LangGraph vs Burr (from Hamilton) for Stateful Apps

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.

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.