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nlweb-ai avatar

nlweb-ai/NLWeb

0
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NLWeb

NLWeb is a platform that enables natural language querying of structured data across the web. It allows users to ask plain-language questions and receive answers grounded in Schema.org structured data from websites, or have those questions translated into SQL queries against enterprise data sources like HubSpot or Jira.

The system can discover and route requests to other NLWeb agents published on the internet, and expose any NLWeb instance as an MCP server for direct querying by AI agents. It evaluates available language models on cost and quality to select the most suitable one for each request, and uses neural scoring to rank search results by relevance.

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Features

  • Cost-Quality Model Routing - Evaluates available language models on cost and quality to select the most suitable one for each request.
  • Agent Discovery & Routing - Discovers and routes requests to other NLWeb agents published on the internet.
  • Natural Language to SQL - Converts plain-English questions into SQL queries against enterprise data sources like HubSpot or Jira.
  • Website Structured Data Querying - Accepts plain-language questions and returns answers grounded in Schema.org structured data from websites.
  • MCP Server Interfaces - Exposes any NLWeb instance as an MCP server for direct querying by AI agents.
  • Neural - Uses a trained neural model to evaluate and order query results by relevance.
6,226 stars·697 forks·Python·MIT·17 views

Star history

Star history chart for nlweb-ai/nlwebStar history chart for nlweb-ai/nlweb

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

Frequently asked questions

What does nlweb-ai/nlweb do?

NLWeb is a platform that enables natural language querying of structured data across the web. It allows users to ask plain-language questions and receive answers grounded in Schema.org structured data from websites, or have those questions translated into SQL queries against enterprise data sources like HubSpot or Jira.

What are the main features of nlweb-ai/nlweb?

The main features of nlweb-ai/nlweb are: Cost-Quality Model Routing, Agent Discovery & Routing, Natural Language to SQL, Website Structured Data Querying, MCP Server Interfaces, Neural.

Which projects share features with nlweb-ai/nlweb?

Projects with overlapping indexed features include: dataherald/dataherald — DataHerald is a natural language text-to-SQL interface and data analytics tool that translates English questions into… executeautomation/mcp-playwright — This project is a Model Context Protocol server that enables Large Language Models to control Playwright browsers for… antvis/mcp-server-chart — This project is a Model Context Protocol server that enables large language models to generate and render data… codelion/optillm — OptiLLM is an inference proxy and gateway router that directs prompts to specific language models based on cost,… defog-ai/sqlcoder — Sqlcoder is a text-to-SQL large language model specialized in converting natural language questions into structured,… ottermind/chat2db — Chat2DB is an AI-powered SQL client and multi-database GUI manager designed for managing various relational and NoSQL…

Projects sharing features with NLWeb

These projects share indexed features with NLWeb. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
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    OptiLLM is an inference proxy and gateway router that directs prompts to specific language models based on cost, performance, and provider health. It functions as a middleware layer designed to optimize requests through intelligent routing, load balancing, and context management. The project provides specialized capabilities for data protection by anonymizing personally identifiable information before requests reach a model. It also acts as a reasoning orchestrator and tool integration layer, using inference-time loops and self-reflection to improve accuracy while connecting models to externa

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  • dataherald/dataheraldDataherald avatar

    Dataherald/dataherald

    3,617View on GitHub↗

    DataHerald is a natural language text-to-SQL interface and data analytics tool that translates English questions into executable database queries using a large language model. It functions as a query generator and connectivity layer capable of retrieving database records and summarizing the results into human-readable explanations. The project features a dedicated business logic layer for defining database constraints and managing verified query pairs to improve translation accuracy. It acts as a multi-warehouse data connector, allowing for the indexing and querying of multiple SQL databases

    Pythonaidatabasefinetuning
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  • antvis/mcp-server-chartantvis avatar

    antvis/mcp-server-chart

    3,688View on GitHub↗

    This project is a Model Context Protocol server that enables large language models to generate and render data visualizations, charts, and diagrams. It functions as a toolset for AI assistants to transform raw data into professional visual representations. The server utilizes an intelligent selection layer to determine the most effective visualization format based on the provided data. It supports remote rendering via external HTTP services and provides the flexibility to route requests to self-hosted rendering endpoints for private network environments. Capabilities cover a wide range of da

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  • defog-ai/sqlcoderdefog-ai avatar

    defog-ai/sqlcoder

    4,035View on GitHub↗

    Sqlcoder is a text-to-SQL large language model specialized in converting natural language questions into structured, executable database queries. It functions as a database interface and query generator that allows for data retrieval without requiring manual code. The system utilizes an instruction-tuned model combined with schema-aware prompting and dynamic context injection. By ingesting database metadata and using in-context learning with example query pairs, it generates syntactically valid queries that match the specific schema of a connected database. The project covers a broader range

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