mem0

# Mem0 Platform Integration > **Skill Graph:** This skill is part of the Mem0 skill graph: > - **mem0** (this skill) -- Platform Client SDK + OSS (Python + TypeScript) > - **[mem0-vercel-ai-sdk](https://github.com/mem0ai/mem0/tree/main/skills/mem0-vercel-ai-sdk)** -- Vercel AI SDK provider Mem0 is a managed memory layer for AI applications. It stores, retrieves, and manages user memories via API — no infrastructure to deploy. For self-hosted usage, see the OSS section in the client references below. ## Step 1: Install and authenticate **Python:** ```bash pip install mem0ai export MEM0_API_KEY="m0-your-api-key" ``` **TypeScript/JavaScript:** ```bash npm install mem0ai export MEM0_API_KEY="m0-your-api-key" ``` Get an API key at: https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=mem0-plugin-skill > **Don't have a `MEM0_API_KEY`?** Sign up at https://app.mem0.ai and create one from the dashboard. Keys start with `m0-`. ## Step 2: Initialize the client **Python:** ```python from mem0 import MemoryClient client = MemoryClient(api_key="m0-xxx") ``` **TypeScript:** ```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 ```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") ``` ### Search memories ```python results = client.search("dietary preferences", filters={"user_id": "alice"}) for mem in results.get("results", []): print(mem["memory"]) ``` ### Get all memories ```python all_memories = client.get_all(filters={"user_id": "alice"}) ``` ### Update a memory ```python client.update("memory-uuid", text="Updated: vegetarian, nut allergy, prefers organic") ``` ### Delete a memory ```python client.delete("memory-uuid") client.delete_all(user_id="alice") # delete all for a user ``` ## Common integration pattern ```python 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:** v3 processes `add()` asynchronously — returns an event ID immediately. Wait 2-3s 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. `{"AND": [{"user_id": "alice"}, {"agent_id": "bot"}]}` returns nothing. Use `OR` instead, or query each separately. - **Duplicate memories:** Don't mix `infer=True` (default) and `infer=False` for the same data. `infer=True` extracts facts via LLM with dedup. `infer=False` stores raw — same text can be stored twice. - **Implicit null scoping:** `filters={"user_id": "alice"}` only returns memories where `agent_id`, `app_id`, `run_id` are ALL null. Wrap in `{"OR": [...]}` to include memories with non-null scoping fields. - **Platform vs OSS imports:** Platform: `from mem0 import MemoryClient`. OSS: `from mem0 import Memory`. Don't mix them — `MemoryClient` talks to `api.mem0.ai`, `Memory` runs locally. - **v3 defaults:** `top_k=20`, `threshold=0.1`, `rerank=False`. Adjust as needed. ## v3 API (Current) Mem0 v3 uses single-pass extraction, entity linking, and multi-signal retrieval. **Key v3 changes from v2:** - **Endpoints:** `POST /v3/memories/add/`, `POST /v3/memories/search/`, `POST /v3/memories/` (paginated list) - **Extraction:** Single ADD-only pass — no more UPDATE/DELETE operations during extraction. Memories accumulate rather than consolidate. - **Entity linking:** Replaces graph memory. Auto-extracted during `add()`, no config needed. Remove `enable_graph` and `graph_store` from any old config. - **Defaults:** `top_k=20`, `threshold=0.1`, `rerank=False` - **Removed params:** `org_id`, `project_id`, `enable_graph` — all removed from SDK - **TypeScript:** Exclusively camelCase (`userId`, `agentId`, `appId`, `topK`) - **Add response:** Async — returns event ID immediately, poll via `GET /v1/event/{event_id}/` See the [migration guide](https://docs.mem0.ai/migration/platform-v2-to-v3) for details. ## Live documentation search For the latest docs beyond what's in the references, use the doc search tool: ```bash 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](client/python.md) | | TypeScript/Node.js (MemoryClient + Memory OSS) | [client/node.md](client/node.md) | | Python vs TypeScript differences | [client/differences.md](client/differences.md) | ## Platform References Load these on demand for deeper detail: | Topic | File | |-------|------| | Quickstart (Python, TS, cURL) | [references/quickstart.md](references/quickstart.md) | | SDK guide (all methods, both languages) | [references/sdk-guide.md](references/sdk-guide.md) | | API reference (endpoints, filters, object schema) | [references/api-reference.md](references/api-reference.md) | | Architecture (pipeline, lifecycle, scoping, performance) | [references/architecture.md](references/architecture.md) | | Platform features (retrieval, graph, categories, MCP, etc.) | [references/features.md](references/features.md) | | Framework integrations (LangChain, CrewAI, OpenAI Agents, etc.) | [references/integration-patterns.md](references/integration-patterns.md) | | Use cases & examples (real-world patterns with code) | [references/use-cases.md](references/use-cases.md) | ## Related Mem0 Skills | Skill | When to use | Link | |-------|-------------|------| | mem0-vercel-ai-sdk | Vercel AI SDK provider with automatic memory | [GitHub](https://github.com/mem0ai/mem0/tree/main/skills/mem0-vercel-ai-sdk) |