Releases June 1st · all content is provisional
stylobot Logo stylobot
Behavioural bot protection

Stop wasting compute on scrapers and click fraud.

Bot and automation blocking. Reverse proxy and sidecar with a rich UI library and dashboard.

Stylobot watches what a client actually does: request timing, path transitions, fingerprint integrity, session shape. 49 detectors vote on every request. No SaaS. Nothing about your visitors leaves your environment. How behavioural inference works →

Download the latest release v6.8.9 published 2026-05-26
All assets & checksums →

Direct one-click downloads — GitHub redirects each link to the latest matching asset. Verify with SHA256SUMS.txt.

Docker — any OS
Bundled gateway + dashboard (the free engine in one container)
  1. 1.
    Run
    docker run -p 8080:8080 scottgal/stylobot-all:latest
  2. 2.
    Open the dashboard
    http://localhost:8080/_stylobot

    This is the FOSS engine, no licence required. stylobot-gateway ships the proxy-only image; stylobot-sidecar is a 36 MB AOT detector your app calls directly.

Live · Your Detection Human
19:57:59 · 15ms
Network Locale Headers Tool Transport Session Quality
30% bot probability
Unknown Monitor

Top Bots

Name Bot % Conf Threat Hits 1h Seen
US bingbot 4 60m: 0 bot · 0 human 4h 56m
US googlebot 1 60m: 0 bot · 0 human 20h 26m
1–2 of 2
1
Not a demo. Your real request, live detection engine.
Observe-only on stylobot.net. Every detector scores you and the dashboard records it, but no request here is blocked. The model is calibrating against real traffic before launch. Self-hosted deployments pick their own action policy (throttle, block, challenge, log-only).
Native binary · or embed (auto-detected; click to switch)
Full getting-started guide →
  1. 1.
    Install (Homebrew)
    brew install scottgal/stylobot/stylobot
  2. 2.
    Run (foreground — shows the live CLI detection table)
    stylobot 5080 http://localhost:3000

    Add -d to background as a daemon (no CLI UI). The web dashboard ships in stylobot-all (Docker), stylobot-ui (remote viewer), and the UI SDKs (TypeScript, ASP.NET) — see the getting-started guide.

  1. 1.
    Install (Chocolatey or winget)
    choco install stylobot
    winget install Mostlylucid.StyloBot
  2. 2.
    Run (foreground — shows the live CLI detection table)
    stylobot 5080 http://localhost:3000

    Add -d to background as a daemon (no CLI UI). The web dashboard ships in stylobot-all (Docker), stylobot-ui (remote viewer), and the UI SDKs (TypeScript, ASP.NET) — see the getting-started guide.

  1. 1.
    Install (apt, Cloudsmith-signed)
    curl -1sLf 'https://dl.cloudsmith.io/public/mostlylucid/stylobot/setup.deb.sh' | sudo bash
    sudo apt update && sudo apt install stylobot
  2. 2.
    Run (foreground — shows the live CLI detection table)
    stylobot 5080 http://localhost:3000

    Add -d to background as a daemon (no CLI UI). The web dashboard ships in stylobot-all (Docker), stylobot-ui (remote viewer), and the UI SDKs (TypeScript, ASP.NET) — see the getting-started guide.

  1. 1.
    Add the NuGet package to your ASP.NET Core app
    dotnet add package mostlylucid.botdetection
  2. 2.
    Wire it up in Program.cs
    builder.Services.AddStyloBot();
    app.UseStyloBot();

    Detection runs in-process — no proxy hop. The ASP.NET UI SDK package ships the dashboard view components your app can mount at any route. TypeScript SDK is available for non-.NET frontends — see the getting-started guide.

  1. 1.
    Download from GitHub Releases

    Pick the asset for your platform: stylobot-linux-x64.tar.gz, stylobot-linux-arm64.tar.gz, stylobot-osx-arm64.tar.gz, stylobot-osx-x64.tar.gz, or stylobot-win-x64.zip from the releases page.

  2. 2.
    Verify provenance + extract
    gh attestation verify stylobot-linux-x64.tar.gz --owner scottgal
    tar xzf stylobot-linux-x64.tar.gz && chmod +x ./stylobot
  3. 3.
    Run (foreground — shows the live CLI detection table)
    ./stylobot 5080 http://localhost:3000

    Add -d to background as a daemon (no CLI UI). The web dashboard ships in stylobot-all (Docker), stylobot-ui (remote viewer), and the UI SDKs (TypeScript, ASP.NET) — see the getting-started guide.

Self-hosted
runs in your VPC, your data stays there
Full decision trace
signals, deltas, action, policy
49 detectors
layered protocol + behavior signals
Privacy-aware
HMACed IDs + stripped UAs

Choose your documentation path

Use Customer Docs for setup and rollout playbooks, and GitHub Docs for deep detector internals, architecture, and API references.

AI without LLMs in the hot path.

The intelligence is emergent: small detectors, behavior memory, signatures, reputation, and feedback loops combine into decisions that improve as traffic repeats. LLMs are optional side analysis, not the thing you depend on for every request.

Useful on day one. Start in observe mode and tune from real traffic
Every decision has a trace: raw signals, derived signals, detector deltas, aggregation, and policy action
Session behavioural analysis catches automation that rotates IPs, user agents, and fingerprints

Per-request output

probability
5%
confidence
88%
risk band
VeryLow
action
observe

Plus detector breakdown, raw and derived signals, threat score, intent classification, policy action, and narrative reasoning.

Built for people who want to see the machinery.

Most bot products hide the decision in a vendor cloud. StyloBot gives you the runtime, the signal trace, and the policy controls inside your own deployment.

StyloBotDataDomeCloudflareHUMAN
Pricing modelFree engine / paid controlsSales-led SaaSEnterprise planCustom
Self-hostedYesNoNoNo
Open sourceFull engineNoNoNo
Per-request meteringNoOftenPlan-dependentCustom
Raw traffic sharingNot requiredCloud-scoredCloud edgeCloud-scored
Policy-controlled frictionYesYesYesYes

From the development blog

How StyloBot was built and the thinking behind it.

All articles →
Core concept
Behavioural Inference Systems
The architectural idea behind StyloBot: inferring intent from request behaviour rather than matching patterns. Why that distinction matters for accuracy and for false-positive rates.
All bot detection posts
The full development series
30+ articles covering detector design, signal composition, LLM integration, HNSW session clustering, fingerprinting, and the commercial layer. Written as StyloBot was built.
Browse all articles →

Try the commercial controls for 30 days

No credit card. Use the paid dashboard, live config, and persistence layer; fall back to the free engine if you do not subscribe.

Open source and charity projects: contact us for a complimentary license.