Projects sharing features with DeepSeek TUI

Swarms is a multi-agent orchestration framework and autonomous agent toolkit designed to coordinate large language model agents. It serves as a workflow engine for managing agent relationships, providing the infrastructure to build autonomous agents with integrated memory, tool-calling capabilities, and reasoning loops. The framework is distinguished by its multi-agent consensus systems, which utilize voting, adversarial debates, and judge agents to synthesize high-quality responses. It supports a variety of collaboration patterns, including director-worker hierarchies, expert synthesis, and

This project is a collection of educational resources and technical guides focused on the development and implementation of large language models. It provides a comprehensive curriculum covering transformer architectures, training methods, and deployment strategies. The materials provide detailed instructions for building autonomous agents using reasoning loops and tool integration, as well as guides for fine-tuning models through supervised learning and preference optimization. It also includes tutorials for constructing retrieval augmented generation pipelines and implementing transformer m

This project is a framework for building AI coding agents that automate software development tasks using large language models. It includes a task lifecycle manager that tracks complex development goals through a persistent graph of dependent tasks and a system for multi-agent orchestration to delegate tasks to specialized sub-agents. The framework implements a Model Context Protocol client to discover and execute tools from external servers and provides a remote development bridge to synchronize local command line interfaces with remote containers or desktop environments. The system covers

This project is an AI-powered IDE extension and LLM coding assistant that provides a conversational interface for generating, refactoring, and debugging code. It functions as an AI agent framework and a Model Context Protocol client, connecting AI models to external data sources and tools to automate complex development tasks. The system is distinguished by its use of autonomous AI agents capable of multi-step task execution, including the ability to read files, modify code, and run terminal commands iteratively. It supports recursive agent orchestration through subagent delegation and employ

Langroid is a multi-agent orchestration framework and tool integration suite designed for building complex AI applications. It serves as a multi-modal integration layer that connects diverse local and remote language models with an agentic retrieval-augmented generation system. The project distinguishes itself through a collaborative message-exchange paradigm, allowing specialized agents to delegate tasks hierarchically and coordinate via structured communication. It features an advanced state management system for conversational AI, including the ability to rewind and prune conversation hist

OpenSquilla is an LLM agent orchestration framework designed to coordinate multi-step AI workflows and tool execution using directed acyclic graphs. It functions as a centralized system for managing specialized skill packages and executing complex reasoning sequences. The project distinguishes itself through a routing gateway that directs tasks to different AI providers based on complexity, cost, and performance. It utilizes a multi-tier AI memory system that organizes working, episodic, and semantic knowledge using local embeddings and SQLite, alongside a secure execution sandbox that isolat

Koog is an LLM agent framework used to build autonomous entities that execute tool-based workflows. It utilizes a graph-based workflow engine to define agent behaviors and decision paths as a directed graph of nodes and edges. The framework distinguishes itself through a model provider orchestrator that enables dynamic switching, load balancing, and automatic fallbacks between different AI backends. It implements the Model Context Protocol to connect agents to remote tool servers and features a RAG memory system using vector embeddings to maintain long-term conversation context. The project

Youtu Agent is an open-source framework for building, running, and evaluating autonomous agents powered by large language models. It provides the core infrastructure for creating agents that follow reasoning loops, use toolkits, and coordinate with other agents to solve complex tasks, all managed through YAML-driven configuration files. The framework distinguishes itself through its support for multi-agent orchestration, where a planner agent decomposes tasks and coordinates specialized worker agents, and through its integration with the Model Context Protocol for connecting to external toolk

Nanoclaw is an LLM agent orchestrator and multi-platform chat gateway designed to deploy and manage isolated AI agents. It provides a containerized runtime that executes agents within sandboxed Linux containers, ensuring filesystem and state isolation through dedicated workspaces and host bind-mounts. The project distinguishes itself through a unified routing pipeline that connects agents to diverse messaging platforms, including WhatsApp, Discord, Slack, Telegram, Signal, and iMessage. It integrates the Model Context Protocol to extend agent capabilities via managed external data and functio

Pi is an autonomous coding agent and framework for building AI agents capable of executing independent loops. It functions as an agent state management system that tracks and persists tool calls throughout complex workflows, utilizing a command-line interface for interaction and control. The system features a self-extensible design, allowing agents to write and implement new capabilities and tools into their own runtime environment. It also includes a provider-agnostic abstraction layer that standardizes interactions across different large language model providers through a unified API. The

Eino is an AI agent development kit and LLM application framework designed for building autonomous agents and orchestrating complex language model workflows. It serves as a multi-agent orchestration engine and workflow orchestrator, providing a graph-based execution model to route data between models, tools, and retrievers. The framework distinguishes itself through a robust set of multi-agent coordination patterns, including supervisor-led management, sequential flows, and autonomous reasoning loops like ReAct. It features advanced agent execution controls such as active turn preemption, che

Agentscope is a comprehensive toolkit for developing and orchestrating autonomous multi-agent systems. It provides a unified framework for building agents that can reason, execute tools, and manage memory, enabling the creation of complex, collaborative workflows where multiple specialized agents interact to solve multi-step objectives. The platform distinguishes itself through a robust orchestration engine that supports both sequential and concurrent agent pipelines. It utilizes a centralized event bus for real-time telemetry, allowing developers to track agent reasoning, tool usage, and sys

Letta is a framework for building, deploying, and managing autonomous AI agents that maintain persistent state across long-term interactions. It provides a comprehensive suite of primitives for defining agents with configurable personas, modular memory blocks, and tool-use capabilities, enabling them to retain user preferences and conversation history over extended sessions. The platform distinguishes itself through its advanced memory management and orchestration capabilities. It allows agents to autonomously update their own memory, perform retrieval-augmented generation, and coordinate com

CAI is a framework for building autonomous security agents and an orchestration system for coordinating multiple specialized agents. It functions as an agentic workflow engine and an autonomous cyber-defense tool that maps language model reasoning to security kill chain functions for threat detection and mitigation. The system distinguishes itself through multi-agent coordination patterns, such as swarms and hierarchies, and the use of stateful conversation handoffs. It implements multi-layer input and output guardrails to block prompt injections and validate commands before they reach the sy

Goose is an autonomous coding assistant and extensible AI agent framework designed to automate software development workflows. It functions as an orchestration engine that can install, execute, and test code, as well as manage local files and shell commands. The platform is model-agnostic, providing a flexible interface to connect with diverse cloud-based or self-hosted large language model providers. It distinguishes itself through a standardized context protocol for integrating external tools and extensions, and a recipe system that allows users to define and repeat complex, multi-step AI w

OpenHarness is a framework for building and orchestrating AI agents that utilize tools and plugins to execute complex tasks. It provides an orchestration system for managing language model lifecycles and a multi-agent coordination system for delegating workloads across teams of specialized subagents. The project features an agent gateway that bridges language model agents to external chat platforms and communication channels. It includes a tool integration engine for executing shell, file, and web operations, supported by a memory and skill manager that handles persistent user preferences and

Qwen-Agent is a development framework for building autonomous software applications that leverage large language models to plan, reason, and execute complex tasks. It functions as an orchestration engine that enables models to interact with external APIs, manage persistent memory, and maintain context across multi-step workflows. The framework distinguishes itself through a multi-agent collaboration platform that allows independent agent instances to exchange structured messages and delegate sub-tasks to one another. By utilizing iterative reasoning loops and dynamic prompt injection, the sys

CrewAI is a multi-agent orchestration framework and autonomous agent workflow engine. It provides a system for coordinating autonomous AI agents with specific roles and goals to solve complex tasks through collaborative intelligence. The framework distinguishes itself through a collaborative AI agent system that enables multiple language model instances to share intelligence and execute multi-step objectives via role-playing. It incorporates human-in-the-loop mechanisms, allowing for manual review checkpoints to validate decisions and refine outcomes within autonomous execution paths. The pl

Agent Zero is an LLM agent framework and multi-agent orchestrator that provides an AI-powered interface for operating system tasks. It functions as a containerized AI workspace, allowing large language models to interact with a filesystem and terminal within an isolated Linux environment. The system distinguishes itself through a hierarchical orchestration model that decomposes complex goals by spawning specialized sub-agents to collaborate and consolidate results. It features a plugin-based architecture for extending capabilities via a community plugin hub, a custom skills system, and extern

This project is a Python framework for building autonomous AI agents capable of executing independent tasks through goal-oriented instructions. It provides a library of tools for managing system operations and processing multimodal data. The framework features a sandboxed system execution environment that restricts shell commands and file access to protect the host system. It also includes an automated OCR text extraction pipeline for converting printed or handwritten text from images and documents into digital formats. Connectivity is handled through a modular tool integration system and a

GenericAgent is an LLM agent framework and autonomous system controller designed to manage local systems, web browsers, and hardware interfaces through action and observation loops. It functions as a tool orchestrator that routes model calls to local executors, enabling the automation of complex tasks on a host machine. The project is distinguished by its self-evolving AI agent capabilities, which convert successful execution paths into reusable procedural scripts and skill trees to reduce future reasoning overhead. It employs a context optimization engine that utilizes layered memory hierarc

Auto-GPT is an autonomous agent framework designed for creating and deploying AI agents that use large language models to plan and execute complex goals independently. The system provides a comprehensive environment for managing the entire agent lifecycle, from initial design and testing to live production deployment. The project features a low-code workflow designer that allows users to define agent behaviors by connecting functional blocks in a visual interface. It includes an agent marketplace for discovering and deploying pre-configured agent templates and a standardized evaluation tool t

Bytebot is an LLM desktop automation framework and virtual Linux desktop environment. It enables AI agents to plan and execute mouse and keyboard actions on a virtual computer using natural language, allowing for autonomous desktop automation and the integration of legacy systems that lack native APIs. The system operates as an LLM API gateway and a Model Context Protocol server, routing requests across multiple language model providers with integrated load balancing and rate limiting. It provides isolated, containerized environments where agents use visual reasoning to interpret screenshots

Hive is an artificial intelligence workflow automation engine and development platform designed for building and deploying autonomous agents. It provides a framework for orchestrating complex, multi-step business processes by coordinating tasks across multiple specialized agents using directed graph structures. The platform distinguishes itself through a focus on production-grade reliability and state management. It maintains persistent execution context and conversation history on disk, enabling crash recovery and continuity for long-running automated sessions. Furthermore, it incorporates a

Tambo is an orchestration platform and framework designed for building generative user interfaces and conversational AI agents. It provides the infrastructure to manage persistent chat threads, execute multi-step reasoning workflows, and integrate large language models with external tools and services. By combining an agent orchestration layer with a component-based library, the project enables developers to create interactive interfaces where AI models dynamically render and update UI elements in real-time. The framework distinguishes itself through its generative UI capabilities, which allo

Conductor is a durable workflow engine designed to orchestrate complex, long-running business processes and autonomous agent loops. It functions as a stateful execution platform that persists the entire history of a process, ensuring that workflows remain reliable and recoverable across infrastructure failures, system restarts, and transient network errors. By managing task lifecycles, worker polling, and state transitions, it provides a centralized coordination layer for distributed systems. The platform distinguishes itself through its specialized support for AI agent orchestration, allowin

This project is a command line interface and GitHub CLI extension that functions as an AI coding agent and model orchestrator. It enables the writing of code and the management of repositories through natural language prompts using large language models. The tool implements the Agent Client Protocol to act as a standardized agent server for external editors. It features a provider-agnostic routing system that allows switching between different hosted AI models or external compatible endpoints. Capabilities include the automation of Git workflows, such as managing pull requests and issues, an