AI Library offers a comprehensive framework for designing and running AI agents. It includes agent builders, chain orchestration, model interfaces, tool integration, and vector store support. The platform features an API-first approach, extensive documentation, and sample projects. Whether you’re creating chatbots, data retrieval agents, or automation assistants, AI Library’s modular architecture ensures each component—such as language models, memory stores, and external tools—can be easily configured, combined, and monitored in production environments.

Flocking Multi-Agent offers a modular library for simulating autonomous agents exhibiting swarm intelligence. It encodes core steering behaviors—cohesion, separation and alignment—alongside obstacle avoidance and dynamic target pursuit. Using Python and Pygame for visualization, the framework allows adjustable parameters such as neighbor radius, maximum speed, and turning force. It supports extensibility through custom behavior functions and integration hooks for robotics or game engines. Ideal for experimentation in AI, robotics, game development, and academic research, it demonstrates how simple local rules lead to complex global formations.

AgenticRAG provides a modular architecture for creating autonomous agents that leverage retrieval-augmented generation (RAG). It offers components to index documents in vector stores, retrieve relevant context, and feed it into LLMs to generate context-aware responses. Users can integrate external APIs and tools, configure memory stores to track conversation history, and define custom workflows to orchestrate multi-step decision-making processes. The framework supports popular vector databases like Pinecone and FAISS, and LLM providers such as OpenAI, allowing seamless switching or multi-model setups. With built-in abstractions for agent loops and tool management, AgenticRAG simplifies development of agents capable of tasks like document QA, automated research, and knowledge-driven automation, reducing boilerplate code and accelerating time to deployment.

AI Agent Example is a hands-on demonstration repository for developers and researchers interested in building intelligent agents powered by large language models. The project includes sample code for agent planning, memory storage, and tool invocation, showcasing how to integrate external APIs or custom functions. It features a simple conversational interface that interprets user intents, formulates action plans, and executes tasks by calling predefined tools. Developers can follow clear patterns to extend the agent with new capabilities, such as scheduling events, web scraping, or automated data processing. By providing a modular architecture, this template accelerates experimentation with AI-driven workflows and personalized digital assistants while offering insights into agent orchestration and state management.

Pipe Pilot is an open-source tool that lets developers build, visualize, and manage AI-driven pipelines in Python. It offers a declarative API or YAML configuration to chain tasks such as text generation, classification, data enrichment, and REST API calls. Users can implement conditional branches, loops, retries, and error handlers to create resilient workflows. Pipe Pilot maintains execution context, logs each step, and supports parallel or sequential execution modes. It integrates with major LLM providers, custom functions, and external services, making it ideal for automating reports, chatbots, intelligent data processing, and complex multi-stage AI applications.

The Gemini Agent Cookbook is a curated open-source toolkit offering a variety of hands-on examples for constructing intelligent agents powered by Google’s Gemini language models. It includes sample code for orchestrating multi-step reasoning chains, dynamically invoking external APIs, integrating toolkits for data retrieval, and managing conversation flows. The cookbook demonstrates best practices for error handling, context management, and prompt engineering, supporting use cases like autonomous chatbots, task automation, and decision support systems. It guides developers through building custom agents that can interpret user requests, fetch real-time data, perform computations, and generate formatted outputs. By following these recipes, engineers can accelerate agent prototyping and deploy robust AI-driven applications in diverse domains.

RModel is a developer-centric AI agent framework designed to simplify the creation of next-generation conversational and autonomous applications. It integrates with any LLM, supports plugin tool chains, memory storage, and dynamic prompt generation. With built-in planning mechanisms, custom tool registration, and telemetry, RModel enables agents to perform tasks like information retrieval, data processing, and decision-making across multiple domains, while maintaining stateful dialogues, asynchronous execution, customizable response handlers, and secure context management for scalable cloud or on-premise deployments.

AutoDRIVE Cooperative MARL is a GitHub-hosted framework combining the AutoDRIVE urban driving simulator with adaptable multi-agent reinforcement learning algorithms. It includes training scripts, environment wrappers, evaluation metrics, and visualization tools to develop and benchmark cooperative driving policies. Users can configure agent observation spaces, reward functions, and training hyperparameters. The repository supports modular extensions, enabling custom task definitions, curriculum learning, and performance tracking for autonomous vehicle coordination research.

AI Agent FletUI provides a modular UI framework for creating intelligent chat applications backed by large language models. It bundles chat widgets, tool integration panels, memory stores and event handlers that connect seamlessly with any LLM provider. Users can define custom tools, manage session context persistently and render rich message formats out of the box. The library abstracts the complexity of UI layout in Flet and streamlines tool invocation, enabling rapid prototyping and deployment of LLM-driven assistants.

Agentic Workflow is a declarative framework enabling developers to define complex AI workflows by chaining multiple LLM-based agents, each with customizable roles, prompts, and execution logic. It provides built-in support for task orchestration, state management, error handling, and plugin integrations, allowing seamless interaction between agents and external tools. The library uses Python and YAML-based configurations to abstract agent definitions, supports asynchronous execution flows, and offers extensibility through custom connectors and plugins. As an open-source project, it includes detailed examples, templates, and documentation to help teams accelerate development and maintain complex AI agent ecosystems.

demo_smolagents is a reference implementation of SmolAgents, a Python-based microframework for creating autonomous AI agents powered by large language models. This demo includes examples of how to configure individual agents with specific toolkits, establish communication channels between agents, and manage task handoffs dynamically. It showcases LLM integration, tool invocation, prompt management, and agent orchestration patterns for building multi-agent systems that can perform coordinated actions based on user input and intermediate results.

Noema Declarative AI allows developers and researchers to specify AI agents and their workflows in a high-level, declarative manner. By writing YAML or JSON configuration files, you define agents, prompts, tools, and memory modules. The Noema runtime then parses these definitions, loads language models, executes each step of your pipeline, handles state and context, and returns structured results. This approach reduces boilerplate, improves reproducibility, and separates logic from execution, making it ideal for prototyping chatbots, automation scripts, and research experiments.

OpenSpiel is a research framework that provides a wide range of environments (from simple matrix games to complex board games such as Chess, Go, and Poker) and implements various reinforcement learning and search algorithms (e.g., value iteration, policy gradient methods, MCTS). Its modular C++ core and Python bindings allow users to plug in custom algorithms, define new games, and compare performance across standard benchmarks. Designed for extensibility, it supports single and multi-agent settings, enabling study of cooperative and competitive scenarios. Researchers leverage OpenSpiel to prototype algorithms quickly, run large-scale experiments, and share reproducible code.

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.

LangGraph provides a graph-based abstraction for designing AI agent workflows. Developers define nodes that represent prompts, tools, data sources, or decision logic, then connect these nodes with edges to form a directed graph. At runtime, LangGraph traverses the graph, executing LLM calls, API requests, and custom functions in sequence or in parallel. Built-in support for caching, error handling, logging, and concurrency ensures robust agent behavior. Extensible node and edge templates let users integrate any external service or model, making LangGraph ideal for building chatbots, data pipelines, autonomous workers, and research assistants without complex boilerplate code.

Claude-Code-OpenAI transforms Anthropic’s Claude API into a drop-in replacement for OpenAI models in Python applications. After installing via pip and configuring your OPENAI_API_KEY and CLAUDE_API_KEY environment variables, you can use familiar methods like openai.ChatCompletion.create(), openai.Completion.create(), or openai.Embedding.create() with Claude model names (e.g., claude-2, claude-1.3). The library intercepts calls, routes them to the corresponding Claude endpoints, and normalizes responses to match OpenAI’s data structures. It supports real-time streaming, rich parameter mapping, error handling, and prompt templating. This allows teams to experiment with Claude and GPT models interchangeably without refactoring code, enabling rapid prototyping for chatbots, content generation, semantic search, and hybrid LLM workflows.

Agent Adapters is designed to provide developers with a consistent interface for connecting AI agents to external services and frameworks. Through its pluggable adapter architecture, it offers prebuilt adapters for HTTP APIs, messaging platforms like Slack and Teams, and custom tool endpoints. Each adapter handles request parsing, response mapping, error handling, and optional logging or monitoring hooks. Developers can also register custom adapters by implementing a defined interface and configuring adapter parameters in their agent settings. This streamlined approach reduces boilerplate code, ensures uniform workflow execution, and accelerates the deployment of agents across multiple environments without rewriting integration logic.

Java-Action-Storage is a core component of the LightJason multi-agent framework designed to handle the end-to-end persistence of agent actions. It defines a generic ActionStorage interface with adapters for popular databases and file systems, supports asynchronous and batched writes, and manages concurrent access from multiple agents. Users can configure storage strategies, query historical action logs, and replay sequences to audit system behavior or recover agent states after failures. The module integrates via simple dependency injection, enabling rapid Adoption in Java-based AI projects.

LinkAgent provides a lightweight microkernel for building AI agents with pluggable components. Users can register language model backends, retrieval modules, and external APIs as tools, then assemble them into workflows using built-in planners and routers. LinkAgent supports memory handlers for context persistence, dynamic tool invocation, and configurable decision logic for complex multi-step reasoning. With minimal code, teams can automate tasks like QA, data extraction, process orchestration, and report generation.