Firekeep

Name: Firekeep
Author: kapella-hub

제작자: kapella-hub버전 1.8.2i★ –i내 컴퓨터에서 실행

레지스트리 이름io.github.kapella-hub/firekeep

설치

개요

제작자가 쓴 설명(영어 원문)

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

PopNx MCP의 세 기둥

이 세 가지는 절대 하나의 점수로 합치지 않아요. 의견, 측정, 인기는 서로 다른 것이니까요.

사용자 의견i아직 리뷰가 없어요리뷰는 다음 단계에서 계정 기능과 함께 열려요
우리의 기본 점검i1개 중 1개 통과공개된 정보로 점검했으며, 코드는 아직 스캔하지 않았어요
사용 정도i아직 측정 안 함★ –

할 수 있는 일

이 서버는 아직 쉬운 말로 된 요약을 쓰지 못했어요. 아래는 레지스트리에서 읽어 온 사실이에요.

연결 방식: 내 컴퓨터에서 실행
전송 방식: stdio
카테고리: 검색 및 AI 기억

설치

쓰고 있는 AI 앱을 고른 다음 설정을 복사하세요.

설치하기 전에

기본 점검: 1개 중 1개 통과
내 컴퓨터에서 내 사용자 계정의 권한(파일, 네트워크, 앱)으로 실행돼요.
레지스트리에 입력해야 할 키가 나와 있지 않아요.

필요한 것

PyPIPython의 uv 설치(uvx 명령어용)

어떤 앱을 쓰세요?

ClaudeClaude CodeCursorVS CodeChatGPT

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

터미널에서 이 명령어를 실행하세요

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

Cursor에서 열기

또는 설정을 직접 복사하기

프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요

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

브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.

VS Code에서 열기

또는 설정을 직접 복사하기

프로젝트의 .vscode/mcp.json에 붙여 넣으세요

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

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.

이 설정은 제작자의 레지스트리 정보로 만들었어요. README와 다르면 README를 따르세요.

할 수 있는 일

내 컴퓨터에서 실행돼요. 설치하면 AI 앱에 도구 목록이 나타나요.

기본 점검i

누구나 볼 수 있는 정보를 점검했고, 코드는 읽지 않았어요. 해당하지 않는 항목은 개수에서 뺐어요. · 2026년 11월 7일 점검

✓ 통과
제작자와 저장소 일치
레지스트리 이름이 GitHub 계정 kapella-hub 기준으로 확인되었고, 이 계정이 저장소를 소유하고 있어요
… 아직 점검 안 함
계속 관리되고 있음
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
… 아직 점검 안 함
라이선스 있음
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
– 해당 없음
엔드포인트 응답
내 컴퓨터에서 실행되는 서버라 인터넷 엔드포인트가 없어요
– 해당 없음
OAuth로 로그인
내 컴퓨터에서 실행되며, 대신 로컬 설정을 사용해요
… 아직 점검 안 함
도구 설명에 숨겨진 지시 없음
내 컴퓨터에서 실행돼요. 도구 목록은 인터넷 엔드포인트가 있는 서버에서만 읽을 수 있어요

검색 및 AI 기억 카테고리의 다른 서버와 비교i

이 카테고리의 다른 서버 7250개 중 0%보다 스타가 많아요
이 카테고리의 다른 서버 7250개 중 76%보다 최근에 레지스트리에 공개됐어요
기본 점검 결과가 이 카테고리의 다른 서버 7250개 중 100%와 같거나 더 좋아요

레지스트리에서 이 카테고리의 활성 서버 전체와 비교했어요. 스타는 게시자가 저장소를 소유한 경우에만 세요.

자주 묻는 질문

Firekeep, 안전한가요?

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.

어떤 키나 코드가 필요한가요?

레지스트리에 필수 키가 나와 있지 않아요. 다만 처음 쓸 때 로그인을 요구하는 서버도 있어요.

내 컴퓨터에서 실행되나요, 클라우드에서 실행되나요?

내 컴퓨터에서 실행돼요. AI 앱이 필요할 때 실행하고, 데이터는 제작자의 서버를 거치지 않아요(서버 자체가 온라인 서비스를 호출하는 경우는 제외).

아직 관리되고 있나요?

GitHub 저장소는 마지막으로 – 변경됐고, 최신 버전(1.8.2)은 11일 전 레지스트리에 올라왔어요.

어떤 앱에서 쓸 수 있나요?

Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. 내 컴퓨터에 Python (uv)도 있어야 해요.

대신 쓸 수 있는 건 뭐가 있나요?

같은 카테고리에는: Context7, Codebase Memory, Exa

대안

같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.

카드 보는 법:★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i

Context7

제작자: upstash

Up-to-date docs for AI agents

컴퓨터 또는 클라우드★ 64,224MIT오늘 업데이트

설치

Codebase Memory

제작자: DeusData

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

내 컴퓨터에서 실행★ 48,148MIT20일 전 업데이트

설치

Exa

제작자: exa-labs

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

클라우드에서 실행★ 5,213MIT지난달 업데이트

설치

제작자의 README

README 열기

제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.

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

앞부분만 보여 드려요. 나머지는 저장소에서 읽어 보세요

Firekeep

개요

PopNx MCP의 세 기둥

할 수 있는 일

설치

설치하기 전에

할 수 있는 일

기본 점검i

제작자와 저장소 일치

계속 관리되고 있음

라이선스 있음

엔드포인트 응답

OAuth로 로그인

도구 설명에 숨겨진 지시 없음

검색 및 AI 기억 카테고리의 다른 서버와 비교i

자주 묻는 질문

대안

Context7

Codebase Memory

Exa

제작자의 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