bedrock-agent is a versatile AI agent framework that integrates with AWS Bedrock’s suite of large language models to orchestrate complex, task-driven workflows. It offers a plugin architecture for registering custom tools, memory modules for context persistence, and a chain-of-thought mechanism for improved reasoning. Through a simple Python API and command-line interface, it enables developers to define agents that can call external services, process documents, generate code, or interact with users via chat. Agents can be configured to automatically select relevant tools based on user prompts and maintain conversational state across sessions. This framework is open-source, extensible, and optimized for rapid prototyping and deployment of AI-powered assistants on local or AWS cloud environments.

GradientJ is an AI-driven platform designed to help non-technical teams automate intricate back-office procedures. It leverages large language models to handle tasks otherwise outsourced to offshore workers. This automation facilitates significant time and cost savings, enhancing overall efficiency. Users can build and deploy robust language model applications, monitor their performance in real-time, and improve model output through continuous feedback.

Lyzr Studio enables organizations to rapidly build custom AI-powered assistants by combining large language models, business rules, and data integrations. In its drag-and-drop interface, users visually orchestrate multi-step workflows, integrate with internal APIs, databases, and third-party services, and customize LLM prompts for domain-specific knowledge. Agents can be tested in real-time, deployed to web widgets, messaging apps or enterprise platforms, and monitored through dashboards tracking performance metrics. Advanced version control, role-based access, and audit logs ensure governance. Whether automating customer support, lead qualification, HR onboarding, or IT troubleshooting, Lyzr Studio streamlines development of reliable, scalable digital workers.

ModelFusion is designed to streamline the use of generative AI by offering a single interface for accessing a wide array of large language models (LLMs). From content creation to data analysis, users can leverage the capabilities of models from providers like OpenAI, Anthropic, and more. With 23 different models available, ModelFusion supports diverse applications, ensuring that users can find the right solution for their specific needs. Fusion credits facilitate the use of these models, making advanced AI accessible and efficient.

OperAgents is a developer-oriented toolkit for building and orchestrating autonomous agents using large language models like GPT. It supports defining custom agent classes, integrating external tools (APIs, databases, code execution), and managing agent memory for context retention. Through configurable pipelines, agents can perform multi-step tasks—such as research, summarization, and decision support—while dynamically invoking tools and maintaining state. The framework includes modules for monitoring agent performance, handling errors automatically, and scaling agent executions. By abstracting LLM interactions and tool management, OperAgents accelerates the development of AI-driven workflows in domains like automated customer support, data analysis, and content generation.

Agentic Framework is a versatile, open-source framework designed to streamline the creation of autonomous AI agents that leverage large language models and MongoDB. It equips developers with modular components for managing agent memory, defining toolsets, orchestrating multi-step workflows, and templating prompts. The integrated MongoDB-backed memory store enables agents to maintain persistent context across sessions, while pluggable tool interfaces allow seamless interaction with external APIs and data sources. Built on Node.js, the framework includes logging, monitoring hooks, and deployment examples to rapidly prototype and scale intelligent agents. With customizable configuration, developers can tailor agents for tasks such as knowledge retrieval, automated customer support, data analysis, and process automation, reducing development overhead and accelerating time-to-production.

Butterfish is a versatile command line tool that enhances your shell environment with AI capabilities. It supports prompting LLMs (Large Language Models), summarizing files, and managing embeddings all from the command line. Ideal for developers and data scientists, Butterfish integrates seamlessly with existing workflows, allowing you to leverage the power of AI without leaving your terminal. Whether you need to generate code, get suggestions, or manage data, Butterfish provides a cohesive set of tools to enhance your command line experience.

ModelOp Center is an advanced platform designed to govern, monitor, and manage AI models across the enterprise. This ModelOps software is essential for the orchestration of AI initiatives, including those involving generative AI and Large Language Models (LLMs). It ensures that all AI models operate efficiently, comply with regulatory standards, and deliver value across their lifecycle. Enterprises can leverage ModelOp Center to enhance the scalability, reliability, and compliance of their AI deployments.

cpp-langchain implements core features from the LangChain ecosystem in C++. Developers can wrap calls to large language models, define prompt templates, assemble chains, and orchestrate agents that call external tools or APIs. It includes memory modules for maintaining conversational state, embeddings support for similarity search, and vector database integrations. The modular design lets you customize each component—LLM clients, prompt strategies, memory backends, and toolkits—to suit specific use cases. By providing a header-only library and CMake support, cpp-langchain simplifies compiling native AI applications across Windows, Linux, and macOS platforms without requiring Python runtimes.

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.

GPA-LM is a Python-based framework designed to simplify the creation and orchestration of AI agents powered by large language models. It features a planner that breaks down high-level instructions into sub-tasks, an executor that manages tool calls and interactions, and a memory module that retains context across sessions. The plugin architecture allows developers to add custom tools, APIs, and decision logic. With multi-agent support, GPA-LM can coordinate roles, distribute tasks, and aggregate results. It integrates seamlessly with popular LLMs like OpenAI GPT and supports deployment on various environments. The framework accelerates the development of autonomous agents for research, automation, and application prototyping.

iNextLabs provides advanced AI-driven solutions designed to help businesses automate their routine operations and enhance customer engagement. With a focus on Generative AI and large language models (LLM), our platform offers industry-specific applications that streamline workflows and provide personalized experiences. Whether you're looking to improve customer service through intelligent chatbots or automate administrative tasks, iNextLabs has the tools and technology to elevate your business performance.

Labs is an open-source, embeddable domain-specific language designed for defining and executing AI agents using large language models. It provides constructs to declare prompts, manage context, conditionally branch, and integrate external tools (e.g., databases, APIs). With Labs, developers describe agent workflows as code, orchestrating multi-step tasks like data retrieval, analysis, and generation. The framework compiles DSL scripts into executable pipelines that can be run locally or in production. Labs supports interactive REPL, command-line tooling, and integrates with standard LLM providers. Its modular architecture allows easy extension with custom functions and utilities, promoting rapid prototyping and maintainable agent development. The lightweight runtime ensures low overhead and seamless embedding in existing applications.

LangBot is a self-hosted, open-source platform that enables seamless integration of large language models into multiple messaging channels. It offers a web-based UI for deploying and managing bots, supports model providers including OpenAI, DeepSeek, and local LLMs, and adapts to platforms such as QQ, WeChat, Discord, Slack, Feishu, and DingTalk. Developers can configure conversation workflows, implement rate limiting strategies, and extend functionality with plugins. Built for scalability, LangBot unifies message handling, model interaction, and analytics into a single framework, accelerating the creation of conversational AI applications for customer service, internal notifications, and community management.

LeanAgent is a Python-based framework designed to streamline the creation of autonomous AI agents. It offers built-in planning modules that leverage large language models for decision making, an extensible tool integration layer for calling external APIs or custom scripts, and a memory management system that retains context across interactions. Developers can configure agent workflows, plug in custom tools, iterate quickly with debugging utilities, and deploy production-ready agents for a variety of domains.

The LLM Maze Agent framework provides a Python-based environment for building intelligent agents capable of navigating grid mazes using large language models. By combining modular environment interfaces with chain-of-thought prompt templates and heuristic planning, the agent iteratively queries an LLM to decide movement directions, adapts to obstacles, and updates its internal state representation. Out-of-the-box support for OpenAI and Hugging Face models allows seamless integration, while configurable maze generation and step-by-step debugging enable experimentation with different strategies. Researchers can adjust reward functions, define custom observation spaces, and visualize agent paths to analyze reasoning processes. This design makes LLM Maze Agent a versatile tool for evaluating LLM-driven planning, teaching AI concepts, and benchmarking model performance on spatial reasoning tasks.

LLM State Machine is an open-source Python framework designed to construct AI agents using explicit state machines. Developers define states as discrete steps—each invoking a large language model or custom logic—and transitions based on outputs. This approach provides clarity, maintainability, and robust error handling for multi-step, LLM-powered workflows, such as document processing, conversational bots, or automation pipelines.

LLMWare serves as a comprehensive toolkit for constructing AI agents powered by large language models. It allows you to define reusable chains, integrate external tools via simple interfaces, manage contextual memory states, and orchestrate multi-step reasoning across language models and downstream services. With LLMWare, developers can plug in different model backends, set up agent decision logic, and attach custom toolkits for tasks like web browsing, database queries, or API calls. Its modular design enables rapid prototyping of autonomous agents, chatbots, or research assistants, offering built-in logging, error handling, and deployment adapters for both development and production environments.

SCOUT-2 provides a modular architecture for building autonomous agents powered by large language models. It includes goal decomposition, task planning, an execution engine, and a feedback-driven reflection module. Developers define a top-level objective, and SCOUT-2 automatically generates a task tree, dispatches worker agents for execution, monitors progress, and refines tasks based on outcomes. It integrates with OpenAI APIs and can be extended with custom prompts and templates to support a wide range of workflows.

Taiat (TypeScript AI Agent Toolkit) is a lightweight, extensible framework for building autonomous AI agents in Node.js and browser environments. It enables developers to define agent behaviors, integrate with large language model APIs such as OpenAI and Hugging Face, and orchestrate multi-step tool execution workflows. The framework supports customizable memory backends for stateful conversations, tool registration for web searches, file operations, and external API calls, as well as pluggable decision strategies. With taiat, you can rapidly prototype agents that plan, reason, and execute tasks autonomously, from data retrieval and summarization to automated code generation and conversational assistants.