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Builderforce Memory

作者:SeanHogg版本 2026.10.43i★ –i在你的电脑上运行
注册名称io.github.SeanHogg/builderforce-memory

安装

概览

作者提供的说明(英文原文)

Token-saving persistent memory for AI agents: recall durable facts instead of re-reading files.

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

  • 连接方式: 在你的电脑上运行
  • 传输方式: stdio
  • 分类: 搜索与 AI 记忆
  • 你需要填写:BUILDERFORCE_API_KEY

安装

选择你使用的 AI 应用,然后复制设置。

安装之前

  • 基础检查:1 项中通过 1 项
  • 在你的电脑上运行,拥有你的用户账户的权限(文件、网络、应用)。
  • 需要你自己填写: BUILDERFORCE_MEMORY_FILE, BUILDERFORCE_MEMORY_READONLY, BUILDERFORCE_API_KEY

标为机密的值是你账户的钥匙。千万不要分享给别人,也不要粘贴到聊天里。

你需要

  • npm已安装 Node.js(用于 npx 命令)

你用的是哪个应用?

在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。

打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 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"
      }
    }
  }
}

在终端中运行这条命令

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

运行前请先填写空着的值。

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json,或 ~/.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"
      }
    }
  }
}

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/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"
      }
    }
  }
}

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。

你的选择通过 Cookie 保存在这台设备上,无需账户。

需要你填写的值

  • BUILDERFORCE_MEMORY_FILE env
    Absolute 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_READONLY env
    Set to 1 to expose recall only (disables memory_remember and memory_forget).
  • BUILDERFORCE_API_KEY 机密 env
    Builderforce 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。

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6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

  • … 尚未检查

    仍在维护

    我们还没有从 GitHub 读取这个仓库

  • … 尚未检查

    有许可证

    我们还没有从 GitHub 读取这个仓库

  • – 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

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 7245 个服务器的 0%
  • 发布到注册中心的时间比此分类中其他 7245 个服务器的 99% 更新
  • 基础检查结果等于或优于此分类中其他 7245 个服务器的 100%

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 1 项适用的基础检查中通过了 1 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–,最新版本(2026.10.43)于今天进入注册中心。

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, Exa

替代选择

同一分类中其他作者的服务器,按星标数排序。

如何看卡片:★ 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

Context7

作者:upstash

Up-to-date docs for AI agents

电脑或云端★ 64,224MIT今天更新
安装

Codebase Memory

作者:DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

在你的电脑上运行★ 48,148MIT20天前更新
安装

Exa

作者:exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

在云端运行★ 5,213MIT上个月更新
安装

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:

TransportFactoryConsumed asUse when
In-process (Claude Agent SDK)createMemoryMcpServer(backend)the returned type:"sdk" objectthe consuming product is TS and runs @anthropic-ai/claude-agent-sdk in-process — lowest latency
stdiorunStdio(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: the authToken check 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 as EVM-3 in 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 in PEER-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_recall returns 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_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.
  • 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:

EnvMeaning
BUILDERFORCE_MEMORY_MODELPath to a .evermind package holding an evermind-lm.
BUILDERFORCE_MEMORY_TOKENIZERTokenizer JSON for that checkpoint. Default .tokenizer.json.
BUILDERFORCE_MEMORY_VECTORSVector-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_infoSnapshot file, screenshot folder, model path, counts.
experience_forget_allDelete every episode, screenshot, skill and run (confirm: true). Memories and the model are untouched.
episode_save / _list / _get / _forgetDemonstrations. Screenshots go in episodes/ / and are deleted with the episode.
skill_preview / _compile / _list / _get / _forget / _schedule, skills_dueTrain Once and routines.
run_start / _step / _finish / _list / _getRuns and their audit trail (newest 500 kept).
experience_trainAdapt 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):

HookBehaviour
SessionStartInject a digest of the top durable memories.
PreCompactFlush durable learnings before the window is summarised.
UserPromptSubmitContextual 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.
StopAutonomous 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

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