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  • LLM Agents Example

    A Python sample demonstrating LLM-based AI agents with integrated tools like search, code execution, and QA.
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    What is LLM Agents Example?
    LLM Agents Example provides a hands-on codebase for building AI agents in Python. It demonstrates registering custom tools (web search, math solver via WolframAlpha, CSV analyzer, Python REPL), creating chat and retrieval-based agents, and connecting to vector stores for document question answering. The repo illustrates patterns for maintaining conversational memory, dispatching tool calls dynamically, and chaining multiple LLM prompts to solve complex tasks. Users learn how to integrate third-party APIs, structure agent workflows, and extend the framework with new capabilities—serving as a practical guide for developer experimentation and prototyping.
    LLM Agents Example Core Features
    • Chat agent implementation using LLMs
    • Retrieval Q&A with document loaders and vector stores
    • Custom tool integration (search, WolframAlpha, CSV analysis, Python REPL)
    • Conversational memory management
    • Dynamic prompt chaining
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