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AutoGen models multi-agent collaboration as a conversation — agents exchange messages until a termination condition is met. The Swarms API maps this same collaborative pattern onto structured workflows with a clean REST interface, eliminating the need to manage conversation loops, termination strings, and local LLM configuration.

Side-by-Side: Basic Two-Agent Chat

AutoGen

Swarms API

For a simple two-agent conversation where UserProxy only relays a message and Assistant responds, a single agent completion is all you need. The UserProxyAgent is not an AI agent — it just passes the task.

Side-by-Side: GroupChat

AutoGen

Swarms API

What changed:
  • UserProxyAgent → removed; the task field replaces it
  • GroupChatManager → managed by the API
  • max_round=5"max_loops": 5 at the top level
  • cache_seed → not needed; the API is stateless
  • llm_config per agent → "model_name" per agent

Side-by-Side: Code Execution Agent

AutoGen

Swarms API

The Swarms API does not provide a sandboxed code-execution tool (/v1/tools/available only exposes auto_search and web_scraper), so there is no direct equivalent of AutoGen’s Docker/subprocess code execution. The agent below writes the code; you run it yourself (or call your own execution function via tools_list_dictionary).

Side-by-Side: Multi-Agent Debate Pattern

AutoGen is often used for debate-style workflows where agents argue positions. The Swarms API has a dedicated DebateWithJudge architecture for this.

AutoGen

Swarms API


LLM Configuration Migration

AutoGen requires per-agent llm_config dicts with API keys and model lists. The Swarms API handles all authentication — you just specify the model name.

AutoGen

Swarms API

The Swarms API supports 300+ models. See Available Models for the full list.

Termination Conditions

AutoGen uses is_termination_msg callbacks to stop conversations. The Swarms API uses max_loops to bound execution.

AutoGen

Swarms API


Key Differences to Keep in Mind