LangGraph: Planning Agents

In this video, we will show you how to build three plan-and-execute style agents using LangGraph, an open-source framework for building stateful, multi-actor AI applications. These agents promise the following properties relative to older “ReACT”-style agents: ⏰ Faster Execution: fewer calls to large models, and execution of tools while the LLM is still decoding 💸 Cost Efficiency: you can use smaller, domain-specific models for sub-tasks 🏆 Enhanced Performance: explicit planning forces the LLM to think about the whole trajectory Links ----------- Basic Plan-and-Execute - Python: https://github.com/langchain-ai/langgraph/blob/main/examples/plan-and-execute/plan-and-execute.ipynb - JS: https://github.com/langchain-ai/langgraphjs/blob/main/examples/plan-and-execute/plan-and-execute.ipynb - Plan and solve paper: https://arxiv.org/abs/2305.04091 ReWOO - Python: https://github.com/langchain-ai/langgraph/blob/main/examples/rewoo/rewoo.ipynb - Paper: https://arxiv.org/abs/2305.18323 LLMCompiler - Python: https://github.com/langchain-ai/langgraph/blob/main/examples/llm-compiler/LLMCompiler.ipynb - Paper: https://arxiv.org/abs/2312.04511 Developing AI applications is easier with LangSmith. Create a free account at https://smith.langchain.com/ By: Will Fu-Hinthorn