Fast-LLM-Agent-MCP is a lightweight, open-source Python framework for building AI agents that combine memory management, chain-of-thought reasoning, and multi-step planning. Developers can integrate it with OpenAI, Azure OpenAI, local Llama, and other models to maintain conversational context, generate structured reasoning traces, and decompose complex tasks into executable subtasks. Its modular design allows custom tool integration and memory stores, making it ideal for applications like virtual assistants, decision support systems, and automated customer support bots.

AIAgentWorkshop is an open-source Python project demonstrating how to build autonomous AI agents capable of planning, decision-making, and tool usage. It includes examples of integrating web search, file management, and system commands, along with simple memory and reasoning modules. Developers can follow guided exercises to create agents that interpret user goals, generate multi-step plans, execute tasks across different tools, and maintain context. The modular architecture makes it easy to swap or extend tools and chain agent actions for complex workflows, turning AI research concepts into runnable prototypes.

Bedrock Engineer provides developers with a structured, modular way to build AI agents leveraging AWS Bedrock foundation models like Amazon Titan and Anthropic Claude. The toolkit includes example workflows for data retrieval, document analysis, automated reasoning, and multi-step planning. It manages session context, integrates with AWS IAM for secure access, and supports customizable prompt templates. By abstracting away boilerplate code, Bedrock Engineer accelerates development of chatbots, summarization tools, and intelligent assistants, while offering scalability and cost optimization through AWS-managed infrastructure.

ChatAgent enables developers to rapidly build and deploy intelligent chatbots by offering an extendable architecture with core modules for memory handling, tool chaining, and strategy orchestration. It integrates seamlessly with popular LLM providers, allowing you to define custom tools for API calls, database queries, or file operations. The framework supports multi-step planning, dynamic decision making, and context-aware memory recall, ensuring coherent interactions across extended conversations. Its plugin system and configuration-driven pipelines facilitate easy customization and experimentation, while structured logs and metrics help monitor performance and troubleshoot issues in production deployments.

Doraemon-Agent is an open-source Python platform and framework designed for developers to build sophisticated AI agents. It allows you to integrate custom plugins and external tools, maintain long-term memory across sessions, and execute chain-of-thought planning with multiple steps. Developers can configure agent roles, manage context, log interactions, and extend functionality through a plugin architecture. It simplifies the creation of autonomous assistants for tasks like data analysis, research support, or customer service automation.