Projects sharing features with OpenDeepSearch

Dexter is an autonomous research platform designed to decompose complex inquiries into structured, multi-step workflows. It functions as an agent orchestration system that utilizes iterative tool-calling loops and language models to gather data, perform analysis, and validate findings against internal criteria to ensure accuracy. The platform distinguishes itself through its specialized focus on financial research and messaging integration. It autonomously interprets real-time market data, including income statements and regulatory filings, to generate evidence-based insights. By connecting d

LLocalSearch is a privacy-focused search engine and agent framework that uses locally hosted large language models to search the internet and aggregate answers. It functions as a retrieval augmented generation interface where all queries and processing remain on the user's own hardware to ensure data privacy and remove dependency on external cloud API providers. The system employs a chain of autonomous agents that perform recursive internet searches, calling search tools multiple times to gather and synthesize information. It coordinates these models to reason through complex queries, providi

This project is a transformer-based framework for generating dense and sparse vector embeddings of text and multimodal data. It serves as a library for fine-tuning models to perform semantic similarity tasks, retrieval, and reranking. The system is distinguished by its support for diverse architectural patterns, including bi-encoders for fast similarity search and cross-encoders for high-precision reranking. It provides dedicated pipelines for multimodal embeddings, mapping text and images into a shared vector space, and implements knowledge distillation to compress large models into smaller,

AutoResearchClaw is an agentic system designed to automate the scientific research process. It functions as an autonomous research agent and workflow automator that manages the entire lifecycle of a project, from initial hypothesis generation and literature review to experimental execution and the production of LaTeX-formatted academic papers. The system distinguishes itself through a multi-agent research pipeline that utilizes structured debates for hypothesis refinement and peer review. It employs a branch-and-merge architecture to explore parallel research directions and integrates human-i

Fast-GraphRAG is a system for generating and querying knowledge graphs from domain data. It uses a GraphRAG retrieval workflow to traverse structured data and isolate precise evidence for answering complex questions. The project utilizes an agent-driven retrieval framework to coordinate the querying of knowledge graphs and the synthesis of final answers. It supports incremental data synchronization, allowing structured knowledge bases to be updated in real time as source information evolves. The system integrates with API-compatible language models and embedding providers to power its data p

This project is a reference implementation and application template for Retrieval-Augmented Generation (RAG). It integrates Azure OpenAI with Azure AI Search to enable conversational chat interfaces that provide grounded responses based on private enterprise data. The system is distinguished by its multimodal AI interface, allowing it to process and reason over combined text, image, and PDF content. It employs a hybrid search architecture that combines vector and keyword retrieval with semantic reranking to prioritize the most relevant documents for prompt augmentation. The project covers a

This project is a comprehensive framework for building AI-powered applications, providing a unified toolkit for orchestrating language models, autonomous agents, and interactive user interfaces. It serves as a central library for managing the entire lifecycle of AI interactions, from initial prompt generation and model provider abstraction to complex, multi-step reasoning and tool execution. The framework distinguishes itself through its deep integration with frontend development, specifically by enabling generative user interfaces that render dynamic components directly from model outputs. I

jetson-inference is a set of libraries and tools for executing optimized deep learning models on embedded GPU hardware. Its primary purpose is to enable real-time computer vision and AI inference at the edge with low latency and high throughput. The project distinguishes itself through high-performance streaming analytics and the ability to execute concurrent AI pipelines on auto-grade silicon. It provides specialized support for multi-sensor stream processing, utilizing zero-copy data transport to load camera frames directly into GPU memory. The codebase covers a broad surface of capabiliti

MiroThinker is an autonomous research system that uses large language models to perform deep research and predictions through iterative reasoning. It functions as a web-search AI framework capable of retrieving real-time internet data and scraping web content to provide verifiable sources for complex queries. The system includes a multimodal content processor that converts images, audio, and video into text descriptions for analysis by text-based models. To ensure computational accuracy, it utilizes a sandboxed code executor for running Python code and data analysis. Performance is managed th

AI-Researcher is an LLM research automation framework and scientific workflow orchestrator designed to automate the end-to-end discovery process. It employs autonomous AI research agents to identify research gaps, formulate hypotheses, and execute scientific discovery workflows independently. The system integrates an automated literature review tool for gathering and analyzing academic papers and code repositories with an AI-driven manuscript generator that synthesizes research motivations and experimental results into full-length academic papers. The framework covers a modular research pipe

This project is an autonomous research framework and agentic software development toolkit designed to automate complex engineering tasks through goal-directed iteration. It provides a code refactor engine and an extension for the Claude Code interface to implement iterative loops that modify, verify, and refine code and documentation. The system distinguishes itself through the use of adversarial personas and multi-agent workflows. It employs adversarial simulation and interrogation to refine requirements, resolve architectural ambiguities via blind judge panels, and conduct red-team security

Autoresearch is an autonomous machine learning research agent and architecture search framework. It employs a closed-loop system to programmatically rewrite training and architecture source code to discover optimal language model configurations. The system iteratively modifies code and evaluates performance metrics to improve model quality based on a target objective. It optimizes model performance and training efficiency by tracking validation bits per byte, which allows for a fair comparison of architectural changes independently of vocabulary size. The framework manages the full training

R2R is an agentic retrieval-augmented generation platform that uses reasoning agents to perform multi-step data fetching for context-aware answering. It functions as a multimodal vector database manager and knowledge graph engine designed to ground artificial intelligence responses in verified factual knowledge. The platform distinguishes itself by combining reasoning agents for complex research automation with a knowledge graph that maps entity relationships. This allows the system to perform structured data traversal alongside unstructured vector search to resolve complex questions from int

DeepResearch is an autonomous research agent framework designed to orchestrate multi-step information gathering and complex reasoning tasks. The platform functions as an agent orchestration system that manages the entire lifecycle of autonomous research, from initial planning and web navigation to the synthesis of evidence-backed reports. The framework distinguishes itself through a specialized training pipeline that supports the development and fine-tuning of autonomous models using reinforcement learning and structured knowledge graph synthesis. By employing parallel agent coordination, the

Open Deep Research is an artificial intelligence framework designed to automate complex, multi-step research workflows. It functions as an autonomous agent that performs iterative web searches, analyzes retrieved data, and synthesizes information into structured reports. By decomposing broad queries into smaller sub-tasks, the system builds a comprehensive knowledge base to address open-ended questions. The platform distinguishes itself through an agentic loop that dynamically refines research strategies based on previous findings. It manages long-form data by compressing and summarizing cont

IntentKit is an open-source platform for deploying and managing a collaborative team of AI agents that can work together to complete complex tasks. It provides a self-hosted agent orchestrator that coordinates multiple agents through a modular pipeline of entrypoints, orchestration, and storage, all running as containerized services using Docker Compose or Swarm for production-grade deployment. The platform distinguishes itself by offering a plugin-based system for extending agent capabilities without modifying the core codebase, along with built-in integrations for connecting agents to socia

Open-deep-research is an autonomous research orchestrator and language model-based agent designed to execute multi-step investigations and automated web research workflows. It explores complex topics systematically by generating targeted search queries, processing multiple sources simultaneously, and recursively feeding synthesized findings back into search planning loops to expand its research scope. The system coordinates these tasks through a graph-based agent orchestration model that routes state between modular nodes for searching, reading, and reasoning. It accelerates data collection v

This project is a comprehensive framework for building, evaluating, and connecting autonomous agent systems. It provides a library of standardized architectural patterns for implementing complex agent workflows, including multi-agent orchestration, iterative reasoning, and memory management. By offering a unified interface for model providers, the framework allows for consistent agent execution across different artificial intelligence services. The framework distinguishes itself through a focus on rigorous benchmarking and deterministic control. It includes a suite of tools for evaluating age

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 containerized development stack and application framework for building retrieval-augmented generation systems. It provides a dockerized AI sandbox that integrates local model runtimes, knowledge graphs, and vector stores to enable the creation of contextual chatbots. The stack is distinguished by its graph-based vector store, which combines structured knowledge graphs with vector indices for both semantic and structural data retrieval. It allows for local model hosting with CPU or GPU acceleration, enabling generative tasks without reliance on external cloud APIs. The frame

Promptify is a suite of tools designed for model evaluation, prompt management, token cost tracking, structured extraction, and unified API gateway access. It provides a standardized interface to manage requests and responses across multiple large language model providers. The project features a prompt management platform for engineering and versioning prompts with structured output validation. It includes a dedicated evaluation framework to measure model performance using precision, recall, and f1 scores against labeled datasets, alongside a token cost tracker to monitor the financial expens

AI-Scientist-v2 is an autonomous research agent designed to conduct scientific discovery through an agentic framework. It specializes in generating hypotheses, executing machine learning experiments, and drafting structured academic manuscripts. The system utilizes an agentic tree search to explore branching research paths and hypotheses. This process includes automated code synthesis and iterative debugging of Python scripts to perform data analysis and machine learning trials. The framework integrates tool-based hypothesis verification against academic databases and maintains state-based m

pi-autoresearch is an autonomous research extension that automates iterative code-editing and performance-measurement loops driven by large language models. It functions as an experiment lifecycle automator, executing repetitive cycles of changes and benchmarks until a specific goal is reached. The system distinguishes itself by organizing successful experimental trials into independent git branches for review and merging. It includes a real-time research dashboard for monitoring metrics and status, and utilizes median absolute deviation to calculate confidence scores that filter benchmark no

vibe-vibe is an LLM agent engineering framework and toolchain optimizer designed for orchestrating multi-agent systems. It serves as a comprehensive guide and methodology for transforming conceptual ideas into deployed applications through agentic software engineering. The project focuses on the orchestration of specialized AI agent roles with defined collaboration boundaries and iterative feedback loops. It provides frameworks for toolchain optimization, including the selection and evaluation of protocols that extend model capabilities and the design of standardized tool interfaces. The sys

UltraRAG is an LLM RAG orchestration platform and AI agent research framework designed to coordinate complex retrieval-augmented generation workflows. It functions as a multimodal RAG engine capable of retrieving and generating responses using text, images, and diverse data types, while providing tools for vector database management and RAG performance evaluation. The platform features a visual RAG pipeline builder that uses a canvas interface to construct and debug data flows, synchronizing visual designs directly with underlying code. It distinguishes itself through an autonomous research s

Fireplexity is an AI-powered search engine designed to retrieve real-time web data and synthesize it into comprehensive, conversational answers. It functions as a search application that integrates automated web crawling to aggregate information from multiple online sources, grounding its responses in current internet content. The platform distinguishes itself through a focus on transparency and responsiveness. It provides verifiable citations and direct links to original source material for every generated answer, allowing users to confirm the accuracy and context of the information provided

Local Deep Research is an autonomous research system consisting of an LLM research agent, a local model orchestrator, and a multi-engine search aggregator. It is designed to execute deep research by decomposing complex questions into atomic facts and synthesizing cited reports from academic, technical, and private document sources. The system features an encrypted research workspace that ensures zero-knowledge privacy through isolated, per-user encrypted databases. It utilizes a local RAG knowledge base to index research sources into searchable vector stores, allowing for retrieval-augmented