OrcaLoca AI Agent

OrcaLoca

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[!NOTE] Maintained SearchAgent repository backend. CodeNib serves OrcaLoca's six-tool SearchAgent contract from an incremental, manifest-backed symbol graph, without building a separate OrcaLoca index. Support begins at SearchAgent with an empty TraceAnalysisOutput; OrcaLoca's trace-analysis and full reproduction pipeline remain in this repository. Documentation · SearchAgent adapter · Repository provider

🔥 News! 🚀

• Apr 5 2025: We support top-K retrieval mode inspired from LocAgent and CoSIL.
• Feb 18 2025: We update the support for Gemini series model through vertex API.

OrcaLoca (previous named Orcar), an LLM agent framework that improves accuracy for software issue localization by integrating priority-based scheduling for LLM-guided action, action decomposition with relevance scoring, and distance-aware context pruning.

Prerequisite

OrcaLoca requires docker to run, so please first pull our docker image (forked from SWE-Agent):

docker pull hejiaz/swe-agent:latest

OrcaLoca also requires API access to LLM. (Currently OpenAI & Anthropic APIs are supported) You can either export them in CLI:

export OPENAI_API_KEY={key_here}
export ANTHROPIC_API_KEY={key_here}

or as a key.cfg file:

OPENAI_API_KEY=key_here
ANTHROPIC_API_KEY=key_here

OrcaLoca also uses torch in its search process. (torch installation guide)

Installation

cd OrcaLoca

conda create -n orca python=3.10
conda activate orca
pip install -e .

After installation succeeded, you can run a quick smoke test (should finish in 5-10 minutes):

python evaluation/run.py --final_stage trace_analysis --instance_ids astropy__astropy-12907 astropy__astropy-6938

Then add search stage into running:

python evaluation/run.py --final_stage search --instance_ids astropy__astropy-12907

Reproducing OrcaLoca Leaderboard Submission

Genrating Search results

About search.cfg We have a customized search configuration for the agent as below:

[SEARCH]
context_control = True # whether to use context cache
redundancy_control = True # prune the redundant action
top_k_search = 12 # search cache size
top_k_output = 3 # k output in retrieval_mode
top_k_retrieval_mode = False # this will output top_k function locations as output
top_k_methods = 3
top_k_disambiguation = 3
top_k_functions = 2
score_threshold = 75
batch_size = 1 # parallel actions batch size to improve efficiency

[SCORE_DECOMPOSITION]
class = True
file = True
disambiguation = True

[PRIORITY]
enable = True
basic = 1
decomposition = 2
related_file = 2

python evaluation/run.py

Genrating output.json

cd evaluation
python process_output.py

Preparing Data for Agentless Edition

Please go through instructions in:

1. evaluation/orcar_agentless/README.md
2. third_party/Agentless/README_orcar.md

Evaluating all_preds.jsonl

Our output all_preds.jsonl can be evaluated with official scripts offered by SWE-Bench. Please check the 'Set Up' and 'Usage' parts in its README.md for details.

License

MIT License

Citation

If our project helps you, please cite our paper with

@misc{yu2025orcalocallmagentframework,
      title={OrcaLoca: An LLM Agent Framework for Software Issue Localization},
      author={Zhongming Yu and Hejia Zhang and Yujie Zhao and Hanxian Huang and Matrix Yao and Ke Ding and Jishen Zhao},
      year={2025},
      eprint={2502.00350},
      archivePrefix={arXiv},
      primaryClass={cs.SE},
      url={https://arxiv.org/abs/2502.00350},
}