Keywords AI

AutoGen vs DSPy

Compare AutoGen and DSPy side by side. Both are tools in the Agent Frameworks category.

Quick Comparison

	AutoGen	DSPy
Category	Agent Frameworks	Agent Frameworks
Pricing	Open Source	—
Best For	Researchers and developers building multi-agent systems with structured conversation patterns	—
Website	microsoft.github.io	dspy.ai
Key Features	Multi-agent conversation framework Flexible agent communication patterns Code execution sandbox Human-in-the-loop support Microsoft-backed development	—
Use Cases	Multi-agent debate and collaboration Code generation with execution verification Research tasks requiring multiple perspectives Group chat agent orchestration Complex problem decomposition	—

When to Choose AutoGen vs DSPy

Choose AutoGen if you need

Multi-agent debate and collaboration
Code generation with execution verification
Research tasks requiring multiple perspectives

Pricing: Open Source

How to Choose a Agent Frameworks Tool

Key criteria to evaluate when comparing Agent Frameworks solutions:

Programming languagePython, TypeScript, or both — must match your team skills and existing codebase.

Architecture patternSingle-agent, multi-agent, or graph-based orchestration depending on task complexity.

Tool ecosystemBuilt-in tools and ease of creating custom tools for your specific needs.

ObservabilityBuilt-in tracing, debugging, and monitoring for understanding agent behavior.

Production readinessError handling, retries, streaming, and deployment options for production use.

About AutoGen

AutoGen is Microsoft's open-source framework for building multi-agent AI systems. It enables the creation of conversational agents that can work together, use tools, and interact with humans to solve complex tasks. AutoGen supports customizable agent behaviors, flexible conversation patterns, and integrations with various LLMs. The framework is popular for building research assistants, coding agents, and automated analysis pipelines.

View AutoGen profile →Visit website

About DSPy

DSPy is a framework from Stanford for programming—not prompting—foundation models. It replaces manual prompt engineering with composable, optimizable modules. DSPy compilers automatically tune prompts and weights for your specific pipeline and dataset, enabling more reliable LLM applications.

View DSPy profile →Visit website

What is Agent Frameworks?

Developer frameworks and SDKs for building autonomous AI agents with tool use, planning, multi-step reasoning, and orchestration capabilities.

Browse all Agent Frameworks tools →

Frequently Asked Questions

What is an AI agent framework?▾

An agent framework provides the building blocks for creating AI agents that can autonomously plan, use tools, and complete multi-step tasks. Instead of building tool use, memory, and orchestration from scratch, you get pre-built components that handle the common patterns.

Do I need a framework or can I build agents with raw API calls?▾

For simple single-tool agents, raw API calls work fine. Frameworks become valuable when you need multi-step planning, tool orchestration, error recovery, memory, or multi-agent coordination. They save significant development time for complex agent architectures.

Which agent framework should I choose?▾

LangChain and LlamaIndex are the most mature with the largest ecosystems. CrewAI is best for multi-agent workflows. Vercel AI SDK is ideal for TypeScript/Next.js applications. Evaluate based on your language preference, use case complexity, and integration needs.