agentpack
FROM node:22-alpine RUN npm install -g agentpack-cli WORKDIR /workspace RUN agentpack init ENTRYPOINT ["agentpack", "mcp"]
README
Agentpack
Repo-native task continuity for AI coding agents.
Coding agents forget. Agentpack gives them the task state they need to continue.
Here it is live. Session 1: Claude Code investigates a flaky test and records what it learns through the Agentpack MCP tools. Session 2, next day, empty context: the agent loads the task state and picks up exactly where the first session stopped — no re-investigation:

Prefer to poke at it by hand? The same flow driven from the CLI is in docs/DEMOS.md.
Every session ends the same way: the context window gets compacted, the chat closes, the task waits until tomorrow. The next session starts from zero — re-reading files, rediscovering decisions, retrying approaches that already failed.
Agentpack keeps a small, reviewable task ledger in .agentpack/ inside your repo. Connected agents record durable state as they work — the goal, decisions, dead ends, verification evidence, checkpoints — and the next session loads it back and continues. That next session can be the same agent after compaction, a different client, or you returning next week.
- Local-first. Plain files in your repo. No cloud, no telemetry, no network calls.
- Agent-oriented. A local MCP server plus generated project instructions (
AGENTS.md,CLAUDE.md, Cursor rules) tell agents when to load and record state. - Human-friendly. The same state is available through the CLI for inspection, debugging, and manual handoff.
Quick start
Requires Node.js >= 20.
npm install -g agentpack-cli
cd path/to/your/repo
agentpack init # once per repo
agentpack install claude --write # per client: codex | claude | cursor | claude-desktop
Restart or reconnect the coding-agent client. From then on the agent loads Agentpack context at session start, records durable decisions, sources, and evidence while working, and checkpoints meaningful progress.
Run agentpack doctor to verify the setup, and agentpack resume --preset agent --query "<topic>" to inspect the task state yourself.
See docs/INTEGRATIONS.md for client-by-client setup, including what each installer writes and why.
How it works
- At session start, the agent loads compact Agentpack context: the current Task Passport, recent checkpoints, decisions, and reviewed source conclusions.
- While working, it records durable state — decisions worth keeping, approaches that failed, verification evidence — and caches reviewed source conclusions with file hashes so unchanged files don't need re-reading.
- At a coherent boundary, it creates a checkpoint with status, next actions, and git state.
- The next session — any MCP-connected agent — continues from that state instead of rebuilding it from chat history.
The ledger is task-scoped: a Task Passport carries the goal, status, constraints, write scope, next actions, and verification for the current task, with an explicit lifecycle (start, park, switch, finalize) for handoffs. An optional task gate (native Claude Code, Codex, and Cursor hooks plus a client-neutral pre-commit hook) warns — or in block mode, stops — when edits bypass the active task.
Context is budgeted: resume output is compressed under a rough token estimate, so agents get the useful state back, not a pile of history.
When it helps
- The context window is compacted mid-task and the next turn needs the state back.
- You start a fresh chat or session on an ongoing task.
- You switch between Claude Code, Cursor, Codex, or another MCP client.
- You return to a refactor or bugfix days later.
- Another agent — or a teammate's agent — continues from your checkpoint.
- You split one session across parts of a monorepo (
api/,frontend/,cron/) with short scoped tasks, and the task gate keeps the agent from drifting outside the folder the current task owns.
A side effect: agents spend fewer tokens re-reading unchanged files and re-explaining old decisions.
Security posture
- Zero runtime dependencies, exact dev dependencies, committed lockfile,
ignore-scripts=true. - No telemetry and no network calls during normal CLI or MCP operation.
- Best-effort redaction of secret-looking values in stored context and handoff output.
- Releases are published from GitHub Actions with npm provenance (Trusted Publisher, no long-lived tokens); verify with
npm audit signatures.
See SECURITY.md for the full policy.
Documentation
- docs/INTEGRATIONS.md — safe setup for Codex, Claude Code, Cursor, Claude Desktop, and git hooks
- docs/CLI.md — full CLI reference, budgets, and manual fallback workflows
- docs/MCP.md — the MCP server contract and tool list
- docs/TASK-PASSPORT.md — task lifecycle, handoffs, and portable bundles
- docs/DEMOS.md — compact continuity demos you can run yourself
- docs/VISION.md — the strategic north star
Contributing / local development
Clone the repo and use Node 20+:
npm ci --ignore-scripts
npm test
npm run mcp:smoke
node dist/src/agentpack.js --help
This repo uses Agentpack on itself through MCP — docs/DOGFOOD.md describes the working protocol, docs/SETUP.md the full setup, and docs/RELEASING.md the release process.
Mirror: Codeberg. Issues, releases, and npm provenance stay on GitHub.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.