langgraph-agents

<quick_start> State schema (foundation):

from typing import TypedDict, Annotated
from langgraph.graph import add_messages

class AgentState(TypedDict, total=False):
    messages: Annotated[list, add_messages]  # Auto-merge
    next_agent: str  # For handoffs

Pattern selection:

API choice: Graph API (explicit nodes/edges) vs Functional API (@entrypoint/@task decorators)

Key packages: pip install langchain langgraph langgraph-supervisor langgraph-swarm langchain-mcp-adapters </quick_start>

<success_criteria> Multi-agent system is successful when:

• State uses Annotated[..., add_messages] for proper message merging
• Termination conditions prevent infinite loops
• Routing uses conditional edges (not hardcoded paths) OR Functional API tasks
• Cost optimization: simple tasks → cheaper models (DeepSeek)
• Complex reasoning → quality models (Claude)
• NO OpenAI used anywhere
• Checkpointers enabled for context preservation
• Human-in-the-loop: interrupt() for approval workflows
• Guardrails: PII detection, budget limits, call limits
• MCP tools standardized via MultiServerMCPClient when appropriate
• Observability: LangSmith tracing enabled in production </success_criteria>

<core_content> Production-tested patterns for building scalable, cost-optimized multi-agent systems with LangGraph and LangChain.

When to Use This Skill

Symptoms:

• "State not updating correctly between agents"
• "Agents not coordinating properly"
• "LLM costs spiraling out of control"
• "Need to choose between supervisor vs swarm vs handoff patterns"
• "Unclear how to structure agent state schemas"
• "Agents losing context or repeating work"
• "Need guardrails for PII, budget, or safety"
• "How to test agent graphs"
• "Need durable execution with crash recovery"
• "Setting up LangSmith tracing / observability"
• "Deploying LangGraph to production"

Use Cases:

• Multi-agent systems with 3+ specialized agents
• Complex workflows requiring orchestration
• Cost-sensitive production deployments
• Self-learning or adaptive agent systems
• Enterprise applications with multiple LLM providers

Quick Reference: Orchestration Pattern Selection

Core Patterns

1. State Schema (Foundation)

from typing import TypedDict, Annotated, Dict, Any
from langchain_core.messages import BaseMessage
from langgraph.graph import add_messages

class AgentState(TypedDict, total=False):
    messages: Annotated[list[BaseMessage], add_messages]  # Auto-merge
    agent_type: str
    metadata: Dict[str, Any]
    next_agent: str  # For handoffs

Deep dive: reference/state-schemas.md (reducers, annotations, multi-level state)

2. Multi-Provider Configuration (via lang-core)

# Use lang-core for unified provider access (NO OPENAI)
from lang_core.providers import get_llm_for_task, LLMPriority

llm_cheap = get_llm_for_task(priority=LLMPriority.COST)     # DeepSeek
llm_smart = get_llm_for_task(priority=LLMPriority.QUALITY)  # Claude
llm_fast = get_llm_for_task(priority=LLMPriority.SPEED)     # Cerebras
llm_local = get_llm_for_task(priority=LLMPriority.LOCAL)    # Ollama

Deep dive: reference/base-agent-architecture.md, reference/cost-optimization.md

3. Supervisor Pattern

from langgraph_supervisor import create_supervisor  # pip install langgraph-supervisor
from langgraph.prebuilt import create_react_agent

research_agent = create_react_agent(model, tools=research_tools, prompt="Research specialist")
writer_agent = create_react_agent(model, tools=writer_tools, prompt="Content writer")

supervisor = create_supervisor(agents=[research_agent, writer_agent], model=model)
result = supervisor.invoke({"messages": [("user", "Write article about LangGraph")]})

4. Swarm Pattern

from langgraph_swarm import create_swarm, create_handoff_tool  # pip install langgraph-swarm

handoff_to_bob = create_handoff_tool(agent_name="Bob", description="Transfer for Python tasks")
alice = create_react_agent(model, tools=[query_db, handoff_to_bob], prompt="SQL expert")
bob = create_react_agent(model, tools=[execute_code], prompt="Python expert")

swarm = create_swarm(agents=[alice, bob], default_active_agent="Alice")

5. Functional API (Alternative to Graph)

from langgraph.func import entrypoint, task
from langgraph.checkpoint.memory import InMemorySaver

@task
def research(query: str) -> str:
    return f"Results for: {query}"

@entrypoint(checkpointer=InMemorySaver())
def workflow(query: str) -> dict:
    result = research(query).result()
    return {"output": result}

Deep dive: reference/functional-api.md (durable execution, time travel, testing)

6. MCP Tool Integration

from langchain_mcp_adapters.client import MultiServerMCPClient

async with MultiServerMCPClient(
    {"tools": {"transport": "stdio", "command": "python", "args": ["./mcp_server.py"]}}
) as client:
    tools = await client.get_tools()
    agent = create_react_agent(model, tools=tools)

Deep dive: reference/mcp-integration.md

7. Deep Agents Framework (Production)

from deep_agents import create_deep_agent
from deep_agents.backends import CompositeBackend, StateBackend, StoreBackend

backend = CompositeBackend({
    "/workspace/": StateBackend(),      # Ephemeral
    "/memories/": StoreBackend()        # Persistent
})
agent = create_deep_agent(
    model=ChatAnthropic(model="claude-opus-4-6"),
    backend=backend,
    interrupt_on=["deploy", "delete"],
    skills_dirs=["./skills/"]
)

Deep dive: reference/deep-agents.md (subagents, skills, long-term memory)

8. Guardrails

# Recursion limit prevents runaway agents (default: 25 steps)
config = {"recursion_limit": 25, "configurable": {"thread_id": "user-123"}}
result = graph.invoke(input_data, config=config)

# Add guardrail nodes for PII, safety checks, HITL — see reference

Deep dive: reference/guardrails.md (input/output validation, tripwires, graph-node guardrails)

Reference Files (14 Deep Dives)

Architecture:

• reference/state-schemas.md - TypedDict, Annotated reducers, multi-level state
• reference/base-agent-architecture.md - Multi-provider setup, agent templates
• reference/tools-organization.md - Modular tool design, InjectedState/InjectedStore

Orchestration:

• reference/orchestration-patterns.md - Supervisor, swarm, handoff, router, skills, master, HITL
• reference/context-engineering.md - Three context types, memory compaction, Anthropic best practices
• reference/cost-optimization.md - Provider routing, caching, token budgets, fallback chains

APIs:

• reference/functional-api.md - @entrypoint/@task, durable execution, time travel, testing
• reference/mcp-integration.md - MultiServerMCPClient, async context manager, tool composition
• reference/deep-agents.md - Harness, backends, subagents, skills, long-term memory
• reference/streaming-patterns.md - 5 streaming modes, v2 format, custom streaming

Production:

• reference/guardrails.md - PII detection, prompt injection, budget tripwires, output filtering
• reference/testing-patterns.md - Unit/integration testing, mocking, snapshot tests, CI/CD
• reference/observability.md - LangSmith tracing, custom metrics, evaluation, monitoring
• reference/deployment-patterns.md - App structure, local server, LangGraph Platform, Docker

Common Pitfalls

lang-core Integration

For production deployments, use lang-core for:

• Middleware: Cost tracking, budget enforcement, retry, caching, PII safety
• LangSmith: Unified tracing with @traced_agent decorators
• Providers: Auto-selection via get_llm_for_task(priority=...)
• Celery: Background agent execution with progress tracking
• Redis: Distributed locks, rate limiting, event pub/sub

from lang_core import traced_agent, get_llm_for_task, LLMPriority
from lang_core.middleware import budget_enforcement_middleware

@traced_agent("QualificationAgent", tags=["sales"])
async def run_qualification(data):
    llm = get_llm_for_task(priority=LLMPriority.SPEED)
    # ... agent logic

Emit Outcome Sidecar

As the final step, write to ~/.claude/skill-analytics/last-outcome-langgraph-agents.json:

{"ts":"[UTC ISO8601]","skill":"langgraph-agents","version":"2.0.0","variant":"default",
 "status":"[success|partial|error]","runtime_ms":[estimated ms from start],
 "metrics":{"agents_created":[n],"nodes_configured":[n],"graphs_built":[n]},
 "error":null,"session_id":"[YYYY-MM-DD]"}

Use status "partial" if some stages failed but results were produced. Use "error" only if no output was generated.