mem0

name mem0 description Mem0 Platform SDK for adding persistent memory to AI applications. TRIGGER when: user mentions "mem0", "MemoryClient", "memory layer", "remember user preferences", "persistent context", "personalization", or needs to add long-term memory to chatbots, agents, or AI apps. Covers Python SDK (mem0ai), TypeScript SDK (mem0ai), and framework integrations (LangChain, CrewAI, OpenAI Agents SDK, Pipecat, LlamaIndex, AutoGen, LangGraph). Also covers the open-source self-hosted Memory class. This is the DEFAULT mem0 skill for ambiguous queries. DO NOT TRIGGER when: user asks about CLI commands, terminal usage, or shell scripts (use mem0-cli), or Vercel AI SDK / @mem0/vercel-ai-provider / createMem0 (use mem0-vercel-ai-sdk). license Apache-2.0 metadata {"author":"mem0ai","version":"3.0.0","category":"ai-memory","tags":"memory, personalization, ai, python, typescript, vector-search"} compatibility Requires Python 3.10+ or Node.js 18+, pip install mem0ai or npm install mem0ai, MEM0_API_KEY env var (Platform), and internet access to api.mem0.ai. SDK v3 with v2 compatibility mode available. Mem0 Platform Integration Skill Graph: This skill is part of the Mem0 skill graph: mem0 (this skill) -- Platform Client SDK + OSS (Python + TypeScript) mem0-cli ( GitHub ) -- Command-line interface mem0-vercel-ai-sdk ( GitHub ) -- Vercel AI SDK provider Mem0 is a managed memory layer for AI applications. It stores, retrieves, and manages user memories via API — no infrastructure to deploy. For self-hosted usage, see the OSS section in the client references below. Step 1: Install and authenticate Python: pip install mem0ai export MEM0_API_KEY= "m0-your-api-key" TypeScript/JavaScript: npm install mem0ai export MEM0_API_KEY= "m0-your-api-key" Get an API key at: https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=skill-mem0 Don't have a MEM0_API_KEY ? Run mem0 init --agent --agent-caller <your-name> --json (after pip install mem0-cli or npm install -g @mem0/cli ), substituting your agent identity (e.g. claude-code , cursor ). If you forgot to pass --agent-caller , run mem0 identify <your-name> after init. The human can claim later with mem0 init --email <your-email> . Step 2: Initialize the client Python: from mem0 import MemoryClient client = MemoryClient(api_key= "m0-xxx" ) TypeScript: import MemoryClient from 'mem0ai' ; const client = new MemoryClient ({ apiKey : 'm0-xxx' }); For async Python, use AsyncMemoryClient . Step 3: Core operations Every Mem0 integration follows the same pattern: retrieve → generate → store . Add memories 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" ) Search memories results = client.search( "dietary preferences" , filters={ "user_id" : "alice" }) for mem in results.get( "results" , []): print (mem[ "memory" ]) Get all memories all_memories = client.get_all(filters={ "user_id" : "alice" }) Update a memory client.update( "memory-uuid" , text= "Updated: vegetarian, nut allergy, prefers organic" ) Delete a memory client.delete( "memory-uuid" ) client.delete_all(user_id= "alice" ) # delete all for a user Common integration pattern from mem0 import MemoryClient from openai import OpenAI mem0 = MemoryClient() openai = OpenAI() def chat ( user_input: str , user_id: str ) -> str : # 1. Retrieve relevant memories memories = mem0.search(user_input, filters={ "user_id" : user_id}) context = "\n" .join([m[ "memory" ] for m in memories.get( "results" , [])]) # 2. Generate response with memory context response = openai.chat.completions.create( model= "gpt-5-mini" , messages=[ { "role" : "system" , "content" : f"User context:\n {context} " }, { "role" : "user" , "content" : user_input}, ] ) reply = response.choices[ 0 ].message.content # 3. Store interaction for future context mem0.add( [{ "role" : "user" , "content" : user_input}, { "role" : "assistant" , "content" : reply}], user_id=user_id ) return reply Common edge cases Search returns empty: Memories process asynchronously. Wait 2-3s after add() before searching. Also verify user_id matches exactly (case-sensitive) and use filters={"user_id": "..."} syntax. AND filter with user_id + agent_id returns empty: Entities are stored separately. Use OR instead, or query separately. Duplicate memories: Don't mix infer=True (default) and infer=False for the same data. Stick to one mode. Wrong import: Always use from mem0 import MemoryClient (or AsyncMemoryClient for async). Do not use from mem0 import Memory . v3 defaults: top_k=20 , threshold=0.1 , rerank=False . Adjust as needed for your use case. v2 Compatibility If you're using SDK v2.x, note these differences: Entity IDs: Pass user_id as top-level kwarg to search() instead of inside filters Defaults: top_k=100 , no threshold, rerank=True Graph memory: Available via enable_graph=True See the migration guide for details. Live documentation search For the latest docs beyond what's in the references, use the doc search tool: python ${CLAUDE_SKILL_DIR} /scripts/mem0_doc_search.py --query "topic" python ${CLAUDE_SKILL_DIR} /scripts/mem0_doc_search.py --page "/platform/features/graph-memory" python ${CLAUDE_SKILL_DIR} /scripts/mem0_doc_search.py --index No API key needed — searches docs.mem0.ai directly. Client SDK References Language-specific deep references (Platform + OSS): Language File Python (MemoryClient + AsyncMemoryClient + Memory OSS) client/python.md TypeScript/Node.js (MemoryClient + Memory OSS) client/node.md Python vs TypeScript differences client/differences.md Platform References Load these on demand for deeper detail: Topic File Quickstart (Python, TS, cURL) references/quickstart.md SDK guide (all methods, both languages) references/sdk-guide.md API reference (endpoints, filters, object schema) references/api-reference.md Architecture (pipeline, lifecycle, scoping, performance) references/architecture.md Platform features (retrieval, graph, categories, MCP, etc.) references/features.md Framework integrations (LangChain, CrewAI, OpenAI Agents, etc.) references/integration-patterns.md Use cases & examples (real-world patterns with code) references/use-cases.md Related Mem0 Skills Skill When to use Link mem0-cli Terminal commands, scripting, CI/CD, agent tool loops local / GitHub mem0-vercel-ai-sdk Vercel AI SDK provider with automatic memory local / GitHub