Web2BigTable

What Is Web2BigTable?

Web2BigTable is a bi-level multi-agent framework for web-to-table search — given a natural-language query and a target schema, it autonomously searches the open web and returns a structured table whose rows are entities, whose columns are the requested attributes, and whose cells are independently verified against web sources. It handles both wide search (broad-coverage tasks that assemble many consistent rows across heterogeneous sources) and deep search (single complex queries resolved by chaining indirect clues across many hops).

An upper-level orchestrator decomposes the task and dispatches sub-problems to lower-level worker agents that solve them in parallel and coordinate through a shared workspace to reduce redundant exploration and reconcile conflicting evidence. The system is self-evolving through a closed-loop run-verify-reflect process that jointly refines how tasks are decomposed and how sub-tasks are solved — adaptation is mediated through persistent, human-readable external memory, leaving the underlying LLMs frozen throughout.

Benchmark Results

We evaluate Web2BigTable on two challenging benchmarks:

• WideSearch — a benchmark for complex, multi-step information retrieval tasks requiring parallel search, data extraction, and structured output across diverse domains.
• XBench-DeepSearch — a benchmark for evaluating deep research capabilities on real-world questions requiring multi-hop reasoning and comprehensive web search.

Key Features

Core question. Web2BigTable is not about building yet another chatbot wrapper. It is about how to decompose hard tasks into parallel subtasks, coordinate workers effectively, and evolve better decomposition strategies from every run.

What Makes It Different?

Web2BigTable is organised as a three-stage Orchestrate → Execute → Evolve pipeline. The first two stages run on every query; the third runs only during training and writes its lessons back into persistent external memory.

The key difference from prior multi-agent systems is bi-level co-evolution: most frameworks adapt either how to plan (decomposition) or how to act (execution skills), but not both. Web2BigTable jointly refines them through the same closed-loop reflection, mediated entirely by persistent, human-readable external memory — the underlying LLMs are never fine-tuned.

One-Click Install

curl -sSL https://raw.githubusercontent.com/Web2BigTable/Web2BigTable/main/install.sh | bash

The installer will:

• Install uv (if not present)
• Clone the repository
• Install all dependencies (Memento-S + orchestrator)
• Download router assets (skill catalog + optional embeddings)
• Configure .env interactively (API keys)
• Create the web2bigtable command

Quick Start (Developer)

git clone https://github.com/Web2BigTable/Web2BigTable.git
cd Web2BigTable

# Install Memento-S worker dependencies
cd Memento-S && uv sync --python 3.12 && cd ..

# Install orchestrator dependencies
uv sync --python 3.12

Create a .env file in the project root:

OPENROUTER_API_KEY=sk-or-...
OPENROUTER_MODEL=anthropic/claude-sonnet-4-5
OPENROUTER_BASE_URL=https://openrouter.ai/api/v1
SERPER_API_KEY=...

Then launch:

web2bigtable

All configuration is centralised in environment variables. Key settings:

Built-in Skills

Workers automatically select the best skill for each subtask via semantic routing (BM25 + embeddings + LLM). If no existing skill matches, the system can dynamically fetch or create new skills on demand.

TUI

web2bigtable

• Submit tasks directly from the interface (Ctrl+Enter or Run Task)
• Session-scoped worker list with per-worker status (live / finished)
• Click any worker row to inspect execution steps and events
• Live workboard view showing real-time inter-worker coordination
• Final orchestrator output panel

Developer Notes

Web2BigTable/
├── tui_app.py                          # Textual TUI — primary interface
├── main.py                             # Standalone entry point (non-TUI)
├── install.sh                          # One-click installer
├── pyproject.toml                      # Root project (orchestrator deps + entry point)
├── orchestrator/
│   ├── orchestrator_agent.py           # LangChain orchestrator agent
│   └── mcp_server.py                   # FastMCP server (execute_subtasks + workboard)
├── orchestrator_skills/                # Auto-generated decomposition strategies
│   ├── task-router/                    # Routes queries to decompose strategies
│   ├── workboard/                      # Shared workboard coordination
│   ├── decompose-split-by-entity/      # Split by entity/brand
│   ├── decompose-split-by-time-period/ # Split by chronological range
│   ├── decompose-split-by-category/    # Split by categorical dimension
│   ├── decompose-split-by-rank-segment/# Split by rank ranges
│   ├── decompose-annual-rank-stats/    # Annual ranking statistics
│   ├── decompose-comparative-data-extraction/ # Comparative data extraction
│   ├── decompose-constrained-set-search/      # Constrained set search
│   ├── decompose-entity-benchmarking/  # Entity benchmarking
│   ├── decompose-geographic-registries/# Geographic registry lookup
│   ├── decompose-linear-multi-hop-dependency/ # Linear multi-hop dependency
│   ├── decompose-multimedia-source-verification/ # Multimedia source verification
│   └── decompose-temporal-event-logs/  # Temporal event log extraction
├── Memento-S/                          # Worker agent (submodule)
│   ├── core/
│   │   ├── agent/memento_s_agent.py    # Worker agent class
│   │   ├── config.py                   # Configuration & constants
│   │   ├── router.py                   # Skill routing (BM25 + embeddings + LLM)
│   │   ├── llm.py                      # LLM wrapper (OpenRouter)
│   │   ├── skill_engine/               # Planning, execution, bridge ops
│   │   └── tools/                      # Tool implementations
│   └── skills/                         # Built-in skill definitions
├── figures/                            # README figures
├── docs/                               # Documentation
└── logs/                               # Worker trajectory logs (*.jsonl)

FAQ

Memento Ecosystem

Web2BigTable is part of the broader Memento project family.

Citation

If you find Web2BigTable useful in your research, please cite:

@misc{huang2026web2bigtablebilevelmultiagentllm,
      title={Web2BigTable: A Bi-Level Multi-Agent LLM System for Internet-Scale Information Search and Extraction},
      author={Yuxuan Huang and Yihang Chen and Zhiyuan He and Yuxiang Chen and Ka Yiu Lee and Huichi Zhou and Weilin Luo and Meng Fang and Jun Wang},
      year={2026},
      eprint={2604.27221},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2604.27221},
}

Chinese Summary

Web2BigTable 是一个多智能体协作系统，核心思路是将复杂任务分解为可并行执行的子任务，由多个 Memento-S 工作智能体同时处理，并通过共享工作板（workboard）进行协调。

系统围绕 路由 → 分解 → 执行 → 合成 的在线流程构建。编排智能体（Orchestrator）通过 task-router 识别任务类型，匹配最佳的 decompose-* 分解策略，将任务拆分为独立子任务；工作智能体通过语义路由选择最佳技能并行执行，通过共享 workboard 进行协调；最后编排智能体聚合结果，生成最终响应。

在 WideSearch 基准测试中，Web2BigTable 在 Row F1（63.5）、Item F1（80.1）和 Success Rate（38.5）三项指标上全面超越 o3-high、Gemini 2.5 Pro、Claude Sonnet 4 等前沿基线。在 XBench-DeepSearch 上达到 73.0% 准确率，超越所有开源智能体模型，接近前沿商业系统。

Licence

MIT