Projects sharing features with Local Deep Research

SillyTavern is a comprehensive interface and orchestration platform designed for immersive AI roleplay and interactive chat experiences. It functions as a unified gateway that connects users to a wide array of local and cloud-based large language models, providing a centralized environment to manage complex character personas, narrative context, and model-driven interactions. The platform distinguishes itself through its advanced prompt engineering and automation capabilities. It utilizes a sophisticated macro-based templating engine and vector-database retrieval to dynamically inject lore, c

Mastra is an orchestration framework designed for building, deploying, and managing autonomous AI agents and multi-agent systems. It provides a comprehensive suite of primitives for creating resilient AI applications, including durable workflow orchestration, event-driven agent loops, and semantic memory management. By integrating these core components, the platform enables developers to build complex, multi-step processes that can reason about goals and execute tasks without manual intervention. The framework distinguishes itself through its focus on observability and secure, isolated execut

qmd is a local semantic search engine and RAG knowledge base indexer that functions as a Model Context Protocol server. It converts local documents, markdown files, and codebases into a searchable database to provide retrieval augmented generation capabilities for AI agents. The system exposes its search and retrieval tools via stdio or HTTP. It utilizes local model files for embeddings and reranking, supporting query expansion across multiple languages. The project employs abstract syntax tree based chunking to split source code at function and class boundaries. It implements hybrid vector-

AI0x0.com is a multimodal AI desktop assistant and cross-application wrapper. It provides a floating interface overlay that integrates large language models into any active software application to facilitate global querying and text automation. The system distinguishes itself through the ability to process real-time screen captures for visual analysis and utilize a voice pipeline for hands-free speech-to-text and text-to-speech interaction. It further enables direct AI content injection by simulating keyboard input to insert generated responses into active software fields. The project includ

Summarize is a command line tool and multimodal content extractor designed to generate concise summaries from web pages, documents, and media files. It functions as an orchestrator that connects developer tools to various language model providers to process and condense information. The system provides specialized capabilities for audio and video processing, including transcription with speaker identification and the extraction of timestamped visual markers from video slides. It also includes a translation utility to convert generated summaries and extracted text into different target languag

Odysseus is a self-hosted AI workspace and autonomous agent framework designed for deploying and managing large language models. It serves as a centralized platform for orchestrating agentic tasks, utilizing a model context protocol server to connect AI models to external system utilities, browser automation, and local hardware. The system distinguishes itself through a combination of retrieval-augmented generation and a RAG knowledge base, using vector stores and local embeddings to provide persistent semantic memory. It further integrates AI-driven communication management to triage email i

This project is a terminal-based command line interface client and agent orchestrator for interacting with multiple large language model providers. It functions as an OpenAI API client and a local API gateway that exposes chat completions and embeddings through an HTTP server. The system distinguishes itself by providing a retrieval-augmented generation tool for indexing local files and URLs into a vector database to provide custom document context. It allows for the creation of specialized AI agents that combine custom system prompts with tool calling and external function execution. The to

This project is a framework for developing multimodal AI agents that function as programmable participants in real-time communication rooms. It enables the construction of agents that can see, hear, and speak by integrating speech-to-text, large language models, and text-to-speech pipelines to facilitate low-latency, natural conversations. The system is distinguished by its advanced orchestration of real-time media and conversational flow, including support for full-duplex speech, preemptive response generation, and sophisticated interruption management. It further differentiates itself throu

Presenton is an AI-powered presentation engine and API designed to transform natural language prompts, uploaded documents, and structured data into professional slide decks. It functions as a generation service that leverages large language models to automate the creation of outlines, slide content, and visual assets. The system is distinguished by its support for both cloud-based and self-hosted infrastructure, allowing for the integration of local language models and image generators to ensure data privacy. It implements a Model Context Protocol server, enabling external AI agents to trigge

Verba is a retrieval-augmented generation interface and chatbot that uses Weaviate to provide factual answers based on private datasets. It functions as a vector database knowledge base, combining a hybrid search engine with an orchestration interface to connect various large language model providers and embedding services. The system differentiates itself through a RAG pipeline manager for adjusting text chunking rules and retrieval settings, alongside a 3D vector space visualization tool for analyzing the spatial organization and clustering of high-dimensional embeddings. It employs a modul

localGPT is a private AI knowledge base and retrieval-augmented generation application. It provides a local document indexer, a hybrid search engine, and an inference interface to enable chatting with private documents and managing a self-hosted information repository without sending data to external servers. The system distinguishes itself through a dual-pass verification pipeline that ensures generated answers are grounded in retrieved sources, accompanied by explicit source attribution. It employs a hybrid retrieval approach combining semantic vector search with keyword matching and rerank

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

PraisonAI is an autonomous AI agent platform that coordinates multiple LLM-powered agents for research, planning, and execution of complex workflows. It functions as a multi-agent orchestration framework, a workflow builder, and a Model Context Protocol server, while also providing retrieval-augmented generation through vector knowledge bases. Agents can interact via CLI, web, or standardized protocols with sandboxed code execution. The platform distinguishes itself with a rich set of agent communication protocols, including A2A, REST, WebSocket, voice and telephony integration, and MCP, allo

SurfSense is a self-hosted platform designed for building retrieval-augmented generation pipelines and managing private knowledge bases. It functions as a containerized research stack that allows users to index diverse data sources and query them using language models, ensuring that all information retrieval is grounded in specific source citations. The platform distinguishes itself through its modular architecture, which supports the integration of custom tools and diverse language models via a unified abstraction layer. It facilitates secure, collaborative research environments by implement

This project is a comprehensive framework for building and managing autonomous agent systems. It provides a unified architecture for orchestrating multi-agent societies, where specialized agents collaborate through roleplay to decompose and solve complex tasks. The system integrates language models with external environments, enabling agents to perform real-world actions through a standardized tool-calling abstraction layer. The framework distinguishes itself through its focus on iterative reasoning and data reliability. It employs automated feedback loops to refine agent outputs and self-eva

RD-Agent is an autonomous framework designed to orchestrate multi-step software engineering and data science workflows. By leveraging large language models, the system decomposes complex technical requirements into actionable research, planning, and execution phases, ultimately generating and running code to solve specific development tasks. The platform distinguishes itself through a containerized execution sandbox that ensures secure dependency management and system stability for all autonomously generated code. It employs multi-agent orchestration to manage iterative feedback loops, allowi

Llama-stack is a standardized orchestration stack and generative AI API gateway. It provides a unified communication layer and a consistent interface for deploying, managing, and interacting with various large language model providers and deployments. The system functions as an agent framework that manages tool execution and versioned skill bundles to automate complex tasks. It includes a batch processing system for handling large volumes of asynchronous requests through offline processing and a vector database interface for storing and searching documents to enable retrieval augmented genera

auto-dev is an AI-native software engineering tool and multi-agent development platform designed to automate the entire software development lifecycle. It functions as an autonomous orchestrator that manages AI-driven coding, testing, and infrastructure configuration through declarative agent chains. The project is built on a Kotlin Multiplatform AI framework, allowing agent logic to run across diverse environments and device interfaces. The platform implements the Model Context Protocol to exchange tools and project information with external AI services. It distinguishes itself through the u

This project is a LangChain-based framework for building retrieval-augmented generation systems, autonomous agents, and multimodal chatbots. It functions as an open-source orchestrator that connects local inference engines and online APIs to manage various large language model deployments. The system distinguishes itself by providing specialized interfaces for local knowledge bases, allowing the loading and vectorization of private documents to create context-aware assistants. It also supports multimodal capabilities, enabling the processing of both text and image inputs through vision-capabl

This project is a multi-channel AI agent and chatbot framework that allows a single AI intelligence to be deployed across various messaging platforms, web interfaces, and email accounts. It functions as a cross-model AI gateway, providing a unified interface to route requests between different large language model providers. The system is distinguished by its autonomous task planning and knowledge management capabilities. It can decompose complex goals into sequential execution steps using external tools and a headless browser, while simultaneously extracting information from conversations to

Miroflow is an agent orchestration framework designed to coordinate multiple large language models and autonomous agents to perform complex research and reasoning tasks. It functions as a hierarchical workflow manager that distributes workloads across specialized agents using intent recognition and structured planning to gather deep information and solve challenging queries. The system distinguishes itself through a multi-model integration gateway and a provider-agnostic interface, allowing it to unify various language model providers. It extends these models via a tool-augmented framework th

This project is a framework for managing generative AI services through a unified provider interface and adapter layer. It provides a standardized API for calling multiple cloud-based and locally hosted models, translating provider-specific parameters and responses into a uniform format. The system includes an agent orchestrator designed for long-running tasks, featuring state persistence for resuming runs and execution tracing to monitor decision-making processes. It integrates the Model Context Protocol to connect models to external servers and filesystems and employs a policy-based executi

LazyLLM is a multi-agent framework and orchestration engine designed for building complex AI applications. It provides a system for chaining large language models into sequential or parallel pipelines, utilizing a tool registry to convert standard functions into discoverable tools that models can invoke via reasoning. The project features an application deployment kit that enables hosting model workflows as web services with integrated chat interfaces and API gateways. It includes an infrastructure abstraction layer that allows users to switch between bare-metal servers, clusters, and public

BrowserOS is an AI agent browser orchestrator and automation framework designed to manage browser state and execute complex web workflows. It functions as a local AI browser assistant and a Model Context Protocol controller, enabling the control of browser tabs, windows, and navigation through programmable AI agents and standardized context protocols. The system distinguishes itself through a graph-based visual workflow builder for creating repeatable automation sequences and the use of markdown-based files to define agent personalities and task recipes. It supports multi-provider orchestrati

Devika is an autonomous AI software engineering system designed to plan, write, and debug code from high-level natural language instructions. It functions as an agentic software engineer that decomposes complex objectives into actionable coding steps for autonomous execution. The system integrates cloud-based and self-hosted large language models through a provider-agnostic layer, allowing for multi-model reasoning and code completion. It distinguishes itself by combining these models with a sandboxed execution environment for running code across different operating systems and a web-browsing

rlm is an LLM code execution engine and orchestration framework designed to coordinate multiple language model calls and recursive sub-tasks through a programmable environment. It provides a sandboxed REPL environment and a recursive context processor to handle inputs that exceed standard token limits by programmatically decomposing prompts. The project differentiates itself through a reinforcement learning training harness used to teach models how to utilize recursive calls and code execution. It includes a reasoning visualization system that records and renders execution trajectories to ana

This project is a toolkit for interacting with large language models through a command line interface, an integration library, and a workflow orchestrator. It provides a framework for embedding language model logic directly into scripts and managing automated sequences of AI tasks. The system utilizes a plugin framework and a provider-agnostic interface to route requests across different model providers. This architecture allows for the addition of custom capabilities and the ability to switch providers without altering the core logic. The project covers several functional areas, including A