langchain-langgraph

You build an AI agent with LangChain and LangGraph by defining tools, reasoning logic, and state machines to orchestrate multi-step workflows. This enables creating intelligent agents capable of accessing knowledge bases and escalating complex issues.

Retrieval-augmented generation is implemented by integrating context-aware retrieval pipelines into your AI agents. This approach allows agents to access external knowledge bases, providing accurate, context-aware responses for complex queries.

Yes, you need LLM API keys to run LangGraph state machines. Execution requires specific Python libraries including langchain-anthropic and langchain-openai, along with API credentials to power the underlying language models.

Yes, you can use LangGraph for complex orchestration pipelines. It provides state machine capabilities to design multi-step workflows, allowing you to build advanced AI applications like customer support agents with predefined escalation logic.

A ReAct agent works by combining reasoning and action within a LangChain pipeline, enabling the agent to search the web and perform calculations. It uses tools and memory to process inputs and execute multi-step orchestration tasks.

Limitations of building AI pipelines with LangGraph include the strict dependency on specific Python libraries like langmem and external LLM API keys. Complex state machines also require advanced orchestration design to manage reasoning, tools, and memory effectively.