Builderforce Memory
What it does
Token-saving persistent memory for AI agents: recall durable facts instead of re-reading files.
- Connects 在你的电脑上运行
- Account or key Needs BUILDERFORCE_API_KEY
- Category 搜索与 AI 记忆
安装
选择你使用的 AI 应用,然后复制设置。
通过 2/3 项检查 · 在你的电脑上运行 · ✓ 命令已检查 2026年11月13日
第一步:你用哪个 AI?
选择你用的应用,步骤会显示在下方。
可连接 Claude,但需要先准备
需要 Builderforce Memory 的密钥(在开发者网站创建) · 需要先在电脑上安装 Node.js(只需一次) · 仅限电脑上的 Claude 应用(Windows 或 Mac),网页和手机不可用
注册并创建 Builderforce Memory 的密钥
打开开发者网站,注册账户并创建 API 密钥,下一步要用。价格和免费方案请看开发者网站。
打开电脑版 Claude 应用的设置文件
SettingsDeveloperEdit Config
粘贴这些设置并保存文件
{ "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" } } } }只把密钥粘贴到应用的输入框里,不要粘贴到聊天中或发给任何人
退出 Claude 再重新打开
试一试
用 Builderforce Memory 告诉我你能帮我做什么,然后做一件给我看✓ 如果 Claude 说正在使用 Builderforce Memory,或回答中出现 Builderforce Memory 的工具名称,就表示连接成功
菜单名称已于 2026年11月11日 对照 Anthropic 帮助页面核实 · 你的界面可能因版本略有不同
ChatGPT 暂时无法连接 Builderforce Memory
Builderforce Memory 需要安装在你的电脑上,而 ChatGPT 只能连接在网上运行的服务器
你可以改用这些方式
A · 使用能连接 Builderforce Memory 的应用
- 需要 Builderforce Memory 的密钥(在开发者网站创建) · 需要先在电脑上安装 Node.js(只需一次) · 仅限电脑上的 Claude 应用(Windows 或 Mac),网页和手机不可用
- 需要 Builderforce Memory 的密钥(在开发者网站创建) · 需要先在电脑上安装 Node.js(只需一次)
- 需要 Builderforce Memory 的密钥(在开发者网站创建) · 需要先在电脑上安装 Node.js(只需一次)
- 需要 Builderforce Memory 的密钥(在开发者网站创建) · 需要先在电脑上安装 Node.js(只需一次)
B · 搜索与 AI 记忆 类中能连接 ChatGPT 的其他服务器
从同类中使用较多的里挑选,仅限用地址即可连接、无需密钥的
- Context7✓ ChatGPT
- AIHOT✓ ChatGPT
- Sui Knowledge Base (kapa.ai)✓ ChatGPT
可连接 Claude Code,但需要先准备
需要 Builderforce Memory 的密钥(在开发者网站创建) · 需要先在电脑上安装 Node.js(只需一次)
注册并创建 Builderforce Memory 的密钥
打开开发者网站,注册账户并创建 API 密钥,下一步要用。价格和免费方案请看开发者网站。
打开终端
Mac:按 Command+空格,输入 Terminal · Windows:打开 PowerShell
粘贴这条命令并按 Enter
claude mcp add --scope user 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按 Enter 前,把“your-value”改成你的密钥
只把密钥粘贴到应用的输入框里,不要粘贴到聊天中或发给任何人
在 Claude Code 中输入 /mcp
应能看到 Builderforce Memory 在列表中并显示已连接
试一试
用 Builderforce Memory 告诉我你能帮我做什么,然后做一件给我看✓ 如果 Claude Code 说正在使用 Builderforce Memory,或回答中出现 Builderforce Memory 的工具名称,就表示连接成功
菜单名称已于 2026年11月11日 对照 Anthropic 帮助页面核实 · 你的界面可能因版本略有不同
可连接 Cursor,但需要先准备
需要 Builderforce Memory 的密钥(在开发者网站创建) · 需要先在电脑上安装 Node.js(只需一次)
注册并创建 Builderforce Memory 的密钥
打开开发者网站,注册账户并创建 API 密钥,下一步要用。价格和免费方案请看开发者网站。
填写你的密钥
在 Builderforce Memory 的设置中,BUILDERFORCE_MEMORY_FILE, BUILDERFORCE_MEMORY_READONLY, BUILDERFORCE_API_KEY 还是空的,填入你的密钥并保存
只把密钥粘贴到应用的输入框里,不要粘贴到聊天中或发给任何人
在 Cursor 中以 Agent 模式打开 AI 聊天
试一试
用 Builderforce Memory 告诉我你能帮我做什么,然后做一件给我看✓ 如果 Cursor 说正在使用 Builderforce Memory,或回答中出现 Builderforce Memory 的工具名称,就表示连接成功
菜单名称已于 2026年11月11日 对照 Cursor 帮助页面核实 · 你的界面可能因版本略有不同
可连接 VS Code,但需要先准备
需要 Builderforce Memory 的密钥(在开发者网站创建) · 需要先在电脑上安装 Node.js(只需一次)
注册并创建 Builderforce Memory 的密钥
打开开发者网站,注册账户并创建 API 密钥,下一步要用。价格和免费方案请看开发者网站。
填写你的密钥
在 Builderforce Memory 的设置中,BUILDERFORCE_MEMORY_FILE, BUILDERFORCE_MEMORY_READONLY, BUILDERFORCE_API_KEY 还是空的,填入你的密钥并保存
只把密钥粘贴到应用的输入框里,不要粘贴到聊天中或发给任何人
在 VS Code 中以 Agent 模式打开 AI 聊天
试一试
用 Builderforce Memory 告诉我你能帮我做什么,然后做一件给我看✓ 如果 VS Code 说正在使用 Builderforce Memory,或回答中出现 Builderforce Memory 的工具名称,就表示连接成功
菜单名称已于 2026年11月11日 对照 VS Code 帮助页面核实 · 你的界面可能因版本略有不同
开发者专用:终端命令和 JSON 设置
安装在电脑上
Claude Code
claude mcp add --scope user 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-mcpClaude Desktop (claude_desktop_config.json)
{
"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"
}
}
}
}Cursor (~/.cursor/mcp.json)
{
"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"
}
}
}
}VS Code (mcp.json)
{
"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"
}
}
}
}需要你填写的值
BUILDERFORCE_MEMORY_FILEenvAbsolute path to the JSON snapshot that makes memory survive restarts. Defaults to ~/.builderforce-memory/memory.json, which every MCP host on the machine shares.BUILDERFORCE_MEMORY_READONLYenvSet to 1 to expose recall only (disables memory_remember and memory_forget).BUILDERFORCE_API_KEY机密 envBuilderforce tenant API key (bfk_*). When set, additionally exposes the token_usage and model_efficiency cost tools.
这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。
一览
哪些应用可以使用它
依据这个服务器的连接方式和各应用自己的文档,检查于 2026年11月10日。
在你的电脑上运行,适用于
ClaudeGooseLM StudioMsty StudioCherry StudioCursorVS Code + GitHub CopilotVisual StudioWindsurf (Devin Desktop)ZedKiroClineContinueJetBrains AI AssistantClaude CodeCodex CLIGemini CLIOpenCodenodecodeWarpLibreChatRaycast
它能做什么
它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。
基础检查i
我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月13日
- ✓ 通过
作者与仓库一致
注册名称已通过 GitHub 账户 SeanHogg 验证,该账户拥有此仓库
- ✓ 通过
仍在维护
代码最后更新于前天
- ✕ 未通过
有许可证
仓库中没有找到许可证文件
- – 不适用
端点有响应
它在你的电脑上运行,没有互联网端点
- – 不适用
使用 OAuth 登录
在你的电脑上运行,改用本地设置
- … 尚未检查
工具描述中没有隐藏指令
它在你的电脑上运行;我们只能读取互联网端点的工具列表
讨论
提问、分享技巧或报告无法使用。按“有帮助”数量排序。
用户评价
还没有评价
与“搜索与 AI 记忆”中的其他服务器比较i
- 星标数多于此分类中其他 6062 个服务器的 0%
- 发布到注册中心的时间比此分类中其他 6062 个服务器的 92% 更新
- 基础检查结果等于或优于此分类中其他 6062 个服务器的 50%
与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。
常见问题
Builderforce Memory 安全吗?
它在 3 项适用的基础检查中通过了 2 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。
它需要哪些密钥或代码?
你需要填写:BUILDERFORCE_API_KEY
它在我的电脑上运行还是在云端?
它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。
它还在维护吗?
GitHub 仓库最后更新于前天,最新版本(2026.10.80)于前天进入注册中心。
哪些应用可以使用它?
支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Node.js。
有什么可以替代它?
同一分类中有:Context7, Codebase Memory, Ruflo
同类别的其他选择
同一分类中其他作者的服务器,按星标数排序。
如何看卡片:↓ 每周下载量i★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i
Codebase Memory
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
作者的 README
打开 README
按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。
@seanhogg/builderforce-memory-mcp
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).
Why this saves tokens
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_recallreturns a ranked top-K (default 5, hard-capped), never the store.- Each entry's content is truncated (default 500 chars) before it hits context.
- There is no "return everything" tool — a dump-all is more expensive than inlining.
memory_compactshrinks 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.- Recall ranks by hybrid fusion (BM25 ⊕ optional CPU SSM embedding, RRF, MMR) and then weights by importance (×(0.5+importance)) and a 90-day half-life recency decay —
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.
The seam
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.
Quick start — in-process with the Claude Agent SDK
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); }
The snapshot is shared — the server watches it
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.
SSM-embedding recall — including on a headless host (no GPU)
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.
Experience — what Evermind has done (opt-in)
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.
One-shot install into any MCP host (not just Claude)
# 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/).
Claude Code: the self-driving "memory combo"
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).
Quick start — stdio (any process / language)
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).
Quick start — HTTP (multi-tenant)
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