Oryntra: Skill for Claude Code

<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:

Pattern	When	Agents
Supervisor	Clear hierarchy	3-10
Swarm	Peer collaboration	5-15
Handoff	Sequential pipeline	2-5
Router	Classify and dispatch	2-10
Master	Learning systems	10-30+

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