# Copilot Python SDK

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

## Installation

```bash
pip install github-copilot-sdk
```

To include OpenTelemetry support:

```bash
pip install "github-copilot-sdk[telemetry]"
```

## Run the Sample

Try the interactive chat sample (from the repo root):

```bash
cd python/samples
python chat.py
```

## Quick Start

```python
import asyncio

from copilot import CopilotClient
from copilot.session_events import AssistantMessageData, SessionIdleData
from copilot.session import PermissionHandler

async def main():
    # Client automatically starts on enter and cleans up on exit
    async with CopilotClient() as client:
        # Create a session with automatic cleanup
        async with await client.create_session(
            on_permission_request=PermissionHandler.approve_all,
            model="gpt-5",
        ) as session:
            # Wait for response using session.idle event
            done = asyncio.Event()

            def on_event(event):
                match event.data:
                    case AssistantMessageData() as data:
                        print(data.content)
                    case SessionIdleData():
                        done.set()

            session.on(on_event)

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

asyncio.run(main())
```

### Manual Resource Management

If you need more control over the lifecycle, you can call `start()`, `stop()`, and `disconnect()` manually:

```python
import asyncio

from copilot import CopilotClient
from copilot.session_events import AssistantMessageData, SessionIdleData
from copilot.session import PermissionHandler

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

    # Create a session (on_permission_request is optional; approve_all allows every tool)
    session = await client.create_session(
        on_permission_request=PermissionHandler.approve_all,
        model="gpt-5",
    )

    done = asyncio.Event()

    def on_event(event):
        match event.data:
            case AssistantMessageData() as data:
                print(data.content)
            case SessionIdleData():
                done.set()

    session.on(on_event)
    await session.send("What is 2+2?")
    await done.wait()

    # Clean up manually
    await session.disconnect()
    await client.stop()

asyncio.run(main())
```

## Features

- ✅ Full JSON-RPC protocol support
- ✅ stdio and TCP transports
- ✅ Real-time streaming events
- ✅ Session history with `get_events()`
- ✅ Type hints throughout
- ✅ Async/await native
- ✅ Async context manager support for automatic resource cleanup

## API Reference

### CopilotClient

```python
from copilot import CopilotClient
from copilot.session import PermissionHandler

async with CopilotClient() as client:
    async with await client.create_session(
        on_permission_request=PermissionHandler.approve_all,
        model="gpt-5",
    ) as session:
        def on_event(event):
            print(f"Event: {event.type}")

        session.on(on_event)
        await session.send("Hello!")

        # ... wait for events ...
```

> **Note:** For manual lifecycle management, see [Manual Resource Management](#manual-resource-management) above.

```python
from copilot import CopilotClient, RuntimeConnection

# Connect to an existing CLI server
client = CopilotClient(connection=RuntimeConnection.for_uri("localhost:3000"))
```

**CopilotClient Constructor:**

```python
CopilotClient()                                                   # spawn the bundled runtime with defaults
CopilotClient(connection=..., log_level="debug", github_token=..., ...)
```

All options are kw-only parameters:

- `connection` (RuntimeConnection | None): How to reach the runtime. Use
  `RuntimeConnection.for_stdio(...)`, `RuntimeConnection.for_tcp(...)`, or
  `RuntimeConnection.for_uri(...)`. Defaults to a stdio connection with the bundled binary.
- `working_directory` (str | None): Working directory for the CLI process (default: current dir).
- `log_level` (str): Log level (default: "info").
- `env` (dict | None): Environment variables for the CLI process.
- `github_token` (str | None): GitHub token for authentication. When provided, takes priority over other auth methods.
- `base_directory` (str | None): Base directory for Copilot data (session state, config, etc.). Sets `COPILOT_HOME` on the spawned CLI process. When `None`, the CLI defaults to `~/.copilot`. Useful in restricted environments where only specific directories are writable. Ignored when using a `UriRuntimeConnection`.
- `use_logged_in_user` (bool | None): Whether to use logged-in user for authentication (default: True, but False when `github_token` is provided).
- `telemetry` (dict | None): OpenTelemetry configuration for the CLI process. Providing this enables telemetry — no separate flag needed. See [Telemetry](#telemetry) below.
- `session_fs` (dict | None): Connection-level session filesystem provider configuration.
- `session_idle_timeout_seconds` (int | None): Server-wide session idle timeout in seconds. Set to `None` or `0` to disable.
- `enable_remote_sessions` (bool): Enable remote/cloud session support (default: False).
- `on_list_models` (callable | None): Custom handler for `list_models()`. When provided, the handler is called instead of querying the runtime.
- `mode` (str): Client mode (default: `"copilot-cli"`).

**RuntimeConnection variants:**

- `RuntimeConnection.for_stdio(path=None, args=None)` — spawn a local CLI process and talk over stdio.
- `RuntimeConnection.for_tcp(port=0, connection_token=None, path=None, args=None)` — spawn a local CLI in TCP mode.
- `RuntimeConnection.for_uri(url, connection_token=None)` — connect to an existing CLI server (e.g. `"localhost:8080"`).

**`CopilotClient.create_session()`:**

These are passed as keyword arguments to `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. Tools with `handler=None` are declaration-only and must be resolved via pending tool-call RPCs.
- `system_message` (SystemMessageConfig): System message configuration
- `streaming` (bool): Enable streaming delta events
- `provider` (ProviderConfig): Custom API provider configuration (BYOK). See [Custom Providers](#custom-providers) section.
- `infinite_sessions` (InfiniteSessionConfig): Automatic context compaction configuration
- `on_permission_request` (callable): Optional handler called before each tool execution to approve or deny it. When omitted, permission requests are emitted as events and left pending for manual resolution. Use `PermissionHandler.approve_all` to allow everything, or provide a custom function for fine-grained control. See [Permission Handling](#permission-handling) section.
- `on_user_input_request` (callable): Handler for user input requests from the agent (enables ask_user tool). See [User Input Requests](#user-input-requests) section.
- `hooks` (SessionHooks): Hook handlers for session lifecycle events. See [Session Hooks](#session-hooks) section.

**Session Lifecycle Methods:**

```python
# 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.session_id}")

unsubscribe = client.on_lifecycle(on_lifecycle)

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

# 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:

```python
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

async with await client.create_session(
    on_permission_request=PermissionHandler.approve_all,
    model="gpt-5",
    tools=[lookup_issue],
) as session:
    ...
```

> **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:

```python
from copilot import CopilotClient
from copilot.tools import Tool, ToolInvocation, ToolResult
from copilot.session import PermissionHandler

async def lookup_issue(invocation: ToolInvocation) -> ToolResult:
    issue_id = invocation.arguments["id"]
    issue = await fetch_issue(issue_id)
    return ToolResult(
        text_result_for_llm=issue.summary,
        result_type="success",
        session_log=f"Fetched issue {issue_id}",
    )

async with await client.create_session(
    on_permission_request=PermissionHandler.approve_all,
    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,
        )
    ],
) as session:
    ...
```

The SDK automatically handles `tool.call`, executes your handler (sync or async), and responds with the final result when the tool completes. If a tool has no handler, it is exposed as a declaration only; observe `external_tool.requested` events and resolve the call with the pending tool RPC.

You can also create a declaration-only tool with generated Pydantic parameters:

```python
tool = define_tool(
    "lookup_issue",
    description="Fetch issue details from our tracker",
    params_type=LookupIssueParams,
)
```

#### Overriding Built-in Tools

If you register a tool with the same name as a built-in CLI tool (e.g. `edit_file`, `read_file`), the SDK will throw an error unless you explicitly opt in by setting `overrides_built_in_tool=True`. This flag signals that you intend to replace the built-in tool with your custom implementation.

```python
class EditFileParams(BaseModel):
    path: str = Field(description="File path")
    content: str = Field(description="New file content")

@define_tool(name="edit_file", description="Custom file editor with project-specific validation", overrides_built_in_tool=True)
async def edit_file(params: EditFileParams) -> str:
    # your logic
```

#### Skipping Permission Prompts

Set `skip_permission=True` on a tool definition to allow it to execute without triggering a permission prompt:

```python
@define_tool(name="safe_lookup", description="A read-only lookup that needs no confirmation", skip_permission=True)
async def safe_lookup(params: LookupParams) -> str:
    # your logic
```

#### Deferring Tools

Set `defer` to control whether a tool may be loaded lazily via tool search rather than always pre-loaded. Use `"auto"` to allow the tool to be deferred and surfaced through tool search, or `"never"` to force it to always be pre-loaded. Defaults to `"auto"`.

```python
@define_tool(name="lookup_issue", description="Fetch issue details", defer="auto")
async def lookup_issue(params: LookupParams) -> str:
    # your logic
```

## Image Support

The SDK supports image attachments via the `attachments` parameter. You can attach images by providing their file path, or by passing base64-encoded data directly using a blob attachment:

```python
# File attachment — runtime reads from disk
await session.send(
    "What's in this image?",
    attachments=[
        {
            "type": "file",
            "path": "/path/to/image.jpg",
        }
    ],
)

# Blob attachment — provide base64 data directly
await session.send(
    "What's in this image?",
    attachments=[
        {
            "type": "blob",
            "data": base64_image_data,
            "mimeType": "image/png",
        }
    ],
)
```

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:

```python
await session.send("What does the most recent jpg in this directory portray?")
```

## Streaming

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

```python
import asyncio

from copilot import CopilotClient
from copilot.session_events import (
    AssistantMessageData,
    AssistantMessageDeltaData,
    AssistantReasoningData,
    AssistantReasoningDeltaData,
    SessionIdleData,
)
from copilot.session import PermissionHandler

async def main():
    async with CopilotClient() as client:
        async with await client.create_session(
            on_permission_request=PermissionHandler.approve_all,
            model="gpt-5",
            streaming=True,
        ) as session:
            # Use asyncio.Event to wait for completion
            done = asyncio.Event()

            def on_event(event):
                match event.data:
                    case AssistantMessageDeltaData() as data:
                        # Streaming message chunk - print incrementally
                        delta = data.delta_content or ""
                        print(delta, end="", flush=True)
                    case AssistantReasoningDeltaData() as data:
                        # Streaming reasoning chunk (if model supports reasoning)
                        delta = data.delta_content or ""
                        print(delta, end="", flush=True)
                    case AssistantMessageData() as data:
                        # Final message - complete content
                        print("\n--- Final message ---")
                        print(data.content)
                    case AssistantReasoningData() as data:
                        # Final reasoning content (if model supports reasoning)
                        print("--- Reasoning ---")
                        print(data.content)
                    case SessionIdleData():
                        # Session finished processing
                        done.set()

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

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.

```python
# Default: infinite sessions enabled with default thresholds
async with await client.create_session(
    on_permission_request=PermissionHandler.approve_all,
    model="gpt-5",
) as session:
    # Access the workspace path for checkpoints and files
    print(session.workspace_path)
    # => ~/.copilot/session-state/{session_id}/

# Custom thresholds
async with await client.create_session(
    on_permission_request=PermissionHandler.approve_all,
    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
    },
) as session:
    ...

# Disable infinite sessions
async with await client.create_session(
    on_permission_request=PermissionHandler.approve_all,
    model="gpt-5",
    infinite_sessions={"enabled": False},
) as session:
    ...
```

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:**

```python
async with await client.create_session(
    on_permission_request=PermissionHandler.approve_all,
    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
    },
) as session:
    await session.send("Hello!")
```

**Example with custom OpenAI-compatible API:**

```python
import os

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

**Example with Azure OpenAI:**

```python
import os

async with await client.create_session(
    on_permission_request=PermissionHandler.approve_all,
    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",
        },
    },
) as session:
    ...
```

> **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.

## Telemetry

The SDK supports OpenTelemetry for distributed tracing. Provide a `telemetry` config to enable trace export and automatic W3C Trace Context propagation.

```python
from copilot import CopilotClient

client = CopilotClient(
    telemetry={
        "otlp_endpoint": "http://localhost:4318",
    },
)
```

**TelemetryConfig options:**

- `otlp_endpoint` (str): OTLP HTTP endpoint URL
- `file_path` (str): File path for JSON-lines trace output
- `exporter_type` (str): `"otlp-http"` or `"file"`
- `source_name` (str): Instrumentation scope name
- `capture_content` (bool): Whether to capture message content

Trace context (`traceparent`/`tracestate`) is automatically propagated between the SDK and CLI on `create_session`, `resume_session`, and `send` calls, and inbound when the CLI invokes tool handlers.

Install with telemetry extras: `pip install "github-copilot-sdk[telemetry]"` (provides `opentelemetry-api`)

## Permission Handling

An `on_permission_request` handler is optional when you create or resume a session. When provided, it is called before the agent executes each tool (file writes, shell commands, custom tools, etc.) and returns a decision. When omitted, permission requests are emitted as events and left pending for the consumer to resolve with the pending permission RPC.

### Approve All (simplest)

Use the built-in `PermissionHandler.approve_all` helper to allow every tool call without any checks:

```python
from copilot import CopilotClient
from copilot.session import PermissionHandler

session = await client.create_session(
    on_permission_request=PermissionHandler.approve_all,
    model="gpt-5",
)
```

### Custom Permission Handler

Provide your own function to inspect each request and apply custom logic (sync or async):

```python
from copilot import PermissionRequest, PermissionRequestResult
from copilot.rpc import (
    PermissionDecisionApproveOnce,
    PermissionDecisionReject,
)
from copilot.session_events import PermissionRequestShell


def on_permission_request(
    request: PermissionRequest, invocation: dict
) -> PermissionRequestResult:
    # ``PermissionRequest`` is a discriminated union — pattern-match on
    # the variant class to access the per-kind fields.
    match request:
        case PermissionRequestShell(full_command_text=cmd):
            # Deny shell commands
            return PermissionDecisionReject(feedback=f"Shell denied: {cmd}")
        case _:
            return PermissionDecisionApproveOnce()


session = await client.create_session(
    on_permission_request=on_permission_request,
    model="gpt-5",
)
```

Async handlers are also supported:

```python
async def on_permission_request(
    request: PermissionRequest, invocation: dict
) -> PermissionRequestResult:
    # Simulate an async approval check (e.g., prompting a user over a network)
    await asyncio.sleep(0)
    return PermissionDecisionApproveOnce()
```

### Permission Result Kinds

The handler returns a ``PermissionRequestResult``, which is an alias for
``PermissionDecision | PermissionNoResult`` (the generated wire-level
union of every decision variant, plus a small sentinel for v1 servers).
Approval decisions are present-tense — they describe the decision to
apply, not the past-tense outcome reported back on `permission.completed`
session events.

| Variant                                       | Meaning                                                                                     |
| --------------------------------------------- | ------------------------------------------------------------------------------------------- |
| `PermissionDecisionApproveOnce()`             | Allow this single request                                                                   |
| `PermissionDecisionReject(feedback="…")`      | Deny the request (optional feedback string forwarded to the LLM)                            |
| `PermissionDecisionUserNotAvailable()`        | Deny the request because no user is available to confirm it (the default)                   |
| `PermissionNoResult()`                        | Leave the request unanswered (only valid with protocol v1; rejected by protocol v2 servers) |

Several richer variants (``PermissionDecisionApproveForSession``,
``PermissionDecisionApproveForLocation``, ``PermissionDecisionApprovePermanently``,
…) are available for granting longer-lived approvals; see the generated
``copilot.rpc`` module for the full list.

### Resuming Sessions

You may pass `on_permission_request` when resuming a session too:

```python
session = await client.resume_session(
    "session-id",
    on_permission_request=PermissionHandler.approve_all,
)
```

### Per-Tool Skip Permission

To let a specific custom tool bypass the permission prompt entirely, set `skip_permission=True` on the tool definition. See [Skipping Permission Prompts](#skipping-permission-prompts) under Tools.

## 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:

```python
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)
    }

async with await client.create_session(
    on_permission_request=PermissionHandler.approve_all,
    model="gpt-5",
    on_user_input_request=handle_user_input,
) as session:
    ...
```

## Session Hooks

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

```python
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_post_tool_use_failure(input, invocation):
    # Fires when a tool's result was a failure. `on_post_tool_use` only fires
    # on success, so register this handler to observe failed tool calls. The
    # CLI extracts the failure message and passes it as the `error` field.
    print(f"Tool {input['toolName']} failed: {input['error']}")
    return {
        "additionalContext": f"Retry guidance for {input['toolName']}",
    }

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"
    }

async with await client.create_session(
    on_permission_request=PermissionHandler.approve_all,
    model="gpt-5",
    hooks={
        "on_pre_tool_use": on_pre_tool_use,
        "on_post_tool_use": on_post_tool_use,
        "on_post_tool_use_failure": on_post_tool_use_failure,
        "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,
    },
) as session:
    ...
```

**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 successful execution. Can modify results or add context.
- `on_post_tool_use_failure` - Observe failed tool executions and inject extra context to guide the model's next step.
- `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.

## Commands

Register slash commands that users can invoke from the CLI TUI. When the user types `/commandName`, the SDK dispatches the event to your handler.

```python
from copilot.session import CommandDefinition, CommandContext, PermissionHandler

async def handle_deploy(ctx: CommandContext) -> None:
    print(f"Deploying with args: {ctx.args}")
    # ctx.session_id  — the session where the command was invoked
    # ctx.command      — full command text (e.g. "/deploy production")
    # ctx.command_name — command name without leading / (e.g. "deploy")
    # ctx.args         — raw argument string (e.g. "production")

async with await client.create_session(
    on_permission_request=PermissionHandler.approve_all,
    commands=[
        CommandDefinition(
            name="deploy",
            description="Deploy the app",
            handler=handle_deploy,
        ),
        CommandDefinition(
            name="rollback",
            description="Rollback to previous version",
            handler=lambda ctx: print("Rolling back..."),
        ),
    ],
) as session:
    ...
```

Commands can also be provided when resuming a session via `resume_session(commands=[...])`.

## Resetting a Session

Use `await session.reset(**config)` to abandon the current runtime session and create a fresh session from explicit configuration. This mirrors the SDK-owned lifecycle part of the CLI TUI `/clear` command; `/reset` is its alias in the TUI.

```python
result = await session.reset(
    on_permission_request=PermissionHandler.approve_all,
    model="gpt-5",
)
session = result.session
# Clear your app's visible transcript, local drafts, and route state here.
```

The returned `previous_session_id` identifies the abandoned session. The old `CopilotSession` object is disconnected after a successful reset, and the new session starts unnamed. If reset fails after teardown starts, treat the old session as no longer usable and create or resume another session explicitly. Host applications own UI cleanup and event listener rebinding.

## UI Elicitation

The `session.ui` API provides convenience methods for asking the user questions through interactive dialogs. These methods are only available when the CLI host supports elicitation — check `session.capabilities` before calling.

### Capability Check

```python
ui_caps = session.capabilities.get("ui", {})
if ui_caps.get("elicitation"):
    # Safe to call session.ui methods
    ...
```

### Confirm

Shows a yes/no confirmation dialog:

```python
ok = await session.ui.confirm("Deploy to production?")
if ok:
    print("Deploying...")
```

### Select

Shows a selection dialog with a list of options:

```python
env = await session.ui.select("Choose environment:", ["staging", "production", "dev"])
if env:
    print(f"Selected: {env}")
```

### Input

Shows a text input dialog with optional constraints:

```python
name = await session.ui.input("Enter your name:")

# With options
email = await session.ui.input("Enter email:", {
    "title": "Email Address",
    "description": "We'll use this for notifications",
    "format": "email",
})
```

### Custom Elicitation

For full control, use the `elicitation()` method with a custom JSON schema:

```python
result = await session.ui.elicitation({
    "message": "Configure deployment",
    "requestedSchema": {
        "type": "object",
        "properties": {
            "region": {"type": "string", "enum": ["us-east-1", "eu-west-1"]},
            "replicas": {"type": "number", "minimum": 1, "maximum": 10},
        },
        "required": ["region"],
    },
})

if result["action"] == "accept":
    region = result["content"]["region"]
    replicas = result["content"].get("replicas", 1)
```

## Elicitation Request Handler

When the server (or an MCP tool) needs to ask the end-user a question, it sends an `elicitation.requested` event. Provide an `on_elicitation_request` handler to respond:

```python
from copilot.session import ElicitationContext, ElicitationResult, PermissionHandler

async def handle_elicitation(
    context: ElicitationContext,
) -> ElicitationResult:
    # context["session_id"]           — the session ID
    # context["message"]              — what the server is asking
    # context.get("requestedSchema")  — optional JSON schema for form fields
    # context.get("mode")             — "form" or "url"

    print(f"Server asks: {context['message']}")

    # Return the user's response
    return {
        "action": "accept",  # or "decline" or "cancel"
        "content": {"answer": "yes"},
    }

async with await client.create_session(
    on_permission_request=PermissionHandler.approve_all,
    on_elicitation_request=handle_elicitation,
) as session:
    ...
```

When `on_elicitation_request` is provided, the SDK automatically:

- Sends `requestElicitation: true` to the server during session creation/resumption
- Reports the `elicitation` capability on the session
- Dispatches `elicitation.requested` events to your handler
- Auto-cancels if your handler throws an error (so the server doesn't hang)

## Requirements

- Python 3.11+
- GitHub Copilot CLI installed and accessible