Key facts
| AutoGen | Microsoft's multi-agent conversation framework for Python and .NET |
| AutoGen models | Bring your own model endpoint and API keys |
| Plugsky | OpenAI-compatible API with live function calling and 30+ models |
| Running together | Point AutoGen agents at Plugsky with a base URL change |
| Platform controls | Scoped keys, RBAC, SSO/SCIM and audit logs are live |
| Deployment | Cloud, VPC, on-prem and air-gapped with region choice |
| Pricing | Flat monthly self-serve plans with unlimited fair-use usage |
| Honest limit | AutoGen's framework features are not replaced by an API |
TL;DR
- AutoGen is orchestration code you host; Plugsky is the model runtime and platform.
- Most teams do not need to choose — point AutoGen at Plugsky and keep the framework.
- Swap the model layer to gain 30+ models, one key and predictable plans.
- You still own agent topology, state, sandboxing and evaluation in AutoGen.
- Move provider-agnostic: the API is OpenAI-compatible, so exit cost stays low.
How it works, step by step
- List what you use AutoGen for: group chat, tool use, code execution or event-driven flows.
- Confirm which model provider the AutoGen agents call and whether they use OpenAI-format clients.
- Create a Plugsky key and test the same prompts against two or three models.
- Change the base URL and model names in the AutoGen model client configuration.
- Re-run your multi-agent scenarios and compare tool-call behaviour and cost per task.
- Keep AutoGen's orchestration and code-execution features, and add scoped keys plus audit logging.
- Route routine agents to small models and reserve frontier models for planning.
Try it yourself
Open the multi-agent workflow tool →
What AutoGen is good at
AutoGen popularised the idea of agents that converse with each other to solve a task: a planner, a coder, a critic and a proxy for human input exchanging messages under a defined termination condition. Its event-driven architecture and code-execution hooks make it a strong toolkit for research and complex multi-agent patterns.
That power comes with operational weight. You host the runtime, manage the event loop, sandbox any code execution, and build the observability and evaluation layers yourself. The model layer is deliberately bring-your-own, which is exactly the seam where a platform like Plugsky fits.
Framework plus runtime: the split that works
Frameworks and runtimes solve different problems. AutoGen decides how agents talk to each other; Plugsky decides how model calls are served, secured, billed and deployed. Keeping that split lets each side do what it is good at.
- Framework: agent topology, conversation patterns, termination logic, code-execution hooks.
- Runtime: model catalogue, function calling, streaming, rate and spend controls, audit.
- Interface: OpenAI-compatible HTTP, so configuration rather than rewriting connects them.
- Evaluation: stays with you, but 30+ models on one key makes A/B comparison cheap.
When a managed runtime is enough
If your needs are simpler than AutoGen's group-chat patterns — a supervisor with a few workers, or a single tool-calling loop — the framework is often more machinery than the task requires. A plain loop over the chat completions endpoint, with your own state and tools, is easier to debug and cheaper to run.
Plugsky serves that path directly: function calling, streaming, JSON mode, embeddings, RAG and agents are live, with scoped keys, RBAC and audit logging for production, and deployment from shared cloud to VPC, on-prem and air-gapped. 30+ models behind one key mean routing decisions are configuration. Chat and tool loops run today; assistants, files, batch and fine-tuning endpoints are coming soon. Plans, including free plugsky-micro and plugsky-lite, are on the live pricing page.
Honest comparison
| Need | AutoGen alone | AutoGen plus Plugsky | Plain tool loop |
|---|---|---|---|
| Agent topology | Rich group-chat patterns | Rich group-chat patterns | Simple supervisor or single loop |
| Model access | Bring your own endpoint | 30+ models behind one API key | 30+ models behind one API key |
| Secrets and keys | You manage per provider | Scoped keys, RBAC, audit logs | Scoped keys, RBAC, audit logs |
| Deployment | Wherever you host it | Cloud, VPC, on-prem, air-gapped | Cloud, VPC, on-prem, air-gapped |
| Operational load | Highest | Moderate | Lowest |
Frequently asked questions
Is Plugsky a drop-in AutoGen replacement?
No. Plugsky replaces the model provider and platform layer, not the multi-agent framework. You can keep AutoGen and point it at Plugsky's OpenAI-compatible endpoint.
How do I connect AutoGen to Plugsky?
Configure the OpenAI-format model client with the Plugsky base URL and your API key, then select model names from the catalogue. No agent logic changes.
Which is more work to operate?
AutoGen requires you to host and scale the runtime and manage provider integrations. Plugsky provides the runtime, keys, audit and deployment options, so the operational surface is smaller.
Do I lose code-execution features?
No. AutoGen's code-execution hooks remain part of your framework layer. Keep them sandboxed with no ambient credentials, as you would with any provider.
How does pricing compare?
Plugsky self-serve plans are flat monthly with unlimited fair-use usage; see the live pricing page. AutoGen itself is open source, but you pay for the models and the infrastructure you run.
Can I still use multiple model providers?
Yes. The OpenAI-compatible interface keeps you portable, and many teams keep a second provider for redundancy while consolidating routine traffic on Plugsky.
What about other frameworks?
The same pattern applies to CrewAI, LangGraph, LangChain, Haystack, Semantic Kernel and the OpenAI Agents SDK: keep the framework, change the base URL.