Agents with ADK is an open-source Python framework designed to streamline the creation of intelligent agents powered by large language models. It includes modular agent templates, built-in memory management, tool execution interfaces, and multi-agent coordination capabilities. Developers can quickly plug in custom functions or external APIs, configure planning and reasoning chains, and monitor agent interactions. The framework supports integration with popular LLM providers and provides logging, retry logic, and extensibility for production deployments.

AI Agent Marketplace is a community-driven directory for AI agents, allowing developers, researchers, and enthusiasts to discover, evaluate, and contribute agents. Users can filter agents by category, view detailed functionality and integration instructions, and submit their own agents via pull requests. The platform aggregates metadata, links, and examples for each agent, making it easier to compare capabilities and find the right tool for specific use cases.

Lagent is a developer-focused framework that enables creation of intelligent agents on top of large language models. It offers dynamic planning modules that break tasks into subgoals, memory stores to maintain context over long sessions, and tool integration interfaces for API calls or external service access. With customizable pipelines, users define agent behaviors, prompting strategies, error handling, and output parsing. Lagent’s logging and debugging tools help monitor decision steps, while its scalable architecture supports local, cloud, or enterprise deployments. It accelerates building autonomous assistants, data analysers, and workflow automations.

PromptsLabs is a platform where users can discover and share prompts to test new language models. The community-driven library provides a wide range of copy-paste prompts along with their expected outputs, helping users to understand and evaluate the performance of various LLMs. Users can also contribute their own prompts, ensuring a continually growing and up-to-date resource.

FastMCP is an open-source Python framework for building MCP (Model Context Protocol) servers and clients that empower LLMs with external tools, data sources, and custom prompts. Developers define tool classes and resource handlers in Python, register them with the FastMCP server, and deploy using transport protocols like HTTP, STDIO, or SSE. The framework’s client library offers an asynchronous interface for interacting with any MCP server, facilitating seamless integration of AI agents into applications.

pyafai is an open-source Python library designed to help developers architect, configure, and execute autonomous AI agents. It offers pluggable modules for memory management to retain context, tool integration for external API calls, observers for environment monitoring, planners for decision making, and an orchestrator to run agent loops. Logging and monitoring features provide visibility into agent performance and behavior. pyafai supports major LLM providers out of the box, enables custom module creation, and reduces boilerplate so teams can rapidly prototype virtual assistants, research bots, and automation workflows with full control over each component.