Awesome Deep Research

An open-source long-horizon SuperAgent harness that researches, codes, and creates. With the help of sandboxes, memories, tools, skill, subagents and message gateway, it handles different levels of tasks that could take minutes to hours.

No fortress, purely open ground. OpenManus is Coming.

A collection of notebooks/recipes showcasing some fun and effective ways of using Claude.

An autonomous agent that conducts deep research on any data using any LLM providers

Tongyi Deep Research, the Leading Open-source Deep Research Agent

An AI-powered research assistant that performs iterative, deep research on any topic by combining search engines, web scraping, and large language models. The goal of this repo is to provide the simplest implementation of a deep research agent - e.g. an agent that can refine its research direction overtime and deep dive into a topic.

Get started with building Fullstack Agents using Gemini 2.5 and LangGraph

"AutoAgent: Fully-Automated and Zero-Code LLM Agent Framework"

PraisonAI 🦞 — Hire a 24/7 AI Workforce. Stop writing boilerplate and start shipping autonomous self-improving agents that research, plan, code, and execute tasks. Deployed in 5 lines of code with built-in memory, RAG, and support for 100+ LLMs.

Open Source Deep Research Alternative to Reason and Search on Private Data. Written in Python.

Keep searching, reading webpages, reasoning until it finds the answer (or exceeding the token budget)

Use any LLMs (Large Language Models) for Deep Research. Support SSE API and MCP server.

No description.

SOTA search powered LLM

DeepResearchAgent is a hierarchical multi-agent system designed not only for deep research tasks but also for general-purpose task solving. The framework leverages a top-level planning agent to coordinate multiple specialized lower-level agents, enabling automated task decomposition and efficient execution across diverse and complex domains.

Open source alternative to Gemini Deep Research. Generate reports with AI based on search results.

Humanity's Last Exam

"Your Fully-Automated Personal AI Assistant"

[NeurIPS 2025] 🌐 WebThinker: Empowering Large Reasoning Models with Deep Research Capability

ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning & ReCall: Learning to Reason with Tool Call for LLMs via Reinforcement Learning

ZeroSearch: Incentivize the Search Capability of LLMs without Searching

🔍 Search-o1: Agentic Search-Enhanced Large Reasoning Models [EMNLP 2025]

Salesforce Enterprise Deep Research

An Open-Source AI Writing Project.

[EMNLP'25] s3 - ⚡ Efficient & Effective Search Agent Training via RL for RAG (RLVR for Search with Minimal Data)

Scaling Deep Research via Reinforcement Learning in Real-world Environments.

DeepResearch Bench: A Comprehensive Benchmark for Deep Research Agents

R1-searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning

CORAL is a robust, lightweight infrastructure for multi-agent autonomous self-evolution, built for autoresearch. Works with Claude Code, Codex, Cursor, OpenCode, Kiro, and more.

Dr. Zero Self-Evolving Search Agents without Training Data

[ACL-2026] MMSearch-R1 is an end-to-end RL framework that enables LMMs to perform on-demand, multi-turn search with real-world multimodal search tools.

recursive rag with r1 reasoning

Deep Research

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A Deep Research agent from scratch

MrlX: A Multi-Agent Reinforcement Learning Framework

Repo for "MaskSearch: A Universal Pre-Training Framework to Enhance Agentic Search Capability"

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Official repository for RAG-Gym

SimpleDeepSearcher: Deep Information Seeking via Web-Powered Reasoning Trajectory Synthesis

The official repo of "WebExplorer: Explore and Evolve for Training Long-Horizon Web Agents"

[NeurIPS'25 D&B] Mind2Web-2 Benchmark: Evaluating Agentic Search with Agent-as-a-Judge

[EMNLP 2025] WebAgent-R1: Training Web Agents via End-to-End Multi-Turn Reinforcement Learning

R1-Searcher++: Incentivizing the Dynamic Knowledge Acquisition of LLMs via Reinforcement Learning

A High-Efficiency System of Large Language Model Based Search Agents

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IKEA: Reinforced Internal-External Knowledge Synergistic Reasoning for Efficient Adaptive Search Agent

Source code of paper: Process vs. Outcome Reward: Which is Better for Agentic RAG Reinforcement Learning

No description.