ai-agent

semantica: Build Trustworthy AI with Graph‑Native Context

Leverage semantica’s Python‑based graph infrastructure to add transparent, accountable memory to LLM agents. Ideal for founders building explainable AI products.

From the repo · “Graph-Native Infrastructure for Context and Accountable AI Systems”

14,173 stars+134★ this week1,655 forksPythonGuide quality 9/10 · reviewed Oct 7Updated 10/21/2026100% free · open source
What it does

Semantica provides a graph-native infrastructure for building context-aware and accountable AI systems, enabling founders to create more transparent and explainable AI models

When to use it
  • •When you need to build AI systems that can provide clear explanations for their decisions
  • •When you're working with complex, graph-structured data and need to integrate it with your AI models
  • •When you want to create AI systems that can adapt to changing contexts and environments
Ready-to-paste prompt
python -m semantica.core.server --graph-name my_graph --agent-name my_agent
Heads up: You need to have Python 3.9 or later and the `graph-tool` library installed to run Semantica, and you may need to adjust the `semantica/config.py` file to match your specific use case
Saves to your device
Use with Claude
New

Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.

🛠️ Technical setup

Expect 20–40 minutes in a terminal — or let your AI agent drive it.

Try it instantly — no install
Claude Code
mkdir -p ~/.claude/skills/semantica && curl -fsSL https://workflowstacks.com/api/skills/semantica/claude-skill -o ~/.claude/skills/semantica/SKILL.md
Open in another AI app

Opens the app with this repo with the prompt ready to go — no copy-paste needed.

Connect the whole catalog (MCP)
claude mcp add --transport http workflowstacks https://workflowstacks.com/api/mcp

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How semantica: Build Trustworthy AI with Graph‑Native Context works
Codeflow
Free to inspect

Semantica: Build Trustworthy AI with Graph‑Native Context is a very large Python project (~230k lines across 511 code files, plus 398 test files). It is a full software project: use it through its install path rather than reading it end to end. Last commit this month, MIT license, has a test suite.

Size
Very large codebase
~230k lines · 511 code files · days to read — use, don't read
Setup
Developer setup
A real software project. Use it via its install path; don't expect to read it all.
Runs on
Python · Docker
No API keys detected
Python 91%TypeScript 9%
Where to start reading
  1. 1
    README.md
    Start here — what it does and how to install it
  2. 2
    semantica/cli.py
    Where the program starts running
  3. 3
    pyproject.toml
    Dependencies and the commands it exposes
  4. 4
    semantica/__init__.py
    Inside semantica/ — the main logic begins here
  5. 5
    cookbook/advanced/01_Advanced_Extraction.ipynb
    A worked example — copy this to get going
What's in each folder
semantica/Core code — the actual logic399 files
plugins/Sub-packages / plugins43 files
integrations/Sub-packages / plugins27 files
docs/Documentation97 files
cookbook/Examples you can copy46 files
examples/Examples you can copy8 files
explorer/Folder122 files
deploy/Deployment / infrastructure34 files
READMEHas testsDocumentedCI checksDocker readyExamples includedMIT licenseUpdated this month
Quick Actions
Details
Creator
semantica-agi
Language
Python
Category
ai-agent
Published
6/25/2025

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