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Firekeep

de kapella-hubversión 1.8.2i★ –iSe ejecuta en tu computadora
Nombre en el registroio.github.kapella-hub/firekeep

Instalar

Resumen

Descripción del creador (original en inglés)

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

Los tres pilares de PopNx MCP

Estos tres nunca se combinan en una sola puntuación: las opiniones, las mediciones y la popularidad son cosas distintas.

Qué puedes hacer con él

Todavía no escribimos un resumen sencillo para este. Abajo están los datos que leímos del registro.

  • Conexión: Se ejecuta en tu computadora
  • Transporte: stdio
  • Categoría: Búsqueda y memoria de IA

Instalación

Elige la app de IA que usas y copia la configuración.

Antes de instalar

Necesitas

  • PyPIuv de Python instalado (para el comando uvx)

¿Qué app usas?

Elige tu app arriba. La recordamos, así que la próxima vez el botón dirá “Instalar en …”.

Abre Claude Desktop → Settings → Developer → Edit Config y pega esto en claude_desktop_config.json

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

Ejecuta esto en tu terminal

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

Abrir en Cursor

O copia la configuración tú

Pégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json

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

Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.

Abrir en VS Code

O copia la configuración tú

Pégalo en .vscode/mcp.json de tu proyecto

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

Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.

Este se ejecuta en tu computadora, y ChatGPT solo puede conectarse a servidores en internet. Elige otra app.

Tu elección se guarda en una cookie en este dispositivo; no necesitas cuenta.

Esta configuración se genera a partir de la ficha del creador en el registro. Si el README dice otra cosa, sigue el README.

Qué puede hacer

Se ejecuta en tu computadora; la lista de herramientas aparece en tu app de IA después de instalarlo.

Revisiones básicasi

Revisamos información que cualquiera puede ver; no hemos leído el código. Las revisiones que no aplican no se cuentan. · Revisado el 7 nov 2026

  • ✓ Superada

    El creador coincide con el repo

    El nombre en el registro se verificó con la cuenta de GitHub kapella-hub, dueña del repo

  • … Aún sin revisar

    Sigue en mantenimiento

    Todavía no hemos leído este repo en GitHub

  • … Aún sin revisar

    Tiene licencia

    Todavía no hemos leído este repo en GitHub

  • – No aplica

    El endpoint responde

    Este se ejecuta en tu computadora; no hay endpoint en internet

  • – No aplica

    Inicio de sesión con OAuth

    Se ejecuta en tu computadora y usa ajustes locales en su lugar

  • … Aún sin revisar

    Sin instrucciones ocultas en las descripciones de herramientas

    Se ejecuta en tu computadora; solo podemos leer listas de herramientas de endpoints en internet

Comparado con otros en Búsqueda y memoria de IAi

  • Más estrellas que el 0% de los otros 7254 servidores de esta categoría
  • Publicado en el registro más recientemente que el 75% de los otros 7254 servidores de esta categoría
  • Revisiones básicas iguales o mejores que el 100% de los otros 7254 servidores de esta categoría

Comparado con todos los servidores activos de esta categoría en el registro. Las estrellas solo cuentan cuando quien lo publica es dueño del repo.

Preguntas frecuentes

¿Es seguro Firekeep?

Superó 1 de 1 revisiones básicas aplicables. No hemos leído el código. Antes de agregar una clave o un token, revisa qué acceso pide el servidor y usa una clave que tenga solo los permisos que necesitas.

¿Qué claves o códigos necesita?

El registro no indica ninguna clave obligatoria, aunque algunos servidores te piden iniciar sesión la primera vez que los usas.

¿Se ejecuta en mi computadora o en la nube?

Se ejecuta en tu computadora. Tu app de IA lo inicia cuando hace falta, y los datos no pasan por los servidores del creador (a menos que el propio servidor llame a un servicio en línea).

¿Sigue en mantenimiento?

El repo de GitHub cambió por última vez –, y la última versión (1.8.2) llegó al registro hace 11 días.

¿Qué apps pueden usarlo?

Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. También necesitas Python (uv) en tu computadora.

¿Qué puedo usar en su lugar?

En la misma categoría: Context7, Codebase Memory, Exa

Alternativas

Servidores de la misma categoría hechos por otros creadores, ordenados por estrellas.

Cómo leer una tarjeta:★ Estrellas de GitHubiTres pilares: reseñas · revisiones · usoiActualizado = fecha en que se publicó la última versión en el registroi

Context7

de upstash

Up-to-date docs for AI agents

Computadora o nube★ 64.224MITActualizado hoy
Instalar

Codebase Memory

de DeusData

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

Se ejecuta en tu computadora★ 48.148MITActualizado hace 20 días
Instalar

Exa

de exa-labs

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

Se ejecuta en la nube★ 5213MITActualizado el mes pasado
Instalar

README del creador

Abrir el README

Copiado tal cual del repo del creador. PopNx MCP no lo ha revisado ni lo respalda. Se quitaron HTML, imágenes y scripts. Los enlaces llevan fuera de este sitio.

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

CapabilityWhat it means
MemoryAgents 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 AutopilotThe 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 ContinuityMemories 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 ContinuityPlans, 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 AwarenessConfigured 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 CoordinationShared 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 GatewayAgents declare intent before consequential actions (action_before → `allowrethinkblock), 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.
SkillsAgents 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 RunbooksA 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 BoardWhen 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 InstructionsThe 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 LedgerA 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 DiscoveryQuality 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 & ExplainabilityEvery 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 SecretsFernet-backed vault for infrastructure credentials, API tokens, and connection strings. Distinct from memory — secrets never appear in recall.
Business KnowledgeIngest 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

Solo se muestra el principio; lee el resto en el repo

Pregunta a PopNx