AutogenAutoGen simplifies multi-agent LLM system development with customizable, collaborating autonomous agents for complex task resolution.

AutoGen is a framework by Microsoft enabling multi-agent LLM applications where customizable agents converse and collaborate to tackle complex tasks, simplifying orchestration of autonomous systems.

Autogen is a framework that enables the development of LLM applications using multiple customizable autonomous agents that can converse and collaborate to solve complex tasks. Developed by Microsoft, it simplifies orchestrating multi-agent systems.

Not yet. This listing is compiled from Microsoft’s public documentation, pricing pages and changelogs — nothing on this page is presented as a hands-on test result.Autogen sits in our testing queue; when we run it, this section will state what we tested, how long for, and what it actually produced. How we review AI tools.

• Automated Code Generation and Review: Agents can collaborate to write, test, and review code, identifying bugs and suggesting improvements.
• Complex Problem Solving: Break down intricate problems into smaller tasks, with specialized agents tackling each part and synthesizing solutions.
• Content Creation and Editing: Agents can brainstorm ideas, draft content, perform fact-checking, and refine text for specific audiences and formats.
• Research and Information Synthesis: Agents can scour vast datasets, extract relevant information, and summarize findings in a coherent manner.

• Autonomous Agent Development
• Multi-Agent Conversation Framework
• Customizable Agent Personalities and Tools
• Task Decomposition and Orchestration
• Human-in-the-Loop Capabilities
• Integration with LLMs like GPT

Prices as of July 25, 2026, taken from Microsoft’s public pricing page. Vendors change pricing without notice — check before you buy.

• Requires robust LLM infrastructure
• Performance depends heavily on LLM quality and configuration

• LangChain
• LlamaIndex
• Guardrails AI

1. Install AutoGen using pip: pip install pyautogen
2. Explore the examples in the AutoGen GitHub repository to understand agent configurations and conversation patterns.
3. Define your agents, assign them roles and tools, and initiate a conversation to tackle your desired task.

Use a SynaBot assistant to produce the thinking, then move the output into Autogen for execution. Every SynaBot assistant is free to try on the Lite plan.

• Content Creator (ZARA) — drafts the copy, captions and campaign angles you'll run through Autogen.
• Business Planner (VIKRAM) — decides whether Autogen belongs in your stack and what it should replace.
• Project Manager (PACE) — turns the rollout of Autogen into owned, dated tasks.

Browse the full AI assistant roster, grab a starting point from the prompt library, or have us wire it together with our AI consultancy service.