Comparisons / AutoGen vs Pydantic AI
AutoGen vs Pydantic AI: Which Agent Framework to Use?
AutoGen autogen by microsoft models agents as conversableagents that chat with each other. Pydantic AI pydantic ai is a type-safe agent framework built by the pydantic team. Here is how they compare — and what the same patterns look like in plain Python.
By the numbers
AutoGen
56.7k
8.5k
Python
CC-BY-4.0
2023-08-18
Microsoft Research
Pydantic AI
16.1k
1.9k
Python
MIT
2024-06-21
Pydantic (Samuel Colvin)
GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.
| Concept | AutoGen | Pydantic AI | Plain Python |
|---|---|---|---|
| Agent | `ConversableAgent` with `system_message`, `llm_config` | `Agent()` class with typed `result_type`, system prompt, and `model` parameter | A function with a system prompt that POSTs to the LLM API |
| Tools | `register_for_llm()` and `register_for_execution()` | `@agent.tool` decorator with typed parameters and Pydantic validation | A dict of callables + JSON schema descriptions |
| Conversation | Two-agent chat with `initiate_chat()`, message history | — | A `messages` array that grows with each turn |
| Multi-Agent | `GroupChat` with `GroupChatManager`, speaker selection | — | Multiple agent functions called in sequence on shared `messages` |
| Nested Chats | `register_nested_chats()` for sub-task handling | — | A task queue (BFS) — agent schedules follow-ups via a tool |
| Termination | `is_termination_msg` callback, `max_consecutive_auto_reply` | — | The `while` loop exits when no `tool_calls` or `max_turns` reached |
| Agent Loop | — | `agent.run()` handles the tool-call loop internally with typed dispatch | A `while` loop: call LLM, check for `tool_calls`, validate args, execute, repeat |
| Structured Output | — | `result_type=MyModel` enforces Pydantic model on final LLM response | Parse the LLM response as JSON, pass to a validation function, retry on failure |
| Model Switching | — | Swap `model='openai:gpt-4o'` to `model='anthropic:claude-sonnet'` in one line | Change the API endpoint URL and adjust the request/response format mapping |
| Dependencies | — | `RunContext[DepsType]` injects typed dependencies into tools at runtime | Pass a `deps` dict to your agent function, tools access it via closure or argument |
What both do in plain Python
Every concept in the table above — agent, tools, loop, memory, state — maps to a handful of Python primitives: a function, a dict, a list, and a while loop. Both AutoGen and Pydantic AI wrap these primitives in their own class hierarchies and APIs. The underlying pattern is the same ~60 lines of code. The difference is how much ceremony each framework adds on top.
When to use AutoGen
AutoGen excels at complex multi-agent workflows where agents need to debate or collaborate. For single-agent use cases or simple tool-calling agents, the plain Python version is significantly simpler.
What AutoGen does
AutoGen's core abstraction is the `ConversableAgent` — an agent that can send and receive messages. Two agents chat by alternating turns on a shared message history. `GroupChat` extends this to N agents, with a `GroupChatManager` that selects the next speaker (round-robin, random, or LLM-based selection). **Nested chats** allow an agent to spin up a sub-conversation to handle a complex subtask before returning to the main thread. AutoGen also provides code execution sandboxes, letting agents write and run code as part of their conversation. The framework thinks in terms of **conversations, not chains or graphs**. This makes it natural for workflows where agents need to debate, critique, or iteratively refine outputs together.
The plain Python equivalent
A `ConversableAgent` is a function that takes a `messages` array, calls the LLM with a system prompt, and returns the assistant message. Two-agent chat is a `while` loop where you alternate between calling `agent_a(messages)` and `agent_b(messages)`, appending each response. `GroupChat` is the same loop but with a **speaker selection step** — either rotate through a list or ask the LLM "who should speak next?" and call that agent function. Nested chats are a function call within the loop: pause the main conversation, run a sub-loop with different agents, and inject the result back. Tool registration is adding functions to a `tools` dict with their JSON schemas. The conversation-as-primitive model is **just `messages` arrays passed between functions**.
When to use Pydantic AI
Pydantic AI adds genuine value if you want compile-time type checking across your agent's tools, outputs, and dependencies. If you already use Pydantic in your stack, it fits naturally. But the core agent logic — loop, dispatch, validate — is still ~60 lines of Python you can own entirely.
What Pydantic AI does
Pydantic AI wraps the agent pattern in **Pydantic's type system**. You define an `Agent` with a `result_type` (a Pydantic model), register tools with typed parameters via decorators, and call `agent.run()` to execute the tool-call loop. The framework validates tool arguments against their type hints, validates the final response against your result model, and retries on validation failures. It supports **25+ model providers** through a unified interface, so switching from OpenAI to Anthropic is a one-line change. Dependencies are injected via typed `RunContext`, giving your tools access to databases, API clients, or configuration without global state. The real value is that your **IDE catches type errors before runtime**.
The plain Python equivalent
Type-safe tool dispatch in plain Python means **validating tool arguments before calling the function**. Parse the LLM's `tool_call` arguments as JSON, check types with `isinstance` or a simple schema, and raise on mismatch. Structured output is the same: parse the final response as JSON, validate against expected keys and types, retry if it fails. Model switching means swapping the API URL and adjusting the request format — a dict mapping provider names to endpoint configs. Dependency injection is passing a `deps` dict to your agent function that tools access via closure. The full typed agent is about **60 lines**, plus maybe 20 for validation helpers. No decorators, no base classes — **just functions with type checks**.
Or build your own in 60 lines
Both AutoGen and Pydantic AI implement the same 8 patterns. An agent is a function. Tools are a dict. The loop is a while loop. The whole thing composes in ~60 lines of Python.
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