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AI · head to head

AutoGen vs Haystack

AutoGen logo

AutoGen

AI

Programming framework for multi-agent agentic AI

From
Free
Rated
-
Full reviewWebsite
Haystack logo

Haystack

Machine Learning

Open-source AI orchestration framework for LLM applications

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: AutoGen framework now in maintenance mode, no new features planned; Haystack requires Python programming knowledge for advanced customization
  • They diverge on capability: AutoGen covers Multi-agent orchestration, Haystack covers Modular pipeline composition.
  • Prices and features above were last checked on 29 August 2026.

Where they differ

Only the attributes on which AutoGen and Haystack actually diverge.

Attributes where AutoGen and Haystack differ
AttributeAutoGenHaystack
Pricing modelOpen source, no pricingOpen-source with optional paid enterprise support
PlatformsPython, .NETPython, Cloud-agnostic
CategoryAIMachine Learning

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated).

What each one covers

Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.

Only in AutoGen

  • Multi-agent orchestration
  • Message passing API
  • AgentChat API
  • Extensions API
  • MCP server support
  • AutoGen Studio
  • Cross-language support
  • Observable agent networks

Only in Haystack

  • Modular pipeline composition
  • Multi-provider LLM support
  • Retrieval-augmented generation
  • Agent framework
  • Memory management
  • Observability and debugging
  • Kubernetes-ready deployment

What people use each for

The jobs each tool is most often brought in to do.

AutoGen

  • Building multi-agent conversational systemsnot Haystack
  • Rapid prototyping of agent applicationsnot Haystack
  • Research on agentic AI patterns and architecturesnot Haystack
  • Distributed agent networks across boundariesnot Haystack

Haystack

  • Building production LLM applications with full controlnot AutoGen
  • Creating retrieval-augmented generation systemsnot AutoGen
  • Developing autonomous AI agentsnot AutoGen
  • Multi-provider LLM orchestrationnot AutoGen
  • Enterprise AI infrastructurenot AutoGen

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

AutoGen

  • Framework now in maintenance mode, no new features planned
  • Steeper learning curve for advanced use cases
  • Microsoft recommends new projects use Agent Framework instead
  • Limited to Python and .NET platforms

Haystack

  • Requires Python programming knowledge for advanced customization
  • Steeper learning curve compared to no-code platforms
  • Community support only on free tier may limit enterprise adoption
  • Ongoing maintenance dependency for open-source framework

Pricing, plan by plan

AutoGen

Free
  • Open SourceFree
    • MIT and CC-BY-4.0 licenses
    • Full framework access
    • Community support

Haystack

Free
  • Open SourceFree
    • Full framework access
    • Community Discord support
    • GitHub community contributions
  • Enterprise Support$undefined/custom
    • Private secure engineering support
    • Best practices templates and deployment guides
    • Flexible services and integrations

Which should you pick?

Choose AutoGen if

  • You need multi-agent orchestration.
  • You want to start without paying.
  • You work on Python, .NET.
  • You also want message passing api.
AutoGen review

Choose Haystack if

  • You need modular pipeline composition.
  • You want to start without paying.
  • You work on Python, Cloud-agnostic.
  • You also want multi-provider llm support.
Haystack review

Questions people ask

Is AutoGen or Haystack better?
Neither clearly leads. AutoGen starts at Free and Haystack at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, AutoGen or Haystack?
AutoGen starts at Free and Haystack at Free.
Does AutoGen or Haystack run on more platforms?
AutoGen runs on Python, .NET. Haystack runs on Python, Cloud-agnostic.
Can I use AutoGen for free?
Both have a free tier, so you can try either at no cost before committing.
What is AutoGen best used for?
AutoGen is most often used for building multi-agent conversational systems, rapid prototyping of agent applications, research on agentic ai patterns and architectures, distributed agent networks across boundaries. Of those, building multi-agent conversational systems and rapid prototyping of agent applications are not what Haystack is typically brought in for.
What can AutoGen do that Haystack cannot?
AutoGen covers Multi-agent orchestration, Message passing API, AgentChat API, Extensions API. Haystack covers Modular pipeline composition, Multi-provider LLM support, Retrieval-augmented generation, Agent framework.

Answered from the vendors’ own pages

AutoGen: Is AutoGen still actively developed?

As of March 2026, AutoGen is in maintenance mode and will not receive new features. Microsoft recommends new projects use the Microsoft Agent Framework instead.

Source
Haystack: Is Haystack completely free to use?

Yes, the open-source Haystack framework is completely free. deepset offers optional paid enterprise support packages for organizations needing secure engineering support and deployment guidance.

Source
AutoGen: Can I still use AutoGen for new projects?

While AutoGen is stable and maintained for existing projects, Microsoft recommends using the Microsoft Agent Framework for new development.

Source
Haystack: What LLM providers does Haystack support?

Haystack supports multiple LLM providers including OpenAI, Anthropic, Mistral, Cohere, and others, allowing teams to avoid vendor lock-in and switch providers as needed.

Source
AutoGen: What LLM providers does AutoGen support?

AutoGen includes extensions for OpenAI and Azure OpenAI through its Extensions API, with community support for other providers.

Source
Haystack: Can I deploy Haystack in production environments?

Yes, Haystack is designed for production use with Kubernetes-ready pipelines, built-in reliability features, and observability tools for enterprise-scale deployments.

Source
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