evaluation/benchmarks/agent_bench/README.md

AgentBench Evaluation

This folder contains evaluation harness for evaluating agents on the AgentBench: Evaluating LLMs as Agents. We currently only support running on the osbench subset.

Setup Environment and LLM Configuration

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

Start the evaluation

./evaluation/benchmarks/agent_bench/scripts/run_infer.sh [model_config] [git-version] [agent] [eval_limit]

• 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.

Following is the basic command to start the evaluation.

You can update the arguments in the script evaluation/benchmarks/agent_bench/scripts/run_infer.sh, such as --max-iterations, --eval-num-workers and so on.

• --agent-cls, the agent to use. For example, CodeActAgent.
• --llm-config: the LLM configuration to use. For example, eval_gpt4_1106_preview.
• --max-iterations: the number of iterations to run the evaluation. For example, 30.
• --eval-num-workers: the number of workers to use for evaluation. For example, 5.
• --eval-n-limit: the number of examples to evaluate. For example, 100.

./evaluation/benchmarks/agent_bench/scripts/run_infer.sh eval_gpt35_turbo HEAD CodeActAgent 1

Run with Remote Runtime (experimental)

You can run the evaluation using a remote runtime instead of a local Docker container. This is useful when you want to run the evaluation in a cloud environment or when you don't have Docker installed locally.

To use the remote runtime, set the following environment variables:

# Required environment variables
export ALLHANDS_API_KEY="your-api-key"  # Contact the team to get an API key
export RUNTIME=remote
export SANDBOX_REMOTE_RUNTIME_API_URL="https://runtime.eval.all-hands.dev"

# Run the evaluation
./evaluation/benchmarks/agent_bench/scripts/run_infer.sh llm.eval_gpt4_1106_preview HEAD CodeActAgent 1

The remote runtime will build a container image and run the evaluation in a cloud environment. The results will be saved locally in the same way as when running with a local runtime.