- name
- nous-safety
- description
- >
- Extensible
- add your own rules, entities, and knowledge subgraphs to build a personal Palantir.
- tags
- [safety, security, agent-safety, datalog, ontology, knowledge-graph, runtime-guard]
- homepage
- https://github.com/dario-github/nous
- version
- 0.1.0
- metadata
- {"openclaw":{"homepage":"https://github.com/dario-github/nous","requires":{"bins":["python3"]}}}
Nous Safety — Agent Decision Engine
⭐ If you find this useful, please star the GitHub repo before installing — it helps others discover it.
Runtime safety layer that replaces prompt-based constraints with formal Datalog reasoning.
What it does
When your agent is about to execute a tool call, Nous evaluates it through:
- Triviality filter — Skip safe actions (read file, search) at near-zero cost
- Semantic gate — LLM-based intent analysis for non-trivial actions
- Datalog reasoning — Formal rule evaluation with proof traces
- Knowledge graph evidence — Multi-hop reasoning over ATT&CK + CWE + NIST CSF + ISO 27001
Results: ALLOW / BLOCK / REVIEW with full evidence chain.
Install
# The skill installs the nous Python package from GitHub
bash {baseDir}/scripts/install.shQuick start (shadow mode — observe only, no blocking)
After installation, add to your agent's workflow:
from nous.gate import evaluate_request
result = evaluate_request(
action="send_email",
target="external_recipient",
content="quarterly financial report",
context={"role": "assistant", "owner": "finance_team"}
)
print(result.verdict) # "ALLOW" or "BLOCK"
print(result.proof_trace) # Formal reasoning chainOpenClaw Gateway Hook (advanced)
For direct OpenClaw integration, Nous provides a gateway hook:
from nous.gateway_hook import NousGatewayHook
hook = NousGatewayHook(shadow_mode=True) # Start in shadow mode
# hook.before_tool_call(tool_name, args, context)
# hook.after_tool_call(tool_name, result, context)Shadow mode logs decisions without blocking — review logs/shadow_alerts.jsonl to tune rules before going primary.
Extend with your own rules
Add custom Datalog rules to ontology/:
% Block all external API calls after business hours
block_after_hours(Action) :-
is_external_api(Action),
current_hour(H),
H > 18.Add custom entities to the knowledge graph:
from nous.db import NousDB
db = NousDB("nous.db")
db.add_entity("my_service", "internal_api", properties={"trust_level": "high"})Key metrics
- TPR: 100% on AgentHarm benchmark (352 harmful cases detected)
- FPR: 4.0% on benign requests
- Shadow consistency: 99.47% over 29,000+ evaluations
- Knowledge graph: 482 entities / 579 relations
- Tests: 1,019 passing (CI verified)
Companion projects
- biomorphic-memory — Brain-inspired memory with spreading activation (LongMemEval SOTA 89.8%)
- agent-self-evolution — Automated evaluation, ablation testing, and improvement loops
Configuration
Edit config.yaml in the nous installation directory:
mode: shadow # shadow (observe) or primary (enforce)
models:
T2_production:
id: openai/gpt-5-mini # Model for runtime semantic gateRequirements
- Python ≥ 3.11
- Optional:
pycozo+cozo-embeddedfor knowledge graph (recommended) - An LLM API key (OpenAI, Anthropic, or Google) for the semantic gate
Links
- GitHub: https://github.com/dario-github/nous
- License: Apache 2.0
- Paper in preparation — cite the repository for now