Four-dimensional optimization solutions to improve operational efficiency
For large code base scenarios, SE-Agent provides the following optimization strategies:
- Trajectory compression technology: Enable the compression function of the trajectory system (on by default), which can reduce the storage occupation of 80%. Set trajectory.compression_level in config to adjust the compression strength (level 1-5).
- batch mode: For standardized test sets such as SWE-bench, use the run-batch command to process multiple problem instances in parallel, controlling the batch size with the -instances.slice parameter.
- Model hierarchical invocation: Implement a "small model for small problems" policy in operator configuration, e.g., specify a lightweight model for simple tasks such as grammar checking.
- caching mechanism: Utilizing the historical scenario repository accumulated by the trajectory system, the cached results are directly invoked for recurring problem patterns.
The measured data shows that the API call cost is reduced by 371 TP3T on average after adopting the hierarchical call strategy, and the disk I/O time is reduced by 631 TP3T after turning on track compression.
This answer comes from the articleSE-Agent: a framework for self-optimizing AI intelligencesThe
May not be reproduced without permission:AI productivity tools " How to optimize the efficiency of SE-Agent running in a large code base?
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