This folder contains code and resources to run experiments and evaluations.

Before starting evaluation, follow the instructions here to setup your local development environment and LLM.

Once you are done with setup, you can follow the benchmark-specific instructions in each subdirectory of the evaluation directory. Generally these will involve running run_infer.py to perform inference with the agents.

To add an agent to OpenHands, you will need to implement it in the agenthub directory. There is a README there with more information.

To evaluate an agent, you can provide the agent's name to the run_infer.py program.

OpenHands in development mode uses config.toml to keep track of most configuration. IMPORTANT: For evaluation, only the LLM section in config.toml will be used. Other configurations, such as save_trajectory_path, are not applied during evaluation.

Here's an example configuration file you can use to define and use multiple LLMs:

[llm] # IMPORTANT: add your API key here, and set the model to the one you want to evaluate model = "gpt-4o-2024-05-13" api_key = "sk-XXX" [llm.eval_gpt4_1106_preview_llm] model = "gpt-4-1106-preview" api_key = "XXX" temperature = 0.0 [llm.eval_some_openai_compatible_model_llm] model = "openai/MODEL_NAME" base_url = "https://OPENAI_COMPATIBLE_URL/v1" api_key = "XXX" temperature = 0.0

For benchmarks that support condenser configuration (like SWE-Bench), you can define multiple condenser configurations in your config.toml file. A condenser is responsible for managing conversation history to maintain context while staying within token limits - you can learn more about how it works here:

# LLM-based summarizing condenser for evaluation [condenser.summarizer_for_eval] type = "llm" llm_config = "haiku" # Reference to an LLM config to use for summarization keep_first = 2 # Number of initial events to always keep max_size = 100 # Maximum size of history before triggering summarization # Recent events condenser for evaluation [condenser.recent_for_eval] type = "recent" keep_first = 2 # Number of initial events to always keep max_events = 50 # Maximum number of events to keep in history

You can then specify which condenser configuration to use when running evaluation scripts, for example:

EVAL_CONDENSER=summarizer_for_eval \ ./evaluation/benchmarks/swe_bench/scripts/run_infer.sh llm.eval_gpt4_1106_preview HEAD CodeActAgent 500 100 1 princeton-nlp/SWE-bench_Verified test

The name is up to you, but should match a name defined in your config.toml file. The last argument in the command specifies the condenser configuration to use. In this case, summarizer_for_eval is used, which refers to the LLM-based summarizing condenser as defined above.

If no condenser configuration is specified, the 'noop' condenser will be used by default, which keeps the full conversation history.

For other configurations specific to evaluation, such as save_trajectory_path, these are typically set in the get_config function of the respective run_infer.py file for each benchmark.

The LLM-Based Editor tool (currently supported only for SWE-Bench) can be enabled by setting:

export ENABLE_LLM_EDITOR=true

You can set the config for the Editor LLM as:

[llm.draft_editor] base_url = "http://localhost:9002/v1" model = "hosted_vllm/lite_coder_qwen_editor_3B" api_key = "" temperature = 0.7 max_input_tokens = 10500 max_output_tokens = 10500

The OpenHands evaluation harness supports a wide variety of benchmarks across software engineering, web browsing, miscellaneous assistance, and real-world tasks.

• SWE-Bench: evaluation/benchmarks/swe_bench
• HumanEvalFix: evaluation/benchmarks/humanevalfix
• BIRD: evaluation/benchmarks/bird
• BioCoder: evaluation/benchmarks/biocoder
• ML-Bench: evaluation/benchmarks/ml_bench
• APIBench: evaluation/benchmarks/gorilla
• ToolQA: evaluation/benchmarks/toolqa
• AiderBench: evaluation/benchmarks/aider_bench
• Commit0: evaluation/benchmarks/commit0_bench
• DiscoveryBench: evaluation/benchmarks/discoverybench
• TerminalBench: evaluation/benchmarks/terminal_bench

• WebArena: evaluation/benchmarks/webarena
• MiniWob++: evaluation/benchmarks/miniwob
• Browsing Delegation: evaluation/benchmarks/browsing_delegation

• GAIA: evaluation/benchmarks/gaia
• GPQA: evaluation/benchmarks/gpqa
• AgentBench: evaluation/benchmarks/agent_bench
• MINT: evaluation/benchmarks/mint
• Entity deduction Arena (EDA): evaluation/benchmarks/EDA
• ProofWriter: evaluation/benchmarks/logic_reasoning
• ScienceAgentBench: evaluation/benchmarks/scienceagentbench

• TheAgentCompany: evaluation/benchmarks/the_agent_company

Check this huggingface space for visualization of existing experimental results.

You can start your own fork of our huggingface evaluation outputs and submit a PR of your evaluation results to our hosted huggingface repo via PR following the guide here.

To learn more about how to integrate your benchmark into OpenHands, check out tutorial here. Briefly,

• Each subfolder contains a specific benchmark or experiment. For example, evaluation/benchmarks/swe_bench should contain all the preprocessing/evaluation/analysis scripts.
• Raw data and experimental records should not be stored within this repo.
• For model outputs, they should be stored at this huggingface space for visualization.
• Important data files of manageable size and analysis scripts (e.g., jupyter notebooks) can be directly uploaded to this repo.