litellm/docs/my-website/docs/providers/github_copilot.md

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GitHub Copilot

https://docs.github.com/en/copilot

:::tip

We support GitHub Copilot Chat API with automatic authentication handling

:::

Property	Details
Description	GitHub Copilot Chat API provides access to GitHub's AI-powered coding assistant.
Provider Route on LiteLLM	github_copilot/
Supported Endpoints	/chat/completions, /embeddings
API Reference	GitHub Copilot docs

Authentication

GitHub Copilot uses OAuth device flow for authentication. On first use, you'll be prompted to authenticate via GitHub:

1. LiteLLM will display a device code and verification URL
2. Visit the URL and enter the code to authenticate
3. Your credentials will be stored locally for future use

Usage - LiteLLM Python SDK

Chat Completion

from litellm import completion

response = completion(
    model="github_copilot/gpt-4",
    messages=[{"role": "user", "content": "Write a Python function to calculate fibonacci numbers"}],
    extra_headers={
        "editor-version": "vscode/1.85.1",
        "Copilot-Integration-Id": "vscode-chat"
    }
)
print(response)

from litellm import completion

stream = completion(
    model="github_copilot/gpt-4",
    messages=[{"role": "user", "content": "Explain async/await in Python"}],
    stream=True,
    extra_headers={
        "editor-version": "vscode/1.85.1",
        "Copilot-Integration-Id": "vscode-chat"
    }
)

for chunk in stream:
    if chunk.choices[0].delta.content is not None:
        print(chunk.choices[0].delta.content, end="")

Responses

For GPT Codex models, only responses API is supported.

import litellm

response = await litellm.aresponses(
    model="github_copilot/gpt-5.1-codex",
    input="Write a Python hello world",
    max_output_tokens=500
)

print(response)

Embedding

import litellm

response = litellm.embedding(
    model="github_copilot/text-embedding-3-small",
    input=["good morning from litellm"]
)
print(response)

Usage - LiteLLM Proxy

Add the following to your LiteLLM Proxy configuration file:

model_list:
  - model_name: github_copilot/gpt-4
    litellm_params:
      model: github_copilot/gpt-4
  - model_name: github_copilot/gpt-5.1-codex
    model_info:
      mode: responses
    litellm_params:
      model: github_copilot/gpt-5.1-codex
  - model_name: github_copilot/text-embedding-ada-002
    model_info:
      mode: embedding
    litellm_params:
      model: github_copilot/text-embedding-ada-002

Start your LiteLLM Proxy server:

litellm --config config.yaml

# RUNNING on http://0.0.0.0:4000

from openai import OpenAI

# Initialize client with your proxy URL
client = OpenAI(
    base_url="http://localhost:4000",  # Your proxy URL
    api_key="your-proxy-api-key"       # Your proxy API key
)

# Non-streaming response
response = client.chat.completions.create(
    model="github_copilot/gpt-4",
    messages=[{"role": "user", "content": "How do I optimize this SQL query?"}],
    extra_headers={
        "editor-version": "vscode/1.85.1",
        "Copilot-Integration-Id": "vscode-chat"
    }
)

print(response.choices[0].message.content)

import litellm

# Configure LiteLLM to use your proxy
response = litellm.completion(
    model="litellm_proxy/github_copilot/gpt-4",
    messages=[{"role": "user", "content": "Review this code for bugs"}],
    api_base="http://localhost:4000",
    api_key="your-proxy-api-key",
    extra_headers={
        "editor-version": "vscode/1.85.1",
        "Copilot-Integration-Id": "vscode-chat"
    }
)

print(response.choices[0].message.content)

curl http://localhost:4000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer your-proxy-api-key" \
  -H "editor-version: vscode/1.85.1" \
  -H "Copilot-Integration-Id: vscode-chat" \
  -d '{
    "model": "github_copilot/gpt-4",
    "messages": [{"role": "user", "content": "Explain this error message"}]
  }'

Getting Started

1. Ensure you have GitHub Copilot access (paid GitHub subscription required)
2. Run your first LiteLLM request - you'll be prompted to authenticate
3. Follow the device flow authentication process
4. Start making requests to GitHub Copilot through LiteLLM

Configuration

Environment Variables

You can customize token storage locations:

# Optional: Custom token directory
export GITHUB_COPILOT_TOKEN_DIR="~/.config/litellm/github_copilot"

# Optional: Custom access token file name
export GITHUB_COPILOT_ACCESS_TOKEN_FILE="access-token"

# Optional: Custom API key file name
export GITHUB_COPILOT_API_KEY_FILE="api-key.json"

Headers

GitHub Copilot supports various editor-specific headers:

extra_headers = {
    "editor-version": "vscode/1.85.1",           # Editor version
    "editor-plugin-version": "copilot/1.155.0",  # Plugin version
    "Copilot-Integration-Id": "vscode-chat",     # Integration ID
    "user-agent": "GithubCopilot/1.155.0"        # User agent
}