Stellar Chat empowers teams to build conversational AI agents by providing a robust framework that abstracts LLM interactions, memory management, and tool integrations. At its core, it features an extensible pipeline that handles user input preprocessing, context enrichment through vector-based memory retrieval, and LLM invocation with configurable prompting strategies. Developers can plug in popular vector storage solutions like Pinecone, Weaviate, or FAISS, and integrate third-party APIs or custom plugins for tasks like web search, database queries, or enterprise application control. With support for streaming outputs and real-time feedback loops, Stellar Chat ensures responsive user experiences. It also includes starter templates and best-practice examples for customer support bots, knowledge search, and internal workflow automation. Deployed with Docker or Kubernetes, it scales to meet production demands while remaining fully open-source under the MIT license.

Strands Agents offers a modular architecture for creating intelligent agents that combine natural language reasoning, long-term memory, and external API/tool calls. It enables developers to configure planner, executor, and memory components, plug in any LLM (e.g., OpenAI, Hugging Face), define custom action schemas, and manage state across tasks. With built-in logging, error handling, and extensible tool registry, it accelerates prototyping and deployment of agents that can research, analyze data, control devices, or serve as digital assistants. By abstracting common agent patterns, it reduces boilerplate and promotes best practices for reliable, maintainable AI-driven automation.

AI Agents Laravel provides a comprehensive framework for defining, managing, and executing AI-driven agents inside Laravel applications. It abstracts interactions with various large language models (OpenAI, Anthropic, Hugging Face) and offers built-in support for tool integrations, such as HTTP requests, database queries, and custom business logic. Developers can define agents with custom prompts, memory backends (in-memory, database, Redis), and decision-making rules to handle complex conversational flows or automated tasks. The package includes event logging, error handling, and monitoring hooks to track agent performance. It facilitates rapid prototyping and seamless integration of intelligent assistants, data parsers, and workflow automation directly in web environments.

AI Coder Buddy is an AI-powered coding assistant geared towards enhancing your productivity. It supports over 90 programming languages, frameworks, and libraries, offering more than 145,000 searchable code examples. Whether you're a beginner needing guidance or a seasoned developer looking to speed up your workflow, AI Coder Buddy provides the tools and support you need to code smarter and more efficiently.

Avalon-LLM is a Python-based multi-agent AI framework that allows users to orchestrate multiple LLM-driven agents in a coordinated environment. Each agent can be configured with specific tools—including web search, file operations, and custom APIs—to perform specialized tasks. The framework supports memory modules for storing conversation context and long-term knowledge, chain-of-thought reasoning to improve decision making, and built-in evaluation pipelines to benchmark agent performance. Avalon-LLM provides a modular plugin system, enabling developers to easily add or replace components such as model providers, toolkits, and memory stores. With simple configuration files and command-line interfaces, users can deploy, monitor, and extend autonomous AI workflows tailored to research, development, and production use cases.

Thousand Birds is an extensible AI agent framework allowing developers to define and configure agent behaviors using a Python SDK and CLI. Agents can plan multi-step workflows, integrate web search, interact with browser sessions, read and write files, call external APIs, and manage stateful memory. It supports plugin modules to add custom tools and data connectors. The built-in orchestration engine schedules tasks, handles retries, and logs execution details. Developers can chain agents, enable parallel execution, and monitor performance through structured outputs. Thousand Birds accelerates deployment of autonomous assistants for research, data extraction, automation, and experimental prototypes.

The Multi Agent Robotic System project offers a modular Python-based platform for developing, simulating, and deploying cooperative robotic teams. At its core, it implements decentralized control strategies, enabling robots to share state information and collaboratively allocate tasks without a central coordinator. The system includes built-in modules for path planning, collision avoidance, environment mapping, and dynamic task scheduling. Developers can integrate new algorithms by extending provided interfaces, adjust communication protocols via configuration files, and visualize robot interactions in simulated environments. Compatible with ROS, it supports seamless transitions from simulation to real-world hardware deployments. This framework accelerates research by providing reusable components for swarm behavior, collaborative exploration, and warehouse automation experiments.

NaturalAgents is an open-source Python library designed to streamline the creation and deployment of LLM-powered agents. It provides modules for memory management, context tracking, and tool integration, allowing agents to store and recall information over long sessions. A hierarchical planner orchestrates multi-step reasoning and actions, while an extension system supports custom plugins and external API calls. Built-in logging and analytics enable developers to monitor agent performance and debug workflow issues. NaturalAgents also supports synchronous and asynchronous execution, making it flexible for both interactive use cases and automated pipelines.

Rigging is a developer-focused framework that streamlines the creation and orchestration of AI agents. It provides tool and function registration, context and memory management, workflow chaining, callback events, and logging. Developers can integrate multiple LLM providers, define custom plugins, and assemble multi-step pipelines. Rigging’s type-safe TypeScript SDK ensures modularity and reusability, accelerating AI agent development for chatbots, data processing, and content generation tasks.

SWE-agent is a developer-focused AI agent framework that integrates with GitHub to autonomously diagnose and resolve code issues. It runs in Docker or GitHub Codespaces, uses your preferred language model, and allows you to configure tool bundles for tasks like linting, testing, and deployment. SWE-agent generates clear action trajectories, applies pull requests with fixes, and provides insights via its trajectory inspector, enabling teams to automate code review, bug fixing, and repository cleanup efficiently.

Team of AI Agents provides a modular architecture to build and deploy multi-agent systems. Each agent operates with distinct roles, utilizing a global memory store and local contexts for knowledge retention. The framework supports asynchronous messaging, tool usage via adapters, and dynamic task reassignment based on agent outcomes. Developers configure agents through YAML or Python scripts, enabling topic specialization, goal hierarchy, and priority handling. It includes built-in metrics for performance evaluation and debugging, facilitating rapid iteration. With extensible plugin architecture, users can integrate custom NLP models, databases, or external APIs. Team of AI Agents accelerates complex workflows by leveraging collective intelligence of specialized agents, making it ideal for research, automation, and simulation environments.

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.

AgenticAppBuilder accelerates AI agent development by providing a one-command CLI to scaffold production-ready applications. It sets up language model configurations, memory backends, tool integrations, and a user interface, enabling developers to focus on custom agent logic. The modular architecture supports extensible toolchains, seamless API key management, and deployment scripts for local or cloud environments, reducing boilerplate and speeding prototyping.

Agent of Code is a versatile AI agent framework enabling developers to offload routine coding tasks to intelligent agents. It leverages large language models to translate natural language prompts into fully functional code, perform automated code reviews, debug existing code, and refactor legacy codebases. Users define agent goals and parameters through YAML or JSON configurations, select plugins for tasks like testing or CI integration, and execute agents via CLI. The framework orchestrates API calls, manages context windows, and assembles modular responses into cohesive code scripts. With an extensible architecture, developers can plug in custom modules, integrate with version control, and tailor the agent pipeline to project workflows.

Agentic Kernel offers a decoupled architecture for constructing AI agents by composing reusable components. Developers can define planning pipelines to break down goals, configure short-term and long-term memory stores using embeddings or file-based backends, and register external tools or APIs for action execution. The framework supports dynamic tool selection, agent reflection cycles, and built-in scheduling to manage agent workflows. Its pluggable design accommodates any LLM provider and custom components, enabling use cases such as conversational assistants, automated research agents, and data-processing bots. With transparent logging, state management, and easy integration, Agentic Kernel accelerates development while ensuring maintainability and scalability in AI-driven applications.

VideoSDK AI Agent Demo combines the power of VideoSDK’s real-time video infrastructure with AI services to create an intelligent virtual assistant for group video calls. The demo features live speech-to-text transcription, enabling participants to read captions in multiple languages through on-the-fly translation. After each session, the agent generates concise meeting summaries highlighting key discussion points and action items. Users can pose natural language questions during calls, and the AI chatbot responds contextually using conversation history. Built using React for UI and Node.js for backend integration with OpenAI APIs, this demo provides a modular architecture for developers to extend or adapt features such as sentiment analysis, custom prompts, and multi-language support, accelerating the creation of AI-driven video collaboration tools.

Augini allows developers to define intelligent agents capable of interpreting user inputs, invoking external APIs, loading context-aware memory, and producing coherent, multi-turn responses. Users can configure each agent with customizable toolkits for web search, database queries, file operations, or custom Python functions. The integrated memory module preserves conversation states across sessions, ensuring contextual continuity. Augini’s declarative API enables construction of complex multi-step workflows with branching logic, retries, and error handling. It seamlessly integrates with major LLM providers including OpenAI, Anthropic, and Azure AI, and supports deployment as standalone scripts, Docker containers, or scalable microservices. Augini empowers teams to rapidly prototype, test, and maintain AI-driven agents in production environments.

CAMEL-AI is a Python-based framework that allows developers and researchers to build, configure, and run multiple autonomous AI agents powered by LLMs. It offers built-in support for retrieval-augmented generation (RAG), external tool usage, agent communication, memory and state management, and scheduling. With modular components and easy integration, teams can prototype complex multi-agent systems, automate workflows, and scale experiments across different LLM backends.

Ernie Bot Agent is a developer framework designed to streamline the creation of AI-driven conversational agents using Baidu ERNIE Bot. It provides abstractions for API calls, prompt templates, memory management, and tool integration. The SDK supports multi-turn conversations with context awareness, custom workflows for task execution, and a plugin system for domain-specific extensions. With built-in logging, error handling, and configuration options, it reduces boilerplate and enables rapid prototyping of chatbots, virtual assistants, and automation scripts.

HMAS is an open-source Python framework that enables development of hierarchical multi-agent systems. It offers abstractions for defining agent hierarchies, inter-agent communication protocols, environment integration, and built-in training loops. Researchers and developers can use HMAS to prototype complex multi-agent interactions, train coordinated policies, and evaluate performance in simulated environments. Its modular design makes it easy to extend and customize agents, environments, and training strategies.