Comparisons / CrewAI vs DSPy
CrewAI vs DSPy: Which Agent Framework to Use?
CrewAI organizes work into Agents, Tasks, and Crews. DSPy replaces hand-written prompts with compiled modules. Here is how they compare — paradigm, ecosystem, and the use cases each one is actually built for.
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 |
CrewAI vs DSPy, head to head
CrewAI CrewAI organizes work into Agents, Tasks, and Crews.
DSPy DSPy replaces hand-written prompts with compiled modules.
Both wrap the same underlying agent pattern — an LLM call, a tool dispatch, a loop — in different abstractions. The choice between them is mostly about which mental model and ecosystem fits the team you have, not which one is technically more capable.
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. CrewAI is the right fit when the tradeoffs in its intro line up with how your team actually wants to work day-to-day; DSPy would force you to translate.
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. DSPy is the right fit when the tradeoffs in its intro line up with how your team actually wants to work day-to-day; CrewAI would force you to translate.
What both add
Both CrewAI and DSPy pull in a class hierarchy and a dependency tree to wrap what is, at the core, an HTTP POST in a while loop. If your use case is straightforward — one provider, a handful of tools, a single agent — the framework cost may exceed the framework benefit. The lesson below shows the same pattern in ~60 lines without either dependency.
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 →