Semantica
github.com/semantica-agi/semantica- Category
- AI Tools
- Rank
- No. 2036Tools index
Previous survey · No. 2041 ·
- Pricing
- Open Source
- Type
- TOOL
- Use case
- Data, Retrieval & Knowledge
- GitHub
- 13.9k stars
- Latest release
- v0.7.0
- Date
About
Graph-native infrastructure for context management and accountable AI systems. It ingests enterprise data, extracts what matters, and builds a context graph and knowledge graph, then runs graph analytics and causal reasoning over it with decision provenance recorded end to end. The project positions itself around ontology management, knowledge modeling, deterministic reasoning, and traceability.
What it does
A Python library that turns documents, databases and other sources into a knowledge graph, then lets you query, analyse and export that graph. Entity and relationship extraction can run on plain pattern rules with no model key, or call an LLM if you configure one. On top sits a decision log: an agent records each choice with its reasoning and confidence, and later queries can pull similar past decisions and trace which facts fed them. It also ships a command line, a local web API with a graph explorer, and a Model Context Protocol server so coding assistants can read the graph.
Why it's ranked here
The breadth is real and the plumbing shows care. It is MIT licensed, installs from PyPI, pins a supported Python range of 3.10 to 3.13, and keeps the core install to about twenty mainstream packages while pushing graph databases, vector stores, cloud connectors and model providers into opt-in extras. The packaging notes explain specific dependency decisions, including dropping a CrewAI extra because its pinned dependencies carried unpatched security advisories. Against that, the surface is very large for a pre-1.0 project, and some public pieces are thinner than the README suggests. A credible pick for graph-backed agent memory, worth testing before trusting it in production.
What's good
Optional machinery stays optional: Neo4j, FalkorDB, Apache AGE and Neptune backends, seven vector stores and ten model providers each sit behind their own install extra, so a basic setup does not drag in a GPU stack. The web server binds to the local machine by default, and the explorer routes refuse requests until an API key is set unless anonymous access is switched on explicitly, with a logged warning. The MCP server reports only the class of an error to the calling tool, keeping paths and connection strings in the server log. The graph module covers centrality, community detection, link prediction and time-bounded queries over facts with validity dates.
Tradeoffs
The REST build endpoint answers "accepted" without doing anything: the actual build call is commented out, so knowledge-base construction over HTTP is not wired up. The docs disagree with themselves. The architecture page says provenance is always on with no opt-in, while the provenance module describes itself as opt-in only. The quickstart still shows an older version number than the package declares. The package marks itself production stable at a 0.x version. And the install surface is wide: the full extras set spans dozens of heavy dependencies, so pinning and conflict management fall on you.
How to use it well
Start with the library, not the server. Install the base package, build a small graph from a handful of documents using the pattern-based extractors, and check the output before adding a model provider. Add one storage backend extra once the in-memory graph outgrows a single process. The decision-recording API is the distinctive part: use it if you need an auditable record of why an agent chose something. Teams that only want retrieval over embeddings will get little from it, and anyone planning to drive builds over HTTP should confirm that path works first.
Technical notes+
pyproject.toml declares version 0.7.0, requires-python >=3.10,<3.14, MIT, and five console scripts (semantica, semantica-server, semantica-worker, semantica-explorer, semantica-mcp); the semantica-mcp script points at semantica.mcp_server:main while the MCP implementation that was read lives at semantica_mcp/mcp/server.py, a hand-rolled JSON-RPC 2.0 stdio loop advertising protocolVersion 2024-11-05 with tools, resources and ping handlers, batch support, and errors reduced to the exception class name. semantica/server.py is FastAPI: CORS defaults to localhost:5173, a security-headers middleware sets nosniff, DENY framing and HSTS on https, explorer routers mount behind require_auth, main() binds 127.0.0.1:8000 unless SEMANTICA_HOST is set, and POST /build returns status accepted with framework.build_knowledge_base commented out. It also instantiates Semantica() at import time. semantica/__init__.py imports submodules on first attribute access through proxy objects with an empty __all__. docs/architecture.md describes four layers and says provenance needs no opt-in; semantica/provenance/__init__.py says opt-in only with stdlib-only storage (InMemoryStorage, SQLiteStorage). docs/quickstart.md shows 0.6.8.
Observed
- License
- MIT
- Language
- Python, supported on 3.10 through 3.13
- Install
- pip install semantica, with optional extras per backend and provider
- Interfaces
- Python library, CLI, FastAPI REST server with web explorer, MCP server over stdio
- Graph backends
- In-memory NetworkX by default; Neo4j, FalkorDB, Apache AGE, Amazon Neptune via extras
- LLM requirement
- Optional; pattern-based extraction runs without an API key
- REST build endpoint
- Returns accepted with the build call commented out in the server source
- Declared maturity
- Production/Stable classifier on a 0.x package version
Read from README.md, pyproject.toml, docs/architecture.md, semantica/__init__.py, semantica/cli.py, semantica/server.py, semantica_mcp/mcp/server.py, semantica/pipeline/__init__.py, semantica/kg/__init__.py, semantica/provenance/__init__.py, docs/quickstart.md.
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