by bibinprathap
VeritasGraph — open-source Knowledge Graph & GraphRAG framework on GitHub. Build multi-hop reasoning, ontology-aware retrieval, and verifiable attribution over your own data. Nodes, edges, RDF, linked-data — runs locally or in the cloud.
# Add to your Claude Code skills
git clone https://github.com/bibinprathap/VeritasGraphGuides for using ai agents skills like VeritasGraph.
See how VeritasGraph compares with popular alternatives.
VeritasGraph is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by bibinprathap. VeritasGraph — open-source Knowledge Graph & GraphRAG framework on GitHub. Build multi-hop reasoning, ontology-aware retrieval, and verifiable attribution over your own data. Nodes, edges, RDF, linked-data — runs locally or in the cloud. It has 326 GitHub stars.
VeritasGraph's catalog security scan is still queued. You can run an instant dependency and prompt-injection check now with the "Scan for vulnerabilities" button above.
Clone the repository with "git clone https://github.com/bibinprathap/VeritasGraph" and add it to your Claude Code skills directory (see the Installation section above).
VeritasGraph is primarily written in Python. It is open-source under bibinprathap on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh VeritasGraph against similar tools.
No comments yet. Be the first to share your thoughts!
Available after the catalog security scan passes.
⚠️ Third-Party Software Notice
This skill is third-party open-source software developed and hosted independently on GitHub. SkillsLLM is an informational directory and does not control or maintain the underlying repository.
Any security checks, ratings, or warnings displayed by SkillsLLM are automated and limited in scope. They do not constitute a security certification or guarantee that the software is safe, error-free, or free from malicious code, vulnerabilities, compromised dependencies, or prompt-injection risks.
Review the source code, permissions, dependencies, and configuration before installing or running any third-party skill. Use is at your own risk. To the maximum extent permitted by applicable law, SkillsLLM is not liable for losses arising from third-party software.
The deep catalog scan for this skill is still queued. Run an instant dependency check now instead.
A knowledge graph your agents can trust: every answer cites its sources, the vocabulary is governed by a certified ontology, and the system says "I don't know" instead of guessing. Runs 100% locally.
🎯 Traditional RAG guesses based on similarity. VeritasGraph reasons based on structure — and refuses to answer when the evidence isn't there.
🌐 Live site · 🎮 Try the Studio live · ⭐ Star · 🍴 Fork · 💬 Discuss · 🐛 Report a bug
| Area | What you get |
|---|---|
| 🧭 Ontology & business glossary | Entity types, predicates (with domain/range) and glossary terms move through proposed → certified → deprecated. Certified vocabulary steers extraction. |
| ⚙️ Ontology compiler | compile normalizes types and predicates, finds duplicate entities, suggests new terms, checks domain/range rules and flags orphans. It only changes the graph when a mapping is unambiguous; everything else becomes a reviewable suggestion. |
| 🛡️ Governed answers | mode="governed" answers only when certified terms cover the question, and withholds answers whose citations don't match retrieved sources. |
| 🧾 Answer receipts | Every answer gets an answer_id. You can replay it later with its question, mode, citations, grounding and feedback. |
| 🚫 Strict citations & abstention | Each response carries grounded, unknown_citations and abstained. If no entity matches, it says so instead of guessing. |
| 💾 Shared SQLite store | Studio, the MCP server and the CLI share one transactional store. Legacy JSON graphs are migrated on first start. |
| 🔐 Governance | A SHA-256 hash-chained audit log with a verify endpoint, snapshots and restore (the graph is snapshotted automatically before it is cleared), and entity merge. |
| 🕵️ PII policy | Choose off, tag, redact_before_extract or reject. Email, SSN, credit card (Luhn-checked), phone and IP are detected and redacted on read for low-privilege roles. |
| 🔑 Auth & roles | Bearer tokens with viewer / editor / admin roles, an SSRF allowlist for HTTP tools, and a CORS allowlist. |
| 🔌 MCP modes | read / write / admin tool tiers, plus new tools: answerability check, ontology, term search, answer receipts and structured data. |
| 🗄️ Structured data layer | Connect read-only to Postgres, MySQL, SQLite, DuckDB, ClickHouse, Trino, Hive, Snowflake or Databricks. Govern a semantic layer and ask questions answered by SQL. |
🧰 veritasgraph CLI |
serve, mcp, migrate, compile, export, import --dry-run, structured …. |
| 🐳 Docker | docker compose up for Studio + Ollama, an on-prem stack with pgvector, and a hardened MCP container. |
git clone https://github.com/bibinprathap/VeritasGraph.git && cd VeritasGraph
pip install -e ".[studio]" # Studio + the `veritasgraph` CLI
ollama serve & ollama pull qwen3:latest # any local chat model
export STUDIO_API_TOKENS="$(openssl rand -hex 24):admin" # auth is on by default
echo "${STUDIO_API_TOKENS%%:*}" # the token to paste into the UI
veritasgraph serve # http://127.0.0.1:8200/studio · API docs at /docs
Studio asks for the token the first time you open it and keeps it for the browser session. To run a local demo without auth, set STUDIO_AUTH=off (Studio logs a warning on startup); never do this on a shared or public host.
STUDIO_API_TOKENS="$(openssl rand -hex 24):admin" docker compose up
docker compose exec ollama ollama pull qwen3:latest
Studio is published on 127.0.0.1:8200 only.
pip install -e .
VERITASGRAPH_MCP_MODE=read python -m veritasgraph_mcp # or: veritasgraph mcp
See MCP server for IDE registration.
This builds a graph and drives a fully wired agent through graph reasoning, memory recall, PII redaction and a guardrail block:
python3 demos/agent-studio/sample_pipeline.py --model qwen3:latest
flowchart LR
subgraph Ingest
D[Documents / graph.json imports] --> PII{PII policy}
PII --> X["LLM extraction<br/>(steered by certified ontology)"]
end
subgraph Store["Shared SQLite store"]
G[(Entities · relations · chunks)]
O[(Ontology & glossary)]
A[(Audit chain · snapshots · receipts)]
end
X --> G
G <--> C["Ontology compiler<br/>normalize · merge · suggest · validate"]
C <--> O
subgraph Query
Q[Question] --> ANS{"Answerability<br/>(governed mode)"}
ANS -->|covered| R["Entity match + multi-hop traversal"]
ANS -->|not covered| AB[Abstain]
R --> L[Local LLM]
L --> V{"Citation check<br/>(doc#chunk)"}
V -->|grounded| OUT["Answer + citations + answer_id"]
V -->|unknown citations| AB
end
G --> R
O --> ANS
OUT --> A
subgraph Surfaces
S[Studio UI & REST API]
M[MCP server]
CLI[veritasgraph CLI]
end
Surfaces --> Ingest
Surfaces --> Query
Studio, MCP and the CLI are thin layers over the same engine and store. A graph built in Studio can be queried from your IDE, and a compile run from the CLI shows up in the Studio Ontology tab. A shared job lock ensures only one compiler runs at a time.
doc_xxx#n, or imp_xxx#n for imported graphs). This prevents hallucinated citations at read time.unknown_citations and set grounded: false.abstained: true instead of guessing.The ontology is the governed vocabulary of the graph:
Drug, Enzyme, Supplier.inhibits, with optional domain/range constraints.stateDiagram-v2
[*] --> proposed: propose (editor, MCP write, compile suggestion)
proposed --> certified: certify (admin)
certified --> deprecated: deprecate (admin)
proposed --> deprecated: deprecate (admin)
Once vocabulary is certified, it changes how the system behaves:
Manage it in the Studio Ontology tab, through /ontology in