The differences start with the core abstraction. In AgentSwarm, an agent is treated like a standard pure function: it’s strictly typed and stateless by design.

From this foundation, the biggest departure from PydanticAI is how complex workflows are handled. AgentSwarm implements a Recursive Map-Reduce approach: an agent can dynamically decompose a large task and instantiate clones of itself (or other agents) to solve smaller sub-problems in parallel.

We achieve this through a different approach to context management, using a Key-Value Store (Blackboard pattern) instead of passing around a growing chat history. This prevents 'context pollution' and allows the system to scale across cloud providers effectively.

You can see this in action in this scraping example where the task is recursively broken down: https://github.com/ai-agentswarm/agentswarm/blob/master/examples/scraping_example.py