---
title: Subagents
description: When a task is too broad for a single reasoning thread, or when parts of it can be done in parallel, the Lead Agent delegates work to **subagents**. A subagent is a self-contained agent invocation that receives a specific task, executes it, and returns the result.
---

import { Callout } from "nextra/components";

# Subagents

<Callout type="info" emoji="👥">
  Subagents are focused workers that the Lead Agent delegates subtasks to. They
  run with isolated context, keeping the main conversation clean while handling
  parallel or specialized work.
</Callout>

When a task is too broad for a single reasoning thread, or when parts of it can be done in parallel, the Lead Agent delegates work to **subagents**. A subagent is a self-contained agent invocation that receives a specific task, executes it, and returns the result.

## Why subagents matter

Subagents solve two key problems in long-horizon workflows:

1. **Context isolation**: a subagent only sees the information it needs for its piece of the task, not the entire parent conversation. This keeps each agent's working context focused and tractable.
2. **Parallelism**: multiple subagents can run concurrently, allowing independent parts of a task (e.g., researching multiple topics simultaneously) to be processed in parallel.

## Built-in subagents

DeerFlow ships with two built-in subagents:

### general-purpose

A general-purpose reasoning and execution agent. Suitable for delegating complex subtasks that require multi-step reasoning, web search, file operations, and artifact production.

- **Default timeout**: 900 seconds (15 minutes)
- **Default max turns**: 160

### bash

A subagent specialized for command-line task execution inside the sandbox. Suitable for scripting, data processing, file transformation, and environment setup tasks.

- **Default timeout**: 900 seconds (15 minutes)
- **Default max turns**: 80
- **Availability**: only exposed when the sandbox's `bash` tool is available (either `allow_host_bash: true` or a container sandbox is configured)

## Delegation flow

The Lead Agent delegates work to a subagent using the built-in `task` tool:

```
task(
  agent="general-purpose",
  task="Research the top 5 competitors of Acme Corp and summarize their pricing",
  context="Focus on B2B SaaS pricing models"
)
```

The runtime then:

1. Looks up the subagent configuration from the registry, applying any `config.yaml` overrides.
2. Creates a new agent invocation with the subagent's own prompt and tools.
3. Runs the subagent to completion (or until timeout / max turns).
4. Returns the subagent's final output to the Lead Agent as the tool result.

## Configuration

Subagent timeouts and max turns are controlled through the `subagents:` section in `config.yaml`:

```yaml
subagents:
  # Default timeout in seconds for all subagents (default: 900 = 15 minutes)
  timeout_seconds: 900

  # Optional: override max turns for all subagents
  # max_turns: 120

  # Optional: per-agent overrides
  agents:
    general-purpose:
      timeout_seconds: 1800 # 30 minutes for complex tasks
      max_turns: 160
    bash:
      timeout_seconds: 300 # 5 minutes for quick commands
      max_turns: 80
```

Per-agent overrides take priority over the global `timeout_seconds` and `max_turns` settings.

## Concurrency limits

The `SubagentLimitMiddleware` controls how many subagents the Lead Agent can invoke in parallel in a single turn. This is controlled through the per-request configuration:

- `subagent_enabled`: whether subagent delegation is active for this session
- `max_concurrent_subagents`: maximum parallel task calls in one turn (default: 3)

If the agent tries to call more subagents than the limit allows, the middleware trims the excess calls.

## ACP agents (external agents)

In addition to the built-in subagents, DeerFlow supports delegating to external agents through the **Agent Connect Protocol (ACP)**. ACP allows DeerFlow to invoke agents running as separate processes (including third-party CLI tools wrapped with an ACP adapter).

Configure ACP agents in `config.yaml`:

```yaml
acp_agents:
  claude_code:
    command: npx
    args: ["-y", "@zed-industries/claude-agent-acp"]
    description: Claude Code for implementation, refactoring, and debugging
    model: null
    # auto_approve_permissions: false
    # env:
    #   ANTHROPIC_API_KEY: $ANTHROPIC_API_KEY

  codex:
    command: npx
    args: ["-y", "@zed-industries/codex-acp"]
    description: Codex CLI for repository tasks and code generation
    model: null
```

The Lead Agent invokes ACP agents through the `invoke_acp_agent` built-in tool.

<Callout type="tip">
  ACP agents run as child processes managed by DeerFlow. They communicate over
  the ACP wire protocol. The standard CLI tools (like the plain `claude` or
  `codex` commands) are not ACP-compatible by default — use the adapter packages
  listed above or a compatible ACP wrapper.
</Callout>

## Custom agents as subagents

Custom agents created through the DeerFlow App UI can also be invoked as subagents using the `task` tool. When you specify `agent="my-custom-agent"`, the runtime loads that agent's configuration (skills, tool groups, model) and runs it as a subagent for the delegated task.

<Cards num={2}>
  <Cards.Card title="Sandbox" href="/docs/harness/sandbox" />
  <Cards.Card title="MCP Integration" href="/docs/harness/mcp" />
</Cards>
