A2A (Agent-to-Agent Architecture) is a Google open-source framework enabling the development and operation of distributed AI agents working together. It offers modular components to define agent roles, communication channels, and shared memory. Developers can integrate various LLM providers, customize agent behaviors, and orchestrate multi-step workflows. A2A includes built-in monitoring, error management, and replay capabilities to trace agent interactions. By providing a standardized protocol for agent discovery, message passing, and task allocation, A2A simplifies complex coordination patterns and enhances reliability when scaling agent-based applications across diverse environments.

AAGPT is an extensible, open-source AI agent framework designed for building autonomous agents. It enables you to define high-level objectives, manage conversational memory, plan multi-step tasks, and integrate external tools or APIs. Using a simple configuration file and Python SDK, you can customize agent behavior, define custom actions, and deploy agents that can interact with data sources, execute commands, and learn from past interactions to improve performance over time.

Abstra is a Python-based workflow engine designed to automate and manage business processes efficiently. It leverages AI to boost productivity while providing full visibility into company operations through real-time insights. With Abstra, users can merge automated tasks with manual processes, track history with git-compatible versioning, and maintain auditable logs. The platform minimizes engineering overhead, making it easier for businesses to build, deploy, and manage their workflows seamlessly.

Accoladeum is a comprehensive AI-powered platform aimed at enhancing innovation and project management. It assists organizations in aligning their innovation initiatives with strategic goals, streamlining workflows, and optimizing resource allocation. Through its robust data analytics and intelligent insights, Accoladeum helps enterprises make data-driven decisions, improve collaboration, and achieve higher efficiency in managing innovation portfolios.

AI Agents offers a structured toolkit for developers to build autonomous agents using large language models. It includes modules for integrating external APIs, managing conversational or long-term memory, orchestrating multi-step workflows, and chaining LLM calls. The framework provides templates for common agent types—data retrieval, question answering, and task automation—while allowing customization of prompts, tool definitions, and memory strategies. With asynchronous support, plugin architecture, and modular design, AI Agents enables scalable, maintainable, and extendable agentic applications.

Agent Forge provides a modular architecture for defining, executing, and coordinating AI agents. It offers built-in task orchestration APIs to sequence and parallelize operations, memory modules for long-term context retention, and a plugin system to integrate external services (e.g., LLMs, databases, third-party APIs). Developers can rapidly prototype, test, and deploy agents in production, weaving together complex workflows without managing low-level infrastructure.

Agent Control Plane offers a centralized control plane for designing, orchestrating, and operating autonomous AI agents at scale. Developers can configure agent behaviors via declarative definitions, integrate external services and APIs as tools, and chain multi-step workflows. It supports containerized deployments with Docker or Kubernetes, real-time monitoring, logging, and metrics through a web-based dashboard. The framework includes a CLI and RESTful API for automation, enabling seamless iteration, versioning, and rollback of agent configurations. With an extensible plugin architecture and built-in scalability, Agent Control Plane accelerates the end-to-end AI agent lifecycle, from local testing to enterprise-grade production environments.

AgentDock provides a drag-and-drop interface for building and managing coordinated AI agents. Each agent can be assigned specific roles—such as web research, summarization, data analysis, or content creation—and linked through triggers and actions. With pre-built templates, API integrations, scheduling, and real-time monitoring, teams can automate end-to-end workflows, gain insights from curated data, and scale operations without developer overhead.

AgenticIR (Agentic Information Retrieval) provides a modular framework where LLM-powered agents autonomously plan and execute IR workflows. It enables the definition of agent roles — such as query generator, document retriever, and summarizer — running in customizable sequences. Agents can fetch raw text, refine queries based on intermediate results, and merge extracted passages into concise summaries. The framework supports multi-step pipelines including iterative web search, API-based data ingestion, and local document parsing. Developers can adjust agent parameters, plug in different LLMs, and fine-tune behavior policies. AgenticIR also offers logging, error handling, and parallel agent execution to accelerate large-scale information gathering. With a minimal code setup, researchers and engineers can prototype and deploy autonomous retrieval systems.

Aegix provides a robust SDK for orchestrating AI Agents capable of handling complex workflows through multi-step reasoning. With support for various LLM providers, it lets developers integrate custom tools—from database connectors to web scrapers—and maintain conversation state with memory modules such as vector stores. Aegix’s flexible agent loop architecture allows the specification of planning, execution, and review phases, enabling agents to refine outputs iteratively. Whether building document question-answering bots, code assistants, or automated support agents, Aegix simplifies development with clear abstractions, configuration-driven pipelines, and easy extension points. It’s designed to scale from prototypes to production, ensuring reliable performance and maintainable codebases for AI-driven applications.

Agentin is an open-source Python library designed to help developers build intelligent agents that can plan, act, and learn. It provides abstractions for managing conversational memory, integrating external tools or APIs, and orchestrating multiple agents in parallel or hierarchical workflows. With configurable planner modules and support for custom tool wrappers, Agentin enables rapid prototyping of autonomous data-processing agents, customer service bots, or research assistants. The framework also offers extensible logging and monitoring hooks, making it easy to track agent decisions and troubleshoot complex multi-step interactions.

AgentScale is an advanced AI agent that specializes in automating customer interactions through intelligent response mechanisms. It leverages natural language processing to understand and respond to user inquiries effectively. Additionally, it streamlines business workflows by efficiently handling repetitive tasks, allowing teams to focus on high-priority projects. The AI enhances user experience by providing responsive support, thereby driving customer satisfaction and engagement.

AgentScope is a developer-focused framework designed to simplify the creation of intelligent agents by providing modular components for dynamic planning, contextual memory storage, and tool/API integration. It supports multiple LLM backends (OpenAI, Anthropic, Hugging Face) and offers customizable pipelines for task execution, answer synthesis, and data retrieval. AgentScope’s architecture enables rapid prototyping of conversational bots, workflow automation agents, and research assistants, all while maintaining extensibility and scalability.

HackerGCLASS Agent API is an open-source Python framework that exposes RESTful endpoints to run AI agents. Developers can define custom tool integrations, configure prompt templates, and maintain agent state and memory across sessions. The framework supports orchestrating multiple agents in parallel, handling complex conversational flows, and integrating external services. It simplifies deployment via Uvicorn or other ASGI servers and offers extensibility with plugin modules, enabling rapid creation of domain-specific AI agents for diverse use cases.

Agent-FLAN is designed to simplify the creation of sophisticated AI agent-driven applications by segmenting tasks into planning and execution roles. Users define agent behaviors and workflows via configuration files, specifying input formats, tool interfaces, and communication protocols. The planning agent generates high-level task plans, while execution agents carry out specific actions, such as calling APIs, processing data, or generating content with large language models. Agent-FLAN’s modular architecture supports plug-and-play tool adapters, custom prompt templates, and real-time monitoring dashboards. It seamlessly integrates with popular LLM providers like OpenAI, Anthropic, and Hugging Face, enabling developers to quickly prototype, test, and deploy multi-agent workflows for scenarios such as automated research assistants, dynamic content generation pipelines, and enterprise process automation.

Simular AI Agent S2 is a comprehensive solution to craft autonomous agents capable of handling complex multistep tasks. Users can ingest domain data for knowledge, set up long-term memory stores to maintain context, and integrate external tools (APIs, web browsers, databases) to fetch real-time information. The platform leverages fine-tuned GPT-4 models for robust decision-making and supports conversational and non-conversational interfaces. Agents can be deployed via API endpoints or embedded in applications, offering monitoring dashboards for performance insights and logs. Simular's built-in security ensures data privacy and compliance, making Agent S2 suitable for customer service, market research, and workflow automation across industries.

Agentic-Systems is designed to streamline the development of sophisticated autonomous AI applications by offering a modular architecture composed of agent, tool, and memory components. Developers can define custom tools that encapsulate external APIs or internal functions, while memory modules retain contextual information across agent iterations. The built-in orchestration engine schedules tasks, resolves dependencies, and manages multi-agent interactions for collaborative workflows. By decoupling agent logic from execution details, the framework enables rapid experimentation, easy scaling, and fine-grained control over agent behavior. Whether prototyping research assistants, automating data pipelines, or deploying decision-support agents, Agentic-Systems provides the necessary abstractions and templates to accelerate end-to-end AI solution development.

Agentle provides a structured framework for developers to build custom AI agents with minimal boilerplate. It supports defining agent workflows as sequences of tasks, seamless integration with external APIs and tools, conversational memory management for context preservation, and built-in logging for auditability. The library also offers plugin hooks to extend functionality, multi-agent coordination for complex pipelines, and a unified interface to run agents locally or deploy via HTTP APIs.

AgentLLM is a web-based AI agent framework that lets users create, configure, and run autonomous agents through a graphical interface or JSON definitions. Agents can plan multi-step workflows by reasoning over tasks, invoke code via Python tools or external APIs, maintain conversation and memory, and adapt based on results. The platform supports OpenAI, Azure, or self-hosted models, offering built-in tool integrations for web search, file handling, mathematical computation, and custom plugins. Designed for experimentation and rapid prototyping, AgentLLM streamlines building intelligent agents capable of automating complex business processes, data analysis, customer support, and personalized recommendations.

Azure AI Agent SDK is a comprehensive framework that enables developers to create intelligent, autonomous agents capable of executing complex tasks. It provides a modular architecture including planners, executors, and memory components that work together to assess user intents, plan actions, invoke external APIs or custom tools, and store state persistently. The SDK supports integration with various LLMs, enabling context-aware conversations and decision-making. With built-in telemetry and Azure service connectors, agents can handle error recovery, scale across cloud environments, and maintain secure interactions. Rapid prototyping is facilitated through CLI templates and prebuilt skills, allowing teams to deploy digital workers that automate workflows, enhance customer support, or perform data analysis independently.