Comparisons / CrewAI vs Haystack
CrewAI vs Haystack: Which Agent Framework to Use?
CrewAI crewai organizes work into agents, tasks, and crews. Haystack haystack by deepset is a framework for building nlp and llm pipelines. Here is how they compare — and what the same patterns look like in plain Python.
By the numbers
CrewAI
48.0k
6.5k
Python
MIT
2023-10-27
João Moura
Haystack
24.7k
2.7k
Python
Apache-2.0
2019-11-14
deepset
GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.
| Concept | CrewAI | Haystack | Plain Python |
|---|---|---|---|
| Agent | Agent(role, goal, backstory, tools, llm) | Agent component with ChatGenerator, tool definitions, and message routing | A function with a system prompt and a tools dict |
| Tools | Tool registration with @tool decorator, custom Tool classes | Tool dataclass with function reference, name, description, parameters schema | A dict: tools[name](**args) |
| Agent Loop | Internal to Agent execution, hidden from user | — | A while loop over messages with tool_calls check |
| Task Delegation | Crew(agents, tasks, process=sequential/hierarchical) | — | A task queue processed in a while loop with a budget cap |
| Memory | ShortTermMemory, LongTermMemory, EntityMemory | ChatMessageStore with ConversationMemory component in pipeline | A dict injected into the system prompt |
| State | Task output passed between agents via Crew orchestration | — | A dict tracking tool calls and results |
| Pipeline Architecture | — | Pipeline() with add_component() and connect() — a directed graph of typed components | A sequence of function calls: output = step_b(step_a(input)) |
| RAG / Retrieval | — | DocumentStore + Retriever + PromptBuilder + Generator wired in a Pipeline | Embed the query, search a list, inject matches into the prompt, call the LLM |
| Deployment | — | Pipeline YAML serialization, Hayhooks REST server | A Python script behind FastAPI or any HTTP server |
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 CrewAI and Haystack 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 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.
When to use Haystack
Haystack earns its complexity when you're building RAG pipelines with multiple retrieval stages, document processing, and production deployment needs. But for straightforward agents with a few tools, the plain Python version is simpler to write and debug.
What Haystack does
Haystack models NLP/LLM applications as directed graphs of components. You create a Pipeline, add components (retrievers, generators, converters, rankers), and connect their inputs and outputs. Each component is a Python class with a @component decorator and a run() method with typed inputs and outputs. The framework handles data routing between components, input validation, and pipeline serialization to YAML. Haystack ships with document stores (Elasticsearch, Qdrant, Pinecone, Weaviate), embedding models, converters for PDFs and HTML, and a ChatGenerator that wraps LLM API calls. The Agent component adds tool-using capabilities on top. For RAG pipelines with multiple retrieval stages, re-ranking, and document processing, the component model provides clear separation of concerns.
The plain Python equivalent
A Haystack Pipeline is a sequence of function calls. The Retriever is a function that takes a query, embeds it, and searches a list of documents. The Generator is a function that calls the LLM API. The PromptBuilder is string formatting. Connecting components is passing the output of one function as the input to the next. The Agent component is the same while loop every agent framework uses — call the LLM, check for tool_calls, dispatch, append results, repeat. Document stores are a list you search with cosine similarity, or a database query. YAML serialization is saving your config to a file. The entire RAG pipeline — embed query, retrieve documents, build prompt, call LLM — fits in about 30 lines. Add tool-using agent behavior and you're at 60.
Or build your own in 60 lines
Both CrewAI and Haystack 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 →