Installation
pip install claude-agent-sdk
Choosing between query() and ClaudeSDKClient
The Python SDK provides two ways to interact with Claude Code:
Quick comparison
| Feature | query() | ClaudeSDKClient |
|---|
| Session | Creates new session each time | Reuses same session |
| Conversation | Single exchange | Multiple exchanges in same context |
| Connection | Managed automatically | Manual control |
| Streaming Input | ✅ Supported | ✅ Supported |
| Interrupts | ❌ Not supported | ✅ Supported |
| Hooks | ✅ Supported | ✅ Supported |
| Custom Tools | ✅ Supported | ✅ Supported |
| Continue Chat | ❌ New session each time | ✅ Maintains conversation |
| Use Case | One-off tasks | Continuous conversations |
When to use query() (new session each time)
Best for:
- One-off questions where you don’t need conversation history
- Independent tasks that don’t require context from previous exchanges
- Simple automation scripts
- When you want a fresh start each time
When to use ClaudeSDKClient (continuous conversation)
Best for:
- Continuing conversations - When you need Claude to remember context
- Follow-up questions - Building on previous responses
- Interactive applications - Chat interfaces, REPLs
- Response-driven logic - When next action depends on Claude’s response
- Session control - Managing conversation lifecycle explicitly
Functions
query()
Creates a new session for each interaction with Claude Code. Returns an async iterator that yields messages as they arrive. Each call to query() starts fresh with no memory of previous interactions.
async def query(
*,
prompt: str | AsyncIterable[dict[str, Any]],
options: ClaudeAgentOptions | None = None,
transport: Transport | None = None
) -> AsyncIterator[Message]
Parameters
| Parameter | Type | Description |
|---|
prompt | str | AsyncIterable[dict] | The input prompt as a string or async iterable for streaming mode |
options | ClaudeAgentOptions | None | Optional configuration object (defaults to ClaudeAgentOptions() if None) |
transport | Transport | None | Optional custom transport for communicating with the CLI process |
Returns
Returns an AsyncIterator[Message] that yields messages from the conversation.
Example - With options
import asyncio
from claude_agent_sdk import query, ClaudeAgentOptions
async def main():
options = ClaudeAgentOptions(
system_prompt="You are an expert Python developer",
permission_mode="acceptEdits",
cwd="/home/user/project",
)
async for message in query(prompt="Create a Python web server", options=options):
print(message)
asyncio.run(main())
Decorator for defining MCP tools with type safety.
def tool(
name: str,
description: str,
input_schema: type | dict[str, Any],
annotations: ToolAnnotations | None = None
) -> Callable[[Callable[[Any], Awaitable[dict[str, Any]]]], SdkMcpTool[Any]]
Parameters
| Parameter | Type | Description |
|---|
name | str | Unique identifier for the tool |
description | str | Human-readable description of what the tool does |
input_schema | type | dict[str, Any] | Schema defining the tool’s input parameters (see below) |
annotations | ToolAnnotations | None | Optional MCP tool annotations providing behavioral hints to clients |
-
Simple type mapping (recommended):
{"text": str, "count": int, "enabled": bool}
-
JSON Schema format (for complex validation):
{
"type": "object",
"properties": {
"text": {"type": "string"},
"count": {"type": "integer", "minimum": 0},
},
"required": ["text"],
}
Returns
A decorator function that wraps the tool implementation and returns an SdkMcpTool instance.
Example
from claude_agent_sdk import tool
from typing import Any
@tool("greet", "Greet a user", {"name": str})
async def greet(args: dict[str, Any]) -> dict[str, Any]:
return {"content": [{"type": "text", "text": f"Hello, {args['name']}!"}]}
Re-exported from mcp.types (also available as from claude_agent_sdk import ToolAnnotations). All fields are optional hints; clients should not rely on them for security decisions.
| Field | Type | Default | Description |
|---|
title | str | None | None | Human-readable title for the tool |
readOnlyHint | bool | None | False | If True, the tool does not modify its environment |
destructiveHint | bool | None | True | If True, the tool may perform destructive updates (only meaningful when readOnlyHint is False) |
idempotentHint | bool | None | False | If True, repeated calls with the same arguments have no additional effect (only meaningful when readOnlyHint is False) |
openWorldHint | bool | None | True | If True, the tool interacts with external entities (for example, web search). If False, the tool’s domain is closed (for example, a memory tool) |
from claude_agent_sdk import tool, ToolAnnotations
from typing import Any
@tool(
"search",
"Search the web",
{"query": str},
annotations=ToolAnnotations(readOnlyHint=True, openWorldHint=True),
)
async def search(args: dict[str, Any]) -> dict[str, Any]:
return {"content": [{"type": "text", "text": f"Results for: {args['query']}"}]}
create_sdk_mcp_server()
Create an in-process MCP server that runs within your Python application.
def create_sdk_mcp_server(
name: str,
version: str = "1.0.0",
tools: list[SdkMcpTool[Any]] | None = None
) -> McpSdkServerConfig
Parameters
| Parameter | Type | Default | Description |
|---|
name | str | - | Unique identifier for the server |
version | str | "1.0.0" | Server version string |
tools | list[SdkMcpTool[Any]] | None | None | List of tool functions created with @tool decorator |
Returns
Returns an McpSdkServerConfig object that can be passed to ClaudeAgentOptions.mcp_servers.
Example
from claude_agent_sdk import tool, create_sdk_mcp_server
@tool("add", "Add two numbers", {"a": float, "b": float})
async def add(args):
return {"content": [{"type": "text", "text": f"Sum: {args['a'] + args['b']}"}]}
@tool("multiply", "Multiply two numbers", {"a": float, "b": float})
async def multiply(args):
return {"content": [{"type": "text", "text": f"Product: {args['a'] * args['b']}"}]}
calculator = create_sdk_mcp_server(
name="calculator",
version="2.0.0",
tools=[add, multiply], # Pass decorated functions
)
# Use with Claude
options = ClaudeAgentOptions(
mcp_servers={"calc": calculator},
allowed_tools=["mcp__calc__add", "mcp__calc__multiply"],
)
list_sessions()
Lists past sessions with metadata. Filter by project directory or list sessions across all projects. Synchronous; returns immediately.
def list_sessions(
directory: str | None = None,
limit: int | None = None,
include_worktrees: bool = True
) -> list[SDKSessionInfo]
Parameters
| Parameter | Type | Default | Description |
|---|
directory | str | None | None | Directory to list sessions for. When omitted, returns sessions across all projects |
limit | int | None | None | Maximum number of sessions to return |
include_worktrees | bool | True | When directory is inside a git repository, include sessions from all worktree paths |
Return type: SDKSessionInfo
| Property | Type | Description |
|---|
session_id | str | Unique session identifier |
summary | str | Display title: custom title, auto-generated summary, or first prompt |
last_modified | int | Last modified time in milliseconds since epoch |
file_size | int | None | Session file size in bytes (None for remote storage backends) |
custom_title | str | None | User-set session title |
first_prompt | str | None | First meaningful user prompt in the session |
git_branch | str | None | Git branch at the end of the session |
cwd | str | None | Working directory for the session |
tag | str | None | User-set session tag (see tag_session()) |
created_at | int | None | Session creation time in milliseconds since epoch |
Example
Print the 10 most recent sessions for a project. Results are sorted by last_modified descending, so the first item is the newest. Omit directory to search across all projects.
from claude_agent_sdk import list_sessions
for session in list_sessions(directory="/path/to/project", limit=10):
print(f"{session.summary} ({session.session_id})")
get_session_messages()
Retrieves messages from a past session. Synchronous; returns immediately.
def get_session_messages(
session_id: str,
directory: str | None = None,
limit: int | None = None,
offset: int = 0
) -> list[SessionMessage]
Parameters
| Parameter | Type | Default | Description |
|---|
session_id | str | required | The session ID to retrieve messages for |
directory | str | None | None | Project directory to look in. When omitted, searches all projects |
limit | int | None | None | Maximum number of messages to return |
offset | int | 0 | Number of messages to skip from the start |
Return type: SessionMessage
| Property | Type | Description |
|---|
type | Literal["user", "assistant"] | Message role |
uuid | str | Unique message identifier |
session_id | str | Session identifier |
message | Any | Raw message content |
parent_tool_use_id | None | Reserved for future use |
Example
from claude_agent_sdk import list_sessions, get_session_messages
sessions = list_sessions(limit=1)
if sessions:
messages = get_session_messages(sessions[0].session_id)
for msg in messages:
print(f"[{msg.type}] {msg.uuid}")
get_session_info()
Reads metadata for a single session by ID without scanning the full project directory. Synchronous; returns immediately.
def get_session_info(
session_id: str,
directory: str | None = None,
) -> SDKSessionInfo | None
Parameters
| Parameter | Type | Default | Description |
|---|
session_id | str | required | UUID of the session to look up |
directory | str | None | None | Project directory path. When omitted, searches all project directories |
Returns SDKSessionInfo, or None if the session is not found.
Example
Look up a single session’s metadata without scanning the project directory. Useful when you already have a session ID from a previous run.
from claude_agent_sdk import get_session_info
info = get_session_info("550e8400-e29b-41d4-a716-446655440000")
if info:
print(f"{info.summary} (branch: {info.git_branch}, tag: {info.tag})")
rename_session()
Renames a session by appending a custom-title entry. Repeated calls are safe; the most recent title wins. Synchronous.
def rename_session(
session_id: str,
title: str,
directory: str | None = None,
) -> None
Parameters
| Parameter | Type | Default | Description |
|---|
session_id | str | required | UUID of the session to rename |
title | str | required | New title. Must be non-empty after stripping whitespace |
directory | str | None | None | Project directory path. When omitted, searches all project directories |
Raises ValueError if session_id is not a valid UUID or title is empty; FileNotFoundError if the session cannot be found.
Example
Rename the most recent session so it’s easier to find later. The new title appears in SDKSessionInfo.custom_title on subsequent reads.
from claude_agent_sdk import list_sessions, rename_session
sessions = list_sessions(directory="/path/to/project", limit=1)
if sessions:
rename_session(sessions[0].session_id, "Refactor auth module")
tag_session()
Tags a session. Pass None to clear the tag. Repeated calls are safe; the most recent tag wins. Synchronous.
def tag_session(
session_id: str,
tag: str | None,
directory: str | None = None,
) -> None
Parameters
| Parameter | Type | Default | Description |
|---|
session_id | str | required | UUID of the session to tag |
tag | str | None | required | Tag string, or None to clear. Unicode-sanitized before storing |
directory | str | None | None | Project directory path. When omitted, searches all project directories |
Raises ValueError if session_id is not a valid UUID or tag is empty after sanitization; FileNotFoundError if the session cannot be found.
Example
Tag a session, then filter by that tag on a later read. Pass None to clear an existing tag.
from claude_agent_sdk import list_sessions, tag_session
# Tag a session
tag_session("550e8400-e29b-41d4-a716-446655440000", "needs-review")
# Later: find all sessions with that tag
for session in list_sessions(directory="/path/to/project"):
if session.tag == "needs-review":
print(session.summary)
Classes
ClaudeSDKClient
Maintains a conversation session across multiple exchanges. This is the Python equivalent of how the TypeScript SDK’s query() function works internally - it creates a client object that can continue conversations.
Key Features
- Session continuity: Maintains conversation context across multiple
query() calls
- Same conversation: The session retains previous messages
- Interrupt support: Can stop execution mid-task
- Explicit lifecycle: You control when the session starts and ends
- Response-driven flow: Can react to responses and send follow-ups
- Custom tools and hooks: Supports custom tools (created with
@tool decorator) and hooks
class ClaudeSDKClient:
def __init__(self, options: ClaudeAgentOptions | None = None, transport: Transport | None = None)
async def connect(self, prompt: str | AsyncIterable[dict] | None = None) -> None
async def query(self, prompt: str | AsyncIterable[dict], session_id: str = "default") -> None
async def receive_messages(self) -> AsyncIterator[Message]
async def receive_response(self) -> AsyncIterator[Message]
async def interrupt(self) -> None
async def set_permission_mode(self, mode: str) -> None
async def set_model(self, model: str | None = None) -> None
async def rewind_files(self, user_message_id: str) -> None
async def get_mcp_status(self) -> McpStatusResponse
async def reconnect_mcp_server(self, server_name: str) -> None
async def toggle_mcp_server(self, server_name: str, enabled: bool) -> None
async def stop_task(self, task_id: str) -> None
async def get_server_info(self) -> dict[str, Any] | None
async def disconnect(self) -> None
Methods
| Method | Description |
|---|
__init__(options) | Initialize the client with optional configuration |
connect(prompt) | Connect to Claude with an optional initial prompt or message stream |
query(prompt, session_id) | Send a new request in streaming mode |
receive_messages() | Receive all messages from Claude as an async iterator |
receive_response() | Receive messages until and including a ResultMessage |
interrupt() | Send interrupt signal (only works in streaming mode) |
set_permission_mode(mode) | Change the permission mode for the current session |
set_model(model) | Change the model for the current session. Pass None to reset to default |
rewind_files(user_message_id) | Restore files to their state at the specified user message. Requires enable_file_checkpointing=True. See File checkpointing |
get_mcp_status() | Get the status of all configured MCP servers. Returns McpStatusResponse |
reconnect_mcp_server(server_name) | Retry connecting to an MCP server that failed or was disconnected |
toggle_mcp_server(server_name, enabled) | Enable or disable an MCP server mid-session. Disabling removes its tools |
stop_task(task_id) | Stop a running background task. A TaskNotificationMessage with status "stopped" follows in the message stream |
get_server_info() | Get server information including session ID and capabilities |
disconnect() | Disconnect from Claude |
Context Manager Support
The client can be used as an async context manager for automatic connection management:
async with ClaudeSDKClient() as client:
await client.query("Hello Claude")
async for message in client.receive_response():
print(message)
Important: When iterating over messages, avoid using break to exit early as this can cause asyncio cleanup issues. Instead, let the iteration complete naturally or use flags to track when you’ve found what you need.
Example - Continuing a conversation
import asyncio
from claude_agent_sdk import ClaudeSDKClient, AssistantMessage, TextBlock, ResultMessage
async def main():
async with ClaudeSDKClient() as client:
# First question
await client.query("What's the capital of France?")
# Process response
async for message in client.receive_response():
if isinstance(message, AssistantMessage):
for block in message.content:
if isinstance(block, TextBlock):
print(f"Claude: {block.text}")
# Follow-up question - the session retains the previous context
await client.query("What's the population of that city?")
async for message in client.receive_response():
if isinstance(message, AssistantMessage):
for block in message.content:
if isinstance(block, TextBlock):
print(f"Claude: {block.text}")
# Another follow-up - still in the same conversation
await client.query("What are some famous landmarks there?")
async for message in client.receive_response():
if isinstance(message, AssistantMessage):
for block in message.content:
if isinstance(block, TextBlock):
print(f"Claude: {block.text}")
asyncio.run(main())
import asyncio
from claude_agent_sdk import ClaudeSDKClient
async def message_stream():
"""Generate messages dynamically."""
yield {
"type": "user",
"message": {"role": "user", "content": "Analyze the following data:"},
}
await asyncio.sleep(0.5)
yield {
"type": "user",
"message": {"role": "user", "content": "Temperature: 25°C, Humidity: 60%"},
}
await asyncio.sleep(0.5)
yield {
"type": "user",
"message": {"role": "user", "content": "What patterns do you see?"},
}
async def main():
async with ClaudeSDKClient() as client:
# Stream input to Claude
await client.query(message_stream())
# Process response
async for message in client.receive_response():
print(message)
# Follow-up in same session
await client.query("Should we be concerned about these readings?")
async for message in client.receive_response():
print(message)
asyncio.run(main())
Example - Using interrupts
import asyncio
from claude_agent_sdk import ClaudeSDKClient, ClaudeAgentOptions, ResultMessage
async def interruptible_task():
options = ClaudeAgentOptions(allowed_tools=["Bash"], permission_mode="acceptEdits")
async with ClaudeSDKClient(options=options) as client:
# Start a long-running task
await client.query("Count from 1 to 100 slowly, using the bash sleep command")
# Let it run for a bit
await asyncio.sleep(2)
# Interrupt the task
await client.interrupt()
print("Task interrupted!")
# Drain the interrupted task's messages (including its ResultMessage)
async for message in client.receive_response():
if isinstance(message, ResultMessage):
print(f"Interrupted task finished with subtype={message.subtype!r}")
# subtype is "error_during_execution" for interrupted tasks
# Send a new command
await client.query("Just say hello instead")
# Now receive the new response
async for message in client.receive_response():
if isinstance(message, ResultMessage) and message.subtype == "success":
print(f"New result: {message.result}")
asyncio.run(interruptible_task())
Buffer behavior after interrupt: interrupt() sends a stop signal but does not clear the message buffer. Messages already produced by the interrupted task, including its ResultMessage (with subtype="error_during_execution"), remain in the stream. You must drain them with receive_response() before reading the response to a new query. If you send a new query immediately after interrupt() and call receive_response() only once, you’ll receive the interrupted task’s messages, not the new query’s response.
Example - Advanced permission control
from claude_agent_sdk import ClaudeSDKClient, ClaudeAgentOptions
from claude_agent_sdk.types import (
PermissionResultAllow,
PermissionResultDeny,
ToolPermissionContext,
)
async def custom_permission_handler(
tool_name: str, input_data: dict, context: ToolPermissionContext
) -> PermissionResultAllow | PermissionResultDeny:
"""Custom logic for tool permissions."""
# Block writes to system directories
if tool_name == "Write" and input_data.get("file_path", "").startswith("/system/"):
return PermissionResultDeny(
message="System directory write not allowed", interrupt=True
)
# Redirect sensitive file operations
if tool_name in ["Write", "Edit"] and "config" in input_data.get("file_path", ""):
safe_path = f"./sandbox/{input_data['file_path']}"
return PermissionResultAllow(
updated_input={**input_data, "file_path": safe_path}
)
# Allow everything else
return PermissionResultAllow(updated_input=input_data)
async def main():
options = ClaudeAgentOptions(
can_use_tool=custom_permission_handler, allowed_tools=["Read", "Write", "Edit"]
)
async with ClaudeSDKClient(options=options) as client:
await client.query("Update the system config file")
async for message in client.receive_response():
# Will use sandbox path instead
print(message)
asyncio.run(main())
Types
@dataclass vs TypedDict: This SDK uses two kinds of types. Classes decorated with @dataclass (such as ResultMessage, AgentDefinition, TextBlock) are object instances at runtime and support attribute access: msg.result. Classes defined with TypedDict (such as ThinkingConfigEnabled, McpStdioServerConfig, SyncHookJSONOutput) are plain dicts at runtime and require key access: config["budget_tokens"], not config.budget_tokens. The ClassName(field=value) call syntax works for both, but only dataclasses produce objects with attributes.
Definition for an SDK MCP tool created with the @tool decorator.
@dataclass
class SdkMcpTool(Generic[T]):
name: str
description: str
input_schema: type[T] | dict[str, Any]
handler: Callable[[T], Awaitable[dict[str, Any]]]
annotations: ToolAnnotations | None = None
| Property | Type | Description |
|---|
name | str | Unique identifier for the tool |
description | str | Human-readable description |
input_schema | type[T] | dict[str, Any] | Schema for input validation |
handler | Callable[[T], Awaitable[dict[str, Any]]] | Async function that handles tool execution |
annotations | ToolAnnotations | None | Optional MCP tool annotations (e.g., readOnlyHint, destructiveHint, openWorldHint). From mcp.types |
Transport
Abstract base class for custom transport implementations. Use this to communicate with the Claude process over a custom channel (for example, a remote connection instead of a local subprocess).
This is a low-level internal API. The interface may change in future releases. Custom implementations must be updated to match any interface changes.
from abc import ABC, abstractmethod
from collections.abc import AsyncIterator
from typing import Any
class Transport(ABC):
@abstractmethod
async def connect(self) -> None: ...
@abstractmethod
async def write(self, data: str) -> None: ...
@abstractmethod
def read_messages(self) -> AsyncIterator[dict[str, Any]]: ...
@abstractmethod
async def close(self) -> None: ...
@abstractmethod
def is_ready(self) -> bool: ...
@abstractmethod
async def end_input(self) -> None: ...
| Method | Description |
|---|
connect() | Connect the transport and prepare for communication |
write(data) | Write raw data (JSON + newline) to the transport |
read_messages() | Async iterator that yields parsed JSON messages |
close() | Close the connection and clean up resources |
is_ready() | Returns True if the transport can send and receive |
end_input() | Close the input stream (for example, close stdin for subprocess transports) |
Import: from claude_agent_sdk import Transport
ClaudeAgentOptions
Configuration dataclass for Claude Code queries.
@dataclass
class ClaudeAgentOptions:
tools: list[str] | ToolsPreset | None = None
allowed_tools: list[str] = field(default_factory=list)
system_prompt: str | SystemPromptPreset | None = None
mcp_servers: dict[str, McpServerConfig] | str | Path = field(default_factory=dict)
permission_mode: PermissionMode | None = None
continue_conversation: bool = False
resume: str | None = None
max_turns: int | None = None
max_budget_usd: float | None = None
disallowed_tools: list[str] = field(default_factory=list)
model: str | None = None
fallback_model: str | None = None
betas: list[SdkBeta] = field(default_factory=list)
output_format: dict[str, Any] | None = None
permission_prompt_tool_name: str | None = None
cwd: str | Path | None = None
cli_path: str | Path | None = None
settings: str | None = None
add_dirs: list[str | Path] = field(default_factory=list)
env: dict[str, str] = field(default_factory=dict)
extra_args: dict[str, str | None] = field(default_factory=dict)
max_buffer_size: int | None = None
debug_stderr: Any = sys.stderr # Deprecated
stderr: Callable[[str], None] | None = None
can_use_tool: CanUseTool | None = None
hooks: dict[HookEvent, list[HookMatcher]] | None = None
user: str | None = None
include_partial_messages: bool = False
fork_session: bool = False
agents: dict[str, AgentDefinition] | None = None
setting_sources: list[SettingSource] | None = None
sandbox: SandboxSettings | None = None
plugins: list[SdkPluginConfig] = field(default_factory=list)
max_thinking_tokens: int | None = None # Deprecated: use thinking instead
thinking: ThinkingConfig | None = None
effort: Literal["low", "medium", "high", "max"] | None = None
enable_file_checkpointing: bool = False
| Property | Type | Default | Description |
|---|
tools | list[str] | ToolsPreset | None | None | Tools configuration. Use {"type": "preset", "preset": "claude_code"} for Claude Code’s default tools |
allowed_tools | list[str] | [] | Tools to auto-approve without prompting. This does not restrict Claude to only these tools; unlisted tools fall through to permission_mode and can_use_tool. Use disallowed_tools to block tools. See Permissions |
system_prompt | str | SystemPromptPreset | None | None | System prompt configuration. Pass a string for custom prompt, or use {"type": "preset", "preset": "claude_code"} for Claude Code’s system prompt. Add "append" to extend the preset |
mcp_servers | dict[str, McpServerConfig] | str | Path | {} | MCP server configurations or path to config file |
permission_mode | PermissionMode | None | None | Permission mode for tool usage |
continue_conversation | bool | False | Continue the most recent conversation |
resume | str | None | None | Session ID to resume |
max_turns | int | None | None | Maximum agentic turns (tool-use round trips) |
max_budget_usd | float | None | None | Maximum budget in USD for the session |
disallowed_tools | list[str] | [] | Tools to always deny. Deny rules are checked first and override allowed_tools and permission_mode (including bypassPermissions) |
enable_file_checkpointing | bool | False | Enable file change tracking for rewinding. See File checkpointing |
model | str | None | None | Claude model to use |
fallback_model | str | None | None | Fallback model to use if the primary model fails |
betas | list[SdkBeta] | [] | Beta features to enable. See SdkBeta for available options |
output_format | dict[str, Any] | None | None | Output format for structured responses (e.g., {"type": "json_schema", "schema": {...}}). See Structured outputs for details |
permission_prompt_tool_name | str | None | None | MCP tool name for permission prompts |
cwd | str | Path | None | None | Current working directory |
cli_path | str | Path | None | None | Custom path to the Claude Code CLI executable |
settings | str | None | None | Path to settings file |
add_dirs | list[str | Path] | [] | Additional directories Claude can access |
env | dict[str, str] | {} | Environment variables |
extra_args | dict[str, str | None] | {} | Additional CLI arguments to pass directly to the CLI |
max_buffer_size | int | None | None | Maximum bytes when buffering CLI stdout |
debug_stderr | Any | sys.stderr | Deprecated - File-like object for debug output. Use stderr callback instead |
stderr | Callable[[str], None] | None | None | Callback function for stderr output from CLI |
can_use_tool | CanUseTool | None | None | Tool permission callback function. See Permission types for details |
hooks | dict[HookEvent, list[HookMatcher]] | None | None | Hook configurations for intercepting events |
user | str | None | None | User identifier |
include_partial_messages | bool | False | Include partial message streaming events. When enabled, StreamEvent messages are yielded |
fork_session | bool | False | When resuming with resume, fork to a new session ID instead of continuing the original session |
agents | dict[str, AgentDefinition] | None | None | Programmatically defined subagents |
plugins | list[SdkPluginConfig] | [] | Load custom plugins from local paths. See Plugins for details |
sandbox | SandboxSettings | None | None | Configure sandbox behavior programmatically. See Sandbox settings for details |
setting_sources | list[SettingSource] | None | None (no settings) | Control which filesystem settings to load. When omitted, no settings are loaded. Note: Must include "project" to load CLAUDE.md files |
max_thinking_tokens | int | None | None | Deprecated - Maximum tokens for thinking blocks. Use thinking instead |
thinking | ThinkingConfig | None | None | Controls extended thinking behavior. Takes precedence over max_thinking_tokens |
effort | Literal["low", "medium", "high", "max"] | None | None | Effort level for thinking depth |
Configuration for structured output validation. Pass this as a dict to the output_format field on ClaudeAgentOptions:
# Expected dict shape for output_format
{
"type": "json_schema",
"schema": {...}, # Your JSON Schema definition
}
| Field | Required | Description |
|---|
type | Yes | Must be "json_schema" for JSON Schema validation |
schema | Yes | JSON Schema definition for output validation |
SystemPromptPreset
Configuration for using Claude Code’s preset system prompt with optional additions.
class SystemPromptPreset(TypedDict):
type: Literal["preset"]
preset: Literal["claude_code"]
append: NotRequired[str]
| Field | Required | Description |
|---|
type | Yes | Must be "preset" to use a preset system prompt |
preset | Yes | Must be "claude_code" to use Claude Code’s system prompt |
append | No | Additional instructions to append to the preset system prompt |
SettingSource
Controls which filesystem-based configuration sources the SDK loads settings from.
SettingSource = Literal["user", "project", "local"]
| Value | Description | Location |
|---|
"user" | Global user settings | ~/.claude/settings.json |
"project" | Shared project settings (version controlled) | .claude/settings.json |
"local" | Local project settings (gitignored) | .claude/settings.local.json |
Default behavior
When setting_sources is omitted or None, the SDK does not load any filesystem settings. This provides isolation for SDK applications.
Why use setting_sources
Load all filesystem settings (legacy behavior):
# Load all settings like SDK v0.0.x did
from claude_agent_sdk import query, ClaudeAgentOptions
async for message in query(
prompt="Analyze this code",
options=ClaudeAgentOptions(
setting_sources=["user", "project", "local"] # Load all settings
),
):
print(message)
Load only specific setting sources:
# Load only project settings, ignore user and local
async for message in query(
prompt="Run CI checks",
options=ClaudeAgentOptions(
setting_sources=["project"] # Only .claude/settings.json
),
):
print(message)
Testing and CI environments:
# Ensure consistent behavior in CI by excluding local settings
async for message in query(
prompt="Run tests",
options=ClaudeAgentOptions(
setting_sources=["project"], # Only team-shared settings
permission_mode="bypassPermissions",
),
):
print(message)
SDK-only applications:
# Define everything programmatically (default behavior)
# No filesystem dependencies - setting_sources defaults to None
async for message in query(
prompt="Review this PR",
options=ClaudeAgentOptions(
# setting_sources=None is the default, no need to specify
agents={...},
mcp_servers={...},
allowed_tools=["Read", "Grep", "Glob"],
),
):
print(message)
Loading CLAUDE.md project instructions:
# Load project settings to include CLAUDE.md files
async for message in query(
prompt="Add a new feature following project conventions",
options=ClaudeAgentOptions(
system_prompt={
"type": "preset",
"preset": "claude_code", # Use Claude Code's system prompt
},
setting_sources=["project"], # Required to load CLAUDE.md from project
allowed_tools=["Read", "Write", "Edit"],
),
):
print(message)
Settings precedence
When multiple sources are loaded, settings are merged with this precedence (highest to lowest):
- Local settings (
.claude/settings.local.json)
- Project settings (
.claude/settings.json)
- User settings (
~/.claude/settings.json)
Programmatic options (like agents, allowed_tools) always override filesystem settings.
AgentDefinition
Configuration for a subagent defined programmatically.
@dataclass
class AgentDefinition:
description: str
prompt: str
tools: list[str] | None = None
model: Literal["sonnet", "opus", "haiku", "inherit"] | None = None
skills: list[str] | None = None
memory: Literal["user", "project", "local"] | None = None
mcpServers: list[str | dict[str, Any]] | None = None
| Field | Required | Description |
|---|
description | Yes | Natural language description of when to use this agent |
prompt | Yes | The agent’s system prompt |
tools | No | Array of allowed tool names. If omitted, inherits all tools |
model | No | Model override for this agent. If omitted, uses the main model |
skills | No | List of skill names available to this agent |
memory | No | Memory source for this agent: "user", "project", or "local" |
mcpServers | No | MCP servers available to this agent. Each entry is a server name or an inline {name: config} dict |
PermissionMode
Permission modes for controlling tool execution.
PermissionMode = Literal[
"default", # Standard permission behavior
"acceptEdits", # Auto-accept file edits
"plan", # Planning mode - no execution
"dontAsk", # Deny anything not pre-approved instead of prompting
"bypassPermissions", # Bypass all permission checks (use with caution)
]
Type alias for tool permission callback functions.
CanUseTool = Callable[
[str, dict[str, Any], ToolPermissionContext], Awaitable[PermissionResult]
]
The callback receives:
tool_name: Name of the tool being called
input_data: The tool’s input parameters
context: A ToolPermissionContext with additional information
Returns a PermissionResult (either PermissionResultAllow or PermissionResultDeny).
ToolPermissionContext
Context information passed to tool permission callbacks.
@dataclass
class ToolPermissionContext:
signal: Any | None = None # Future: abort signal support
suggestions: list[PermissionUpdate] = field(default_factory=list)
| Field | Type | Description |
|---|
signal | Any | None | Reserved for future abort signal support |
suggestions | list[PermissionUpdate] | Permission update suggestions from the CLI |
PermissionResult
Union type for permission callback results.
PermissionResult = PermissionResultAllow | PermissionResultDeny
PermissionResultAllow
Result indicating the tool call should be allowed.
@dataclass
class PermissionResultAllow:
behavior: Literal["allow"] = "allow"
updated_input: dict[str, Any] | None = None
updated_permissions: list[PermissionUpdate] | None = None
| Field | Type | Default | Description |
|---|
behavior | Literal["allow"] | "allow" | Must be “allow” |
updated_input | dict[str, Any] | None | None | Modified input to use instead of original |
updated_permissions | list[PermissionUpdate] | None | None | Permission updates to apply |
PermissionResultDeny
Result indicating the tool call should be denied.
@dataclass
class PermissionResultDeny:
behavior: Literal["deny"] = "deny"
message: str = ""
interrupt: bool = False
| Field | Type | Default | Description |
|---|
behavior | Literal["deny"] | "deny" | Must be “deny” |
message | str | "" | Message explaining why the tool was denied |
interrupt | bool | False | Whether to interrupt the current execution |
PermissionUpdate
Configuration for updating permissions programmatically.
@dataclass
class PermissionUpdate:
type: Literal[
"addRules",
"replaceRules",
"removeRules",
"setMode",
"addDirectories",
"removeDirectories",
]
rules: list[PermissionRuleValue] | None = None
behavior: Literal["allow", "deny", "ask"] | None = None
mode: PermissionMode | None = None
directories: list[str] | None = None
destination: (
Literal["userSettings", "projectSettings", "localSettings", "session"] | None
) = None
| Field | Type | Description |
|---|
type | Literal[...] | The type of permission update operation |
rules | list[PermissionRuleValue] | None | Rules for add/replace/remove operations |
behavior | Literal["allow", "deny", "ask"] | None | Behavior for rule-based operations |
mode | PermissionMode | None | Mode for setMode operation |
directories | list[str] | None | Directories for add/remove directory operations |
destination | Literal[...] | None | Where to apply the permission update |
PermissionRuleValue
A rule to add, replace, or remove in a permission update.
@dataclass
class PermissionRuleValue:
tool_name: str
rule_content: str | None = None
Preset tools configuration for using Claude Code’s default tool set.
class ToolsPreset(TypedDict):
type: Literal["preset"]
preset: Literal["claude_code"]
ThinkingConfig
Controls extended thinking behavior. A union of three configurations:
class ThinkingConfigAdaptive(TypedDict):
type: Literal["adaptive"]
class ThinkingConfigEnabled(TypedDict):
type: Literal["enabled"]
budget_tokens: int
class ThinkingConfigDisabled(TypedDict):
type: Literal["disabled"]
ThinkingConfig = ThinkingConfigAdaptive | ThinkingConfigEnabled | ThinkingConfigDisabled
| Variant | Fields | Description |
|---|
adaptive | type | Claude adaptively decides when to think |
enabled | type, budget_tokens | Enable thinking with a specific token budget |
disabled | type | Disable thinking |
Because these are TypedDict classes, they’re plain dicts at runtime. Either construct them as dict literals or call the class like a constructor; both produce a dict. Access fields with config["budget_tokens"], not config.budget_tokens:
from claude_agent_sdk import ClaudeAgentOptions, ThinkingConfigEnabled
# Option 1: dict literal (recommended, no import needed)
options = ClaudeAgentOptions(thinking={"type": "enabled", "budget_tokens": 20000})
# Option 2: constructor-style (returns a plain dict)
config = ThinkingConfigEnabled(type="enabled", budget_tokens=20000)
print(config["budget_tokens"]) # 20000
# config.budget_tokens would raise AttributeError
SdkBeta
Literal type for SDK beta features.
SdkBeta = Literal["context-1m-2025-08-07"]
Use with the betas field in ClaudeAgentOptions to enable beta features.
The context-1m-2025-08-07 beta is retired as of April 30, 2026. Passing this header with Claude Sonnet 4.5 or Sonnet 4 has no effect, and requests that exceed the standard 200k-token context window return an error. To use a 1M-token context window, migrate to Claude Sonnet 4.6 or Claude Opus 4.6, which include 1M context at standard pricing with no beta header required.
McpSdkServerConfig
Configuration for SDK MCP servers created with create_sdk_mcp_server().
class McpSdkServerConfig(TypedDict):
type: Literal["sdk"]
name: str
instance: Any # MCP Server instance
McpServerConfig
Union type for MCP server configurations.
McpServerConfig = (
McpStdioServerConfig | McpSSEServerConfig | McpHttpServerConfig | McpSdkServerConfig
)
McpStdioServerConfig
class McpStdioServerConfig(TypedDict):
type: NotRequired[Literal["stdio"]] # Optional for backwards compatibility
command: str
args: NotRequired[list[str]]
env: NotRequired[dict[str, str]]
McpSSEServerConfig
class McpSSEServerConfig(TypedDict):
type: Literal["sse"]
url: str
headers: NotRequired[dict[str, str]]
McpHttpServerConfig
class McpHttpServerConfig(TypedDict):
type: Literal["http"]
url: str
headers: NotRequired[dict[str, str]]
McpServerStatusConfig
The configuration of an MCP server as reported by get_mcp_status(). This is the union of all McpServerConfig transport variants plus an output-only claudeai-proxy variant for servers proxied through claude.ai.
McpServerStatusConfig = (
McpStdioServerConfig
| McpSSEServerConfig
| McpHttpServerConfig
| McpSdkServerConfigStatus
| McpClaudeAIProxyServerConfig
)
McpSdkServerConfigStatus is the serializable form of McpSdkServerConfig with only type ("sdk") and name (str) fields; the in-process instance is omitted. McpClaudeAIProxyServerConfig has type ("claudeai-proxy"), url (str), and id (str) fields.
McpStatusResponse
Response from ClaudeSDKClient.get_mcp_status(). Wraps the list of server statuses under the mcpServers key.
class McpStatusResponse(TypedDict):
mcpServers: list[McpServerStatus]
McpServerStatus
Status of a connected MCP server, contained in McpStatusResponse.
class McpServerStatus(TypedDict):
name: str
status: McpServerConnectionStatus # "connected" | "failed" | "needs-auth" | "pending" | "disabled"
serverInfo: NotRequired[McpServerInfo]
error: NotRequired[str]
config: NotRequired[McpServerStatusConfig]
scope: NotRequired[str]
tools: NotRequired[list[McpToolInfo]]
| Field | Type | Description |
|---|
name | str | Server name |
status | str | One of "connected", "failed", "needs-auth", "pending", or "disabled" |
serverInfo | dict (optional) | Server name and version ({"name": str, "version": str}) |
error | str (optional) | Error message if the server failed to connect |
config | McpServerStatusConfig (optional) | Server configuration. Same shape as McpServerConfig (stdio, SSE, HTTP, or SDK), plus a claudeai-proxy variant for servers connected through claude.ai |
scope | str (optional) | Configuration scope |
tools | list (optional) | Tools provided by this server, each with name, description, and annotations fields |
SdkPluginConfig
Configuration for loading plugins in the SDK.
class SdkPluginConfig(TypedDict):
type: Literal["local"]
path: str
| Field | Type | Description |
|---|
type | Literal["local"] | Must be "local" (only local plugins currently supported) |
path | str | Absolute or relative path to the plugin directory |
Example:
plugins = [
{"type": "local", "path": "./my-plugin"},
{"type": "local", "path": "/absolute/path/to/plugin"},
]
For complete information on creating and using plugins, see Plugins.
Message Types
Message
Union type of all possible messages.
Message = (
UserMessage
| AssistantMessage
| SystemMessage
| ResultMessage
| StreamEvent
| RateLimitEvent
)
UserMessage
User input message.
@dataclass
class UserMessage:
content: str | list[ContentBlock]
uuid: str | None = None
parent_tool_use_id: str | None = None
tool_use_result: dict[str, Any] | None = None
| Field | Type | Description |
|---|
content | str | list[ContentBlock] | Message content as text or content blocks |
uuid | str | None | Unique message identifier |
parent_tool_use_id | str | None | Tool use ID if this message is a tool result response |
tool_use_result | dict[str, Any] | None | Tool result data if applicable |
AssistantMessage
Assistant response message with content blocks.
@dataclass
class AssistantMessage:
content: list[ContentBlock]
model: str
parent_tool_use_id: str | None = None
error: AssistantMessageError | None = None
usage: dict[str, Any] | None = None
message_id: str | None = None
| Field | Type | Description |
|---|
content | list[ContentBlock] | List of content blocks in the response |
model | str | Model that generated the response |
parent_tool_use_id | str | None | Tool use ID if this is a nested response |
error | AssistantMessageError | None | Error type if the response encountered an error |
usage | dict[str, Any] | None | Per-message token usage (same keys as ResultMessage.usage) |
message_id | str | None | API message ID. Multiple messages from one turn share the same ID |
AssistantMessageError
Possible error types for assistant messages.
AssistantMessageError = Literal[
"authentication_failed",
"billing_error",
"rate_limit",
"invalid_request",
"server_error",
"max_output_tokens",
"unknown",
]
SystemMessage
System message with metadata.
@dataclass
class SystemMessage:
subtype: str
data: dict[str, Any]
ResultMessage
Final result message with cost and usage information.
@dataclass
class ResultMessage:
subtype: str
duration_ms: int
duration_api_ms: int
is_error: bool
num_turns: int
session_id: str
total_cost_usd: float | None = None
usage: dict[str, Any] | None = None
result: str | None = None
stop_reason: str | None = None
structured_output: Any = None
model_usage: dict[str, Any] | None = None
The usage dict contains the following keys when present:
| Key | Type | Description |
|---|
input_tokens | int | Total input tokens consumed. |
output_tokens | int | Total output tokens generated. |
cache_creation_input_tokens | int | Tokens used to create new cache entries. |
cache_read_input_tokens | int | Tokens read from existing cache entries. |
The model_usage dict maps model names to per-model usage. The inner dict keys use camelCase because the value is passed through unmodified from the underlying CLI process, matching the TypeScript ModelUsage type:
| Key | Type | Description |
|---|
inputTokens | int | Input tokens for this model. |
outputTokens | int | Output tokens for this model. |
cacheReadInputTokens | int | Cache read tokens for this model. |
cacheCreationInputTokens | int | Cache creation tokens for this model. |
webSearchRequests | int | Web search requests made by this model. |
costUSD | float | Cost in USD for this model. |
contextWindow | int | Context window size for this model. |
maxOutputTokens | int | Maximum output token limit for this model. |
StreamEvent
Stream event for partial message updates during streaming. Only received when include_partial_messages=True in ClaudeAgentOptions. Import via from claude_agent_sdk.types import StreamEvent.
@dataclass
class StreamEvent:
uuid: str
session_id: str
event: dict[str, Any] # The raw Claude API stream event
parent_tool_use_id: str | None = None
| Field | Type | Description |
|---|
uuid | str | Unique identifier for this event |
session_id | str | Session identifier |
event | dict[str, Any] | The raw Claude API stream event data |
parent_tool_use_id | str | None | Parent tool use ID if this event is from a subagent |
RateLimitEvent
Emitted when rate limit status changes (for example, from "allowed" to "allowed_warning"). Use this to warn users before they hit a hard limit, or to back off when status is "rejected".
@dataclass
class RateLimitEvent:
rate_limit_info: RateLimitInfo
uuid: str
session_id: str
| Field | Type | Description |
|---|
rate_limit_info | RateLimitInfo | Current rate limit state |
uuid | str | Unique event identifier |
session_id | str | Session identifier |
RateLimitInfo
Rate limit state carried by RateLimitEvent.
RateLimitStatus = Literal["allowed", "allowed_warning", "rejected"]
RateLimitType = Literal[
"five_hour", "seven_day", "seven_day_opus", "seven_day_sonnet", "overage"
]
@dataclass
class RateLimitInfo:
status: RateLimitStatus
resets_at: int | None = None
rate_limit_type: RateLimitType | None = None
utilization: float | None = None
overage_status: RateLimitStatus | None = None
overage_resets_at: int | None = None
overage_disabled_reason: str | None = None
raw: dict[str, Any] = field(default_factory=dict)
| Field | Type | Description |
|---|
status | RateLimitStatus | Current status. "allowed_warning" means approaching the limit; "rejected" means the limit was hit |
resets_at | int | None | Unix timestamp when the rate limit window resets |
rate_limit_type | RateLimitType | None | Which rate limit window applies |
utilization | float | None | Fraction of the rate limit consumed (0.0 to 1.0) |
overage_status | RateLimitStatus | None | Status of pay-as-you-go overage usage, if applicable |
overage_resets_at | int | None | Unix timestamp when the overage window resets |
overage_disabled_reason | str | None | Why overage is unavailable, if status is "rejected" |
raw | dict[str, Any] | Full raw dict from the CLI, including fields not modeled above |
TaskStartedMessage
Emitted when a background task starts. A background task is anything tracked outside the main turn: a backgrounded Bash command, a subagent spawned via the Agent tool, or a remote agent. The task_type field tells you which. This naming is unrelated to the Task-to-Agent tool rename.
@dataclass
class TaskStartedMessage(SystemMessage):
task_id: str
description: str
uuid: str
session_id: str
tool_use_id: str | None = None
task_type: str | None = None
| Field | Type | Description |
|---|
task_id | str | Unique identifier for the task |
description | str | Description of the task |
uuid | str | Unique message identifier |
session_id | str | Session identifier |
tool_use_id | str | None | Associated tool use ID |
task_type | str | None | Which kind of background task: "local_bash", "local_agent", "remote_agent" |
TaskUsage
Token and timing data for a background task.
class TaskUsage(TypedDict):
total_tokens: int
tool_uses: int
duration_ms: int
TaskProgressMessage
Emitted periodically with progress updates for a running background task.
@dataclass
class TaskProgressMessage(SystemMessage):
task_id: str
description: str
usage: TaskUsage
uuid: str
session_id: str
tool_use_id: str | None = None
last_tool_name: str | None = None
| Field | Type | Description |
|---|
task_id | str | Unique identifier for the task |
description | str | Current status description |
usage | TaskUsage | Token usage for this task so far |
uuid | str | Unique message identifier |
session_id | str | Session identifier |
tool_use_id | str | None | Associated tool use ID |
last_tool_name | str | None | Name of the last tool the task used |
TaskNotificationMessage
Emitted when a task completes, fails, or is stopped.
@dataclass
class TaskNotificationMessage(SystemMessage):
task_id: str
status: TaskNotificationStatus # "completed" | "failed" | "stopped"
output_file: str
summary: str
uuid: str
session_id: str
tool_use_id: str | None = None
usage: TaskUsage | None = None
| Field | Type | Description |
|---|
task_id | str | Unique identifier for the task |
status | TaskNotificationStatus | One of "completed", "failed", or "stopped" |
output_file | str | Path to the task output file |
summary | str | Summary of the task result |
uuid | str | Unique message identifier |
session_id | str | Session identifier |
tool_use_id | str | None | Associated tool use ID |
usage | TaskUsage | None | Final token usage for the task |
Content Block Types
ContentBlock
Union type of all content blocks.
ContentBlock = TextBlock | ThinkingBlock | ToolUseBlock | ToolResultBlock
TextBlock
Text content block.
@dataclass
class TextBlock:
text: str
ThinkingBlock
Thinking content block (for models with thinking capability).
@dataclass
class ThinkingBlock:
thinking: str
signature: str
Tool use request block.
@dataclass
class ToolUseBlock:
id: str
name: str
input: dict[str, Any]
Tool execution result block.
@dataclass
class ToolResultBlock:
tool_use_id: str
content: str | list[dict[str, Any]] | None = None
is_error: bool | None = None
Error Types
ClaudeSDKError
Base exception class for all SDK errors.
class ClaudeSDKError(Exception):
"""Base error for Claude SDK."""
CLINotFoundError
Raised when Claude Code CLI is not installed or not found.
class CLINotFoundError(CLIConnectionError):
def __init__(
self, message: str = "Claude Code not found", cli_path: str | None = None
):
"""
Args:
message: Error message (default: "Claude Code not found")
cli_path: Optional path to the CLI that was not found
"""
CLIConnectionError
Raised when connection to Claude Code fails.
class CLIConnectionError(ClaudeSDKError):
"""Failed to connect to Claude Code."""
ProcessError
Raised when the Claude Code process fails.
class ProcessError(ClaudeSDKError):
def __init__(
self, message: str, exit_code: int | None = None, stderr: str | None = None
):
self.exit_code = exit_code
self.stderr = stderr
CLIJSONDecodeError
Raised when JSON parsing fails.
class CLIJSONDecodeError(ClaudeSDKError):
def __init__(self, line: str, original_error: Exception):
"""
Args:
line: The line that failed to parse
original_error: The original JSON decode exception
"""
self.line = line
self.original_error = original_error
Hook Types
For a comprehensive guide on using hooks with examples and common patterns, see the Hooks guide.
HookEvent
Supported hook event types.
HookEvent = Literal[
"PreToolUse", # Called before tool execution
"PostToolUse", # Called after tool execution
"PostToolUseFailure", # Called when a tool execution fails
"UserPromptSubmit", # Called when user submits a prompt
"Stop", # Called when stopping execution
"SubagentStop", # Called when a subagent stops
"PreCompact", # Called before message compaction
"Notification", # Called for notification events
"SubagentStart", # Called when a subagent starts
"PermissionRequest", # Called when a permission decision is needed
]
The TypeScript SDK supports additional hook events not yet available in Python: SessionStart, SessionEnd, Setup, TeammateIdle, TaskCompleted, ConfigChange, WorktreeCreate, and WorktreeRemove.
HookCallback
Type definition for hook callback functions.
HookCallback = Callable[[HookInput, str | None, HookContext], Awaitable[HookJSONOutput]]
Parameters:
input: Strongly-typed hook input with discriminated unions based on hook_event_name (see HookInput)
tool_use_id: Optional tool use identifier (for tool-related hooks)
context: Hook context with additional information
Returns a HookJSONOutput that may contain:
decision: "block" to block the action
systemMessage: System message to add to the transcript
hookSpecificOutput: Hook-specific output data
HookContext
Context information passed to hook callbacks.
class HookContext(TypedDict):
signal: Any | None # Future: abort signal support
HookMatcher
Configuration for matching hooks to specific events or tools.
@dataclass
class HookMatcher:
matcher: str | None = (
None # Tool name or pattern to match (e.g., "Bash", "Write|Edit")
)
hooks: list[HookCallback] = field(
default_factory=list
) # List of callbacks to execute
timeout: float | None = (
None # Timeout in seconds for all hooks in this matcher (default: 60)
)
Union type of all hook input types. The actual type depends on the hook_event_name field.
HookInput = (
PreToolUseHookInput
| PostToolUseHookInput
| PostToolUseFailureHookInput
| UserPromptSubmitHookInput
| StopHookInput
| SubagentStopHookInput
| PreCompactHookInput
| NotificationHookInput
| SubagentStartHookInput
| PermissionRequestHookInput
)
Base fields present in all hook input types.
class BaseHookInput(TypedDict):
session_id: str
transcript_path: str
cwd: str
permission_mode: NotRequired[str]
| Field | Type | Description |
|---|
session_id | str | Current session identifier |
transcript_path | str | Path to the session transcript file |
cwd | str | Current working directory |
permission_mode | str (optional) | Current permission mode |
Input data for PreToolUse hook events.
class PreToolUseHookInput(BaseHookInput):
hook_event_name: Literal["PreToolUse"]
tool_name: str
tool_input: dict[str, Any]
tool_use_id: str
agent_id: NotRequired[str]
agent_type: NotRequired[str]
| Field | Type | Description |
|---|
hook_event_name | Literal["PreToolUse"] | Always “PreToolUse” |
tool_name | str | Name of the tool about to be executed |
tool_input | dict[str, Any] | Input parameters for the tool |
tool_use_id | str | Unique identifier for this tool use |
agent_id | str (optional) | Subagent identifier, present when the hook fires inside a subagent |
agent_type | str (optional) | Subagent type, present when the hook fires inside a subagent |
PostToolUseHookInput
Input data for PostToolUse hook events.
class PostToolUseHookInput(BaseHookInput):
hook_event_name: Literal["PostToolUse"]
tool_name: str
tool_input: dict[str, Any]
tool_response: Any
tool_use_id: str
agent_id: NotRequired[str]
agent_type: NotRequired[str]
| Field | Type | Description |
|---|
hook_event_name | Literal["PostToolUse"] | Always “PostToolUse” |
tool_name | str | Name of the tool that was executed |
tool_input | dict[str, Any] | Input parameters that were used |
tool_response | Any | Response from the tool execution |
tool_use_id | str | Unique identifier for this tool use |
agent_id | str (optional) | Subagent identifier, present when the hook fires inside a subagent |
agent_type | str (optional) | Subagent type, present when the hook fires inside a subagent |
PostToolUseFailureHookInput
Input data for PostToolUseFailure hook events. Called when a tool execution fails.
class PostToolUseFailureHookInput(BaseHookInput):
hook_event_name: Literal["PostToolUseFailure"]
tool_name: str
tool_input: dict[str, Any]
tool_use_id: str
error: str
is_interrupt: NotRequired[bool]
agent_id: NotRequired[str]
agent_type: NotRequired[str]
| Field | Type | Description |
|---|
hook_event_name | Literal["PostToolUseFailure"] | Always “PostToolUseFailure” |
tool_name | str | Name of the tool that failed |
tool_input | dict[str, Any] | Input parameters that were used |
tool_use_id | str | Unique identifier for this tool use |
error | str | Error message from the failed execution |
is_interrupt | bool (optional) | Whether the failure was caused by an interrupt |
agent_id | str (optional) | Subagent identifier, present when the hook fires inside a subagent |
agent_type | str (optional) | Subagent type, present when the hook fires inside a subagent |
Input data for UserPromptSubmit hook events.
class UserPromptSubmitHookInput(BaseHookInput):
hook_event_name: Literal["UserPromptSubmit"]
prompt: str
| Field | Type | Description |
|---|
hook_event_name | Literal["UserPromptSubmit"] | Always “UserPromptSubmit” |
prompt | str | The user’s submitted prompt |
Input data for Stop hook events.
class StopHookInput(BaseHookInput):
hook_event_name: Literal["Stop"]
stop_hook_active: bool
| Field | Type | Description |
|---|
hook_event_name | Literal["Stop"] | Always “Stop” |
stop_hook_active | bool | Whether the stop hook is active |
Input data for SubagentStop hook events.
class SubagentStopHookInput(BaseHookInput):
hook_event_name: Literal["SubagentStop"]
stop_hook_active: bool
agent_id: str
agent_transcript_path: str
agent_type: str
| Field | Type | Description |
|---|
hook_event_name | Literal["SubagentStop"] | Always “SubagentStop” |
stop_hook_active | bool | Whether the stop hook is active |
agent_id | str | Unique identifier for the subagent |
agent_transcript_path | str | Path to the subagent’s transcript file |
agent_type | str | Type of the subagent |
Input data for PreCompact hook events.
class PreCompactHookInput(BaseHookInput):
hook_event_name: Literal["PreCompact"]
trigger: Literal["manual", "auto"]
custom_instructions: str | None
| Field | Type | Description |
|---|
hook_event_name | Literal["PreCompact"] | Always “PreCompact” |
trigger | Literal["manual", "auto"] | What triggered the compaction |
custom_instructions | str | None | Custom instructions for compaction |
Input data for Notification hook events.
class NotificationHookInput(BaseHookInput):
hook_event_name: Literal["Notification"]
message: str
title: NotRequired[str]
notification_type: str
| Field | Type | Description |
|---|
hook_event_name | Literal["Notification"] | Always “Notification” |
message | str | Notification message content |
title | str (optional) | Notification title |
notification_type | str | Type of notification |
Input data for SubagentStart hook events.
class SubagentStartHookInput(BaseHookInput):
hook_event_name: Literal["SubagentStart"]
agent_id: str
agent_type: str
| Field | Type | Description |
|---|
hook_event_name | Literal["SubagentStart"] | Always “SubagentStart” |
agent_id | str | Unique identifier for the subagent |
agent_type | str | Type of the subagent |
Input data for PermissionRequest hook events. Allows hooks to handle permission decisions programmatically.
class PermissionRequestHookInput(BaseHookInput):
hook_event_name: Literal["PermissionRequest"]
tool_name: str
tool_input: dict[str, Any]
permission_suggestions: NotRequired[list[Any]]
| Field | Type | Description |
|---|
hook_event_name | Literal["PermissionRequest"] | Always “PermissionRequest” |
tool_name | str | Name of the tool requesting permission |
tool_input | dict[str, Any] | Input parameters for the tool |
permission_suggestions | list[Any] (optional) | Suggested permission updates from the CLI |
HookJSONOutput
Union type for hook callback return values.
HookJSONOutput = AsyncHookJSONOutput | SyncHookJSONOutput
SyncHookJSONOutput
Synchronous hook output with control and decision fields.
class SyncHookJSONOutput(TypedDict):
# Control fields
continue_: NotRequired[bool] # Whether to proceed (default: True)
suppressOutput: NotRequired[bool] # Hide stdout from transcript
stopReason: NotRequired[str] # Message when continue is False
# Decision fields
decision: NotRequired[Literal["block"]]
systemMessage: NotRequired[str] # Warning message for user
reason: NotRequired[str] # Feedback for Claude
# Hook-specific output
hookSpecificOutput: NotRequired[HookSpecificOutput]
Use continue_ (with underscore) in Python code. It is automatically converted to continue when sent to the CLI.
HookSpecificOutput
A TypedDict containing the hook event name and event-specific fields. The shape depends on the hookEventName value. For full details on available fields per hook event, see Control execution with hooks.
A discriminated union of event-specific output types. The hookEventName field determines which fields are valid.
class PreToolUseHookSpecificOutput(TypedDict):
hookEventName: Literal["PreToolUse"]
permissionDecision: NotRequired[Literal["allow", "deny", "ask"]]
permissionDecisionReason: NotRequired[str]
updatedInput: NotRequired[dict[str, Any]]
additionalContext: NotRequired[str]
class PostToolUseHookSpecificOutput(TypedDict):
hookEventName: Literal["PostToolUse"]
additionalContext: NotRequired[str]
updatedMCPToolOutput: NotRequired[Any]
class PostToolUseFailureHookSpecificOutput(TypedDict):
hookEventName: Literal["PostToolUseFailure"]
additionalContext: NotRequired[str]
class UserPromptSubmitHookSpecificOutput(TypedDict):
hookEventName: Literal["UserPromptSubmit"]
additionalContext: NotRequired[str]
class NotificationHookSpecificOutput(TypedDict):
hookEventName: Literal["Notification"]
additionalContext: NotRequired[str]
class SubagentStartHookSpecificOutput(TypedDict):
hookEventName: Literal["SubagentStart"]
additionalContext: NotRequired[str]
class PermissionRequestHookSpecificOutput(TypedDict):
hookEventName: Literal["PermissionRequest"]
decision: dict[str, Any]
HookSpecificOutput = (
PreToolUseHookSpecificOutput
| PostToolUseHookSpecificOutput
| PostToolUseFailureHookSpecificOutput
| UserPromptSubmitHookSpecificOutput
| NotificationHookSpecificOutput
| SubagentStartHookSpecificOutput
| PermissionRequestHookSpecificOutput
)
AsyncHookJSONOutput
Async hook output that defers hook execution.
class AsyncHookJSONOutput(TypedDict):
async_: Literal[True] # Set to True to defer execution
asyncTimeout: NotRequired[int] # Timeout in milliseconds
Use async_ (with underscore) in Python code. It is automatically converted to async when sent to the CLI.
Hook Usage Example
This example registers two hooks: one that blocks dangerous bash commands like rm -rf /, and another that logs all tool usage for auditing. The security hook only runs on Bash commands (via the matcher), while the logging hook runs on all tools.
from claude_agent_sdk import query, ClaudeAgentOptions, HookMatcher, HookContext
from typing import Any
async def validate_bash_command(
input_data: dict[str, Any], tool_use_id: str | None, context: HookContext
) -> dict[str, Any]:
"""Validate and potentially block dangerous bash commands."""
if input_data["tool_name"] == "Bash":
command = input_data["tool_input"].get("command", "")
if "rm -rf /" in command:
return {
"hookSpecificOutput": {
"hookEventName": "PreToolUse",
"permissionDecision": "deny",
"permissionDecisionReason": "Dangerous command blocked",
}
}
return {}
async def log_tool_use(
input_data: dict[str, Any], tool_use_id: str | None, context: HookContext
) -> dict[str, Any]:
"""Log all tool usage for auditing."""
print(f"Tool used: {input_data.get('tool_name')}")
return {}
options = ClaudeAgentOptions(
hooks={
"PreToolUse": [
HookMatcher(
matcher="Bash", hooks=[validate_bash_command], timeout=120
), # 2 min for validation
HookMatcher(
hooks=[log_tool_use]
), # Applies to all tools (default 60s timeout)
],
"PostToolUse": [HookMatcher(hooks=[log_tool_use])],
}
)
async for message in query(prompt="Analyze this codebase", options=options):
print(message)
Documentation of input/output schemas for all built-in Claude Code tools. While the Python SDK doesn’t export these as types, they represent the structure of tool inputs and outputs in messages.
Agent
Tool name: Agent (previously Task, which is still accepted as an alias)
Input:
{
"description": str, # A short (3-5 word) description of the task
"prompt": str, # The task for the agent to perform
"subagent_type": str, # The type of specialized agent to use
}
Output:
{
"result": str, # Final result from the subagent
"usage": dict | None, # Token usage statistics
"total_cost_usd": float | None, # Total cost in USD
"duration_ms": int | None, # Execution duration in milliseconds
}
AskUserQuestion
Tool name: AskUserQuestion
Asks the user clarifying questions during execution. See Handle approvals and user input for usage details.
Input:
{
"questions": [ # Questions to ask the user (1-4 questions)
{
"question": str, # The complete question to ask the user
"header": str, # Very short label displayed as a chip/tag (max 12 chars)
"options": [ # The available choices (2-4 options)
{
"label": str, # Display text for this option (1-5 words)
"description": str, # Explanation of what this option means
}
],
"multiSelect": bool, # Set to true to allow multiple selections
}
],
"answers": dict | None, # User answers populated by the permission system
}
Output:
{
"questions": [ # The questions that were asked
{
"question": str,
"header": str,
"options": [{"label": str, "description": str}],
"multiSelect": bool,
}
],
"answers": dict[str, str], # Maps question text to answer string
# Multi-select answers are comma-separated
}
Bash
Tool name: Bash
Input:
{
"command": str, # The command to execute
"timeout": int | None, # Optional timeout in milliseconds (max 600000)
"description": str | None, # Clear, concise description (5-10 words)
"run_in_background": bool | None, # Set to true to run in background
}
Output:
{
"output": str, # Combined stdout and stderr output
"exitCode": int, # Exit code of the command
"killed": bool | None, # Whether command was killed due to timeout
"shellId": str | None, # Shell ID for background processes
}
Edit
Tool name: Edit
Input:
{
"file_path": str, # The absolute path to the file to modify
"old_string": str, # The text to replace
"new_string": str, # The text to replace it with
"replace_all": bool | None, # Replace all occurrences (default False)
}
Output:
{
"message": str, # Confirmation message
"replacements": int, # Number of replacements made
"file_path": str, # File path that was edited
}
Read
Tool name: Read
Input:
{
"file_path": str, # The absolute path to the file to read
"offset": int | None, # The line number to start reading from
"limit": int | None, # The number of lines to read
}
Output (Text files):
{
"content": str, # File contents with line numbers
"total_lines": int, # Total number of lines in file
"lines_returned": int, # Lines actually returned
}
Output (Images):
{
"image": str, # Base64 encoded image data
"mime_type": str, # Image MIME type
"file_size": int, # File size in bytes
}
Write
Tool name: Write
Input:
{
"file_path": str, # The absolute path to the file to write
"content": str, # The content to write to the file
}
Output:
{
"message": str, # Success message
"bytes_written": int, # Number of bytes written
"file_path": str, # File path that was written
}
Glob
Tool name: Glob
Input:
{
"pattern": str, # The glob pattern to match files against
"path": str | None, # The directory to search in (defaults to cwd)
}
Output:
{
"matches": list[str], # Array of matching file paths
"count": int, # Number of matches found
"search_path": str, # Search directory used
}
Grep
Tool name: Grep
Input:
{
"pattern": str, # The regular expression pattern
"path": str | None, # File or directory to search in
"glob": str | None, # Glob pattern to filter files
"type": str | None, # File type to search
"output_mode": str | None, # "content", "files_with_matches", or "count"
"-i": bool | None, # Case insensitive search
"-n": bool | None, # Show line numbers
"-B": int | None, # Lines to show before each match
"-A": int | None, # Lines to show after each match
"-C": int | None, # Lines to show before and after
"head_limit": int | None, # Limit output to first N lines/entries
"multiline": bool | None, # Enable multiline mode
}
Output (content mode):
{
"matches": [
{
"file": str,
"line_number": int | None,
"line": str,
"before_context": list[str] | None,
"after_context": list[str] | None,
}
],
"total_matches": int,
}
Output (files_with_matches mode):
{
"files": list[str], # Files containing matches
"count": int, # Number of files with matches
}
NotebookEdit
Tool name: NotebookEdit
Input:
{
"notebook_path": str, # Absolute path to the Jupyter notebook
"cell_id": str | None, # The ID of the cell to edit
"new_source": str, # The new source for the cell
"cell_type": "code" | "markdown" | None, # The type of the cell
"edit_mode": "replace" | "insert" | "delete" | None, # Edit operation type
}
Output:
{
"message": str, # Success message
"edit_type": "replaced" | "inserted" | "deleted", # Type of edit performed
"cell_id": str | None, # Cell ID that was affected
"total_cells": int, # Total cells in notebook after edit
}
WebFetch
Tool name: WebFetch
Input:
{
"url": str, # The URL to fetch content from
"prompt": str, # The prompt to run on the fetched content
}
Output:
{
"response": str, # AI model's response to the prompt
"url": str, # URL that was fetched
"final_url": str | None, # Final URL after redirects
"status_code": int | None, # HTTP status code
}
WebSearch
Tool name: WebSearch
Input:
{
"query": str, # The search query to use
"allowed_domains": list[str] | None, # Only include results from these domains
"blocked_domains": list[str] | None, # Never include results from these domains
}
Output:
{
"results": [{"title": str, "url": str, "snippet": str, "metadata": dict | None}],
"total_results": int,
"query": str,
}
TodoWrite
Tool name: TodoWrite
Input:
{
"todos": [
{
"content": str, # The task description
"status": "pending" | "in_progress" | "completed", # Task status
"activeForm": str, # Active form of the description
}
]
}
Output:
{
"message": str, # Success message
"stats": {"total": int, "pending": int, "in_progress": int, "completed": int},
}
BashOutput
Tool name: BashOutput
Input:
{
"bash_id": str, # The ID of the background shell
"filter": str | None, # Optional regex to filter output lines
}
Output:
{
"output": str, # New output since last check
"status": "running" | "completed" | "failed", # Current shell status
"exitCode": int | None, # Exit code when completed
}
KillBash
Tool name: KillBash
Input:
{
"shell_id": str # The ID of the background shell to kill
}
Output:
{
"message": str, # Success message
"shell_id": str, # ID of the killed shell
}
ExitPlanMode
Tool name: ExitPlanMode
Input:
{
"plan": str # The plan to run by the user for approval
}
Output:
{
"message": str, # Confirmation message
"approved": bool | None, # Whether user approved the plan
}
ListMcpResources
Tool name: ListMcpResources
Input:
{
"server": str | None # Optional server name to filter resources by
}
Output:
{
"resources": [
{
"uri": str,
"name": str,
"description": str | None,
"mimeType": str | None,
"server": str,
}
],
"total": int,
}
ReadMcpResource
Tool name: ReadMcpResource
Input:
{
"server": str, # The MCP server name
"uri": str, # The resource URI to read
}
Output:
{
"contents": [
{"uri": str, "mimeType": str | None, "text": str | None, "blob": str | None}
],
"server": str,
}
Advanced Features with ClaudeSDKClient
Building a Continuous Conversation Interface
from claude_agent_sdk import (
ClaudeSDKClient,
ClaudeAgentOptions,
AssistantMessage,
TextBlock,
)
import asyncio
class ConversationSession:
"""Maintains a single conversation session with Claude."""
def __init__(self, options: ClaudeAgentOptions | None = None):
self.client = ClaudeSDKClient(options)
self.turn_count = 0
async def start(self):
await self.client.connect()
print("Starting conversation session. Claude will remember context.")
print(
"Commands: 'exit' to quit, 'interrupt' to stop current task, 'new' for new session"
)
while True:
user_input = input(f"\n[Turn {self.turn_count + 1}] You: ")
if user_input.lower() == "exit":
break
elif user_input.lower() == "interrupt":
await self.client.interrupt()
print("Task interrupted!")
continue
elif user_input.lower() == "new":
# Disconnect and reconnect for a fresh session
await self.client.disconnect()
await self.client.connect()
self.turn_count = 0
print("Started new conversation session (previous context cleared)")
continue
# Send message - the session retains all previous messages
await self.client.query(user_input)
self.turn_count += 1
# Process response
print(f"[Turn {self.turn_count}] Claude: ", end="")
async for message in self.client.receive_response():
if isinstance(message, AssistantMessage):
for block in message.content:
if isinstance(block, TextBlock):
print(block.text, end="")
print() # New line after response
await self.client.disconnect()
print(f"Conversation ended after {self.turn_count} turns.")
async def main():
options = ClaudeAgentOptions(
allowed_tools=["Read", "Write", "Bash"], permission_mode="acceptEdits"
)
session = ConversationSession(options)
await session.start()
# Example conversation:
# Turn 1 - You: "Create a file called hello.py"
# Turn 1 - Claude: "I'll create a hello.py file for you..."
# Turn 2 - You: "What's in that file?"
# Turn 2 - Claude: "The hello.py file I just created contains..." (remembers!)
# Turn 3 - You: "Add a main function to it"
# Turn 3 - Claude: "I'll add a main function to hello.py..." (knows which file!)
asyncio.run(main())
Using Hooks for Behavior Modification
from claude_agent_sdk import (
ClaudeSDKClient,
ClaudeAgentOptions,
HookMatcher,
HookContext,
)
import asyncio
from typing import Any
async def pre_tool_logger(
input_data: dict[str, Any], tool_use_id: str | None, context: HookContext
) -> dict[str, Any]:
"""Log all tool usage before execution."""
tool_name = input_data.get("tool_name", "unknown")
print(f"[PRE-TOOL] About to use: {tool_name}")
# You can modify or block the tool execution here
if tool_name == "Bash" and "rm -rf" in str(input_data.get("tool_input", {})):
return {
"hookSpecificOutput": {
"hookEventName": "PreToolUse",
"permissionDecision": "deny",
"permissionDecisionReason": "Dangerous command blocked",
}
}
return {}
async def post_tool_logger(
input_data: dict[str, Any], tool_use_id: str | None, context: HookContext
) -> dict[str, Any]:
"""Log results after tool execution."""
tool_name = input_data.get("tool_name", "unknown")
print(f"[POST-TOOL] Completed: {tool_name}")
return {}
async def user_prompt_modifier(
input_data: dict[str, Any], tool_use_id: str | None, context: HookContext
) -> dict[str, Any]:
"""Add context to user prompts."""
original_prompt = input_data.get("prompt", "")
# Add a timestamp as additional context for Claude to see
from datetime import datetime
timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
return {
"hookSpecificOutput": {
"hookEventName": "UserPromptSubmit",
"additionalContext": f"[Submitted at {timestamp}] Original prompt: {original_prompt}",
}
}
async def main():
options = ClaudeAgentOptions(
hooks={
"PreToolUse": [
HookMatcher(hooks=[pre_tool_logger]),
HookMatcher(matcher="Bash", hooks=[pre_tool_logger]),
],
"PostToolUse": [HookMatcher(hooks=[post_tool_logger])],
"UserPromptSubmit": [HookMatcher(hooks=[user_prompt_modifier])],
},
allowed_tools=["Read", "Write", "Bash"],
)
async with ClaudeSDKClient(options=options) as client:
await client.query("List files in current directory")
async for message in client.receive_response():
# Hooks will automatically log tool usage
pass
asyncio.run(main())
Real-time Progress Monitoring
from claude_agent_sdk import (
ClaudeSDKClient,
ClaudeAgentOptions,
AssistantMessage,
ToolUseBlock,
ToolResultBlock,
TextBlock,
)
import asyncio
async def monitor_progress():
options = ClaudeAgentOptions(
allowed_tools=["Write", "Bash"], permission_mode="acceptEdits"
)
async with ClaudeSDKClient(options=options) as client:
await client.query("Create 5 Python files with different sorting algorithms")
# Monitor progress in real-time
async for message in client.receive_response():
if isinstance(message, AssistantMessage):
for block in message.content:
if isinstance(block, ToolUseBlock):
if block.name == "Write":
file_path = block.input.get("file_path", "")
print(f"Creating: {file_path}")
elif isinstance(block, ToolResultBlock):
print("Completed tool execution")
elif isinstance(block, TextBlock):
print(f"Claude says: {block.text[:100]}...")
print("Task completed!")
asyncio.run(monitor_progress())
Example Usage
Basic file operations (using query)
from claude_agent_sdk import query, ClaudeAgentOptions, AssistantMessage, ToolUseBlock
import asyncio
async def create_project():
options = ClaudeAgentOptions(
allowed_tools=["Read", "Write", "Bash"],
permission_mode="acceptEdits",
cwd="/home/user/project",
)
async for message in query(
prompt="Create a Python project structure with setup.py", options=options
):
if isinstance(message, AssistantMessage):
for block in message.content:
if isinstance(block, ToolUseBlock):
print(f"Using tool: {block.name}")
asyncio.run(create_project())
Error handling
from claude_agent_sdk import query, CLINotFoundError, ProcessError, CLIJSONDecodeError
try:
async for message in query(prompt="Hello"):
print(message)
except CLINotFoundError:
print(
"Claude Code CLI not found. Try reinstalling: pip install --force-reinstall claude-agent-sdk"
)
except ProcessError as e:
print(f"Process failed with exit code: {e.exit_code}")
except CLIJSONDecodeError as e:
print(f"Failed to parse response: {e}")
Streaming mode with client
from claude_agent_sdk import ClaudeSDKClient
import asyncio
async def interactive_session():
async with ClaudeSDKClient() as client:
# Send initial message
await client.query("What's the weather like?")
# Process responses
async for msg in client.receive_response():
print(msg)
# Send follow-up
await client.query("Tell me more about that")
# Process follow-up response
async for msg in client.receive_response():
print(msg)
asyncio.run(interactive_session())
from claude_agent_sdk import (
ClaudeSDKClient,
ClaudeAgentOptions,
tool,
create_sdk_mcp_server,
AssistantMessage,
TextBlock,
)
import asyncio
from typing import Any
# Define custom tools with @tool decorator
@tool("calculate", "Perform mathematical calculations", {"expression": str})
async def calculate(args: dict[str, Any]) -> dict[str, Any]:
try:
result = eval(args["expression"], {"__builtins__": {}})
return {"content": [{"type": "text", "text": f"Result: {result}"}]}
except Exception as e:
return {
"content": [{"type": "text", "text": f"Error: {str(e)}"}],
"is_error": True,
}
@tool("get_time", "Get current time", {})
async def get_time(args: dict[str, Any]) -> dict[str, Any]:
from datetime import datetime
current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
return {"content": [{"type": "text", "text": f"Current time: {current_time}"}]}
async def main():
# Create SDK MCP server with custom tools
my_server = create_sdk_mcp_server(
name="utilities", version="1.0.0", tools=[calculate, get_time]
)
# Configure options with the server
options = ClaudeAgentOptions(
mcp_servers={"utils": my_server},
allowed_tools=["mcp__utils__calculate", "mcp__utils__get_time"],
)
# Use ClaudeSDKClient for interactive tool usage
async with ClaudeSDKClient(options=options) as client:
await client.query("What's 123 * 456?")
# Process calculation response
async for message in client.receive_response():
if isinstance(message, AssistantMessage):
for block in message.content:
if isinstance(block, TextBlock):
print(f"Calculation: {block.text}")
# Follow up with time query
await client.query("What time is it now?")
async for message in client.receive_response():
if isinstance(message, AssistantMessage):
for block in message.content:
if isinstance(block, TextBlock):
print(f"Time: {block.text}")
asyncio.run(main())
Sandbox Configuration
SandboxSettings
Configuration for sandbox behavior. Use this to enable command sandboxing and configure network restrictions programmatically.
class SandboxSettings(TypedDict, total=False):
enabled: bool
autoAllowBashIfSandboxed: bool
excludedCommands: list[str]
allowUnsandboxedCommands: bool
network: SandboxNetworkConfig
ignoreViolations: SandboxIgnoreViolations
enableWeakerNestedSandbox: bool
| Property | Type | Default | Description |
|---|
enabled | bool | False | Enable sandbox mode for command execution |
autoAllowBashIfSandboxed | bool | True | Auto-approve bash commands when sandbox is enabled |
excludedCommands | list[str] | [] | Commands that always bypass sandbox restrictions (e.g., ["docker"]). These run unsandboxed automatically without model involvement |
allowUnsandboxedCommands | bool | True | Allow the model to request running commands outside the sandbox. When True, the model can set dangerouslyDisableSandbox in tool input, which falls back to the permissions system |
network | SandboxNetworkConfig | None | Network-specific sandbox configuration |
ignoreViolations | SandboxIgnoreViolations | None | Configure which sandbox violations to ignore |
enableWeakerNestedSandbox | bool | False | Enable a weaker nested sandbox for compatibility |
Filesystem and network access restrictions are NOT configured via sandbox settings. Instead, they are derived from permission rules:
- Filesystem read restrictions: Read deny rules
- Filesystem write restrictions: Edit allow/deny rules
- Network restrictions: WebFetch allow/deny rules
Use sandbox settings for command execution sandboxing, and permission rules for filesystem and network access control.
Example usage
from claude_agent_sdk import query, ClaudeAgentOptions, SandboxSettings
sandbox_settings: SandboxSettings = {
"enabled": True,
"autoAllowBashIfSandboxed": True,
"network": {"allowLocalBinding": True},
}
async for message in query(
prompt="Build and test my project",
options=ClaudeAgentOptions(sandbox=sandbox_settings),
):
print(message)
Unix socket security: The allowUnixSockets option can grant access to powerful system services. For example, allowing /var/run/docker.sock effectively grants full host system access through the Docker API, bypassing sandbox isolation. Only allow Unix sockets that are strictly necessary and understand the security implications of each.
SandboxNetworkConfig
Network-specific configuration for sandbox mode.
class SandboxNetworkConfig(TypedDict, total=False):
allowLocalBinding: bool
allowUnixSockets: list[str]
allowAllUnixSockets: bool
httpProxyPort: int
socksProxyPort: int
| Property | Type | Default | Description |
|---|
allowLocalBinding | bool | False | Allow processes to bind to local ports (e.g., for dev servers) |
allowUnixSockets | list[str] | [] | Unix socket paths that processes can access (e.g., Docker socket) |
allowAllUnixSockets | bool | False | Allow access to all Unix sockets |
httpProxyPort | int | None | HTTP proxy port for network requests |
socksProxyPort | int | None | SOCKS proxy port for network requests |
SandboxIgnoreViolations
Configuration for ignoring specific sandbox violations.
class SandboxIgnoreViolations(TypedDict, total=False):
file: list[str]
network: list[str]
| Property | Type | Default | Description |
|---|
file | list[str] | [] | File path patterns to ignore violations for |
network | list[str] | [] | Network patterns to ignore violations for |
Permissions Fallback for Unsandboxed Commands
When allowUnsandboxedCommands is enabled, the model can request to run commands outside the sandbox by setting dangerouslyDisableSandbox: True in the tool input. These requests fall back to the existing permissions system, meaning your can_use_tool handler will be invoked, allowing you to implement custom authorization logic.
excludedCommands vs allowUnsandboxedCommands:
excludedCommands: A static list of commands that always bypass the sandbox automatically (e.g., ["docker"]). The model has no control over this.
allowUnsandboxedCommands: Lets the model decide at runtime whether to request unsandboxed execution by setting dangerouslyDisableSandbox: True in the tool input.
from claude_agent_sdk import (
query,
ClaudeAgentOptions,
HookMatcher,
PermissionResultAllow,
PermissionResultDeny,
ToolPermissionContext,
)
async def can_use_tool(
tool: str, input: dict, context: ToolPermissionContext
) -> PermissionResultAllow | PermissionResultDeny:
# Check if the model is requesting to bypass the sandbox
if tool == "Bash" and input.get("dangerouslyDisableSandbox"):
# The model is requesting to run this command outside the sandbox
print(f"Unsandboxed command requested: {input.get('command')}")
if is_command_authorized(input.get("command")):
return PermissionResultAllow()
return PermissionResultDeny(
message="Command not authorized for unsandboxed execution"
)
return PermissionResultAllow()
# Required: dummy hook keeps the stream open for can_use_tool
async def dummy_hook(input_data, tool_use_id, context):
return {"continue_": True}
async def prompt_stream():
yield {
"type": "user",
"message": {"role": "user", "content": "Deploy my application"},
}
async def main():
async for message in query(
prompt=prompt_stream(),
options=ClaudeAgentOptions(
sandbox={
"enabled": True,
"allowUnsandboxedCommands": True, # Model can request unsandboxed execution
},
permission_mode="default",
can_use_tool=can_use_tool,
hooks={"PreToolUse": [HookMatcher(matcher=None, hooks=[dummy_hook])]},
),
):
print(message)
This pattern enables you to:
- Audit model requests: Log when the model requests unsandboxed execution
- Implement allowlists: Only permit specific commands to run unsandboxed
- Add approval workflows: Require explicit authorization for privileged operations
Commands running with dangerouslyDisableSandbox: True have full system access. Ensure your can_use_tool handler validates these requests carefully.If permission_mode is set to bypassPermissions and allow_unsandboxed_commands is enabled, the model can autonomously execute commands outside the sandbox without any approval prompts. This combination effectively allows the model to escape sandbox isolation silently.
See also