AI Inference in GitHub Actions

Use AI models from GitHub Models in your workflows.

Usage

Create a workflow to use the AI inference action:

name: 'AI inference'
on: workflow_dispatch

jobs:
  inference:
    permissions:
      models: read
    runs-on: ubuntu-latest
    steps:
      - name: Test Local Action
        id: inference
        uses: actions/ai-inference@v1
        with:
          prompt: 'Hello!'

      - name: Print Output
        id: output
        run: echo "${{ steps.inference.outputs.response }}"

Using a prompt file

You can also provide a prompt file instead of an inline prompt. The action supports both plain text files and structured .prompt.yml files:

steps:
  - name: Run AI Inference with Text File
    id: inference
    uses: actions/ai-inference@v1
    with:
      prompt-file: './path/to/prompt.txt'

Using GitHub prompt.yml files

For more advanced use cases, you can use structured .prompt.yml files that support templating, custom models, and JSON schema responses:

steps:
  - name: Run AI Inference with Prompt YAML
    id: inference
    uses: actions/ai-inference@v1
    with:
      prompt-file: './.github/prompts/sample.prompt.yml'
      input: |
        var1: hello
        var2: ${{ steps.some-step.outputs.output }}
        var3: |
          Lorem Ipsum
          Hello World
      file_input: |
        var4: ./path/to/long-text.txt
        var5: ./path/to/config.json

Simple prompt.yml example

messages:
  - role: system
    content: Be as concise as possible
  - role: user
    content: 'Compare {{a}} and {{b}}, please'
model: openai/gpt-4o

Prompt.yml with JSON schema support

messages:
  - role: system
    content: You are a helpful assistant that describes animals using JSON format
  - role: user
    content: |-
      Describe a {{animal}}
      Use JSON format as specified in the response schema
model: openai/gpt-4o
responseFormat: json_schema
jsonSchema: |-
  {
    "name": "describe_animal",
    "strict": true,
    "schema": {
      "type": "object",
      "properties": {
        "name": {
          "type": "string",
          "description": "The name of the animal"
        },
        "habitat": {
          "type": "string",
          "description": "The habitat the animal lives in"
        }
      },
      "additionalProperties": false,
      "required": [
        "name",
        "habitat"
      ]
    }
  }

Variables in prompt.yml files are templated using {{variable}} format and are supplied via the input parameter in YAML format. Additionally, you can provide file-based variables via file_input, where each key maps to a file path.

Prompt.yml with model parameters

You can specify model parameters directly in your .prompt.yml files using the modelParameters key:

messages:
  - role: system
    content: Be as concise as possible
  - role: user
    content: 'Compare {{a}} and {{b}}, please'
model: openai/gpt-4o
modelParameters:
  maxCompletionTokens: 500
  temperature: 0.7

![Note] Parameters set in modelParameters take precedence over the corresponding action inputs.

Using a system prompt file

In addition to the regular prompt, you can provide a system prompt file instead of an inline system prompt:

steps:
  - name: Run AI Inference with System Prompt File
    id: inference
    uses: actions/ai-inference@v1
    with:
      prompt: 'Hello!'
      system-prompt-file: './path/to/system-prompt.txt'

Read output from file instead of output

This can be useful when model response exceeds actions output limit

steps:
  - name: Test Local Action
    id: inference
    uses: actions/ai-inference@v1
    with:
      prompt: 'Hello!'

  - name: Use Response File
    run: |
      echo "Response saved to: ${{ steps.inference.outputs.response-file }}"
      cat "${{ steps.inference.outputs.response-file }}"

Using custom headers

You can include custom HTTP headers in your API requests, which is useful for integrating with API Management platforms, adding tracking information, or routing requests through custom gateways.

YAML format (recommended for multiple headers)

steps:
  - name: AI Inference with Azure APIM
    id: inference
    uses: actions/ai-inference@v1
    with:
      prompt: 'Analyze this code for security issues...'
      endpoint: ${{ secrets.APIM_ENDPOINT }}
      token: ${{ secrets.APIM_KEY }}
      custom-headers: |
        Ocp-Apim-Subscription-Key: ${{ secrets.APIM_SUBSCRIPTION_KEY }}
        serviceName: code-review-workflow
        env: production
        team: security
        computer: github-actions

JSON format (alternative for compact syntax)

steps:
  - name: AI Inference with Custom Headers
    id: inference
    uses: actions/ai-inference@v1
    with:
      prompt: 'Hello!'
      custom-headers: '{"X-Custom-Header": "value", "X-Team": "engineering", "X-Request-ID": "${{ github.run_id }}"}'

Use cases for custom headers

• API Management: Integrate with Azure APIM, AWS API Gateway, Kong, or other API management platforms
• Request tracking: Add correlation IDs, request IDs, or workflow identifiers
• Rate limiting: Include quota or tier information for custom rate limiting
• Multi-tenancy: Identify teams, services, or environments
• Observability: Add metadata for logging, monitoring, and debugging
• Routing: Control request routing through custom gateways or load balancers

Header name requirements: Header names must follow the HTTP token syntax defined in RFC 7230 (which permits underscores). For maximum compatibility with intermediaries and tooling, we recommend using only alphanumeric characters and hyphens.

Security note: Always use GitHub secrets for sensitive header values like API keys, tokens, or passwords. The action automatically masks common sensitive headers (containing key, token, secret, password, or authorization) in logs.

Using GitHub Copilot CLI as the inference provider

By default the action calls the GitHub Models REST API. You can instead route inference through the GitHub Copilot CLI by setting provider: copilot.

Because the Copilot CLI is not pre-installed on GitHub-hosted runners, you'll need to install and authenticate it in earlier steps before invoking this action. The pattern follows the official GitHub Actions docs:

name: 'AI inference (Copilot)'
on: workflow_dispatch

jobs:
  inference:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v6

      - uses: actions/setup-node@v6

      - name: Install Copilot CLI
        run: npm install -g @github/copilot

      - name: Run AI Inference via Copilot
        id: inference
        uses: actions/ai-inference@v1
        with:
          prompt: 'Summarise the latest changes in this repo.'
          provider: copilot
          model: gpt-4.1 # any model the Copilot CLI accepts; omit to use the CLI default
        env:
          # Create a fine-grained PAT with the "Copilot Requests" permission and
          # store it as a repository secret. See the docs linked above.
          COPILOT_GITHUB_TOKEN: ${{ secrets.COPILOT_PAT }}

      - run: echo "${{ steps.inference.outputs.response }}"

Notes when provider: copilot:

• The Copilot CLI must be on PATH (or pass copilot-cli-path) and authenticated via COPILOT_GITHUB_TOKEN (or another env var Copilot CLI accepts) before this action runs.
• The action's default model (openai/gpt-4o) is a GitHub Models identifier and is not forwarded to Copilot. Set model: to a Copilot-compatible model (e.g. gpt-4.1, claude-sonnet-4.5) when you want to override the CLI default.
• enable-github-mcp, custom-headers, endpoint, and responseFormat / jsonSchema are ignored under this provider — Copilot has its own tools and configuration mechanism. Use copilot-allow-tools (e.g. shell(git:*),write) to opt in to specific Copilot tools.
• The action invokes the CLI with -p <prompt> -s --no-ask-user, so it never blocks on interactive prompts and stdout is just the model response.
• Permissions are denied by default. No --allow-tool flags are passed unless you set copilot-allow-tools, so Copilot can't run shell commands, write files, or fetch URLs out of the box.

GitHub MCP Integration (Model Context Protocol)

This action supports read-only integration with the GitHub-hosted Model Context Protocol (MCP) server, which provides access to GitHub tools like repository management, issue tracking, and pull request operations.

Authentication

You can authenticate the MCP server with either:

1. Personal Access Token (PAT) – user-scoped token
2. GitHub App Installation Token (ghs_…) – short-lived, app-scoped token

   The built-in GITHUB_TOKEN is not accepted by the MCP server. Using a GitHub App installation token is recommended in most CI environments because it is short-lived and least-privilege by design.

Enabling MCP in the action

Set enable-github-mcp: true and provide a token via github-mcp-token.

steps:
  - name: AI Inference with GitHub Tools
    id: inference
    uses: actions/ai-inference@v1.2
    with:
      prompt: 'List my open pull requests and create a summary'
      enable-github-mcp: true
      token: ${{ secrets.USER_PAT }} # or a ghs_ installation token

If you want, you can use separate tokens for the AI inference endpoint and the GitHub MCP server:

steps:
  - name: AI Inference with Separate MCP Token
    id: inference
    uses: actions/ai-inference@v1.2
    with:
      prompt: 'List my open pull requests and create a summary'
      enable-github-mcp: true
      token: ${{ secrets.GITHUB_TOKEN }}
      github-mcp-token: ${{ secrets.USER_PAT }} # or a ghs_ installation token

Configuring GitHub MCP Toolsets

By default, the GitHub MCP server provides a standard set of tools (context, repos, issues, pull_requests, users). You can customize which toolsets are available by specifying the github-mcp-toolsets parameter:

steps:
  - name: AI Inference with Custom Toolsets
    id: inference
    uses: actions/ai-inference@v2
    with:
      prompt: 'Analyze recent workflow runs and check security alerts'
      enable-github-mcp: true
      token: ${{ secrets.USER_PAT }}
      github-mcp-toolsets: 'repos,issues,pull_requests,actions,code_security'

Available toolsets: See: Tool configuration

When MCP is enabled, the AI model will have access to GitHub tools and can perform actions like searching issues and PRs.

Inputs

Various inputs are defined in action.yml to let you configure the action:

Outputs

The AI inference action provides the following outputs:

Required Permissions

In order to run inference with GitHub Models, the GitHub AI inference action requires models permissions.

permissions:
  contents: read
  models: read

Publishing a New Release

This project includes a helper script, script/release designed to streamline the process of tagging and pushing new releases for GitHub Actions. For more information, see Versioning in the GitHub Actions toolkit.

GitHub Actions allows users to select a specific version of the action to use, based on release tags. This script simplifies this process by performing the following steps:

1. Retrieving the latest release tag: The script starts by fetching the most recent SemVer release tag of the current branch, by looking at the local data available in your repository.
2. Prompting for a new release tag: The user is then prompted to enter a new release tag. To assist with this, the script displays the tag retrieved in the previous step, and validates the format of the inputted tag (vX.X.X). The user is also reminded to update the version field in package.json.
3. Tagging the new release: The script then tags a new release and syncs the separate major tag (e.g. v1, v2) with the new release tag (e.g. v1.0.0, v2.1.2). When the user is creating a new major release, the script auto-detects this and creates a releases/v# branch for the previous major version.
4. Pushing changes to remote: Finally, the script pushes the necessary commits, tags and branches to the remote repository. From here, you will need to create a new release in GitHub so users can easily reference the new tags in their workflows.

License

This project is licensed under the terms of the MIT open source license. Please refer to MIT for the full terms.

Contributions

Contributions are welcome! See the Contributor's Guide.