Contributing

Contributing#

We welcome contributions to the project. Here is some information to get you started.

Note

This document is a work in progress. PRs are welcome.

Install#

# checkout the code and navigate to the root of the project
git clone https://github.com/gptme/gptme.git
cd gptme

# install poetry (if not installed)
pipx install poetry

# activate the virtualenv
poetry shell

# build the project
make build

You can now start gptme from your development environment using the regular commands.

You can also install it in editable mode with pipx using pipx install -e . which will let you use your development version of gptme regardless of venv.

Tests#

Run tests with make test.

Some tests make LLM calls, which might take a while and so are not run by default. You can run them with make test SLOW=true.

There are also some integration tests in ./tests/test-integration.sh which are used to manually test more complex tasks.

There is also the Evals.

Telemetry#

gptme includes optional OpenTelemetry integration for performance monitoring and debugging. This is useful for development to understand performance characteristics and identify bottlenecks.

Setup#

To enable telemetry during development:

  1. Install telemetry dependencies:

    poetry install -E telemetry

  2. Run Jaeger for trace visualization:

    docker run --rm --name jaeger \
          -p 16686:16686 \
          -p 4317:4317 \
          -p 4318:4318 \
          -p 5778:5778 \
          -p 9411:9411 \
          cr.jaegertracing.io/jaegertracing/jaeger:latest

  3. Set the telemetry environment variable:

    export GPTME_TELEMETRY_ENABLED=true
export OTLP_ENDPOINT=http://localhost:4317  # optional (default)

  4. Run gptme:

    poetry run gptme 'hello'
# or gptme-server
poetry run gptme-server

  5. View traces in Jaeger UI:

    You can view traces in the Jaeger UI at http://localhost:16686.

Once enabled, gptme will automatically:

  • Trace function execution times

  • Record token processing metrics

  • Monitor request durations

  • Instrument Flask and HTTP requests

The telemetry data helps identify:

  • Slow operations and bottlenecks

  • Token processing rates

  • Tool execution performance

Release#

To make a release, simply run make release and follow the instructions.