32850ef7c5
* fix(coding-agent): resume after context-limited length stops Treat length stops as context overflow when prompt usage is within 1% of Pi's configured context window. Include cache-write tokens in that usage and allow non-zero output because OpenAI may emit reasoning tokens before returning an incomplete response. After compaction, remove any retryable error or truncated-length assistant message restored from persisted session history so the interrupted agent turn can continue. This is specifically for handling this case for openai models: https://developers.openai.com/api/docs/guides/reasoning > If the generated tokens reach the context window limit or the max_output_tokens value you’ve set, you’ll receive a response with a status of incomplete and incomplete_details with reason set to max_output_tokens. This might occur before any visible output tokens are produced, meaning you could incur costs for input and reasoning tokens without receiving a visible response. This detection currently assumes the reported prompt usage is within 1% of the context window configured in Pi. Its reliability therefore depends on Pi's model contextWindow metadata matching the provider's effective limit. Maybe we should consider treating OpenAI incomplete responses as requiring compaction regardless of the local context-window estimate? fixes #7020 * fix(coding-agent): recover truncated responses after compaction Classify length stops by comparing provider-reported output usage with the model's intended output limit. Compact and retry once when generation ends below that limit, avoiding unreliable context-window estimates while preserving genuine max-output stops. Normalize OpenAI Responses incomplete reasons so only max_output_tokens is treated as a length stop. Preserve qualified provider reasons for diagnostics, surface other reasons as errors, and use neutral truncation wording in the TUI. |
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| AGENTS.md | ||
| biome.json | ||
| CONTRIBUTING.md | ||
| LICENSE | ||
| package-lock.json | ||
| package.json | ||
| pi-test.bat | ||
| pi-test.ps1 | ||
| pi-test.sh | ||
| README.md | ||
| SECURITY.md | ||
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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.
- @earendil-works/pi-coding-agent: Interactive coding agent CLI
- @earendil-works/pi-agent-core: Agent runtime with tool calling and state management
- @earendil-works/pi-ai: Unified multi-provider LLM API (OpenAI, Anthropic, Google, …)
To learn more about Pi:
- Visit pi.dev, the project website with demos
- Read the documentation, but you can also ask the agent to explain itself
All Packages
| Package | Description |
|---|---|
| @earendil-works/pi-ai | Unified multi-provider LLM API (OpenAI, Anthropic, Google, etc.) |
| @earendil-works/pi-agent-core | Agent runtime with tool calling and state management |
| @earendil-works/pi-coding-agent | Interactive coding agent CLI |
| @earendil-works/pi-tui | Terminal UI library with differential rendering |
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
piand provider auth on the host while routing built-in tools and!commands into a local Linux micro-VM. - Plain Docker: run the whole
piprocess in a local container for simple isolation. - OpenShell: run the whole
piprocess 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 source archive includes the generated provider model data used for the release. --offline-model-data builds with that snapshot instead of refreshing it from live provider catalogs. The script still installs dependencies, builds the monorepo, compiles the Bun executable, and stages its runtime assets. Package maintainers who provide dependencies separately can pass --skip-install --skip-deps.
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.
.npmrcsetssave-exact=trueandmin-release-age=2to avoid same-day dependency releases during npm resolution.package-lock.jsonis the dependency ground truth. Pre-commit blocks accidental lockfile commits unlessPI_ALLOW_LOCKFILE_CHANGE=1is set.npm run checkverifies 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:localto 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 --selfuse--ignore-scriptswhere supported. - CI installs with
npm ci --ignore-scripts, and a scheduled GitHub workflow runsnpm audit --omit=devplusnpm 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
