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ai agents

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Multi-Agent Platforms & Frameworks
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Basic Information

This repository provides a provider-agnostic Ruby SDK for building multi-agent AI workflows. It is designed to let developers define specialized, immutable, thread-safe agents with instructions, tools, and registered handoff relationships. The SDK includes an AgentRunner/Runner orchestration layer to execute conversations and route turns between agents automatically, while a serializable Context & State mechanism persists conversation history and current agent information for storage and later restoration. It supports integration with multiple LLM providers such as OpenAI, Anthropic, and Gemini, and ships example demos, configuration hooks, and installation via a Ruby gem to help teams prototype and run multi-agent conversational systems in production-grade Ruby applications.

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App URL
https://github.com/chatwoot/ai-agents
Github Repository
https://github.com/chatwoot/ai-agents/blob/main/README.md

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App Details

Features
The README highlights multi-agent orchestration and transparent agent-to-agent handoffs, tool integration for custom functions, and JSON schema validated structured output for reliable data extraction. Core components include Agent, AgentRunner, Runner, Context & State, Tools, and Handoffs. Agents are immutable and thread-safe, runners are reusable across threads, and context is fully serializable for persistence. The project is provider agnostic with configuration support for OpenAI, Anthropic, and Gemini API keys, default models and timeout settings. It also documents patterns like hub-and-spoke handoffs, examples under an examples/ folder, and a simple API for defining custom tools that accept typed parameters and perform external actions.
Use Cases
This SDK simplifies building conversational systems that need specialized roles and seamless transfers between specialists without exposing handoffs to end users. It helps teams persist and restore conversation context between sessions, orchestrate multi-agent workflows reliably across threads, and validate structured outputs for downstream automation. Custom tools let agents interact with external systems such as CRMs, ticketing, or email, enabling practical integrations. Configuration options allow switching LLM providers and tuning runtime behavior. Example demos and serialized context support make it easier to prototype, test, and operate multi-agent assistants in Ruby applications while keeping implementations modular and extensible under an MIT license.

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