SyntropAI is a developer-focused Python library designed to simplify the construction of autonomous AI agents. It provides a modular architecture with core components for memory management, tool and API integration, LLM backend abstraction, and a planning engine that orchestrates multi-step workflows. Users can define custom tools, configure persistent or short-term memory, and select from supported LLM providers. SyntropAI also includes logging and monitoring hooks to track agent decisions. Its plug-and-play modules let teams iterate quickly on agent behaviors, making it ideal for chatbots, knowledge assistants, task automation bots, and research prototypes.

Agent-Go provides a modular framework for building autonomous AI agents in Go. It integrates LLM providers (such as OpenAI), vector-based memory stores for long-term context retention, and a flexible planning engine that breaks down user requests into executable steps. Developers define and register custom tools (APIs, databases, or shell commands) that agents can invoke. A conversation manager tracks dialog history, while a configurable planner orchestrates tool calls and LLM interactions. This allows teams to rapidly prototype AI-driven assistants, automated workflows, and task-oriented bots in a production-ready Go environment.

FastAPI Agents provides a robust service layer for developing LLM-based agents using the FastAPI web framework. It allows you to define agent behaviors with LangChain chains, tools, and memory systems. Each agent can be exposed as a standard REST endpoint, supporting asynchronous requests, streaming responses, and customizable payloads. Integration with vector stores enables retrieval-augmented generation for knowledge-driven applications. The framework includes built-in logging, monitoring hooks, and Docker support for containerized deployment. You can easily extend agents with new tools, middleware, and authentication. FastAPI Agents accelerates the production readiness of AI solutions, ensuring security, scalability, and maintainability of agent-based applications in enterprise and research settings.

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.

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.

ai-agents-trial is an open-source Python project demonstrating how to build autonomous AI agents using LLMs. It provides modular abstractions for agent planning, tool invocation (e.g., web search, calculators), and memory management. Developers can define custom tools, chain actions across multiple steps, and persist context across sessions. The codebase uses OpenAI APIs alongside helper utilities to orchestrate workflows, making it ideal for rapid prototyping of chat-based assistants, research bots, or domain-specific automation agents. Integration points allow extending functionality with new connectors and data sources without altering core logic.

At its core, AI Orchestra offers a modular orchestration engine that lets developers define nodes representing AI agents, tools, and custom modules. Each node can be configured with specific LLMs (e.g., OpenAI, Hugging Face), parameters, and input/output mapping, enabling dynamic task delegation. The framework supports composable pipelines, concurrency controls, and branching logic, allowing complex flows that adapt based on intermediate results. Built-in telemetry and logging capture execution details, while callback hooks handle errors and retries. AI Orchestra also includes a plugin system for integrating external APIs or custom functionalities. With YAML or Python-based pipeline definitions, users can prototype and deploy robust multi-agent systems in minutes, from chat-based assistants to automated data analytics workflows.

AiQuickHelp is an innovative AI assistant platform aimed at boosting productivity. Through its suite of advanced AI-powered tools and features, users can experience a more streamlined and efficient workflow. The platform offers functionalities like intelligent search, personalized AI characters, and an extensive prompt library, making it a comprehensive solution for modern workplace demands. Whether you're looking to automate tasks or integrate multiple systems, AiQuickHelp has you covered.

autogen4j is a lightweight Java library designed to abstract the complexity of building autonomous AI agents. It offers core modules for planning, memory storage, and action execution, letting agents decompose high-level goals into sequential sub-tasks. The framework integrates with LLM providers (e.g., OpenAI, Anthropic) and allows registration of custom tools (HTTP clients, database connectors, file I/O). Developers define agents through a fluent DSL or annotations, quickly assembling pipelines for data enrichment, automated reporting, and conversational bots. An extensible plugin system ensures flexibility, enabling fine-tuned behaviors across diverse applications.

Autonomous Agents is an open-source Python library designed to simplify the creation of autonomous AI agents powered by large language models. By abstracting core components such as perception, reasoning, and action, it allows developers to define custom tools, memories, and strategies. Agents can autonomously plan multi-step tasks, query external APIs, process results through custom parsers, and maintain conversational context. The framework supports dynamic tool selection, sequential and parallel task execution, and memory persistence, enabling robust automation for tasks ranging from data analysis and research to email summarization and web scraping. Its extensible design facilitates easy integration with different LLM providers and custom modules.

Botsnap delivers a platform where users can create and discover custom AI assistants tailored to their needs. It offers a marketplace with over 100 personalized assistants suitable for time management, business workflows, and creative projects. The platform allows users to become creators, enabling them to develop, monetize, and optimize AI-driven tools. Botsnap aims to enhance user engagement by providing solutions that are uniquely tailored to individual user preferences.

Minimalist Agent provides a bare-bones framework for building AI agents in Python. It leverages LangChain’s agent classes and OpenAI’s API to perform multi-step reasoning, dynamically select tools, and execute functions. You can clone the repository, configure your OpenAI API key, define custom tools or endpoints, and run the CLI script to interact with the agent. The design emphasizes clarity and extensibility, making it easy to study, modify, and extend core agent behaviors for experimentation or teaching.

Cyrano is an open-source Python framework and CLI for creating AI agents that orchestrate large language models and external tools through natural language prompts. Users can define custom tools (functions), configure memory and token limits, and handle callbacks. Cyrano handles parsing JSON responses from LLMs and executes specified tools in sequence. It emphasizes simplicity, modularity, and zero external dependencies, enabling developers to prototype chatbots, build automated workflows, and integrate AI capabilities into applications quickly.

Fast-LLM-Agent-MCP is a lightweight, open-source Python framework for building AI agents that combine memory management, chain-of-thought reasoning, and multi-step planning. Developers can integrate it with OpenAI, Azure OpenAI, local Llama, and other models to maintain conversational context, generate structured reasoning traces, and decompose complex tasks into executable subtasks. Its modular design allows custom tool integration and memory stores, making it ideal for applications like virtual assistants, decision support systems, and automated customer support bots.

FAgent offers a modular architecture for constructing AI agents, including environment abstractions, policy interfaces, and tool connectors. It supports integration with popular LLM services, implements memory management for context retention, and provides an observability layer for logging and monitoring agent actions. Developers can define custom tools and actions, orchestrate multi-step workflows, and run simulation-based evaluations. FAgent also includes plugins for data collection, performance metrics, and automated testing, making it suitable for research, prototyping, and production deployments of autonomous agents in various domains.

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.

LLM-Powered AI Agents is designed to streamline the creation of autonomous agents by orchestrating large language models and external tools through a modular architecture. Developers can define custom tools with standardized interfaces, configure memory backends to persist state, and set up multi-step reasoning chains that use LLM prompts to plan and execute tasks. The AgentExecutor module manages tool invocation, error handling, and asynchronous workflows, while built-in templates illustrate real-world scenarios like data extraction, customer support, and scheduling assistants. By abstracting API calls, prompt engineering, and state management, the framework reduces boilerplate code and accelerates experimentation, making it ideal for teams building custom intelligent automation solutions in Python.

llama-cpp-agent is an open-source C++ framework for running AI agents entirely offline. It leverages the llama.cpp inference engine to provide fast, low-latency interactions and supports a modular plugin system, configurable memory, and task execution. Developers can integrate custom tools, switch between different local LLM models, and build privacy-focused conversational assistants without external dependencies.

OSU NLP Middleware is a lightweight framework built in Python that simplifies the development of AI agent systems. It provides a core agent loop that orchestrates interactions between natural language models and external tool functions defined as plugins. The framework supports popular LLM providers (OpenAI, Hugging Face, etc.), and enables developers to register custom tools for tasks like database queries, document retrieval, web search, mathematical computation, and RESTful API calls. Middleware manages conversation history, handles rate limits, and logs all interactions. It also offers configurable caching and retry policies for improved reliability, making it easy to build intelligent assistants, chatbots, and autonomous workflows with minimal boilerplate code.

MiniAgent is a minimalistic open-source framework built in Python for constructing autonomous AI agents capable of planning and executing complex workflows. At its core, MiniAgent includes a task planning module that decomposes high-level goals into ordered steps, an execution controller that runs each step sequentially, and built-in adapters for integrating external tools and APIs, including web services, databases, and custom scripts. It also features a lightweight memory management system to persist conversational or task context. Developers can easily register custom action plugins, define policy rules for decision-making, and extend tool functionality. With support for OpenAI models and local LLMs, MiniAgent enables rapid prototyping of chatbots, digital workers, and automated pipelines, all under an MIT license.