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Netdryrun

by netdryrunversion 0.4.0i★ –iRuns on your computer
Registry nameio.github.netdryrun/netdryrun

Install

Overview

Description from the maker (original English)

Validate BGP and routing changes in a real containerlab lab: pass/fail verdict with evidence.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

  • Connection: Runs on your computer
  • Transport: stdio
  • Category: Other

Install

Pick the AI app you use, then copy the settings.

Before you install

  • Basic checks: 1 of 1 passed
  • Runs on your computer with your user’s permissions (files, network, apps).
  • The registry lists no key to fill in.

You need

  • PyPIPython’s uv installed (for the uvx command)

Which app do you use?

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "netdryrun": {
      "command": "uvx",
      "args": [
        "netdryrun",
        "mcp"
      ]
    }
  }
}

Run this in your terminal

claude mcp add netdryrun -- uvx netdryrun mcp

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "netdryrun": {
      "command": "uvx",
      "args": [
        "netdryrun",
        "mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "netdryrun": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "netdryrun",
        "mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account netdryrun, which owns the repo

  • … Not checked yet

    Still maintained

    We have not read this repo from GitHub yet

  • … Not checked yet

    Has a licence

    We have not read this repo from GitHub yet

  • – Not applicable

    Endpoint responds

    This one runs on your computer; there is no internet endpoint

  • – Not applicable

    Sign-in with OAuth

    Runs on your computer and uses local settings instead

  • … Not checked yet

    No hidden instructions in tool descriptions

    Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Otheri

  • More stars than 0% of the other 32433 servers in this category
  • Published to the registry more recently than 84% of the other 32433 servers in this category
  • Basic checks equal to or better than 100% of the other 32433 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Netdryrun safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.4.0) reached the registry 6 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Spec Driven Development, Remote Desktop Commander, Symantec Cloud DLP AIops

Alternatives

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Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

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Runs on your computer★ 440MITUpdated 14 months ago
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Remote Desktop Commander

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Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

Runs in the cloud★ 211Updated yesterday
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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

Test network changes before they touch production.

Netdryrun is an open-source (Apache-2.0) tool that dry-runs network changes in a real containerlab lab and returns a pass/fail verdict with evidence. It runs as a CLI, a GitHub Action, and an MCP server for AI agents.

Software teams don't ship code without running its tests. Network changes deserve the same step: run the change in a real lab, check how the network behaves, and ship only on a pass. Netdryrun makes that one command, one CI step, or one tool call for an AI agent.

Already have a containerlab lab? [Add Netdryrun to it in 10 minutes](https://netdryrun.com/guides/your-lab/): record its steady state, add one guard, run it on every pull request.

Netdryrun™ boots your topology as a real virtual network (containerlab), applies your configs, and checks how the network actually *behaves*: BGP sessions, prefix counts, routes, reachability. It catches the mistakes your checks describe, such as a route leak, before the change reaches production, and leaves the commands and raw output for a reviewer to inspect.

$ netdryrun run examples/frr-bgp-broken

Deploying lab ndr-frr-broken (3 nodes)... [PASS] r1: BGP session to 10.0.12.2 is established -- state is established (6.3s) [PASS] r1: ping 192.168.3.1 from 192.168.1.1 expects success -- 3/3 replies (2.3s) ... (3 more passing checks trimmed) [FAIL] r1: route 0.0.0.0/0 via bgp is absent -- unexpectedly present via bgp (not selected) (0.1s) FAILED: 5 passed, 1 failed (6 checks)

That run caught a leaked default route that was *received but not selected*. Ping and session checks alone would miss this example: every session is up and every address answers. (A static analyzer such as Batfish also catches this particular leak; the Batfish page shows one that it misses and a lab catches.)

Status: v0.4.0. Runs on Linux and WSL2, with drivers for FRR, Nokia SR Linux, SONiC (docker-sonic-vs), Arista EOS (cEOS, bring your own image) and Juniper cJunosEvolved (beta, bring your own image). On the verdict corpus v1 (30 realistic BGP changes: 19 harmful, 11 safe), Netdryrun judged 24/30 correctly, ping-only checks 20/30 and all-sessions-up checks 15/30. Netdryrun approved 5 of the 19 harmful changes (ping-only checks: 10 of 19) and blocked 1 of the 11 safe ones. On v2 (18 cases written after v1 to target v1's misses): Netdryrun 16 of 17 deterministic cases correct, ping-only 9/17, all-sessions-up 7/17; case 18 is nondeterministic in this lab and excluded from the score (listed with its reruns). Expected verdicts were committed before any run, on one FRR topology: a comparison of testing methods, not a production reliability estimate. Method, every miss and a one-command rerun are on the corpus page. Provenance: both ran on Netdryrun 0.3.1.

Why

  • It tests behavior, not syntax. A config diff shows what changed, not whether BGP comes up or which routes each router ends up with. A real control plane shows what the routing software actually does.
  • It's built for CI. One GitHub Action, a pass/fail exit code, a Markdown summary in the job, JUnit XML, and a JSON evidence file with every command and raw output.
  • It waits for convergence, with stated rules. A positive check passes after 2 passing samples taken 3 s apart, within its timeout. Negative checks ("this route must be absent") and best-path attribute checks wait until every configured BGP session on their node is up with stable prefix counts (sessions a check expects down don't count), then a 10 s settle, then must hold for 10 s (settle and hold are configurable). If a node still reports no BGP neighbors at the timeout, its checks run anyway and the result says BGP convergence was not seen. In a failure scenario, checks start only once the fault is visible in BGP (or the run ends with exit 2), and positive checks must hold too. Exit 0 passed, 1 checks failed, 2 not tested (config or lab problem), 3 inconclusive. The rules are sample-based; see convergence semantics.

Quick start

Try it in your browser (no install)

The codespace comes with Docker, containerlab and Netdryrun already installed. When the terminal shows Ready, run:

bash examples/agent-demo/demo.sh

A route-leaking change is rejected, then the fix passes every check. It uses your GitHub account's free Codespaces hours (2-core machine). Details.

On your own machine

Requirements: Linux or WSL2, Docker, containerlab, Python 3.10+. On Ubuntu 24.04 and Debian 12 a plain pip install outside a virtualenv is refused ("externally managed environment"), so use pipx: sudo apt install pipx && pipx ensurepath, then open a new terminal. uv tool install netdryrun, or pip install netdryrun inside an activated virtualenv, work too.

pipx install netdryrun

git clone https://github.com/netdryrun/Netdryrun netdryrun && cd netdryrun netdryrun run examples/frr-bgp # 3 FRR routers, should pass netdryrun run examples/srl-frr # SR Linux + FRR, should pass netdryrun run examples/frr-bgp-broken # a bad change, should fail

On your own containerlab topology (3 commands)
netdryrun record my-lab/topology.clab.yml --deploy   # snapshot today's behavior into my-lab/netdryrun.yml

...edit your configs (the change you want to test)...

netdryrun run my-lab # re-run the checks in my-lab/netdryrun.yml

record writes checks for every established BGP session, a prefix-count range for each (today's count up to 20% more, at least 2), guards against a default route or a link subnet arriving via BGP, and up to 50 BGP routes per node. A session that is down today isn't recorded.

Advisory (0.3.3 and 0.4.0): that range lets a leak of one or two extra prefixes from a neighbor pass. Until 0.4.1, record with --exact-prefixes and add present: false checks for prefixes that must never arrive. Advisory. Review it (remove what is allowed to change, add negative checks such as present: false), commit it, and every future change runs against it, locally or in CI. A pass means those checks passed, nothing more.

netdryrun.yml

lab: topology.clab.yml      # your containerlab topology

timeout: 90 # seconds a positive check may wait for convergence

checks:

  • type: bgp_session # session reaches a state (default: established) node: r1 neighbor: 10.0.12.2
  • type: bgp_prefixes # accepted prefix count within a range node: r1 neighbor: 10.0.12.2 min: 1 max: 10
  • type: route # route present (or absent), optionally via a protocol node: r1 prefix: 192.168.3.0/24 protocol: bgp
  • type: ping # reachability, optionally from a source address node: r1 source: 192.168.1.1 target: 192.168.3.1
  • type: route # negative check: must stay absent after convergence node: r1 prefix: 0.0.0.0/0 protocol: bgp present: false

Failure scenarios break something, check the network still behaves, then restore it. They can take links down, or (from 0.3.1) make an FRR neighbor announce routes it shouldn't, to prove a max-prefix limit or an import filter actually holds:

scenarios:
  • name: r1-r2 link fails link_down: {node: r1, interface: eth1} checks:
  • {type: ping, node: r1, source: 192.168.1.1, target: 192.168.2.1} # rerouted via r3
  • {type: bgp_session, node: r1, neighbor: 10.0.12.2, state: down}
  • name: customer floods 200 prefixes inject_routes: {node: ce1, count: 200, from: 10.100.16.0/24} checks:
  • {type: bgp_prefixes, node: up1, neighbor: 10.0.2.1, max: 51} # the limit stops the flood

Every check also accepts name, timeout and vrf. Ping accepts expect: failure and count. Drivers are detected from the topology; override them with nodes: {r1: {driver: frr}}. Nodes that are configured after boot (SONiC) take setup commands: nodes: {sonic1: {setup: [...]}}.

CheckFRRSR LinuxArista EOS (cEOS)SONiC (VS)
bgp_session✅✅✅✅
bgp_prefixes (accepted = received minus rejected by policy)✅✅✅✅
route present / absent✅✅✅✅
bgp_route best-path attributes: communities (standard, large, well-known), AS path (exact or regex), local preference, MED, next hop✅✅✅✅
ping success / failure✅✅✅✅
How it is testedLive lab in CI on every pushLive lab in CI on every pushParsers tested against output captured from live labs; no live lab in CIParsers tested against output captured from live labs; no live lab in CI

Commands

CommandWhat it does
netdryrun run [path]Deploy the lab, run the checks, destroy the lab. Add --keep to leave it up for debugging
netdryrun check [path]Run the checks against a lab that is already running
netdryrun validate [path]Validate netdryrun.yml and its topology without running anything
netdryrun record topology.clab.ymlSnapshot a known-good lab into a netdryrun.yml baseline, so you don't write checks by hand

Reports: --json FILE (full evidence), --markdown FILE, --junit FILE. Under GitHub Actions, a summary is appended to the job page automatically. Exit codes: 0 passed, 1 a check failed, 2 no verdict (config or lab error, a fault that did not take effect, or an internal error; add --debug for the traceback), 3 inconclusive (the lab never reached a testable state, so the run says nothing about the change), 130/143/129 interrupted by SIGINT/SIGTERM/SIGHUP after cleaning up the lab.

GitHub Action

name: Network dry run

on: pull_request jobs: dryrun: runs-on: ubuntu-latest steps:

  • uses: actions/checkout@v4
  • uses: netdryrun/Netdryrun@v0.4.0 with: path: network/ # folder containing netdryrun.yml

The action installs containerlab, runs the checks, uploads the JSON and JUnit reports as an artifact, and fails the job if any check fails. Outputs: status, passed, failed, inconclusive-reason.

For AI agents (MCP)

Listed in the official MCP Registry (io.github.netdryrun/netdryrun) and on Glama; the agent skill is also on Smithery.

AI can propose network changes. Netdryrun verifies the outcome. Agents: read [AG

Only the beginning is shown; read the rest in the repo

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