copilot-sdk

name	copilot-sdk
description	Build agentic applications with GitHub Copilot SDK. Use when embedding AI agents in apps, creating custom tools, implementing streaming responses, managing sessions, connecting to MCP servers, or creating custom agents. Triggers on Copilot SDK, GitHub SDK, agentic app, embed Copilot, programmable agent, MCP server, custom agent.

GitHub Copilot SDK

Embed Copilot's agentic workflows in any application using Python, TypeScript, Go, or .NET.

Overview

The GitHub Copilot SDK exposes the same engine behind Copilot CLI: a production-tested agent runtime you can invoke programmatically. No need to build your own orchestration - you define agent behavior, Copilot handles planning, tool invocation, file edits, and more.

Copilot Python SDK

Python SDK for programmatic control of GitHub Copilot CLI via JSON-RPC.

Note: This SDK is in technical preview and may change in breaking ways.

Installation

pip install github-copilot-sdk
# or
uv ppip install github-copilot-sdk

Quick Start

import asyncio
from copilot import CopilotClient

async def main():
    # Create and start client
    client = CopilotClient()
    await client.start()

    # Create a session
    session = await client.create_session({"model": "gpt-5"})

    # Wait for response using session.idle event
    done = asyncio.Event()

    def on_event(event):
        if event.type.value == "assistant.message":
            print(event.data.content)
        elif event.type.value == "session.idle":
            done.set()

    session.on(on_event)

    # Send a message and wait for completion
    await session.send({"prompt": "What is 2+2?"})
    await done.wait()

    # Clean up
    await session.destroy()
    await client.stop()

asyncio.run(main())

Features

• ✅ Full JSON-RPC protocol support
• ✅ stdio and TCP transports
• ✅ Real-time streaming events
• ✅ Session history with get_messages()
• ✅ Type hints throughout
• ✅ Async/await native

API Reference

CopilotClient

client = CopilotClient({
    "cli_path": "copilot",  # Optional: path to CLI executable
    "cli_url": None,        # Optional: URL of existing server (e.g., "localhost:8080")
    "log_level": "info",    # Optional: log level (default: "info")
    "auto_start": True,     # Optional: auto-start server (default: True)
    "auto_restart": True,   # Optional: auto-restart on crash (default: True)
})
await client.start()

session = await client.create_session({"model": "gpt-5"})

def on_event(event):
    print(f"Event: {event['type']}")

session.on(on_event)
await session.send({"prompt": "Hello!"})

# ... wait for events ...

await session.destroy()
await client.stop()

CopilotClient Options:

• cli_path (str): Path to CLI executable (default: "copilot" or COPILOT_CLI_PATH env var)
• cli_url (str): URL of existing CLI server (e.g., "localhost:8080", "http://127.0.0.1:9000", or just "8080"). When provided, the client will not spawn a CLI process.
• cwd (str): Working directory for CLI process
• port (int): Server port for TCP mode (default: 0 for random)
• use_stdio (bool): Use stdio transport instead of TCP (default: True)
• log_level (str): Log level (default: "info")
• auto_start (bool): Auto-start server on first use (default: True)
• auto_restart (bool): Auto-restart on crash (default: True)
• github_token (str): GitHub token for authentication. When provided, takes priority over other auth methods.
• use_logged_in_user (bool): Whether to use logged-in user for authentication (default: True, but False when github_token is provided). Cannot be used with cli_url.

SessionConfig Options (for create_session):

• model (str): Model to use ("gpt-5", "claude-sonnet-4.5", etc.). Required when using custom provider.
• reasoning_effort (str): Reasoning effort level for models that support it ("low", "medium", "high", "xhigh"). Use list_models() to check which models support this option.
• session_id (str): Custom session ID
• tools (list): Custom tools exposed to the CLI
• system_message (dict): System message configuration
• streaming (bool): Enable streaming delta events
• provider (dict): Custom API provider configuration (BYOK). See Custom Providers section.
• infinite_sessions (dict): Automatic context compaction configuration
• on_user_input_request (callable): Handler for user input requests from the agent (enables ask_user tool). See User Input Requests section.
• hooks (dict): Hook handlers for session lifecycle events. See Session Hooks section.

Session Lifecycle Methods:

# Get the session currently displayed in TUI (TUI+server mode only)
session_id = await client.get_foreground_session_id()

# Request TUI to display a specific session (TUI+server mode only)
await client.set_foreground_session_id("session-123")

# Subscribe to all lifecycle events
def on_lifecycle(event):
    print(f"{event.type}: {event.sessionId}")

unsubscribe = client.on(on_lifecycle)

# Subscribe to specific event type
unsubscribe = client.on("session.foreground", lambda e: print(f"Foreground: {e.sessionId}"))

# Later, to stop receiving events:
unsubscribe()

Lifecycle Event Types:

• session.created - A new session was created
• session.deleted - A session was deleted
• session.updated - A session was updated
• session.foreground - A session became the foreground session in TUI
• session.background - A session is no longer the foreground session

Tools

Define tools with automatic JSON schema generation using the @define_tool decorator and Pydantic models:

from pydantic import BaseModel, Field
from copilot import CopilotClient, define_tool

class LookupIssueParams(BaseModel):
    id: str = Field(description="Issue identifier")

@define_tool(description="Fetch issue details from our tracker")
async def lookup_issue(params: LookupIssueParams) -> str:
    issue = await fetch_issue(params.id)
    return issue.summary

session = await client.create_session({
    "model": "gpt-5",
    "tools": [lookup_issue],
})

Note: When using from __future__ import annotations, define Pydantic models at module level (not inside functions).

Low-level API (without Pydantic):

For users who prefer manual schema definition:

from copilot import CopilotClient, Tool

async def lookup_issue(invocation):
    issue_id = invocation["arguments"]["id"]
    issue = await fetch_issue(issue_id)
    return {
        "textResultForLlm": issue.summary,
        "resultType": "success",
        "sessionLog": f"Fetched issue {issue_id}",
    }

session = await client.create_session({
    "model": "gpt-5",
    "tools": [
        Tool(
            name="lookup_issue",
            description="Fetch issue details from our tracker",
            parameters={
                "type": "object",
                "properties": {
                    "id": {"type": "string", "description": "Issue identifier"},
                },
                "required": ["id"],
            },
            handler=lookup_issue,
        )
    ],
})

The SDK automatically handles tool.call, executes your handler (sync or async), and responds with the final result when the tool completes.

Image Support

The SDK supports image attachments via the attachments parameter. You can attach images by providing their file path:

await session.send({
    "prompt": "What's in this image?",
    "attachments": [
        {
            "type": "file",
            "path": "/path/to/image.jpg",
        }
    ]
})

Supported image formats include JPG, PNG, GIF, and other common image types. The agent's view tool can also read images directly from the filesystem, so you can also ask questions like:

await session.send({"prompt": "What does the most recent jpg in this directory portray?"})

Streaming

Enable streaming to receive assistant response chunks as they're generated:

import asyncio
from copilot import CopilotClient

async def main():
    client = CopilotClient()
    await client.start()

    session = await client.create_session({
        "model": "gpt-5",
        "streaming": True
    })

    # Use asyncio.Event to wait for completion
    done = asyncio.Event()

    def on_event(event):
        if event.type.value == "assistant.message_delta":
            # Streaming message chunk - print incrementally
            delta = event.data.delta_content or ""
            print(delta, end="", flush=True)
        elif event.type.value == "assistant.reasoning_delta":
            # Streaming reasoning chunk (if model supports reasoning)
            delta = event.data.delta_content or ""
            print(delta, end="", flush=True)
        elif event.type.value == "assistant.message":
            # Final message - complete content
            print("\n--- Final message ---")
            print(event.data.content)
        elif event.type.value == "assistant.reasoning":
            # Final reasoning content (if model supports reasoning)
            print("--- Reasoning ---")
            print(event.data.content)
        elif event.type.value == "session.idle":
            # Session finished processing
            done.set()

    session.on(on_event)
    await session.send({"prompt": "Tell me a short story"})
    await done.wait()  # Wait for streaming to complete

    await session.destroy()
    await client.stop()

asyncio.run(main())

When streaming=True:

• assistant.message_delta events are sent with delta_content containing incremental text
• assistant.reasoning_delta events are sent with delta_content for reasoning/chain-of-thought (model-dependent)
• Accumulate delta_content values to build the full response progressively
• The final assistant.message and assistant.reasoning events contain the complete content

Note: assistant.message and assistant.reasoning (final events) are always sent regardless of streaming setting.

Infinite Sessions

By default, sessions use infinite sessions which automatically manage context window limits through background compaction and persist state to a workspace directory.

# Default: infinite sessions enabled with default thresholds
session = await client.create_session({"model": "gpt-5"})

# Access the workspace path for checkpoints and files
print(session.workspace_path)
# => ~/.copilot/session-state/{session_id}/

# Custom thresholds
session = await client.create_session({
    "model": "gpt-5",
    "infinite_sessions": {
        "enabled": True,
        "background_compaction_threshold": 0.80,  # Start compacting at 80% context usage
        "buffer_exhaustion_threshold": 0.95,  # Block at 95% until compaction completes
    },
})

# Disable infinite sessions
session = await client.create_session({
    "model": "gpt-5",
    "infinite_sessions": {"enabled": False},
})

When enabled, sessions emit compaction events:

• session.compaction_start - Background compaction started
• session.compaction_complete - Compaction finished (includes token counts)

Custom Providers

The SDK supports custom OpenAI-compatible API providers (BYOK - Bring Your Own Key), including local providers like Ollama. When using a custom provider, you must specify the model explicitly.

ProviderConfig fields:

• type (str): Provider type - "openai", "azure", or "anthropic" (default: "openai")
• base_url (str): API endpoint URL (required)
• api_key (str): API key (optional for local providers like Ollama)
• bearer_token (str): Bearer token for authentication (takes precedence over api_key)
• wire_api (str): API format for OpenAI/Azure - "completions" or "responses" (default: "completions")
• azure (dict): Azure-specific options with api_version (default: "2024-10-21")

Example with Ollama:

session = await client.create_session({
    "model": "deepseek-coder-v2:16b",  # Required when using custom provider
    "provider": {
        "type": "openai",
        "base_url": "http://localhost:11434/v1",  # Ollama endpoint
        # api_key not required for Ollama
    },
})

await session.send({"prompt": "Hello!"})

Example with custom OpenAI-compatible API:

import os

session = await client.create_session({
    "model": "gpt-4",
    "provider": {
        "type": "openai",
        "base_url": "https://my-api.example.com/v1",
        "api_key": os.environ["MY_API_KEY"],
    },
})

Example with Azure OpenAI:

import os

session = await client.create_session({
    "model": "gpt-4",
    "provider": {
        "type": "azure",  # Must be "azure" for Azure endpoints, NOT "openai"
        "base_url": "https://my-resource.openai.azure.com",  # Just the host, no path
        "api_key": os.environ["AZURE_OPENAI_KEY"],
        "azure": {
            "api_version": "2024-10-21",
        },
    },
})

Important notes:

• When using a custom provider, the model parameter is required. The SDK will throw an error if no model is specified.
• For Azure OpenAI endpoints (*.openai.azure.com), you must use type: "azure", not type: "openai".
• The base_url should be just the host (e.g., https://my-resource.openai.azure.com). Do not include /openai/v1 in the URL - the SDK handles path construction automatically.

User Input Requests

Enable the agent to ask questions to the user using the ask_user tool by providing an on_user_input_request handler:

async def handle_user_input(request, invocation):
    # request["question"] - The question to ask
    # request.get("choices") - Optional list of choices for multiple choice
    # request.get("allowFreeform", True) - Whether freeform input is allowed

    print(f"Agent asks: {request['question']}")
    if request.get("choices"):
        print(f"Choices: {', '.join(request['choices'])}")

    # Return the user's response
    return {
        "answer": "User's answer here",
        "wasFreeform": True,  # Whether the answer was freeform (not from choices)
    }

session = await client.create_session({
    "model": "gpt-5",
    "on_user_input_request": handle_user_input,
})

Session Hooks

Hook into session lifecycle events by providing handlers in the hooks configuration:

async def on_pre_tool_use(input, invocation):
    print(f"About to run tool: {input['toolName']}")
    # Return permission decision and optionally modify args
    return {
        "permissionDecision": "allow",  # "allow", "deny", or "ask"
        "modifiedArgs": input.get("toolArgs"),  # Optionally modify tool arguments
        "additionalContext": "Extra context for the model",
    }

async def on_post_tool_use(input, invocation):
    print(f"Tool {input['toolName']} completed")
    return {
        "additionalContext": "Post-execution notes",
    }

async def on_user_prompt_submitted(input, invocation):
    print(f"User prompt: {input['prompt']}")
    return {
        "modifiedPrompt": input["prompt"],  # Optionally modify the prompt
    }

async def on_session_start(input, invocation):
    print(f"Session started from: {input['source']}")  # "startup", "resume", "new"
    return {
        "additionalContext": "Session initialization context",
    }

async def on_session_end(input, invocation):
    print(f"Session ended: {input['reason']}")

async def on_error_occurred(input, invocation):
    print(f"Error in {input['errorContext']}: {input['error']}")
    return {
        "errorHandling": "retry",  # "retry", "skip", or "abort"
    }

session = await client.create_session({
    "model": "gpt-5",
    "hooks": {
        "on_pre_tool_use": on_pre_tool_use,
        "on_post_tool_use": on_post_tool_use,
        "on_user_prompt_submitted": on_user_prompt_submitted,
        "on_session_start": on_session_start,
        "on_session_end": on_session_end,
        "on_error_occurred": on_error_occurred,
    },
})

Available hooks:

• on_pre_tool_use - Intercept tool calls before execution. Can allow/deny or modify arguments.
• on_post_tool_use - Process tool results after execution. Can modify results or add context.
• on_user_prompt_submitted - Intercept user prompts. Can modify the prompt before processing.
• on_session_start - Run logic when a session starts or resumes.
• on_session_end - Cleanup or logging when session ends.
• on_error_occurred - Handle errors with retry/skip/abort strategies.

Requirements

• Python 3.11+
• GitHub Copilot CLI installed and accessible