AutoGen vs Haystack: Which Agent Framework to Use?

Paradigm

AutoGen treats every actor as a ConversableAgent and frames the whole system as a chat — initiate_chat(), GroupChat, GroupChatManager, and register_nested_chats() for sub-tasks. Haystack treats the system as a typed DAG: a Pipeline of @component classes wired through add_component() and connect(), with Agent being just one node alongside Retriever, PromptBuilder, and ChatGenerator.

In short: AutoGen's primitive is a message between agents, Haystack's is a tensor of typed I/O between components.

Ecosystem

AutoGen ships speaker-selection strategies, code-execution sandboxes, and is_termination_msg callbacks — its center of gravity is agent choreography. Haystack ships DocumentStore integrations (Elasticsearch, Qdrant, Pinecone, Weaviate), PDF/HTML converters, embedders, rankers, and Hayhooks for REST deployment — its center of gravity is retrieval and document processing.

Both wrap the same /chat/completions call underneath, but the surface area you import is completely different: AutoGen pulls in conversation orchestration; Haystack pulls in an indexing stack.

Use case

Reach for AutoGen when the hard part is multiple agents arguing toward an answer — author/reviewer loops, planner/executor splits, debate-style refinement where the next speaker isn't known statically. Reach for Haystack when the hard part is getting the right documents into the prompt — hybrid sparse+dense retrieval, re-ranking, multi-format ingestion, swappable vector stores.

A single tool-calling agent fits both frameworks awkwardly: AutoGen wraps it in a chat that has nobody to chat with; Haystack wraps it in a Pipeline that has nothing to pipe.

Pick AutoGen if your project lives or dies on multiple agents talking to each other with non-trivial turn-taking logic.

• Dynamic speaker selection: You need GroupChatManager to pick the next agent via LLM routing, round-robin, or custom logic — not a hardcoded sequence.
• Nested sub-conversations: An agent should pause the main thread, spin up a sub-chat via register_nested_chats(), and inject the result back without you hand-rolling a task queue.
• Sandboxed code execution: Your agents write and run Python as part of the conversation, and you'd rather use AutoGen's executor than build container isolation yourself.

Pick Haystack if your project lives or dies on the retrieval pipeline, not the agent loop.

• Multi-stage RAG: You want sparse + dense retrievers, a re-ranker, and a PromptBuilder wired through Pipeline.connect() with typed contracts catching mismatches at build time.
• Document store portability: You need to swap Elasticsearch for Qdrant or Weaviate without rewriting the pipeline — the DocumentStore interface is doing real work for you.
• Config-as-deployment: YAML serialization plus Hayhooks lets ops or non-developers version pipelines and deploy them as REST endpoints without touching Python.

Both frameworks bring a worldview before they bring features. AutoGen wants you to think in ConversableAgent and GroupChat; Haystack wants you to think in @component classes with typed run() signatures. If your actual problem is a single agent calling three tools in a loop, you're paying for orchestration or graph machinery that has nothing to do.

There's also the dependency tail: Microsoft's agent stack on one side, deepset's retrieval stack with optional vector DB clients on the other. Onboarding a new engineer means teaching the framework's mental model before they touch your business logic — fine when the abstractions earn it, friction when they don't.