Subagents in the SDK
Define and invoke subagents to isolate context, run tasks in parallel, and apply specialized instructions in your Claude Agent SDK applications.
Subagents are separate agent instances that your main agent can spawn to handle focused subtasks. Use them to isolate context, run multiple analyses in parallel, and apply specialized instructions without adding to the main agent's prompt.
This guide explains how to define and use subagents in the SDK using the agents parameter.
Overview
You can create subagents in three ways:
- Programmatically: use the
agentsparameter in yourquery()options. See the TypeScript and Python references - Filesystem-based: define agents as markdown files in
.claude/agents/directories. See defining subagents as files - Built-in general-purpose: Claude can invoke the built-in
general-purposesubagent at any time via the Agent tool without you defining anything
This guide focuses on the programmatic approach, which is recommended for SDK applications.
When you define subagents, Claude determines whether to invoke them based on each subagent's description field. Write clear descriptions that explain when to use the subagent, and Claude automatically delegates appropriate tasks. You can also explicitly request a subagent by name in your prompt, for example "Use the code-reviewer agent to...".
Benefits of using subagents
Context isolation
Each subagent runs in its own fresh conversation. Intermediate tool calls and results stay inside the subagent; only its final message returns to the parent. See What subagents inherit for exactly what's in the subagent's context.
Example: a research-assistant subagent can explore dozens of files without any of that content accumulating in the main conversation. The parent receives a concise summary, not every file the subagent read.
Parallelization
Multiple subagents can run concurrently, so independent subtasks finish in the time of the slowest one rather than the sum of all of them.
Example: during a code review, you can run style-checker, security-scanner, and test-coverage subagents simultaneously instead of sequentially.
Specialized instructions and knowledge
Each subagent can have tailored system prompts with specific expertise, best practices, and constraints.
Example: a database-migration subagent can have detailed knowledge about SQL best practices, rollback strategies, and data integrity checks that would be unnecessary noise in the main agent's instructions.
Tool restrictions
Subagents can be limited to specific tools, reducing the risk of unintended actions.
Example: a doc-reviewer subagent might only have access to Read and Grep tools, ensuring it can analyze but never accidentally modify your documentation files.
Create subagents
Programmatic definition (recommended)
Define subagents directly in your code using the agents parameter. Claude invokes subagents through the Agent tool, so include Agent in allowedTools to auto-approve subagent invocations without a permission prompt.
Most examples on this page print only the final result. To confirm that Claude delegated to a subagent rather than answering directly, see Detect subagent invocation.
This example creates two subagents: a code reviewer with read-only access and a test runner that can execute commands.
import asyncio
from claude_agent_sdk import query, ClaudeAgentOptions, AgentDefinition
async def main():
async for message in query(
prompt="Review the authentication module for security issues",
options=ClaudeAgentOptions(
# Auto-approve these tools, including Agent for subagent invocation
allowed_tools=["Read", "Grep", "Glob", "Agent"],
agents={
"code-reviewer": AgentDefinition(
# description tells Claude when to use this subagent
description="Expert code review specialist. Use for quality, security, and maintainability reviews.",
# prompt defines the subagent's behavior and expertise
prompt="""You are a code review specialist with expertise in security, performance, and best practices.
When reviewing code:
- Identify security vulnerabilities
- Check for performance issues
- Verify adherence to coding standards
- Suggest specific improvements
Be thorough but concise in your feedback.""",
# tools restricts what the subagent can do (read-only here)
tools=["Read", "Grep", "Glob"],
# model overrides the default model for this subagent
model="sonnet",
),
"test-runner": AgentDefinition(
description="Runs and analyzes test suites. Use for test execution and coverage analysis.",
prompt="""You are a test execution specialist. Run tests and provide clear analysis of results.
Focus on:
- Running test commands
- Analyzing test output
- Identifying failing tests
- Suggesting fixes for failures""",
# Bash access lets this subagent run test commands
tools=["Bash", "Read", "Grep"],
),
},
),
):
if hasattr(message, "result"):
print(message.result)
asyncio.run(main())
import { query } from "@anthropic-ai/claude-agent-sdk";
for await (const message of query({
prompt: "Review the authentication module for security issues",
options: {
// Auto-approve these tools, including Agent for subagent invocation
allowedTools: ["Read", "Grep", "Glob", "Agent"],
agents: {
"code-reviewer": {
// description tells Claude when to use this subagent
description:
"Expert code review specialist. Use for quality, security, and maintainability reviews.",
// prompt defines the subagent's behavior and expertise
prompt: `You are a code review specialist with expertise in security, performance, and best practices.
When reviewing code:
- Identify security vulnerabilities
- Check for performance issues
- Verify adherence to coding standards
- Suggest specific improvements
Be thorough but concise in your feedback.`,
// tools restricts what the subagent can do (read-only here)
tools: ["Read", "Grep", "Glob"],
// model overrides the default model for this subagent
model: "sonnet"
},
"test-runner": {
description:
"Runs and analyzes test suites. Use for test execution and coverage analysis.",
prompt: `You are a test execution specialist. Run tests and provide clear analysis of results.
Focus on:
- Running test commands
- Analyzing test output
- Identifying failing tests
- Suggesting fixes for failures`,
// Bash access lets this subagent run test commands
tools: ["Bash", "Read", "Grep"]
}
}
}
})) {
if ("result" in message) console.log(message.result);
}
AgentDefinition configuration
| Field | Type | Required | Description |
|---|---|---|---|
description |
string |
Yes | Natural language description of when to use this agent |
prompt |
string |
Yes | The agent's system prompt defining its role and behavior |
tools |
string[] |
No | Array of allowed tool names. If omitted, inherits all tools |
disallowedTools |
string[] |
No | Array of tool names to remove from the agent's tool set. MCP server-level patterns are also accepted: mcp__server or mcp__server__* removes every tool from that server, and mcp__* removes every MCP tool from any server |
model |
string |
No | Model override for this agent. Accepts an alias such as 'fable', 'opus', 'sonnet', 'haiku', 'inherit', or a full model ID. Defaults to main model if omitted |
skills |
string[] |
No | List of skill names to preload into the agent's context at startup. Unlisted skills remain invocable through the Skill tool |
memory |
'user' | 'project' | 'local' |
No | Memory source for this agent |
mcpServers |
(string | object)[] |
No | MCP servers available to this agent, by name or inline config |
initialPrompt |
string |
No | Auto-submitted as the first user turn when this agent runs as the main thread agent. Ignored when the agent is invoked as a subagent |
maxTurns |
number |
No | Maximum number of agentic turns before the agent stops |
background |
boolean |
No | Run this agent as a non-blocking background task when invoked |
effort |
'low' | 'medium' | 'high' | 'xhigh' | 'max' | number |
No | Reasoning effort level for this agent |
permissionMode |
PermissionMode |
No | Permission mode for tool execution within this agent |
In the Python SDK, multi-word field names such as disallowedTools and mcpServers keep their camelCase spelling to match the wire format rather than following Python's snake_case convention. See the AgentDefinition reference for details.
Two subagent behaviors changed in Claude Code v2.1.198:
- Subagents run in the background by default. An Agent tool call that omits the
run_in_backgroundinput launches a background subagent, and Claude setsrun_in_background: falsewhen it needs the result before continuing. Before v2.1.198, omittingrun_in_backgroundran the subagent synchronously. Set thebackgroundfield totrueto force background execution for a specific agent regardless of what Claude requests. - A subagent inherits the main session's extended thinking configuration. On earlier versions, extended thinking is disabled inside subagents regardless of the main session's setting.
{/* min-version: 2.1.172 */}As of Claude Code v2.1.172, subagents can spawn their own subagents. A subagent five levels below the main agent can't spawn further subagents, regardless of whether it runs in the foreground or background. To prevent a subagent from spawning others, omit Agent from its tools array or add it to disallowedTools. See nested subagents for the full depth rules.
Filesystem-based definition (alternative)
You can also define subagents as markdown files in .claude/agents/ directories. See the Claude Code subagents documentation for details on this approach. Programmatically defined agents take precedence over filesystem-based agents with the same name.
Even without defining custom subagents, Claude can spawn the built-in general-purpose subagent. This is useful for delegating research or exploration tasks without creating specialized agents. Include Agent in allowedTools so these invocations auto-approve without a permission prompt.
What subagents inherit
A subagent's context window starts fresh, with no parent conversation, but isn't empty. The only channel from parent to subagent is the Agent tool's prompt string, so include any file paths, error messages, or decisions the subagent needs directly in that prompt.
| The subagent receives | The subagent doesn't receive |
|---|---|
Its own system prompt (AgentDefinition.prompt) and the Agent tool's prompt |
The parent's conversation history or tool results |
Project CLAUDE.md (loaded via settingSources) |
Preloaded skill content, unless listed in AgentDefinition.skills |
Tool definitions (inherited from parent, or the subset in tools) |
The parent's system prompt |
The parent receives the subagent's final message verbatim as the Agent tool result, but may summarize it in its own response. To preserve subagent output verbatim in the user-facing response, include an instruction to do so in the prompt or systemPrompt option you pass to the main query() call.
{/* min-version: 2.1.199 /}An API error that ends the subagent early, such as a rate limit, is never delivered as its result. If a rate limit, overload, or server error cuts off a foreground subagent that already produced text output, the Agent tool returns that partial output with a note that the subagent didn't finish. {/ min-version: 2.1.200 */}A subagent that produced nothing, or whose only output was tool calls with no text, fails with an error message, Agent terminated early due to an API error, followed by the error detail. See API errors in subagents for the foreground and background behavior.
This partial-output handling requires Claude Code v2.1.199 or later. In v2.1.199, a rate limit, overload, or server error left the tool-calls-only shape with an empty partial result containing only the cutoff note.
Invoke subagents
Automatic invocation
Claude automatically decides when to invoke subagents based on the task and each subagent's description. For example, if you define a performance-optimizer subagent with the description "Performance optimization specialist for query tuning", Claude will invoke it when your prompt mentions optimizing queries.
Write clear, specific descriptions so Claude can match tasks to the right subagent.
Explicit invocation
To guarantee Claude uses a specific subagent, mention it by name in your prompt:
"Use the code-reviewer agent to check the authentication module"
This bypasses automatic matching and directly invokes the named subagent.
Dynamic agent configuration
You can create agent definitions dynamically based on runtime conditions. This example creates a security reviewer with different strictness levels, using a more powerful model for strict reviews.
import asyncio
from claude_agent_sdk import query, ClaudeAgentOptions, AgentDefinition
# Factory function that returns an AgentDefinition
# This pattern lets you customize agents based on runtime conditions
def create_security_agent(security_level: str) -> AgentDefinition:
is_strict = security_level == "strict"
return AgentDefinition(
description="Security code reviewer",
# Customize the prompt based on strictness level
prompt=f"You are a {'strict' if is_strict else 'balanced'} security reviewer...",
tools=["Read", "Grep", "Glob"],
# Key insight: use a more capable model for high-stakes reviews
model="opus" if is_strict else "sonnet",
)
async def main():
# The agent is created at query time, so each request can use different settings
async for message in query(
prompt="Review this PR for security issues",
options=ClaudeAgentOptions(
allowed_tools=["Read", "Grep", "Glob", "Agent"],
agents={
# Call the factory with your desired configuration
"security-reviewer": create_security_agent("strict")
},
),
):
if hasattr(message, "result"):
print(message.result)
asyncio.run(main())
import { query, type AgentDefinition } from "@anthropic-ai/claude-agent-sdk";
// Factory function that returns an AgentDefinition
// This pattern lets you customize agents based on runtime conditions
function createSecurityAgent(securityLevel: "basic" | "strict"): AgentDefinition {
const isStrict = securityLevel === "strict";
return {
description: "Security code reviewer",
// Customize the prompt based on strictness level
prompt: `You are a ${isStrict ? "strict" : "balanced"} security reviewer...`,
tools: ["Read", "Grep", "Glob"],
// Key insight: use a more capable model for high-stakes reviews
model: isStrict ? "opus" : "sonnet"
};
}
// The agent is created at query time, so each request can use different settings
for await (const message of query({
prompt: "Review this PR for security issues",
options: {
allowedTools: ["Read", "Grep", "Glob", "Agent"],
agents: {
// Call the factory with your desired configuration
"security-reviewer": createSecurityAgent("strict")
}
}
})) {
if ("result" in message) console.log(message.result);
}
Detect subagent invocation
Claude invokes subagents through the Agent tool. To detect when a subagent is invoked, check for tool_use blocks where name is "Agent". Messages from within a subagent's context include a parent_tool_use_id field.
The tool name was renamed from "Task" to "Agent" in Claude Code v2.1.63. Current SDK releases emit "Agent" in tool_use blocks but still use "Task" in the system:init tools list and in result.permission_denials[].tool_name. Checking both values in block.name ensures compatibility across SDK versions.
The message structure differs between SDKs. In Python, you access content blocks directly via message.content. In TypeScript, SDKAssistantMessage wraps the Claude API message, so you access content via message.message.content.
This example iterates through streamed messages, logging when a subagent is invoked and when subsequent messages originate from within that subagent's execution context.
import asyncio
from claude_agent_sdk import query, ClaudeAgentOptions, AgentDefinition, ToolUseBlock
async def main():
async for message in query(
prompt="Use the code-reviewer agent to review this codebase",
options=ClaudeAgentOptions(
allowed_tools=["Read", "Glob", "Grep", "Agent"],
agents={
"code-reviewer": AgentDefinition(
description="Expert code reviewer.",
prompt="Analyze code quality and suggest improvements.",
tools=["Read", "Glob", "Grep"],
)
},
),
):
# Check for subagent invocation. Match both names: older SDK
# versions emitted "Task", current versions emit "Agent".
if hasattr(message, "content") and message.content:
for block in message.content:
if isinstance(block, ToolUseBlock) and block.name in (
"Task",
"Agent",
):
print(f"Subagent invoked: {block.input.get('subagent_type')}")
# Check if this message is from within a subagent's context
if hasattr(message, "parent_tool_use_id") and message.parent_tool_use_id:
print(" (running inside subagent)")
if hasattr(message, "result"):
print(message.result)
asyncio.run(main())
import { query } from "@anthropic-ai/claude-agent-sdk";
for await (const message of query({
prompt: "Use the code-reviewer agent to review this codebase",
options: {
allowedTools: ["Read", "Glob", "Grep", "Agent"],
agents: {
"code-reviewer": {
description: "Expert code reviewer.",
prompt: "Analyze code quality and suggest improvements.",
tools: ["Read", "Glob", "Grep"]
}
}
}
})) {
const msg = message as any;
// Check for subagent invocation. Match both names: older SDK versions
// emitted "Task", current versions emit "Agent".
for (const block of msg.message?.content ?? []) {
if (block.type === "tool_use" && (block.name === "Task" || block.name === "Agent")) {
console.log(`Subagent invoked: ${block.input.subagent_type}`);
}
}
// Check if this message is from within a subagent's context
if (msg.parent_tool_use_id) {
console.log(" (running inside subagent)");
}
if ("result" in message) {
console.log(message.result);
}
}
Resume subagents
You can resume a subagent to continue where it left off rather than starting fresh. A resumed subagent retains its full conversation history, including all previous tool calls, results, and reasoning.
When a subagent completes, the Agent tool result includes a text block containing agentId: <id>. The built-in Explore and Plan agents are one-shot and don't return an agentId, so use a custom agent or general-purpose when you need to resume. To resume a subagent programmatically:
- Capture the session ID: extract
session_idfrom messages during the first query - Extract the agent ID: parse
agentIdfrom the Agent tool result text - Resume the session: pass
resume: sessionIdin the second query's options, and include the agent ID in your prompt
You must resume the same session to access the subagent's transcript. Each query() call starts a new session by default, so pass resume: sessionId to continue in the same session.
When using a custom agent, pass the same agent definition in the agents parameter for both queries.
The example below defines a custom endpoint-finder agent. The first query runs it and captures the session ID and agent ID from the Agent tool result, then the second query resumes the session to ask a follow-up question that requires context from the first analysis.
import asyncio
import re
from claude_agent_sdk import query, ClaudeAgentOptions, AgentDefinition, ToolResultBlock
AGENTS = {
"endpoint-finder": AgentDefinition(
description="Locates and catalogs API endpoints in a codebase.",
prompt="You find and document API endpoints. Report each endpoint's path, method, and handler.",
tools=["Read", "Grep", "Glob"],
)
}
def extract_agent_id(block: ToolResultBlock) -> str | None:
"""Extract agentId from an Agent tool result's text content."""
parts = block.content if isinstance(block.content, list) else [{"text": block.content}]
for part in parts:
if match := re.search(r"agentId:\s*([\w-]+)", part.get("text") or ""):
return match.group(1)
return None
async def main():
agent_id = None
session_id = None
# First invocation - run the endpoint-finder subagent
try:
async for message in query(
prompt="Use the endpoint-finder agent to find all API endpoints in this codebase",
options=ClaudeAgentOptions(allowed_tools=["Read", "Grep", "Glob", "Agent"], agents=AGENTS),
):
# Capture session_id from ResultMessage (needed to resume this session)
if hasattr(message, "session_id"):
session_id = message.session_id
# Search tool results for the agentId trailer
for block in getattr(message, "content", None) or []:
if isinstance(block, ToolResultBlock):
agent_id = extract_agent_id(block) or agent_id
# Print the final result
if hasattr(message, "result"):
print(message.result)
except Exception as error:
# A single-shot query() raises after yielding an error result,
# so session_id and agent_id have already been captured by the loop above.
print(f"Session ended with an error: {error}")
# Second invocation - resume and ask follow-up
if agent_id and session_id:
async for message in query(
prompt=f"Resume agent {agent_id} and list the top 3 most complex endpoints",
options=ClaudeAgentOptions(
allowed_tools=["Read", "Grep", "Glob", "Agent"], agents=AGENTS, resume=session_id
),
):
if hasattr(message, "result"):
print(message.result)
else:
print("No agentId found in the first query, so there is no subagent to resume.")
asyncio.run(main())
import { query, type SDKMessage } from "@anthropic-ai/claude-agent-sdk";
const agents = {
"endpoint-finder": {
description: "Locates and catalogs API endpoints in a codebase.",
prompt: "You find and document API endpoints. Report each endpoint's path, method, and handler.",
tools: ["Read", "Grep", "Glob"]
}
};
// Stringify content to search for agentId without traversing nested block types
function extractAgentId(message: SDKMessage): string | undefined {
if (message.type !== "assistant" && message.type !== "user") return undefined;
const content = JSON.stringify(message.message.content);
const match = content.match(/agentId:\s*([\w-]+)/);
return match?.[1];
}
let agentId: string | undefined;
let sessionId: string | undefined;
// First invocation - run the endpoint-finder subagent
try {
for await (const message of query({
prompt: "Use the endpoint-finder agent to find all API endpoints in this codebase",
options: { allowedTools: ["Read", "Grep", "Glob", "Agent"], agents }
})) {
// Capture session_id from ResultMessage (needed to resume this session)
if ("session_id" in message) sessionId = message.session_id;
// Search message content for the agentId (appears in Agent tool results)
const extractedId = extractAgentId(message);
if (extractedId) agentId = extractedId;
// Print the final result
if ("result" in message) console.log(message.result);
}
} catch (error) {
// A single-shot query() throws after yielding an error result,
// so sessionId and agentId have already been captured by the loop above.
console.error(`Session ended with an error: ${error}`);
}
// Second invocation - resume and ask follow-up
if (agentId && sessionId) {
for await (const message of query({
prompt: `Resume agent ${agentId} and list the top 3 most complex endpoints`,
options: { allowedTools: ["Read", "Grep", "Glob", "Agent"], agents, resume: sessionId }
})) {
if ("result" in message) console.log(message.result);
}
} else {
console.log("No agentId found in the first query, so there is no subagent to resume.");
}
Subagent transcripts persist independently of the main conversation:
- Main conversation compaction: when the main conversation compacts, subagent transcripts are unaffected. They're stored in separate files.
- Session persistence: subagent transcripts persist within their session. You can resume a subagent after restarting Claude Code by resuming the same session.
- Automatic cleanup: transcripts are cleaned up based on the
cleanupPeriodDayssetting, which defaults to 30 days.
Tool restrictions
Subagents can have restricted tool access via the tools field:
- Omit the field: agent inherits all available tools (default)
- Specify tools: agent can only use listed tools
This example creates a read-only analysis agent that can examine code but can't modify files or run commands.
import asyncio
from claude_agent_sdk import query, ClaudeAgentOptions, AgentDefinition
async def main():
async for message in query(
prompt="Analyze the architecture of this codebase",
options=ClaudeAgentOptions(
allowed_tools=["Read", "Grep", "Glob", "Agent"],
agents={
"code-analyzer": AgentDefinition(
description="Static code analysis and architecture review",
prompt="""You are a code architecture analyst. Analyze code structure,
identify patterns, and suggest improvements without making changes.""",
# Read-only tools: no Edit, Write, or Bash access
tools=["Read", "Grep", "Glob"],
)
},
),
):
if hasattr(message, "result"):
print(message.result)
asyncio.run(main())
import { query } from "@anthropic-ai/claude-agent-sdk";
for await (const message of query({
prompt: "Analyze the architecture of this codebase",
options: {
allowedTools: ["Read", "Grep", "Glob", "Agent"],
agents: {
"code-analyzer": {
description: "Static code analysis and architecture review",
prompt: `You are a code architecture analyst. Analyze code structure,
identify patterns, and suggest improvements without making changes.`,
// Read-only tools: no Edit, Write, or Bash access
tools: ["Read", "Grep", "Glob"]
}
}
}
})) {
if ("result" in message) console.log(message.result);
}
Common tool combinations
| Use case | Tools | Description |
|---|---|---|
| Read-only analysis | Read, Grep, Glob |
Can examine code but not modify or execute |
| Test execution | Bash, Read, Grep |
Can run commands and analyze output |
| Code modification | Read, Edit, Write, Grep, Glob |
Full read/write access without command execution |
| Full access | All tools | Inherits all tools from parent (omit tools field) |
Scale up with dynamic workflows
Subagents work well for a few delegated tasks per turn. For runs that coordinate dozens to hundreds of agents, use the Workflow tool, which moves the orchestration into a script the runtime executes outside the conversation context. See dynamic workflows for how workflows differ from turn-by-turn subagent delegation.
The Workflow tool is available in the TypeScript Agent SDK v0.3.149 and later. Include Workflow in allowedTools to auto-approve workflow runs. The tool input and output schemas are listed in the TypeScript reference.
Troubleshooting
Claude not delegating to subagents
If Claude completes tasks directly instead of delegating to your subagent:
- Check Agent invocations are approved: include
AgentinallowedToolsto auto-approve subagent calls. Without it, Agent invocations fall through to yourcanUseToolcallback or, indontAskmode, are denied - Use explicit prompting: mention the subagent by name in your prompt, for example "Use the code-reviewer agent to..."
- Write a clear description: explain exactly when to use the subagent so Claude can match tasks appropriately
Filesystem-based agents not loading
Claude Code watches ~/.claude/agents/ and .claude/agents/ and picks up a new or edited agent file within a few seconds, with no restart needed. If a definition never appears, work through these causes:
- New
agentsdirectory: the watcher covers only directories that existed when the session started, so the first file in a new directory needs a session restart. This is the most common cause. - Invalid frontmatter or a duplicate
name: check the file's YAML, and whether an existing agent already uses thename. --disable-slash-commands: sessions started with this flag don't watch these directories and always need a restart to load new files.- A programmatic agent with the same name:
agentspassed toquery()override a filesystem agent with the same name.
For the file format, see how to write subagent files.
Long prompt failures on Windows
On Windows, subagents with very long prompts may fail due to the command line length limit of 8191 characters. Keep prompts concise or use filesystem-based agents for complex instructions.
Related documentation
- Claude Code subagents: comprehensive subagent documentation including filesystem-based definitions
- Dynamic workflows: orchestrate many subagents from a script for jobs too large for one conversation
- SDK overview: getting started with the Claude Agent SDK