Promptix Python is an open-source framework for building autonomous AI agents in Python. With a simple installation via pip, you can instantiate agents powered by any major LLM, register domain-specific tools, configure in-memory or persistent data stores, and orchestrate multi-step decision loops. The SDK supports real-time streaming of token outputs, callback handlers for logging or custom processing, and built-in memory modules to retain context across interactions. Developers can leverage this library to prototype chatbot assistants, automations, data pipelines, or research agents in minutes. Its modular design allows swapping models, adding custom tools, and extending memory backends, providing flexibility for a wide range of AI agent use cases.

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.

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.

Micro-agent is a lightweight, unopinionated JavaScript library designed to simplify the creation of sophisticated AI agents using large language models. It exposes core abstractions such as agents, tools, planners, and memory stores, allowing developers to assemble custom conversational flows. Agents can invoke external APIs or internal utilities as tools, enabling dynamic data retrieval and action execution. The library supports both short-term conversational memory and long-term persistent memory to maintain context across sessions. Planners orchestrate chain-of-thought processes, breaking down complex tasks into tool calls or language model queries. With configurable prompt templates and execution strategies, micro-agent adapts seamlessly to frontend web applications, Node.js services, and edge environments, providing a flexible foundation for chatbots, virtual assistants, or autonomous decision-making systems.