Microsoft AutoGen vs LlamaIndex

Detailed side-by-side comparison to help you choose the right tool

Microsoft AutoGen

AI Automation Platforms

Microsoft's open-source framework for building multi-agent AI systems with asynchronous, event-driven architecture.

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Starting Price

Free

LlamaIndex

🔴Developer

AI agent framework

LlamaIndex is an open-source Python and TypeScript framework for building RAG, document workflows, and AI agents — with LlamaCloud for managed parsing, extraction, and indexing.

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Starting Price

Free

Feature Comparison

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FeatureMicrosoft AutoGenLlamaIndex
CategoryAI Automation PlatformsAI agent framework
Pricing Plans11 tiers8 tiers
Starting PriceFreeFree
Key Features
  • Multi-agent conversation orchestration with flexible topologies
  • Built-in observability via OpenTelemetry integration
  • Cross-language interoperability between Python and .NET
  • LlamaParse for 50+ unstructured file types
  • Document parsing, extraction, indexing, and retrieval
  • Open-source repos plus LiteParse for local document parsing

Microsoft AutoGen - Pros & Cons

Pros

  • MIT-licensed open source with active development
  • Backed by Microsoft Research with strong academic foundations
  • v0.4's async event-driven architecture enables scalable agent systems
  • Native cross-language support for Python and .NET
  • AutoGen Studio provides a no-code interface for rapid prototyping
  • Tight Azure AI Foundry integration for enterprise deployment

Cons

  • Microsoft's agent strategy is evolving; monitor official announcements for roadmap changes
  • v0.4 introduced major breaking changes from v0.2, requiring significant migration effort
  • Steep learning curve compared to simpler frameworks like CrewAI
  • AutoGen Studio is experimental and not production-ready
  • No commercial support tier outside of Azure AI Foundry

LlamaIndex - Pros & Cons

Pros

  • Best-in-class retrieval strategies: hybrid, parent-child, summary indexes, knowledge graphs
  • LlamaParse is the strongest PDF/document parser for enterprise RAG today
  • Open-source library is MIT-licensed and runs anywhere
  • Workflows agent layer is a clean alternative to LangGraph for stateful task graphs
  • 10,000 free LlamaCloud credits make evaluation painless

Cons

  • LlamaCloud paid pricing is credit-based and harder to model than seat pricing
  • Workflows ecosystem is younger than LangGraph's; fewer multi-agent examples in the wild
  • Library API has churned over major releases — older tutorials are often out of date
  • Visual builder UX is not part of the product; teams that want no-code go elsewhere
  • Pure agent orchestration with complex branching is still cleaner in LangGraph

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🔒 Security & Compliance Comparison

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Security FeatureMicrosoft AutoGenLlamaIndex
SOC2
GDPR
HIPAA
SSO🏢 Enterprise
Self-Hosted✅ Yes🔀 Hybrid
On-Prem✅ Yes
RBAC
Audit Log
Open Source✅ Yes✅ Yes
API Key Auth✅ Yes
Encryption at Rest
Encryption in Transit
Data Residencynot publicly confirmed
Data Retentionconfigurablecached data retained for 48 hours by default for LlamaParse, with caching optional
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