A technical comparison of LangGraph and Burr, two Python frameworks for building durable, stateful applications in the age of Agentic AI.
Comparison
LangGraph vs Burr (from Hamilton) for Stateful Apps
A technical deep dive comparing LangGraph, an LLM-native orchestration library, and Burr, a general-purpose framework for durable state and event-driven workflows. This analysis helps CTOs and engineering leads choose the right tool for building stateful, agentic applications.
THE ANALYSIS
Introduction: The Stateful Application Dilemma
LangGraph excels at orchestrating LLM-powered agents because it is built on top of LangChain, providing native primitives for tools, chat history, and Runnable components. Its graph-based abstraction, with explicit StateGraph definitions and built-in persistence, is optimized for the non-deterministic, branching nature of LLM reasoning. For example, its checkpointer system can automatically snapshot an agent's state after each node execution, enabling long-running conversations and complex task decomposition without data loss.
Burr (from Hamilton) takes a different approach by being a general-purpose framework for durable state and event-driven workflows, independent of any LLM library. Its core abstraction is a state machine defined by pure functions (@action decorators), resulting in superior debuggability, testability, and deterministic replay of application state. This makes Burr a strong choice for applications where LLM calls are just one type of event in a broader, mission-critical business process that might also involve database transactions or API calls.
The key trade-off: If your priority is rapid development of complex, LLM-centric agents with built-in tooling and community support, choose LangGraph. Its design mirrors common agentic patterns like planning, tool execution, and human-in-the-loop steps. If you prioritize engineering rigor, deterministic state management, and integrating AI into existing, fault-tolerant systems, choose Burr. Its agnosticism and pure-function model offer greater control and reliability for production-grade, stateful applications. For broader context on this architectural choice, see our comparison of LangGraph vs Temporal for Agent Workflows.
HEAD-TO-HEAD COMPARISON
LangGraph vs Burr (from Hamilton) for Stateful Apps
Direct comparison of two Python libraries for building durable, stateful applications and agentic workflows.
| Metric / Feature | LangGraph | Burr (from Hamilton) |
|---|---|---|
Primary Design Goal | LLM Agent & Multi-Agent Orchestration | General-Purpose Stateful Application Framework |
State Persistence Model | Checkpointing to memory/disk (optional) | Built-in durable storage (DB-backed) |
Core Abstraction | Stateful graph (nodes & edges) | State machine (applications & actions) |
Native LLM/Tool Integration | ||
Human-in-the-Loop (HITL) Support | Built-in (interrupts) | Manual implementation required |
Time Travel Debugging | ||
Primary Use Case | Chatbots, Autonomous Agents, RAG Pipelines | Event-driven workflows, Data Pipelines, Backend Services |
LangGraph vs. Burr (Hamilton)
TL;DR: Key Differentiators
LangGraph is purpose-built for LLM agents, while Burr is a general-purpose framework for durable state. Your choice hinges on whether you prioritize LLM-native abstractions or robust, event-driven application logic.
01
Choose LangGraph For LLM-Centric Agents
Native LLM & Tool Integration: Built by LangChain, it provides first-class support for LLM calls, tool execution, and chat history management out-of-the-box. This matters for rapidly prototyping and deploying conversational agents and complex reasoning loops without wiring low-level API calls.
02
Choose Burr For Durable, Event-Driven Apps
General-Purpose State Management: Burr treats state as a first-class citizen, offering built-in persistence, time travel (debugging), and strong typing via Pydantic. This matters for building production-grade, long-running applications (e.g., user onboarding flows, background jobs) where state integrity and auditability are critical beyond just LLM interactions.
03
LangGraph's Graph-Based Control Flow
Explicit, Visualizable Workflows: You define your agent as a state graph with conditional edges (conditional_edge). This provides clear control flow, easy debugging, and built-in persistence for the graph's state. This matters for implementing deterministic multi-step processes like agentic coding, planning, or approval chains.
04
Burr's Action & State Reducer Model
Event-Sourcing Inspired Architecture: Applications are built as a series of actions that reduce application state. This enables powerful features like replayability, easy state inspection, and seamless integration with external event streams. This matters for applications requiring rigorous audit trails, complex business logic, or integration with existing event-driven systems.
CHOOSE YOUR PRIORITY
When to Choose LangGraph vs. Burr
LangGraph for LLM Agents
Verdict: The default choice for building stateful, reasoning-based agents.
Strengths: LangGraph is purpose-built for LLM workflows. Its StateGraph abstraction natively manages LLM context, tool calls, and conditional routing (e.g., tools_condition). It integrates seamlessly with LangChain's tool ecosystem and provides built-in persistence for chat memory. The graph paradigm perfectly maps to agentic reasoning loops (think → act → observe).
Trade-offs: It's specialized. While excellent for agent logic, it's not a general-purpose workflow engine for non-LLM tasks.
Burr for LLM Agents
Verdict: A powerful, lower-level framework for durable, auditable agent execution. Strengths: Burr treats an agent as a state machine with explicit actions and transitions, offering superior control over persistence, replay, and debugging. Every state transition is logged, which is critical for governance in regulated environments. It's framework-agnostic, so you can use any LLM SDK. Trade-offs: Requires more boilerplate to set up standard agent patterns compared to LangGraph's batteries-included approach. It's better for teams needing deep observability over rapid prototyping. Related Reading: For more on multi-agent patterns, see our comparison of LangGraph vs AutoGen.
THE ANALYSIS
Final Verdict and Recommendation
Choosing between LangGraph and Burr hinges on whether your primary focus is LLM-native agent orchestration or building robust, general-purpose stateful applications.
LangGraph excels at building complex, LLM-driven agents and workflows because it is purpose-built for this domain, integrating seamlessly with the LangChain ecosystem. Its primary strength is the StateGraph abstraction, which allows developers to explicitly define and visualize control flow between nodes (like an LLM call or tool execution) with built-in support for cycles, human-in-the-loop checkpoints, and streaming. For example, a customer support agent that routes queries, calls a knowledge base, and escalates to a human can be modeled intuitively as a directed graph, with LangGraph managing the state transitions and context between each step.
Burr (from Hamilton) takes a different approach by providing a general-purpose framework for durable, event-driven state machines. Its core abstraction is an Application, where state is mutated by a series of pure, deterministic Action functions. This results in a trade-off: while not LLM-optimized out of the box, Burr offers superior robustness for mission-critical applications through features like automatic state persistence, time-travel debugging, and exactly-once execution guarantees. Its design prioritizes auditability and fault tolerance over rapid LLM agent prototyping.
The key trade-off is specialization versus generality. If your priority is rapid development of sophisticated AI agents with tight integration to tools, chat models, and retrieval systems, choose LangGraph. Its design patterns and community focus are tailored for this. If you prioritize production resilience, strict state management, and building durable workflows that may include but are not limited to LLM steps, choose Burr. Its engineering rigor ensures your stateful app can handle failures and scale reliably. For broader context on orchestrating these systems, see our guide on Agentic Workflow Orchestration Frameworks and the comparison of LangGraph vs Temporal for Agent Workflows.
Contact
Talk to the team about your AI system.
Share what you are building, where you need help, and what needs to ship next. We will reply with the right next step.
01
NDA available
We can start under NDA when the work requires it.
02
Direct team access
You speak directly with the team doing the technical work.
03
Clear next step
We reply with a practical recommendation on scope, implementation, or rollout.
30m
working session
Direct
team access