hindsight vs Mem0
Two of the top projects in Memory, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
hindsight
Agent memory server with retain, recall and reflect operations
Mem0
Long-term memory layer that stores and retrieves user facts for LLM agents
Verdict
hindsight ranks higher, 94 to 89. Of the 4 score parts we measure for both, hindsight leads on 2, Mem0 on 1 and 1 is even.
- AdoptionMem0 leads by 8 points, 97 to 89.
- FreshnessEven at 100.
- Maintenancehindsight leads by 20 points, 96 to 76: a wide gap.
- Agent-readyhindsight leads by 15 points, 85 to 70: a wide gap.
Pick hindsight if you are
Developers adding long-term memory to LLM agents and coding assistants
- Four parallel retrieval strategies merged with reciprocal rank fusion and cross-encoder reranking
- Works with 25+ LLM providers, including local ollama, lmstudio and llamacpp
Pick Mem0 if you are
Developers adding persistent per-user memory to agents or chatbots
- Python (pip) and npm SDKs plus a CLI, so it embeds in existing agents
- Hybrid retrieval: semantic, BM25 keyword and entity linking fused in one search
| What we compare | hindsight | Mem0 |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 89, widely used | 97, widely used |
| Freshness | 100, active | 100, active |
| Maintenance | 96, healthy | 76, fair |
| Agent-ready | 85, ready | 70, partly |
| Facts from GitHub and the README | ||
| Stars | 53.3k | 67.7k |
| License | MIT (permissive) | Apache-2.0 (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Sep 2026 |
| Language | Python | Python |
| Docker | Yes | No |
| GPU | Not stated | Optional |
| arm64 or Apple Silicon | Mentioned | Not stated |
hindsight
Hindsight stores agent memories in banks and extracts facts, entities and timestamps from retained text using an LLM. Recall runs semantic, BM25, graph and temporal retrieval in parallel, then reranks the merged results. Background jobs consolidate facts into observations and mental models. It exposes a REST API, Python, Node.js and Go clients, a CLI, and a per-bank MCP endpoint.
Who it is for: Developers adding long-term memory to LLM agents and coding assistants
Strengths
- Four parallel retrieval strategies merged with reciprocal rank fusion and cross-encoder reranking
- Works with 25+ LLM providers, including local ollama, lmstudio and llamacpp
- Built-in MCP endpoint per bank, plus 60+ listed integrations
- Embedded mode runs in-process via pip with a bundled pg0 database
Weaknesses
- Every retain call requires an LLM, adding cost and latency
- Accuracy claims are the vendor's own benchmarks; independent reproduction is partial
- Managed Cloud and Enterprise tiers exist; feature differences from self-hosted are unclear
- Minimum RAM and GPU needs are not stated in the README
- Docker
- Needs LLM provider (hosted or local), PostgreSQL (embedded pg0 by default), Oracle AI Database (optional)
- Models: openai, anthropic, gemini, groq, bedrock
- port 9999
Mem0
Mem0 extracts facts from conversations with an LLM, stores them per user, session or agent, and retrieves them with combined semantic, BM25 keyword and entity matching. It ships as a Python and npm library, a self-hosted server with a dashboard and API keys, a CLI, and a managed cloud platform. The published benchmark numbers come from the managed platform, not the open-source SDK.
Who it is for: Developers adding persistent per-user memory to agents or chatbots
Strengths
- Python (pip) and npm SDKs plus a CLI, so it embeds in existing agents
- Hybrid retrieval: semantic, BM25 keyword and entity linking fused in one search
- Evaluation framework is open-sourced so benchmark numbers can be reproduced
- Supports several LLMs and embedders; defaults are configurable
Weaknesses
- Benchmarks reflect the managed platform; OSS results may differ
- Needs an LLM; default is OpenAI gpt-5-mini and text-embedding-3-small
- BM25 and entity features need the optional mem0ai[nlp] extra and a spaCy model
- Self-hosted dashboard is listed as limited; advanced features are cloud-only
- GPU optional
- Needs LLM provider (default OpenAI), Embedding model (default OpenAI text-embedding-3-small), Docker Compose (self-hosted server), spaCy en_core_web_sm (optional NLP extra)
- Models: OpenAI gpt-5-mini (default), text-embedding-3-small (default), other LLMs and embedders via configuration
- port 3000
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