Devon provides a comprehensive suite of tools for defining, orchestrating, and running autonomous agents within Python applications. Users can outline agent goals, specify callable tasks, and chain actions based on conditional logic. Through seamless integration with language models like GPT and local vector stores, agents ingest and interpret user inputs, retrieve contextual knowledge, and generate plans. The framework supports long-term memory via pluggable storage backends, enabling agents to recall past interactions. Built-in monitoring and logging components allow real-time tracking of agent performance, while a CLI and SDK facilitate rapid development and deployment. Suitable for automating customer support, data analysis pipelines, and routine business operations, Devon accelerates the creation of scalable digital workers.

OpenSilver is a free, open-source UI framework designed for building modern .NET web applications using C# and XAML. It supports WPF and Silverlight applications and facilitates seamless transition away from legacy Silverlight technology. The framework ensures 100% code reusability, compatibility with various modern web technologies like Blazor, React, and Angular, and offers an AI-enhanced XAML designer for Visual Studio Code. With OpenSilver, developers can build cross-platform applications that run on any browser and device, preserving the original look and feel of the apps, while significantly reducing the migration cost and time.

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.

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.

BotPlayers is a versatile open-source framework designed to streamline the development and deployment of AI-driven game-playing agents. It features a flexible environment abstraction layer that supports screen scraping, web APIs, or custom simulation interfaces, allowing bots to interact with various games. The framework includes built-in reinforcement learning algorithms, genetic algorithms, and rule-based heuristics, along with tools for data logging, model checkpointing, and performance visualization. Its modular plugin system enables developers to customize sensors, actions, and AI policies in Python or Java. BotPlayers also offers YAML-based configuration for rapid prototyping and automated pipelines for training and evaluation. With cross-platform support on Windows, Linux, and macOS, this framework accelerates experimentation and production of intelligent game agents.

Eliza is a lightweight, open-source conversational framework that simulates a psychotherapist via pattern matching and scripted templates. Developers can define custom scripts, patterns, and memory variables to tailor responses and conversation flows. It runs in any modern browser or webview environment, supports multiple sessions, and logs interactions for analysis. Its extensible architecture allows integration into web pages, mobile apps, or desktop wrappers, making it a versatile tool for education, research, prototype development, and interactive installations.

SwarmZero offers a scalable, open-source environment for defining, managing, and executing swarms of AI agents. Developers can declare agent roles, customize prompts, and chain workflows via a unified Orchestrator API. The framework integrates with major LLM providers, supports plugin extensions, and logs session data for debugging and performance analysis. Whether coordinating research bots, content creators, or data analyzers, SwarmZero streamlines multi-agent collaboration and ensures transparent, reproducible results.

RAGENT is designed to create autonomous AI agents that can interact with diverse tools and data sources. Under the hood, it uses retrieval-augmented generation to fetch relevant context from local files or external sources and then composes responses via OpenAI models. Developers can plug in tools for web search, browser automation with Selenium, file read/write operations, code execution in secure sandboxes, and OCR for image text extraction. The framework manages conversation memory, handles tool orchestration, and supports custom prompt templates. With RAGENT, teams can rapidly prototype intelligent agents for document Q&A, research automation, content summarization, and end-to-end workflow automation, all within a Python environment.

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-Powered RAG System is a developer-focused framework for building retrieval-augmented generation (RAG) pipelines. It provides modules for embedding document collections, indexing via FAISS, Pinecone, or Weaviate, and retrieving relevant context at runtime. The system uses LangChain wrappers to orchestrate LLM calls, supports prompt templates, streaming responses, and multi-vector store adapters. It simplifies end-to-end RAG deployment for knowledge bases, allowing customization at each stage—from embedding model configuration to prompt design and result post-processing.

llm-lab provides a flexible toolkit for creating intelligent agents using large language models. It includes an agent orchestration engine, support for custom prompt templates, memory and state tracking, and seamless integration with external APIs and plugins. Users can write scenarios, define toolchains, simulate interactions, and collect performance logs. The framework also offers a built-in testing suite to validate agent behavior against expected outcomes. Extensible by design, llm-lab enables developers to swap LLM providers, add new tools, and evolve agent logic through iterative experimentation.

mini-AlphaStar demystifies the complex AlphaStar architecture by offering an accessible, open-source PyTorch framework for StarCraft II AI development. It features spatial feature encoders for screen and minimap inputs, non-spatial feature processing, LSTM memory modules, and separate policy and value networks for action selection and state evaluation. Using imitation learning to bootstrap and reinforcement learning with self-play for fine-tuning, it supports environment wrappers compatible with StarCraft II via pysc2, logging through TensorBoard, and configurable hyperparameters. Researchers and students can generate datasets from human gameplay, train models on custom scenarios, evaluate agent performance, and visualize learning curves. The modular codebase enables easy experimentation with network variants, training schedules, and multi-agent setups. Designed for education and prototyping rather than production deployment.

Multi-Agent Architecture provides a scalable, extensible platform to define, register, and coordinate multiple AI agents working together on a shared objective. It includes a message broker, lifecycle management, dynamic agent spawning, and customizable communication protocols. Developers can build specialized agents (e.g., data fetchers, NLP processors, decision-makers) and plug them into the core runtime to handle tasks ranging from data aggregation to autonomous decision workflows. The framework’s modular design supports plugin extensions and integrates with existing ML models or APIs.

multiagent-golang provides a structured approach to building multi-agent systems in Go. It introduces an Agent abstraction where each agent can be equipped with various sensors to perceive its environment and actuators to take actions. Agents run concurrently using Go routines and communicate through dedicated messaging channels. The framework also includes an environment simulation layer to handle events, manage the agent lifecycle, and track state changes. Developers can easily extend or customize agent behaviors, configure simulation parameters, and integrate additional modules for logging or analytics. It streamlines the creation of scalable, concurrent simulations for research and prototyping.

MultiAgentPacman offers a Python-based game environment where users can implement, visualize, and benchmark multiple AI agents in the Pacman domain. It supports adversarial search algorithms like minimax, expectimax, alpha-beta pruning, as well as custom reinforcement learning or heuristic-based agents. The framework includes a simple GUI, command-line controls, and utilities to log game statistics and compare agent performance under competitive or cooperative scenarios.

MultiAgentPuzzleSolver provides a modular environment where independent AI agents work together to solve puzzles such as sliding tiles, Rubik’s Cube, and logic grids. Agents share state information, negotiate subtask assignments, and apply diverse heuristics to explore the solution space more effectively than single-agent approaches. Developers can plug in new agent behaviors, customize communication protocols, and add novel puzzle definitions. The framework includes tools for real-time visualization of agent interactions, performance metrics collection, and experiment scripting. It supports Python 3.8+, standard libraries, and popular ML toolkits for seamless integration into research projects.

RoboCup Rescue Agent Simulation is an open-source framework that models urban disaster environments where multiple AI-driven agents collaborate to locate and rescue victims. It offers interfaces for navigation, mapping, communication, and sensor integration. Users can script custom agent strategies, run batch experiments, and visualize agent performance metrics. The platform supports scenario configuration, logging, and result analysis to accelerate research in multi-agent systems and disaster response algorithms.

SelfDrivingCarSimulator is a lightweight Python framework built on Pygame that offers a 2D driving environment for training autonomous vehicle agents using reinforcement learning. It supports customizable track layouts, configurable sensor models (like LiDAR and camera emulation), real-time visualization, and data logging for performance analysis. Developers can integrate their RL algorithms, adjust physics parameters, and monitor metrics such as speed, collision rate, and reward functions to iterate quickly on self-driving research and educational projects.

SPEAR (Scalable Platform for Edge AI Real-Time) is designed to manage the full lifecycle of AI inference at the edge. Developers can define streaming pipelines that ingest sensor data, videos, or logs via connectors to Kafka, MQTT, or HTTP sources. SPEAR dynamically deploys containerized models to worker nodes, balancing loads across clusters while ensuring low-latency responses. It includes built-in model versioning, health checks, and telemetry, exposing metrics to Prometheus and Grafana. Users can apply custom transformations or alerts through a modular plugin architecture. With automated scaling and fault recovery, SPEAR delivers reliable real-time analytics for IoT, industrial automation, smart cities, and autonomous systems in heterogeneous environments.

Base OnChain Agent is an open-source framework designed to deploy autonomous AI agents on Ethereum-like blockchains. It connects to blockchain nodes via Web3 and uses OpenAI's GPT models to interpret on-chain events such as token transfers or protocol-specific logs. The agent can process natural language prompts or predefined strategies to decide when to execute transactions, call smart contract functions, or respond to governance proposals. Developers can extend modules for custom event listeners, integrate off-chain data feeds, and manage private keys securely. This solution enables automated DeFi operations like liquidity provisioning, arbitrage trading, and portfolio rebalancing with minimal manual intervention.