Firekeep

Name: Firekeep
Author: kapella-hub

作者：kapella-hub版本 1.8.2i★ –i在你的电脑上运行

注册名称io.github.kapella-hub/firekeep

安装

概览

作者提供的说明（英文原文）

Self-hosted operating layer for AI agents: knowledge, continuity, coordination, and governance.

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。

用户评价i暂无评价评价功能将在后续阶段与账户功能一起开放
我们的基础检查i1 项中通过 1 项依据公开信息检查；尚未扫描代码
使用情况i尚未统计★ –

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

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）。
注册中心没有列出需要填写的密钥。

你需要

PyPI已安装 Python 的 uv（用于 uvx 命令）

你用的是哪个应用？

ClaudeClaude CodeCursorVS CodeChatGPT

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": {
    "firekeep": {
      "command": "uvx",
      "args": [
        "firekeep-client",
        "gateway",
        "--runtime",
        "generic"
      ]
    }
  }
}

在终端中运行这条命令

claude mcp add firekeep -- uvx firekeep-client gateway --runtime generic

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": {
    "firekeep": {
      "command": "uvx",
      "args": [
        "firekeep-client",
        "gateway",
        "--runtime",
        "generic"
      ]
    }
  }
}

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": {
    "firekeep": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "firekeep-client",
        "gateway",
        "--runtime",
        "generic"
      ]
    }
  }
}

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。

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7日

✓ 通过
作者与仓库一致
注册名称已通过 GitHub 账户 kapella-hub 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 7271 个服务器的 0%
发布到注册中心的时间比此分类中其他 7271 个服务器的 75% 更新
基础检查结果等于或优于此分类中其他 7271 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

Firekeep 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？

注册中心没有列出必需的密钥，不过有些服务器会在首次使用时要求你登录。

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（1.8.2）于12天前进入注册中心。

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 Python (uv)。

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昨天更新

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★ 48,148MIT21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。

Firekeep

Your agents shouldn’t work like strangers.

Connect Claude Code, Codex, Kiro, OpenCode, and other MCP clients through one shared, self-hosted Keep.

Every solved problem should make the next one easier. Firekeep preserves the experience behind the work — durable knowledge, working state, procedures, coordination, and inspectable evidence — and puts it back to work in the next session, tool, or machine.

Use it for your own continuity on one workstation. Add teammates when you need them without starting the team’s context over.

[Website](https://firekeep.ai/?src=github) · [Install](https://firekeep.ai/docs.html?src=github#server) · [Product tour](https://firekeep.ai/?src=github#outcomes) · [Firekeep Studio](https://firekeep.ai/?src=github#studio) · [Evidence](https://firekeep.ai/?src=github#proof) · [Privacy](https://firekeep.ai/privacy.html) · [Support](https://github.com/kapella-hub/FirekeepHQ/issues)

One Keep, across runtimes

flowchart LR

A["Claude Code stores a proven fix"] --> K[("your self-hosted Keep")] K --> B["Codex recalls the fix + evidence"] K --> C["Kiro reuses the procedure"] K --> D["OpenCode / MCP client continues informed"]

Agents keep their native interfaces and provider-owned sessions. What carries forward is the Firekeep context they explicitly record and recall through the same local MCP gateway. That means one person can switch tools without losing the thread, and a team can earn context once and carry it forward.

[See Claude Code and Codex share one Keep →](https://firekeep.ai/claude-code-codex-shared-memory.html?src=github)

Install Firekeep

The installer configures Claude Code, Codex, Kiro, and OpenCode together (plus Claude Desktop when its config directory exists), then asks where your Firekeep server should live. The full setup flow is in the [install guide](https://firekeep.ai/docs.html#server).

curl -fsSL 'https://firekeep.ai/latest/install?src=github' | sh    # macOS / Linux

irm 'https://firekeep.ai/latest/install.ps1?src=github' | iex # Windows PowerShell

The server runs on linux/amd64 or linux/arm64 (server releases after v1.6.2; v1.6.2 and earlier are amd64-only) and needs Docker Compose v2, two CPU cores, and Git; 16 GB RAM is recommended for the default stack. It binds to 127.0.0.1 by default. See the [requirements and sizing notes](https://firekeep.ai/docs.html#requirements) before choosing a host.

Trying Firekeep for the first time? [Share what worked and where you got stuck →](https://github.com/kapella-hub/FirekeepHQ/discussions/15)

Runtime coverage

Client Kit: configures Claude Code, Codex, Kiro, and OpenCode together; Claude Desktop is added when its config directory exists. Other MCP clients can use the generic path.
Claude Code: managed MCP, instructions, and lifecycle hooks. Its supported pre-tool hook can hard-block an edit, write, or shell action.
Codex: managed MCP plus AGENTS.md guidance. Codex exposes no native hooks, so lifecycle automation and pre-edit enforcement are not implied.
Kiro CLI: a named Firekeep agent with MCP, steering, and lifecycle hooks. On the validated Kiro CLI 2.12.1 surface, its pre-edit hook is advisory.
Firekeep Studio: runs Codex, Claude Code, Kiro CLI, and Grok in one desktop workspace. The direct Grok adapter has no Keep memory, hooks, or briefing; Studio reports that boundary instead of inheriting another runtime’s status.

See the [integration guide](docs/INTEGRATIONS.md) and [Studio runtime matrix](studio/README.md#runtime-support) for the maintained details.

Measured retrieval, with the method attached

On the accepted 2026-08-28 LongMemEval-S run, Firekeep achieved 97.7% Evidence Recall@10: at least one labeled evidence session appeared in the first ten results for 459 of 470 scored questions. The measurement covers retrieval, not generated-answer accuracy; it is one corrected deterministic run rather than a multi-run confidence interval.

[Methodology](https://firekeep.ai/benchmarks/METHODOLOGY.md) · [Result data](https://firekeep.ai/benchmarks/longmemeval-s-20260828.json)

The problem Firekeep solves

People do not build professional judgment by rereading every email, document, ticket, or source file from the beginning. They remember what happened, why a decision worked, how a failure was diagnosed, and which procedure held up in practice. That experience changes how they approach the next problem.

Agents should improve the same way. Firekeep connects the knowledge, working state, coordination, and evidence behind the work so the next agent can begin with the experience the last one earned.

Detailed capability map

Capability	What it means
Memory	Agents remember what worked, what failed, and what matters across sessions. Semantic + graph retrieval, confidence scoring, contradiction handling, four memory types (reference / procedural / episodic / transient) with type-aware recall decay, recoverable archive-first aging, and token-conscious recall with optional LLM synthesis. Recall is re-ranked by recorded session outcomes (outcome-weighted memory) and by agent feedback on knowledge that was actually acted on (`memory_feedback`).
Knowledge Autopilot	The knowledge base maintains itself without deciding anything on its own. When two unconfirmed memories genuinely conflict, neither is silently dropped — both stay recallable, marked contested, until a human verdict (`/memory/contested/resolve`). A session reaper closes out crashed/walked-away sessions so failures count in outcome scoring. Every review queue (draft skills, stale skills, procedure proposals, contested pairs, eval dead letters) lands in one inbox with a weekly digest, and `/memory/{id}/evidence` shows every signal behind a memory's rank in one read.
Fleet-as-GPU (Night Shift)	The connected agents' own machines do the knowledge base's homework overnight, against a local model, and only ever produce drafts. The session-end hook queues a distill job per session; the server's nightly passes queue one job per stale skill (re-author it) and per contested memory pair (propose a verdict); `firekeep night-shift` drains the queue — started for you at session start whenever LM Studio or Ollama is listening. A human still activates every draft and resolves every pair, and an approval-rate ledger per job type on the Autopilot tab says whether the fleet's work is worth keeping.
Team Continuity	Memories carry verified workspace/member provenance plus an untrusted runtime `agent_id` label and project. Per-contributor activity reports and LLM-synthesized handoff briefs let one agent pick up where another left off.
Session Continuity	Plans, decisions, and progress recorded through the session tools survive context compression. Crashed sessions are auto-detected on next start and offered for resumption with a periodic workspace snapshot (git branch, recent commits, diff stats) embedded in the shadow.
Environment Awareness	Configured Docker, git, and file collectors monitor operational state instead of relying only on prompts. Container restarts, new commits, and file changes flow into a replayable event stream.
Agent Coordination	Shared channels, bulletin board, structured task queue, resource leases with monotonic fencing tokens, presence registry, and direct messages. Concurrent agents can assign work, track progress, and use leases to prevent overlapping edits; hook-enabled clients block an edit when another agent already holds the file lease.
Predict-then-Act Gateway	Agents declare intent before consequential actions (`action_before` → `allow	rethink	block`), then reconcile outcomes (`action_after`). Combines a runtime policy engine (lease, file risk, path deny, session health, recent failure) with a fast-path cache for repeated low-risk actions.
Skills	Agents author reusable "what to do when X happens" playbooks via the `skill_create` tool (client-side, with full session context); a docs→skills pipeline drafts more from wikis/runbooks under human review. Top matches are injected into the next session's briefing. (Server-side auto-synthesis exists behind `SKILL_SYNTHESIS_ENABLED` but is off by default — the CPU-only deploy can't run the generation LLM in workable time.)
Enforced Runbooks	A skill whose steps carry command matchers is a runbook a human can arm: `advise` (round-1 advisories), `require_ack` (a matched command is challenged and proceeds only after an audited acknowledgement — one-use permit bound to workspace, member, session, command hash, step, bundle version and execution), or `block` (fails closed, with a server receipt the client requires before honoring an allow). Evidence is scored by success — a step counts only when its command exits 0 — and every enforcement event lands in a deviation ledger (dashboard + inbox), storing command hashes, never command text. Modes are set by a human on an admin-only route; agents can propose runbooks, never arm them. Opt-in via `PROCEDURE_ENABLED`, currently being dogfooded on our own deploys — see docs/guides/living-procedures.md.
Decision Board	When a clarification needs more than a couple of questions, the agent opens a local browser board pre-populated with evidence retrieved from team memory — better questions, informed by what the team already learned. The local gateway fronts the Decision Board process and Cortex `/decision/synthesize`.
Living Instructions	The instruction layer measures itself: a per-instruction compliance table computed deterministically from replay (did sessions recall before answering, record as they went, declare consequential actions), with trend over time, per-runtime slices, and exposure receipts — sessions carry a content hash of the instruction text that actually reached them, and anything unverifiable reports as unknown rather than counted. Honest about its limits by construction: it measures behavior, not whether the behavior helped. Fleet-drafted rewrites under human verdict and A/B validation are roadmap.
Trust Ledger	A per-agent employment record built from the declarations agents already make through the gateway (`action_before`/`action_after`): declared-action count, reconciliation rate, prediction-match calibration (Brier over stated confidence vs reconciled outcome), reversals, sessions, first/last seen. Visibility only — it reports, it never gates. Honest by construction: calibration is behavior not competence, only declared actions are seen, `agent_id` is a self-reported label so the record is per declared identity, and calibration reads not enough signal below a threshold rather than inventing a number. The enforcing half (a capability broker turning the record into earned autonomy) is roadmap.
Auto-Evals + Pattern Discovery	Quality metrics computed from replay traces on session completion. Pattern detection is enabled; automated promotion/validation and A/B experiment endpoints are implemented but disabled by default until a deployment has enough session volume to use them responsibly.
Replay & Explainability	Every memory read/write, session lifecycle event, environment change, coordination action, and gateway decision is recorded as a structured trace. Inspect, narrow, and reconstruct context at any prior event.
Encrypted Secrets	Fernet-backed vault for infrastructure credentials, API tokens, and connection strings. Distinct from memory — secrets never appear in recall.
Business Knowledge	Ingest company documents (wiki pages, tickets, API docs) — manually, or via scheduled Confluence collectors. Chunks land in the vector store and surface naturally during memory recall alongside operational memories.

| **Cod

只显示了开头部分；其余内容请在仓库中阅读

Firekeep

概览

PopNx MCP 的三大支柱

它能帮你做什么

安装

安装之前

它能做什么

基础检查i

作者与仓库一致

仍在维护

有许可证

端点有响应

使用 OAuth 登录

工具描述中没有隐藏指令

与“搜索与 AI 记忆”中的其他服务器比较i

常见问题

替代选择

Context7

Codebase Memory

Exa

作者的 README

Firekeep

Your agents shouldn’t work like strangers.

One Keep, across runtimes

Install Firekeep

Runtime coverage

Measured retrieval, with the method attached

The problem Firekeep solves

Detailed capability map