hindsight vs Cognee
Two of the top memory, side by side: score, setup, license, activity and what each review found.
hindsight
Agent memory server with retain, recall and reflect operations
Cognee
Memory engine that turns documents and code into a knowledge graph
| What we compare | hindsight | Cognee |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 81, widely used | 64, popular |
| Freshness | 100, active | 100, active |
| Maintenance | 95, healthy | 82, healthy |
| Easy to run | 50, easy | 67, easy |
| Agent-ready | 85, ready | 85, ready |
| Facts from GitHub and the README | ||
| Stars | 48.7k | 32.1k |
| License | MIT (permissive) | Apache-2.0 (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Oct 2026 |
| Language | Python | Python |
| Docker | Yes | Yes |
| GPU | Not stated | Not needed |
| arm64 or Apple Silicon | Mentioned | Mentioned |
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
Cognee
Cognee builds persistent agent memory by extracting entities, relationships and chunks from text, code and sessions into a graph and vector index with hybrid recall. Without an LLM key it uses local GLiNER extraction and embeddings; adding a key enables generated answers via OpenAI, Ollama or other providers. It runs as a library, CLI, REST API (port 8000), UI (3000) and MCP server (8001).
Who it is for: Developers building graph-backed memory for agents and company knowledge
Strengths
- Keyless mode: local GLiNER extraction and embeddings, no cloud LLM required
- Claude Code and Codex plugins, OpenClaw plugin, MCP server, Python, TypeScript and Rust SDKs
- Imports memory from Mem0, Letta, Zep or Graphiti via the COGX format
- Apache-2.0; research paper and BEAM evaluation published
Weaknesses
- Single-Postgres graph store is a demo; production version is a licensed product
- API defaults to multi-tenant mode; local use needs ENABLE_BACKEND_ACCESS_CONTROL=false
- Bundled GLiNER extractor is a demo; higher-accuracy version requires contacting the vendor
- UI launcher needs Node.js/npm and Docker for its MCP service
- no GPU
- Docker + Compose
- Compose runs Neo4j, PostgreSQL, Redis
- Models: local GLiNER + embeddings (keyless), OpenAI, Ollama, other providers per docs
- port 8000
More in Memory
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