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.

SimplerLLM provides developers a minimalistic API to compose LLM chains, define agent actions, and orchestrate tool calls. With built-in abstractions for memory retention, prompt templates, and output parsing, users can rapidly assemble conversational agents that maintain context across interactions. The framework seamlessly integrates with OpenAI, Azure, and HuggingFace models, and supports pluggable toolkits for searches, calculators, and custom APIs. Its lightweight core minimizes dependencies, allowing agile development and easy deployment on cloud or edge. Whether building chatbots, QA assistants, or task automators, SimplerLLM simplifies end-to-end LLM agent pipelines.

The EVM Agent Kit offers a modular architecture to construct intelligent agents that seamlessly interact with Ethereum networks. At its core, it leverages large language models to generate instruction chains parsed into JSON-RPC calls for on-chain data retrieval and transaction execution. Developers can plug in custom logic for wallet management, gas estimation, and result validation. The kit includes templates for scenarios like token swaps, contract audits, and on-chain analytics. By abstracting low-level EVM complexities, it enables rapid prototyping of agents that can monitor wallet balances, decode smart contract events, and autonomously execute trades based on predefined strategies. Extensible connectors allow integration with major LLM providers and blockchain networks, ensuring flexibility in agent design.