Technology Radar
Since the previous Radar, we’ve observed that the LangGraph architecture — which treats every multi-agent system as stateful graphs with a global shared state — is not always the best approach for building agentic systems. We’ve also seen an alternative approach, used in frameworks such as Pydantic AI, that also works well.
Instead of starting with a rigid graph and a massive shared state, this approach favors simple agents communicating through code execution, with graph structures added later when needed. It often results in leaner and more effective systems for many use cases. Because each agent only has access to the state it needs, reasoning, testing and debugging become easier. As a result, we’ve moved LangGraph out of Adopt. While it remains a powerful tool, we no longer see it as the default choice for building every agentic system.
LangGraph is an orchestration framework for building stateful multi-agent applications using LLMs. It provides low-level primitives such as nodes and edges, along with built-in features that give developers granular control over agent workflows, memory management and state persistence. This means developers can start with a simple pre-built graph and scale to complex, evolving agent architectures. With support for streaming, advanced context management and resilience patterns like model fallbacks and tool error handling, LangGraph enables you to build robust, production-grade agentic applications. Its graph-based approach ensures predictable, customizable workflows and simplifies debugging and scaling. Our teams have had strong results using LangGraph to build multi-agent systems thanks to its lightweight and modular design.
LangGraph is an orchestration framework designed to build stateful multi-agent applications using LLMs. It provides a lower-level set of primitives like edges and nodes compared to LangChain’s higher-level abstractions, offering developers fine-grained control over agent workflows, memory management and state persistence. This graph-based approach ensures predictable and customizable workflows, making debugging, scaling and maintaining production applications easier. Although it has a steeper learning curve, LangGraph's lightweight design and modularity make it a powerful framework for creating agentic applications.
