hindsight vs agentmemory
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
agentmemory
Persistent memory server for coding agents, exposed over MCP and REST
| What we compare | hindsight | agentmemory |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 81, widely used | 48, known |
| Freshness | 100, active | 100, active |
| Maintenance | 93, healthy | 81, healthy |
| Easy to run | 50, easy | 83, very easy |
| Agent-ready | 85, ready | 30, minimal |
| Facts from GitHub and the README | ||
| Stars | 48.2k | 29.3k |
| License | MIT (permissive) | Apache-2.0 (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Oct 2026 |
| Language | Python | TypeScript |
| 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
agentmemory
agentmemory captures what a coding agent does across sessions, stores it as searchable memory, and injects relevant context at the start of the next session. It runs as a local Node.js server on the pinned iii engine and connects to agents through hooks, MCP, or REST, with 20 adapters listed. Keyless mode uses BM25 search; vector embeddings need a provider or the local Xenova/all-MiniLM-L6-v2 model.
Who it is for: Developers using coding agents who want memory kept across sessions
Strengths
- No external database; state lives in a local iii engine data directory
- Works with 20 listed agents through hooks, MCP, or REST
- Keyless BM25 mode works without any API key
- Local embeddings via EMBEDDING_PROVIDER=local after a one-time model download
Weaknesses
- Keyless mode has no vector search, so semantic queries can return nothing
- Pinned to iii-engine v0.22.1; refuses to attach to other engine versions
- Native Windows needs manual iii.exe install; WSL2 or Docker recommended
- Uses four local ports (3111, 3112, 3113, 49134)
- no GPU
- Docker + Compose
- Needs Node.js 20+, iii-engine v0.22.1
- Models: Xenova/all-MiniLM-L6-v2 (local embeddings)
- port 3113