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Head-to-head · LLM Application Development Frameworks

LangGraph vs Apache Burr

  • Updated Oct 2026
  • Both researched from official sources
  • 5 checks side by side
Higher score LangGraph #2 in LLM Application Development Frameworks 9.2/10 Free plan Free plan✓ 0 of 5 features Visit LangGraph
Apache Burr #6 in LLM Application Development Frameworks 8.8/10 Free plan Free plan✓ 3 of 5 features Visit Apache Burr

LangGraph leads on 0 checks, Apache Burr on 3, and 2 are even. Who comes out ahead on the 5 yes/no, price and count checks where we have data for both products. The editor score weighs everything else too.

Our verdict

  • Highest scoreLangGraph · 9.2/10
  • Free planboth
  • Most featuresApache Burr · 3 of 5

LangGraph scores higher on our rubric for llm application development frameworks: 9.2 against 8.8 out of 10; our editors rank them #2 and #6.

Apache Burr offers rag support; LangGraph doesn't publish it. Apache Burr offers agent and tool use; LangGraph doesn't publish it. Apache Burr offers workflow graphs; LangGraph doesn't publish it.

LangGraph is the better fit for developers building durable, stateful agent workflows. Apache Burr is the better fit for stateful, inspectable workflow applications.

  • LangGraph fits best

    Developers building durable, stateful agent workflows

  • Apache Burr fits best

    Stateful, inspectable workflow applications

Advertiser disclosure: iTechGuides is reader-supported. We may earn a commission when you click some links. How we rank.

Side by side

Feature LangGraph 9.2/10 Visit ↗ Apache Burr 8.8/10 Visit ↗
At a glance
Editor score 9.2 8.8
Ranking #2 in LLM Application Development Frameworks #6 in LLM Application Development Frameworks
Best for Developers building durable, stateful agent workflows Stateful, inspectable workflow applications
Pricing model Free Free
Starting price Not published Not published
Free plan ✓ ✓
Free trial — —
Deployment Self-hosted, Cloud Self-hosted
Integrations 2 integrations 15 integrations
Built for Solo, Small business, Mid-market, Enterprise Solo, Small business, Mid-market, Enterprise
Features LangGraph 0/5 · Apache Burr 3/5
RAG support Not published ✓ (best)
Agent and tool use Not published ✓ (best)
Workflow graphs Not published ✓ (best)
Prompt optimization Not published Not published
JavaScript/TypeScript SDK Not published —
Specs
Primary language Not published Python
Open-source license Not published Apache-2.0
Our review
Pros
  • Combines deterministic code and LLM-driven steps in graph workflows
  • Supports durable execution, checkpointing, and resumption after failures
  • Offers memory, streaming, and human review of agent state
  • Immutable state, persistence, and resumable human-input pauses
  • Parallel actions and branches support complex workflows
  • Burr UI exposes execution telemetry for inspection and debugging
Cons
  • Low-level control requires developers to define workflow behavior
  • Does not prescribe a high-level agent architecture
  • Verified integrations are narrower than LangChain’s
  • Python-focused; no official JavaScript or TypeScript SDK is identified
  • Requires developers to implement LLM calls within actions
  • Fewer ready-made model integrations and agent capabilities than some alternatives
Our verdict

LangGraph is an open-source framework and runtime for developers building long-running, stateful agents and workflows. Its graph model combines deterministic code with model-driven steps, and supports single-agent, multi-agent, and…

Read the review →

Apache Burr is an open-source Python framework for building applications from actions, transitions, and explicit state. It is aimed at developers orchestrating chatbots, agents, conversational retrieval workflows, and other applications…

Read the review →
  1. LangGraphLLM Application Development Frameworks 9.2Free plan
  2. Apache BurrLLM Application Development Frameworks 8.8Free plan

Strengths and trade-offs

  • LangGraph — where it wins

    • Combines deterministic code and LLM-driven steps in graph workflows
    • Supports durable execution, checkpointing, and resumption after failures
    • Offers memory, streaming, and human review of agent state

    Where it doesn't

    • Low-level control requires developers to define workflow behavior
    • Does not prescribe a high-level agent architecture
    • Verified integrations are narrower than LangChain’s

  • Apache Burr — where it wins

    • Immutable state, persistence, and resumable human-input pauses
    • Parallel actions and branches support complex workflows
    • Burr UI exposes execution telemetry for inspection and debugging

    Where it doesn't

    • Python-focused; no official JavaScript or TypeScript SDK is identified
    • Requires developers to implement LLM calls within actions
    • Fewer ready-made model integrations and agent capabilities than some alternatives

More comparisons

All LLM application development frameworks comparisons → · Full ranking →

Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

Last updated · How we research and update