deep-agents-orchestration

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")

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
)

# 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')

// WRONG: Subagents don't remember previous calls
// task research: Find data
// task research: What did you find?  // Starts fresh!
 
// CORRECT: Complete instructions upfront
// task research: Find data on AI, save to /research/, return summary

# WRONG: Custom subagent won't have main agent's skills
agent = create_deep_agent(
    skills=["/main-skills/"],
    subagents=[{"name": "helper", ...}]  # No skills inherited
)
 
# CORRECT: Provide skills explicitly (general-purpose subagent DOES inherit)
agent = create_deep_agent(
    skills=["/main-skills/"],
    subagents=[{"name": "helper", "skills": ["/helper-skills/"], ...}]
)

write_todos(todos: list[dict]) -> None

from deepagents import create_deep_agent
 
agent = create_deep_agent()  # TodoListMiddleware included by default
 
result = agent.invoke({
    "messages": [{"role": "user", "content": "Create a REST API: design models, implement CRUD, add auth, write tests"}]
}, config={"configurable": {"thread_id": "session-1"}})
 
# Agent's planning via write_todos:
# [
#   {"content": "Design data models", "status": "in_progress"},
#   {"content": "Implement CRUD endpoints", "status": "pending"},
#   {"content": "Add authentication", "status": "pending"},
#   {"content": "Write tests", "status": "pending"}
# ]

import { createDeepAgent } from "deepagents";
 
const agent = await createDeepAgent();  // TodoListMiddleware included
 
const result = await agent.invoke({
  messages: [{ role: "user", content: "Create a REST API: design models, implement CRUD, add auth, write tests" }]
}, { configurable: { thread_id: "session-1" } });

result = agent.invoke({...}, config={"configurable": {"thread_id": "session-1"}})
 
# Access todo list from final state
todos = result.get("todos", [])
for todo in todos:
    print(f"[{todo['status']}] {todo['content']}")

# WRONG: Fresh state each time without thread_id
agent.invoke({"messages": [...]})
 
# CORRECT: Use thread_id
config = {"configurable": {"thread_id": "user-session"}}
agent.invoke({"messages": [...]}, config=config)  # Todos preserved

from deepagents import create_deep_agent
from langgraph.checkpoint.memory import MemorySaver
 
agent = create_deep_agent(
    interrupt_on={
        "write_file": True,  # All decisions allowed
        "execute_sql": {"allowed_decisions": ["approve", "reject"]},
        "read_file": False,  # No interrupts
    },
    checkpointer=MemorySaver()  # REQUIRED for interrupts
)

import { createDeepAgent } from "deepagents";
import { MemorySaver } from "@langchain/langgraph";
 
const agent = await createDeepAgent({
  interruptOn: {
    write_file: true,
    execute_sql: { allowedDecisions: ["approve", "reject"] },
    read_file: false,
  },
  checkpointer: new MemorySaver()  // REQUIRED
});

from deepagents import create_deep_agent
from langgraph.checkpoint.memory import MemorySaver
from langgraph.types import Command
 
agent = create_deep_agent(
    interrupt_on={"write_file": True},
    checkpointer=MemorySaver()
)
 
config = {"configurable": {"thread_id": "session-1"}}
 
# Step 1: Agent proposes write_file - execution pauses
result = agent.invoke({
    "messages": [{"role": "user", "content": "Write config to /prod.yaml"}]
}, config=config)
 
# Step 2: Check for interrupts
state = agent.get_state(config)
if state.next:
    print(f"Pending action")
 
# Step 3: Approve and resume
result = agent.invoke(Command(resume={"decisions": [{"type": "approve"}]}), config=config)

import { createDeepAgent } from "deepagents";
import { MemorySaver, Command } from "@langchain/langgraph";
 
const agent = await createDeepAgent({
  interruptOn: { write_file: true },
  checkpointer: new MemorySaver()
});
 
const config = { configurable: { thread_id: "session-1" } };
 
// Step 1: Agent proposes write_file - execution pauses
let result = await agent.invoke({
  messages: [{ role: "user", content: "Write config to /prod.yaml" }]
}, config);
 
// Step 2: Check for interrupts
const state = await agent.getState(config);
if (state.next) {
  console.log("Pending action");
}
 
// Step 3: Approve and resume
result = await agent.invoke(
  new Command({ resume: { decisions: [{ type: "approve" }] } }), config
);

result = agent.invoke(
    Command(resume={"decisions": [{"type": "reject", "message": "Run tests first"}]}),
    config=config,
)

const result = await agent.invoke(
  new Command({ resume: { decisions: [{ type: "reject", message: "Run tests first" }] } }),
  config,
);

result = agent.invoke(
    Command(resume={"decisions": [{
        "type": "edit",
        "edited_action": {
            "name": "execute_sql",
            "args": {"query": "DELETE FROM users WHERE last_login < '2020-01-01' LIMIT 100"},
        },
    }]}),
    config=config,
)

# WRONG
agent = create_deep_agent(interrupt_on={"write_file": True})
 
# CORRECT
agent = create_deep_agent(interrupt_on={"write_file": True}, checkpointer=MemorySaver())

// WRONG
const agent = await createDeepAgent({ interruptOn: { write_file: true } });
 
// CORRECT
const agent = await createDeepAgent({ interruptOn: { write_file: true }, checkpointer: new MemorySaver() });

# WRONG: Can't resume without thread_id
agent.invoke({"messages": [...]})
 
# CORRECT
config = {"configurable": {"thread_id": "session-1"}}
agent.invoke({...}, config=config)
# Resume with Command using same config
agent.invoke(Command(resume={"decisions": [{"type": "approve"}]}), config=config)

// WRONG: Can't resume without thread_id
await agent.invoke({ messages: [...] });
 
// CORRECT
const config = { configurable: { thread_id: "session-1" } };
await agent.invoke({ messages: [...] }, config);
// Resume with Command using same config
await agent.invoke(new Command({ resume: { decisions: [{ type: "approve" }] } }), config);

result = agent.invoke({...}, config=config)       # Step 1: triggers interrupt
if "__interrupt__" in result:                      # Step 2: check for interrupt
    result = agent.invoke(                         # Step 3: resume
        Command(resume={"decisions": [{"type": "approve"}]}),
        config=config,
    )