Comparisons / AutoGen vs Haystack
AutoGen vs Haystack: Which Agent Framework to Use?
AutoGen autogen by microsoft models agents as conversableagents that chat with each other. 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
AutoGen
56.7k
8.5k
Python
CC-BY-4.0
2023-08-18
Microsoft Research
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 | AutoGen | Haystack | Plain Python |
|---|---|---|---|
| Agent | ConversableAgent with system_message, llm_config | Agent component with ChatGenerator, tool definitions, and message routing | A function with a system prompt that POSTs to the LLM API |
| Tools | register_for_llm() and register_for_execution() | Tool dataclass with function reference, name, description, parameters schema | A dict of callables + JSON schema descriptions |
| Conversation | Two-agent chat with initiate_chat(), message history | — | A messages array that grows with each turn |
| Multi-Agent | GroupChat with GroupChatManager, speaker selection | — | Multiple agent functions called in sequence on shared messages |
| Nested Chats | register_nested_chats() for sub-task handling | — | A task queue (BFS) — agent schedules follow-ups via a tool |
| Termination | is_termination_msg callback, max_consecutive_auto_reply | — | The while loop exits when no tool_calls or max_turns reached |
| 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 |
| Memory | — | ChatMessageStore with ConversationMemory component in pipeline | A messages list that persists outside the function |
| 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 AutoGen 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 AutoGen
AutoGen excels at complex multi-agent workflows where agents need to debate or collaborate. For single-agent use cases or simple tool-calling agents, the plain Python version is significantly simpler.
What AutoGen does
AutoGen's core abstraction is the ConversableAgent — an agent that can send and receive messages. Two agents chat by alternating turns on a shared message history. GroupChat extends this to N agents, with a GroupChatManager that selects the next speaker (round-robin, random, or LLM-based selection). Nested chats allow an agent to spin up a sub-conversation to handle a complex subtask before returning to the main thread. AutoGen also provides code execution sandboxes, letting agents write and run code as part of their conversation. The framework thinks in terms of conversations, not chains or graphs. This makes it natural for workflows where agents need to debate, critique, or iteratively refine outputs together.
The plain Python equivalent
A ConversableAgent is a function that takes a messages array, calls the LLM with a system prompt, and returns the assistant message. Two-agent chat is a while loop where you alternate between calling agent_a(messages) and agent_b(messages), appending each response. GroupChat is the same loop but with a speaker selection step — either rotate through a list or ask the LLM "who should speak next?" and call that agent function. Nested chats are a function call within the loop: pause the main conversation, run a sub-loop with different agents, and inject the result back. Tool registration is adding functions to a tools dict with their JSON schemas. The conversation-as-primitive model is just messages arrays passed between functions.
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 AutoGen 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.
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