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README.md

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Introduction

This folder contains backend integration tests that rely on a mock LLM. It serves two purposes:

  1. Ensure the quality of development, including OpenDevin framework and agents.
  2. Help contributors learn the workflow of OpenDevin, and examples of real interactions with (powerful) LLM, without spending real money.

Why don't we launch an open-source model, e.g. LLAMA3? There are two reasons:

  1. LLMs cannot guarantee determinism, meaning the test behavior might change.
  2. CI machines are not powerful enough to run any LLM that is sophisticated enough to finish the tasks defined in tests.

Note: integration tests are orthogonal to evaluations/benchmarks as they serve different purposes. Although benchmarks could also capture bugs, some of which may not be caught by tests, benchmarks require real LLMs which are non-deterministic and costly. We run integration test suite for every single commit, which is not possible with benchmarks.

Known limitations:

  1. To avoid the potential impact of non-determinism, we remove all special characters and numbers (often used as PIDs) when doing the comparison. If two prompts for the same task only differ in non-alpha characters, a wrong mock response might be picked up.
  2. It is required that the agent itself doesn't do anything non-deterministic, including but not limited to using randomly generated numbers.

The folder is organised as follows:

├── README.md
├── conftest.py
├── mock
│   ├── [AgentName]
│   │   └── [TestName]
│   │       ├── prompt_*.log
│   │       ├── response_*.log
└── [TestFiles].py

where conftest.py defines the infrastructure needed to load real-world LLM prompts and responses for mocking purpose. Prompts and responses generated during real runs of agents with real LLMs are stored under mock/AgentName/TestName folders.

Run Integration Tests

Take a look at run-integration-tests.yml to learn how integration tests are launched in CI environment. Assuming you want to use workspace for testing, an example is as follows:

rm -rf workspace; AGENT=PlannerAgent \
WORKSPACE_BASE="/Users/admin/OpenDevin/workspace" WORKSPACE_MOUNT_PATH="/Users/admin/OpenDevin/workspace" MAX_ITERATIONS=10 \
poetry run pytest -s ./tests/integration

Note: in order to run integration tests correctly, please ensure your workspace is empty.

Regenerate Integration Tests

When you make changes to an agent's prompt, the integration tests will fail. You'll need to regenerate them by running:

./tests/integration/regenerate.sh

Note that this will make several calls to your LLM_MODEL, potentially costing money! If you don't want to cover the cost, ask one of the maintainers to regenerate for you. You might also be able to fix the tests by hand.

Write a new Integration Test

To write an integration test, there are essentially two steps:

  1. Decide your task prompt, and the result you want to verify.
  2. Add your prompt to ./regenerate.sh

NOTE: If your agent decide to support user-agent interaction via natural language (e.g., you will prompted to enter user resposes when running the above main.py command), you should create a file named tests/integration/mock/<AgentName>/<TestName>/user_responses.log containing all the responses in order you provided to the agent, delimited by newline ('\n'). This will be used to mock the STDIN during testing.

That's it, you are good to go! When you launch an integration test, mock responses are loaded and used to replace a real LLM, so that we get deterministic and consistent behavior, and most importantly, without spending real money.