What is the benefit of using Pydantic for tool schemas?

Pydantic ensures strict type validation and automatic JSON schema generation, significantly reducing runtime errors when passing data between the LLM and your backend Python functions.

Can I run multiple agents at the same time?

Yes, the skill provides a ParallelOrchestrator pattern using Python's asyncio.gather to execute multiple specialized agents concurrently on the same input.

Does this support state persistence across sessions?

Yes, it includes an AgentStateManager pattern designed to manage conversation history and tool call logs, which can be easily connected to databases like Redis or PostgreSQL.

How does this skill handle tool execution failures?

It implements a ToolExecutor pattern that manages timeouts, validation errors, and runtime exceptions, ensuring the agent receives structured feedback rather than a system crash.

Agent Orchestration Patterns

Name: Agent Orchestration Patterns
Author: ricardoroche

byricardoroche

Data Science & ML

Implements robust multi-agent systems using Pydantic tool schemas, state management, and advanced orchestration patterns.

About

This skill provides a standardized framework for building complex multi-agent systems and tool-calling workflows within Claude Code. It guides developers through the creation of type-safe tool schemas using Pydantic, implements reliable state management for persistent conversation history, and establishes proven patterns for sequential, parallel, and router-based agent coordination. By focusing on structured error handling and validated tool execution, it ensures that AI-driven applications remain maintainable, scalable, and resilient to the unpredictability of LLM tool-calling.

Key Features

Pydantic-based tool schema generation for Anthropic-compatible tool definitions
Comprehensive agent state management with session persistence and metadata tracking
Robust tool execution framework with built-in validation, timeouts, and error handling
Standardized orchestration patterns including Sequential, Parallel, and Router-based logic
Seamless integration logic for LangChain, LlamaIndex, and native Anthropic tool workflows
0 GitHub stars

Use Cases

Building multi-step research and synthesis pipelines that require hand-offs between agents
Implementing complex LLM applications that require dynamic routing to specialized sub-agents
Developing concurrent code review or security auditing systems using parallel execution

About

Key Features

Pydantic-based tool schema generation for Anthropic-compatible tool definitions
Comprehensive agent state management with session persistence and metadata tracking
Robust tool execution framework with built-in validation, timeouts, and error handling
Standardized orchestration patterns including Sequential, Parallel, and Router-based logic
Seamless integration logic for LangChain, LlamaIndex, and native Anthropic tool workflows
0 GitHub stars

Use Cases

Building multi-step research and synthesis pipelines that require hand-offs between agents
Implementing complex LLM applications that require dynamic routing to specialized sub-agents
Developing concurrent code review or security auditing systems using parallel execution