Context7
Up-to-date docs for AI agents
作者による説明(英語の原文)
Token-saving persistent memory for AI agents: recall durable facts instead of re-reading files.
この3つを1つのスコアにまとめることはしません。意見、測定、人気はそれぞれ別のものだからです。
このサーバーのやさしい解説はまだ書いていません。以下はレジストリから読み取った事実です。
使っているAIアプリを選んで、設定をコピーしてください。
BUILDERFORCE_MEMORY_FILE, BUILDERFORCE_MEMORY_READONLY, BUILDERFORCE_API_KEY「秘密」と表示された値は、あなたのアカウントの鍵です。絶対に人に教えたり、チャットに貼り付けたりしないでください。
必要なもの
どのアプリを使っていますか?
上でアプリを選んでください。選んだアプリを覚えておくので、次からはボタンに「…にインストール」と表示されます。
Claude Desktop → Settings → Developer → Edit Config を開き、claude_desktop_config.json にこれを貼り付けます
{
"mcpServers": {
"builderforce-memory": {
"command": "npx",
"args": [
"-y",
"@seanhogg/builderforce-memory-mcp"
],
"env": {
"BUILDERFORCE_MEMORY_FILE": "your-value",
"BUILDERFORCE_MEMORY_READONLY": "your-value",
"BUILDERFORCE_API_KEY": "your-value"
}
}
}
}ターミナルでこれを実行します
claude mcp add builderforce-memory --env BUILDERFORCE_MEMORY_FILE=your-value --env BUILDERFORCE_MEMORY_READONLY=your-value --env BUILDERFORCE_API_KEY=your-value -- npx -y @seanhogg/builderforce-memory-mcp動かす前に、空欄の値を入力してください。
プロジェクトの .cursor/mcp.json、または ~/.cursor/mcp.json に貼り付けます
{
"mcpServers": {
"builderforce-memory": {
"command": "npx",
"args": [
"-y",
"@seanhogg/builderforce-memory-mcp"
],
"env": {
"BUILDERFORCE_MEMORY_FILE": "your-value",
"BUILDERFORCE_MEMORY_READONLY": "your-value",
"BUILDERFORCE_API_KEY": "your-value"
}
}
}
}ブラウザがアプリを開いてよいか確認し、そのあとアプリが設定を表示して確認を求めます。キーの値は空欄のままなので、自分で入力してください。
プロジェクトの .vscode/mcp.json に貼り付けます
{
"servers": {
"builderforce-memory": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@seanhogg/builderforce-memory-mcp"
],
"env": {
"BUILDERFORCE_MEMORY_FILE": "your-value",
"BUILDERFORCE_MEMORY_READONLY": "your-value",
"BUILDERFORCE_API_KEY": "your-value"
}
}
}
}ブラウザがアプリを開いてよいか確認し、そのあとアプリが設定を表示して確認を求めます。キーの値は空欄のままなので、自分で入力してください。
これはパソコンで動くサーバーですが、ChatGPTがつなげるのはインターネット上のサーバーだけです。ほかのアプリを選んでください。
選んだアプリは、この端末のCookieに保存されます。アカウントは不要です。
BUILDERFORCE_MEMORY_FILE envBUILDERFORCE_MEMORY_READONLY envこの設定は、作者のレジストリ登録情報から作成しています。READMEと違う場合は、READMEに従ってください。
パソコンで動くサーバーです。インストールすると、ツール一覧がAIアプリに表示されます。
誰でも見られる情報をチェックしており、コードは読んでいません。当てはまらない項目は数に入れていません。 · 2026年11月6日にチェック
レジストリ名は、リポジトリを所有するGitHubアカウント SeanHogg で確認済みです
このリポジトリはまだGitHubから読み取っていません
このリポジトリはまだGitHubから読み取っていません
これはパソコンで動くため、インターネット上のエンドポイントはありません
パソコンで動き、代わりにローカルの設定を使います
パソコンで動くサーバーです。ツール一覧を読み取れるのは、インターネット上のエンドポイントがある場合だけです
レジストリでこのカテゴリにある有効なすべてのサーバーと比較しています。スターは、公開者がリポジトリの所有者である場合だけ数えます。
当てはまる基本チェック1項目のうち1項目に合格しました。コードは読んでいません。キーやトークンを追加する前に、サーバーがどんなアクセスを求めているかを確認し、必要な権限だけを持つキーを使ってください。
入力が必要なもの:BUILDERFORCE_API_KEY
あなたのパソコンで動きます。必要なときにAIアプリが起動し、データは作者のサーバーを通りません(サーバー自体がオンラインサービスを呼び出す場合を除きます)。
GitHubリポジトリの最終更新は–、最新バージョン(2026.10.43)がレジストリに届いたのは今日です。
Claude Desktop、Claude Code、Cursor、VS CodeなどのMCP対応アプリで使えます。パソコンにNode.jsも必要です。
同じカテゴリには:Context7, Codebase Memory, Exa
同じカテゴリで、ほかの作者が作ったサーバー。スターの多い順。
カードの見方:★ GitHubスターi3つの柱:レビュー · チェック · 利用状況i更新=最新バージョンをレジストリに公開した日i
Up-to-date docs for AI agents
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.
作者のリポジトリから書かれたとおりにコピーしたものです。PopNx MCPは内容を確認・推奨していません。HTML、画像、スクリプトは取り除いています。リンクはこのサイトの外へ移動します。
Expose @seanhogg/builderforce-memory to any MCP client. One token-saving tool core over a pluggable MemoryBackend, three transports:
| Transport | Factory | Consumed as | Use when |
|---|---|---|---|
| In-process (Claude Agent SDK) | createMemoryMcpServer(backend) | the returned type:"sdk" object | the consuming product is TS and runs @anthropic-ai/claude-agent-sdk in-process — lowest latency |
| stdio | runStdio(backend) / npx @seanhogg/builderforce-memory-mcp | { type:"stdio", command, args } | any language / separate process; decouples the SSM+IndexedDB deps from the consumer |
| HTTP (Streamable) | createMemoryHttpHandler(backend, { authToken }) | { type:"http", url, headers } | multi-tenant / networked (builderforce.ai hosting, remote agents) |
Technical report & peer review. This transport is reviewed in the Evermind technical report's adversarial
PEER-REVIEW.md. Note before exposing the HTTP handler publicly: theauthTokencheck is a non-constant-time string compare (timing channel) and the handler shares one `backend` with no per-tenant isolation — a single token reads every tenant's facts. Despite the "multi-tenant" note above, real isolation (token→namespace mapping, constant-time compare, rate limiting) is not yet implemented. Tracked asEVM-3in the Builderforce.ai Consolidated Gap Register. Resolved in v2026.6.34: the HTTP handler now uses a constant-time token compare and per-tenant namespace isolation (see the resolution addendum inPEER-REVIEW.md).
The point of an external memory store is fetch-on-demand instead of pinning your whole memory file into every prompt. That only pays off if recall is *selective*, so the caps are enforced server-side in tools.ts:
memory_recall returns a ranked top-K (default 5, hard-capped), never the store.memory_compact shrinks a fact the local Evermind has already ABSORBED down to a one-line pointer stub, so it stops filling context. It is lossless: a recall probe must pass first, and the original is archived (recall still returns it) — see below. It writes through the live store, which keeps memory and the on-disk snapshot in agreement.createLocalMemoryStoreBackend({ ranking: false }) turns the signals off.Tool descriptions are prescriptive ("call this *before* answering when…") because recent Claude models reach for tools more conservatively; the trigger condition in the description is what drives should-call rate. Pair this with prompt caching (keep the tool set identical across tenants so the cached prefix survives) and context editing (prune stale recalled facts) for the full win.
Everything is written against MemoryBackend:
interface MemoryBackend {
recall(query: string, topK: number): Promise ; // semantic get(key: string): Promise ; // exact recallByTag(tag: string, limit: number): Promise ; remember?(input: RememberInput): Promise ; // optional → read-only backends omit forget?(key: string): Promise ; }
Ship the local MemoryStoreBackend (IndexedDB via @seanhogg/builderforce-memory) today; drop in a networked builderforce.ai adapter later with zero changes to tools or transports.
import { query } from "@anthropic-ai/claude-agent-sdk";
import { createMemoryMcpServer, createLocalMemoryStoreBackend } from "@seanhogg/builderforce-memory-mcp";
const backend = await createLocalMemoryStoreBackend(); // IndexedDB (fake-indexeddb in Node) const memory = await createMemoryMcpServer(backend); // type:"sdk" config
for await (const msg of query({ prompt: "What language does this user prefer? Check memory first.", options: { mcpServers: { builderforce_memory: memory }, allowedTools: ["mcp__builderforce_memory__*"], // auto-approve, no prompts }, })) { if (msg.type === "result" && msg.subtype === "success") console.log(msg.result); }
With persistFile set, the store lives in memory and the JSON snapshot is the source of truth across process lifetimes. Other processes edit that file too (the BuilderForce VS Code extension rewrites absorbed entries to stubs in place), so before every operation the backend stats the file and re-hydrates from disk when it changed underneath — otherwise this server, still holding the pre-edit bodies, would re-snapshot over that work on its next write. Its own writes are stamped (mtime + size + content hash) the moment they land, so the steady-state cost is one stat and there is no write-loop.
With no model configured the local backend ranks recall lexically (BM25). Point it at an Evermind checkpoint and the ordering becomes the SSM embedding's cosine ranking fused with the lexical one (Reciprocal Rank Fusion — dense ⊕ sparse, so exact tokens like identifiers and error codes are still caught). Every memory_recall result names the ranker it used, so a degrade to lexical is never silent:
Ranked by: semantic recall (SSM embedding fused with lexical).
Ranked by: lexical recall (fallback - no embedding model loaded).
The stdio/HTTP servers do not need a GPU for this. EvermindLM is a pure-CPU model and EvermindLM.embed() mean-pools + L2-normalises the same hidden state the GPU HybridMambaModel.embed() does, to the same contract. Configure it with:
| Env | Meaning |
|---|---|
BUILDERFORCE_MEMORY_MODEL | Path to a .evermind package holding an evermind-lm. |
BUILDERFORCE_MEMORY_TOKENIZER | Tokenizer JSON for that checkpoint. Default .tokenizer.json. |
BUILDERFORCE_MEMORY_VECTORS | Vector-cache path. Defaults beside the memory snapshot. |
Vectors are cached across process lifetimes (stamped with the model's fingerprint, so an adapted checkpoint invalidates them), because a per-session subprocess that re-embedded the whole store on its first recall would cost more than the lexical ranking it replaces.
In-process hosts pass an embedder directly — reuse an already-loaded GPU Evermind runtime rather than standing up a second model:
const backend = await createLocalMemoryStoreBackend({ runtime: ssmMemoryService.runtime });
Either way, recall quality improves automatically as that model is adapted/distilled.
Facts are one kind of learned item; experience is another. An *episode* is a recorded demonstration (a program and the UI steps a person took in it), a *skill* is what Train Once compiles from one (typed values become parameters, secrets never stored, irreversible clicks gated for approval), and a *run* is one execution with its audit trail. The domain lives in @seanhogg/builderforce-memory-engine; this server keeps it durable and exposes it.
Set BUILDERFORCE_MEMORY_EXPERIENCE=1 to register the tools (Synapse does). Off by default, so ordinary coding-agent installs never carry them in their tool list.
| Tools | |
|---|---|
experience_info | Snapshot file, screenshot folder, model path, counts. |
experience_forget_all | Delete every episode, screenshot, skill and run (confirm: true). Memories and the model are untouched. |
episode_save / _list / _get / _forget | Demonstrations. Screenshots go in episodes/ / and are deleted with the episode. |
skill_preview / _compile / _list / _get / _forget / _schedule, skills_due | Train Once and routines. |
run_start / _step / _finish / _list / _get | Runs and their audit trail (newest 500 kept). |
experience_train | Adapt the private model (BUILDERFORCE_MEMORY_MODEL) on every procedure it has not learned, through the engine's one adaptation recipe. The previous model is kept as .prev. Opt-in curate: true first leaves out procedures whose runs mostly fail and near-copies (of each other, or of the 200 most recently learned), listing each one dropped; it is reconsidered next time. |
Stored beside the memory snapshot as experience.json (shared across processes exactly like memory.json) plus episodes/. Never served to a token-selected tenant of the HTTP handler.
# Auto-detect installed agents and register the memory server into each:
npx -y -p @seanhogg/builderforce-memory-mcp builderforce-memory-install
npx -y -p @seanhogg/builderforce-memory-mcp builderforce-memory-install --host=all npx -y -p @seanhogg/builderforce-memory-mcp builderforce-memory-install --host=cursor,windsurf
Writes the stdio server entry into each host's own config, idempotently and with .bak backups. Supported hosts: Claude Code, Claude Desktop, Cursor, Windsurf, VS Code, Cline, Gemini CLI, Codex CLI. Every agent points at one shared store (resolveMemoryFile(): ~/.builderforce-memory/memory.json, or the legacy ~/.claude/builderforce-memory/memory.json when that is where the live store is; override with --memory-file / BUILDERFORCE_MEMORY_FILE; the hook script resolves the same way), so a fact remembered in one agent is recalled in another. --readonly drops the write tools; --local= runs a checkout for development.
The same launch spec is exported for programmatic use: buildServerSpec() / installMemoryServer() (see src/install/).
Registering the server gives Claude Code the memory *tools*. For Claude Code the installer ALSO wires hooks so memory is self-driving instead of advisory (skip with --no-hooks):
| Hook | Behaviour |
|---|---|
SessionStart | Inject a digest of the top durable memories. |
PreCompact | Flush durable learnings before the window is summarised. |
UserPromptSubmit | Contextual recall — score memories against the prompt and inject the relevant ones, so a stored fact is retrieved at the *decision point*, not only at session start. |
Stop | Autonomous capture — if the user's last message was a durable instruction/correction and it wasn't consolidated this turn, BLOCK the stop with a directive to memory_remember. Capture flows through the MCP write path (never racing the snapshot); a stop_hook_active guard means at most one nudge per turn. |
All four point at one generated, dependency-free hook script (~/.claude/builderforce-memory/bfmem-hook.mjs, four modes) plus a companion skill. Idempotent + .bak-safe. Source of truth: src/install/claude-hooks.ts (installClaudeCombo / bfmemHookSource).
npx -y @seanhogg/builderforce-memory-mcp # serves the local MemoryStore over stdio
mcpServers: {
builderforce_memory: { type: "stdio", command: "npx", args: ["-y", "@seanhogg/builderforce-memory-mcp"] }, }
Env: BUILDERFORCE_MEMORY_DB (db name), BUILDERFORCE_MEMORY_READONLY=1 (drop write tools).
BUILDERFORCE_MEMORY_TOKEN=secret PORT=8787 npx @seanhogg/builderforce-memory-mcp-http
mcpServers: {
builderforce_memory: { type: "http", url: "http://localhost:8787", headers: { Authorization: "Bearer secret" }, }, }
Or moun
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