Multi-Agent Orchestration Stack
Tooling for coordinating several specialised agents with reliable handoffs.
/ quick answer
Run supervisor, pipeline or swarm topologies without losing traceability. Tooling for coordinating several specialised agents with reliable handoffs.
- LangGraph (state graph and checkpoints)
- CrewAI (role-based teams)
- Temporal or Inngest (durable execution)
- Langfuse (per-agent traces)
- Redis (shared scratchpad)
- AutoGen
- OpenAI Swarm-style handoffs
What is the Multi-Agent Orchestration Stack stack for?
Run supervisor, pipeline or swarm topologies without losing traceability.
Which tools are in this stack?
LangGraph (state graph and checkpoints), CrewAI (role-based teams), Temporal or Inngest (durable execution), Langfuse (per-agent traces), Redis (shared scratchpad).
Are there alternatives to this stack?
Yes — AutoGen, OpenAI Swarm-style handoffs.
/ continue exploring
Related concepts
The vocabulary this page depends on.
- →Multi-Agent System
A multi-agent system splits a job across several specialised AI agents that coordinate through a shared plan, message bus or orchestrator.
- →Agent Handoff
Agent handoff is the controlled transfer of a task — with its context — from one agent to another agent or to a human.
- →AI Orchestration
Coordinating multiple AI models, tools and steps into a single reliable workflow.
- →Agent Architecture
Agent architecture is the structural blueprint of an AI agent: the model, the planning loop, the tools, the memory layer and the guardrails that decide how it acts.
Related workflows
Turn this into a repeatable process.
- →Build a Multi-Agent Content Team
Researcher, writer and editor agents under a supervisor, producing publishable drafts.
- →Design Agent-to-Human Escalation
A handoff contract that gives humans everything they need in one screen.
- →Design an Agent Architecture Before Writing Code
A one-page design process that prevents the most expensive agent rebuilds.
- →Cut Agent Costs by 60% Without Losing Quality
A measurable cost-reduction pass for any agent already in production.
Related tool stacks
The tools that run it in production.
- →Multi-Agent Orchestration Stack (CrewAI)
This stack outlines the essential components for deploying a robust multi-agent system using CrewAI, focusing on agent definition, tool integration, and monitoring for collaborative task execution.
- →Agent Architecture Stack
The minimum tooling to design, run and observe a production agent.
- →AI Employee Stack
Everything a role-owning agent needs: knowledge, tools, memory and reporting.
- →Autonomous Operations Stack
Run autonomous workflows with approvals, audit trail and a kill switch.
Related prompts
Reusable prompts for this job.
- →Multi-Agent Role Definition Prompt
Generates crisp role prompts and handoff contracts for a team of agents.
- →Agent Architecture Spec Prompt
Turns a fuzzy agent idea into a reviewable five-layer architecture spec.
- →Crypto Agent Design Prompt
Specifies an AI crypto agent's tools, permissions, guardrails and evaluation before a single line is written.
Related use cases
How people apply it, and what came out.
- →Ecommerce Team Runs a Multi-Agent Catalog Pipeline
Three agents enrich, write and QA 12,000 product listings.
- →SaaS Cuts First-Response Time from 6h to 4min with an Agent
A 12-person SaaS shipped a tier-1 support agent and kept humans on exceptions only.
Comparisons & alternatives
Pick between the options.
- →Single Agent vs Multi-Agent System
One well-equipped agent beats a crowd for most jobs; multi-agent wins on genuinely separable, parallel work.
- →LangGraph vs CrewAI vs AI SDK for Agents
LangGraph for stateful control, CrewAI for fast role-based teams, AI SDK for shipping agents inside a product.
- →AI Agent vs Trading Bot
A trading bot executes fixed rules deterministically; an AI agent interprets context and decides which steps to take — powerful for research, risky for execution.