Agent Orchestrator

Here's a list of Agent Orchestrator tools and libraries you can explore to build, coordinate, or manage multi-agent systems — especially for LLM-driven or hybrid (LLM + Tool) environments:


⚙️ Top Agent Orchestrator Tools & Libraries

Tool/Library
Description
Language
Highlights

Autogen (Microsoft)

Framework for multi-agent conversation orchestration using LLMs.

Python

Supports memory, tools, group chats, customizable agents.

CrewAI

Lightweight framework for task-based multi-agent collaboration.

Python

Role-based agents (e.g., Researcher, Engineer, Reviewer).

LangGraph (LangChain)

Graph-based multi-agent orchestration using LangChain.

Python

Async support, dynamic routing, perfect for LLM flows.

Haystack Agents

Pipeline-based framework extended for agent workflows.

Python

Emphasizes RAG + Agent flows with tools.

MetaGPT

Builds multi-role agents for software development teams.

Python

Generates PRDs, tasks, code, and reviews like a human team.

CAMEL

Pairs of agents (User & Assistant) roleplay to solve problems.

Python

Good for research and simulations.

AgentVerse

Simulates multi-agent environments.

Python

Focused on social interaction and behavior simulation.

Orca (OpenAgents)

Open framework for multi-agent workflows with tools.

Python

Agent registration, messaging, memory.

OpenDevin (early)

Dev-focused agent orchestrator inspired by Devin.

JS/Python

Still evolving — devops/coding assistant focus.

Superagent.sh

Open-source platform to deploy, schedule, and monitor agents.

TypeScript

UI, prompt management, vector store plugins.

Flowise

No-code orchestration for agents and LLMs using nodes.

Node.js

Best for non-dev teams or fast prototyping.


🧠 Bonus Libraries for Building Custom Orchestrators

Library
Description

Ray

Parallel task scheduling and distributed agent execution.

FastAPI + Celery

Build API-driven agent systems with async task queuing.

LangChain Expression Language (LCEL)

DSL for chaining and routing LLM agents with conditions.

ReAct (Reason + Act)

Pattern to coordinate thought-action loops in agents.


Want help picking the best one for a specific use case (like resume scoring, customer support, or workflow automation)? Just tell me your goal and I can recommend the most efficient stack.

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