examples

Symfony AI Examples

This directory contains various examples of how to use the Symfony AI components. They are meant to provide a reference implementation to help you get started.

On top, the examples are used as integration tests to ensure that the components work as expected.

Running the examples

For setting up and running the examples, you can either run them standalone or via the example runner. You find the commands for that in this section. Make sure to change into the examples directory before running the commands.

cd examples

Setup

Dependencies

Before running the examples, you need to install the dependencies. You can do this by running:

composer install

If you want to run the examples together with local changes, for example while developing a feature, you need to link the AI components into the vendor directory after composer install. You can use the link script in the root directory for this:

../link

Configuration

Depending on the examples you want to run, you may need to configure the needed API keys. Therefore, you need to create a .env.local file in the root of the examples' directory. This file should contain the environment variables for the corresponding example you want to run.

Now you can run examples standalone or via the example runner.

Store with Docker

Some of the store examples require locally running services, meaning that you need to have Docker installed and running to test these examples.

docker compose up -d

Running examples standalone

Every example script is a standalone PHP script that can be run from the command line. You can run an example by executing the following command:

php openai/chat.php

To get more insights into what is happening at runtime, e.g. HTTP and tool calls, you can add -vv or -vvv:

php openai/toolcall-stream.php -vvv

Running examples via the example runner

You can also run the examples via the example runner, which takes care of running the examples parallel in sub-processes. This is useful if you are contributing to the Symfony AI components and want to ensure that all examples work as expected.

You can run the example runner by executing the following command:

./runner

If you only want to run examples of one or multiple specific subdirectories, you can pass the name as an argument:

./runner openai mistral

If you only want to run a specific subset of examples, you can use a filter option:

./runner --filter=toolcall

Record & replay

Examples don't only run against the live provider APIs — every HTTP interaction can be recorded into a cassette (a JSON file under tests/fixtures/, mirroring the example's path) and replayed later without any API keys. Replaying drives the recorded bytes through the real bridge pipeline (Platform → ModelClient → ResultConverter), which turns the example corpus into deterministic, credential-free integration tests: CI replays every recorded example via PHPUnit and compares its output against a committed golden (tests/fixtures/<path>.out), so a result-converter regression fails the build.

The switch is the CASSETTE environment variable (record or replay), but you usually don't set it yourself — the runner and the test suite handle it:

# Record: run the examples live (API keys required), write cassettes with credentials
# redacted, then verify each recording replays and refresh its golden.
./runner --record openai

# Replay: re-run every recorded example offline and compare against its golden.
# No keys needed - this is also what CI runs.
vendor/bin/phpunit

Recording is a local maintainer task, since CI has no provider credentials — a good habit is recording alongside the live example run before tagging a release: it costs no extra API calls, and the cassette diff shows exactly what the providers changed since the last recording. Commit cassettes and goldens together.

Binary responses (generated images, audio, ...) are not stored byte-for-byte: the cassette keeps a metadata stub (content type, byte size) and replay serves a small placeholder body instead.

Recordings never store your credentials or account specific values like an Azure endpoint: every variable of .env.test whose value in .env.local differs is replaced by its placeholder, and replay loads .env.test instead of .env.local. When an example reads a new environment variable, add a placeholder for it to .env.test.

To record a single example (or a subset), narrow the record run with a filter — the golden refresh is included:

./runner --record --filter=chat openai