Advanced jobs / Multi-agent workflow orchestration
Multi-agent workflow orchestration
Several specialized agents hand work to each other, with state, retries and a person in the loop.
- Difficulty
- Very hard (5/5)
- Human checkpoints
- 3 to plan for
- Matching bots
- 8 on BotPickr
The job
A system where a planner, specialists and reviewers (separate agents) divide a task, pass context along, recover from failures and stop for approval when needed.
Why it is hard
- Errors compound: a small mistake by one agent becomes the next agent's input.
- Shared state and memory must be managed explicitly or agents contradict each other.
- Costs and latency multiply with each extra agent and loop.
- Debugging is hard without traces that show every hand-off.
What a good setup needs
Tools
- An orchestration framework (LangGraph, CrewAI, OpenAI Agents SDK, Microsoft Agent Framework, Mastra, Pydantic AI)
- Persistent state and checkpoints so runs can resume
- Tracing and evaluation (LangSmith or the framework's own tools)
- Tool access defined per agent, not shared by all
Guardrails
- Give each agent the minimum tools and permissions it needs
- Set step, time and cost limits so loops cannot run away
- Validate each hand-off with a schema before the next agent uses it
- Consider whether one agent would do the job before adding more
Human-in-the-loop points
- Interrupt points before irreversible actions (sending, paying, deploying)
- A person approves the plan for high-impact runs
- Regular review of traces to find weak hand-offs
How to start small
Start here: Build the workflow as one agent first, find where it fails, and split out a second agent only for the part that needs it.
How to know it works: End-to-end task success rate with cost per task, compared to a single-agent baseline.
Bots that fit this job
Pick from the listings below. Open one for setup steps; those marked Paid have plan prices as shown by the vendor.
LangGraph
Library for building stateful, controllable agents as graphs with memory and human checkpoints.
CrewAI
Python framework for teams of role-playing agents that split up and finish a job together.
OpenAI Agents SDK
Small Python SDK for agents with tools, handoffs, guardrails and tracing.
Microsoft Agent Framework
Microsoft's open framework for building and running AI agents and multi-agent workflows in Python and .NET. The successor to AutoGen.
AutoGen
Microsoft's multi-agent framework. It is now in maintenance mode, so new projects should look at Microsoft Agent Framework instead.
Mastra
TypeScript framework for agents, workflows, RAG and evals.
Pydantic AI
Type-safe Python agent framework from the Pydantic team with structured outputs.
smolagents
Hugging Face's minimal library where agents write and run Python code as their actions.
Deep dives for these bots
Official docs and guides
Quick answers
Do I need multiple agents?
Often not. Start with one agent and tools. Add agents when separate roles, permissions or parallel work clearly help, because each added agent multiplies cost and failure points.
How do I keep a person in the loop?
Use framework features that pause a run for approval before important actions, such as LangGraph interrupts, and log every decision.
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