MemoryAtlas

Single-pass ADD-only extraction: each add() fires one LLM call (default gpt-5-mini) that accumulates facts — no UPDATE or DELETE on ingestion. Entity linking via spaCy (installed as `mem0ai[nlp]`) extracts named entities from every stored memory and writes them to a parallel vector sub-collection (`{collection}_entities`); retrieval folds entity-match scores into the ranking alongside dense and keyword signals. External graph DB support (Neo4j, Memgraph, Kuzu, AGE, Neptune) was removed in 2026; graph-style association is now vector-native. Three memory scopes: user (cross-session persistence), session (ephemeral per run), agent (runtime state). Two deployment paths: OSS library (BYO LLM + BYO vector store) or fully managed cloud (Qdrant-backed, auto-scaled). 23+ official integrations span LangChain, LangGraph, CrewAI, AutoGen, Vercel AI SDK, OpenAI Agents SDK, Google ADK, AWS Bedrock, LiveKit, ElevenLabs, Dify, Flowise, and more.

Deep diveMem0 deep dive: how the token-efficient memory layer works in productionRead →
Storage
Cloud: Mem0-hosted Qdrant vectors with automatic scaling, reranking, and no infrastructure work. OSS: pluggable vector store (Qdrant, Chroma, Pinecone, Weaviate, PGVector, Valkey, and others) plus an auto-created entities sub-collection (`{collection}_entities`) for entity-linked memories. Requires BYO LLM and BYO embedder in OSS mode (default text-embedding-3-small; Qwen 600M+ recommended for hybrid search). No external graph database required — entity storage is vector-native since Jun 2026.
Retrieval
Multi-signal fusion: dense semantic similarity, BM25 keyword matching, and entity matching (spaCy-extracted entities against the `{collection}_entities` sub-collection) are scored in parallel and fused at query time. Metadata filter support. No LLM call at retrieval time — retrieval is embed-then-search only. Reported p50 latency 0.88–1.09s per call. Token consumption under 7,000 per retrieval across all four benchmarks (LoCoMo 6,956; LongMemEval 6,787; BEAM 1M 6,719; BEAM 10M 6,914), vs 25,000+ for full-context approaches (self-reported).
Self-host
Self-host: moderate
License
Apache-2.0
Pricing
Free/Hobby (10k adds, 1k retrievals/mo, 1 project); Starter $19/mo (50k adds, 5k retrievals, 1 project); Pro $249/mo (500k adds, 50k retrievals, unlimited projects, private Slack, advanced analytics, graph memory); Enterprise custom (unlimited adds/retrievals/projects, on-prem, audit logs). The former Growth $79/mo tier has been discontinued — Starter now jumps directly to Pro. · Freemium
GitHub stars
62,937
Last release
2026-08-05
Last commit
2026-08-07
First catalogued
2026-06-28

Strengths

  • ~30s to wire up: one API key and two lines of code via the cloud SDK; OSS mode requires BYO vector store but still ships a working config in minutes
  • 23+ official framework integrations: LangChain, LangGraph, CrewAI, AutoGen, Vercel AI SDK, OpenAI Agents SDK, Google ADK, AWS Bedrock, LiveKit, ElevenLabs, Dify, Flowise, and more
  • Token-efficient retrieval: under 7,000 tokens per retrieval call vs 25,000+ for full-context; extraction is one LLM call per add()
  • Generous free tier: 10k memory adds and 1k retrievals per month at no cost with no credit card required
  • Entity-linked recall without an external graph DB: spaCy NLP extracts entities into a parallel vector sub-collection and boosts precision on named-entity queries

Watch out

  • ADD-only extraction accumulates facts without deduplication: contradictory or stale memories can pile up in long-running deployments; there is no automatic conflict resolution or overwrite on ingestion
  • Each add() fires one LLM call (gpt-5-mini by default): extraction API costs accrue at production scale on top of embedding costs — budget for LLM usage on every memory write, not just on reads
  • All published benchmark scores (LoCoMo 92.5, LongMemEval 94.4, BEAM 1M 64.1) are self-reported by Mem0; independently reproduced results in this catalog are lower; no neutral same-config multi-framework comparison exists yet
  • OSS/platform feature gap: entity linking requires `mem0ai[nlp]` + spaCy in OSS; cloud platform adds automatic reranking, webhooks, audit logs, custom categories, and SOC 2/HIPAA controls not present in the OSS tier

Best for

  • Fastest drop-in memory with the largest integration ecosystem when time-to-working-agent is the constraint
  • Token-cost-sensitive production agents needing sub-7k retrieval tokens and single-pass extraction without full-context overhead
  • SOC 2 / HIPAA workloads and AWS Agent SDK users needing managed, auditable memory with no infrastructure to run

How it integrates

Benchmark results

BenchmarkValueBackboneTrustSource
longmemeval94.4 accuracySelf-reportedMem0
longmemeval49 accuracyGPT-4oIndependentZep (competitor harness)
locomo92.5 accuracySelf-reportedMem0
beam-1m64.1 accuracySelf-reportedMem0
beam-10m48.6 accuracySelf-reportedMem0
locomo66.88 accuracyIndependentHindsight/Vectorize (competitor re-run)
longmemeval62.6 accuracygpt-4o-miniIndependentLiCoMemory (Huang et al., HKUST et al.) — competitor re-run
locomo54.68 accuracygpt-4o-miniIndependentLiCoMemory (Huang et al., HKUST et al.) — competitor re-run

Sources

Last verified 2026-08-01 · updated by refresh-framework-cards