Context7
Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.
Description from the maker (original English)
Local-first cognitive agent memory with bi-temporal SQLite, replayable history, hybrid retrieval.
These three are never merged into one score: opinions, measurements and popularity are different things.
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
Pick the AI app you use, then copy the settings.
You need
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Pick your app above. We remember it, so next time the button says “Install in …”.
Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"fastrmemory": {
"command": "npx",
"args": [
"-y",
"fastrmemory",
"mcp"
]
}
}
}Run this in your terminal
claude mcp add fastrmemory -- npx -y fastrmemory mcpPaste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"fastrmemory": {
"command": "npx",
"args": [
"-y",
"fastrmemory",
"mcp"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Paste into .vscode/mcp.json in your project
{
"servers": {
"fastrmemory": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"fastrmemory",
"mcp"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
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Runs on your computer; the tool list appears in your AI app after you install it.
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026
The registry name was verified with the GitHub account atifjubaer, which owns the repo
We have not read this repo from GitHub yet
We have not read this repo from GitHub yet
This one runs on your computer; there is no internet endpoint
Runs on your computer and uses local settings instead
Runs on your computer; we can only read tool lists from internet endpoints
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
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It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
The GitHub repo last changed –, and the latest version (0.9.3) reached the registry 6 days ago.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.
In the same category: Context7, Codebase Memory, Exa
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Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Connect AI agents to Exa for web search, content fetching, and multi-step research.
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Zero Daemon • Native Node 22+ `node:sqlite` • Bi-Temporal Git Sync • Triple-Stream RRF • Official MCP v2
Status (2026-11-01): `fastrmemory@0.9.3` is published and is `latest`. Evidence:
npm view fastrmemory version->0.9.3;npm view fastrmemory dist-tags --json->{"latest":"0.9.3"}; tagv0.9.3(15a772f); published 2026-11-01 via GitHub Actions OIDC with npm provenance; the release workflow passed the full gate, clean tarball install, registry verification, and GitHub Release upload; the npm tarball is 358 files, 819,117 bytes packed, 3,289,741 bytes unpacked; GitHub Release asset SHA-256eebda8ab3f90b73ee5f4bd4a64a6ff511f9c93468576c167de3a1c1e2efbcf22; a cleannpm install fastrmemory@0.9.3+fastr --version->0.9.3;npm audit signaturesreports 5 verified registry signatures and 4 verified attestations. The official MCP registry listsio.github.atifjubaer/fastrmemory@0.9.3asactiveandisLatest. Caveat: Node.js >=22.16.0;0.9.2,0.9.1,0.9.0,0.8.2,0.8.1,0.8.0,0.7.2,0.7.1,0.7.0,0.6.0,0.5.0,0.4.0,0.3.1, and0.3.0remain published historical. This release makes code-graph indexing automatic: setup and hook install index once, SessionStart repairs an empty index, and prompt/session-end hooks refresh recent edits; the schema is unchanged. The old scoped npm entries (@atifjubaer/fastrmemoryandfastrmemory@0.0.0-stage) were removed on 2026-10-23; the GitHubv0.2.1tag and release remain as history. This README tracks currentmain. Currentmainis `0.9.3` and has 481 tests / 74 files. Use the exact0.9.3version for reproducibility. PR #46 at5da9b2epassed all required checks (six matrix jobs plus Replayable Memory Diff and Pre-Merge Trap Guardian); the historical0.9.2release commitb2c6502,0.9.1release commit5da9b2e,0.9.0release commitc204f50,0.8.2release commit8cc925b,0.8.1release commit4e7d9ac,0.8.0release commit44b2d9a,0.7.2release commit583223f,0.7.1release commit31d8e12,0.7.0release commit5512557,0.6.0release commita57a656,0.5.0release commit54a79ab, and0.4.0release commitbb365c2remain published historical. The exact release surface is in CHANGELOG.md. The headline benchmark uses 240 deterministic synthetic cases with a 20% holdout, not SWE-bench or LongMemEval. Public LongMemEval, LoCoMo, and self-repository retrieval-only results plus pinned external baselines are in BENCHMARKS.md; no QA-leaderboard parity is claimed. Performance, packaging, and retrieval claims are tracked in CLAIMS.md; every README section is mapped to claim IDs in the README claim trace at the end of that ledger. Methodology, caveats, and raw JSON are in BENCHMARKS.md.
Overview • Design Principles • Benchmarks • Web UI • Supported Agents • Quick Start • MCP Setup • MCP Tools • Architecture • Migration • Security • Scorecard
| Claim | Current result | Evidence |
|---|---|---|
| Install path | Published fastrmemory@0.9.3 | npm view fastrmemory version -> 0.9.3; npm view fastrmemory dist-tags --json -> {"latest":"0.9.3"}; npm install fastrmemory@0.9.3; fastr --version -> 0.9.3; GitHub Release tarball SHA-256 eebda8ab...; old scoped npm entries removed |
| MCP registry | io.github.atifjubaer/fastrmemory@0.9.3, status active, isLatest | curl --get --data-urlencode "search=io.github.atifjubaer/fastrmemory" https://registry.modelcontextprotocol.io/v0.1/servers; gh run view 40262643667 |
| Synthetic retrieval (optional MiniLM) | Holdout 95.8% Hit@1, 100% Hit@5, MRR 0.969 | npx tsx scripts/eval-bench.ts --split all --embeddings minilm --out bench/results/minilm.json |
| Test suite | 481 tests across 74 files on current main; published 0.9.3 is 481 / 74; published 0.9.2 is 476 / 74; published 0.9.1 is 476 / 74; published 0.9.0 is 476 / 74; published 0.8.2 is 461 / 69; published 0.8.1 is 461 / 69; published 0.8.0 is 461 / 69; published 0.7.2 is 456 / 67; published 0.7.1 is 456 / 67; published 0.7.0 is 455 / 67; published 0.6.0 is 388 / 61; 0.5.0 is 297 / 53; historical 0.4.0 is 293 / 53; 0.3.1 is 282 / 52; v0.2.1 is 235 / 43 | npm test; npm run check:ci |
| MCP setup | fastr mcp init merges portable registry configs or active-CLI local configs for 9 JSON clients and prints a Codex TOML snippet; --source local ships in published 0.9.3, 0.9.2, 0.9.1, 0.9.0, 0.8.2, 0.8.1, 0.8.0, 0.7.2, 0.7.1, 0.7.0, 0.6.0, and 0.5.0 | npm test -- tests/mcp-init.test.ts -> 15 passed; fastr mcp init --client claude --source local --dry-run --json -> node .../dist/bin/fastr.js mcp --project ... |
| Cross-engine (MCP stdio) | 80/120 overall Hit@1 (66.7%, 95% CI 57.8-74.5): exact 40/40, multi 40/40, paraphrase 0/40; 1.094 ms/doc ingest, 1.86 ms recall p50 | npm run bench:vs; bench/results/vs-engines.json |
| Crash safety | 10/10 runs, 0 lost, 0 duplicate ids/titles | npm run test:crash; bench/results/crash-test.json |
| Public retrieval (0.3.0) | LongMemEval +10.4pp / +11.0pp Hit@5; LoCoMo +4.4pp / +2.3pp (target missed) | docs/EXTERNAL_BASELINE.md |
| Release score | Provisional 9.4 / 10; 9.2 target met; Retrieval remains Partial | SCORECARD.md |
The synthetic table is not SWE-bench or LongMemEval. Public numbers are retrieval-only and are not QA-leaderboard results. The 95.8% row uses optional MiniLM; the default offline hash path scores 77.1% Hit@1 / 93.8% Hit@5 on the same holdout. Full provenance is in CLAIMS.md.
AI coding agents (Claude Code, Cursor, Windsurf, Codex, Antigravity) degrade on long-running codebases when project memory is lossy or operationally heavy. Common designs make one of three trade-offs:
FastrMemory keeps a verbatim event log, bi-temporal memory history, and a replayable Git mirror in a single in-process TypeScript engine (3 runtime dependencies; local embeddings and the daemon are optional):
Pure Node 22+ built-in node:sqlite in WAL mode. On the 240-case suite, engine-level recall is 0.95 ms p50 / 1.60 ms p95 with hash embeddings and 3.30 ms p50 / 5.11 ms p95 with MiniLM on the train split (CLI startup measured separately). The default CLI/MCP path has no background daemon and no open port; daemon start is optional. Native on Windows, macOS, and Linux (the current CI matrix uses Intel macOS; Apple Silicon is not covered); CI matrix status is reported in Actions.
Never deletes facts during consolidation (there is no destructive sleep step). Replaces half-life decay with valid_from / valid_to timestamps and auto-mirrors human-readable .fastr/pages/*.md files into Git.
Fuses FTS5 BM25 + optional local embeddings + file-graph links. Error traps receive a conditional 2.0x boost only for trap-intent or file-scoped queries, with per-ID outcome feedback (+0.2 / -0.5). Three consecutive bad outcomes move a memory to dormant; confidence tiers are high, medium, low, dormant, and superseded. Counting questions ("how many projects have I led?") additionally run an aggregate-coverage pass: a second lexical query over their content terms, fused with the original, returning up to 20 rows so the caller can count. retrieval.temporalFilter adds opt-in date windows ("past two weeks", "this year") parsed against the question date.
| Capability | Hippo Memory (hippo-memory) | Memoria (matrixorigin/Memoria) | AgentMemory (@agentmemory/mcp) | FastrMemory (0.9.3+) |
|---|---|---|---|---|
| Runtime Architecture | SQLite store | MatrixOne relational/vector DB container | Dual-layer engine + Redis/Docker | Zero-Daemon • Native `node:sqlite` in WAL mode |
| Ingestion Speed | 88.9 ms/doc | Container roundtrip | 1.74 ms/doc | 1.09 ms/doc (80x faster than Hippo) |
| Recall Latency (p50) | 12.8 ms (spikes to 386 ms p95) | 50–100 ms | 1.52 ms | 0.95 ms p50 / 1.60 ms p95 |
| AST Symbol Anchoring | ❌ None (file path only) | ❌ None | ❌ None | ✅ `declaration_id` (AST hash survives renames & file moves) |
| Self-Verifying Traps | ❌ None | ❌ None | ❌ None | ✅ Machine assertions (`text_present`, etc.) auto-retire fixed bugs |
| Context Token Overhead | ⚠️ High (dumps entire rule sets) | ⚠️ High (dumps knowledge graphs) | ⚠️ High (dumps session contexts) | Ultra-low (≤150 tokens targeted per-turn injection) |
| Passive Learning | ❌ Manual commands only | ❌ Manual commands only | ❌ Manual commands only | ✅ Auto-distills traps from test failures & subsequent edits |
| Cross-Machine Team Sync | Manual markdown git commits | Cloud database cluster | Custom daemon | ✅ Bidirectional `fastr sync` on Git pull |
| Editor Integration | Basic CLI instructions | None | Basic plugin | ✅ Native VS Code & Cursor extension with Problems tab diagnostics |
| MCP Protocol Support | stdio | REST / MCP adapter | stdio | Official MCP v2 (12-tool core & 45-tool full profiles) |
*Verified benchmark evidence from bench/results/vs-engines.json and docs/PUBLIC_BENCHMARKS.md.*
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