https://github.com/epappas/agentroot#readme

Agentroot

Fast local semantic search for your codebase and knowledge base. Agentroot provides hybrid search combining BM25 full-text search with vector similarity search, powered by AST-aware semantic chunking for code files.

Why Agentroot?

Traditional code search tools fall short in several ways:

The Problem

Keyword search (grep, ripgrep, GitHub search):

• Finds exact matches only
• Misses semantically similar code
• Splits functions at arbitrary boundaries
• No understanding of code structure

Naive semantic search:

• Chunks text at character boundaries
• Breaks functions mid-implementation
• Loses context (docstrings, comments)
• Poor embedding quality

The Solution

Agentroot solves these problems with:

1. AST-Aware Semantic Chunking

Code files are parsed with tree-sitter and chunked by semantic units (functions, classes, methods):

Traditional chunking:              Agentroot (AST-aware):
─────────────────────              ───────────────────────

fn process_data() {                /// Process input data
    let x = parse();               fn process_data() {
    let y = validate();                let x = parse();
} ← Split here!                        let y = validate();
                                       transform(x, y)
fn next_function() {               }  ← Kept intact

Benefits:

• Functions stay intact
• Context preserved (docstrings, comments)
• Better embedding quality
• More accurate search results

2. Smart Content-Addressable Caching

Each chunk gets a blake3 hash based on its content and context. On re-indexing:

Edit 1 function out of 100:
❌ Without cache: Re-embed all 100 functions (30s)
✅ With cache: Re-embed 1 function (0.3s)

Typical cache hit rates:
- Minor edits: 90-95%
- Feature additions: 80-90%
- Major refactoring: 60-80%

Result: 5-10x faster re-indexing for typical development workflows.

3. Hybrid Search with RRF

Combines the best of both worlds:

• BM25: Fast exact keyword matching (<10ms)
• Vector search: Semantic understanding (~100ms)
• RRF fusion: Intelligently combines rankings

Query: "error handling patterns"

BM25 finds:
- Exact matches: "error", "handling"
- Technical terms: "Result<T>", "anyhow"

Vector search finds:
- Semantic matches: exception handling code
- Similar patterns without exact keywords
- Related concepts

Hybrid combines both for best results

4. Privacy-First Local Operation

• All data stays on your machine
• No API keys required
• No cloud services
• Works completely offline (after model download)

Comparison to Alternatives

Feature	Agentroot	ripgrep	GitHub Search	Semantic Code Search
Keyword search	✅ BM25	✅ Fast	✅ Advanced	⚠️ Limited
Semantic search	✅ Hybrid	❌ No	❌ No	✅ Yes
AST-aware chunking	✅ Yes	❌ No	❌ No	⚠️ Varies
Local-first	✅ Yes	✅ Yes	❌ Cloud	⚠️ Varies
Smart caching	✅ 80-90% hit	N/A	N/A	❌ No
Speed (keyword)	✅ <10ms	✅ <10ms	⚠️ 100ms+	❌ Slow
Speed (semantic)	✅ ~100ms	❌ N/A	❌ N/A	⚠️ 500ms+
Setup complexity	✅ One command	✅ None	⚠️ OAuth	⚠️ Complex

When to use Agentroot:

• You want semantic understanding of code
• You need privacy (local-first)
• You frequently re-index (cache helps)
• You want best-of-both-worlds hybrid search

When to use alternatives:

• ripgrep: Pure keyword search, need maximum speed
• GitHub Search: Already on GitHub, want web interface
• Other tools: Specific enterprise requirements

Features

• Multi-Source Indexing: Pluggable provider system for indexing from local files, GitHub repositories, URLs, databases, and more
• Hybrid Search: Combines BM25 full-text search with vector similarity search using Reciprocal Rank Fusion
• AI-Powered Search: Natural language queries with LLM-based query understanding and metadata generation (optional vLLM integration)
• Response Caching: 7,000-10,000x speedup for repeated queries with intelligent cache management
• AST-Aware Chunking: Intelligently chunks code by semantic units (functions, classes, methods) using tree-sitter
• Smart Cache Invalidation: Content-addressable chunk hashing achieves 80-90% cache hit rates on re-indexing
• Multi-Language Support: Rust, Python, JavaScript/TypeScript, Go (with fallback for other languages)
• Local-First or Cloud: Run entirely offline with local models, or connect to Basilica for GPU-accelerated inference
• MCP Server: Model Context Protocol support for AI assistant integration

Powered by Basilica

AgentRoot integrates seamlessly with Basilica (GitHub) - a trustless GPU compute marketplace built on Bittensor's decentralized infrastructure. Basilica provides production-grade AI inference with verified hardware, automatic failover, and 99.9% uptime. When connected to Basilica, AgentRoot achieves 10x faster embeddings and GPU-accelerated search while maintaining privacy through decentralized compute verification.

Why Basilica works so well with AgentRoot:

• ⚡ OpenAI-compatible API - zero custom integration needed
• 🔒 Trustless verification - binary validation of GPU compute
• 🚀 10x faster - GPU acceleration with intelligent load balancing
• 💾 Smart caching - AgentRoot + Basilica layers for 7,000x speedup
• 🌐 Decentralized - 100+ verified GPU nodes on Bittensor Subnet 39

See VLLM_SETUP.md for Basilica integration details.

Installation

From crates.io (Recommended)

cargo install agentroot

Verify installation:

agentroot --version

From Source

git clone https://github.com/epappas/agentroot
cd agentroot
cargo build --release

# Install to PATH
cargo install --path crates/agentroot-cli

Dependencies

Agentroot requires an embedding model for vector search. On first run, it will download nomic-embed-text-v1.5 (~100MB) to ~/.local/share/agentroot/models/.

Quick Start

Option 1: Local-Only (Privacy-First)

# 1. Add a collection (index files from a directory)
agentroot collection add /path/to/your/code --name myproject --mask '**/*.rs'

# 2. Index the files
agentroot update

# 3. Generate embeddings (downloads model on first run)
agentroot embed

# 4. Search
agentroot search "error handling"      # BM25 full-text search
agentroot vsearch "error handling"     # Vector similarity search
agentroot query "error handling"       # Hybrid search (best quality)

Option 2: AI-Powered with Basilica (Recommended)

# 1. Get Basilica endpoints at https://basilica.ai (instant access)
# 2. Configure endpoints (see VLLM_SETUP.md for details)
export AGENTROOT_LLM_URL="https://your-id.deployments.basilica.ai"
export AGENTROOT_LLM_MODEL="Qwen/Qwen2.5-7B-Instruct"
export AGENTROOT_EMBEDDING_URL="https://your-id.deployments.basilica.ai"
export AGENTROOT_EMBEDDING_MODEL="intfloat/e5-mistral-7b-instruct"
export AGENTROOT_EMBEDDING_DIMS="4096"

# 2. Add and index collection
agentroot collection add /path/to/your/code --name myproject
agentroot update

# 3. Generate embeddings (uses vLLM, 10x faster with GPU)
agentroot embed

# 4. Generate AI metadata (optional but recommended)
agentroot metadata refresh myproject

# 5. Smart natural language search
agentroot smart "show me files dealing with error handling"

Benefits of Basilica Integration:

• 🚀 10x faster with GPU acceleration (decentralized Bittensor network)
• 🧠 Smarter queries with LLM understanding
• 📊 Rich metadata generation
• ⚡ 7,000x speedup for cached queries
• 🔒 Trustless compute with hardware verification
• 🌐 99.9% uptime with automatic failover

See Complete Workflow Guide for step-by-step tutorials and VLLM_SETUP.md for Basilica setup.

Multi-Source Indexing

Agentroot can index content from multiple sources beyond local files using its pluggable provider system:

Local Files (Default)

# Add local directory
agentroot collection add /path/to/code --name myproject --mask '**/*.rs'

GitHub Repositories

# Add GitHub repository
agentroot collection add https://github.com/rust-lang/rust \
  --name rust-lang \
  --mask '**/*.md' \
  --provider github

# Optionally provide GitHub token for higher rate limits
export GITHUB_TOKEN=ghp_your_token_here

Provider Architecture

The provider system is extensible and designed to support:

Provider	Status	Description
FileProvider	✅ Available	Local file system with glob patterns
GitHubProvider	✅ Available	GitHub repositories and files
URLProvider	✅ Available	Web pages and HTTP(S) documents
PDFProvider	✅ Available	PDF document text extraction
SQLProvider	✅ Available	SQLite database content indexing
CalendarProvider	🔄 Planned	Calendar events and notes

Adding a new provider is simple - implement the SourceProvider trait and register it. See Provider Documentation for details.

Using Providers in Code

use agentroot_core::{Database, GitHubProvider, ProviderConfig};

let db = Database::open("index.db")?;
db.initialize()?;

// Add GitHub collection
db.add_collection(
    "rust-docs",
    "https://github.com/rust-lang/rust",
    "**/*.md",
    "github",
    None,
)?;

// Index using provider
db.reindex_collection("rust-docs")?;

See examples/github_provider.rs for a complete working example.

Code Examples

Working code examples demonstrating library usage are available in examples/:

# Basic search example (database setup, indexing, BM25 search)
cargo run -p agentroot-core --example basic_search

# Semantic chunking example (AST-aware code parsing)
cargo run -p agentroot-core --example semantic_chunking

# Custom indexing pipeline example
cargo run -p agentroot-core --example custom_index

# Provider examples
cargo run -p agentroot-core --example github_provider  # GitHub repositories
cargo run -p agentroot-core --example url_provider     # Web pages/HTTP
cargo run -p agentroot-core --example pdf_provider     # PDF documents
cargo run -p agentroot-core --example sql_provider     # SQLite databases
cargo run -p agentroot-core --example custom_provider  # Custom provider template

All examples are production-ready, compile cleanly, and demonstrate real functionality. See examples/README.md for details.

Commands

Command	Description	Speed	Quality
collection add <path>	Add a new collection	-	-
collection list	List all collections	-	-
collection remove <name>	Remove a collection	-	-
update	Re-index all collections	Fast	-
embed	Generate vector embeddings	Medium	-
metadata refresh	Generate AI metadata (vLLM)	Medium	-
search <query>	BM25 full-text search	⚡ <10ms	⭐⭐⭐
vsearch <query>	Vector similarity search	~100ms	⭐⭐⭐⭐
query <query>	Hybrid search with RRF	~150ms	⭐⭐⭐⭐⭐
smart <query>	AI natural language search (vLLM)	~150ms*	⭐⭐⭐⭐⭐
get <docid>	Get document by path or docid	<1ms	-
multi-get <pattern>	Get multiple documents	<10ms	-
ls [collection]	List files in a collection	<1ms	-
status	Show index status	<1ms	-
mcp	Start MCP server for AI integration	-	-

*First query ~1.5s, cached queries ~150ms (10x faster)

See CLI Reference for complete documentation.

Example Usage

Index a Rust Project

agentroot collection add ~/projects/myapp --name myapp \
  --mask '**/*.rs' \
  --exclude '**/target/**'

agentroot update
agentroot embed

Search for Error Handling Patterns

# Keyword search (fast, <10ms)
agentroot search "Result<T>"

# Semantic search (understands meaning, ~100ms)
agentroot vsearch "how to handle database errors"

# Hybrid search (best quality, ~150ms)
agentroot query "error handling patterns in async code"

# AI natural language search (with vLLM, understands complex queries)
agentroot smart "show me all files that deal with async error handling"

Example output:

🤖 Parsed query: async error handling
📊 Search type: Hybrid
🔍 Expanded terms: error handling, async, Result, tokio

  94% src/async/error.rs #a1b2c3
  Async error handling utilities with retry and backoff

  91% src/api/handlers.rs #d4e5f6
  HTTP handlers with async error propagation

  87% src/database/pool.rs #g7h8i9
  Connection pool error recovery strategies

Retrieve Specific Files

# By path
agentroot get myapp/src/error.rs

# By docid (from search results)
agentroot get "#a1b2c3"

# Multiple files
agentroot multi-get "myapp/src/*.rs"

Integration with AI Assistants

Start MCP server for Claude Desktop or Continue.dev:

agentroot mcp

See MCP Server Documentation for integration details.

Architecture

agentroot/
├── agentroot-core/     # Core library
│   ├── db/             # SQLite database layer
│   ├── index/          # Indexing and chunking
│   │   └── ast_chunker/  # AST-aware semantic chunking
│   ├── providers/      # Pluggable content sources
│   ├── search/         # Search algorithms
│   └── llm/            # Embedding model integration
├── agentroot-cli/      # Command-line interface
├── agentroot-mcp/      # MCP server for AI assistants
└── agentroot-tui/      # Terminal UI (experimental)

Key Components

AST Chunker: Uses tree-sitter to parse code and extract semantic units. Supports Rust, Python, JavaScript, TypeScript, and Go.

Embedding Cache: blake3-hashed chunks enable smart cache invalidation. Only changed chunks are re-embedded, achieving 80-90% cache hit rates.

Hybrid Search: Reciprocal Rank Fusion combines BM25 (keyword) and vector (semantic) results for optimal quality.

SQLite Storage: FTS5 for full-text search, BLOB storage for embeddings, content-addressable deduplication.

See Architecture Documentation for detailed design.

Supported Languages

Language	File Extensions	Semantic Units
Rust	.rs	functions, impl blocks, structs, enums, traits, modules
Python	.py	functions, classes, decorated definitions
JavaScript	.js, .jsx	functions, classes, methods, arrow functions
TypeScript	.ts, .tsx	functions, classes, interfaces, type aliases
Go	.go	functions, methods, types, interfaces
Other	*	Character-based chunking (fallback)

See Semantic Chunking Documentation for technical details.

Performance

Indexing Speed

• Scanning: ~1000 files/second
• AST parsing: ~1-5ms per file
• Embedding (local): ~50-100 chunks/second (CPU-dependent)
• Embedding (vLLM): ~200-500 chunks/second (GPU-accelerated)

Search Speed

Operation	First Query	Cached Query	Speedup
BM25 search	<10ms	<10ms	1x
Vector search	~100ms	~100ms	1x
Hybrid search	~150ms	~150ms	1x
Smart search (vLLM)	~1500ms	~150ms	10x
Embedding (vLLM)	600ms	80µs	7,500x

Response Caching (vLLM)

AgentRoot intelligently caches LLM responses and embeddings:

Cache Performance:
  Embedding cache:   7,000-10,000x speedup (600ms → 80µs)
  Query cache:       10x speedup (1.5s → 0.15s)
  TTL:               1 hour (auto-expiration)
  Thread-safe:       Concurrent access supported

Chunk Cache Efficiency

Initial indexing:  0% cache hits (all chunks computed)
Minor edits:       90-95% cache hits
Feature additions: 80-90% cache hits
Major refactor:    60-80% cache hits

Real-World Example:

# Test caching yourself
cargo run --release --example test_cache

# Output:
# First embed:  632ms  (cache miss)
# Second embed: 80µs   (cache hit - 7,900x faster!)

See Performance Documentation for detailed benchmarks.

Configuration

Database Location

~/.cache/agentroot/index.sqlite

Model Location (Local Mode)

~/.local/share/agentroot/models/

Environment Variables

Basic Configuration

# Override database path
export AGENTROOT_DB=/custom/path/index.sqlite

# Override models directory (local mode)
export AGENTROOT_MODELS=/custom/path/models

# Set log level
export RUST_LOG=debug

Basilica Integration (Optional - Recommended)

For AI-powered features with Basilica's decentralized GPU network:

# Get endpoints at https://basilica.ai (instant access)

# LLM Service (for query parsing, metadata generation)
export AGENTROOT_LLM_URL="https://your-id.deployments.basilica.ai"
export AGENTROOT_LLM_MODEL="Qwen/Qwen2.5-7B-Instruct"

# Embedding Service (for vector search)
export AGENTROOT_EMBEDDING_URL="https://your-id.deployments.basilica.ai"
export AGENTROOT_EMBEDDING_MODEL="intfloat/e5-mistral-7b-instruct"
export AGENTROOT_EMBEDDING_DIMS="4096"

# Optional: Timeouts
export AGENTROOT_LLM_TIMEOUT="120"

When to use Basilica:

• ✅ Want GPU-accelerated search (10x faster)
• ✅ Need AI metadata generation
• ✅ Natural language queries
• ✅ Trust decentralized compute verification
• ✅ Team with shared infrastructure
• ✅ Production reliability (99.9% uptime)

When to use Local:

• ✅ Privacy-critical code (air-gapped)
• ✅ Offline development
• ✅ No external dependencies

See VLLM_SETUP.md for complete Basilica integration guide.

Development

# Build all workspace members
cargo build

# Run tests
cargo test

# Run with debug logging
RUST_LOG=debug cargo run --bin agentroot -- status

# Run clippy
cargo clippy --all-targets --all-features

# Format code
cargo fmt

See AGENTS.md for developer guidelines.

Documentation

Start Here:

• ⚡ Quick Reference - Fast 30-second start (NEW!)
• 🚀 End-to-End Workflow - Complete real-world tutorial (NEW!)
• Getting Started - Step-by-step tutorial for new users
• vLLM Setup Guide - Configure AI-powered features (NEW!)
• How-To Guide - Practical recipes for common tasks

Reference:

• CLI Reference - Complete command reference
• Provider System - Multi-source indexing guide (files, GitHub, etc.)
• Troubleshooting - Common issues and solutions

Technical Details:

• Architecture - System design and components
• Semantic Chunking - AST-aware chunking details
• Embedding Cache - Smart cache invalidation
• Performance - Benchmarks and optimization

Integration:

• MCP Server - AI assistant integration (Claude, Continue.dev)

Development:

• TODO - Known issues and planned improvements
• AGENTS.md - Guidelines for AI coding agents

Index:

• Documentation Index - Complete documentation overview

Contributing

Contributions are welcome! See CONTRIBUTING.md for guidelines.

License

MIT License - see LICENSE for details.

Acknowledgments

Built with:

• tree-sitter - AST parsing
• llama.cpp - Embedding model inference
• SQLite with FTS5 - Database and full-text search
• blake3 - Content hashing

Embedding model: nomic-embed-text-v1.5 by Nomic AI.