# How to Contribute to the Semantica AGI Project: A Complete Developer Guide

> Learn how to contribute to the Semantica AGI project. Follow this developer guide to fork the repo, implement changes, run quality checks, and submit a pull request to join the effort.

- Repository: [Semantica /semantica](https://github.com/semantica-agi/semantica)
- Tags: how-to-guide
- Published: 2026-08-13

---

**To contribute to the Semantica AGI project, fork the repository, create a descriptive feature branch, implement changes within the appropriate modular pipeline stage (such as `semantica.ingest` or `semantica.semantic_extract`), validate your code with the full quality-check suite (black, isort, flake8, mypy), and submit a Pull Request that references the associated issue.**

The Semantica AGI project is an open-source knowledge-graph construction framework that transforms raw data into actionable intelligence through an 11-stage modular pipeline. Contributing to this repository involves extending capabilities in specific architectural layers, from data ingestion to semantic extraction and reasoning. Understanding the repository's stage-based structure ensures your contributions integrate seamlessly with the existing data-to-insight workflow.

## Understanding the Semantica Architecture

The Semantica codebase follows a strict modular design where each pipeline stage is implemented as a distinct Python package under the `semantica/` namespace. According to the [`ARCHITECTURE.md`](https://github.com/semantica-agi/semantica/blob/main/ARCHITECTURE.md) specification, the processing workflow proceeds through eleven distinct stages:

- **Ingest** (`semantica.ingest`): Normalizes diverse sources (files, web pages, databases, streams) into **Raw Document** representations.
- **Parse** (`semantica.parse`): Converts raw documents into structured data formats.
- **Normalize** (`semantica.normalize`): Cleans and standardizes textual content.
- **Split** (`semantica.split`): Segments normalized data into entity-aware or graph-aware chunks.
- **Extract** (`semantica.semantic_extract`): Generates semantic extracts including entities, relations, events, triples, and coreference data.
- **Conflict Detection** (`semantica.conflicts`): Identifies overlapping or contradictory information.
- **Deduplication** (`semantica.deduplication`): Merges duplicate facts across sources.
- **KG Construction** (`semantica.kg`): Builds the knowledge graph from processed extracts.
- **Intelligence Layer** (`semantica.ontology`, `semantica.reasoning`, `semantica.provenance`, `semantica.context`): Applies ontology enrichment, reasoning, provenance tracking, and decision-context handling.
- **Storage** (`semantica.vector_store`, `semantica.graph_store`): Persists the enriched graph to vector or graph databases.
- **Outputs** (`semantica.export`, `semantica.visualization`, `semantica` CLI): Exports data, generates visualizations, or exposes services via REST API and MCP tools.

Because each stage is isolated in its own sub-module, contributors can locate the correct layer quickly. For example, adding a new **Extractor** requires creating a class in `semantica/semantic_extract/`, while a new data source connector belongs in `semantica/ingest/`.

## Contribution Workflow

The [`CONTRIBUTING.md`](https://github.com/semantica-agi/semantica/blob/main/CONTRIBUTING.md) file defines the standard workflow for all contributors. Follow this checklist to ensure your PR meets repository standards:

1. **Select an Issue**: Browse open issues for labels like "good first issue" and comment to request assignment, or create a new issue to propose features.
2. **Fork and Clone**: Fork the repository on GitHub, then clone your local copy and add the upstream remote.
3. **Branch Naming**: Create a dedicated branch using the pattern `feature/<description>` or `fix/<description>`.
4. **Development**: Implement your changes within the appropriate `semantica/` sub-module, adhering to existing class hierarchies.
5. **Testing**: Add or update unit tests in the `tests/` directory to cover new functionality.
6. **Documentation**: Update docstrings, README files, or add tutorials to the `cookbook/` directory.
7. **Quality Checks**: Run formatting, linting, and type checking before committing (see commands below).
8. **Submit PR**: Push your branch and open a Pull Request that references the issue using keywords like `Closes #123`.

## Setting Up the Development Environment

Begin by forking the repository and installing development dependencies. The project uses [`pyproject.toml`](https://github.com/semantica-agi/semantica/blob/main/pyproject.toml) to manage optional development extras.

```bash

# Clone your fork

git clone https://github.com/your-username/semantica.git
cd semantica
git remote add upstream https://github.com/semantica-agi/semantica.git

# Install with development dependencies

python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate

pip install -e ".[dev]"

```

Create a feature branch before making changes:

```bash
git checkout -b feature/add-custom-extractor

```

## Implementing a New Feature

Most contributions involve extending base classes within specific pipeline stages. For example, to add a custom semantic extractor, inherit from `BaseExtractor` in the `semantica.semantic_extract` module.

Create a new file at [`semantica/semantic_extract/custom_extractor.py`](https://github.com/semantica-agi/semantica/blob/main/semantica/semantic_extract/custom_extractor.py):

```python
from semantica.semantic_extract import BaseExtractor
from typing import List, Dict

class CustomExtractor(BaseExtractor):
    """Extracts uppercase words as dummy entities."""
    def extract(self, text: str) -> List[Dict]:
        return [{"entity": w, "type": "UPPERCASE"} for w in text.split() if w.isupper()]

```

This implementation follows the established pattern where extractors return structured dictionaries containing entity metadata and classification types.

## Testing and Code Quality Standards

All contributions must include unit tests and pass the automated quality suite configured in the repository. Place tests in the `tests/` directory following existing naming conventions.

Example test file at [`tests/test_custom_extractor.py`](https://github.com/semantica-agi/semantica/blob/main/tests/test_custom_extractor.py):

```python
import pytest
from semantica.semantic_extract.custom_extractor import CustomExtractor

def test_custom_extractor():
    extractor = CustomExtractor()
    result = extractor.extract("Hello WORLD from SEMANTICA")
    assert {"entity": "WORLD", "type": "UPPERCASE"} in result
    assert {"entity": "SEMANTICA", "type": "UPPERCASE"} in result

```

Before submitting, run the full quality-check suite to ensure compliance with project standards:

```bash
pytest                        # Execute all tests

black . && isort .            # Format code and sort imports

flake8 . && mypy .            # Lint and type-check

pre-commit run --all-files    # Optional: run all git hooks locally

```

## Key Project Files and Resources

Understanding these core files accelerates the contribution process and provides architectural context:

- **[`CONTRIBUTING.md`](https://github.com/semantica-agi/semantica/blob/main/CONTRIBUTING.md)**: Complete contribution guidelines, coding standards, and detailed testing commands.
- **[`ARCHITECTURE.md`](https://github.com/semantica-agi/semantica/blob/main/ARCHITECTURE.md)**: Visual documentation of the 11-stage pipeline and data flow between modules.
- **[`README.md`](https://github.com/semantica-agi/semantica/blob/main/README.md)**: High-level project overview, installation instructions, and CLI entry points.
- **[`pyproject.toml`](https://github.com/semantica-agi/semantica/blob/main/pyproject.toml)**: Declares package dependencies, optional `[dev]` extras for development tools, and build settings.
- **`semantica/`**: Core library containing stage-specific modules including `ingest`, `parse`, `semantic_extract`, `kg`, and `reasoning`.
- **`tests/`**: Unit and integration test suites that must remain green for every PR.
- **`cookbook/`**: Jupyter notebooks and tutorials illustrating real-world usage patterns for contributors and users.

## Summary

- The Semantica AGI project uses an 11-stage modular architecture where each pipeline phase lives in a dedicated `semantica/` sub-package (e.g., `semantica.ingest`, `semantica.semantic_extract`).
- Contributors should locate the correct architectural layer before implementing changes, ensuring new classes inherit from appropriate base classes like `BaseExtractor`.
- The contribution workflow requires forking, creating descriptive branches (`feature/<name>` or `fix/<description>`), and submitting PRs that reference specific issues.
- All code must pass automated checks: **black** for formatting, **isort** for import sorting, **flake8** for linting, and **mypy** for type safety.
- Include unit tests in `tests/` and update documentation in docstrings, README files, or the `cookbook/` directory to maintain project quality.

## Frequently Asked Questions

### What programming language is the Semantica AGI project written in?

The project is implemented in **Python 3.x**, utilizing type hints throughout the codebase for improved developer experience and static analysis compatibility with `mypy`.

### Do I need to understand the entire 11-stage pipeline to make a contribution?

No. The modular architecture allows contributors to focus on specific stages in isolation. You can extend the ingestion layer in `semantica/ingest/` or add extractors in `semantica/semantic_extract/` without modifying downstream knowledge graph construction logic, provided you maintain the expected input/output interfaces for your specific module.

### How do I run tests locally before submitting my contribution?

Execute `pytest` from the repository root to run the full test suite. For rapid iteration during development, target specific test files with `pytest tests/test_specific_file.py`. Ensure you also run `black`, `isort`, `flake8`, and `mypy` to validate code quality before pushing.

### Are there specific commit message conventions I should follow?

Yes. Use descriptive messages that reference the component changed, following the pattern shown in the contribution guide: `git commit -m "feat(semantic_extract): add CustomExtractor for uppercase entities"`. This convention maintains a clear git history and facilitates automated changelog generation.