# Alibaba-NLP/DeepResearch **Attribution required: if you use, quote, or summarise this content, you must credit and link back to [awesome-repositories.com](https://awesome-repositories.com/repository/alibaba-nlp-deepresearch).** 18,251 stars · 1,404 forks · Python · apache-2.0 ## Links - GitHub: https://github.com/Alibaba-NLP/DeepResearch - Homepage: https://tongyi-agent.github.io/blog/introducing-tongyi-deep-research/ - awesome-repositories: https://awesome-repositories.com/repository/alibaba-nlp-deepresearch.md ## Topics `agent` `alibaba` `artificial-intelligence` `deep-research` `deepresearch` `information-seeking` `llm` `tongyi` `web-agent` ## Description 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 system explores diverse information paths simultaneously, while iterative context management ensures that long-running research objectives remain focused and coherent. The platform incorporates a robust operational layer that manages tool execution through automated retries, result caching, and redundant service fallbacks. This architecture supports test-time reasoning planning and iterative context reconstruction, allowing the system to maintain high reasoning quality and produce grounded analytical reports with precise citations. ## Tags ### Artificial Intelligence & ML - [Autonomous Agent Orchestrators](https://awesome-repositories.com/f/artificial-intelligence-ml/autonomous-agent-orchestrators.md) — Provides a runtime environment that decomposes complex research goals into multi-step plans by invoking external tools and managing long-term memory. - [Autonomous Research Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/autonomous-research-frameworks.md) — Orchestrates autonomous agents to perform complex web research and generate grounded, cited reports. - [Autonomous Web Researchers](https://awesome-repositories.com/f/artificial-intelligence-ml/agentic-systems-frameworks/agent-orchestration-multi-agent/autonomous-agents/autonomous-web-researchers.md) — Navigates the web and synthesizes answers to complex questions without human guidance. ([source](https://tongyi-agent.github.io/blog/introducing-tongyi-deep-research/)) - [Reinforcement Learning Training Pipelines](https://awesome-repositories.com/f/artificial-intelligence-ml/reinforcement-learning-training-pipelines.md) — Provides a platform for developing autonomous models using reinforcement learning and knowledge graph synthesis. - [Multi-Agent Orchestration](https://awesome-repositories.com/f/artificial-intelligence-ml/multi-agent-orchestration.md) — Orchestrates multiple independent agents to execute concurrent search paths for comprehensive information gathering. - [Multi-Agent Orchestration Systems](https://awesome-repositories.com/f/artificial-intelligence-ml/multi-agent-orchestration-systems.md) — Orchestrates multiple parallel agents to execute collaborative research workflows. - [Multi-Agent Research Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/multi-agent-research-frameworks.md) — Coordinates multiple research agents in parallel to explore diverse information paths and aggregate findings. ([source](https://tongyi-agent.github.io/blog/introducing-tongyi-deep-research/)) - [Reasoning-Action Loops](https://awesome-repositories.com/f/artificial-intelligence-ml/agent-architectures/orchestration-engines/ai-agent/reasoning-action-loops.md) — Implements autonomous cycles of thought, tool execution, and result analysis to solve complex problems. - [Agent Planning Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/agent-planning-frameworks.md) — Enables autonomous agents to generate and refine step-by-step task plans and reconstruct workspaces during long-running research operations. ([source](https://tongyi-agent.github.io/blog/introducing-tongyi-deep-research/)) - [Automated Knowledge Synthesis Tools](https://awesome-repositories.com/f/artificial-intelligence-ml/automated-knowledge-synthesis-tools.md) — Synthesizes data from diverse sources into evidence-based reports through automated reasoning. - [Autonomous Task Execution](https://awesome-repositories.com/f/artificial-intelligence-ml/autonomous-task-execution.md) — Executes complex information gathering tasks autonomously through iterative reasoning and observation. ([source](https://tongyi-agent.github.io/blog/introducing-tongyi-deep-research/)) - [Reasoning Pipelines](https://awesome-repositories.com/f/artificial-intelligence-ml/reasoning-models/reasoning-pipelines.md) — Divides high-level research objectives into sequential reasoning steps to retrieve and synthesize data. ([source](https://tongyi-agent.github.io/blog/introducing-tongyi-deep-research/)) - [End-to-End Training Pipelines](https://awesome-repositories.com/f/artificial-intelligence-ml/end-to-end-training-pipelines.md) — Provides end-to-end pipelines for pre-training, fine-tuning, and reinforcement learning of autonomous research models. ([source](https://tongyi-agent.github.io/blog/introducing-tongyi-deep-research/)) - [Reinforcement Learning Alignment](https://awesome-repositories.com/f/artificial-intelligence-ml/reinforcement-learning-alignment.md) — Refines agent decision-making through reinforcement learning feedback loops aligned with research goals. - [Agent Optimization Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/agent-optimization-frameworks.md) — Applies reinforcement learning strategies to align agent decision-making patterns with high-level research objectives. ([source](https://tongyi-agent.github.io/blog/introducing-tongyi-deep-research/)) - [External Tool Execution](https://awesome-repositories.com/f/artificial-intelligence-ml/external-tool-execution.md) — Manages external tool interactions with automated retries, caching, and fallback mechanisms for operational stability. - [Knowledge Graphs](https://awesome-repositories.com/f/artificial-intelligence-ml/knowledge-graphs.md) — Transforms retrieved web information into structured knowledge graphs to support complex reasoning. - [Training Data Generation](https://awesome-repositories.com/f/artificial-intelligence-ml/training-data-generation.md) — Generates large-scale training datasets by transforming web information into structured knowledge graphs and reasoning sequences. ([source](https://tongyi-agent.github.io/blog/introducing-tongyi-deep-research/)) ### Data & Databases - [Evidence-Based Reporting](https://awesome-repositories.com/f/data-databases/data-analysis-visualization/analytical-platforms-engines/data-reporting/evidence-based-reporting.md) — Generates detailed, evidence-backed analytical reports with precise citations to original sources. ([source](https://tongyi-agent.github.io/blog/introducing-tongyi-deep-research/)) ### Development Tools & Productivity - [Tool Execution Resilience](https://awesome-repositories.com/f/development-tools-productivity/workflow-automation-tools/automation-execution-frameworks/tool-execution-resilience.md) — Ensures reliable tool usage through automated retries, result caching, and redundant service fallbacks. ([source](https://tongyi-agent.github.io/blog/introducing-tongyi-deep-research/))