoryClient from 'mem0ai'` | `import { Memory } from 'mem0ai/oss'` |
|
||||
| **Auth** | API key required (`MEM0_API_KEY`) | No API key -- config-based |
|
||||
| **Execution** | API calls to `api.mem0.ai` | Local execution |
|
||||
| **Infrastructure** | Fully managed | Self-managed vector DB, embedder, LLM |
|
||||
| **Param style** | `snake_case` in options (`user_id`) | `camelCase` in config (`userId`) |
|
||||
| **Batch ops** | `batchUpdate`, `batchDelete` | Not available |
|
||||
| **Webhooks** | Full CRUD | Not available |
|
||||
| **Export** | `createMemoryExport` | Not available |
|
||||
| **Feedback** | `feedback()` | Not available |
|
||||
| **Project mgmt** | `getProject`, `updateProject` | Not available |
|
||||
| **User listing** | `users()`, `deleteUser()` | Not available |
|
||||
| **Graph store** | Platform-managed | Self-managed (Neo4j) |
|
||||
| **History** | Platform-managed | SQLite (configurable) |
|
||||
@@ -0,0 +1,481 @@
|
||||
# Mem0 Python SDK Reference
|
||||
|
||||
Complete reference for the `mem0ai` Python package. Covers both the Platform client (managed API) and the Open Source self-hosted variant.
|
||||
|
||||
---
|
||||
|
||||
## Platform Client
|
||||
|
||||
### Installation
|
||||
|
||||
```bash
|
||||
pip install mem0ai
|
||||
export MEM0_API_KEY="m0-your-api-key"
|
||||
```
|
||||
|
||||
### MemoryClient (Synchronous)
|
||||
|
||||
```python
|
||||
from mem0 import MemoryClient
|
||||
|
||||
client = MemoryClient(api_key="m0-xxx")
|
||||
```
|
||||
|
||||
**Constructor:** `MemoryClient(api_key=None)`. If `api_key` is not provided, reads from `MEM0_API_KEY` environment variable. Raises `ValueError` if no key found.
|
||||
|
||||
- HTTP library: `httpx`
|
||||
- Timeout: 300 seconds
|
||||
- Base URL: `https://api.mem0.ai`
|
||||
|
||||
### AsyncMemoryClient (Asynchronous)
|
||||
|
||||
```python
|
||||
from mem0 import AsyncMemoryClient
|
||||
|
||||
client = AsyncMemoryClient(api_key="m0-xxx")
|
||||
|
||||
# Or use as context manager
|
||||
async with AsyncMemoryClient(api_key="m0-xxx") as client:
|
||||
results = await client.search("query", user_id="alice")
|
||||
```
|
||||
|
||||
Same methods as `MemoryClient`, all `async`/`await`. Supports async context manager.
|
||||
|
||||
---
|
||||
|
||||
### Memory Methods
|
||||
|
||||
#### add(messages, **kwargs)
|
||||
|
||||
Store new memories from messages.
|
||||
|
||||
```python
|
||||
messages = [
|
||||
{"role": "user", "content": "I'm a vegetarian and allergic to nuts."},
|
||||
{"role": "assistant", "content": "Got it! I'll remember that."}
|
||||
]
|
||||
client.add(messages, user_id="alice")
|
||||
```
|
||||
|
||||
| Parameter | Type | Default | Description |
|
||||
|-----------|------|---------|-------------|
|
||||
| `messages` | str \| dict \| list[dict] | required | Message content. Strings auto-convert to user messages |
|
||||
| `user_id` | str | None | User identifier |
|
||||
| `agent_id` | str | None | Agent identifier |
|
||||
| `app_id` | str | None | Application identifier |
|
||||
| `run_id` | str | None | Session/run identifier |
|
||||
| `metadata` | dict | None | Custom key-value pairs |
|
||||
| `enable_graph` | bool | None | Activate knowledge graph extraction |
|
||||
| `infer` | bool | True | If False, store raw text without LLM inference |
|
||||
| `immutable` | bool | None | If True, prevents future modification |
|
||||
| `expiration_date` | str | None | Auto-expiry date (`YYYY-MM-DD`) |
|
||||
| `includes` | str | None | Preference filter for inclusion |
|
||||
| `excludes` | str | None | Preference filter for exclusion |
|
||||
| `async_mode` | bool | True | If False, wait for processing to complete |
|
||||
| `custom_categories` | list | None | Override project categories |
|
||||
| `custom_instructions` | str | None | Override extraction instructions |
|
||||
| `timestamp` | int \| float \| str | None | Custom timestamp (Unix epoch or ISO 8601) |
|
||||
|
||||
**Returns:** `dict` -- list of events: `[{"id": "...", "event": "ADD|UPDATE|DELETE", "data": {"memory": "..."}}]`
|
||||
|
||||
#### search(query, **kwargs)
|
||||
|
||||
Search memories by semantic similarity.
|
||||
|
||||
```python
|
||||
results = client.search("dietary preferences", user_id="alice")
|
||||
for mem in results.get("results", []):
|
||||
print(mem["memory"], mem["score"])
|
||||
```
|
||||
|
||||
| Parameter | Type | Default | Description |
|
||||
|-----------|------|---------|-------------|
|
||||
| `query` | str | required | Natural language search query |
|
||||
| `user_id` | str | None | Filter by user |
|
||||
| `agent_id` | str | None | Filter by agent |
|
||||
| `app_id` | str | None | Filter by app |
|
||||
| `top_k` | int | 10 | Number of results |
|
||||
| `filters` | dict | None | V2 filter object (`AND`/`OR`/`NOT`) |
|
||||
| `rerank` | bool | None | Enable deep semantic reranking (+150-200ms) |
|
||||
| `threshold` | float | 0.3 | Minimum similarity score |
|
||||
| `keyword_search` | bool | None | Enable keyword-based search (+10ms) |
|
||||
| `enable_graph` | bool | None | Include graph relations in results |
|
||||
| `filter_memories` | bool | None | Precision filtering, removes low-relevance (+200-300ms) |
|
||||
| `fields` | list | None | Specific fields to return |
|
||||
| `categories` | list | None | Filter by category |
|
||||
|
||||
**Returns:** `dict` -- `{"results": [{id, memory, user_id, categories, score, created_at, ...}], "relations": [...]}`
|
||||
|
||||
#### get(memory_id)
|
||||
|
||||
Retrieve a single memory by ID.
|
||||
|
||||
```python
|
||||
memory = client.get(memory_id="ea925981-...")
|
||||
```
|
||||
|
||||
**Returns:** `dict` -- full memory object
|
||||
|
||||
#### get_all(**kwargs)
|
||||
|
||||
Retrieve all memories with optional filtering. Requires at least one entity identifier.
|
||||
|
||||
```python
|
||||
memories = client.get_all(user_id="alice")
|
||||
# With filters
|
||||
memories = client.get_all(filters={"AND": [{"user_id": "alice"}, {"categories": {"contains": "health"}}]})
|
||||
```
|
||||
|
||||
| Parameter | Type | Default | Description |
|
||||
|-----------|------|---------|-------------|
|
||||
| `user_id` | str | None | Filter by user |
|
||||
| `agent_id` | str | None | Filter by agent |
|
||||
| `app_id` | str | None | Filter by app |
|
||||
| `top_k` | int | None | Limit results |
|
||||
| `page` | int | None | Page number |
|
||||
| `page_size` | int | None | Results per page |
|
||||
| `filters` | dict | None | V2 filter object |
|
||||
| `enable_graph` | bool | None | Include graph relations |
|
||||
|
||||
**Returns:** `dict` -- `{"results": [...]}`
|
||||
|
||||
#### update(memory_id, text=None, metadata=None, timestamp=None)
|
||||
|
||||
Update a memory's content, metadata, or timestamp. At least one parameter required. Cannot update immutable memories.
|
||||
|
||||
```python
|
||||
client.update("ea925981-...", text="Updated: vegan since 2024")
|
||||
client.update("ea925981-...", metadata={"verified": True})
|
||||
```
|
||||
|
||||
**Returns:** `dict` -- updated memory
|
||||
|
||||
#### delete(memory_id)
|
||||
|
||||
Permanently delete a single memory.
|
||||
|
||||
```python
|
||||
client.delete("ea925981-...")
|
||||
```
|
||||
|
||||
#### delete_all(**kwargs)
|
||||
|
||||
Delete all memories matching filters. Irreversible.
|
||||
|
||||
```python
|
||||
client.delete_all(user_id="alice")
|
||||
```
|
||||
|
||||
#### history(memory_id)
|
||||
|
||||
Get the change history of a memory.
|
||||
|
||||
```python
|
||||
history = client.history("ea925981-...")
|
||||
# Returns: [{previous_value, new_value, action, timestamps}]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Batch Methods
|
||||
|
||||
#### batch_update(memories)
|
||||
|
||||
Update up to 1000 memories in a single request.
|
||||
|
||||
```python
|
||||
client.batch_update([
|
||||
{"memory_id": "uuid-1", "text": "Updated text"},
|
||||
{"memory_id": "uuid-2", "text": "Another update", "metadata": {"verified": True}},
|
||||
])
|
||||
```
|
||||
|
||||
#### batch_delete(memories)
|
||||
|
||||
Delete up to 1000 memories in a single request.
|
||||
|
||||
```python
|
||||
client.batch_delete([
|
||||
{"memory_id": "uuid-1"},
|
||||
{"memory_id": "uuid-2"},
|
||||
])
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### User/Entity Management
|
||||
|
||||
#### users()
|
||||
|
||||
List all users, agents, and sessions that have memories.
|
||||
|
||||
```python
|
||||
users = client.users()
|
||||
# Returns: {"results": [{"type": "user", "name": "alice"}, ...]}
|
||||
```
|
||||
|
||||
#### delete_users(user_id=None, agent_id=None, app_id=None, run_id=None)
|
||||
|
||||
Delete a specific entity and all its memories.
|
||||
|
||||
```python
|
||||
client.delete_users(user_id="alice")
|
||||
```
|
||||
|
||||
#### reset()
|
||||
|
||||
Delete ALL users, agents, sessions, and memories. Complete data reset.
|
||||
|
||||
```python
|
||||
client.reset()
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Export & Summary
|
||||
|
||||
#### create_memory_export(schema, **kwargs)
|
||||
|
||||
Create a structured export of memories.
|
||||
|
||||
```python
|
||||
import json
|
||||
|
||||
schema = json.dumps({
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"name": {"type": "string"},
|
||||
"preferences": {"type": "array", "items": {"type": "string"}},
|
||||
}
|
||||
})
|
||||
export = client.create_memory_export(schema=schema, user_id="alice")
|
||||
```
|
||||
|
||||
#### get_memory_export(**kwargs)
|
||||
|
||||
Retrieve a previously created export.
|
||||
|
||||
```python
|
||||
result = client.get_memory_export(memory_export_id=export["id"])
|
||||
```
|
||||
|
||||
#### get_summary(filters=None)
|
||||
|
||||
Get a summary of memories.
|
||||
|
||||
```python
|
||||
summary = client.get_summary(filters={"user_id": "alice"})
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Feedback
|
||||
|
||||
#### feedback(memory_id, feedback=None, feedback_reason=None)
|
||||
|
||||
Provide quality feedback on a memory.
|
||||
|
||||
```python
|
||||
client.feedback(
|
||||
memory_id="mem-123",
|
||||
feedback="POSITIVE", # POSITIVE | NEGATIVE | VERY_NEGATIVE | None (clear)
|
||||
feedback_reason="Accurately captured preference"
|
||||
)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Webhooks
|
||||
|
||||
```python
|
||||
# List
|
||||
webhooks = client.get_webhooks(project_id="proj_123")
|
||||
|
||||
# Create
|
||||
webhook = client.create_webhook(
|
||||
url="https://your-app.com/webhook",
|
||||
name="Memory Logger",
|
||||
project_id="proj_123",
|
||||
event_types=["memory_add", "memory_update"]
|
||||
)
|
||||
|
||||
# Update
|
||||
client.update_webhook(webhook_id=123, name="Updated", url="https://new-url.com")
|
||||
|
||||
# Delete
|
||||
client.delete_webhook(webhook_id=123)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Project Management
|
||||
|
||||
Access via `client.project.*`:
|
||||
|
||||
```python
|
||||
# Get project config
|
||||
config = client.project.get(fields=["custom_categories", "custom_instructions"])
|
||||
|
||||
# Update project settings
|
||||
client.project.update(
|
||||
custom_instructions="Extract dietary preferences and health info",
|
||||
custom_categories=[{"health": "Medical and dietary info"}],
|
||||
enable_graph=True,
|
||||
multilingual=True,
|
||||
)
|
||||
|
||||
# Create/delete project (requires org_id)
|
||||
client.project.create(name="My Project", description="...")
|
||||
client.project.delete()
|
||||
|
||||
# Member management
|
||||
members = client.project.get_members()
|
||||
client.project.add_member(email="user@example.com", role="READER") # READER or OWNER
|
||||
client.project.update_member(email="user@example.com", role="OWNER")
|
||||
client.project.remove_member(email="user@example.com")
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Open Source / Self-Hosted
|
||||
|
||||
### Installation
|
||||
|
||||
```bash
|
||||
pip install mem0ai
|
||||
```
|
||||
|
||||
### Memory Class
|
||||
|
||||
```python
|
||||
from mem0 import Memory
|
||||
|
||||
m = Memory() # Uses default config (OpenAI embedder + in-memory vector store)
|
||||
```
|
||||
|
||||
**Import:** `from mem0 import Memory` (NOT `MemoryClient` -- that is the Platform client)
|
||||
|
||||
### Configuration
|
||||
|
||||
```python
|
||||
config = {
|
||||
"llm": {
|
||||
"provider": "openai", # openai, groq, azure, ollama, lmstudio, google, anthropic, mistral
|
||||
"config": {
|
||||
"model": "gpt-4o-mini",
|
||||
"api_key": "sk-xxx",
|
||||
}
|
||||
},
|
||||
"embedder": {
|
||||
"provider": "openai", # openai, ollama, azure, lmstudio, google, huggingface
|
||||
"config": {
|
||||
"model": "text-embedding-3-small",
|
||||
"api_key": "sk-xxx",
|
||||
}
|
||||
},
|
||||
"vector_store": {
|
||||
"provider": "qdrant", # faiss, qdrant, pgvector, redis, supabase, azure_ai_search, memory
|
||||
"config": {
|
||||
"collection_name": "my_memories",
|
||||
"host": "localhost",
|
||||
"port": 6333,
|
||||
}
|
||||
},
|
||||
"graph_store": { # Optional
|
||||
"provider": "neo4j",
|
||||
"config": {
|
||||
"url": "neo4j://localhost:7687",
|
||||
"username": "neo4j",
|
||||
"password": "password",
|
||||
}
|
||||
},
|
||||
"history_db_path": "history.db", # SQLite path for change history
|
||||
"custom_fact_extraction_prompt": "...", # Custom LLM prompt for extraction
|
||||
"custom_update_memory_prompt": "...", # Custom LLM prompt for updates
|
||||
"enable_graph": False, # Enable graph memory
|
||||
}
|
||||
|
||||
m = Memory.from_config(config)
|
||||
```
|
||||
|
||||
### Context Manager
|
||||
|
||||
```python
|
||||
with Memory(config) as m:
|
||||
m.add("I prefer dark mode", user_id="alice")
|
||||
results = m.search("preferences", user_id="alice")
|
||||
# SQLite connections released automatically
|
||||
```
|
||||
|
||||
### Methods
|
||||
|
||||
All methods mirror the Platform client but run locally:
|
||||
|
||||
#### add(messages, *, user_id, agent_id, run_id, metadata, infer=True)
|
||||
|
||||
```python
|
||||
m.add("I'm a vegetarian", user_id="alice")
|
||||
m.add([
|
||||
{"role": "user", "content": "I like hiking"},
|
||||
{"role": "assistant", "content": "Great outdoor activity!"}
|
||||
], user_id="alice")
|
||||
```
|
||||
|
||||
At least one of `user_id`, `agent_id`, `run_id` required.
|
||||
|
||||
**Returns:** `{"results": [...], "relations": [...]}`
|
||||
|
||||
#### search(query, *, user_id, agent_id, run_id, limit=100, filters=None, threshold=None, rerank=True)
|
||||
|
||||
```python
|
||||
results = m.search("dietary preferences", user_id="alice", limit=5)
|
||||
```
|
||||
|
||||
Supports filter operators: `eq`, `ne`, `in`, `nin`, `gt`, `gte`, `lt`, `lte`, `contains`, `not_contains`.
|
||||
|
||||
#### get(memory_id) / get_all(**kwargs) / update(memory_id, data, metadata=None) / delete(memory_id) / delete_all(**kwargs) / history(memory_id)
|
||||
|
||||
Same interface as Platform client.
|
||||
|
||||
#### reset()
|
||||
|
||||
Clear the entire vector store collection and history database. Recreates the vector store.
|
||||
|
||||
```python
|
||||
m.reset()
|
||||
```
|
||||
|
||||
#### close()
|
||||
|
||||
Release SQLite connections. Called automatically when using context manager.
|
||||
|
||||
### AsyncMemory
|
||||
|
||||
```python
|
||||
from mem0 import AsyncMemory
|
||||
|
||||
m = AsyncMemory(config)
|
||||
await m.add("text", user_id="alice")
|
||||
results = await m.search("query", user_id="alice")
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Key Differences: Platform vs OSS
|
||||
|
||||
| Aspect | Platform (`MemoryClient`) | OSS (`Memory`) |
|
||||
|--------|--------------------------|----------------|
|
||||
| **Import** | `from mem0 import MemoryClient` | `from mem0 import Memory` |
|
||||
| **Auth** | API key required (`MEM0_API_KEY`) | No API key -- config-based |
|
||||
| **Execution** | API calls to `api.mem0.ai` | Local execution |
|
||||
| **Infrastructure** | Fully managed | Self-managed vector DB, embedder, LLM |
|
||||
| **Batch ops** | `batch_update`, `batch_delete` | Not available |
|
||||
| **Webhooks** | Full CRUD | Not available |
|
||||
| **Export** | `create_memory_export`, `get_memory_export` | Not available |
|
||||
| **Feedback** | `feedback()` | Not available |
|
||||
| **Project mgmt** | `client.project.*` | Not available |
|
||||
| **User listing** | `users()`, `delete_users()` | Not available |
|
||||
| **Custom prompts** | Via project settings | Direct config |
|
||||
| **Graph store** | Platform-managed | Self-managed (Neo4j) |
|
||||
| **History** | Platform-managed | SQLite (configurable) |
|
||||
| **Async** | `AsyncMemoryClient` | `AsyncMemory` |
|
||||
@@ -116,73 +116,24 @@ result = crew.kickoff()
|
||||
|
||||
## Vercel AI SDK
|
||||
|
||||
Source: [docs.mem0.ai/integrations/vercel-ai-sdk](https://docs.mem0.ai/integrations/vercel-ai-sdk)
|
||||
> **Dedicated skill available.** For comprehensive Vercel AI SDK documentation, see the [mem0-vercel-ai-sdk skill](../mem0-vercel-ai-sdk/SKILL.md) ([GitHub](https://github.com/mem0ai/mem0/tree/main/skills/mem0-vercel-ai-sdk)).
|
||||
|
||||
Install: `npm install @mem0/vercel-ai-provider`
|
||||
|
||||
### Basic Text Generation with Memory
|
||||
Quick example (wrapped model with automatic memory):
|
||||
|
||||
```typescript
|
||||
import { generateText } from "ai";
|
||||
import { createMem0 } from "@mem0/vercel-ai-provider";
|
||||
|
||||
const mem0 = createMem0({
|
||||
provider: "openai",
|
||||
mem0ApiKey: "m0-xxx",
|
||||
apiKey: "openai-api-key",
|
||||
});
|
||||
|
||||
const { text } = await generateText({
|
||||
model: mem0("gpt-4-turbo", { user_id: "borat" }),
|
||||
prompt: "Suggest me a good car to buy!",
|
||||
});
|
||||
```
|
||||
|
||||
### Streaming with Memory
|
||||
|
||||
```typescript
|
||||
import { streamText } from "ai";
|
||||
import { createMem0 } from "@mem0/vercel-ai-provider";
|
||||
|
||||
const mem0 = createMem0();
|
||||
|
||||
const { textStream } = streamText({
|
||||
const { text } = await generateText({
|
||||
model: mem0("gpt-4-turbo", { user_id: "borat" }),
|
||||
prompt: "Suggest me a good car to buy!",
|
||||
});
|
||||
|
||||
for await (const textPart of textStream) {
|
||||
process.stdout.write(textPart);
|
||||
}
|
||||
```
|
||||
|
||||
### Using Memory Utilities Standalone
|
||||
|
||||
```typescript
|
||||
import { openai } from "@ai-sdk/openai";
|
||||
import { generateText } from "ai";
|
||||
import { retrieveMemories, addMemories } from "@mem0/vercel-ai-provider";
|
||||
|
||||
// Retrieve memories and inject into any provider
|
||||
const prompt = "Suggest me a good car to buy.";
|
||||
const memories = await retrieveMemories(prompt, { user_id: "borat", mem0ApiKey: "m0-xxx" });
|
||||
|
||||
const { text } = await generateText({
|
||||
model: openai("gpt-4-turbo"),
|
||||
prompt: prompt,
|
||||
system: memories,
|
||||
});
|
||||
|
||||
// Store new memories
|
||||
await addMemories(
|
||||
[{ role: "user", content: [{ type: "text", text: "I love red cars." }] }],
|
||||
{ user_id: "borat", mem0ApiKey: "m0-xxx" }
|
||||
);
|
||||
```
|
||||
|
||||
### Supported Providers
|
||||
|
||||
`openai`, `anthropic`, `google`, `groq`
|
||||
Supported providers: `openai`, `anthropic`, `google`, `groq`, `cohere`
|
||||
|
||||
---
|
||||
|
||||
|
||||
@@ -2,6 +2,8 @@
|
||||
|
||||
Complete SDK reference for Python and TypeScript. All methods use `MemoryClient` (Platform API).
|
||||
|
||||
> **For language-specific deep references (including OSS):** See [client/python.md](../client/python.md) and [client/node.md](../client/node.md). For Python vs TypeScript differences: [client/differences.md](../client/differences.md).
|
||||
|
||||
## Initialization
|
||||
|
||||
**Python:**
|
||||
|
||||
Reference in New Issue
Block a user