maris 0.1.1

Local Multi-Agent Repository Intelligence System

MARIS

MARIS is a local-first, multi-agent repository intelligence system for understanding source code. It indexes repositories with language-aware parsers, stores repository knowledge locally, and uses local Ollama models for search, Q&A, documentation, and impact analysis.

The goal is to help developers reason about codebases without sending source code to external services. MARIS focuses on repository understanding rather than code generation.

Current Status

MARIS is an alpha-stage Python package.

Implemented:

• CLI entry point: maris
• Local storage with DuckDB metadata and LanceDB vectors
• Ollama-based embeddings and local model validation
• Parser implementations for Python, Java, and Scala
• Repository indexing, semantic search, symbol explanations, Q&A, documentation generation, and repository stats
• Git-based incremental indexing
• Impact analysis commands for impact, edge cases, test coverage, and breaking changes

Planned or incomplete:

• Parser factory lists additional planned languages, but Kotlin, JavaScript, TypeScript, Go, Bash, and Rust parsers are not implemented yet
• Git archaeology and architecture evolution agents are roadmap items
• Some secondary docs may lag the CLI; the root README should be treated as the current quick-start reference
• AGENT.md is contributor guidance and product direction, not an executable agent spec

Requirements

• Python 3.11+
• Ollama running locally
• Required Ollama models:
  • nomic-embed-text for embeddings
  • qwen2.5:7b by default for Q&A and documentation

Install or start Ollama separately, then pull the default models:

ollama pull nomic-embed-text
ollama pull qwen2.5:7b

Installation

For local development:

python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
pip install -r requirements-dev.txt
pip install -e .

Alternatively, use the setup script:

./setup.sh
source venv/bin/activate

Verify the CLI:

maris --help

Quick Start

Run MARIS from the repository you want to analyze. By default, MARIS stores project-specific data in .maris/ in the current working directory unless MARIS_DATA_DIR is set.

# Index supported source files recursively
maris index src/ --recursive

# Show indexed repository statistics
maris stats

# Search indexed symbols
maris search "RepositoryKnowledge"

# Ask a question grounded in indexed symbols
maris ask "How does indexing work?"

# Explain a symbol
maris explain IndexingAgent

# Generate documentation for one file
maris document src/maris/agents/indexing_agent.py --output docs/indexing_agent.md

Incremental indexing uses Git change detection:

maris index --incremental

Impact analysis examples:

maris impact analyze --symbol "GitAgent.detect_changes"
maris impact edge-cases --file "src/maris/agents/git_agent.py"
maris impact tests --symbol "QAAgent.answer_question"
maris impact breaking-changes --symbol "RepositoryKnowledgeImpl"

Interactive Q&A:

maris interactive

CLI Reference

Global options:

maris --config-file .env --skip-validation COMMAND

Commands:

• maris index [PATH]: index a file or directory
• maris index --incremental: index files changed since the last indexed commit
• maris search QUERY: semantic symbol search
• maris explain SYMBOL_NAME: explain a symbol with relevant indexed context
• maris ask QUESTION: ask a natural-language repository question
• maris impact analyze: analyze callers, callees, affected files, and recommendations
• maris impact edge-cases: detect likely edge case risks
• maris impact tests: inspect test coverage signals
• maris impact breaking-changes: detect potential breaking change risks
• maris document FILE_PATH: generate Markdown documentation for a file
• maris stats: show indexed symbol counts
• maris clear: clear indexed metadata and vectors
• maris interactive: start an interactive Q&A session

Use command help for exact options:

maris COMMAND --help
maris impact COMMAND --help

Configuration

Configuration is loaded in this order:

1. Environment variables with the MARIS_ prefix
2. .env in the current directory
3. ~/.maris/.env
4. Defaults

Common settings:

MARIS_DATA_DIR=.maris
MARIS_OLLAMA_HOST=http://localhost:11434
MARIS_EMBEDDING_MODEL=nomic-embed-text
MARIS_EMBEDDING_BATCH_SIZE=32
MARIS_QA_MODEL=qwen2.5:7b
MARIS_QA_TEMPERATURE=0.7
MARIS_QA_MAX_TOKENS=2048
MARIS_DOC_MODEL=qwen2.5:7b
MARIS_DOC_TEMPERATURE=0.3
MARIS_DOC_MAX_TOKENS=4096
MARIS_MAX_SEARCH_RESULTS=20
MARIS_MAX_CONTEXT_SYMBOLS=10
MARIS_ENABLE_CACHING=true
MARIS_PARALLEL_INDEXING=false
MARIS_LOG_LEVEL=INFO

For first-time setup, maris index ... --auto-pull can pull missing Ollama models automatically. Use --skip-validation only when you intentionally want to bypass Ollama and model checks.

Architecture

MARIS is organized around a shared repository knowledge layer:

Source repository
    -> Indexing Agent
    -> Repository Knowledge Layer
       -> DuckDB metadata store
       -> LanceDB vector store
       -> Ollama embeddings
    -> Specialized agents
       -> Q&A Agent
       -> Documentation Agent
       -> Git Agent
       -> Impact Analysis Agent

Core source layout:

src/maris/
  agents/       specialized agents and orchestrator
  cli/          Click-based CLI
  config/       configuration loading
  core/         domain models
  embeddings/   Ollama embedding service
  indexing/     Tree-sitter parsers and parser factory
  knowledge/    repository knowledge service
  storage/      DuckDB and LanceDB adapters
  utils/        shared validation helpers

Development

Run tests:

pytest

Run targeted tests:

pytest tests/test_python_parser.py
pytest tests/test_orchestrator_agent.py

Formatting and linting tools are configured in pyproject.toml:

black src tests
ruff check src tests

Contributor Guidance

AGENT.md contains contributor guidance and product direction. The key expectations are:

• Read .codex/project-profile.md before changing architecture or design direction
• Read relevant files in .codex/specs/ before changing behavior
• Preserve the local-first, retrieval-first, symbol-aware design
• Prefer deterministic workflows and specialized agents over broad autonomous loops
• Update specs when behavior, acceptance criteria, API contracts, or domain rules change

Gaps found during review:

• AGENT.md refers to a pragmatic-developer skill and says the guidance is intended for Claude; that dependency is not represented in this repository and may confuse other agents or contributors
• The root README previously read more like a product vision than a setup and usage guide
• Some docs mention stale or unsupported CLI flags; verify behavior against src/maris/cli/main.py
• Runtime dependencies are split unevenly between requirements.txt and pyproject.toml; use requirements.txt for a reliable local setup until package metadata is reconciled
• The roadmap and parser support docs should distinguish implemented languages from planned languages consistently

Additional Docs

• Installation Guide
• Configuration
• Architecture
• Git Agent
• Impact Analysis Agent
• Multi-Language Support