How to Build Semantica AGI from Source: Complete Installation Guide

To build Semantica AGI from source, clone the semantica-agi/semantica repository, install the core package in editable mode via pip install -e ., add optional backend dependencies using extras like [faiss] or [neo4j], and verify the build with the semantica doctor CLI command.

Semantica AGI is a modular, graph-native platform that provides deterministic context graphs, decision provenance, and ontology management for LLM-based agents. Building from source gives you full access to the complete pipeline—from raw data ingestion through knowledge graph construction to REST-MCP serving. This guide walks you through compiling the platform from the official repository and configuring its polyglot storage backends.

Prerequisites

Before you begin building Semantica AGI from source, ensure your environment meets the baseline requirements.

• Python 3.8 or higher is required for core compatibility.
• Git is necessary to clone the repository.
• A C compiler may be required for optional native extensions like FAISS.

Step-by-Step Build Instructions

Follow these steps to compile and install Semantica AGI from the source code.

Clone the Repository

First, download the source code from the official GitHub repository.

git clone https://github.com/semantica-agi/semantica.git
cd semantica

Create a Virtual Environment

Isolate your dependencies to prevent conflicts with system packages.

python -m venv .venv
source .venv/bin/activate

Install the Core Package

Install Semantica in editable mode. This compiles any optional C-extensions and registers CLI entry-points as defined in pyproject.toml.

pip install -e .

Install Optional Backend Dependencies

Semantica AGI uses an extensible backend system. Install only the extras you need for your specific deployment. Each backend is available as a pip extra defined in the source pyproject.toml.

Vector Store Backends:

pip install "semantica[faiss]"      # FAISS for CPU-based similarity search

pip install "semantica[qdrant]"     # Qdrant RPC client

pip install "semantica[neo4j]"      # Neo4j graph database driver

pip install "semantica[weaviate]"   # Weaviate vector search

Enterprise Data Platform Connectors:

pip install "semantica[db-databricks]"  # Databricks Unity Catalog integration

pip install "semantica[db-snowflake]"   # Snowflake connector

Multi-Agent Integrations:

pip install "semantica[agno]"        # Agno shared context for multi-agent systems

Verify the Installation

Run the built-in sanity check to confirm that the core package and optional components are functional.

semantica doctor

Expected output includes Python version, Semantica version, backend statuses, and configuration file locations.

Understanding the Modular Architecture

Semantica AGI is organized as a pipeline of independent, importable modules. Each layer handles a specific stage of the knowledge graph lifecycle.

Layer	Purpose	Core Package	Typical Entry Points
Ingestion	Pull data from files, web, databases, and streams	semantica.ingest	FileIngestor(), WebIngestor(), DatabricksIngestor()
Normalization & Chunking	Clean documents and split with entity-aware boundaries for GraphRAG	semantica.normalize, semantica.split	TextSplitter(method="entity_aware")
Semantic Extraction	Named entity recognition, relation detection, and triplet generation	semantica.semantic_extract	NamedEntityRecognizer() in semantica/semantic_extract/ner.py
Conflict Detection	Flag contradictory facts and merge duplicate entities	semantica.conflicts, semantica.deduplication	ConflictDetector(), DuplicateDetector()
Knowledge Graph Construction	Assemble entities into a bi-temporal KG	semantica.kg	GraphBuilder().build(sources) in semantica/kg/graph_builder.py
Ontology & Reasoning	OWL generation, SHACL validation, and forward-chaining	semantica.ontology, semantica.reasoning	OntologyGenerator(), ReteEngine() in semantica/reasoning/rete_engine.py
Decision Intelligence	Record decisions as first-class graph nodes	semantica.context	ContextGraph class in semantica/context/__init__.py
Vector & Polyglot Store	Hybrid semantic search with pluggable backends	semantica.vector_store	VectorStore(backend="faiss") in semantica/vector_store/vector_store.py
Provenance & Export	W3C PROV-O lineage and RDF export	semantica.provenance, semantica.export	RDFExporter() in semantica/export/rdf_exporter.py
Visualization	Interactive force-directed graphs and dashboards	semantica.visualization	KGVisualizer()
MCP Server	REST-MCP protocol for multi-client access	semantica.mcp	semantica/mcp/server.py

Starting the Platform

Once built, you can launch the MCP server or use the CLI to interact with the platform.

Start the MCP server (defaults to port 8000):

semantica start-server

Launch the interactive visualization workbench:

semantica viz

Access the server programmatically via HTTP:

import requests

resp = requests.get("http://localhost:8000/api/v1/graph")
graph_json = resp.json()

Practical Code Examples

Recording Decisions with ContextGraph

The ContextGraph class in semantica/context/__init__.py provides the core interface for decision provenance.

from semantica.context import ContextGraph

# Initialize with advanced analytics enabled

graph = ContextGraph(advanced_analytics=True)

# Record a regulated decision with full provenance

decision_id = graph.record_decision(
    category="vendor_selection",
    scenario="Choose cloud provider for HIPAA workload",
    reasoning="AWS offers BAA, mature HIPAA tooling, and existing team expertise",
    outcome="selected_aws",
    confidence=0.93,
)

# Create causal links between decisions

graph.add_causal_relationship(decision_id, "some_next_id", relationship_type="CAUSED")

# Query decision chains and precedents

chain = graph.trace_decision_chain(decision_id)
similar = graph.find_similar_decisions("cloud vendor", max_results=5)

Hybrid Vector Search

Implement semantic search using the unified vector store façade.

from semantica.vector_store import VectorStore, HybridSearch

# Initialize FAISS backend

vs = VectorStore(backend="faiss", dimension=1536)

# Store a decision with embedding

vs.store_decision(
    scenario="Personal loan A-7291, $85k income, 31% DTI",
    outcome="approved",
    confidence=0.94,
    category="loan_underwriting",
)

# Pure dense retrieval

dense_hits = vs.search(query="personal loan approval", limit=5)

# Hybrid dense + sparse search

hs = HybridSearch(vector_store=vs)
hybrid_hits = hs.search("high-risk transactions 2024")

# Explain provenance for audit trails

explanation = vs.explain_decision(hybrid_hits[0]["id"])

Exporting to RDF for Regulatory Compliance

Use RDFExporter to generate audit-ready Turtle or JSON-LD files.

from semantica.export import RDFExporter
from semantica.context import ContextGraph

graph = ContextGraph(advanced_analytics=True)

# Populate graph with nodes and edges...

kg_dict = graph.to_dict()

# Transform to RDF-compatible structure

kg = {
    "entities": [{"id": n["id"], "type": n["type"], "text": n["content"]} for n in kg_dict["nodes"]],
    "relationships": [
        {"source_id": e["source"], "target_id": e["target"], "type": e["type"]}
        for e in kg_dict["edges"]
    ],
}

# Export to W3C PROV-O compliant Turtle

RDFExporter().export(kg, "audit_trail.ttl", format="turtle")

Running Integration Tests

Validate your source build before production deployment.

pytest -n auto  # Parallel test execution

Summary

Building Semantica AGI from source provides complete control over the modular pipeline:

• Clone and install the core package with pip install -e . to register CLI entry-points and compile optional extensions.
• Select backends using extras like [faiss], [neo4j], or [db-snowflake] to match your infrastructure requirements.
• Verify functionality immediately with semantica doctor before launching the MCP server or workbench.
• Leverage the modular architecture by importing specific layers like semantica.ingest or semantica.reasoning independently.

Frequently Asked Questions

What Python version is required to build Semantica AGI?

Semantica AGI requires Python 3.8 or higher. This ensures compatibility with the async features used in semantica/mcp/server.py and the type hints throughout the codebase.

How do I install only specific vector store backends?

Use pip extras to install only the backends you need. For example, run pip install "semantica[faiss]" for local CPU-based search, or pip install "semantica[qdrant]" for managed vector databases. The full list of extras is documented in the repository's pyproject.toml.

Where is the MCP server implementation located?

The MCP (Multi-Client Protocol) server is implemented in semantica/mcp/server.py. This file contains the REST endpoints for graph access and decision management, which you can launch via the semantica start-server CLI command.

Can I use Semantica AGI with existing multi-agent frameworks?

Yes. The integrations/agno/agno_shared_context.py module provides the AgnoSharedContext class, allowing multiple agents to read from and write to a single ContextGraph instance. Install this integration using pip install "semantica[agno]".

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