Stop AI agents from doing things they shouldn't. The action authorization layer — pre-execution, deterministic, policies included. Drop in. Day one.
Open protocol and TypeScript SDK for AI agent authority: identity, scoped delegation, enforcement before the action, and signed receipts.
A cognition kernel that wraps a frozen LLM in a persistent identity + trust + governance loop — testing whether capability comes from structure, not weights. Research-stage; honest about what's real vs mocked.
The authorization kernel for AI agents. Block, allow, or escalate agent tool calls with YAML rules — deterministic-first, LLM fallback.
Deterministic execution authorization for AI agents and automation systems
Deterministic authorization + tamper-evident audit for AI agents, humans, and workloads — one principal type, safety invariants machine-checked by an SMT solver (cvc5), every decision independently verifiable offline. Pure Rust.
Web4: an open standard for verifiable AI presence — LCT identity, T3/V3 trust tensors, MRH context, ATP/ADP. Rust + Python SDKs on crates.io/PyPI. AGPL-3.0-or-later.
TrapDefense — Open-source AI Security Gateway for HTTP APIs and MCP tools. Inspect content, enforce agent permissions, and audit decisions.
Authorization, delegation, provenance, and verifiable-audit engine for AI agents. MCP adapter published.
Jev agent authorization for MCP tool calls: Kinde identity and permissions plus Jev's typed, calibrated decisions, checked server-side before every call runs
Hestia — universal Web4 presence for humans and AI. Local-first cryptographic identity, encrypted vault, delegation authority, witnessed trust record, and device constellation. Cross-platform app + AI plugin + CLI. EUDI-compatible credential issuance. AGPL-3.0.
Language-neutral authorization and governance framework for AI agents, Spring AI, and Model Context Protocol (MCP)
Draco-Tech is the identity and authorization layer that lets autonomous agents operate with zero static credentials and full policy adherence.
Permissions that narrow when one AI agent delegates: a sub-agent never holds more than its parent. In-process, with a hash-chained audit log you verify offline. Python and TypeScript, adapters for 19 frameworks and A2A.
Runtime guardrails for TypeScript AI agents. Prevents duplicate tool calls, enforces per-user cost budgets, and gates irreversible actions. MIT.
Authorization and execution control for AI agents — independent policy and judge approval before consequential tool actions execute. MCP gateway, capability tokens, auditability, and fail-closed execution.
A dual-authorization layer for AI agents with independent approvals, single-use capability tokens, auditability, and fail-closed execution.
Aiyo Execution Authorization Protocol — open specification for runtime execution authorization of autonomous agents.
Attenu Derive: computes the permission set each AI agent task needs from the app itself — roster, tools, observed calls — for attenu-guard to enforce on every tool call and sub-agent handoff.
Deterministic pre-execution policy for AI agent tool calls in Go.
To associate your repository with the agent-authorization topic, visit your repo's landing page and select "manage topics."