a37306d437
* feat(ai): support Azure Foundry Chat Completions deployments The Azure provider only implemented the Responses API, so Foundry deployments that speak Chat Completions could not be used. On Chat Completions the deepseek/deepseek-v4-pro model sends `thinking` and `prompt_cache_key`, both of which Azure rejects with 400. The provider now dispatches openai-completions alongside azure-openai-responses. A stream wrapper resolves the Azure endpoint onto the model first, since the shared openai-completions implementation reads it off the model. The request-shape differences are catalog compat rather than api code: reasoning_effort instead of DeepSeek's thinking field, no prompt cache parameters, reasoning_content kept on assistant turns so the prefix Foundry cached stays byte-identical, and effort clamped to the low/medium/high the deployment accepts. The provider is renamed from azure-openai-responses to azure, since it now serves more than the Responses API. The azure-openai-responses api id is unchanged. Breaking: auth.json and models.json entries keyed on azure-openai-responses must move to azure, and existing sessions see a different provider on their next turn. closes #9645 * feat(ai): apply the Azure deployment name map to Chat Completions The Responses path already sent the deployment name from AZURE_OPENAI_DEPLOYMENT_NAME_MAP. The Chat Completions path sent the catalog model id, so any deployment not named after the model 404'd. The Azure provider now swaps the deployment name into the request via onPayload before dispatch, then runs the caller's own onPayload on it. model.id stays the catalog id. The map helpers move to azure-openai-config.ts so the provider can share them without importing the SDK. * docs(ai,coding-agent): add changelog entries for the Azure provider rename Breaking: the azure-openai-responses provider is now azure. Users rename the key in auth.json, models.json, and settings.json; SDK users switch to getModel("azure", ...) and providers/azure. Also note Azure Foundry Chat Completions support (DeepSeek V4 Pro). --------- Co-authored-by: David Brailovsky <davidbrai@gmail.com> |
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New issues and PRs from new contributors are closed automatically. Maintainers review closed submissions daily. See CONTRIBUTING.md.
Pi
Pi is a minimal, extensible agent harness that you can make your own.
Adapt Pi to your workflows, not the other way around. Customize Pi with extensions, skills, prompt templates, and themes. Bundle them as Pi packages and share via npm or git.
Pi ships with powerful defaults but skips features like sub-agents and plan mode. Ask Pi to build what you want, or install a package that does it your way.
Use Pi interactively, automate it in print or JSON mode, control it over RPC, or build apps with the Pi TypeScript SDK. See OpenClaw for a real-world integration.
Getting started
Install the command-line interface:
curl -fsSL https://pi.dev/install.sh | sh
On Windows:
powershell -c "irm https://pi.dev/install.ps1 | iex"
The installer pins all dependencies and updates Pi with pi update. Alternatively, install directly with npm, which does not pin transitive dependencies:
npm install -g --ignore-scripts @earendil-works/pi-coding-agent
Pi requires Node.js 22.19 or newer. The macOS, Linux, and Windows installers can install it if needed. Pi does not require dependency lifecycle scripts for a normal npm installation.
On macOS and Linux, Nix users can install the latest release with nix profile add github:earendil-works/pi/stable. See the quickstart for updating and pinning releases.
Start Pi in the directory where you want it to work:
cd /path/to/project
pi
For a built-in AI provider, run /login inside Pi to connect a subscription or API key. Then give Pi a task.
See the documentation for full setup and usage instructions.
Share your OSS coding agent sessions
If you use Pi for open source work, please share your coding agent sessions.
Public OSS session data helps improve models, prompts, tools, and evaluations using real development workflows.
For the full explanation, see this post on X.
To publish sessions, use badlogic/pi-share-hf using a Hugging Face account and the Hugging Face CLI.
- Demo video on how to publish Pi sessions
- Published Pi development sessions:
badlogicgames/pi-monoon Hugging Face.
Development
Clone the repository, install its dependencies, and run Pi from source:
git clone https://github.com/earendil-works/pi
cd pi
npm install --ignore-scripts
./pi-test.sh
pi-test.sh can be called from any directory and preserves the caller's working directory.
Before submitting changes, run:
npm run check
./test.sh
Read CONTRIBUTING.md before opening an issue or pull request. It defines the contribution gate, issue quality bar, and required checks. Read AGENTS.md for repository-specific implementation, testing, dependency, and release rules.
License
MIT