Practical AI Agents provides developers with a comprehensive framework and ready-to-use examples to construct autonomous agents powered by large language models. It demonstrates how to integrate API tools (e.g., web browsers, databases, custom functions), implement RAG-style memory, manage conversation context, and perform dynamic planning. You can adapt examples for chatbots, data analysis assistants, task automation scripts, or research tools. The repository includes notebooks, Dockerfiles, and configuration files to streamline setup and deployment across environments.

AgentServe provides a unified interface for creating and deploying AI agents. Users define agent behaviors in configuration files or code, integrate external tools or knowledge sources, and expose agents over REST endpoints. The framework handles model routing, parallel requests, health checks, logging, and metrics out of the box. AgentServe’s modular design allows plugging in new models, custom tools, or scheduling policies, making it ideal for building chatbots, automated workflows, and multi-agent systems in a scalable, maintainable way.

AgentForge provides a structured environment for defining, combining, and orchestrating individual AI skills into cohesive autonomous agents. It supports conversation memory for context retention, plugin integration for external services, multi-agent communication, task scheduling, and error handling. Developers can configure custom skill handlers, leverage built-in modules for natural language understanding, and integrate with popular LLMs like OpenAI’s GPT series. AgentForge’s modular design accelerates development cycles, facilitates testing, and simplifies deployment of chatbots, virtual assistants, data analysis agents, and domain-specific automation bots.