Context7
Up-to-date docs for AI agents
제작자가 쓴 설명(영어 원문)
Token-saving persistent memory for AI agents: recall durable facts instead of re-reading files.
이 세 가지는 절대 하나의 점수로 합치지 않아요. 의견, 측정, 인기는 서로 다른 것이니까요.
이 서버는 아직 쉬운 말로 된 요약을 쓰지 못했어요. 아래는 레지스트리에서 읽어 온 사실이에요.
쓰고 있는 AI 앱을 고른 다음 설정을 복사하세요.
BUILDERFORCE_MEMORY_FILE, BUILDERFORCE_MEMORY_READONLY, BUILDERFORCE_API_KEY비밀로 표시된 값은 내 계정의 열쇠예요. 절대 다른 사람과 공유하거나 채팅에 붙여 넣지 마세요.
필요한 것
어떤 앱을 쓰세요?
위에서 앱을 고르세요. 기억해 두었다가 다음부터는 버튼에 “…에 설치”라고 표시해요.
Claude Desktop → Settings → Developer → Edit Config를 열고 claude_desktop_config.json에 이 내용을 붙여 넣으세요
{
"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"
}
}
}
}터미널에서 이 명령어를 실행하세요
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-mcp실행하기 전에 빈 값을 채워 주세요.
프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"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"
}
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"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"
}
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
이 서버는 내 컴퓨터에서 실행되는데, ChatGPT는 인터넷에 있는 서버에만 연결할 수 있어요. 다른 앱을 고르세요.
선택한 앱은 이 기기의 쿠키에만 저장돼요. 계정은 필요 없어요.
BUILDERFORCE_MEMORY_FILE envBUILDERFORCE_MEMORY_READONLY env이 설정은 제작자의 레지스트리 정보로 만들었어요. README와 다르면 README를 따르세요.
내 컴퓨터에서 실행돼요. 설치하면 AI 앱에 도구 목록이 나타나요.
누구나 볼 수 있는 정보를 점검했고, 코드는 읽지 않았어요. 해당하지 않는 항목은 개수에서 뺐어요. · 2026년 11월 6일 점검
레지스트리 이름이 GitHub 계정 SeanHogg 기준으로 확인되었고, 이 계정이 저장소를 소유하고 있어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
내 컴퓨터에서 실행되는 서버라 인터넷 엔드포인트가 없어요
내 컴퓨터에서 실행되며, 대신 로컬 설정을 사용해요
내 컴퓨터에서 실행돼요. 도구 목록은 인터넷 엔드포인트가 있는 서버에서만 읽을 수 있어요
레지스트리에서 이 카테고리의 활성 서버 전체와 비교했어요. 스타는 게시자가 저장소를 소유한 경우에만 세요.
해당하는 기본 점검 1개 중 1개를 통과했어요. 코드는 읽지 않았어요. 키나 토큰을 넣기 전에 서버가 어떤 권한을 요구하는지 확인하고, 꼭 필요한 권한만 있는 키를 쓰세요.
입력해야 할 값: BUILDERFORCE_API_KEY
내 컴퓨터에서 실행돼요. AI 앱이 필요할 때 실행하고, 데이터는 제작자의 서버를 거치지 않아요(서버 자체가 온라인 서비스를 호출하는 경우는 제외).
GitHub 저장소는 마지막으로 – 변경됐고, 최신 버전(2026.10.41)은 오늘 레지스트리에 올라왔어요.
Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. 내 컴퓨터에 Node.js도 있어야 해요.
같은 카테고리에는: Context7, Codebase Memory, Exa
같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.
카드 보는 법:★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i
Up-to-date docs for AI agents
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제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.
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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