---
name: multi-agent-architect
description: "Design and optimize production-grade multi-agent systems with LangGraph, LangChain, and DeepAgents for complex AI workflows."
risk: safe
source: community
date_added: "2026-05-08"
metadata:
  category: ai-engineering
  source_repo: pravin-python/antigravity-awesome-skills
  source_type: community
  date_added: "2025-05-07"
  author: community
  tags: [langgraph, langchain, multi-agent, orchestration, deepagents, rag, tool-calling]
  tools: [claude, cursor, gemini]
  license: "MIT"
  license_source: "https://github.com/pravin-python/antigravity-awesome-skills/blob/main/LICENSE"
---


# Multi-Agent Architect & Updater Skill

## Overview

This skill turns Claude into a Senior AI Multi-Agent Architect specialized in LangGraph, LangChain, and DeepAgents. It provides structured workflows for creating and updating production-grade multi-agent systems — including supervisor agents, planners, researchers, coders, and memory-backed autonomous pipelines. Use it whenever you need to design, build, debug, or scale any multi-agent AI system.

If this skill adapts material from an external GitHub repository, declare both:

- `source_repo: owner/repo`
- `source_type: official` or `source_type: community`

## When to Use This Skill

- Use when you need to create a new agent or multi-agent workflow from scratch
- Use when working with LangGraph state graphs, nodes, edges, or conditional routing
- Use when the user asks about agent communication, memory systems, or tool-calling pipelines
- Use when debugging or optimizing an existing LangChain/LangGraph agent system
- Use when architecting supervisor, planner, research, coding, or validation agent roles
- Use when integrating DeepAgents with hierarchical planning and delegation

Inline code is a partial architecture sketch, not a production-ready or currently verified API integration. `bind_tools` only produces tool-call requests: a connected messages state, ToolNode route and return edge are required to execute them. The shown ToolNode is disconnected and must not be claimed to have researched or written anything. Redis history is not wired to the graph; use an authenticated tenant/session namespace and actual checkpoint/history integration. Protect the optional FastAPI endpoint with authentication, authorization, input/rate/time/token budgets and isolation. Increment and enforce a step budget before model calls; textual critique containing “bug” is not a test or reliable routing criterion.

## How It Works

### Step 1: Understand the Goal

Before writing any code, clarify:
- What is the **business objective** this agent system must achieve?
- What **agent roles** are needed (supervisor, planner, researcher, coder, validator)?
- What **tools** does each agent require?
- What **memory** strategy is needed (Redis, Vector DB, LangChain Memory)?
- What **communication protocol** connects agents (shared state, message passing)?

### Step 2: Define the State Schema

All agents share a typed state object passed through the graph:

```python
from typing import TypedDict

class AgentState(TypedDict):
    user_goal: str
    tasks: list[str]
    completed_tasks: list[str]
    next_agent: str
    context: dict
    step_count: int          # guards against infinite loops
    error: str | None
```

### Step 3: Define Agent Nodes

Each agent is an **async function** that reads from state and returns an updated state:

```python
import logging
from langchain_openai import ChatOpenAI

logger = logging.getLogger(__name__)

async def research_node(state: AgentState) -> AgentState:
    logger.info("research_node: starting")
    llm = ChatOpenAI(model="gpt-4o")
    result = await llm.bind_tools(research_tools).ainvoke(state["user_goal"])
    state["context"]["research"] = result.content
    state["next_agent"] = "coder"
    return state
```

### Step 4: Build the LangGraph

Wire nodes together with edges and conditional routing:

```python
from langgraph.graph import StateGraph, END
from langgraph.prebuilt import ToolNode

def build_graph() -> StateGraph:
    graph = StateGraph(AgentState)

    graph.add_node("supervisor", supervisor_node)
    graph.add_node("research",   research_node)
    graph.add_node("coder",      coding_node)
    graph.add_node("validator",  validation_node)
    graph.add_node("tools",      ToolNode(all_tools))

    graph.set_entry_point("supervisor")

    graph.add_conditional_edges(
        "supervisor",
        route_next,
        {"research": "research", "coder": "coder", "validator": "validator", "end": END}
    )

    graph.add_edge("research",  "supervisor")
    graph.add_edge("coder",     "validator")
    graph.add_edge("validator", "supervisor")

    return graph.compile()

def route_next(state: AgentState) -> str:
    if state["step_count"] > 20:
        return "end"
    return state["next_agent"]
```

### Step 5: Add Memory

```python
from langchain_community.chat_message_histories import RedisChatMessageHistory

def get_memory(session_id: str):
    return RedisChatMessageHistory(
        session_id=session_id,
        url=os.getenv("REDIS_URL"),
        ttl=3600
    )
```

### Step 6: Run the Graph

```python
async def run(user_goal: str, session_id: str):
    graph = build_graph()
    initial_state = AgentState(
        user_goal=user_goal,
        tasks=[],
        completed_tasks=[],
        next_agent="supervisor",
        context={},
        step_count=0,
        error=None,
    )
    return await graph.ainvoke(initial_state, config={"recursion_limit": 100})
```

### Step 7: Expose via FastAPI (optional)

```python
from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class RunRequest(BaseModel):
    goal: str
    session_id: str

@app.post("/run")
async def run_agent(req: RunRequest):
    result = await run(req.goal, req.session_id)
    return {"result": result}
```

---

## Updating an Existing Agent

When the user wants to update or debug an existing agent, structure the response as:

```
## Existing Issue
[Describe the current problem]

## Root Cause
[Identify why it's happening in the architecture]

## Proposed Update
[Outline the changes at architecture level]

## Updated Code
[Generate only the changed modules]

## Migration Notes
[What breaks, what's backward-compatible]

## Performance Impact
[Latency / token / memory delta]
```

---

## Standard Folder Structure

Always generate code in this layout:

```
multi_agent_system/
├── agents/          # One file per agent role
├── tools/           # Tool definitions and wrappers
├── memory/          # Redis, VectorDB, LangChain memory helpers
├── prompts/         # Prompt templates (one per agent)
├── workflows/       # High-level orchestration logic
├── graphs/          # LangGraph state + compiled graph definitions
├── api/             # FastAPI routes (optional)
├── configs/         # Config loader — no secrets in code
├── tests/           # Unit + integration tests per agent
└── main.py
```

---

## Examples

### Example 1: Research + Coding Multi-Agent Workflow

```python
# agents/research_agent.py
async def research_node(state: AgentState) -> AgentState:
    llm = ChatOpenAI(model="gpt-4o").bind_tools([web_search, rag_search])
    response = await llm.ainvoke(
        f"Research the following and return structured findings:\n{state['user_goal']}"
    )
    state["context"]["research"] = response.content
    state["next_agent"] = "coder"
    return state

# agents/coding_agent.py
async def coding_node(state: AgentState) -> AgentState:
    llm = ChatOpenAI(model="gpt-4o").bind_tools([python_repl, github_tool])
    response = await llm.ainvoke(
        f"Given this research:\n{state['context']['research']}\n\nWrite production Python code."
    )
    state["context"]["code"] = response.content
    state["next_agent"] = "validator"
    return state
```

### Example 2: Supervisor with Dynamic Delegation

```python
# agents/supervisor_agent.py
DELEGATION_PROMPT = """
You are a supervisor. Given the current state, decide the next agent.
Available agents: research, coder, validator, end.
Respond with ONLY the agent name.

Goal: {goal}
Completed: {completed}
Context keys available: {context}
"""

async def supervisor_node(state: AgentState) -> AgentState:
    state["step_count"] += 1
    llm = ChatOpenAI(model="gpt-4o")
    decision = await llm.ainvoke(
        DELEGATION_PROMPT.format(
            goal=state["user_goal"],
            completed=state["completed_tasks"],
            context=list(state["context"].keys()),
        )
    )
    next_agent = decision.content.strip().lower()
    # Validate against allowlist before setting
    allowed = {"research", "coder", "validator", "end"}
    state["next_agent"] = next_agent if next_agent in allowed else "end"
    return state
```

### Example 3: DeepAgents Reflection Loop

```python
async def reflection_node(state: AgentState) -> AgentState:
    llm = ChatOpenAI(model="gpt-4o")
    critique = await llm.ainvoke(
        f"Evaluate this output critically:\n{state['context'].get('code', '')}\n"
        "List any bugs, gaps, or improvements. Be concise."
    )
    state["context"]["critique"] = critique.content
    state["next_agent"] = "coder" if "bug" in critique.content.lower() else "end"
    return state
```

---

## Best Practices

- ✅ One agent = one responsibility — never combine planning + coding + testing in one node
- ✅ Use `TypedDict` for all state schemas — enables type checking and graph validation
- ✅ Bind only the tools each agent needs — reduces hallucinated tool calls
- ✅ Always add a `step_count` guard to prevent infinite routing loops
- ✅ Use `async`/`await` throughout — LangGraph supports async natively
- ✅ Store all secrets in environment variables loaded via `os.getenv()`
- ✅ Set TTLs on all Redis keys scoped to `session_id`
- ✅ Log at every node entry and tool call for observability
- ✅ Validate supervisor routing output against an allowlist of agent names
- ❌ Don't hardcode API keys, model names, or Redis URLs
- ❌ Don't share tool lists across agents that don't need them
- ❌ Don't skip error handling — tool failures and empty LLM responses are common
- ❌ Don't trust unvalidated LLM routing decisions — always check against an allowlist

---

## Limitations

- This skill does not replace environment-specific testing, load testing, or security review before production deployment.
- Generated LangGraph code targets the current stable API — always verify method signatures against your installed version (`pip show langgraph`).
- Stop and ask for clarification if the agent's goal, tool permissions, or routing logic is ambiguous before generating a full architecture.
- DeepAgents integration patterns assume the library is installed and configured in the target environment.

---

## Security & Safety Notes

- Never expose API keys in generated code. All secrets must use environment variables:
  ```python
  OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")   # ✅ correct
  leaked_openai_token = "[redacted API key]"       # ❌ never do this
  ```
- Always validate and sanitize user inputs before injecting them into agent prompts — treat all user input as untrusted.
- Add a permission layer before allowing agents to execute shell commands or write to filesystems.
- If generating a Python REPL tool node, document that it must only run in a sandboxed, isolated environment.
  <!-- security-allowlist: python_repl tool examples are for sandboxed execution environments only -->
- For production deployments, add rate-limit handling and exponential backoff on all LLM and external API calls.
- Scope all Redis session keys to `session_id` and set a TTL to prevent memory leaks across sessions.

---

## Common Pitfalls

- **Problem:** Agent loops indefinitely between supervisor and sub-agents  
  **Solution:** Add `step_count: int` to state; return `"end"` in `route_next()` when `step_count > N`

- **Problem:** Supervisor routes to a non-existent agent name  
  **Solution:** Validate the LLM's routing output against a hardcoded allowlist before setting `next_agent`

- **Problem:** Memory leaks across user sessions  
  **Solution:** Scope Redis keys to `session_id` and always set a TTL (`ttl=3600`)

- **Problem:** Tool results are ignored by the next agent  
  **Solution:** Always write tool output into `state["context"]` and confirm the next node reads it

- **Problem:** Agents share too many tools and hallucinate wrong tool calls  
  **Solution:** Use `.bind_tools([only_relevant_tools])` per agent instead of a global tool list

- **Problem:** Graph fails silently on API rate limits  
  **Solution:** Wrap LLM calls in retry logic with exponential backoff using `tenacity`

---

## Related Skills

- `@langchain-rag` - When you need retrieval-augmented generation pipelines specifically
- `@fastapi-backend` - When deploying agent systems as production REST APIs
- `@python-async` - When deepening async/await patterns used throughout agent nodes