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README.md

SWE-Bench Evaluation with OpenHands SWE-Bench Docker Image

This folder contains the evaluation harness that we built on top of the original SWE-Bench benchmark (paper).

UPDATE (03/27/2025): We now support SWE-Bench multimodal evaluation! Simply use "princeton-nlp/SWE-bench_Multimodal" as the dataset name in the run_infer.sh script to evaluate on multimodal instances.

UPDATE (2/18/2025): We now support running SWE-Gym using the same evaluation harness here. For more details, checkout this README.

UPDATE (7/1/2024): We now support the official SWE-Bench dockerized evaluation as announced here.

The evaluation consists of three steps:

  1. Environment setup: install python environment and configure LLM config.
  2. Run inference: Generate a edit patch for each Github issue
  3. Evaluate patches using SWE-Bench docker

Setup Environment and LLM Configuration

Please follow instruction here to setup your local development environment and LLM.

Run Inference (Rollout) on SWE-Bench Instances: Generate Patch from Problem Statement

Note

Iterative Evaluation Protocol

We have an iterative approach for more stable and reproducible results:

  • For each instance, we attempt to generate a solution up to 3 times
  • Each attempt continues until either:
    1. The agent successfully produces a patch with AgentFinishAction, or
    2. The attempt reaches the maximum iteration limit
  • If an attempt fails, we retry with a fresh attempt (up to the 3-attempt maximum)
  • If your LLM config has temperature=0, we will automatically use temperature=0.1 for the 2nd and 3rd attempts

To enable this iterative protocol, set export ITERATIVE_EVAL_MODE=true

Running Locally with Docker

Make sure your Docker daemon is running, and you have ample disk space (at least 200-500GB, depends on the SWE-Bench set you are running on) for the instance-level docker image.

When the run_infer.sh script is started, it will automatically pull the relevant SWE-Bench images. For example, for instance ID django_django-11011, it will try to pull our pre-build docker image sweb.eval.x86_64.django_s_django-11011 from DockerHub. This image will be used create an OpenHands runtime image where the agent will operate on.

./evaluation/benchmarks/swe_bench/scripts/run_infer.sh [model_config] [git-version] [agent] [eval_limit] [max_iter] [num_workers] [dataset] [dataset_split]

# Example
./evaluation/benchmarks/swe_bench/scripts/run_infer.sh llm.eval_gpt4_1106_preview HEAD CodeActAgent 500 100 1 princeton-nlp/SWE-bench_Verified test

where model_config is mandatory, and the rest are optional.

  • model_config, e.g. eval_gpt4_1106_preview, is the config group name for your LLM settings, as defined in your config.toml.
  • git-version, e.g. HEAD, is the git commit hash of the OpenHands version you would like to evaluate. It could also be a release tag like 0.6.2.
  • agent, e.g. CodeActAgent, is the name of the agent for benchmarks, defaulting to CodeActAgent.
  • eval_limit, e.g. 10, limits the evaluation to the first eval_limit instances. By default, the script evaluates the entire SWE-bench_Lite test set (300 issues). Note: in order to use eval_limit, you must also set agent.
  • max_iter, e.g. 20, is the maximum number of iterations for the agent to run. By default, it is set to 60.
  • num_workers, e.g. 3, is the number of parallel workers to run the evaluation. By default, it is set to 1.
  • dataset, a huggingface dataset name. e.g. princeton-nlp/SWE-bench, princeton-nlp/SWE-bench_Lite, princeton-nlp/SWE-bench_Verified, or princeton-nlp/SWE-bench_Multimodal, specifies which dataset to evaluate on.
  • dataset_split, split for the huggingface dataset. e.g., test, dev. Default to test.

Caution

Setting num_workers larger than 1 is not officially tested, YMMV.

There is also one optional environment variable you can set.

export USE_HINT_TEXT=true # if you want to use hint text in the evaluation. Default to false. Ignore this if you are not sure.

Let's say you'd like to run 10 instances using llm.eval_gpt4_1106_preview and CodeActAgent,

then your command would be:

./evaluation/benchmarks/swe_bench/scripts/run_infer.sh llm.eval_gpt4_1106_preview HEAD CodeActAgent 10

For multimodal evaluation, you can use:

# Example for running multimodal SWE-Bench evaluation
./evaluation/benchmarks/swe_bench/scripts/run_infer.sh llm.eval_gpt4_vision HEAD CodeActAgent 10 100 1 princeton-nlp/SWE-bench_Multimodal test

Running in parallel with RemoteRuntime

OpenHands Remote Runtime is currently in beta (read here for more details), it allows you to run rollout in parallel in the cloud, so you don't need a powerful machine to run evaluation.

Fill out this form to apply if you want to try this out!

./evaluation/benchmarks/swe_bench/scripts/run_infer.sh [model_config] [git-version] [agent] [eval_limit] [max_iter] [num_workers] [dataset] [dataset_split]

# Example - This runs evaluation on CodeActAgent for 300 instances on "princeton-nlp/SWE-bench_Lite"'s test set, with max 30 iteration per instances, with 16 number of workers running in parallel
ALLHANDS_API_KEY="YOUR-API-KEY" RUNTIME=remote SANDBOX_REMOTE_RUNTIME_API_URL="https://runtime.eval.all-hands.dev" EVAL_DOCKER_IMAGE_PREFIX="us-central1-docker.pkg.dev/evaluation-092424/swe-bench-images" \
./evaluation/benchmarks/swe_bench/scripts/run_infer.sh llm.eval HEAD CodeActAgent 300 30 16 "princeton-nlp/SWE-bench_Lite" test

To clean-up all existing runtime you've already started, run:

ALLHANDS_API_KEY="YOUR-API-KEY" ./evaluation/utils/scripts/cleanup_remote_runtime.sh

Specify a subset of tasks to run infer

If you would like to specify a list of tasks you'd like to benchmark on, you could create a config.toml under ./evaluation/benchmarks/swe_bench/ folder, and put a list attribute named selected_ids, e.g.

selected_ids = ['sphinx-doc__sphinx-8721', 'sympy__sympy-14774', 'scikit-learn__scikit-learn-10508']

Then only these tasks (rows whose instance_id is in the above list) will be evaluated. In this case, eval_limit option applies to tasks that are in the selected_ids list.

After running the inference, you will obtain a output.jsonl (by default it will be saved to evaluation/evaluation_outputs).

Evaluate Generated Patches

Run evaluation with official SWE-Bench harness (Recommend if you have local disk space)

With output.jsonl file, you can run eval_infer.sh to evaluate generated patches, and produce a fine-grained report. This evaluation is performed using the official dockerized evaluation announced here.

Note

This process will automatically download docker images from SWE-Bench official docker hub, please make sure you have enough disk space!

./evaluation/benchmarks/swe_bench/scripts/eval_infer.sh $YOUR_OUTPUT_JSONL [instance_id] [dataset_name] [split]

# Example
./evaluation/benchmarks/swe_bench/scripts/eval_infer.sh evaluation/evaluation_outputs/outputs/swe_bench/CodeActAgent/gpt-4-1106-preview_maxiter_50_N_v1.0/output.jsonl

The script now accepts optional arguments:

  • instance_id: Specify a single instance to evaluate (optional)
  • dataset_name: The name of the dataset to use (default: "princeton-nlp/SWE-bench_Lite")
  • split: The split of the dataset to use (default: "test")

For example, to evaluate a specific instance with a custom dataset and split:

./evaluation/benchmarks/swe_bench/scripts/eval_infer.sh $YOUR_OUTPUT_JSONL instance_123 princeton-nlp/SWE-bench test

You can also pass in a JSONL with SWE-Bench format to ./evaluation/benchmarks/swe_bench/scripts/eval_infer.sh, where each line is a JSON of {"model_patch": "XXX", "model_name_or_path": "YYY", "instance_id": "ZZZ"}.

The final results will be saved to evaluation/evaluation_outputs/outputs/swe_bench/CodeActAgent/gpt-4-1106-preview_maxiter_50_N_v1.0/ with the following files/directory:

  • README.md: a report showing what are the instances that passed, failed, etc.
  • report.json: a JSON file that contains keys like "resolved_ids" pointing to instance IDs that are resolved by the agent.
  • logs/: a directory of test logs

Run evaluation with RemoteRuntime

OpenHands Remote Runtime is currently in beta (read here for more details), it allows you to run rollout in parallel in the cloud, so you don't need a powerful machine to run evaluation. Fill out this form to apply if you want to try this out!

./evaluation/benchmarks/swe_bench/scripts/eval_infer_remote.sh [output.jsonl filepath] [num_workers]

# Example - This evaluates patches generated by CodeActAgent on Llama-3.1-70B-Instruct-Turbo on "princeton-nlp/SWE-bench_Lite"'s test set, with 16 number of workers running in parallel
ALLHANDS_API_KEY="YOUR-API-KEY" RUNTIME=remote SANDBOX_REMOTE_RUNTIME_API_URL="https://runtime.eval.all-hands.dev" EVAL_DOCKER_IMAGE_PREFIX="us-central1-docker.pkg.dev/evaluation-092424/swe-bench-images" \
evaluation/benchmarks/swe_bench/scripts/eval_infer_remote.sh evaluation/evaluation_outputs/outputs/swe-bench-lite/CodeActAgent/Llama-3.1-70B-Instruct-Turbo_maxiter_30_N_v1.9-no-hint/output.jsonl 16 "princeton-nlp/SWE-bench_Lite" "test"

To clean-up all existing runtimes that you've already started, run:

ALLHANDS_API_KEY="YOUR-API-KEY" ./evaluation/utils/scripts/cleanup_remote_runtime.sh