Automatisierung
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Fair-code workflow automation platform with native AI capabilities. Combine visual building with custom code, self-host or cloud, 400+…
★ 206,6 Tsd. SterneCustom (check repo)
Anleitung ansehen →Tracecat ist ein Open-Source-Projekt aus der Kategorie Automatisierung von TracecatHQ (TracecatHQ/tracecat auf GitHub). Es hat 3.824 Sterne auf GitHub und 428 Forks, wird unter der Lizenz AGPL-3.0 veröffentlicht und wurde zuletzt am 2. Oktober 2026 auf GitHub aktualisiert.
Open-source security automation platform for teams and AI agents
Link the services you already use — chat, email, sheets, CRMs, databases — through built-in integrations or generic webhooks and API calls.
Drag triggers and actions onto a canvas: when this happens, do that. Branch on conditions, loop over items, and transform data between steps.
Pipe steps through language models for classification, extraction, summarising, or drafting — most platforms here treat AI as just another node.
Turn the workflow on, watch executions in the run history, and get alerted when something fails.
Design automations on a canvas instead of writing glue code for every integration.
Prebuilt connectors for popular apps plus HTTP, webhooks, and database nodes for everything else.
Your credentials and execution data stay on your infrastructure — no third-party middleman.
Call LLMs mid-workflow for decisions, extraction, and content generation.
Cron schedules, webhooks, and event triggers keep flows running around the clock.
Open-source means the automation itself is free; you only pay for your own server.
Download the source and move into the project folder.
git clone https://github.com/TracecatHQ/tracecat.git
cd tracecatIsolate dependencies so nothing clashes with your other projects.
python -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activateInstall the requirements, then follow the README's launch command (it varies per project).
pip install -r requirements.txtThe core software is open-source and free to self-host. Managed cloud versions exist for most of these if you would rather not run a server.
Most flows are built visually. Light expressions or code nodes help for advanced logic, but simple automations need none.
It is an open-source project at TracecatHQ/tracecat with 428 forks. Check the repo's contributors and recent commits to see how active maintenance is.
Same idea — triggers and actions across apps — but self-hosted, with AI steps first-class and no per-task pricing.
Typically Docker and a small VPS or home server; several offer one-command installs.
Yes — LLM nodes are standard, working with hosted APIs or your own local models.
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Fair-code workflow automation platform with native AI capabilities. Combine visual building with custom code, self-host or cloud, 400+…
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Kostenlos und offen — hol dir den Quellcode, gib dem Repository einen Stern und leg los.