LangChain on X: "💨 Plan-and-Execute Agents We've added new documentation on how to make plan-and-execute style agents using LangGraph. Relative to older agent designs, these agents promise: 🔹 Faster Execution: fewer calls to large models, and execution of tools while the LLM is still https://t.co/
Quick Answer
LangChain introduces new documentation for plan-and-execute agents using LangGraph, promising faster execution with fewer calls to large models, cost efficiency through smaller domain-specific models, and enhanced performance via explicit planning.
Quick Take
This approach allows tools to be executed while the is still decoding, optimizing overall task execution.
Key Points
- Faster execution with fewer calls to large models.
- Cost efficiency by using smaller, domain-specific models for sub-tasks.
- Enhanced performance through explicit planning of the entire trajectory.
- Allows tool execution while the LLM is still decoding.
- New documentation and tutorial available for implementation.
📖 Reader Mode
~1 min read💨 Plan-and-Execute Agents We've added new documentation on how to make plan-and-execute style agents using LangGraph. Relative to older agent designs, these agents promise: 🔹 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 and avoid redundant computation 🔹 Enhanced Performance: explicit planning forces the LLM to think about the whole trajectory Check out the overview here: blog.langchain.dev/planning-agent… Or the Youtube tutorial: youtu.be/uRya4zRrRx4
— Originally published at x.com
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