Mem0

Salient Memory Extraction & Recall

Mem0 distills interactions into compact, factual memories and retrieves them when relevant. It scores importance, freshness, and scope to avoid clutter, and can attach metadata like source, tags, and entities for targeted recall. Memories persist across sessions and tools, so assistants remember preferences, biographical facts, and past outcomes. This makes conversations feel continuous without stuffing prompts, and improves accuracy by grounding answers in user-specific context.

APIs, SDKs, Open Source & Cloud

Start with the open-source server or use Mem0 Cloud for hosted scale. REST and Python/JS SDKs expose endpoints to create, search, update, and delete memories across users, agents, apps, and runs. OpenAPI docs and quickstarts make integration straightforward. You can run locally for privacy, then shift to cloud as traffic grows—keeping the same API surface. This flexibility lets startups prototype quickly and enterprises meet governance requirements.

Graph & Vector Stores with Integrations

Combine vector search with graph relationships to link people, preferences, and events over time. Mem0 supports embeddings for semantic match plus edges for explicit ties, so agents infer context and constraints. Integrations exist for LangChain, AutoGen, CrewAI, Camel, and voice stacks, making memories available to chains and tools with minimal glue code. The result is richer recall than plain keyword or vector only, and cleaner reasoning paths.

Policies, Scoping & Privacy Controls

Keep memories safe and relevant with namespaces, TTLs, and access policies. Control which agents can read or write, redact or hash sensitive fields, and auto-expire stale items. Audit logs show when memories were created, updated, or used in responses. This limits over-reach, reduces leakage between tenants, and supports compliance reviews. Combining scoring, retention, and permissions keeps the store lean, explainable, and compliant when stakes are high.

Personalization & Cost Reduction

By remembering stable facts and preferences, Mem0 lets assistants skip repetitive Q&A and shorter prompts, cutting tokens and latency. Support bots recall prior issues and outcomes; copilots pick defaults that match user history; research agents retain sources and stances per project. Because recall is precise, you avoid over-stuffing context windows and can run smaller models more often. The result is better UX at lower cost, with measurable gains in retention and task completion.