Test lt memory

[sujalgawas]

Summary

This PR introduces a new unit test file (test_lt_memory.py) to provide dedicated test coverage for the LongTermMemory class (mesa_llm/memory/lt_memory.py).

Motive

I noticed that while LongTermMemory functionality is partially tested via the test_STLT_memory.py integration test, it didn't have its own dedicated unit test file.

Adding a separate unit test file is valuable for:

Isolating Failures: If a bug is introduced in LongTermMemory, these unit tests will fail directly, making it faster to pinpoint the problem.

Thoroughness: It allows for testing the class's internal methods (like _update_long_term_memory) and specific edge cases in isolation.

Safer Refactoring: It provides a safety net, allowing the LongTermMemory class to be modified or optimized in the future with confidence that its core contract is being met.

This PR addresses this testing gap and improves overall code coverage.

Implementation

Created a new test file: tests/memory/test_lt_memory.py.

Followed the project's existing testing patterns by using pytest for the test class and fixtures (e.g., mock_agent).

Utilized unittest.mock (specifically patch and mock fixtures) to isolate the LongTermMemory class from its dependencies (like the LLM and rich.console).

Tests cover the following key functionalities:

test_memory_initialization: Ensures the class initializes correctly with the expected attributes.

test_update_long_term_memory: Verifies the private update method by mocking the LLM's generate call. It asserts that the prompt sent to the LLM is structured correctly and that the long_term_memory attribute is updated with the mock response.

test_process_step: Tests the process_step logic for both pre_step=True (buffer creation) and pre_step=False (committing the summary to long-term memory), again mocking the LLM call.

test_format_long_term: A simple test to ensure the format_long_term method returns the memory string as expected.

Usage Examples

This PR adds development tests, not a new user-facing feature. The tests can be run by executing pytest from the root of the repository

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✨ Finishing touches

🧪 Generate unit tests (beta)

• Create PR with unit tests
• Post copyable unit tests in a comment

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[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 86.27%. Comparing base (55be38c) to head (381eb26).
⚠️ Report is 1 commits behind head on main.

Additional details and impacted files

@@            Coverage Diff             @@
##             main      #27      +/-   ##
==========================================
+ Coverage   86.17%   86.27%   +0.09%     
==========================================
  Files          18       19       +1     
  Lines        1273     1311      +38     
==========================================
+ Hits         1097     1131      +34     
- Misses        176      180       +4     

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[colinfrisch]

Lots of good things, I'll review it in details asap.
On a different note, have you tried mesa-llm to build models yourself ? If yes do you have any feedback ? It's a recent extension of mesa so we are trying to figure out what works with the users and what doesn't :)

[sujalgawas]

I haven’t tried it yet, but I was thinking about recreating the Sugarscape Constant Growback Model from Mesa’s main project (in the Examples directory) to better understand how simulations work and how they can be integrated with mesa-llm. I’m still fairly new to open source, but the idea of using AI agents to create simulations and run tests seemed fascinating, so I started contributing.

If I do end up building sugarscape_g1mt, do you think it would make a good example? And would it make sense to open a PR to add it under mesa-llm/examples?

[colinfrisch]

If I do end up building sugarscape_g1mt, do you think it would make a good example? And would it make sense to open a PR to add it under mesa-llm/examples?

Definitely, it's a very interesting model ! If you need anything don't hesitate to ask :)

[colinfrisch]

Code looks good, thanks for your work !