Comparisons / AutoGen vs DSPy

AutoGen vs DSPy: Which Agent Framework to Use?

AutoGen vs DSPy, head to head

AutoGen and DSPy both let you build an agent, but they sit in different parts of the stack and they assume different things about who's writing the code.

AutoGen by Microsoft models agents as ConversableAgents that chat with each other.

DSPy replaces hand-written prompts with compiled modules.

Underneath, both wrap the same thing: a model call, a tool dispatch, a loop. The decision is about which abstraction your team wants to think in day to day, and which ecosystem you're willing to inherit along with it. There's an honest, framework-free version of the same pattern in about 60 lines of Python in the lesson at the bottom of this page — useful as a baseline regardless of which framework wins.

Pick AutoGen if

Pick AutoGen if 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. The tradeoffs in its intro should match how your team already thinks about agents; DSPy will feel like translation if they don't.

Full AutoGencomparison →

Pick DSPy if

Pick DSPy if dSPy's real innovation is automated prompt optimization — replacing manual prompt engineering with algorithmic tuning. This is genuinely novel and valuable for production systems where prompt quality matters at scale. For simple agents or learning, hand-written prompts are easier to understand and modify. The tradeoffs in its intro should match how your team already thinks about agents; AutoGen will feel like translation if they don't.

Full DSPycomparison →

What both add

Whichever you pick, you're inheriting a dependency tree and a vocabulary your team has to learn before they ship anything. AutoGen has its own class hierarchy and tool registration conventions; DSPy has its. Either way, when something misbehaves you'll be reading framework source before you reach the actual HTTP call.

If the real workload is one model and a handful of tools, both can feel like a workbench for driving a nail. The lesson below builds the same pattern in plain Python — useful as a comparison point even if you ultimately keep the framework.

By the numbers

By the numbers

AutoGen

GitHub Stars

56.7k

Forks

8.5k

Language

Python

License

CC-BY-4.0

Created

2023-08-18

Created by

Microsoft Research

github.com/microsoft/autogen

DSPy

GitHub Stars

33.4k

Forks

2.8k

Language

Python

License

MIT

Created

2023-01-09

Created by

Stanford NLP (Omar Khattab)

github.com/stanfordnlp/dspy

GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.

ConceptAutoGenDSPy
Agent`ConversableAgent` with `system_message`, `llm_config``dspy.ReAct` module with signature and tools
Tools`register_for_llm()` and `register_for_execution()`Tools passed to `ReAct` module as callable list
ConversationTwo-agent chat with `initiate_chat()`, message history
Multi-Agent`GroupChat` with `GroupChatManager`, speaker selection
Nested Chats`register_nested_chats()` for sub-task handling
Termination`is_termination_msg` callback, `max_consecutive_auto_reply`
Prompts`dspy.Signature` defines input/output fields, compiled to optimized prompts
Optimization`dspy.BootstrapFewShot`, `MIPROv2` auto-tune prompts against a metric
Chaining`dspy.ChainOfThought`, `dspy.Module` with `forward()` composition
Evaluation`dspy.Evaluate` with metric functions and dev sets

Or build your own in 60 lines

Both AutoGen and DSPy 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.

No framework. No dependencies. No opinions. Just the code.

Build it from scratch →