Comparisons / BabyAGI vs CrewAI
BabyAGI vs CrewAI: Which Agent Framework to Use?
BabyAGI babyagi popularized the task-driven autonomous agent in ~100 lines of python. CrewAI crewai organizes work into agents, tasks, and crews. Here is how they compare — and what the same patterns look like in plain Python.
By the numbers
BabyAGI
22.2k
2.8k
Python
MIT
2023-04-03
Yohei Nakajima
CrewAI
48.0k
6.5k
Python
MIT
2023-10-27
João Moura
GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.
| Concept | BabyAGI | CrewAI | Plain Python |
|---|---|---|---|
| Agent | Three sub-agents: execution agent, task creation agent, prioritization agent | Agent(role, goal, backstory, tools, llm) | Three LLM calls with different system prompts inside one while loop |
| Tools | Task execution via LLM completion with context from vector DB retrieval | Tool registration with @tool decorator, custom Tool classes | A function that calls the LLM with the task description and relevant context |
| Agent Loop | Pop task → execute → create new tasks → reprioritize → repeat | Internal to Agent execution, hidden from user | A while loop: pop from a list, call LLM, extend the list, sort, repeat |
| Memory | Pinecone or Chroma vector DB storing task results as embeddings | ShortTermMemory, LongTermMemory, EntityMemory | A list of past results; optionally embed and search with a similarity function |
| Task Queue | Deque of task dicts managed by the prioritization agent | — | A Python list of strings, sorted by a priority LLM call or simple heuristic |
| Context Retrieval | Vector similarity search over stored results to build execution context | — | Search your results list for relevant entries, inject the top N into the prompt |
| Task Delegation | — | Crew(agents, tasks, process=sequential/hierarchical) | A task queue processed in a while loop with a budget cap |
| State | — | Task output passed between agents via Crew orchestration | A dict tracking tool calls and results |
What both do in plain Python
Every concept in the table above — agent, tools, loop, memory, state — maps to a handful of Python primitives: a function, a dict, a list, and a while loop. Both BabyAGI and CrewAI wrap these primitives in their own class hierarchies and APIs. The underlying pattern is the same ~60 lines of code. The difference is how much ceremony each framework adds on top.
When to use BabyAGI
BabyAGI proved that an autonomous agent can be elegantly simple — the original was ~100 lines. The value is in the pattern (task creation, execution, prioritization loop), not the framework. You can reimplement it in an afternoon and customize the stopping criteria that BabyAGI leaves open-ended.
What BabyAGI does
BabyAGI runs a loop with three LLM-powered steps. First, an execution agent takes the top task and produces a result, using context retrieved from a vector database of previous results. Second, a task creation agent looks at the result and the objective to generate new tasks. Third, a prioritization agent reorders the task list based on the objective. The loop repeats until the task queue is empty or a limit is reached. Created by Yohei Nakajima in 2023, the original was about 100 lines of Python — deliberately minimal to show that the pattern, not the framework, is what matters. It inspired dozens of agent frameworks and proved that task decomposition could be surprisingly simple.
The plain Python equivalent
The BabyAGI pattern translates directly to plain Python. A while loop pops tasks from a list. For each task, you make an LLM call with the task description and any relevant context from previous results. You append the result to a results list. Then you make a second LLM call asking for new tasks based on the result and objective, and extend your task list. Optionally, a third call reprioritizes — or you just sort by a simple heuristic. The vector database becomes a list you search with cosine similarity, or even just keyword matching for simple cases. The whole thing fits in 40-60 lines without any external dependencies beyond an HTTP client.
When to use CrewAI
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.
What CrewAI does
CrewAI models multi-agent systems as a crew of specialists. Each Agent has a role ("Senior Researcher"), a goal ("Find the best data sources"), a backstory that shapes its behavior, and a set of tools it can use. Tasks define discrete units of work with expected outputs. The Crew orchestrates execution — sequentially, hierarchically, or with a custom process. CrewAI also provides memory systems (short-term, long-term, entity) and delegation, where one agent can hand off subtasks to another. The mental model is a team of people collaborating on a project. For prototyping multi-agent workflows where you want to reason about roles and responsibilities, it provides a clean vocabulary.
The plain Python equivalent
An Agent in CrewAI is a function with a system prompt that includes the role, goal, and backstory. The tools dict maps names to callables. Task delegation is a list of tasks processed in order — each task calls the assigned agent function with the task description appended to the messages. Hierarchical execution is a manager agent that decides which sub-agent to call next (just another tool choice). Memory is a dict injected into the system prompt. The entire crew pattern — multiple agents, task queue, delegation — is a for-loop over tasks, where each iteration calls the right agent function. No Crew class, no process kwarg. Just functions calling functions with a shared state dict passed between them.
Or build your own in 60 lines
Both BabyAGI and CrewAI 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.
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