Ross ROSS = Recommend OSS · open-source software intelligence for agents

SafeAI-Lab-X/ClawKeeper

ClawKeeper: Comprehensive Safety Protection for OpenClaw Agents Through Skills, Plugins, and Watchers (aka The Norton for OpenClaw) observed · 2026-08-28

github.com/SafeAI-Lab-X/ClawKeeper · TypeScript observed · 2026-08-28

Health v2 · maintenance only

59/100

  • Activity 98
  • Release rhythm 35
  • Longevity 11

Flags: no_releases young no_license

How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.

  • gap_med: n/a
  • age_days: 166
  • days_rel: n/a
  • days_push: 17
  • n_releases_24m: 0

Full methodology

Adoption not part of the score

1022 stars · 57 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

ClawKeeper is a host-agnostic safety middleware layer that sits between AI agents and their tools, blocking risky tool calls, redacting sensitive results, and detecting attack patterns like prompt injection and credential exfiltration. It ships as a Python library with adapters (e.g., Hermes Agent), an HTTP Judge API for non-Python hosts, and an optional LLM-powered Watcher daemon that reasons over multi-step agent trajectories and self-improves guard coverage.

Use cases

  • block dangerous shell commands before an agent executes them
  • detect prompt injection and poisoned tool output in agent runs
  • prevent agents from reading credentials and exfiltrating them over the network
  • redact secrets from tool results returned to an LLM
  • guard filesystem, browser, and network tool calls with inspectable policies
  • add trajectory-level safety review to multi-step agent workflows
  • run a centralized safety judge service for agents in any language

When to choose

  • you run tool-using agents (shell, filesystem, browser, network) and need pre-execution safety guards
  • you want a host-independent safety layer that works across agent frameworks via adapters or HTTP
  • you need detection of agent-specific attack chains like credential discovery plus exfiltration
  • you want an LLM-based watcher for decisions that depend on intent and recent tool history

When to avoid

  • you need a general-purpose web application firewall rather than agent tool-call guarding
  • you require a formally verified or certified security product with a mature audit trail
  • your agents make no tool calls and only generate text
  • you need guaranteed protection - the policies are intentionally small and inspectable, not exhaustive

Facets

library · maturity active

security middleware agent-framework monitoring logging security developer-tools large-language-models python cross-platform self-hosted agent-safety guardrails prompt-injection tool-call-filtering openclaw mcp watcher-daemon redaction ai-agents

1 source

Member repositories

RepositoryRoleHealth v2
SafeAI-Lab-X/ClawKeepermain59

For agents

markdown · JSON · MCP: product_card(name="SafeAI-Lab-X/ClawKeeper")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem