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
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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
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