Comparisons / CrewAI vs DSPy

CrewAI vs DSPy: Which Agent Framework to Use?

CrewAI vs DSPy, head to head

CrewAI 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.

CrewAI organizes work into Agents, Tasks, and Crews.

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 CrewAI if

Pick CrewAI if crewAI shines for multi-agent setups where you want named roles ("researcher", "writer"). But the core mechanics — tool dispatch, the agent loop, task scheduling — are the same patterns you can build in plain Python. The tradeoffs in its intro should match how your team already thinks about agents; DSPy will feel like translation if they don't.

Full CrewAIcomparison →

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; CrewAI 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. CrewAI 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

CrewAI

GitHub Stars

48.0k

Forks

6.5k

Language

Python

License

MIT

Created

2023-10-27

Created by

João Moura

github.com/crewAIInc/crewAI

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.

ConceptCrewAIDSPy
Agent`Agent(role, goal, backstory, tools, llm)``dspy.ReAct` module with signature and tools
ToolsTool registration with `@tool` decorator, custom `Tool` classesTools passed to `ReAct` module as callable list
Agent LoopInternal to `Agent` execution, hidden from user
Task Delegation`Crew(agents, tasks, process=sequential/hierarchical)`
Memory`ShortTermMemory`, `LongTermMemory`, `EntityMemory`
StateTask output passed between agents via `Crew` orchestration
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 CrewAI 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 →