New issues and PRs from new contributors are auto-closed by default. Maintainers review auto-closed issues daily. See CONTRIBUTING.md.
Pi Agent Harness
This is the home of the Pi agent harness project including our self extensible coding agent.
To learn more about Pi:
All Packages
For Slack/chat automation and workflows see earendil-works/pi-chat.
Permissions & Containerization
Pi does not include a built-in permission system for restricting filesystem, process, network, or credential access. By default, it runs with the permissions of the user and process that launched it.
If you need stronger boundaries, containerize or sandbox Pi. See packages/coding-agent/docs/containerization.md for three patterns:
- Gondolin extension: keep
pi and provider auth on the host while routing built-in tools and ! commands into a local Linux micro-VM.
- Plain Docker: run the whole
pi process in a local container for simple isolation.
- OpenShell: run the whole
pi process in a policy-controlled sandbox.
Contributing
See CONTRIBUTING.md for contribution guidelines and AGENTS.md for project-specific rules (for both humans and agents). Longer term plans for Pi can also be found in RFCs.
Development
npm install --ignore-scripts # Install all dependencies without running lifecycle scripts
npm run build # Refresh model data, then build all packages
npm run build:offline # Rebuild using existing model data without network access
npm run check # Lint, format, and type check
./test.sh # Run tests (skips LLM-dependent tests without API keys)
./pi-test.sh # Run pi from sources (can be run from any directory)
Building standalone binaries from release source
GitHub releases include a versioned source archive covered by the release's SHA256SUMS file. Extract it and run the same build script used for the official standalone binaries:
VERSION="<release-version>"
tar -xzf "pi-${VERSION}-source.tar.gz"
cd "pi-${VERSION}"
./scripts/build-binaries.sh --offline-model-data --platform linux-x64 --out "$PWD/out"
The archive includes release model data and native prebuilds. --offline-model-data uses that model data without refreshing provider catalogs. The script installs dependencies and builds the executable with its runtime assets; pass --skip-install if dependencies are already provided.
Supply-chain hardening
We treat npm dependency changes as reviewed code changes.
- Direct external dependencies are pinned to exact versions. Internal workspace packages remain version-ranged.
.npmrc sets save-exact=true and min-release-age=2 to avoid same-day dependency releases during npm resolution.package-lock.json is the dependency ground truth. Pre-commit blocks accidental lockfile commits unless PI_ALLOW_LOCKFILE_CHANGE=1 is set.npm run check verifies pinned direct deps, native TypeScript import compatibility, and the generated coding-agent shrinkwrap.- The published CLI package includes
packages/coding-agent/npm-shrinkwrap.json, generated from the root lockfile, to pin transitive deps for npm users.
- Release smoke tests use
npm run release:local to build, pack, and create isolated npm and Bun installs outside the repo before tagging a release.
- Local release installs, documented npm installs, and
pi update --self use --ignore-scripts where supported.
- CI installs with
npm ci --ignore-scripts, and a scheduled GitHub workflow runs npm audit --omit=dev plus npm audit signatures --omit=dev.
- Shrinkwrap generation has an explicit allowlist for dependency lifecycle scripts; new lifecycle-script deps fail checks until reviewed.
Share your OSS coding agent sessions
If you use Pi or other coding agents for open source work, please share your sessions.
Public OSS session data helps improve coding agents with real-world tasks, tool use, failures, and fixes instead of toy benchmarks.
For the full explanation, see this post on X.
To publish sessions, use badlogic/pi-share-hf. Read its README.md for setup instructions. All you need is a Hugging Face account, the Hugging Face CLI, and pi-share-hf.
You can also watch this video, where I show how I publish my pi-mono sessions.
I regularly publish my own pi-mono work sessions here:
License
MIT
pi.dev domain graciously donated by
exe.dev
Pi 是一个智能体框架（agent harness），即一套用于构建和运行 AI 智能体的软件包工具集，包括一个可自我扩展的交互式编程智能体命令行工具、统一支持多家模型提供商的大语言模型 API、智能体运行时，以及终端用户界面库。