AI agent orchestration

Agent orchestration platforms sit above individual agent frameworks. Rather than providing tools to build a single agent, they coordinate teams of agents working together toward shared goals — managing scheduling, delegation, budgets, and governance across a whole system of autonomous workers.

Orchestration is distinct from two neighbouring concerns. Agent development frameworks (such as LlamaIndex or Semantic Kernel) provide the building blocks to write a single agent; harness engineering provides the runtime controls around one agent. Orchestration is the coordination layer above both: how multiple agents divide work, communicate, and converge on a result. Inter-agent communication is increasingly standardized through protocols such as A2A.

The line between a development framework and an orchestration platform is not always sharp — several tools below do both.

LangChain and LangGraph

LangChain is an open-source framework for building applications powered by large language models (LLMs). Created by Harrison Chase in October 2022, it became one of the most widely adopted frameworks for LLM application development and now backs a venture-funded company of the same name (Series A led by Sequoia Capital in April 2023).

LangChain’s core idea is composition: it provides building blocks — prompts, models, retrievers, memory, and tools — chained together into pipelines for use cases such as retrieval-augmented generation (RAG), chatbots, document analysis, and structured extraction. It is written in Python, with a parallel JavaScript/TypeScript implementation (LangChain.js).

The orchestration-focused part of the ecosystem is LangGraph — a library for modelling stateful, multi-step, multi-agent workflows as graphs of nodes, with cycles, persistence, and human-in-the-loop checkpoints, plus a managed LangGraph Platform for deployment. The wider family also includes the LangChain Expression Language (LCEL), a declarative syntax for defining chains; LangSmith, a closed-source observability and evaluation platform (2024); and LangServe, for deploying chains as production APIs.

LangChain is sometimes criticized for over-abstraction — its layers can obscure what is underneath a relatively simple sequence of model calls — and some teams prefer to call model APIs directly for production systems. It remains a popular starting point and a useful tool for rapid prototyping. (Repo.)

Other orchestration platforms

  • Claude Cowork — Anthropic’s agentic AI for knowledge work. It runs on the desktop, connecting to local files and applications to complete multi-step tasks end to end. Built on the Claude Code stack, it dynamically writes orchestration scripts that run tens to hundreds of parallel subagents in a single session, checking its own work. Aimed at researchers, analysts, and operations, legal, and finance teams, with enterprise plugin marketplaces.

  • Ace — A research prototype from the GitHub Next team (forthcoming, not yet a production tool). A multiplayer development workspace blending communication, coding, and cloud compute: Slack-like shared sessions backed by isolated microVMs on separate git branches, where several people can prompt agents simultaneously, edit code in real time, run terminals, preview, and open PRs together. Its premise is that as agentic coding gets cheap, the bottleneck shifts to team alignment — agreeing on what to build before agents execute. (Described in Maggie Appleton’s essay Zero Alignment.)

  • AG2 — Open-source framework for building multi-agent systems through structured conversations between agents. The community-driven successor to Microsoft’s AutoGen, it supports conversational agents, group chats, and human-in-the-loop workflows.

  • CrewAI — Open-source Python framework for orchestrating role-playing autonomous agents. Agents are organized into "crews" with defined roles, goals, and tools, collaborating on tasks through sequential or hierarchical processes.

  • Mastra — JS/TS-first framework for building and orchestrating agents, with workflows, agent memory, RAG, and evals built in.

  • MetaGPT — Multi-agent framework that assigns role-based agents (product manager, architect, engineer, QA) to collaborate like a simulated software company, turning a single requirement into designs, code, and tests.

  • SuperAGI — Open-source, developer-first framework and platform for building, deploying, and managing autonomous agents, with a GUI, a tool ecosystem, and support for running multiple agents concurrently.

  • Multica — Open-source, self-hosted platform for managing a mixed human + AI-agent workforce. Assign tasks to coding agents (Claude, Copilot, Gemini, and others), track progress in real time over WebSocket streaming, and build a shared library of reusable skills that any teammate — human or agent — can apply.

  • OpenAI Symphony — Open-source orchestration framework (reference implementation in Elixir, plus a spec) that monitors a work queue such as Linear, spawns an autonomous agent per task, and returns each completed task as a validated deliverable with proof of work (CI status, PR feedback). Shifts the model from supervising individual agents to managing work; designed for codebases built on harness-engineering principles.

  • Paperclip — Open-source, self-hosted platform for running organizations of AI agents, with org charts, heartbeat-based scheduling, per-agent cost budgets, and full audit logging.

  • Warp Oz — Built in to the Warp agentic terminal, Oz orchestrates and tracks fleets of agents to automate work. Intro video.

See also