Open-source alternatives to Onyx

Yao is an LLM agent framework and low-code web app builder designed for orchestrating autonomous AI agents. It provides a platform to design, deploy, and coordinate agents with specialized personas that can plan tasks, utilize external tools, and execute multi-stage pipelines. The project distinguishes itself through a Model Context Protocol server for connecting assistants to external binaries and HTTP services, and a gRPC remote execution engine that allows agents to manage remote servers and devices. It includes a model-agnostic provider bridge that supports dynamic switching between vario

Hermes-webui is a self-hosted AI orchestrator and web interface for managing autonomous agents. It serves as a multi-provider gateway that connects cloud and local large language models, providing a central hub to execute scheduled background jobs, run shell commands, and manage agent memory on private hardware. The system distinguishes itself through a persistent memory manager that utilizes knowledge graphs and markdown files for long-term context across sessions. It features a model context protocol host for extending agent capabilities with standardized tools and supports the orchestratio

Nexent is an enterprise AI control plane and LLM agent orchestration platform. It provides a zero-code environment for designing, deploying, and managing production AI agents through a multi-agent collaboration framework that coordinates specialized autonomous agents using standardized messaging protocols. The platform integrates the Model Context Protocol to connect agents with external tools, plugins, and services via a universal communication interface. It further distinguishes itself with a dedicated RAG knowledge base manager that imports unstructured documents and utilizes hybrid search

Awesome Copilot is a comprehensive framework for autonomous software development, providing the infrastructure to orchestrate multi-agent teams and automate complex coding workflows. It functions as a centralized platform for managing AI-driven development, enabling developers to deploy specialized agents that interact with local files, terminal commands, and external APIs to execute end-to-end software delivery tasks. The project distinguishes itself through its focus on governance and extensibility, offering a suite of security controls, policy-based execution guardrails, and audit trails t

Kotaemon is an orchestration framework designed for building modular, agentic workflows that integrate document processing, retrieval-augmented generation, and multi-step reasoning. It provides a comprehensive platform for developing document-based question answering systems, allowing users to chain language models, prompt templates, and external tools into complex, automated pipelines. The system distinguishes itself through a highly modular architecture that emphasizes component-based composition and schema-driven data exchange. It supports autonomous agents capable of decomposing complex q

This project provides a comprehensive framework for building, deploying, and orchestrating autonomous agents within a decentralized network. It serves as a collection of patterns and examples for developing intelligent software entities capable of performing complex tasks, making decisions, and interacting with other agents to achieve shared goals. The framework distinguishes itself through its focus on multi-agent orchestration and decentralized communication. It enables the coordination of specialized agent teams that collaborate on workflows through structured messaging protocols, allowing

OpenHuman is an AI application framework for building private intelligence systems and personal AI layers. It provides a system for deploying private AI assistants that execute technical tasks and manage personal knowledge bases. The project features a model-agnostic request proxy that routes AI workloads to different large language models based on requirements for reasoning, speed, or vision. It integrates an OAuth-driven data integrator to synchronize personal information from external services into a local knowledge base composed of hierarchical Markdown summaries. The framework also inclu

Quivr is a retrieval-augmented generation platform designed to transform raw documents into searchable knowledge bases. It functions as a centralized environment where users can ingest files, index them into vector databases, and interact with language models to receive contextually relevant, data-backed responses. The platform distinguishes itself through an agentic workflow orchestrator that sequences retrieval tasks, tool execution, and model interactions to resolve complex, multi-step queries. This engine is entirely configuration-driven, allowing users to define document ingestion, chunk

This project is a Python framework for building autonomous, event-driven agent systems. It provides a unified runtime for orchestrating multi-agent workflows, managing persistent conversation state, and executing code within secure, isolated sandbox environments. The framework is designed to handle complex task delegation, allowing agents to invoke other agents as tools while maintaining context across multi-turn interactions. The framework distinguishes itself through its deep integration with the Model Context Protocol, enabling agents to connect to external data sources and remote services

llmware is a Python framework for AI agent orchestration and model management, designed to coordinate multi-model workflows and autonomous agents. It provides a unified model catalog and standardized interface to execute specialized language models for complex research, analysis, and structured data generation. The project distinguishes itself through its heavy emphasis on local execution and quantized inference, allowing models to run on private infrastructure using CPU, GPU, and NPU acceleration via runtimes like ONNX and OpenVino. It features a specialized ability to translate natural lang

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

CSGHub is a model management platform and dataset registry designed for storing, distributing, and managing large language models. It provides a centralized hub for AI assets accessible via a web interface, software development kit, and command line. The platform functions as a self-hosted AI infrastructure, allowing for on-premise installation within private networks for secure and offline operations. It includes a model storage system that maintains Python SDK compatibility with the Hugging Face ecosystem to facilitate the migration of existing scripts. The system covers the full model lif

53AIHub is a centralized orchestration platform for deploying and managing AI agents and prompts across multiple large language model providers. It functions as a multi-model AI gateway and an operation portal for AI services, providing a unified interface to coordinate agents and prompts from various external platforms. The project distinguishes itself as a white-label AI portal designed for self-hosted infrastructure, allowing for full control over operational data on private servers or containers. It includes a comprehensive AI SaaS administration layer with a multi-tenant subscription eng

Osaurus is a local AI workflow engine and LLM agent orchestration framework designed for private execution on local hardware. It functions as a desktop application automator and a voice-controlled AI interface, enabling the development of autonomous agents that can write code, execute tools, and operate a computer without keyboard or mouse input. The system is distinguished by its ability to control native desktop applications via accessibility APIs and manage web interactions through a headless browser automation tool. It supports a local-first execution model and on-premises deployment with

This project is a framework for integrating Large Language Models into the Feishu messaging platform to create automated assistants. It functions as a self-hosted AI assistant and a chatbot gateway that routes messages between chat platforms and remote AI cloud providers. The system features a multi-channel messaging bridge and provider-agnostic model routing, allowing for orchestration between different AI models with automatic failover management. It includes a browser automation agent capable of programmatically controlling web browsers and capturing page snapshots to extend the assistant'

Rig is a framework for building large language model applications, featuring a multi-provider client and a workflow builder for retrieval-augmented generation systems. It serves as an orchestrator for creating autonomous agents that can maintain conversation state and execute complex tasks through custom prompting and plugins. The project provides standardized interfaces for both completion and embedding model providers, allowing for unified request and response patterns across different engines. It also includes a vector database integration layer that defines a common interface for indexing

TradingAgents-CN is a multi-agent framework designed for autonomous financial market analysis and automated trading execution. It functions as a containerized orchestrator that leverages large language models to perform complex reasoning, research, and decision-making tasks within financial environments. The platform distinguishes itself through a modular architecture that integrates diverse artificial intelligence providers and financial data sources into a unified pipeline. It provides granular control over agent behavior through prompt-driven logic configuration and multi-model orchestrati

Nanobot is an orchestration framework designed for building, deploying, and managing autonomous AI agents. It provides a secure runtime environment that supports persistent memory, multi-step workflow management, and tool integration, allowing agents to maintain context and state across long-running tasks. The platform distinguishes itself through a unified model gateway that normalizes requests across diverse local and remote language models, alongside a multi-channel integration layer that connects agents to various messaging platforms. It enforces security through containerized sandboxing

Casibase is an open-source platform that orchestrates multi-turn conversations with large language models and manages retrieval-augmented knowledge bases from a single interface. It provides a unified system for connecting to over 30 AI model providers, ingesting documents into vector embeddings for semantic search, and running autonomous agent loops that can drive a browser, search the web, execute commands, and integrate with external tools. The platform distinguishes itself by combining AI conversation management with infrastructure and application orchestration capabilities. It includes a

Suna is an orchestration platform designed for the deployment, management, and governance of autonomous AI agents. It provides a centralized system for defining agent behaviors and tool integrations, enabling the automation of complex business processes through a unified interface. The platform distinguishes itself by applying infrastructure-as-code principles to AI, utilizing version-controlled repositories to manage agent configurations, skills, and guardrails. It ensures secure and predictable operations by spawning ephemeral, isolated virtual machines for every individual task, preventing

DataHub is a metadata management platform designed to unify technical, operational, and business context across diverse data ecosystems. By utilizing a graph-based metadata model and an event-driven ingestion architecture, it creates a centralized source of truth that maps complex data relationships, lineage, and ownership. This foundational framework enables organizations to maintain a synchronized view of their data landscape, supporting both human-led discovery and automated data operations. The platform distinguishes itself through its focus on grounding artificial intelligence and autono

Zeroclaw is a modular framework for building and deploying autonomous agents that integrate AI models, messaging platforms, and hardware interfaces. It functions as a multi-agent orchestrator and embedded systems controller, providing a unified runtime for managing agent lifecycles, memory, and security policies across diverse environments. The system distinguishes itself through its focus on secure, verifiable hardware and software orchestration. It enforces strict security boundaries, including command allowlisting, resource throttling, and interactive human-in-the-loop approval for sensiti

This project is an autonomous agent framework designed to integrate large language models with popular messaging platforms. It functions as a middleware platform that enables automated, multimodal interactions by decomposing complex user goals into sequential plans, executing them through external tools, and maintaining persistent context across sessions. The framework distinguishes itself through a modular skill architecture and a hybrid memory system. Users can extend system capabilities by installing custom logic modules from community hubs or generating them through natural language. The

Eigent is a comprehensive platform for developing, configuring, and orchestrating autonomous AI agents. It functions as an agent development environment and workflow automation engine, enabling users to build modular agents equipped with custom toolsets, domain-specific skill packages, and external API connections to perform targeted operational tasks. The framework distinguishes itself through a robust multi-agent orchestration layer that coordinates teams of specialized agents to execute complex workflows. By utilizing hierarchical task decomposition, the system breaks high-level goals into

RAG-Anything is a retrieval-augmented generation framework designed to index diverse document formats and perform semantic search using local machine learning models. It functions as a local multimodal data processor, extracting and organizing information from various file types into a unified knowledge base to facilitate private document analysis. The system distinguishes itself through its high-throughput ingestion engine, which processes large batches of documents into searchable vector embeddings. By executing machine learning models directly on local hardware, the framework ensures that

Baserow is a no-code relational database and application builder that allows users to create structured data tables and business tools through a visual interface. It functions as a headless REST API data backend and a self-hosted data workspace, providing a platform for managing collaborative databases while maintaining full control over data residency. The platform integrates large language models to serve as an LLM-powered data platform, capable of generating database structures, record content, and technical workflows from natural language. It also acts as a Model Context Protocol server,

Briefer is an interactive data notebook platform and business intelligence dashboard tool used for collaborative data analysis and reporting. It provides a containerized environment for building reports that combine SQL, Python, and Markdown with native visualizations. The platform features an integrated code assistant that uses large language models to generate SQL and Python snippets from natural language prompts. It is designed as a Kubernetes data application, deploying via Helm charts to manage isolated compute environments and ensure separate resources per page through pod-based isolati