We build multi-agent AI systems using Microsoft's AutoGen framework — specialized agents that collaborate through conversation to complete complex tasks. From research assistants and code review bots to automated analysis pipelines and decision support systems, AutoGen enables sophisticated multi-agent orchestration with built-in human-in-the-loop capabilities.
AutoGen is Microsoft's open-source framework for multi-agent conversations — agents chat with humans, execute code, and compose specialist roles. v0.4 rewrote the core for production with async and actor-model runtime.
We build multi-agent AI systems using Microsoft's AutoGen framework — specialized agents that collaborate through conversation to complete complex tasks. From research assistants and code review bots to automated analysis pipelines and decision support systems, AutoGen enables sophisticated multi-agent orchestration with built-in human-in-the-loop capabilities.
Key capabilities and advantages that make AutoGen Multi-Agent Development the right choice for your project
Design agents that collaborate through structured conversation — each with defined roles, capabilities, and knowledge. AutoGen's conversation-driven approach handles complex multi-step tasks naturally.
Built-in support for human intervention at critical decision points — agents can request approval, clarification, or expert input before proceeding with high-stakes actions.
Agents that write, execute, and debug code in sandboxed environments — enabling data analysis, automation, and tool creation as part of multi-agent workflows.
AutoGen's GroupChat manager coordinates multiple agents working together — managing turn-taking, context sharing, and task delegation across agent teams.
Discover how AutoGen Multi-Agent Development can transform your business
Build agent teams that research topics, gather data, analyze findings, and produce reports — with human review at key checkpoints.
Agents that review code, suggest improvements, write tests, and fix bugs through iterative conversation — with developer approval before changes.
Multi-agent systems for complex business processes — data collection, analysis, document generation, and decision support with human oversight.
Real numbers that demonstrate the power of AutoGen Multi-Agent Development
GitHub Stars
One of the most popular AI agent frameworks
+120% YoY
Microsoft Backing
Developed and maintained by Microsoft Research
Active development
Agent Patterns
Built-in conversation patterns for common multi-agent scenarios
Growing pattern library
Our proven approach to delivering successful AutoGen Multi-Agent Development projects
Define agent roles, capabilities, conversation flows, and human intervention points for your multi-agent system.
Build individual agents with their system prompts, tools, knowledge bases, and code execution capabilities.
Configure GroupChat orchestration, test agent interactions, and validate outputs across diverse scenarios.
Deploy with monitoring, logging, cost tracking, and human escalation workflows for production reliability.
Find answers to common questions about AutoGen Multi-Agent Development
AutoGen excels at conversation-driven agent collaboration with strong human-in-the-loop support — backed by Microsoft Research. CrewAI is simpler for role-based agent teams. LangGraph provides more control over state management and conditional flows. We choose based on your specific orchestration needs.
Let's discuss how we can help you achieve your goals
When each option wins, what it costs, and its biggest gotcha.
| Alternative | Best For | Cost Signal | Biggest Gotcha |
|---|---|---|---|
| LangGraph | Explicit graphs, checkpointing, production durability | Free OSS + LLM costs | More structured; less conversational than AutoGen |
| CrewAI | Simple role-based teams, fast prototyping | Free OSS | Less control over message routing and state |
| OpenAI Swarm / Agents SDK | Lightweight handoffs, OpenAI-native | Free SDK + API | Tied to OpenAI; less multi-provider flexibility |
| Custom code (raw SDKs) | Maximum control and minimal deps | Engineering time only | You reinvent messaging, memory, tool calls, state |
AutoGen cost drivers are LLM tokens x number of agent turns. A 5-agent system running a single task can consume 20-60x tokens of a single-prompt equivalent—$0.02 -> $0.40-1.20/task. 1K tasks/day at Sonnet ~$400-1,200/day ($12-36K/mo). ROI positive only when each task replaces human work worth $5-20. Build cost $25-120K depending on complexity. Simpler architectures (LangGraph with explicit branches) often hit same outcomes at 30-60% of token spend—benchmark both on your task before committing.
Specific production failures that have tripped up real teams.
Older tutorials reference v0.2 APIs that no longer exist; ensure docs/samples match your version—expect significant migration if upgrading.
AutoGen's code execution tool runs arbitrary model-generated code; always run in Docker or isolated containers, never production shell.
Agents discussing 'are we done?' can cycle for dozens of turns—set explicit max_consecutive_auto_reply and termination conditions.
Each agent keeps its own message history by default; shared context requires explicit passing or group chat patterns.
No built-in tracing like LangSmith; instrument with your own logging + cost tracking or debugging multi-agent failures is painful.
We say this out loud because lying to close a lead always backfires.
Multi-agent overhead wasted; a direct LLM call or simple chain is enough.
Conversational agents introduce non-determinism and token bloat; rules + functions give tighter bounds.
AutoGen production use requires orchestration, monitoring, and LLM-cost discipline.
Multi-turn agent chats compound latency; use single-agent with tools for interactive UIs.