Sistava

AutoGen

Open-source multi-agent conversation framework with inter-agent chat patterns

About AutoGen

AutoGen pioneered the multi-agent conversation pattern where AI agents collaborate by talking to each other, and has now evolved into Microsoft Agent Framework combining AutoGen with Semantic Kernel for enterprise use. While powerful for developers, it requires significant engineering for production workflows. Sistava delivers a managed AI workforce where business users hire employees and get work done immediately.

Platform details

Official website

What does AutoGen actually do?

AutoGen is an open source Python framework from Microsoft for building multi agent AI applications where agents talk to each other to solve a task. Everything is modeled as a conversation between agents, and you orchestrate by writing the participants, the chat manager, and the termination conditions. AutoGen is now in maintenance mode. Microsoft has rolled the project forward into the Microsoft Agent Framework, which merges AutoGen ideas with Semantic Kernel for enterprise grade orchestration. AutoGen still works and has an active community, but new features land in the successor. The framework is influential. It pioneered the agent conversation pattern that CrewAI and others later refined, and it remains a strong research and prototyping tool for multi agent ideas. Sistava takes the multi agent idea and hides it behind an employee abstraction. You do not see the chat between agents. You see a single AI employee taking on work in a shared workspace.

How much does AutoGen cost?

AutoGen is free and open source. You pay for the LLM calls each agent in the conversation makes, plus any infrastructure you run it on. Like CrewAI and LangGraph, the main cost driver is tokens, not the framework itself. Microsoft Agent Framework, the successor, is also free as an open source library, but it is designed to plug into Azure OpenAI and Azure infrastructure where pricing is the standard Azure model. Hidden cost is conversation explosion. AutoGen agents are designed to chat until a termination condition is met, and a poorly tuned setup can ping pong for many turns before stopping. Each turn is more tokens. Sistava charges a single credit quota that includes the underlying reasoning, regardless of how many internal agent steps the employee runs. The trade off is less ability to design the exact conversation pattern.

When does AutoGen beat the alternatives?

AutoGen is the right pick when you want the original conversation centric model, are doing research, or already live in the Microsoft ecosystem. The codebase is mature, the patterns are well documented, and there is a large community of academic users. It is also a good prototyping tool. You can stand up two agents and a chat manager in a few dozen lines of Python and watch them solve a toy task end to end. That makes it useful for teaching and exploration. It loses to LangGraph for stateful, durable, production agents. AutoGen is not built around graph orchestration, checkpointing, or human in the loop in the same first class way. It loses to Sistava when you want a finished product instead of a framework. AutoGen is a way to build agents. Sistava is the AI employee you hire.

Where does AutoGen fall short for production use?

Maintenance mode is the headline weakness. New features and roadmap energy are moving to Microsoft Agent Framework, which means new AutoGen production projects in 2026 are starting on a slowly cooling stack. Existing projects still work but face a future migration. Operational maturity is also limited. Tracing, evals, deployment, and tenancy are not first class in AutoGen the way they are in LangGraph plus LangSmith. Teams typically wrap AutoGen in their own observability layer. There is also no business surface. AutoGen does not ship a workspace, a CRM, a task board, or a way for non engineers to interact with agents. That is by design, since it is a framework, but it is a real gap for small teams. Sistava handles those concerns natively. The employee runs on managed infrastructure with tracing, retries, and a shared workspace baked in. You give up the right to design the agent conversation yourself.

How does AutoGen handle the move to Microsoft Agent Framework?

Microsoft positions Microsoft Agent Framework as the enterprise ready successor, combining AutoGen orchestration with Semantic Kernel stability. It targets production agents that need to run reliably for large numbers of steps, with first class checkpointing. For existing AutoGen users the migration path is real but not trivial. Concepts map across, but the APIs are different enough that production code needs a rewrite, and the documentation is still maturing. If you are starting fresh in 2026 and you want a Microsoft aligned stack, the framework is the right call. If you want the largest open source community, LangGraph and Browser Use both have more momentum. Sistava is intentionally framework agnostic. The employee abstraction does not change when the underlying agent libraries do. That stability is the point of buying an employee instead of building one.

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