js-agent provides developers with a minimalistic yet powerful toolkit to create autonomous AI agents in JavaScript. It offers abstractions for conversation memory, function-calling tools, customizable planning strategies, and error handling. With js-agent, you can quickly wire up prompts, manage state, invoke external APIs, and orchestrate complex agent behaviors through a simple, modular API. It's designed to run in Node.js environments and integrates seamlessly with the OpenAI API to power intelligent, context-aware agents.

Just Chat delivers a complete self-hosted chat interface for interacting with large language models. By inputting API keys for providers like OpenAI, Anthropic, or Hugging Face, users can start multi-turn conversations with memory support. The platform enables attachments, letting users upload documents for context-aware Q&A. Plugin integration allows external tool calls such as web search, calculations, or database queries. Developers can design custom prompt templates, control system messages, and switch between models seamlessly. The UI is built using React and Node.js, offering a responsive web experience on desktop and mobile. With its modular plugin system, users can add or remove features easily, tailoring Just Chat to customer support bots, research assistants, content generators, or educational tutors.

LangChain is an open-source Python framework designed to accelerate development of AI-powered applications. It provides abstractions for chaining multiple language model calls (chains), building agents that interact with external tools, and managing conversation memory. Developers can define prompts, output parsers, and run end-to-end workflows. Integrations include vector stores, databases, APIs, and hosting platforms, enabling production-ready chatbots, document analysis, code assistants, and custom AI pipelines.

This bootcamp covers the LangChain framework end-to-end, enabling you to build AI Agents in Python. You’ll explore prompt templates, chain composition, agent tooling, conversational memory, and document retrieval. Through interactive notebooks and detailed exercises, you’ll implement chatbots, automated workflows, question-answering systems, and custom agent chains. By course end, you’ll understand how to deploy and optimize LangChain-based agents for diverse tasks.

Linguistic Agent System is an open-source Python framework designed for constructing intelligent agents that leverage language models to plan and execute tasks. It includes components for memory management, tool registry, planner, and executor, allowing agents to maintain context, call external APIs, perform web searches, and automate workflows. Configurable via YAML, it supports multiple LLM providers, enabling rapid prototyping of chatbots, content summarizers, and autonomous assistants. Developers can extend functionality by creating custom tools and memory backends, deploying agents locally or on servers.

LLM-Blender-Agent enables developers to build modular, multi-agent AI systems by wrapping LLMs into collaborative agents. Each agent can access tools like Python execution, web scraping, SQL databases, and external APIs. The framework handles conversation memory, step-by-step reasoning, and tool orchestration, allowing tasks such as report generation, data analysis, automated research, and workflow automation. Built on top of LangChain, it’s lightweight, extensible, and works with GPT-3.5, GPT-4, and other LLMs.

LLM-Agent is a lightweight, extensible framework for building AI agents powered by large language models. It provides abstractions for conversation memory, dynamic prompt templates, and seamless integration of custom tools or APIs. Developers can orchestrate multi-step reasoning processes, maintain state across interactions, and automate complex tasks such as data retrieval, report generation, and decision support. By combining memory management with tool usage and planning, LLM-Agent streamlines the development of intelligent, task-oriented agents in Python.

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.

NagaAgent is an open-source Python library designed to simplify the creation, orchestration, and scaling of AI agents. It provides a plug-and-play tool integration system, persistent conversational memory objects, and an asynchronous multi-agent controller. Developers can register custom tools as functions, manage agent state, and choreograph interactions between multiple agents. The framework includes logging, error-handling hooks, and configuration presets for rapid prototyping. NagaAgent is ideal for building complex workflows—customer support bots, data processing pipelines, or research assistants—without infrastructure overhead.

Nuzon-AI provides a Python-based agent framework that lets you define tasks, manage conversational memory, and extend capabilities via plugins. It supports integration with major LLMs (OpenAI, local models), enabling agents to perform web interactions, data analysis, and automated workflows. The architecture includes a skill registry, tool invocation system, and multi-agent orchestration layer, allowing you to compose agents for customer support, research assistance, and personal productivity. With configuration files, you can tailor each agent’s behavior, memory retention policy, and logging for debugging or audit purposes.

Tongui Agent provides a modular architecture for creating AI agents that can maintain conversation state, leverage external tools, and coordinate multiple sub-agents. Developers configure LLM backends, define custom actions, and attach memory modules to store context. The framework includes an SDK, CLI, and middleware hooks for observability, making it easy to integrate into web or Node.js applications. Supported LLMs include OpenAI, Azure OpenAI, and open-source models.

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.

Discord AI Agent leverages the Discord API and OpenAI-compatible LLMs to transform any server into an interactive AI chat environment. Developers can register custom plugins to handle slash commands, message events, or scheduled tasks, while built-in memory storage retains conversation context for coherent multi-turn dialogues. The framework supports asynchronous execution, configurable models, prompt templates, and logging for debugging. By editing a single YAML or JSON configuration, you can define API keys, model preferences, command prefixes, and plugin directories. Its extension-friendly architecture allows adding specialized functionality such as moderation, trivia games, or customer support bots. Whether running locally or deploying on cloud platforms, Discord AI Agent simplifies the process of building flexible, maintainable AI agents for community engagement.

LazyLL external APIs or custom utilities. Agents execute defined tasks through sequential or branching workflows, supporting synchronous or asynchronous operation. LazyLLM also offers built-in logging, testing utilities, and extension points for customizing prompts or retrieval strategies. By handling the underlying orchestration of LLM calls, memory management, and tool execution, LazyLLM enables rapid prototyping and deployment of intelligent assistants, chatbots, and automation scripts with minimal boilerplate code.

LLM Agents Example provides a hands-on codebase for building AI agents in Python. It demonstrates registering custom tools (web search, math solver via WolframAlpha, CSV analyzer, Python REPL), creating chat and retrieval-based agents, and connecting to vector stores for document question answering. The repo illustrates patterns for maintaining conversational memory, dispatching tool calls dynamically, and chaining multiple LLM prompts to solve complex tasks. Users learn how to integrate third-party APIs, structure agent workflows, and extend the framework with new capabilities—serving as a practical guide for developer experimentation and prototyping.

LLMLing Agent is a modular framework for building, configuring, and deploying AI agents powered by large language models. Users can instantiate multiple agent roles, connect external tools or APIs, manage conversational memory, and orchestrate complex workflows. The platform includes a browser-based playground that visualizes agent interactions, logs message history, and allows real-time adjustments. With a Python SDK, developers can script custom behaviors, integrate vector databases, and extend the system through plugins. LLMLing Agent streamlines creation of chatbots, data analysis bots, and automated assistants by providing reusable components and clear abstractions for multi-agent collaboration.

Minerva is an extensible AI agent framework designed to automate complex workflows using large language models. Developers can integrate external tools—such as web search, API calls, or file processors—define custom planning strategies, and manage conversational or persistent memory. Minerva supports both synchronous and asynchronous task execution, configurable logging, and a plugin architecture, making it easy to prototype, test, and deploy intelligent agents capable of reasoning, planning, and tool use in real-world scenarios.

Personal AI Assistant is a modular AI agent built in Python to deliver conversational chat, context-aware memory, and automated task execution. It features a plugin system for web browsing, file management, email sending, and calendar scheduling. Backed by OpenAI or local language models and SQLite-based memory storage, it preserves conversation history and adapts responses over time. Developers can extend capabilities with custom modules, creating a tailored assistant for productivity, research, or home automation.

Arcade is a developer-oriented framework that simplifies building AI agents by providing a cohesive SDK and command-line interface. Using familiar JS/TS syntax, you can define workflows that integrate large language model calls, external API endpoints, and custom logic. Arcade handles conversation memory, context batching, and error handling out of the box. With features like pluggable models, tool invocation, and a local testing playground, you can iterate quickly. Whether you're automating customer support, generating reports, or orchestrating complex data pipelines, Arcade streamlines the process and provides deployment tools for production rollout.

At its core, SpongeCake is a high-level abstraction layer over Langchain designed to accelerate AI agent development. It offers built-in support for registering tools—like web search, database connectors, or custom APIs—managing prompt templates, and persisting conversational memory. With both code-based and YAML-based configurations, teams can declaratively define agent behaviors, chain multi-step workflows, and enable dynamic tool selection. The included CLI facilitates local testing, debugging, and deployment, making SpongeCake ideal for building chatbots, task automators, and domain-specific assistants without repetitive boilerplate.