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Introducing Tongyi Deep Research

22 settembre 2025·324 parole·2 minuti

GitHub AI Agent Python Open Source

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DeepResearch repository preview

#### Fonte

Tipo: GitHub Repository
Link originale: https://github.com/Alibaba-NLP/DeepResearch
Data pubblicazione: 2025-09-22

Sintesi
#

WHAT - Tongyi DeepResearch è un agente di ricerca basato su un modello linguistico di grandi dimensioni open-source sviluppato da Alibaba, con 30,5 miliardi di parametri totali.

WHY - È rilevante per il business AI perché offre capacità avanzate di ricerca e generazione di dati sintetici, migliorando l’efficacia delle interazioni agenti-utente e la qualità delle risposte.

WHO - Gli attori principali sono Alibaba-NLP e la community open-source che contribuisce al progetto.

WHERE - Si posiziona nel mercato degli agenti di ricerca basati su AI, competendo con altre soluzioni open-source e proprietarie.

WHEN - È un progetto relativamente nuovo ma già consolidato, con una base di utenti attiva e una roadmap di sviluppo chiara.

BUSINESS IMPACT:

Opportunità: Integrazione con sistemi di ricerca aziendali per migliorare la qualità delle risposte e l’efficienza delle interazioni.
Rischi: Competizione con soluzioni proprietarie di grandi aziende tecnologiche.
Integrazione: Possibile integrazione con stack esistenti tramite API e modelli disponibili su piattaforme come HuggingFace e ModelScope.

TECHNICAL SUMMARY:

Core technology stack: Python, HuggingFace, ModelScope, framework di deep learning personalizzati.
Scalabilità: Alta scalabilità grazie a un pipeline di generazione dati sintetici automatizzato e pre-training continuo su grandi volumi di dati.
Differenziatori tecnici: Utilizzo di un framework di ottimizzazione delle politiche relative di gruppo personalizzato per il reinforcement learning, compatibilità con paradigmi di inferenza avanzati come ReAct.

Casi d’uso
#

Private AI Stack: Integrazione in pipeline proprietarie
Client Solutions: Implementazione per progetti clienti
Development Acceleration: Riduzione time-to-market progetti
Strategic Intelligence: Input per roadmap tecnologica
Competitive Analysis: Monitoring ecosystem AI

Risorse
#

Link Originali
#

Introducing Tongyi Deep Research - Link originale

Articolo segnalato e selezionato dal team Human Technology eXcellence elaborato tramite intelligenza artificiale (in questo caso con LLM HTX-EU-Mistral3.1Small) il 2025-09-22 15:19 Fonte originale: https://github.com/Alibaba-NLP/DeepResearch

Articoli Correlati
#

Enterprise Deep Research - Python, Open Source
AI-Researcher: Autonomous Scientific Innovation - Python, Open Source, AI
💾🎉 copyparty - Open Source, Python

Articoli Interessanti - This article is part of a series.

Part : GitHub - rbalestr-lab/lejepa

Part : Use Cases | Claude

Part : Improving frontend design through Skills | Claude

Part : Sim: Open-source platform to build and deploy AI agent workflows

Part : Context Retrieval for AI Agents across Apps & Databases

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Part : You Should Write An Agent · The Fly Blog

Part : 🚀 Hello, Kimi K2 Thinking! The Open-Source Thinking Agent Model is here

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Part : Source: Thanks and Bharat for showing the world you can in fact tra...

Part : This Claude Code prompt literally turns Claude Code into ultrathink...

Part : Wren AI | Official Blog

Part : Tongyi DeepResearch: A New Era of Open-Source AI Researchers | Tongyi DeepResearch

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Part : MiniMax-M2

Part : AI Act Single Information Platform | AI Act Service Desk

Part : eurollm.io

Part : Introducing Mistral AI Studio. | Mistral AI

Part : OpenSnowcat - Enterprise-grade behavioral data platform.

Part : Dr Milan Milanović (@milan_milanovic) on X

Part : Game Theory | Open Yale Courses

Part : DeepSeek-OCR

Part : Airbyte: The Leading Data Integration Platform for ETL/ELT Pipelines

Part : Enterprise Deep Research

Part : I quite like the new DeepSeek-OCR paper

Part : olmOCR 2: Unit test rewards for document OCR | Ai2

Part : We used DeepSeek OCR to extract every dataset from tables/charts ac...

Part : Scripts I wrote that I use all the time

Part : DeepSeek OCR - More than OCR - YouTube

Part : How to Get Consistent Classification From Inconsistent LLMs?

Part : Production RAG: what I learned from processing 5M+ documents

Part : Stanford's ALL FREE Courses [2024 & 2025] ❯ CS230 - Deep Learni...

Part : Syllabus

Part : Make Any App Searchable for AI Agents

Part : PaddleOCR-VL: Boosting Multilingual Document Parsing via a 0.9B Ultra-Compact Vision-Language Model

Part : Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting

Part : nanochat

Part : ROMA: Recursive Open Meta-Agents

Part : NeuTTS Air

Part : Cua: Open-source infrastructure for Computer-Use Agents

Part : MCP Analytics and Authentication Platform

Part : My trick for getting consistent classification from LLMs

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Part : DeepLearning.AI: Start or Advance Your Career in AI

Part : Claude Code best practices | Code w/ Claude - YouTube

Part : EU-funded TildeOpen LLM delivers European AI breakthrough for multilingual innovation | Shaping Europe’s digital future

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Part : Introducing Qwen3-Max-Preview (Instruct)

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Part : Anthropic's Interactive Prompt Engineering Tutorial

Part : swiss-ai/Apertus-70B-2509 · Hugging Face

Part : Making a font of my handwriting · Chameth.com

Part : SurfSense

Part : LoRAX: Multi-LoRA inference server that scales to 1000s of fine-tuned LLMs

Part : NextChat

Part : The LLM Red Teaming Framework

Part : Colette - ci ricorda molto Kotaemon

Part : VibeVoice: A Frontier Open-Source Text-to-Speech Model

Part : [2502.12110] A-MEM: Agentic Memory for LLM Agents

Part : [2504.19413] Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory

Part : Apertus 70B: Truly Open - Swiss LLM by ETH, EPFL and CSCS

Part : HumanLayer

Part : PageIndex: Document Index for Reasoning-based RAG

Part : Deploying DeepSeek on 96 H100 GPUs

Part : Claude Code: A Highly Agentic Coding Assistant - DeepLearning.AI

Part : DyG-RAG: Dynamic Graph Retrieval-Augmented Generation with Event-Centric Reasoning

Part : [2508.15126] aiXiv: A Next-Generation Open Access Ecosystem for Scientific Discovery Generated by AI Scientists

Part : Alexander Kruel - Links for 2025-08-24

Part : AI Agents for Beginners - A Course

Part : Turning Claude Code into my best design partner

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Part : Tiledesk Design Studio

Part : Build a Large Language Model (From Scratch)

Part : Data Formulator: Create Rich Visualizations with AI

Part : browser-use/web-ui

Part : Casper Capital - 100 AI Tools You Can’t Ignore in 2025...

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Part : Show HN: Whispering – Open-source, local-first dictation you can trust

Part : Fallinorg v1.0.0-beta

Part : paperetl

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Part : My AI Had Already Fixed the Code Before I Saw It

Part : Llama-Scan: Convert PDFs to Text W Local LLMs

Part : Claudia – Desktop companion for Claude code

Part : Show HN: Fallinorg - Offline Mac app that organizes files by meaning

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Part : The race for LLM cognitive core

Part : [2507.07935] Working with AI: Measuring the Occupational Implications of Generative AI

Part : Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting

Part : Prava - Teaching GPT‑5 to use a computer

Part : InstaVM - Secure Code Execution Platform

Part : Litestar is worth a look

Part : Jobs at Kaizen | Y Combinator

Part : Launch HN: Lucidic (YC W25) – Debug, test, and evaluate AI agents in production

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Part : Agentic Design Patterns - Documenti Google

Part : [2507.14447] Routine: A Structural Planning Framework for LLM Agent System in Enterprise

Part : Qwen3-Coder: Agentic coding in the world

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Part : Nice - my AI startup school talk is now up! Chapters: 0:00 Imo fair to say that software is changing quite fundamentally again

Part : Automated 73% of his remote job using basic automation tools, told his manager everything, and got a promotion

Part : Building Effective AI Agents

Part : How Anthropic Teams Use Claude Code

Part : Snorting the AGI with Claude Code

Part : Nanonets-OCR-s – OCR model that transforms documents into structured markdown

Part : The Illusion of Thinking

Part : Trends – Artificial Intelligence | BOND

Part : Claude Code is My Computer | Peter Steinberger

Part : [2505.24863] AlphaOne: Reasoning Models Thinking Slow and Fast at Test Time

Part : [2505.24864] ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Part : My AI Skeptic Friends Are All Nuts · The Fly Blog

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Part : BillionMail 📧 An Open-Source MailServer, NewsLetter, Email Marketing Solution for Smarter Campaigns

Part : Ask HN: What is the best LLM for consumer grade hardware?

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Part : Agent Development Kit (ADK)

Part : Strands Agents

Part : Show HN: AutoThink – Boosts local LLM performance with adaptive reasoning

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Part : Show HN: My LLM CLI tool can run tools now, from Python code or plugins

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Part : [2505.03335] Absolute Zero: Reinforced Self-play Reasoning with Zero Data

Part : Requests for Startups | Y Combinator

Part : Token & Token Usage | DeepSeek API Docs

Part : Cua is Docker for Computer-Use AI Agents

Part : [2504.07139] Artificial Intelligence Index Report 2025

Part : Gemma 3 QAT Models: Bringing state-of-the-Art AI to consumer GPUs

Part : DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning | Nature

Part : A foundation model to predict and capture human cognition | Nature

Part : Large language models are proficient in solving and creating emotional intelligence tests | Communications Psychology

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