Why Vectorless?​

Understanding a document requires more than finding keywords — it requires navigating structure, cross-referencing sections, and evaluating whether the evidence is sufficient. Vectorless agents do exactly this: they reason through documents the way a human expert would.

Key capabilities:

• Hierarchical Semantic Trees — Documents are parsed into a tree of sections, preserving structure and relationships.
• LLM Agent Navigation — Queries are resolved by agents that navigate the tree using commands (ls, cd, cat, find, grep), making every decision through LLM reasoning.
• Zero Infrastructure — Just an LLM API key, nothing else to deploy.

Quick Start​

Python​

import asyncio
from vectorless import Engine, IndexContext, QueryContext

async def main():
    engine = Engine(
        api_key="sk-...",
        model="gpt-4o",
    )

    # Index a document
    result = await engine.index(IndexContext.from_path("./report.pdf"))
    doc_id = result.doc_id

    # Query
    answer = await engine.query(
        QueryContext("What is the total revenue?").with_doc_ids([doc_id])
    )
    print(answer.single().content)

asyncio.run(main())

Rust​

use vectorless::{EngineBuilder, IndexContext, QueryContext};

#[tokio::main]
async fn main() -> vectorless::Result<()> {
    let engine = EngineBuilder::new()
        .with_key("sk-...")
        .with_model("gpt-4o")
        .build()
        .await?;

    let result = engine.index(IndexContext::from_path("./report.pdf")).await?;
    let doc_id = result.doc_id().unwrap();

    let result = engine.query(
        QueryContext::new("What is the total revenue?").with_doc_ids(vec![doc_id.to_string()])
    ).await?;
    println!("{}", result.content);

    Ok(())
}

How It Works​

1. Index — Documents are parsed into hierarchical semantic trees with pre-computed navigation indexes and keyword mappings.
2. Query — The Orchestrator coordinates multi-document retrieval by dispatching Worker agents. Each Worker navigates the tree using commands, collects evidence, and self-evaluates sufficiency.
3. Result — Evidence is deduplicated, ranked by BM25 relevance, and returned as original document text.

What's Next​

• Cross-document graph-aware retrieval with score boosting
• DOCX format support
• Streaming query results with real-time progress events

The project is open source under Apache-2.0. Contributions welcome!

• GitHub
• PyPI
• crates.io