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ADR secures enterprise AI agents through observability, security benchmarking, and threat detection. Deployed at Uber.
This repository page has enough original analysis, source evidence, and comparison context to be treated as an indexable Git-Stars review.
100
review score
Problem solved
ADR addresses the lack of visibility and security controls for AI agents in enterprise environments. It solves the problem of detecting and preventing malicious or unsafe agent behavior, which traditional security tools are not designed to handle, by providing a unified telemetry schema, a realistic benchmark for testing defenses, and an efficient two-tier detection architecture.
Deployment reality
The available setup signal starts with: git clone https://github.com/uber/ADR && cd ADR/Detection && uv sync. Treat this as a starting point, then ask Codex or Claude to inspect the README, environment variables, runtime version, and deployment target before production use.
Commercial use
The recorded license is Apache-2.0. This is a useful commercial-use signal, but teams should still verify license text, dependencies, model/API terms, and trademark constraints.
Capability ceiling
Developers can build comprehensive security monitoring and response systems for AI agents, including real-time observability of agent actions, automated threat detection, and integration with existing enterprise security workflows. The framework supports a wide range of agents (e.g., Cursor, Claude Code, Codex) and can be extended to custom agents. The benchmark enables continuous evaluation and hardening of agent security postures, and the detection system can be deployed to flag risky sessions in production.
Git-Stars is independent and not affiliated with GitHub or this project. Analysis may be AI-assisted and based on public repository metadata plus short README-derived summaries. We do not mirror full README files, docs, issues, or social comments.
uber/ADR is tracked as a Python project in the AI Agent, LLM Tool, MCP Server, Observability area. This evaluation combines public GitHub metadata, category signals, short source summaries, and Git-Stars editorial rules rather than copying project documentation.
Momentum check: the repository has 1.4k total stars, with +0 today, +424 this week, and +0 this month. These growth windows help distinguish durable adoption from short-lived visibility spikes.
Maintenance check: current activity is Active; the latest push was 13 days ago, and the open issue queue is 7, about 0.50% of total stars. Treat this as an adoption signal, not a substitute for engineering due diligence.
Adoption check: 123 forks and 1.4k watchers suggest how often the project is reused or followed. License signal: Apache-2.0. Always verify license compatibility before commercial or internal use.
Practical fit: this project is most relevant when you need Python teams evaluating ecosystem-native tooling. Be cautious when you need a legal review, security audit, or production SLA.
Source check: Git-Stars currently has 2 explicit source reference(s) for this report and a recent growth signal of 714. Follow the original GitHub repository for final install, security, and release information.
Analysis
ReadyOriginal problem, capability, and verdict guidance are available.
Sources
ReadyRepository metadata and README/source references are attached.
License
ReadyApache-2.0 is recorded for review.
Maintenance
ReadyRecent activity is visible in repository metadata.
Alternatives
ReadyEnough nearby projects exist for comparison.
Momentum
1.4k Stars
Reuse
123 Forks
Attention
1.4k Watchers
Maintenance
active
License
Apache-2.0
Open issues
7
ADR (Agentic AI Detection and Response) is an enterprise security system for AI agents, deployed in production at Uber. It provides observability, benchmarking, detection, and prevention capabilities to secure employee-facing agents like Cursor, Claude Code, and Codex, as well as customer-facing agents. The open-source release includes the ADR Sensor, ADR-Bench, and ADR Detector.
Key Features
- Captures agent intent, tool use, and execution traces across 7+ AI coding tools on macOS, Linux, and Windows. - Includes ADR-Bench with 300+ tasks, 133 MCP servers, and coverage of all 17 agent attack techniques. - Two-tier detection architecture combining high-recall triage with deeper agentic reasoning.
AI Agent
Agent frameworks, autonomous workflows, and tool-use systems
LLM Tool
Libraries and tools for LLM apps, RAG, prompts, and evals
MCP Server
Model Context Protocol servers, clients, and integrations
Observability
Monitoring, tracing, logging, profiling, and reliability tools
git clone https://github.com/uber/ADR && cd ADR/Detection && uv sync73
Health Score
Active
Commit Activity
Apr 19, 2026
Created
Aug 11, 2026
Last push
+355
Today's growth
+714
7-day growth
+714
30-day growth
Forks
Open
Watchers
uber
GitHub profileobra/superpowers
An agentic skills framework & software development methodology that works.
affaan-m/ECC
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
NousResearch/hermes-agent
The agent that grows with you
mattpocock/skills
Skills for Real Engineers. Straight from my .agents directory.
multica-ai/andrej-karpathy-skills
A single CLAUDE.md file to improve Claude Code behavior, derived from Andrej Karpathy's observations on LLM coding pitfalls.
✓
License
✓
Forked
✓ Active
Maintained
Problem Solved
ADR addresses the lack of visibility and security controls for AI agents in enterprise environments. It solves the problem of detecting and preventing malicious or unsafe agent behavior, which traditional security tools are not designed to handle, by providing a unified telemetry schema, a realistic benchmark for testing defenses, and an efficient two-tier detection architecture.
Capabilities
Developers can build comprehensive security monitoring and response systems for AI agents, including real-time observability of agent actions, automated threat detection, and integration with existing enterprise security workflows. The framework supports a wide range of agents (e.g., Cursor, Claude Code, Codex) and can be extended to custom agents. The benchmark enables continuous evaluation and hardening of agent security postures, and the detection system can be deployed to flag risky sessions in production.
Bottom Line
ADR is a robust, production-tested security framework ideal for enterprises deploying AI agents at scale, offering comprehensive observability and detection. It is less suitable for small projects or those without the infrastructure to support its complexity. The key trade-off is the significant setup and resource investment required versus the high level of security assurance it provides.