mini-agi is designed to simplify the creation of autonomous AI agents by providing a minimal, modular framework. Built in Python, it leverages OpenAI’s language models to interpret high-level goals, decompose them into sub-tasks, and orchestrate tool calls such as HTTP requests, file operations, or custom actions. The framework includes memory storage to track agent state and results, a planner module for task decomposition with cost-based heuristics, and an executor module that sequentially invokes tools. With configuration files, users can inject custom tools, define prompt templates, and adjust planning depth. mini-agi’s lightweight architecture makes it ideal for prototyping AI agents that perform research queries, automate workflows, or generate code autonomously.

Rawr Agent is a modular, open-source Python framework that empowers developers to build autonomous AI agents by orchestrating complex workflows of LLM interactions. Leveraging LangChain under the hood, Rawr Agent lets you define task sequences either through YAML configurations or Python code, specifying tool integrations such as web APIs, database queries, and custom scripts. It includes memory components for storing conversational history and vector embeddings, caching mechanisms to optimize repeated calls, and robust logging and error handling to monitor agent behavior. Rawr Agent’s extensible architecture allows adding custom tools and adapters, making it suitable for tasks like automated research, data analysis, report generation, and interactive chatbots. With its simple API, teams can rapidly prototype and deploy intelligent agents for diverse applications.

Real-Agents is designed to simplify the creation and orchestration of AI-powered agents that can perform complex tasks autonomously. Built on Python and compatible with major large language models, the framework features a modular design comprising core components for language understanding, reasoning, memory storage, and tool execution. Developers can rapidly integrate external services like web APIs, databases, and custom functions to extend agent capabilities. Real-Agents supports memory mechanisms to retain context across interactions, enabling multi-turn conversations and long-running workflows. The platform also includes utilities for logging, debugging, and scaling agents in production environments. By abstracting low-level details, Real-Agents streamlines the development cycle, allowing teams to focus on task-specific logic and deliver powerful automated solutions.

Semantic Kernel Copilot Demo is an end-to-end reference application illustrating how to build advanced AI agents with Microsoft’s Semantic Kernel framework. The demo features prompt chaining for multi-step reasoning, memory management to recall context across sessions, and a plugin-based skill architecture enabling integration with external APIs or services. Developers can configure connectors for Azure OpenAI or OpenAI models, define custom prompt templates, and implement domain-specific skills such as calendar access, file operations, or data retrieval. The sample shows how to orchestrate these components to create a conversational Copilot capable of understanding user intents, executing tasks, and maintaining context over time, fostering rapid development of personalized AI assistants.

AI-Agents provides a flexible agent architecture that orchestrates language model planners, persistent memory modules, and pluggable toolkits. Developers define tools for HTTP requests, file operations, and custom logic, then configure an LLM planner to decide which tool to invoke. Memory stores context and conversation history. The framework handles asynchronous execution, error recovery, and logging, enabling rapid prototyping of intelligent assistants, data analyzers, or automation bots without reinventing core orchestration logic.

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.

AgentLab is a ServiceNow framework for creating AI agents—also called digital workers—using a visual, drag-and-drop editor. Users link large language models with ServiceNow tables, define intents and actions, and orchestrate workflows for tasks like incident resolution, change approvals, and knowledge retrieval. Agents can be tested in built-in sandboxes, versioned, and monitored in real time. With connectors for external APIs and chat interfaces, AgentLab enables deployment across portals, Microsoft Teams, and Slack. The platform offers governance controls, audit trails, and analytics dashboards to ensure compliance and performance at scale.

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.

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.

10x Rules is a comprehensive AI agent framework allowing organizations to construct and deploy smart agents based on custom rule sets and business logic. By defining triggers, conditions, and actions in an intuitive interface, users can instruct AI agents to perform tasks such as extracting data from documents, scoring leads, sending personalized emails, and updating CRM records. The platform seamlessly integrates with popular services through pre-built connectors, supports real-time monitoring and debugging, and provides analytics on agent performance. Both technical and non-technical users can streamline repetitive workflows, reduce manual errors, and accelerate operations with AI-driven automation.

autoMate leverages OpenAI's GPT models and a modular tooling system to perform end-to-end automation workflows. Users define objectives in natural language, and autoMate breaks them into subtasks such as reading or writing files, scraping web pages, summarizing data, and composing emails. It dynamically invokes the appropriate functions, handles API interactions, logs progress, and outputs results in the desired format. Its extensible architecture allows adding custom tools, enabling scalable automation across data processing, content generation, and system operations.

Swarms is a developer-focused framework enabling the creation, orchestration, and execution of multi-agent AI workflows. You define agents with specific roles, configure their behavior via LLM prompts, and link them to external tools or APIs. Swarms manages inter-agent communication, task planning, and memory persistence. Its plugin architecture allows seamless integration of custom modules—such as retrievers, databases, or monitoring dashboards—while built-in connectors support popular LLM providers. Whether you need coordinated data analysis, automated customer support, or complex decision-making pipelines, Swarms provides the building blocks to deploy scalable, autonomous agent ecosystems.

Council provides a structured environment for designing AI agents by defining roles, chaining tasks, and integrating external tools or APIs. Users can configure memory stores, manage agent state, and implement custom reasoning pipelines. Council’s plugin architecture allows seamless integration with NLP services, data sources, and third-party tools, enabling you to rapidly prototype and deploy multi-agent systems that coordinate to perform complex tasks reliably.

At its core, LionAGI provides a modular architecture for defining and executing dependent task stages, breaking complex problems into logical components that can be processed sequentially or in parallel. Each stage can leverage a custom prompt, memory storage, and decision logic to adapt behavior based on previous results. Developers can integrate any supported LLM API or self-hosted model, configure observation spaces, and define action mappings to create agents that plan, reason, and learn over multiple cycles. Built-in logging, error recovery, and analytics tools enable real-time monitoring and iterative refinement. Whether automating research workflows, generating reports, or orchestrating autonomous processes, LionAGI accelerates the delivery of intelligent, adaptable AI agents with minimal boilerplate.

ManasAI is a Python-based framework that enables the creation of autonomous AI agents with built-in state and modular components. It offers core abstractions for agent reasoning, short-term and long-term memory, external tool and API integrations, message-driven event handling, and multi-agent orchestration. Agents can be configured to manage context, execute tasks, handle retries, and gather feedback. Its pluggable architecture allows developers to tailor memory backends, tools, and orchestrators to specific workflows, making it ideal for prototyping chatbots, digital workers, and automated pipelines that require persistent context and complex interactions.

The Multi-Agent System Framework offers a modular structure for building and orchestrating multiple AI agents within Python applications. It includes an agent manager to spawn and supervise agents, a communication backbone supporting various protocols (e.g., message passing, event broadcasting), and customizable memory stores for long-term knowledge retention. Developers can define distinct agent roles, assign specialized tasks, and configure cooperative strategies such as consensus-building or voting. The framework integrates seamlessly with external AI models and knowledge bases, enabling agents to reason, learn, and adapt. Ideal for distributed simulations, conversational agent clusters, and automated decision-making pipelines, the system accelerates complex problem solving by leveraging parallel autonomy.

Multi-Agent-Blueprint is a comprehensive open-source codebase for building and orchestrating multiple AI-driven agents that collaborate to address complex tasks. At its core, it offers a modular system for defining distinct agent roles—such as researchers, analysts, and executors—each with dedicated memory stores and prompt templates. The framework integrates seamlessly with large language models, external knowledge APIs, and custom tools, enabling dynamic task delegation and iterative feedback loops between agents. It also includes built-in logging and monitoring to track agent interactions and outputs. With customizable workflows and interchangeable components, developers and researchers can rapidly prototype multi-agent pipelines for applications like content generation, data analysis, product development, or automated customer support.

Multi-Agent Coordination provides a lightweight API to define AI agents, register them with a central coordinator, and dispatch tasks for collaborative problem solving. It handles message routing, concurrency control, and result aggregation. Developers can plug in custom agent behaviors, extend communication channels, and monitor interactions through built-in logging and hooks. This framework simplifies the development of distributed AI workflows, where each agent specializes in a subtask and the coordinator ensures smooth collaboration.

OpenAI Agents MCP Server provides a robust foundation for deploying and managing autonomous agents powered by OpenAI models. It exposes a flexible RESTful API to create, configure, and control agents, enabling developers to orchestrate multi-step tasks, coordinate interactions between agents, and maintain persistent memory across sessions. The framework supports plugin-like tool integrations, advanced conversation logging, and customizable planning strategies. By abstracting infrastructure concerns, MCP Server streamlines the development pipeline, facilitating rapid prototyping and scalable deployment of conversational assistants, workflow automations, and AI-driven digital workers in production environments.

SonAgent is an extensible open-source framework designed for building, organizing, and running AI agents in Python. It provides core modules for memory storage, tool wrappers, planning logic, and asynchronous event handling. Developers can register custom tools, integrate language models, manage long-term agent memory, and orchestrate multiple agents to collaborate on complex tasks. SonAgent’s modular design accelerates the development of conversational bots, workflow automations, and distributed agent systems.