Ripple: open-source local-first authorization for AI coding agents
Ripple is an open-source, local-first authorization layer that restricts AI coding agents to only modify files they are explicitly permitted to change. It solves the problem of AI agents making unauthorized or unintended modifications across a codebase, giving developers control and trust in agentic coding workflows.
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Runeward: Sandbox AI agents with policy gates
Runeward is a sandboxing tool for AI agents that enforces policy gates to restrict agent actions. It uses LLMs to interpret and enforce user-defined policies, solving the problem of unsafe or unintended agent behavior for developers building autonomous AI systems.
Attestor: open-source zero-trust boundary for autonomous AI agents
Attestor is an open-source execution boundary that enforces zero-trust security for autonomous AI agents. It uses LLMs to define and enforce policies that restrict agent actions, preventing unauthorized access or data leaks. This solves the problem of safely deploying autonomous agents in production environments.
Cinchor: accountability layer for AI agents with bound-before and prove-after
Cinchor is a primitive that enforces policy on AI agents before they act and provides cryptographic proof of actions afterward. It allows teams to give agents real capabilities (e.g., moving money, shipping changes) while maintaining auditability and preventing out-of-policy actions. The system mints scoped capabilities (spend cap, allowlist, expiry) and checks every action against them, refusing violations before execution; actions are hashed, signed, and anchored append-only for later verification.
Enola: engineering intelligence layer for AI coding agents
Enola is an open-source engineering intelligence layer that helps AI coding agents understand existing codebases. It answers questions about change impact, dependency reachability, safe module deletion, refactoring priorities, and architecture drift. The tool uses LLMs to analyze code context and provide insights that reduce mistakes from both humans and AI agents.
Human approval inbox for AI agents
Impri is a human-in-the-loop approval system for AI agents. It provides a shared inbox where agents submit drafts (emails, posts, replies) and a human must approve or reject before the action is taken. It solves the problem of developers repeatedly building the same approval UI and cron infrastructure for agentic workflows.