A pasteable skeleton generated from the repository name. Check the project README for the exact package and arguments.
Star count
9 ★ · HBarefoot/engram · read from the repository page · (checked 2026-10-16 20:00:20)
Source
collected from the punkpeye/awesome-mcp-servers list · type: MCP server
Harvest history
first seen 2026-10-09 · last seen 2026-10-16 · seen 32× · listed by 7 sources
Score
score 703 · 9 ★ · 7 sources · mcp
FAQ (answers come from our records)
How do I install it?
We did not find an install command for this repository (our install coverage is only about 4%). Check its README or releases page.
Who is it for?
We filed it under these tags: MCP 服务. The tags come from the entry itself, not a taxonomy we imposed.
Who maintains it?
repository HBarefoot/engram;first catalogued 2026-10-09;last seen 2026-10-16。There is no official certification or quality rating here — we record sources and dates only.
**Persistent memory for AI agents. In-process. No infra.** > Give your AI agent the memory of a colleague who's worked with you for years — without cloud, API keys, or Docker. ⭐ **Useful to you? Star it on GitHub** — it's the simplest way to help others find Engram. Your AI agent now has long-term memory. Two minutes, no setup, no cloud. - 🧠 **In-process** — runs inside your agent's stack. No separate server to deploy, no IPC overhead, nothing to fork. - 📴 **Offline** — local SQLite + bundled embeddings (~23 MB). No API keys, no data leaving your machine. - 🔌 **MCP-native** — first-class Model Context Protocol integration with Claude Desktop, Claude Code, Cursor, Windsurf, and Cline. - 🔐 **Safety by default** — automatic secret detection on every write. API keys, private keys, connection strings, JWTs blocked before they hit the database. --- Why local-first, in numbers Engram runs *inside* your agent's process — no service to deploy, no account, nothing leaving your machine. That design choice is measurable: | Metric | Engram | | |---|---|---| | Cold start → first recall | **under 200 ms** | import → first answer, model load included (M-series; hardware-dependent) | | Warm recall (p50, 1k memories) | **~4 ms** | median query latency once the model is in memory | | Package download | **~571 KB** | the npm package (1.3 MB unpacked) | | Embedding model | **~23 MB** | all-MiniLM-L6-v2, fetched once, cached at ~/.engram/models | | External services | **0** | no database, broker, or cloud account | | Works offline | **✅** | zero network calls on the default path | Measured on an Apple M4 Pro over 1,000 seeded memories — reproduce with npm run bench. These are footprint and latency numbers, **not** an accuracy claim: Engram doesn't try to out-rank Mem0 or Zep on memory benchmarks. The point is solid recall with none of the operational surface. **Optional accuracy lift — still 100% local.** If you already run a local model, the opt-in LLM layer sharpens fact extraction: entity-extraction accuracy climbs from 45.8% (rule-based) to 95.8% with the recommended henrybarefoot1987/engram-extract model (qwen3:1.7b) — **+50 pts** — without a single byte leaving your device. --- Support Engram **Engram is free and MIT-licensed — and always will be.** No paywalls, no tier-locked features, no telemetry. Every feature ships in the open-source package. Sponsorship is purely a way to fund continued development, not to unlock anything. If Engram saves you time, you can sponsor it via Polar: | Tier | Price / month | For | |---|---|---| | 🌱 **Supporter** | $5 | Individuals who want the project to keep shipping. | | ⚡ **Power User** | $25 | Heavy users who rely on Engram day to day. | | 👥 **Team** | $100 | Teams standardizing on Engram across projects. | | 🏢 **Enterprise** | $499 | Priority response on issues + dedicated integration help. | **About Enterprise.** Engram is MIT-licensed, so commercial use is *already* granted — you don't need to buy a license to use it at work. The Enterprise tier buys **priority response** on issues and **dedicated help wiring Engram into your stack**. For organizations whose policy precludes depending on MIT-licensed software, an optional commercial-license override is available on request. (Engram is maintained by a solo developer, so this is best-effort priority response, not a contractual SLA.) --- Why Engram? Most agent-memory products are services you run alongside your agent — Postgres, Docker, cloud accounts, API keys. Engram embeds *inside* your agent's process: a focused, stable npm package with practical guardrails. | | **Engram** | **Lodis** | **Mem0 / OpenMemory** | **Zep** | **Letta** | |---|---|---|---|---|---| | **Maturity** | v1.9.x, stable | v0.5.x, early | mature / SaaS | v0.x | v0.x | | **Infra to operate** | None (npm package) | None (npx package) | Cloud account *or* multi-container Docker | Docker + Postgres + Graphiti | Docker + Postgres | | **Install footprint** | ~23 MB | ~22 MB | Hundreds of MB containers (self-hosted) | Hundreds of MB | Hundreds of MB | | **Works offline** | ✅ | ✅ | ❌ Cloud / ✅ if self-hosted | ❌ External embed provider | ❌ External LLM provider | | **MCP-native** | ✅ Primary | ✅ Primary | 🟡 OpenMemory ships an MCP server | ❌ REST/SDK | ❌ REST/SDK |