deep-agents-orchestration

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• SKILL.md

SKILL.md (first lines)

<overview>
Deep Agents include three orchestration capabilities:

1. **SubAgentMiddleware**: Delegate work via `task` tool to specialized agents
2. **TodoListMiddleware**: Plan and track tasks via `write_todos` tool
3. **HumanInTheLoopMiddleware**: Require approval before sensitive operations

All three are automatically included in `create_deep_agent()`.
</overview>

---

## Subagents (Task Delegation)

<when-to-use-subagents>

| Use Subagents When | Use Main Agent When |
|-------------------|-------------------|
| Task needs specialized tools | General-purpose tools sufficient |
| Want to isolate complex work | Single-step operation |
| Need clean context for main agent | Context bloat acceptable |

</when-to-use-subagents>

<how-subagents-work>
Main agent has `task` tool -> creates fresh subagent -> subagent executes autonomously -> returns final report.

**Default subagent**: "general-purpose" - automatically available with same tools/config as main agent.
</how-subagents-work>

<ex-custom-subagents>
<python>
Create a custom "researcher" subagent with specialized tools for academic paper search.
```python
from deepagents import create_deep_agent
from langchain.tools import tool

@tool
def search_papers(query: str) -> str:
    """Search academic papers."""
    return f"Found 10 papers about {query}"

agent = create_deep_agent(
    subagents=[
        {
            "name": "researcher",
            "description": "Conduct web research and compile findings",
            "system_prompt": "Search thoroughly, return concise summary",
            "tools": [search_papers],
        }
    ]
)

# Main agent delegates: task(agent="researcher", instruction="Research AI trends")
```
</python>
<typescript>
Create a custom "researcher" subagent with specialized tools for academic paper search.
```typescript
import { createDeepAgent } from "deepagents";
import { tool } from "@langchain/core/tools";
import { z } from "zod";

const searchPapers = tool(
  async ({ query }) => `Found 10 papers about ${query}`,
  { name: "search_papers", description: "Search papers", schema: z.object({ query: z.string() }) }
);

const agent = await createDeepAgent({
  subagents: [
    {
      name: "researcher",
      description: "Conduct web research and compile findings",
      systemPrompt: "Search thoroughly, return concise summary",
      tools: [searchPapers],
    }
  ]
});

// Main agent delegates: task(agent="researcher", instruction="Research AI trends")
```
</typescript>
</ex-custom-subagents>

<ex-subagent-with-hitl>
<python>
Configure a subagent with HITL approval for sensitive operations.
```python
from deepagents import create_deep_agent
from langgraph.checkpoint.memory import MemorySaver

agent = create_deep_agent(
    subagents=[
        {
            "name": "code-deployer",
            "description": "Deploy code to production",
            "system_prompt": "You deploy code after tests pass.",
            "tools": [run_tests, deploy_to_prod],
            "interrupt_on": {"deploy_to_prod": True},  # Require approval
        }
    ],
    checkpointer=MemorySaver()  # Required for interrupts
)
```
</python>
</ex-subagent-with-hitl>

<fix-subagents-are-stateless>
<python>
Subagents are stateless - provide complete instructions in a single call.
```python
# WRONG: Subagents don't remember previous calls
# task(agent='research', instruction='Find data')
# task(agent='research', instruction='What did you find?')  # Starts fresh!

# CORRECT: Complete instructions upfront
# task(agent='research', instruction='Find data on AI, save to /research/, return summary')
```
</python>

From Harmeet10000/skills, MIT.

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