10 Open-source Deep Research assistants 
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10 Open-source Deep Research assistants 
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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:
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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:
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1. DeerFlow -> 
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1. DeerFlow -> 
https://github.com/bytedance/deer-flow
https://github.com/bytedance/deer-flow
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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) 
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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) 
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2. Alita -> 
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2. Alita -> 
https://github.com/CharlesQ9/Alita
https://github.com/CharlesQ9/Alita
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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
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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
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3. WebThinker -> 
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3. WebThinker -> 
https://github.com/RUC-NLPIR/WebThinker
https://github.com/RUC-NLPIR/WebThinker
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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
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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
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4. SimpleDeepSearcher -> 
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4. SimpleDeepSearcher -> 
https://github.com/RUCAIBox/SimpleDeepSearcher
https://github.com/RUCAIBox/SimpleDeepSearcher
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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
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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
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5. AgenticSeek -> 
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5. AgenticSeek -> 
https://github.com/Fosowl/agenticSeek
https://github.com/Fosowl/agenticSeek
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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
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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
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6. Suna -> 
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6. Suna -> 
https://github.com/kortix-ai/suna
https://github.com/kortix-ai/suna
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Offers web browsing, file and doc handling, CLI execution, site deployment, and API/service integration—all in one assistant
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Offers web browsing, file and doc handling, CLI execution, site deployment, and API/service integration—all in one assistant
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Read further ⬇️
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Read further ⬇️
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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