// agents · uber/ADR

ADR

Uber's ADR: discover, observe, benchmark, and detect threats across enterprise AI agents, deployed at Uber.

★ 1.9k stars⑂ 202 forksApache-2.0updated 2026-10
▦ visual — this project's README ships no embeddable screenshot,
so here's a placeholder. The guide below is complete regardless.
// project facts

Project facts

ADR is an open-source ai agents project by uber (uber/ADR on GitHub). It has 1,950 GitHub stars and 202 forks, is released under the Apache-2.0 license, and was last updated on GitHub on 8 October 2026.

Repository
uber/ADR
GitHub stars
1,950
Forks
202
License
Apache-2.0
Last updated on GitHub
8 October 2026
Category
AI Agents
Listing
Full install guide
Official site
Official site
// how it works

How it works

  1. Clarify your goal

    Decide which component you need first: endpoint discovery, telemetry collection, or the detection benchmark.

  2. Get the project

    Clone the repository and move into the component you are evaluating, such as Detection.

  3. Install and configure

    Sync the Python environment with uv and configure the model credentials the detector needs, or choose the keyless smoke-test detector.

  4. Run your first task

    Follow the reproducibility workflow to inflate the benchmark, run detectors, and review results before adapting components to your fleet.

// features

Why people love it

ADR Discovery

Inventories installed AI applications, CLI agents, IDE extensions, local model runtimes, and MCP servers on an endpoint, flagging unknown surfaces for review.

ADR Sensor observability

Collects and normalises agent intent, tool use, and execution traces from many coding tools across macOS, Linux, and Windows into a unified schema.

ADR-Bench benchmark

A security benchmark with hundreds of tasks and many MCP servers covering a range of agent attack techniques, with baselines included.

Two-tier ADR Detector

Combines high-recall triage with deeper agentic reasoning on suspicious sessions to detect risky agent behaviour efficiently.

Synthetic, safe fixtures

Benchmark fixtures are synthetic (fake credentials and emulated environments) intended for defensive security research.

Reproducibility documentation

A step-by-step workflow documents how to reproduce benchmark detection results and the paper's figures.

Advertisement
// run it

Run it yourself

  1. Clone and enter the Detection component

    The quickstart in the README focuses on ADR Detection.

    git clone https://github.com/uber/ADR.git
    cd ADR/Detection
  2. Sync the environment and set credentials

    The default adr detector uses an Anthropic key; a keyless smoke-test detector is also documented.

    uv sync
    export ANTHROPIC_API_KEY=<your key>
  3. Run the benchmark workflow

    Follow the reproducibility guide to inflate the packed benchmark and run detectors. # see README docs/REPRODUCIBILITY.md

    # see docs/REPRODUCIBILITY.md for the full evaluation workflow
// use cases

What you can build

Advertisement
// faq

Questions, answered

Who maintains it?

It is developed by Uber, where the README says it is deployed in production, and published under the uber GitHub organisation.

Is it free to use?

Yes, the released components are Apache-2.0 licensed. Running the default detector uses your own Anthropic API key; a keyless smoke-test detector option is documented.

Does it need a paid API key?

For the default detector, yes, an Anthropic API key is configured in the quickstart. Organisations can adapt components to their own model setups.

How does it compare to a well-known alternative?

Endpoint security tools watch processes and files; ADR is purpose-built for agent behaviour: it inventories AI tools, observes agent traces, and benchmarks agent-specific attacks such as prompt injection through MCP servers.

How do I get started?

Clone the repo, cd into Detection, run uv sync, set ANTHROPIC_API_KEY, and follow docs/REPRODUCIBILITY.md.

// keep exploring

Similar tools

AI Agents

koog

Koog is a JVM (Java and Kotlin) framework for building predictable, fault-tolerant and enterprise-ready AI agents across all platforms – from backend service…

★ 4.6k starsApache-2.0
View guide →
AI Agents

AgentBench

A rigorous benchmark for LLMs acting as agents.

★ 3.8k starsApache-2.0
View guide →
AI Agents

loopx

A control plane with a durable state kernel that keeps long-horizon agents moving across sessions.

★ 6.2k starsApache-2.0
View guide →
AI Agents

OSWorld

OSWorld — benchmark multimodal agents on open-ended tasks in real computer environments.

★ 3.2k starsApache-2.0
View guide →
AI Agents

ToolJet

Open-source foundation of ToolJet AI - the enterprise app generation platform for internal tools, dashboards, business applications, workflows and AI agents.…

★ 41.0k starsAGPL-3.0
View guide →
AI Agents

ClawBench

An open benchmark for browser AI agents on everyday tasks.

★ 975 starsApache-2.0
View guide →
// alternatives

Alternatives in AI Agents

The most-starred other AI Agents projects in the LibreAI catalogue — the realistic shortlist if ADR is not the right fit.

Get ADR

Free and open — grab the source, star the repo, and start creating.