skills/copilot-sdk/SKILL.md
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.
npx skillsauth add jyjeanne/ai-setup-forge copilot-sdkInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Embed Copilot's agentic workflows in any application using Python, TypeScript, Go, or .NET.
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.
Verify CLI: copilot --version
mkdir copilot-demo && cd copilot-demo
npm init -y --init-type module
npm install @github/copilot-sdk tsx
pip install github-copilot-sdk
mkdir copilot-demo && cd copilot-demo
go mod init copilot-demo
go get github.com/github/copilot-sdk/go
dotnet new console -n CopilotDemo && cd CopilotDemo
dotnet add package GitHub.Copilot.SDK
import { CopilotClient } from "@github/copilot-sdk";
const client = new CopilotClient();
const session = await client.createSession({ model: "gpt-4.1" });
const response = await session.sendAndWait({ prompt: "What is 2 + 2?" });
console.log(response?.data.content);
await client.stop();
process.exit(0);
Run: npx tsx index.ts
import asyncio
from copilot import CopilotClient
async def main():
client = CopilotClient()
await client.start()
session = await client.create_session({"model": "gpt-4.1"})
response = await session.send_and_wait({"prompt": "What is 2 + 2?"})
print(response.data.content)
await client.stop()
asyncio.run(main())
package main
import (
"fmt"
"log"
"os"
copilot "github.com/github/copilot-sdk/go"
)
func main() {
client := copilot.NewClient(nil)
if err := client.Start(); err != nil {
log.Fatal(err)
}
defer client.Stop()
session, err := client.CreateSession(&copilot.SessionConfig{Model: "gpt-4.1"})
if err != nil {
log.Fatal(err)
}
response, err := session.SendAndWait(copilot.MessageOptions{Prompt: "What is 2 + 2?"}, 0)
if err != nil {
log.Fatal(err)
}
fmt.Println(*response.Data.Content)
os.Exit(0)
}
using GitHub.Copilot.SDK;
await using var client = new CopilotClient();
await using var session = await client.CreateSessionAsync(new SessionConfig { Model = "gpt-4.1" });
var response = await session.SendAndWaitAsync(new MessageOptions { Prompt = "What is 2 + 2?" });
Console.WriteLine(response?.Data.Content);
Run: dotnet run
Enable real-time output for better UX:
import { CopilotClient, SessionEvent } from "@github/copilot-sdk";
const client = new CopilotClient();
const session = await client.createSession({
model: "gpt-4.1",
streaming: true,
});
session.on((event: SessionEvent) => {
if (event.type === "assistant.message_delta") {
process.stdout.write(event.data.deltaContent);
}
if (event.type === "session.idle") {
console.log(); // New line when done
}
});
await session.sendAndWait({ prompt: "Tell me a short joke" });
await client.stop();
process.exit(0);
import asyncio
import sys
from copilot import CopilotClient
from copilot.generated.session_events import SessionEventType
async def main():
client = CopilotClient()
await client.start()
session = await client.create_session({
"model": "gpt-4.1",
"streaming": True,
})
def handle_event(event):
if event.type == SessionEventType.ASSISTANT_MESSAGE_DELTA:
sys.stdout.write(event.data.delta_content)
sys.stdout.flush()
if event.type == SessionEventType.SESSION_IDLE:
print()
session.on(handle_event)
await session.send_and_wait({"prompt": "Tell me a short joke"})
await client.stop()
asyncio.run(main())
session, err := client.CreateSession(&copilot.SessionConfig{
Model: "gpt-4.1",
Streaming: true,
})
session.On(func(event copilot.SessionEvent) {
if event.Type == "assistant.message_delta" {
fmt.Print(*event.Data.DeltaContent)
}
if event.Type == "session.idle" {
fmt.Println()
}
})
_, err = session.SendAndWait(copilot.MessageOptions{Prompt: "Tell me a short joke"}, 0)
await using var session = await client.CreateSessionAsync(new SessionConfig
{
Model = "gpt-4.1",
Streaming = true,
});
session.On(ev =>
{
if (ev is AssistantMessageDeltaEvent deltaEvent)
Console.Write(deltaEvent.Data.DeltaContent);
if (ev is SessionIdleEvent)
Console.WriteLine();
});
await session.SendAndWaitAsync(new MessageOptions { Prompt = "Tell me a short joke" });
Define tools that Copilot can invoke during reasoning. When you define a tool, you tell Copilot:
import { CopilotClient, defineTool, SessionEvent } from "@github/copilot-sdk";
const getWeather = defineTool("get_weather", {
description: "Get the current weather for a city",
parameters: {
type: "object",
properties: {
city: { type: "string", description: "The city name" },
},
required: ["city"],
},
handler: async (args: { city: string }) => {
const { city } = args;
// In a real app, call a weather API here
const conditions = ["sunny", "cloudy", "rainy", "partly cloudy"];
const temp = Math.floor(Math.random() * 30) + 50;
const condition = conditions[Math.floor(Math.random() * conditions.length)];
return { city, temperature: `${temp}°F`, condition };
},
});
const client = new CopilotClient();
const session = await client.createSession({
model: "gpt-4.1",
streaming: true,
tools: [getWeather],
});
session.on((event: SessionEvent) => {
if (event.type === "assistant.message_delta") {
process.stdout.write(event.data.deltaContent);
}
});
await session.sendAndWait({
prompt: "What's the weather like in Seattle and Tokyo?",
});
await client.stop();
process.exit(0);
import asyncio
import random
import sys
from copilot import CopilotClient
from copilot.tools import define_tool
from copilot.generated.session_events import SessionEventType
from pydantic import BaseModel, Field
class GetWeatherParams(BaseModel):
city: str = Field(description="The name of the city to get weather for")
@define_tool(description="Get the current weather for a city")
async def get_weather(params: GetWeatherParams) -> dict:
city = params.city
conditions = ["sunny", "cloudy", "rainy", "partly cloudy"]
temp = random.randint(50, 80)
condition = random.choice(conditions)
return {"city": city, "temperature": f"{temp}°F", "condition": condition}
async def main():
client = CopilotClient()
await client.start()
session = await client.create_session({
"model": "gpt-4.1",
"streaming": True,
"tools": [get_weather],
})
def handle_event(event):
if event.type == SessionEventType.ASSISTANT_MESSAGE_DELTA:
sys.stdout.write(event.data.delta_content)
sys.stdout.flush()
session.on(handle_event)
await session.send_and_wait({
"prompt": "What's the weather like in Seattle and Tokyo?"
})
await client.stop()
asyncio.run(main())
type WeatherParams struct {
City string `json:"city" jsonschema:"The city name"`
}
type WeatherResult struct {
City string `json:"city"`
Temperature string `json:"temperature"`
Condition string `json:"condition"`
}
getWeather := copilot.DefineTool(
"get_weather",
"Get the current weather for a city",
func(params WeatherParams, inv copilot.ToolInvocation) (WeatherResult, error) {
conditions := []string{"sunny", "cloudy", "rainy", "partly cloudy"}
temp := rand.Intn(30) + 50
condition := conditions[rand.Intn(len(conditions))]
return WeatherResult{
City: params.City,
Temperature: fmt.Sprintf("%d°F", temp),
Condition: condition,
}, nil
},
)
session, _ := client.CreateSession(&copilot.SessionConfig{
Model: "gpt-4.1",
Streaming: true,
Tools: []copilot.Tool{getWeather},
})
using GitHub.Copilot.SDK;
using Microsoft.Extensions.AI;
using System.ComponentModel;
var getWeather = AIFunctionFactory.Create(
([Description("The city name")] string city) =>
{
var conditions = new[] { "sunny", "cloudy", "rainy", "partly cloudy" };
var temp = Random.Shared.Next(50, 80);
var condition = conditions[Random.Shared.Next(conditions.Length)];
return new { city, temperature = $"{temp}°F", condition };
},
"get_weather",
"Get the current weather for a city"
);
await using var session = await client.CreateSessionAsync(new SessionConfig
{
Model = "gpt-4.1",
Streaming = true,
Tools = [getWeather],
});
When Copilot decides to call your tool:
Copilot decides when to call your tool based on the user's question and your tool's description.
Build a complete interactive assistant:
import { CopilotClient, defineTool, SessionEvent } from "@github/copilot-sdk";
import * as readline from "readline";
const getWeather = defineTool("get_weather", {
description: "Get the current weather for a city",
parameters: {
type: "object",
properties: {
city: { type: "string", description: "The city name" },
},
required: ["city"],
},
handler: async ({ city }) => {
const conditions = ["sunny", "cloudy", "rainy", "partly cloudy"];
const temp = Math.floor(Math.random() * 30) + 50;
const condition = conditions[Math.floor(Math.random() * conditions.length)];
return { city, temperature: `${temp}°F`, condition };
},
});
const client = new CopilotClient();
const session = await client.createSession({
model: "gpt-4.1",
streaming: true,
tools: [getWeather],
});
session.on((event: SessionEvent) => {
if (event.type === "assistant.message_delta") {
process.stdout.write(event.data.deltaContent);
}
});
const rl = readline.createInterface({
input: process.stdin,
output: process.stdout,
});
console.log("Weather Assistant (type 'exit' to quit)");
console.log("Try: 'What's the weather in Paris?'\n");
const prompt = () => {
rl.question("You: ", async (input) => {
if (input.toLowerCase() === "exit") {
await client.stop();
rl.close();
return;
}
process.stdout.write("Assistant: ");
await session.sendAndWait({ prompt: input });
console.log("\n");
prompt();
});
};
prompt();
import asyncio
import random
import sys
from copilot import CopilotClient
from copilot.tools import define_tool
from copilot.generated.session_events import SessionEventType
from pydantic import BaseModel, Field
class GetWeatherParams(BaseModel):
city: str = Field(description="The name of the city to get weather for")
@define_tool(description="Get the current weather for a city")
async def get_weather(params: GetWeatherParams) -> dict:
conditions = ["sunny", "cloudy", "rainy", "partly cloudy"]
temp = random.randint(50, 80)
condition = random.choice(conditions)
return {"city": params.city, "temperature": f"{temp}°F", "condition": condition}
async def main():
client = CopilotClient()
await client.start()
session = await client.create_session({
"model": "gpt-4.1",
"streaming": True,
"tools": [get_weather],
})
def handle_event(event):
if event.type == SessionEventType.ASSISTANT_MESSAGE_DELTA:
sys.stdout.write(event.data.delta_content)
sys.stdout.flush()
session.on(handle_event)
print("Weather Assistant (type 'exit' to quit)")
print("Try: 'What's the weather in Paris?'\n")
while True:
try:
user_input = input("You: ")
except EOFError:
break
if user_input.lower() == "exit":
break
sys.stdout.write("Assistant: ")
await session.send_and_wait({"prompt": user_input})
print("\n")
await client.stop()
asyncio.run(main())
Connect to MCP (Model Context Protocol) servers for pre-built tools. Connect to GitHub's MCP server for repository, issue, and PR access:
const session = await client.createSession({
model: "gpt-4.1",
mcpServers: {
github: {
type: "http",
url: "https://api.githubcopilot.com/mcp/",
},
},
});
session = await client.create_session({
"model": "gpt-4.1",
"mcp_servers": {
"github": {
"type": "http",
"url": "https://api.githubcopilot.com/mcp/",
},
},
})
session, _ := client.CreateSession(&copilot.SessionConfig{
Model: "gpt-4.1",
MCPServers: map[string]copilot.MCPServerConfig{
"github": {
Type: "http",
URL: "https://api.githubcopilot.com/mcp/",
},
},
})
await using var session = await client.CreateSessionAsync(new SessionConfig
{
Model = "gpt-4.1",
McpServers = new Dictionary<string, McpServerConfig>
{
["github"] = new McpServerConfig
{
Type = "http",
Url = "https://api.githubcopilot.com/mcp/",
},
},
});
Define specialized AI personas for specific tasks:
const session = await client.createSession({
model: "gpt-4.1",
customAgents: [{
name: "pr-reviewer",
displayName: "PR Reviewer",
description: "Reviews pull requests for best practices",
prompt: "You are an expert code reviewer. Focus on security, performance, and maintainability.",
}],
});
session = await client.create_session({
"model": "gpt-4.1",
"custom_agents": [{
"name": "pr-reviewer",
"display_name": "PR Reviewer",
"description": "Reviews pull requests for best practices",
"prompt": "You are an expert code reviewer. Focus on security, performance, and maintainability.",
}],
})
Customize the AI's behavior and personality:
const session = await client.createSession({
model: "gpt-4.1",
systemMessage: {
content: "You are a helpful assistant for our engineering team. Always be concise.",
},
});
session = await client.create_session({
"model": "gpt-4.1",
"system_message": {
"content": "You are a helpful assistant for our engineering team. Always be concise.",
},
})
Run the CLI in server mode separately and connect the SDK to it. Useful for debugging, resource sharing, or custom environments.
copilot --server --port 4321
const client = new CopilotClient({
cliUrl: "localhost:4321"
});
const session = await client.createSession({ model: "gpt-4.1" });
client = CopilotClient({
"cli_url": "localhost:4321"
})
await client.start()
session = await client.create_session({"model": "gpt-4.1"})
client := copilot.NewClient(&copilot.ClientOptions{
CLIUrl: "localhost:4321",
})
if err := client.Start(); err != nil {
log.Fatal(err)
}
session, _ := client.CreateSession(&copilot.SessionConfig{Model: "gpt-4.1"})
using var client = new CopilotClient(new CopilotClientOptions
{
CliUrl = "localhost:4321"
});
await using var session = await client.CreateSessionAsync(new SessionConfig { Model = "gpt-4.1" });
Note: When cliUrl is provided, the SDK will not spawn or manage a CLI process - it only connects to the existing server.
| Event | Description |
|-------|-------------|
| user.message | User input added |
| assistant.message | Complete model response |
| assistant.message_delta | Streaming response chunk |
| assistant.reasoning | Model reasoning (model-dependent) |
| assistant.reasoning_delta | Streaming reasoning chunk |
| tool.execution_start | Tool invocation started |
| tool.execution_complete | Tool execution finished |
| session.idle | No active processing |
| session.error | Error occurred |
| Option | Description | Default |
|--------|-------------|---------|
| cliPath | Path to Copilot CLI executable | System PATH |
| cliUrl | Connect to existing server (e.g., "localhost:4321") | None |
| port | Server communication port | Random |
| useStdio | Use stdio transport instead of TCP | true |
| logLevel | Logging verbosity | "info" |
| autoStart | Launch server automatically | true |
| autoRestart | Restart on crashes | true |
| cwd | Working directory for CLI process | Inherited |
| Option | Description |
|--------|-------------|
| model | LLM to use ("gpt-4.1", "claude-sonnet-4.5", etc.) |
| sessionId | Custom session identifier |
| tools | Custom tool definitions |
| mcpServers | MCP server connections |
| customAgents | Custom agent personas |
| systemMessage | Override default system prompt |
| streaming | Enable incremental response chunks |
| availableTools | Whitelist of permitted tools |
| excludedTools | Blacklist of disabled tools |
Save and resume conversations across restarts:
const session = await client.createSession({
sessionId: "user-123-conversation",
model: "gpt-4.1"
});
const session = await client.resumeSession("user-123-conversation");
await session.send({ prompt: "What did we discuss earlier?" });
const sessions = await client.listSessions();
await client.deleteSession("old-session-id");
try {
const client = new CopilotClient();
const session = await client.createSession({ model: "gpt-4.1" });
const response = await session.sendAndWait(
{ prompt: "Hello!" },
30000 // timeout in ms
);
} catch (error) {
if (error.code === "ENOENT") {
console.error("Copilot CLI not installed");
} else if (error.code === "ECONNREFUSED") {
console.error("Cannot connect to Copilot server");
} else {
console.error("Error:", error.message);
}
} finally {
await client.stop();
}
process.on("SIGINT", async () => {
console.log("Shutting down...");
await client.stop();
process.exit(0);
});
const session = await client.createSession({ model: "gpt-4.1" });
await session.sendAndWait({ prompt: "My name is Alice" });
await session.sendAndWait({ prompt: "What's my name?" });
// Response: "Your name is Alice"
await session.send({
prompt: "Analyze this file",
attachments: [{
type: "file",
path: "./data.csv",
displayName: "Sales Data"
}]
});
const timeoutId = setTimeout(() => {
session.abort();
}, 60000);
session.on((event) => {
if (event.type === "session.idle") {
clearTimeout(timeoutId);
}
});
Query available models at runtime:
const models = await client.getModels();
// Returns: ["gpt-4.1", "gpt-4o", "claude-sonnet-4.5", ...]
try-finally or defer to ensure client.stop() is calledsendAndWait with timeout for long operationsYour Application
|
SDK Client
| JSON-RPC
Copilot CLI (server mode)
|
GitHub (models, auth)
The SDK manages the CLI process lifecycle automatically. All communication happens via JSON-RPC over stdio or TCP.
This SDK is in Technical Preview and may have breaking changes. Not recommended for production use yet.
development
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development
Apply lean thinking to UX: hypothesis-driven design, collaborative sketching, and rapid experiments instead of heavy deliverables. Use when the user mentions "Lean UX", "design hypothesis", "UX experiment", "collaborative design", or "outcome over output". Covers hypothesis statements, MVPs for UX, and cross-functional collaboration. For Build-Measure-Learn, see lean-startup. For usability audits, see ux-heuristics.
development
Design MVPs, validated learning experiments, and pivot-or-persevere decisions using Build-Measure-Learn. Use when the user mentions "MVP scope", "validated learning", "pivot or persevere", "vanity metrics", or "test assumptions". Covers innovation accounting and actionable metrics. For 5-day prototype testing, see design-sprint. For customer motivation analysis, see jobs-to-be-done.
tools
Instrument, trace, evaluate, and monitor LLM applications and AI agents with LangSmith. Use when setting up observability for LLM pipelines, running offline or online evaluations, managing prompts in the Prompt Hub, creating datasets for regression testing, or deploying agent servers. Triggers on: langsmith, langchain tracing, llm tracing, llm observability, llm evaluation, trace llm calls, @traceable, wrap_openai, langsmith evaluate, langsmith dataset, langsmith feedback, langsmith prompt hub, langsmith project, llm monitoring, llm debugging, llm quality, openevals, langsmith cli, langsmith experiment, annotate llm, llm judge.