teslamate-org/teslamate
teslamate
A self-hosted data logger for your Tesla 🚘 [main maintainer=@JakobLichterfeld]
Usage guide
teslamate is an open-source project around dashboard, datalogger, docker with 8,150 GitHub stars. This guide focuses on when to use it, how to install it, how to run the first example, and what to verify before adopting it.
Key features
- Implemented mainly in Elixir, useful for judging integration effort in a similar stack.
- GitHub detected the AGPL-3.0 repository license, which does not by itself confirm commercial permission. Review repository obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
- The project has a homepage, so cross-check docs, examples, and release information beyond GitHub.
Best for
- Evaluating teslamate for Elixir AI workflows.
- Comparing a GitHub project with 8,150 stars and current repository activity.
Pros
- teslamate has visible GitHub traction with 8,150 stars. Topics: dashboard, datalogger, docker.
- The project provides an external homepage for deeper evaluation.
Cons
- Production fit still depends on documentation depth, issue activity, and release cadence.
- License review should confirm the AGPL-3.0 terms fit your use case.
Production readiness
teslamate should be validated with its README, release history, open issues, and integration requirements before production use.
License risk
AGPL-3.0 is reported by GitHub; review the repository license before redistribution or commercial use.
teslamate architecture preview
teslamate's main path starts at the entry surface, runs through teslamate core runtime, combines LLM / model client, PostgreSQL, GitHub / APIs / webhooks, and returns User-facing result.
Entry
Web / product entry
Users start from a web UI, hosted product surface, or browser-based workflow.
https://docs.teslamate.org
Runtime
teslamate core runtime
The core coordinates project logic, configuration, and AI-related execution in Elixir.
Elixir
Model
LLM / model client
The project connects its core runtime to local models or hosted AI APIs when model inference is required.
model signal
Context
PostgreSQL
Context comes from PostgreSQL, which constrains what the model or runtime can use.
PostgreSQL
Tools
GitHub / APIs / webhooks
Tool adapters let the runtime act outside the model through GitHub / APIs / webhooks.
GitHub, APIs / webhooks
Output
User-facing result
The final output is returned to the user, workflow, API caller, or downstream system.
output
Install tutorial
Before you install
- A clean working directory for the first test run
Check the runtime environment
Confirm your system can run a Elixir project before starting the installation steps.
Get the project files
Start from the official repository or package so the first run matches the documented behavior.
$ git clone https://github.com/teslamate-org/teslamate.gitInstall or build dependencies
No extra setup command was detected. Check the README before adding custom configuration.
Adoption guidance and sources
Practical use cases
A self-hosted data logger for your Tesla 🚘 [main maintainer=@JakobLic
This is one of the documented reasons to evaluate teslamate before choosing a stack.
Focus area: dashboard
This is one of the documented reasons to evaluate teslamate before choosing a stack.
Automation project comparison
Compare teslamate with similar projects before committing to a stack.
Before adopting
- Complete one clean-environment verification using the official teslamate setup path.
- Review repository license, model weights, external services, and dependency terms for your use case.
- Check recent commits, release cadence, issue response, and documentation depth.
- Evaluate output quality, latency, resource usage, and recovery behavior with a small dataset.
Configuration notes
- Review README configuration notes before using production data.
Sources checked
These links are used to verify repository, documentation, or tutorial details. Review the source pages before adopting the project.
Troubleshooting
- If installation fails, first confirm the command is being run from the README-specified directory.
- If dependencies conflict, retry in a fresh virtual environment, container, or working directory.
- If output looks wrong, return to the smallest documented teslamate example before adding complex data.
- For keys, model files, or external services, verify environment variables, local paths, and permissions one by one.
- Before production use, review recent updates, open issues, license terms, and safety boundaries.
What is teslamate?
teslamate is an open-source automation project. A self-hosted data logger for your Tesla 🚘 [main maintainer=@JakobLichterfeld]
How do I install teslamate?
Start with the official README. The first detected setup step is: git clone https://github.com/teslamate-org/teslamate.git.
Is teslamate beginner-friendly?
If you already know the Elixir ecosystem, start with the smallest example. Otherwise test it in an isolated environment first.
Can teslamate be used commercially?
GitHub detected the AGPL-3.0 repository license, which does not by itself confirm commercial permission. Review repository obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
Does teslamate need a GPU?
GPU requirements depend on the workload, model, and dataset size. Start with the smallest README example before scaling up.
How should I decide whether to adopt teslamate?
Evaluate setup cost, maintenance activity, issue health, license terms, and fit with your real workflow.