What Are the Dependencies for Semantica AGI? A Complete Guide

Semantica AGI ships with a core set of safe defaults defined in pyproject.toml plus modular optional extras for LLM providers, graph databases, and vector stores, while the Explorer dashboard lists its JavaScript dependencies in explorer/package.json.

Semantica AGI is a modular Python library designed for building AI agents that reason over knowledge graphs. Unlike monolithic frameworks, it uses an opt-in dependency model where you install only what your use case requires. All Python dependencies are centralized in the repository root's pyproject.toml, while the React-based Explorer UI declares its requirements separately in explorer/package.json.

Core Runtime Dependencies

When you run pip install semantica, you receive approximately 40 core packages that provide fundamental capabilities for data manipulation, NLP, embedding generation, and graph manipulation. These are pinned with minimum versions in pyproject.toml lines 45–89 to ensure compatibility without forcing unnecessary bloat.

Data Science and Machine Learning

The core stack relies on standard scientific Python libraries:

• numpy (≥2.0.2) and pandas (≥1.3.0) for tensor and tabular data operations
• scipy (≥1.13.1) for scientific algorithms and optimization
• scikit-learn (≥1.7.2) for classical ML models and clustering
• torch (≥1.13.1) as the deep learning backbone
• umap-learn (≥0.5.12) for dimensionality reduction

Natural Language Processing and Embeddings

Text processing is handled by industry-standard libraries:

• spacy (≥3.4.0) for tokenization, NER, and linguistic pipelines
• transformers (≥4.20.0) for accessing HuggingFace models
• sentence-transformers (≥2.2.0) for ready-made sentence embeddings
• tokenizers (≥0.15.0) for fast tokenization
• fastembed (≥0.2.0) for lightweight embedding generation

Graph and Semantic Web

Knowledge graph operations depend on:

• rdflib (≥6.2.0) for RDF graph handling and SPARQL queries
• networkx (≥2.8.0) for in-memory graph algorithms
• faiss-cpu (≥1.7.0) for CPU-based vector similarity search

Visualization and Utilities

Interactive capabilities and developer experience are supported by:

• matplotlib (≥3.9.4), seaborn (≥0.13.2), and plotly (≥6.8.0) for plotting
• ipywidgets (≥8.0.0) for Jupyter UI components
• pydantic (≥2.13.4) for data validation and settings management
• click (≥8.4.2) and rich (≥12.5.0) for CLI creation and terminal output

Document and Media Processing

Core ingestion handles multiple formats:

• beautifulsoup4 (≥4.15.0) and lxml (≥6.1.1) for HTML/XML parsing
• python-docx (≥1.2.0) and openpyxl (≥3.1.5) for Office documents
• pillow (≥12.2.0) for image processing
• librosa (≥0.9.0) and opencv-python (≥4.13.0.92) for audio and computer vision

Optional Extras for Extended Functionality

Semantica AGI organizes additional capabilities into logical extras that can be installed via pip install semantica[<extra>]. These groups are defined in pyproject.toml lines 102–176.

LLM Provider Integrations

Direct API bindings for major providers include:

• llm-openai: openai client library
• llm-anthropic: anthropic SDK
• llm-gemini: google-genai
• llm-groq, llm-ollama, llm-deepseek, llm-litellm, and llm-instructor: respective API clients

Graph and Vector Database Backends

Connect to production storage systems:

• graph-neo4j: neo4j driver
• graph-falkordb: redis connector
• graph-amazon-neptune: boto3 for AWS access
• graph-apache-age: psycopg2-binary for PostgreSQL-based graph storage
• Vector stores: vectorstore-qdrant, vectorstore-weaviate, vectorstore-pinecone, vectorstore-milvus, vectorstore-pgvector, and vectorstore-sqlite with their respective client libraries (qdrant-client, weaviate-client, etc.)

Document Parsing and Validation

Advanced processing capabilities:

• parse-docling: High-performance PDF/Docx parsing via docling
• shacl: RDF shape validation using pyshacl

Infrastructure and Cloud

Production deployment dependencies:

• infra: Message queues and workers including redis, celery, kafka-python, and pika
• cloud: Storage APIs including boto3, azure-storage-blob, and google-cloud-storage
• monitoring: Observability via prometheus-client and OpenTelemetry libraries

Specialized Hardware and Performance

• gpu: GPU-accelerated FAISS and CuPy (faiss-gpu, cupy)
• agno: Integration with the Agno agentic framework

Development and UI Tools

• explorer: FastAPI-based backend for the web dashboard (fastapi, uvicorn, websockets)
• explorer-lite: Streamlit-based minimal UI (streamlit, streamlit-agraph)
• dev: Testing and linting tools (pytest, black, mypy, pre-commit)

The meta-extra all bundles every optional component (excluding GPU) for a one-stop install.

Explorer Dashboard Frontend Dependencies

The optional Explorer UI is a React/TypeScript application whose dependencies are listed in explorer/package.json. It requires:

• Core framework: react and react-dom (^19.2.4)
• Graph visualization: sigma (^3.0.2), @xyflow/react (^12.10.2), and graphology plugins
• UI components: @monaco-editor/react for code editing, react-arborist for tree views, react-dropzone for file uploads, and lucide-react for icons
• Data management: @tanstack/react-query (^5.95.2) for server state management
• Testing: playwright (^1.59.1) for end-to-end testing

Installing Semantica AGI

Install only what you need to keep your environment lightweight.

Core Installation

pip install semantica

Targeted Installation with Extras

Combine multiple extras for specific workflows:

pip install "semantica[graph-neo4j,llm-openai,vectorstore-qdrant]"

Installing the Explorer UI

pip install "semantica[explorer]"
semantica-explorer  # launches FastAPI backend + React frontend

Complete Installation

Install every optional component (excluding GPU) for maximum compatibility:

pip install "semantica[all]"

How Dependencies Map to Source Code

The dependency declarations directly support specific modules in the codebase:

• pyproject.toml: Central definition of all Python dependencies, optional groups, and CLI entry points. Lines 45–89 define core dependencies; lines 100–176 define optional groups.
• explorer/package.json: Lists JavaScript libraries for the web dashboard UI.
• semantica/graph/__init__.py: Implements the core graph abstraction that wires together networkx, rdflib, and optional backends like Neo4j.
• semantica/mcp/__init__.py: Entry point for the Multi-Chain Processor (MCP), which orchestrates LLM calls using the optional provider libraries and manages provenance tracking.
• semantica/cli.py: Exposes commands like semantica, semantica-server, and semantica-worker, relying on click and rich from the core dependencies.
• semantica/visualization/utils/layout_algorithms.py: Implements layout algorithms used by the viz extra and the Explorer dashboard.

Summary

• Core dependencies in pyproject.toml (lines 45–89) provide essential data science, NLP, and graph capabilities via packages like torch, spacy, rdflib, and networkx.
• Optional extras (lines 102–176) enable specific LLM providers, graph databases, vector stores, and infrastructure components through the pip install semantica[<extra>] syntax.
• Frontend dependencies in explorer/package.json power the React-based Explorer dashboard with libraries like sigma, @xyflow/react, and react.
• The all meta-extra bundles every optional Python component except GPU-specific libraries.
• Key source files like semantica/graph/__init__.py and semantica/mcp/__init__.py directly consume these dependencies to provide agentic reasoning and knowledge graph operations.

Frequently Asked Questions

What is the minimum Python version required for Semantica AGI?

The project requires Python 3.9 or higher as specified in the requires-python field of pyproject.toml. Several core dependencies like numpy (≥2.0.2) and pandas (≥1.3.0) enforce additional compatibility constraints that ensure stable operation on supported Python versions.

Can I install Semantica AGI without GPU dependencies?

Yes. GPU support is completely optional and isolated in the gpu extra, which installs faiss-gpu and cupy. The default faiss-cpu package handles vector similarity search on standard hardware. Install the core library or use the all extra to avoid GPU-specific packages entirely.

How do I install only the backend API without the React frontend?

Install the explorer extra, which provides the FastAPI backend (fastapi, uvicorn) defined in the optional dependencies. The React frontend in the explorer/ directory is a separate Node.js application that you can choose to build and serve independently, or you can interact with the API directly via HTTP clients like requests or httpx.

What is the difference between explorer and explorer-lite extras?

The explorer extra installs a full FastAPI-based backend suitable for production deployments with WebSocket support (websockets, python-multipart). The explorer-lite extra installs a minimal Streamlit-based interface (streamlit, streamlit-agraph) for quick local prototyping without the overhead of a Node.js build process or separate frontend server.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}