Goat SDK is designed to simplify the creation and orchestration of AI agents in Go. It provides pluggable LLM integrations (OpenAI, Anthropic, Azure, local models), a tool registry for custom actions, and memory stores for stateful conversations. Developers can define chains, representer strategies, and publishers to output interactions via CLI, WebSocket, REST endpoints, or a built-in Web UI. Goat supports streaming responses, customizable logging, and easy error handling. By combining these components, you can develop chatbots, automation workflows, and decision-support systems in Go with minimal boilerplate, while maintaining flexibility to swap or extend providers and tools as needed.

GRASP provides a structured pipeline for building AI agents in TypeScript or JavaScript environments. At its core, developers define agents by registering a set of tools—functions or external API connectors—and specifying prompt templates that guide agent behavior. Built-in memory modules allow agents to store and retrieve contextual information, enabling multi-turn conversations with persistent state. The planning component orchestrates tool selection and execution based on user input, while the execution layer handles API calls and result processing. GRASP’s plugin system supports custom extensions, enabling capabilities such as retrieval-augmented generation (RAG), scheduling tasks, and logging. Its modular design means teams can choose only the components they need, facilitating integration with existing systems and services for chatbots, virtual assistants, and automated workflows.

Haystack is designed to help developers easily create custom search solutions that leverage the latest advancements in machine learning. With its components like document stores, retrievers, and readers, Haystack can connect to various data sources and effectively process queries. Its modular architecture supports mixed search strategies, including semantic search and traditional keyword-based search, making it a versatile tool for enterprises looking to enhance their search capabilities.

Hive is a robust AI agent orchestration platform built for Node.js environments. It provides a modular system for defining, managing, and executing multiple AI agents in parallel or sequential workflows. Each agent can be configured with specific roles, prompt templates, memory stores, and external tool integrations such as APIs or plugins. Hive streamlines communication paths between agents, enabling data sharing, decision-making, and task delegation. Its extensible design allows developers to implement custom utilities, monitor execution logs, and deploy agents at scale. Hive also includes features like error handling, retry policies, and performance optimizations to ensure reliable automation. With minimal setup, teams can prototype complex AI-driven services, including chatbots, data analysis pipelines, and content generators.

The IntelligentMASPlatform is built to accelerate development and deployment of multi-agent systems by offering a modular architecture with distinct agent, environment, and service layers. Agents communicate using FIPA-compliant ACL messaging, enabling dynamic negotiation and coordination. The platform includes a versatile environment simulator allowing developers to model complex scenarios, schedule agent tasks, and visualize agent interactions in real-time through a built-in dashboard. For advanced behaviors, it integrates reinforcement learning modules and supports custom behavior plugins. Deployment tools allow packaging agents into standalone applications or distributed networks. Additionally, the platform's API facilitates integration with databases, IoT devices, or third-party AI services, making it suitable for research, industrial automation, and smart city use cases.

KitchenAI is an open-source control plane designed to simplify the orchestration of AI frameworks. It allows users to manage various AI implementations through a single, standardized API endpoint. The KitchenAI platform supports a modular architecture, real-time monitoring, and high-performance messaging, providing a unified interface for integrating, deploying, and monitoring AI workflows. It is framework-agnostic and can be deployed on various platforms such as AWS, GCP, and on-premises environments.

LAWLIA provides a structured interface to define agent behaviors, plugin tools, and memory management for conversational or autonomous workflows. Developers can integrate with major LLM APIs, configure prompt templates, and register custom tools like search, calculators, or database connectors. Through its Agent class, LAWLIA handles planning, action execution, and response interpretation, allowing multi-turn interactions and dynamic tool invocation. Its modular design supports extending capabilities via plugins, enabling agents for customer support, data analysis, code assistance, or content generation. The framework streamlines agent development by managing context, memory, and error handling under a unified API.

LazyLL external APIs or custom utilities. Agents execute defined tasks through sequential or branching workflows, supporting synchronous or asynchronous operation. LazyLLM also offers built-in logging, testing utilities, and extension points for customizing prompts or retrieval strategies. By handling the underlying orchestration of LLM calls, memory management, and tool execution, LazyLLM enables rapid prototyping and deployment of intelligent assistants, chatbots, and automation scripts with minimal boilerplate code.

LLaVA-Plus builds upon leading vision-language foundations to deliver an agent capable of interpreting and reasoning over multiple images simultaneously. It integrates assembly learning and vision-language planning to perform complex tasks such as visual question answering, step-by-step problem-solving, and multi-stage inference workflows. The framework offers a modular plugin architecture to connect with various LLM backends, enabling custom prompt strategies and dynamic chain-of-thought explanations. Users can deploy LLaVA-Plus locally or through the hosted web demo, uploading single or multiple images, issuing natural language queries, and receiving rich explanatory answers along with planning steps. Its extensible design supports rapid prototyping of multimodal applications, making it an ideal platform for research, education, and production-grade vision-language solutions.

LLM-Agent provides a structured architecture for building intelligent agents using LLMs. It includes a toolkit for defining custom tools, memory modules for context preservation, and executors that orchestrate complex chains of actions. Agents can call APIs, run local processes, query databases, and manage conversational state. Prompt templates and plugin hooks allow fine-tuning of agent behavior. Designed for extensibility, LLM-Agent supports adding new tool interfaces, custom evaluators, and dynamic routing of tasks, enabling automated research, data analysis, code generation, and more.

MindSearch provides a modular Retrieval-Augmented Generation architecture designed to enhance large language models with real-time knowledge access. By connecting to various data sources including local file systems, document stores, and cloud-based vector databases, MindSearch indexes and embeds documents using configurable embedding models. During runtime, it retrieves the most relevant context, re-ranks results using customizable scoring functions, and composes a comprehensive prompt for LLMs to generate accurate responses. It also supports caching, multi-modal data types, and pipelines combining multiple retrievers. MindSearch’s flexible API allows developers to tinker with embedding parameters, retrieval strategies, chunking methods, and prompt templates. Whether building conversational AI assistants, question-answering systems, or domain-specific chatbots, MindSearch simplifies the integration of external knowledge into LLM-driven applications.

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.

NKC Multi-Agent Models provides researchers and developers with a comprehensive toolkit for designing, training, and evaluating multi-agent reinforcement learning systems. It features a modular architecture where users define custom agent policies, environment dynamics, and reward structures. Seamless integration with OpenAI Gym allows for rapid prototyping, while support for TensorFlow and PyTorch enables flexibility in selecting learning backends. The framework includes utilities for experience replay, centralized training with decentralized execution, and distributed training across multiple GPUs. Extensive logging and visualization modules capture performance metrics, facilitating benchmarking and hyperparameter tuning. By simplifying the setup of cooperative, competitive, and mixed-motive scenarios, NKC Multi-Agent Models accelerates experimentation in domains such as autonomous vehicles, robotic swarms, and game AI.

Overeasy is a Python-based open-source framework for orchestrating LLM-driven AI agents across various domains. It provides a modular architecture to define agents, configure memory stores, and integrate external tools such as APIs, knowledge bases, and databases. Developers can connect to OpenAI, Azure, or self-hosted LLM endpoints and design dynamic workflows involving single or multiple agents. Overeasy’s orchestration engine handles task delegation, decision making, and fallback strategies, enabling robust digital workers for research, customer support, data analysis, scheduling, and more. Comprehensive documentation and example projects accelerate deployment on Linux, macOS, and Windows.

Pillar is a comprehensive AI agent framework designed to simplify the development and deployment of intelligent multi-step workflows. It features a modular architecture with planners for task decomposition, memory stores for context retention, and executors that perform actions via external APIs or custom code. Developers can define agent pipelines in YAML or JSON, integrate any LLM provider, and extend functionality through custom plugins. Pillar handles asynchronous execution and context management out of the box, reducing boilerplate code and accelerating time-to-market for AI-driven applications such as chatbots, data analysis assistants, and automated business processes.

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.

The RAG-based Intelligent Conversational AI Agent combines a vector store-backed retrieval layer with Google’s Gemini LLM via LangChain to power context-rich, conversational knowledge extraction. Users ingest and index documents—PDFs, web pages, or databases—into a vector database. When a query is posed, the agent retrieves top relevant passages, feeds them into a prompt template, and generates concise, accurate answers. Modular components allow customization of data sources, vector stores, prompt engineering, and LLM backends. This open-source framework simplifies the development of domain-specific Q&A bots, knowledge explorers, and research assistants, delivering scalable, real-time insights from large document collections.

Rags provides a modular pipeline to build retrieval-augmented generative applications. It integrates with popular vector stores (e.g., FAISS, Pinecone), offers configurable prompt templates, and includes memory modules to maintain conversational context. Developers can switch between LLM providers like Llama-2, GPT-4, and Claude2 through a unified API. Rags supports streaming responses, custom preprocessing, and evaluation hooks. Its extensible design enables seamless integration into production services, allowing automated document ingestion, semantic search, and generation tasks for chatbots, knowledge assistants, and document summarization at scale.

Self Collab AI provides a modular framework where developers define autonomous agents, communication channels, and task objectives. Agents use predefined prompts and patterns to negotiate responsibilities, exchange data, and iterate on solutions. Built on Python and easy-to-extend interfaces, it supports integration with LLMs, custom plugins, and external APIs. Teams can rapidly prototype complex workflows—such as research assistants, content generation, or data analysis pipelines—by configuring agent roles and collaboration rules without deep orchestration code.

sma-begin sets up a streamlined codebase to create AI-driven agents by abstracting common components like input processing, decision logic, and output generation. At its core, it implements an agent loop that queries an LLM, interprets the response, and optionally executes integrated tools, such as HTTP clients, file handlers, or custom scripts. Memory modules allow the agent to recall previous interactions or context, while prompt chaining supports multi-step workflows. Error handling catches API failures or invalid tool outputs. Developers only need to define the prompts, tools, and desired behaviors. With minimal boilerplate, sma-begin accelerates prototyping of chatbots, automation scripts, or domain-specific assistants on any Python-supported platform.