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Fast LLM & Agents & MCPs

@omerbsezer

This repo covers LLM, Agents concepts both theoretically and practically: LLMs, RAG, Fine Tuning, Agents, Tools, MCP, Agent Frameworks, Reference Documents, Links
Overview

What is Fast LLM & Agents & MCPs?

Fast LLM & Agents & MCPs is a comprehensive repository that explores the concepts of Large Language Models (LLMs), Agents, and Model Context Protocols (MCPs) both theoretically and practically. It includes various tools, frameworks, and sample codes for developing intelligent agents and applications using LLMs.

How to use Fast LLM & Agents & MCPs?

Users can explore the repository on GitHub, where they can find sample codes and projects demonstrating the implementation of LLMs and agents using the Google Agent Development Kit (ADK) and other frameworks. Users can clone the repository and run the provided examples to understand the functionalities.

Key features of Fast LLM & Agents & MCPs?

  • In-depth exploration of LLM architectures and fine-tuning techniques.
  • Sample codes for building agents with Google ADK, FastAPI, and Streamlit.
  • Examples of multi-agent workflows and integration with external tools.
  • Documentation on prompt engineering and retrieval-augmented generation (RAG).

Use cases of Fast LLM & Agents & MCPs?

  1. Developing chatbots and virtual assistants using LLMs.
  2. Automating complex tasks like summarization and translation.
  3. Creating intelligent applications that leverage external data sources.
  4. Building educational tools that utilize LLMs for content generation.

FAQ from Fast LLM & Agents & MCPs?

  • What is the purpose of this repository?

    The repository aims to provide resources and tools for developers interested in building applications using LLMs and agents.

  • Is there any cost associated with using the tools?

    No, the tools and resources provided in this repository are free to use.

  • Can I contribute to this project?

    Yes, contributions are welcome! You can submit pull requests or open issues on GitHub.

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