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.

Minerva is an extensible AI agent framework designed to automate complex workflows using large language models. Developers can integrate external tools—such as web search, API calls, or file processors—define custom planning strategies, and manage conversational or persistent memory. Minerva supports both synchronous and asynchronous task execution, configurable logging, and a plugin architecture, making it easy to prototype, test, and deploy intelligent agents capable of reasoning, planning, and tool use in real-world scenarios.

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.

Saga provides a flexible architecture for building AI agents that plan and execute multi-step workflows. Core components include a planner module that breaks goals into actions, a memory store for conversational and task context, and a tool registry for integrating external services or scripts. Agents run asynchronously, manage state across sessions, and support custom tool development. Saga enables rapid prototyping of autonomous assistants, automating tasks such as data collection, alerting, and interactive Q&A within your own Python environment.

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.