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Kseniase 
posted an update 9 days ago
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10 Open-source Deep Research assistants

Deep Research agents are quickly becoming our daily co-workers — built for complex investigations, not just chat. With modular architecture, advanced tool use and real web access, they go far beyond typical AI. While big-name agents get the spotlight, we want to highlight some powerful recent open-source alternatives:

1. DeerFlow -> https://github.com/bytedance/deer-flow
A modular multi-agent system combining LMs and tools for automated research and code analysis. It links a coordinator, planner, team of specialized agent, and reporter, and converts reports to speech via Text-to-Speech (TTS)

2. Alita -> https://github.com/CharlesQ9/Alita
Uses a single problem-solving module for scalable reasoning through simplicity. It self-evolves by generating and reusing Model Context Protocols (MCPs) from open-source tools to build external capabilities for diverse tasks

3. WebThinker -> https://github.com/RUC-NLPIR/WebThinker
Lets reasoning models autonomously search the web and navigate pages. Deep Web Explorer allows interaction with links and follow-up searches. Through a Think-Search-and-Draft process models generate and refine reports in real time. RL training with preference pairs improves the workflow

4. SimpleDeepSearcher -> https://github.com/RUCAIBox/SimpleDeepSearcher
A lightweight framework showing that supervised fine-tuning is a real alternative to complex RL, using simulated web interactions and multi-criteria curation to generate high-quality training data

5. AgenticSeek -> https://github.com/Fosowl/agenticSeek
A private, on-device assistant that picks the best agent expert for browsing, coding, or planning—no cloud needed. Includes voice input via speech-to-text

6. Suna -> https://github.com/kortix-ai/suna
Offers web browsing, file and doc handling, CLI execution, site deployment, and API/service integration—all in one assistant

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Kseniase

  1. DeepResearcher -> https://github.com/GAIR-NLP/DeepResearcher
    An RL framework for training deep research agents end-to-end in real-world environments with web search, exhibiting emergent behaviour like planning, multi-source validation, self-reflection, and honest defining when the agent doesn't know the answer

  2. Search-R1 -> https://github.com/PeterGriffinJin/Search-R1
    Features interleaved search access and an open-source RL training pipeline supporting various algorithms (PPO, GRPO, etc.), LLMs (LLaMA3, Qwen2.5, etc.), and search engines (online, local, retrievers)

  3. ReCall -> https://github.com/Agent-RL/ReCall
    Trains LLMs to reason with tools via RL, no supervised tool-use data needed. It enables agentic use of tools like OpenAI o3 and supports synthetic data generation across diverse environments and multi-step tasks

  4. OWL -> https://github.com/camel-ai/owl
    A framework built on CAMEL-AI framework enabling dynamic multi-agent collaboration for task automation across diverse domains

Here's an awesome study exploring the entire roadmap of Deep Research assistants. Don't forget to check it out -> https://huggingface.co/papers/2506.18096

SohanVichat

The projects you shared are fantastic examples of how open-source deep research assistants are evolving—definitely worth exploring! You might also check out our work, which uses secure APIs to upload documents, RAG-based retrieval to answer any questions, and robust safeguards to ensure data security and privacy. It’s built to make complex investigations easier, safer, and more accurate.

Please check out: https://www.teravera.com/

Also, to request the API access and documentation, please sign up here: www.teravera.com/api-access-form/

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