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.
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.
By the numbers
By the numbers
CrewAI
48.0k
6.5k
Python
MIT
2023-10-27
João Moura
DSPy
33.4k
2.8k
Python
MIT
2023-01-09
Stanford NLP (Omar Khattab)
GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.
| Concept | CrewAI | DSPy |
|---|---|---|
| Agent | `Agent(role, goal, backstory, tools, llm)` | `dspy.ReAct` module with signature and tools |
| Tools | Tool registration with `@tool` decorator, custom `Tool` classes | Tools passed to `ReAct` module as callable list |
| Agent Loop | Internal to `Agent` execution, hidden from user | — |
| Task Delegation | `Crew(agents, tasks, process=sequential/hierarchical)` | — |
| Memory | `ShortTermMemory`, `LongTermMemory`, `EntityMemory` | — |
| State | Task 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 →