Turn your codebase into a searchable book.

Overview · Install · Usage · Docs

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Important

🚧 Beta Release Notice This is a beta version – features are stable but subject to improvement. We expect no breaking changes for the next few releases. The first stable release is scheduled for late June / early July 2026. Please report issues on GitHub – contributions to documentation are especially welcome.

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Eulix transforms your codebase into a structured, searchable knowledge base. Ask questions about your code — get accurate answers grounded in actual source structure, not hallucinations.

Using ML algorithms and LLMs, Eulix analyzes your code's architecture (symbols, call graphs, control flow), then intelligently retrieves relevant context to answer questions with precision.

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How It Works

1. Index Your Codebase

Eulix analyzes your source code and creates a structured knowledge base:

• Symbol Index — Maps all functions, classes, variables, and their locations
• Call Graphs — Tracks which code calls what (dependencies, relationships) Note: uses PRISM (Polyglot Resolution via inverted Symbol Map) – an approximation algorithm. Call graphs are precise enough for most queries but may have false positives.
• Control Flow — Captures structure, complexity, and error handling
• Embeddings — Generates semantic vectors for each code unit

Result: Your codebase becomes a "book" with chapters (files), sections (classes), and indexed content (functions).

2. Answer Questions Accurately

When you ask a question, Eulix:

1. Finds relevant code using multi-layer retrieval (symbol lookup → keyword search → semantic search → call graph traversal)
2. Builds precise context — Only includes code that matters, with accurate relationships
3. Feeds to LLM — Local model explains based on grounded facts, not guesses

Architecture

Three focused binaries working in concert:

Component	Language	Role	License
eulix	Go	Orchestrator — CLI, config, and the retrieval pipeline	GPLv3
eulix_parser	Rust	Static Analyzer — Extracts symbols, call graphs, and complexity	GPLv3
eulix_embed	Python	Embedder — Runs transformers via PyTorch with GPU acceleration (ROCm/CUDA support)	Apache 2.0

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Why Eulix

Accurate answers grounded in your actual code. Most AI code tools hallucinate because they guess at context. Eulix builds structured knowledge of your codebase first, so answers are precise.

Works offline, keeps your code private. All parsing, embedding, and reasoning happen locally. No code exposure.

Fast on small models. With accurate context, a local 7B model explains code as well as ChatGPT-4. No API costs, no latency, no rate limits.

Production‑ready. Handles millions of lines of code. Built for large teams, legacy systems, and complex architectures.

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Features

• Multi-Language Parsing — Python, Go, C, C++, Rust. Extract structure, not just text.
• PRISM Call Graph Approximation — Fast, polyglot call graph resolution (with documented limitations – see docs).
• Local Intelligence – All analysis runs on your machine. No cloud dependency(Cloud llm can be used and code will be sent).
• GPU Acceleration — CUDA/ROCm support for fast embedding generation.
• MCP Integration — Plugs into any editor or tool via Model Context Protocol (coming soon).
• Anti-Hallucination Design — Retrieval-augmented answering grounded in actual code structure.

Supported Languages

Stable: Python · Go · C · C++ · Rust

Coming soon: TypeScript · JavaScript · Java

Use Cases

• Onboarding new engineers — Explain "what does this module do?" in seconds
• Debugging unfamiliar code — Trace execution flow and dependencies
• Refactoring legacy systems — Understand impact of changes before making them
• Security audits — Find all callers of sensitive functions
• Architecture decisions — Explore how components interact

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Installation

Requirements

• Go 1.23+
• Rust (stable)
• Python 3.10-3.11
• uv

  Install PyTorch for your platform from: https://pytorch.org/

Linux/Mac

curl -fsSL https://raw.githubusercontent.com/nurysso/eulix/main/install.sh | bash

Windows

USERS WILL NEED VISUAL STUDIO CODE FOR C++ LINKER USED BY RUST

Invoke-WebRequest -Uri "https://raw.githubusercontent.com/nurysso/eulix/main/install.ps1" -OutFile "$env:TEMP\install.ps1"

or look at install doc

Usage

1. Initialize a project

cd fooProj
eulix init

2. Analyze the codebase

eulix analyze

This triggers the parser and embedding pipeline, generating a .eulix folder which becomes the knowledge base for the LLM.

3. Chat with your code

eulix chat

Opens an interactive session to query your codebase using the multi-layer retrieval pipeline.

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CLI Reference

eulix (Go)

• init : Initialize eulix in current directory
• analyze : Analyze codebase and generate knowledge base
• chat : Start interactive chat interface
• cache : Manage cache entries
• config : Manage eulix configuration
• history : View query history interactively
• embed: Run the eulix_embed pipeline (Python venv)
• version : Displays version of eulix, eulix_parser, and eulix_embed
• checksum: Creates checksum without running analyze
• aspirine : Attempts to fix embeddings.bin and KB (meant for testing)
• glados : Checks for errors in knowledge base and embeddings size (testing)

eulix_parser (Rust)

Fast static analysis tool.

• -V, --version : Parser version
• -r, --root : Project root directory
• -o, --output : Output file for knowledge base [default: knowledge_base.json]
• -t, --threads : Number of threads for parallel parsing [default: 4]
• -v, --verbose : Verbose output
• -l, --languages : Languages to parse (comma-separated, or "all") [default: all]
• --no-analyze : Skip analysis phase (faster, only parse files)
• --euignore : Path to custom .euignore file (defaults to /.euignore)
• -h, --help : Print help
• -V, --version : Print version

eulix_embed (Python)

Vector generation via PyTorch. Supports sentence-transformers/all-MiniLM-L6-v2, BAAI/bge-small-en-v1.5, BAAI/bge-base-en-v1.5, and more. Native CUDA/ROCm support.

eulix_embed [COMMAND] [OPTIONS]

COMMANDS:

• embed : Generate embeddings for knowledge base (default)
• query : Generate embedding for a query string
• compare : Compare embeddings.bin with vectors.bin

EMBED OPTIONS:

• -k, --kb-path : Path to knowledge base JSON file
• -o, --output : Output directory for embeddings
• -m, --model : HuggingFace model name or local path

QUERY OPTIONS:

• -q, --query : Query text to embed

• -m, --model : HuggingFace model name or local path

• -f, --format : Output format: json (default) or binary

• -h, --help : Show help

• -v, --version : Show version

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Documentation

Full documentation is available in the docs/ directory:

• Architecture Overview – System design and data flow
• Parser Internals – How eulix_parser works
• Context Builder – Retrieval and MMR selection
• Classifier – Query intent recognition
• Cache Architecture – Redis/SQL caching
• Eulix Embed – Embedding pipeline
• Parser Benchmarks – Performance numbers
• Known Issues – Current limitations (including PRISM call graph approximation)
• Installation Guide – Detailed setup
• Models to Use – Recommended embedding/LLM models

Contributions to docs are highly welcome – many sections are old and needs time to be updated.

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Contributing

Contributions are welcome! Please open an issue before submitting a pull request for significant changes.

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License

• eulix (Go CLI) and eulix_parser (Rust) – GNU General Public License v3.0
• eulix_embed (Python embedding module) – Apache License 2.0

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Happy code understanding with Eulix!