Context7
by upstash
Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.
Computer or cloud★ 64,224MITUpdated yesterday
Install
Firekeep
Overview
Description from the maker (original English)
Self-hosted operating layer for AI agents: knowledge, continuity, coordination, and governance.
PopNx MCP’s three pillars
These three are never merged into one score: opinions, measurements and popularity are different things.
- User voiceiNo reviews yetReviews open together with accounts in a later step
- Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
- UsageiNot measured yet★ –
What you can do with it
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
- Connection: Runs on your computer
- Transport: stdio
- Category: Search & AI memory
Install
Pick the AI app you use, then copy the settings.
Before you install
- Basic checks: 1 of 1 passed
- Runs on your computer with your user’s permissions (files, network, apps).
- The registry lists no key to fill in.
You need
- PyPIPython’s uv installed (for the uvx command)
Which app do you use?
Pick your app above. We remember it, so next time the button says “Install in …”.
Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"firekeep": {
"command": "uvx",
"args": [
"firekeep-client",
"gateway",
"--runtime",
"generic"
]
}
}
}Run this in your terminal
claude mcp add firekeep -- uvx firekeep-client gateway --runtime genericOr copy the settings yourself
Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"firekeep": {
"command": "uvx",
"args": [
"firekeep-client",
"gateway",
"--runtime",
"generic"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Or copy the settings yourself
Paste into .vscode/mcp.json in your project
{
"servers": {
"firekeep": {
"type": "stdio",
"command": "uvx",
"args": [
"firekeep-client",
"gateway",
"--runtime",
"generic"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.
Your choice stays on this device in a cookie; no account needed.
These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
What it can do
Runs on your computer; the tool list appears in your AI app after you install it.
Basic checksi
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026
- ✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account kapella-hub, which owns the repo
- … Not checked yet
Still maintained
We have not read this repo from GitHub yet
- … Not checked yet
Has a licence
We have not read this repo from GitHub yet
- – Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
- – Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
- … Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints
Compared with others in Search & AI memoryi
- More stars than 0% of the other 7270 servers in this category
- Published to the registry more recently than 75% of the other 7270 servers in this category
- Basic checks equal to or better than 100% of the other 7270 servers in this category
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
Common questions
Is Firekeep safe?
It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.
What keys or codes does it need?
The registry does not list a required key, though some servers ask you to sign in on first use.
Does it run on my computer or in the cloud?
It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
Is it still maintained?
The GitHub repo last changed –, and the latest version (1.8.2) reached the registry 12 days ago.
Which apps can use it?
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.
What can I use instead?
In the same category: Context7, Codebase Memory, Exa
Alternatives
Servers in the same category from other makers, sorted by stars.
Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
Codebase Memory
by DeusData
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Runs on your computer★ 48,148MITUpdated 21 days ago
Install
Exa
by exa-labs
Connect AI agents to Exa for web search, content fetching, and multi-step research.
Runs in the cloud★ 5,213MITUpdated last month
Install
README from the maker
Open the README
Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.
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 LRA["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 / Linuxirm '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.mdguidance. 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
Only the beginning is shown; read the rest in the repo