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 subagents to isolate context for focused subtasks, run multiple analyses in parallel, and apply specialized instructions without bloating 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 agents parameter in your query() options (TypeScript, Python)
• Filesystem-based: define agents as markdown files in .claude/agents/ directories (see the Claude Code documentation)
• Built-in general-purpose: Claude can invoke the built-in general-purpose subagent at any time via the Task tool without you defining anything

This guide focuses on the programmatic approach, which is recommended for SDK applications.

When you define subagents, Claude decides whether to invoke them based on each subagent's description field. Write clear descriptions that explain when the subagent should be used, and Claude will automatically delegate appropriate tasks. You can also explicitly request a subagent by name in your prompt (e.g., "Use the code-reviewer agent to...").

Benefits of using subagents

Context management

Subagents maintain separate context from the main agent, preventing information overload and keeping interactions focused. This isolation ensures that specialized tasks don't pollute the main conversation context with irrelevant details.

Example: a research-assistant subagent can explore dozens of files and documentation pages without cluttering the main conversation with all the intermediate search results, returning only the relevant findings.

Parallelization

Multiple subagents can run concurrently, dramatically speeding up complex workflows.

Example: during a code review, you can run style-checker, security-scanner, and test-coverage subagents simultaneously, reducing review time from minutes to seconds.

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.

Creating subagents

Programmatic definition (recommended)

Define subagents directly in your code using the agents parameter. This example creates two subagents: a code reviewer with read-only access and a test runner that can execute commands. The Task tool must be included in allowedTools since Claude invokes subagents through the Task tool.

async def main(): async for message in query( prompt="Review the authentication module for security issues", options=ClaudeAgentOptions(

Task tool is required for subagent invocation

allowed_tools=["Read", "Grep", "Glob", "Task"], 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())

```typescript TypeScript
import { query } from '@anthropic-ai/claude-agent-sdk';

for await (const message of query({
  prompt: "Review the authentication module for security issues",
  options: {
    // Task tool is required for subagent invocation
    allowedTools: ['Read', 'Grep', 'Glob', 'Task'],
    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

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.

Invoking 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.

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", "Task"], agents={

Call the factory with your desired configuration

"security-reviewer": create_security_agent("strict") } ) ): if hasattr(message, "result"): print(message.result)

asyncio.run(main())

```typescript TypeScript
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', 'Task'],
    agents: {
      // Call the factory with your desired configuration
      'security-reviewer': createSecurityAgent('strict')
    }
  }
})) {
  if ('result' in message) console.log(message.result);
}

Detecting subagent invocation

Subagents are invoked via the Task tool. To detect when a subagent is invoked, check for tool_use blocks with name: "Task". Messages from within a subagent's context include a parent_tool_use_id field.

This example iterates through streamed messages, logging when a subagent is invoked and when subsequent messages originate from within that subagent's execution context.

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", "Task"], agents={ "code-reviewer": AgentDefinition( description="Expert code reviewer.", prompt="Analyze code quality and suggest improvements.", tools=["Read", "Glob", "Grep"] ) } ) ):

Check for subagent invocation in message content

if hasattr(message, 'content') and message.content: for block in message.content: if getattr(block, 'type', None) == 'tool_use' and block.name == 'Task': 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())

```typescript TypeScript
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", "Task"],
    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 in message content
  for (const block of msg.message?.content ?? []) {
    if (block.type === "tool_use" && block.name === "Task") {
      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);
  }
}

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 cannot modify files or run commands.

async def main(): async for message in query( prompt="Analyze the architecture of this codebase", options=ClaudeAgentOptions( allowed_tools=["Read", "Grep", "Glob", "Task"], 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())

```typescript TypeScript
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', 'Task'],
    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

Troubleshooting

Claude not delegating to subagents

If Claude completes tasks directly instead of delegating to your subagent:

1. Include the Task tool: subagents are invoked via the Task tool, so it must be in allowedTools
2. Use explicit prompting: mention the subagent by name in your prompt (e.g., "Use the code-reviewer agent to...")
3. Write a clear description: explain exactly when the subagent should be used so Claude can match tasks appropriately

Filesystem-based agents not loading

Agents defined in .claude/agents/ are loaded at startup only. If you create a new agent file while Claude Code is running, restart the session to load it.

Windows: long prompt failures

On Windows, subagents with very long prompts may fail due to command line length limits (8191 chars). Keep prompts concise or use filesystem-based agents for complex instructions.