Advanced Agents Cookbooks is a community-driven GitHub project offering a library of AI agent recipes built on LangChain. It covers memory modules for context retention, custom tool integrations for external data and API calls, function-calling patterns for structured responses, chain-of-thought planning for complex decision-making, and multi-step workflow orchestration. Developers can use these ready-made examples to understand best practices, customize behavior, and accelerate the development of intelligent agents that automate tasks such as scheduling, data retrieval, and customer support.

EasyAgent provides a comprehensive framework for constructing autonomous AI agents in Python. It offers pluggable LLM backends such as OpenAI, Azure, and local models, customizable planning and reasoning modules, API tool integration, and persistent memory storage. Developers can define agent behaviors through simple YAML or code-based configurations, leverage built-in function calling for external data access, and orchestrate multiple agents for complex workflows. EasyAgent also includes features like logging, monitoring, error handling, and extension points for tailored implementations. Its modular architecture accelerates prototyping and deployment of specialized agents in domains like customer support, data analysis, automation, and research.

LangGraph4j represents AI agent operations—LLM calls, function invocations, data transforms—as nodes in a directed graph, with edges modeling data flow. You create a graph, add nodes for chat, embeddings, external APIs or custom logic, connect them, and execute. The framework manages execution order, handles caching, logs inputs and outputs, and lets you extend with new node types. It supports synchronous and asynchronous processing, making it ideal for chatbots, document QA, and complex reasoning pipelines.

LLM Functions provides a simple framework to bridge large language model responses with real code execution. You define functions via JSON schemas, register them with the library, and the LLM will return structured function calls when appropriate. The library parses those responses, validates the parameters, and invokes the correct handler. It supports synchronous and asynchronous callbacks, custom error handling, and plugin extensions, making it ideal for applications that require dynamic data lookup, external API calls, or complex business logic within AI-driven conversations.