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Head-to-head · LLM Application Development Frameworks

LangChain vs Microsoft Agent Framework

  • Updated Oct 2026
  • Both researched from official sources
  • 1 check side by side
Higher score LangChain #1 in LLM Application Development Frameworks 9.4/10 Free plan Free plan✓ 0 of 5 features Visit LangChain

LangChain leads on 0 checks, Microsoft Agent Framework on 0, and 1 is even. Who comes out ahead on the 1 yes/no, price and count check where we have data for both products. The editor score weighs everything else too.

Our verdict

  • Highest scoreLangChain · 9.4/10
  • Free planboth

LangChain scores higher on our rubric for llm application development frameworks: 9.4 against 9.1 out of 10; our editors rank them #1 and #3.

LangChain is the better fit for general-purpose LLM app and agent development. Microsoft Agent Framework is the better fit for multi-agent workflows across model providers.

  • LangChain fits best

    General-purpose LLM app and agent development

  • Microsoft Agent Framework fits best

    Multi-agent workflows across model providers

Advertiser disclosure: iTechGuides is reader-supported. Vendors can pay for top positions in our rankings and for a place on other products' pages, and we may earn a commission when you click some links. How we rank.

Side by side

Feature LangChain 9.4/10 Visit ↗ Microsoft Agent Framework 9.1/10 Visit ↗
At a glance
Editor score 9.4 9.1
Ranking #1 in LLM Application Development Frameworks #3 in LLM Application Development Frameworks
Best for General-purpose LLM app and agent development Multi-agent workflows across model providers
Pricing model Free Free
Starting price Not published Not published
Free plan ✓ ✓
Free trial — —
Deployment Self-hosted Cloud, Self-hosted
Support Community, Docs Docs, Community
Integrations 1,000+ integrations 8 integrations
Built for Solo, Small business, Mid-market, Enterprise Small business, Mid-market, Enterprise
Features LangChain 0/5 · Microsoft Agent Framework 0/5
RAG support Not published Not published
Agent and tool use Not published Not published
Workflow graphs Not published Not published
Prompt optimization Not published Not published
JavaScript/TypeScript SDK Not published Not published
Specs
Primary language Not published Not published
Open-source license Not published Not published
Our review
Pros
  • Connects applications to a broad range of models, tools, databases, and data sources
  • Provides document-processing and retrieval components for RAG workflows
  • Supports human approval and durable agent execution through LangGraph
  • Supports sequential, concurrent, handoff, group-chat, and Magentic orchestration
  • Adds human pauses, tool-approval gates, checkpoints, and workflow resumption
  • Exports workflow telemetry and topology; supports OpenTelemetry tracing
Cons
  • Requires developers to run it in their own application environment
  • Its broad set of abstractions may exceed the needs of a narrowly scoped app
  • Tracing, evaluation, and deployment workflows are associated with separate LangSmith
  • Requires development work rather than offering a visual builder
  • Cloud and model-service costs depend on chosen infrastructure and providers
  • Support channels are documentation and community
Our verdict

LangChain is an open-source framework for developers building LLM applications and agents. It is aimed at a wide range of project sizes, from solo developers to enterprise teams, and runs self-hosted in application code. Its scope covers…

Read the review →

Microsoft Agent Framework is an open-source developer framework for building AI agents and multi-agent workflows with Python, .NET, and Go. Its shared agent interface, tools, middleware, and orchestration options suit teams that want to…

Read the review →
  1. LangChainLLM Application Development Frameworks 9.4Free plan
  2. Microsoft Agent FrameworkLLM Application Development Frameworks 9.1Free plan

Strengths and trade-offs

  • LangChain — where it wins

    • Connects applications to a broad range of models, tools, databases, and data sources
    • Provides document-processing and retrieval components for RAG workflows
    • Supports human approval and durable agent execution through LangGraph

    Where it doesn't

    • Requires developers to run it in their own application environment
    • Its broad set of abstractions may exceed the needs of a narrowly scoped app
    • Tracing, evaluation, and deployment workflows are associated with separate LangSmith
  • Microsoft Agent Framework — where it wins

    • Supports sequential, concurrent, handoff, group-chat, and Magentic orchestration
    • Adds human pauses, tool-approval gates, checkpoints, and workflow resumption
    • Exports workflow telemetry and topology; supports OpenTelemetry tracing

    Where it doesn't

    • Requires development work rather than offering a visual builder
    • Cloud and model-service costs depend on chosen infrastructure and providers
    • Support channels are documentation and community

More comparisons

Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

Last updated · How we research and update