Fix issue #4700: 'Cache google sheet contents, so they'"'"'re retrieved no more than once per minute'

.github/ISSUE_TEMPLATE/bug_template.yml

@@ -31,8 +31,6 @@ body:
       options:
         - Docker command in README
         - Development workflow
-        - app.all-hands.dev
-        - Other
       default: 0
 
   - type: input

.github/workflows/ghcr-build.yml

@@ -286,6 +286,7 @@ jobs:
           image_name=ghcr.io/${{ github.repository_owner }}/runtime:${{ env.RELEVANT_SHA }}-${{ matrix.base_image }}
           image_name=$(echo $image_name | tr '[:upper:]' '[:lower:]')
 
+          SKIP_CONTAINER_LOGS=true \
           TEST_RUNTIME=eventstream \
           SANDBOX_USER_ID=$(id -u) \
           SANDBOX_RUNTIME_CONTAINER_IMAGE=$image_name \
@@ -363,6 +364,7 @@ jobs:
           image_name=ghcr.io/${{ github.repository_owner }}/runtime:${{ env.RELEVANT_SHA }}-${{ matrix.base_image }}
           image_name=$(echo $image_name | tr '[:upper:]' '[:lower:]')
 
+          SKIP_CONTAINER_LOGS=true \
           TEST_RUNTIME=eventstream \
           SANDBOX_USER_ID=$(id -u) \
           SANDBOX_RUNTIME_CONTAINER_IMAGE=$image_name \
@@ -422,9 +424,9 @@ jobs:
             -p 3000:3000 \
             -v /var/run/docker.sock:/var/run/docker.sock \
             --add-host host.docker.internal:host-gateway \
-            -e SANDBOX_RUNTIME_CONTAINER_IMAGE=docker.all-hands.dev/all-hands-ai/runtime:$SHORT_SHA-nikolaik \
+            -e SANDBOX_RUNTIME_CONTAINER_IMAGE=ghcr.io/all-hands-ai/runtime:$SHORT_SHA-nikolaik \
             --name openhands-app-$SHORT_SHA \
-            docker.all-hands.dev/all-hands-ai/openhands:$SHORT_SHA"
+            ghcr.io/all-hands-ai/runtime:$SHORT_SHA"
 
           PR_BODY=$(gh pr view $PR_NUMBER --json body --jq .body)
 

.github/workflows/openhands-resolver.yml

@@ -3,13 +3,11 @@ name: Resolve Issues with OpenHands
 on:
   issues:
     types: [labeled]
-  pull_request:
-    types: [labeled]
 
 jobs:
   call-openhands-resolver:
     uses: All-Hands-AI/openhands-resolver/.github/workflows/openhands-resolver.yml@main
     if: github.event.label.name == 'fix-me'
     with:
-      max_iterations: 50
+      issue_number: ${{ github.event.issue.number }}
     secrets: inherit

Development.md

@@ -100,7 +100,7 @@ poetry run pytest ./tests/unit/test_*.py
 ### 9. Use existing Docker image
 To reduce build time (e.g., if no changes were made to the client-runtime component), you can use an existing Docker container image. Follow these steps:
 1. Set the SANDBOX_RUNTIME_CONTAINER_IMAGE environment variable to the desired Docker image.
-2. Example: export SANDBOX_RUNTIME_CONTAINER_IMAGE=ghcr.io/all-hands-ai/runtime:0.13-nikolaik
+2. Example: export SANDBOX_RUNTIME_CONTAINER_IMAGE=ghcr.io/all-hands-ai/runtime:0.9-nikolaik
 
 ## Develop inside Docker container
 

README.md

@@ -38,16 +38,15 @@ See the [Installation](https://docs.all-hands.dev/modules/usage/installation) gu
 system requirements and more information.
 
 ```bash
-docker pull docker.all-hands.dev/all-hands-ai/runtime:0.13-nikolaik
+docker pull docker.all-hands.dev/all-hands-ai/runtime:0.12-nikolaik
 
-docker run -it --pull=always \
-    -e SANDBOX_RUNTIME_CONTAINER_IMAGE=docker.all-hands.dev/all-hands-ai/runtime:0.13-nikolaik \
+docker run -it --rm --pull=always \
+    -e SANDBOX_RUNTIME_CONTAINER_IMAGE=docker.all-hands.dev/all-hands-ai/runtime:0.12-nikolaik \
     -v /var/run/docker.sock:/var/run/docker.sock \
     -p 3000:3000 \
-    -e LOG_ALL_EVENTS=true \
     --add-host host.docker.internal:host-gateway \
     --name openhands-app \
-    docker.all-hands.dev/all-hands-ai/openhands:0.13
+    docker.all-hands.dev/all-hands-ai/openhands:0.12
 ```
 
 You'll find OpenHands running at [http://localhost:3000](http://localhost:3000)!

compose.yml

@@ -7,7 +7,7 @@ services:
     image: openhands:latest
     container_name: openhands-app-${DATE:-}
     environment:
-      - SANDBOX_RUNTIME_CONTAINER_IMAGE=${SANDBOX_RUNTIME_CONTAINER_IMAGE:-ghcr.io/all-hands-ai/runtime:0.13-nikolaik}
+      - SANDBOX_RUNTIME_CONTAINER_IMAGE=${SANDBOX_RUNTIME_CONTAINER_IMAGE:-ghcr.io/all-hands-ai/runtime:0.9-nikolaik}
       - SANDBOX_USER_ID=${SANDBOX_USER_ID:-1234}
       - WORKSPACE_MOUNT_PATH=${WORKSPACE_BASE:-$PWD/workspace}
     ports:

config.template.toml

@@ -32,8 +32,7 @@ workspace_base = "./workspace"
 # Enable saving and restoring the session when run from CLI
 #enable_cli_session = false
 
-# Path to store trajectories, can be a folder or a file
-# If it's a folder, the session id will be used as the file name
+# Path to store trajectories
 #trajectories_path="./trajectories"
 
 # File store path

containers/dev/compose.yml

@@ -11,7 +11,7 @@ services:
       - BACKEND_HOST=${BACKEND_HOST:-"0.0.0.0"}
       - SANDBOX_API_HOSTNAME=host.docker.internal
       #
-      - SANDBOX_RUNTIME_CONTAINER_IMAGE=${SANDBOX_RUNTIME_CONTAINER_IMAGE:-ghcr.io/all-hands-ai/runtime:0.13-nikolaik}
+      - SANDBOX_RUNTIME_CONTAINER_IMAGE=${SANDBOX_RUNTIME_CONTAINER_IMAGE:-ghcr.io/all-hands-ai/runtime:0.9-nikolaik}
       - SANDBOX_USER_ID=${SANDBOX_USER_ID:-1234}
       - WORKSPACE_MOUNT_PATH=${WORKSPACE_BASE:-$PWD/workspace}
     ports:

docs/i18n/fr/docusaurus-plugin-content-docs/current/usage/how-to/evaluation-harness.md

@@ -161,7 +161,7 @@ Pour créer un workflow d'évaluation pour votre benchmark, suivez ces étapes :
            instruction=instruction,
            test_result=evaluation_result,
            metadata=metadata,
-           history=compatibility_for_eval_history_pairs(state.history),
+           history=state.history.compatibility_for_eval_history_pairs(),
            metrics=state.metrics.get() if state.metrics else None,
            error=state.last_error if state and state.last_error else None,
        )
@@ -260,7 +260,7 @@ def codeact_user_response(state: State | None) -> str:
         # vérifier si l'agent a essayé de parler à l'utilisateur 3 fois, si oui, faire savoir à l'agent qu'il peut abandonner
         user_msgs = [
             event
-            for event in state.history
+            for event in state.history.get_events()
             if isinstance(event, MessageAction) and event.source == 'user'
         ]
         if len(user_msgs) >= 2:

docs/i18n/zh-Hans/docusaurus-plugin-content-docs/current/usage/how-to/evaluation-harness.md

@@ -158,7 +158,7 @@ OpenHands 的主要入口点在 `openhands/core/main.py` 中。以下是它工
            instruction=instruction,
            test_result=evaluation_result,
            metadata=metadata,
-           history=compatibility_for_eval_history_pairs(state.history),
+           history=state.history.compatibility_for_eval_history_pairs(),
            metrics=state.metrics.get() if state.metrics else None,
            error=state.last_error if state and state.last_error else None,
        )
@@ -257,7 +257,7 @@ def codeact_user_response(state: State | None) -> str:
         # 检查代理是否已尝试与用户对话 3 次，如果是，让代理知道它可以放弃
         user_msgs = [
             event
-            for event in state.history
+            for event in state.history.get_events()
             if isinstance(event, MessageAction) and event.source == 'user'
         ]
         if len(user_msgs) >= 2:

docs/modules/usage/how-to/cli-mode.md

@@ -50,7 +50,7 @@ LLM_API_KEY="sk_test_12345"
 ```bash
 docker run -it \
     --pull=always \
-    -e SANDBOX_RUNTIME_CONTAINER_IMAGE=docker.all-hands.dev/all-hands-ai/runtime:0.13-nikolaik \
+    -e SANDBOX_RUNTIME_CONTAINER_IMAGE=docker.all-hands.dev/all-hands-ai/runtime:0.12-nikolaik \
     -e SANDBOX_USER_ID=$(id -u) \
     -e WORKSPACE_MOUNT_PATH=$WORKSPACE_BASE \
     -e LLM_API_KEY=$LLM_API_KEY \
@@ -59,7 +59,7 @@ docker run -it \
     -v /var/run/docker.sock:/var/run/docker.sock \
     --add-host host.docker.internal:host-gateway \
     --name openhands-app-$(date +%Y%m%d%H%M%S) \
-    docker.all-hands.dev/all-hands-ai/openhands:0.13 \
+    docker.all-hands.dev/all-hands-ai/openhands:0.12 \
     python -m openhands.core.cli
 ```
 

docs/modules/usage/how-to/evaluation-harness.md

@@ -158,7 +158,7 @@ To create an evaluation workflow for your benchmark, follow these steps:
            instruction=instruction,
            test_result=evaluation_result,
            metadata=metadata,
-           history=compatibility_for_eval_history_pairs(state.history),
+           history=state.history.compatibility_for_eval_history_pairs(),
            metrics=state.metrics.get() if state.metrics else None,
            error=state.last_error if state and state.last_error else None,
        )
@@ -257,7 +257,7 @@ def codeact_user_response(state: State | None) -> str:
         # check if the agent has tried to talk to the user 3 times, if so, let the agent know it can give up
         user_msgs = [
             event
-            for event in state.history
+            for event in state.history.get_events()
             if isinstance(event, MessageAction) and event.source == 'user'
         ]
         if len(user_msgs) >= 2:

docs/modules/usage/how-to/gui-mode.md

@@ -19,15 +19,6 @@ OpenHands provides a user-friendly Graphical User Interface (GUI) mode for inter
 3. Enter the corresponding `API Key` for your chosen provider.
 4. Click "Save" to apply the settings.
 
-### GitHub Token Setup
-
-OpenHands automatically exports a `GITHUB_TOKEN` to the shell environment if it is available. This can happen in two ways:
-
-1. Locally (OSS): The user directly inputs their GitHub token.
-2. Online (SaaS): The token is obtained through GitHub OAuth authentication.
-
-When you reach the `/app` route, the app checks if a token is present. If it finds one, it sets it in the environment for the agent to use.
-
 ### Advanced Settings
 
 1. Toggle `Advanced Options` to access additional settings.

docs/modules/usage/how-to/headless-mode.md

@@ -44,16 +44,15 @@ LLM_API_KEY="sk_test_12345"
 ```bash
 docker run -it \
     --pull=always \
-    -e SANDBOX_RUNTIME_CONTAINER_IMAGE=docker.all-hands.dev/all-hands-ai/runtime:0.13-nikolaik \
+    -e SANDBOX_RUNTIME_CONTAINER_IMAGE=docker.all-hands.dev/all-hands-ai/runtime:0.12-nikolaik \
     -e SANDBOX_USER_ID=$(id -u) \
     -e WORKSPACE_MOUNT_PATH=$WORKSPACE_BASE \
     -e LLM_API_KEY=$LLM_API_KEY \
     -e LLM_MODEL=$LLM_MODEL \
-    -e LOG_ALL_EVENTS=true \
     -v $WORKSPACE_BASE:/opt/workspace_base \
     -v /var/run/docker.sock:/var/run/docker.sock \
     --add-host host.docker.internal:host-gateway \
     --name openhands-app-$(date +%Y%m%d%H%M%S) \
-    docker.all-hands.dev/all-hands-ai/openhands:0.13 \
+    docker.all-hands.dev/all-hands-ai/openhands:0.12 \
     python -m openhands.core.main -t "write a bash script that prints hi"
 ```

docs/modules/usage/installation.mdx

@@ -11,16 +11,15 @@
 The easiest way to run OpenHands is in Docker.
 
 ```bash
-docker pull docker.all-hands.dev/all-hands-ai/runtime:0.13-nikolaik
+docker pull docker.all-hands.dev/all-hands-ai/runtime:0.12-nikolaik
 
 docker run -it --rm --pull=always \
-    -e SANDBOX_RUNTIME_CONTAINER_IMAGE=docker.all-hands.dev/all-hands-ai/runtime:0.13-nikolaik \
+    -e SANDBOX_RUNTIME_CONTAINER_IMAGE=docker.all-hands.dev/all-hands-ai/runtime:0.12-nikolaik \
     -v /var/run/docker.sock:/var/run/docker.sock \
     -p 3000:3000 \
-    -e LOG_ALL_EVENTS=true \
     --add-host host.docker.internal:host-gateway \
     --name openhands-app \
-    docker.all-hands.dev/all-hands-ai/openhands:0.13
+    docker.all-hands.dev/all-hands-ai/openhands:0.12
 ```
 
 You can also run OpenHands in a scriptable [headless mode](https://docs.all-hands.dev/modules/usage/how-to/headless-mode), as an [interactive CLI](https://docs.all-hands.dev/modules/usage/how-to/cli-mode), or using the [OpenHands GitHub Action](https://docs.all-hands.dev/modules/usage/how-to/github-action).

docs/modules/usage/llms/litellm-proxy.md

@@ -1,20 +0,0 @@
-# LiteLLM Proxy
-
-OpenHands supports using the [LiteLLM proxy](https://docs.litellm.ai/docs/proxy/quick_start) to access various LLM providers.
-
-## Configuration
-
-To use LiteLLM proxy with OpenHands, you need to:
-
-1. Set up a LiteLLM proxy server (see [LiteLLM documentation](https://docs.litellm.ai/docs/proxy/quick_start))
-2. When running OpenHands, you'll need to set the following in the OpenHands UI through the Settings:
-  * Enable `Advanced Options`
-  * `Custom Model` to the prefix `litellm_proxy/` + the model you will be using (e.g. `litellm_proxy/anthropic.claude-3-5-sonnet-20241022-v2:0`)
-  * `Base URL` to your LiteLLM proxy URL (e.g. `https://your-litellm-proxy.com`)
-  * `API Key` to your LiteLLM proxy API key
-
-## Supported Models
-
-The supported models depend on your LiteLLM proxy configuration. OpenHands supports any model that your LiteLLM proxy is configured to handle.
-
-Refer to your LiteLLM proxy configuration for the list of available models and their names.

docs/modules/usage/llms/llms.md

@@ -4,11 +4,11 @@ OpenHands can connect to any LLM supported by LiteLLM. However, it requires a po
 
 ## Model Recommendations
 
-Based on our evaluations of language models for coding tasks (using the SWE-bench dataset), we can provide some recommendations for model selection. Some analyses can be found in [this blog article comparing LLMs](https://www.all-hands.dev/blog/evaluation-of-llms-as-coding-agents-on-swe-bench-at-30x-speed) and [this blog article with some more recent results](https://www.all-hands.dev/blog/openhands-codeact-21-an-open-state-of-the-art-software-development-agent).
+Based on a recent evaluation of language models for coding tasks (using the SWE-bench dataset), we can provide some recommendations for model selection. The full analysis can be found in [this blog article](https://www.all-hands.dev/blog/evaluation-of-llms-as-coding-agents-on-swe-bench-at-30x-speed).
 
 When choosing a model, consider both the quality of outputs and the associated costs. Here's a summary of the findings:
 
-- Claude 3.5 Sonnet is the best by a fair amount, achieving a 53% resolve rate on SWE-Bench Verified with the default agent in OpenHands.
+- Claude 3.5 Sonnet is the best by a fair amount, achieving a 27% resolve rate with the default agent in OpenHands.
 - GPT-4o lags behind, and o1-mini actually performed somewhat worse than GPT-4o. We went in and analyzed the results a little, and briefly it seemed like o1 was sometimes "overthinking" things, performing extra environment configuration tasks when it could just go ahead and finish the task.
 - Finally, the strongest open models were Llama 3.1 405 B and deepseek-v2.5, and they performed reasonably, even besting some of the closed models.
 
@@ -63,7 +63,6 @@ We have a few guides for running OpenHands with specific model providers:
 - [Azure](llms/azure-llms)
 - [Google](llms/google-llms)
 - [Groq](llms/groq)
-- [LiteLLM Proxy](llms/litellm-proxy)
 - [OpenAI](llms/openai-llms)
 - [OpenRouter](llms/openrouter)
 

docs/modules/usage/runtimes.md

@@ -59,7 +59,8 @@ docker run # ...
     -e RUNTIME=remote \
     -e SANDBOX_REMOTE_RUNTIME_API_URL="https://runtime.app.all-hands.dev" \
     -e SANDBOX_API_KEY="your-all-hands-api-key" \
-    -e SANDBOX_KEEP_RUNTIME_ALIVE="true" \
+    -e SANDBOX_KEEP_REMOTE_RUNTIME_ALIVE="true" \
+    -e SANDBOX_RUNTIME_CONTAINER_IMAGE=docker.all-hands.dev/all-hands-ai/runtime:0.11-nikolaik \
     # ...
 ```
 
@@ -74,4 +75,5 @@ docker run # ...
     -e RUNTIME=modal \
     -e MODAL_API_TOKEN_ID="your-id" \
     -e MODAL_API_TOKEN_SECRET="your-secret" \
+    -e SANDBOX_RUNTIME_CONTAINER_IMAGE=docker.all-hands.dev/all-hands-ai/runtime:0.11-nikolaik \
 ```

docs/package.json

@@ -15,10 +15,10 @@
     "typecheck": "tsc"
   },
   "dependencies": {
-    "@docusaurus/core": "^3.6.0",
-    "@docusaurus/plugin-content-pages": "^3.6.0",
-    "@docusaurus/preset-classic": "^3.6.0",
-    "@docusaurus/theme-mermaid": "^3.6.0",
+    "@docusaurus/core": "^3.5.2",
+    "@docusaurus/plugin-content-pages": "^3.5.2",
+    "@docusaurus/preset-classic": "^3.5.2",
+    "@docusaurus/theme-mermaid": "^3.5.2",
     "@mdx-js/react": "^3.1.0",
     "clsx": "^2.0.0",
     "prism-react-renderer": "^2.4.0",
@@ -29,7 +29,7 @@
   },
   "devDependencies": {
     "@docusaurus/module-type-aliases": "^3.5.1",
-    "@docusaurus/tsconfig": "^3.6.0",
+    "@docusaurus/tsconfig": "^3.5.2",
     "@docusaurus/types": "^3.5.1",
     "typescript": "~5.6.3"
   },

docs/sidebars.ts

@@ -76,11 +76,6 @@ const sidebars: SidebarsConfig = {
                   label: 'Groq',
                   id: 'usage/llms/groq',
                 },
-                {
-                  type: 'doc',
-                  label: 'LiteLLM Proxy',
-                  id: 'usage/llms/litellm-proxy',
-                },
                 {
                   type: 'doc',
                   label: 'OpenAI',

evaluation/EDA/run_infer.py

@@ -8,7 +8,6 @@ from evaluation.EDA.game import Q20Game, Q20GameCelebrity
 from evaluation.utils.shared import (
     EvalMetadata,
     EvalOutput,
-    compatibility_for_eval_history_pairs,
     make_metadata,
     prepare_dataset,
     reset_logger_for_multiprocessing,
@@ -35,8 +34,7 @@ def codeact_user_response_eda(state: State) -> str:
 
     # retrieve the latest model message from history
     if state.history:
-        last_agent_message = state.get_last_agent_message()
-        model_guess = last_agent_message.content if last_agent_message else ''
+        model_guess = state.history.get_last_agent_message()
 
     assert game is not None, 'Game is not initialized.'
     msg = game.generate_user_response(model_guess)
@@ -141,8 +139,7 @@ def process_instance(
     if state is None:
         raise ValueError('State should not be None.')
 
-    last_agent_message = state.get_last_agent_message()
-    final_message = last_agent_message.content if last_agent_message else ''
+    final_message = state.history.get_last_agent_message()
 
     logger.info(f'Final message: {final_message} | Ground truth: {instance["text"]}')
     test_result = game.reward()
@@ -151,7 +148,7 @@ def process_instance(
     # history is now available as a stream of events, rather than list of pairs of (Action, Observation)
     # for compatibility with the existing output format, we can remake the pairs here
     # remove when it becomes unnecessary
-    histories = compatibility_for_eval_history_pairs(state.history)
+    histories = state.history.compatibility_for_eval_history_pairs()
 
     # Save the output
     output = EvalOutput(

evaluation/agent_bench/run_infer.py

@@ -16,7 +16,6 @@ from evaluation.agent_bench.helper import (
 from evaluation.utils.shared import (
     EvalMetadata,
     EvalOutput,
-    compatibility_for_eval_history_pairs,
     make_metadata,
     prepare_dataset,
     reset_logger_for_multiprocessing,
@@ -243,7 +242,7 @@ def process_instance(
         raw_ans = ''
 
         # retrieve the last agent message or thought
-        for event in reversed(state.history):
+        for event in state.history.get_events(reverse=True):
             if event.source == 'agent':
                 if isinstance(event, AgentFinishAction):
                     raw_ans = event.thought
@@ -272,7 +271,7 @@ def process_instance(
     # history is now available as a stream of events, rather than list of pairs of (Action, Observation)
     # for compatibility with the existing output format, we can remake the pairs here
     # remove when it becomes unnecessary
-    histories = compatibility_for_eval_history_pairs(state.history)
+    histories = state.history.compatibility_for_eval_history_pairs()
 
     metrics = state.metrics.get() if state.metrics else None
 

evaluation/aider_bench/run_infer.py

@@ -15,7 +15,6 @@ from evaluation.aider_bench.helper import (
 from evaluation.utils.shared import (
     EvalMetadata,
     EvalOutput,
-    compatibility_for_eval_history_pairs,
     make_metadata,
     prepare_dataset,
     reset_logger_for_multiprocessing,
@@ -251,7 +250,7 @@ def process_instance(
     # history is now available as a stream of events, rather than list of pairs of (Action, Observation)
     # for compatibility with the existing output format, we can remake the pairs here
     # remove when it becomes unnecessary
-    histories = compatibility_for_eval_history_pairs(state.history)
+    histories = state.history.compatibility_for_eval_history_pairs()
     metrics = state.metrics.get() if state.metrics else None
 
     # Save the output

evaluation/biocoder/run_infer.py

@@ -13,7 +13,6 @@ from evaluation.utils.shared import (
     EvalMetadata,
     EvalOutput,
     codeact_user_response,
-    compatibility_for_eval_history_pairs,
     make_metadata,
     prepare_dataset,
     reset_logger_for_multiprocessing,
@@ -300,7 +299,7 @@ def process_instance(
     # history is now available as a stream of events, rather than list of pairs of (Action, Observation)
     # for compatibility with the existing output format, we can remake the pairs here
     # remove when it becomes unnecessary
-    histories = compatibility_for_eval_history_pairs(state.history)
+    histories = state.history.compatibility_for_eval_history_pairs()
 
     test_result['generated'] = test_result['metadata']['1_copy_change_code']
 

evaluation/bird/run_infer.py

@@ -16,7 +16,6 @@ from tqdm import tqdm
 from evaluation.utils.shared import (
     EvalMetadata,
     EvalOutput,
-    compatibility_for_eval_history_pairs,
     make_metadata,
     prepare_dataset,
     reset_logger_for_multiprocessing,
@@ -47,7 +46,7 @@ def codeact_user_response(state: State) -> str:
         # check if the agent has tried to talk to the user 3 times, if so, let the agent know it can give up
         user_msgs = [
             event
-            for event in state.history
+            for event in state.history.get_events()
             if isinstance(event, MessageAction) and event.source == 'user'
         ]
         if len(user_msgs) > 2:
@@ -432,7 +431,7 @@ def process_instance(
     # history is now available as a stream of events, rather than list of pairs of (Action, Observation)
     # for compatibility with the existing output format, we can remake the pairs here
     # remove when it becomes unnecessary
-    histories = compatibility_for_eval_history_pairs(state.history)
+    histories = state.history.compatibility_for_eval_history_pairs()
 
     # Save the output
     output = EvalOutput(

evaluation/browsing_delegation/run_infer.py

@@ -9,7 +9,6 @@ from datasets import load_dataset
 from evaluation.utils.shared import (
     EvalMetadata,
     EvalOutput,
-    compatibility_for_eval_history_pairs,
     make_metadata,
     prepare_dataset,
     reset_logger_for_multiprocessing,
@@ -90,7 +89,7 @@ def process_instance(
     # history is now available as a stream of events, rather than list of pairs of (Action, Observation)
     # for compatibility with the existing output format, we can remake the pairs here
     # remove when it becomes unnecessary
-    histories = compatibility_for_eval_history_pairs(state.history)
+    histories = state.history.compatibility_for_eval_history_pairs()
 
     # find the last delegate action
     last_delegate_action = None

evaluation/discoverybench/README.md

@@ -1,37 +0,0 @@
-# DiscoveryBench with OpenHands
-
-[DiscoveryBench](https://github.com/allenai/discoverybench/) [(Paper)](https://arxiv.org/abs/2407.01725v1) contains 264 tasks collected across 6 diverse domains, such as biology, economics, and sociology. It incorporates discovery workflows from published papers to approximate the real-world challenges faced by researchers.
-
-<p align="center">
-  <a href="[https://github.com/allenai/discoverybench](https://github.com/allenai/discoverybench)">
-    <img src="https://raw.githubusercontent.com/allenai/discoverybench/refs/heads/main/assets/discoverybench-openhands-teaser.png" width="100%" alt="DiscoveryBench Background" />
-  </a>
-</p>
-
-
-## Setup Environment and LLM Configuration
-
-1. Please follow instructions mentioned [here](https://github.com/openlocus/OpenHands/blob/discoverybench-openhands-integration/evaluation/README.md#setup) to setup OpenHands development environment and LLMs locally
-
-2. Execute the bash script to start DiscoveryBench Evaluation
-
-```
-./evaluation/discoverybench/scripts/run_infer.sh [YOUR MODEL CONFIG]
-```
-Replace `[YOUR MODEL CONFIG]` with any model the model that you have set up in `config.toml`
-
-
-## Run Inference on DiscoveryBench Instances
-
-When the `run_infer.sh` script is started, it will automatically pull the latest DiscoveryBench instances & set up the agent environment. The OpenHands agent is invoked to process the task within this environment, producing a hypothesis. We then evaluate it against the “gold” hypothesis provided by DiscoveryBench. The evaluation result, along with the agent chat history is logged to `output.jsonl` under `evaluation_outputs`.
-
-
-```
-./evaluation/discoverybench/scripts/run_infer.sh [MODEL_CONFIG] [GIT_COMMIT] [AGENT] [EVAL_LIMIT] [NUM_WORKERS]
-```
-
-- `MODEL_CONFIG`: Name of the model you want to evaluate with
-- `GIT_COMMIT`: This should be the git commit hash or release tag for OpenHands, e.g., HEAD or a specific tag like 0.6.2.
-- `AGENT`: Use CoderActAgent, right now it only supports that.
-- `EVAL_LIMIT`: Number of samples to evaluate.
-- `NUM_WORKERS`: Number of workers to parallelize the evaluation process.

evaluation/discoverybench/eval_utils/README.md

@@ -1,7 +0,0 @@
-## DiscoveryBench Evaluation Utils
-
-- **`eval_w_subhypo_gen.py`**: Implements the DiscoveryBench logic for evaluating agent-generated hypotheses.
-- **`lm_utils.py`**: Provides utility functions necessary for the evaluation process.
-- **`openai_helpers.py`**: Includes helper functions for OpenAI-related tasks.
-- **`openai_semantic_gen_prompts.py`**: Contains prompts used for semantic generation.
-- **`response_parser.py`**: Handles the parsing of agent-generated hypotheses.

evaluation/discoverybench/eval_utils/eval_w_subhypo_gen.py

@@ -1,538 +0,0 @@
-import json
-import logging
-
-from openai import OpenAI
-
-from .lm_utils import run_chatgpt_query_multi_turn
-from .openai_helpers import get_response
-
-logging.basicConfig(
-    format='%(asctime)s - %(levelname)s - %(name)s -   %(message)s',
-    datefmt='%m/%d/%Y %H:%M:%S',
-    level=logging.INFO,
-)
-logger = logging.getLogger(__name__)
-
-
-def get_score_from_answer(type, answer):
-    if type == 'context':
-        answer = answer.replace('Answer:', '').strip()
-        if answer.startswith('A)'):
-            return 1.0
-        elif answer.startswith('B)'):
-            return 0.0
-        return -1.0
-
-    elif type == 'var':
-        try:
-            var_json = json.loads(answer)
-            # print(f"var_json:{var_json}")
-            p = 0.0
-            r = 0.0
-            f1 = 0.0
-            if var_json['sizeB']:
-                p = var_json['intersection'] / var_json['sizeB']
-            if var_json['sizeA']:
-                r = var_json['intersection'] / var_json['sizeA']
-            if p > 0.0 and r > 0.0:
-                f1 = (2 * p * r) / (p + r)
-            else:
-                f1 = 0.0
-            eval_rec = {
-                'p': p,
-                'r': r,
-                'f1': f1,
-                'sizeA': var_json['sizeA'],
-                'sizeB': var_json['sizeB'],
-                'intersection': var_json['intersection'],
-                'explanation': var_json['explanation'],
-            }
-            print(f'var_eval: {eval_rec}')
-            return eval_rec
-        except Exception:  # COMMENT: added Exception
-            return {'p': -1.0, 'r': -1.0, 'f1': -1.0}
-    elif type == 'rel':
-        print(answer)
-        rel_json = json.loads(answer)
-        answer_str = rel_json['answer'].strip()
-        if answer_str.startswith('A') or 'very similar' in answer_str:
-            return 1.0
-        elif (
-            answer_str.startswith('B') or 'similar but general than HypoA' in answer_str
-        ):
-            return 0.5
-        elif answer_str.startswith('C') or 'different' in answer_str:
-            return 0.0
-        return -1.0
-    return -1.0
-
-
-def ask_dimension_question(
-    query,
-    gold_hypo,
-    gold_workflow,
-    gen_hypo,
-    gen_workflow,
-    dataset_meta,
-    llm_used,
-    dimension,
-    dataset_type,
-    use_column_metadata=True,
-):
-    dimension_question = ''
-    answer = ''
-    score = 0.0
-    if dimension == 'var':
-        score = {'p': -1.0, 'r': -1.0, 'f1': -1.0}
-    num_tokens = 256
-    num_retries = 1
-    json_response = False
-
-    messages = [
-        {
-            'role': 'system',
-            'content': 'You are an AI assistant that helps evaluate a data-driven hypothesis. You are a helpful assistant who is not talkative. You only respond with the exact answer to a query without additional conversation.',
-        },
-    ]
-    if dimension == 'context':
-        dimension_question = """\
-        Question: Is HypoB defined in the same context as HypoA?
-        (Context refers to assumptions/stratification under which the hypotheses are defined.)
-        Options: A) same   B) different
-        What is your answer?"""
-    elif dimension == 'var':
-        dimension_question = """\
-        Question: For both HypoA and HypoB, what are the different variables found in the hypotheses? \
-        Return your answer as a JSON object in the following format:
-        ```json
-        {{
-        "sizeA": num of variables used in HypoA
-        "sizeB": num of variables used in HypoB
-        "intersection": num of variables common in HypoA and HypoB. Use *fuzzy matching* to determine intersection, accounting for paraphrases or slightly different surface forms
-        "explanation": a short text explanation about the variables
-        }}```
-        Answer:"""
-        num_tokens = 512
-        num_retries = 1
-        json_response = True
-    elif dimension == 'rel':
-        dimension_question = """\
-        Question: Does HypoB exhibit the same relation as HypoA?
-        Compare using following example hierarchy of relationships (based on specificity): \
-        "there exists a relationship" > "positive relationship" > "positive AND (linear OR quadratic)" > "positive AND linear".
-        Options: A) very similar B) similar but general than HypoA C) different
-        Return your answer as a JSON object in the following format:
-        ```json
-        {{
-        "answer": one of the options from A) very similar B) similar but general than HypoA C) different
-        "explanation": a short text explanation about the relationship comparison
-        }}```
-        Answer:"""
-        num_tokens = 512
-        num_retries = 1
-        json_response = True
-
-    datasets_json = prepare_dataset_metadata_json(
-        dataset_meta, dataset_type=dataset_type, use_column_metadata=use_column_metadata
-    )
-
-    dimension_question_str = f"""\
-        You are going to compare two natural-language hypotheses HypoA and HypoB accompanied with optional workflows: WorkflowA for HypoA and WorkflowB for HypoB. \
-        Both the hypotheses answer the natural language query "QUERY" over the dataset(s) described by dataset description(s) and column description(s) below. \
-        Compare HypoA and HypoB in terms of three aspects: Contexts, Variables, and Relations. \
-        E.g., for the hypothesis "From 1995 to 2009, the number of sandhill cranes around the tundra (Indigilka River) surged by an astounding ~10X":
-        * Contexts refer to stratification of the data under which the given hypothesis is True. E.g., "For all women", "From 1995 to 2009".
-        * Variables refer to the set of variables (either dependent or independent) that are mentioned in the hypothesis. E.g., number of sandhill cranes, location.
-        * Relations refer to the form of relation between the variables. E.g., "surged by ~10x".
-
-        Answer following questions for a given pair of hypotheses, HypoA and HypoB, along with an explanation grounded on the QUERY and the DATASET(S).
-
-        Here is the metadata for the task:
-        ```json
-        {{
-        "datasets": {datasets_json},
-        "query": {query},
-        "HypoA": {gold_hypo},
-        "WorkflowA": {gold_workflow},
-        "HypoB": {gen_hypo},
-        "WorkflowB": {gen_workflow}
-        }}
-        ```
-
-        {dimension_question}"""
-
-    messages.append({'role': 'user', 'content': dimension_question_str})
-    for retry in range(num_retries):
-        response = run_chatgpt_query_multi_turn(
-            messages=messages,
-            model_name=llm_used,
-            max_tokens=num_tokens,
-            temperature=0,  # 0 for greedy best decoding
-            json_response=json_response,
-        )
-        if response is not None:  # COMMENT: changed from != to is not
-            break
-
-    if response is not None:  # COMMENT: changed from != to is not
-        answer = response.choices[0].message.content.strip()
-        score = get_score_from_answer(type=dimension, answer=answer)
-
-    return dimension_question, answer, score
-
-
-def prepare_dataset_metadata_json(dataset_meta, dataset_type, use_column_metadata=True):
-    if dataset_meta is None:  # COMMENT: changed from == to is None
-        return [
-            {
-                'dataset_description': '',
-                'columns': [],
-            }
-        ]
-    datasets_json = []
-    if dataset_type == 'real':
-        for d in dataset_meta['datasets']:
-            datasets_json.append(
-                {
-                    'dataset_description': d['description'],
-                    'columns': [
-                        {'name': col['name'], 'description': col['description']}
-                        for col in d['columns']['raw']
-                    ]
-                    if use_column_metadata
-                    else [],
-                }
-            )
-    else:
-        for d in dataset_meta['datasets']:
-            datasets_json.append(
-                {
-                    'dataset_description': d['description'],
-                    'columns': [
-                        {'name': col['name'], 'description': col['description']}
-                        for col in d['columns']
-                    ]
-                    if use_column_metadata
-                    else [],
-                }
-            )
-    return datasets_json
-
-
-def get_sub_hypotheses(
-    query,
-    hypo,
-    workflow,
-    dataset_meta,
-    llm_used,
-    dataset_type,
-    use_column_metadata=True,
-):
-    client = OpenAI()
-    extraction_prompt = """\
-        Given a set of dataset columns, a ground-truth hypothesis, and the analysis workflow used, your task is to extract three dimensions that define the hypothesis: Context, Variables, and Relations. \
-        Here are the definitions for these dimensions:
-        - Contexts: Boundary conditions that limit the scope of a hypothesis. E.g., “for men over \
-        the age of 30”, “in Asia and Europe”. If the context applies to the full dataset, then extract the context from the dataset_descrption.
-        - Variables: Known concepts that interact in a meaningful way under a given context to \
-        produce the hypothesis. E.g., gender, age, income, or "None" if there is no interacting variable.
-        - Relations: Interactions between a given set of variables under a given context to produce \
-        the hypothesis. E.g., “quadratic relationship”, “inversely proportional”, piecewise conditionals, \
-        or "None" if there is no interacting relationship.
-        Make sure to only use the information present in the hypothesis and the workflow. Do not add any new information. \
-        For each dimension, be specific, and do not omit any important details.
-
-        Here is the metadata for the task:
-        ```json
-        {
-        "datasets": %s,
-        "hypothesis": "%s",
-        "workflow": "%s"
-        }
-        ```
-
-        Return your answer as a JSON object in the following format:
-        ```json
-        {
-        "sub_hypo": [
-            {
-                "text": the hypothesis in natural language,
-                "context": a short text description of the context of the hypothesis,
-                "variables": a list of columns involved in the hypothesis,
-                "relations": a short text description of the relationship between the variables of the hypothesis
-            },
-            ...
-        ]
-        }```
-        """
-    datasets_json = prepare_dataset_metadata_json(
-        dataset_meta, dataset_type, use_column_metadata=use_column_metadata
-    )
-    _prompt = extraction_prompt % (datasets_json, hypo, workflow)
-    sub_hypo_json = get_response(client, _prompt, model=llm_used, max_retry=1)
-
-    if sub_hypo_json is not None:  # COMMENT: changed from != to is not
-        # print(f"full hypothesis: {hypo}")
-        print(f'sub_hypo_json: {sub_hypo_json}')
-    else:
-        sub_hypo_json = {
-            'sub_hypo': [],
-        }
-
-    sub_hypo_json['full_hypo'] = hypo
-
-    return sub_hypo_json
-
-
-def match_context_with_gpt(
-    gold_hyp, gold_context, pred_hyp, pred_context, model='gpt-3.5-turbo'
-):
-    prompt = f"""\
-        Given a gold hypothesis, a gold context, a predicted hypothesis, and a predicted context, your task is \
-        to determine if the predicted context semantically matches the ground-truth context. \
-        Here is the definition for Context: Boundary conditions that limit the scope of a sub-hypothesis. E.g., “for men over the age of 30”, “in Asia and Europe”. If the context applies to the full dataset, then the context is derived from the dataset_descrption. \
-        Here is the definition for Context: Boundary conditions that limit the scope of a sub-hypothesis. E.g., “for men over the age of 30”, “in Asia and Europe”. If the context applies to the full dataset, then the context is derived from the dataset_descrption. \
-        If the predicted context matches the gold context, return true, otherwise return false.
-        If both gold and predicted hypotheses are defined over the context of the full dataset, then also return true.
-        If both gold and predicted hypotheses are defined over the context of the full dataset, then also return true.
-
-        Here is the metadata for the task:
-        ```json
-        {{
-            "gold_hypothesis": "{gold_hyp}",
-            "gold_context": "{gold_context}",
-            "predicted_hypothesis": "{pred_hyp}",
-            "predicted_context": "{pred_context}"
-        }}
-        ```
-
-        Return your answer as a JSON object in the following format:
-        ```json
-        {{
-            "match": true or false
-        }}
-        ```"""
-
-    client = OpenAI()
-    output = get_response(client, prompt, model=model)
-    return output.get('match', False)
-
-
-def is_matching_context(gold_hyp, gold_context, pred_hyp, pred_context, llm_used):
-    if gold_context == pred_context:
-        return True
-    if 'None' in [gold_context, pred_context]:
-        return False
-    return match_context_with_gpt(
-        gold_hyp, gold_context, pred_hyp, pred_context, model=llm_used
-    )
-
-
-def run_eval_gold_vs_gen_NL_subhypo(
-    query,
-    gold_hypo,
-    gold_workflow,
-    gen_hypo,
-    gen_workflow,
-    dataset_meta,
-    llm_used,
-    context_score,
-    dataset_type,
-    use_column_metadata=True,
-):
-    # GPT-4 based evaluation to evaluate generated hypothesis in terms of context, variables, relation
-
-    eval_rec = {
-        'query': query,
-        'HypoA': gold_hypo,
-        'WorkflowA': gold_workflow,
-        'HypoB': gen_hypo,
-        'WorkflowB': gen_workflow,
-    }
-
-    for dimension in ['var', 'rel']:
-        question, answer, score = ask_dimension_question(
-            query,
-            gold_hypo,
-            gold_workflow,
-            gen_hypo,
-            gen_workflow,
-            dataset_meta,
-            llm_used,
-            dimension=dimension,
-            dataset_type=dataset_type,
-            use_column_metadata=use_column_metadata,
-        )
-
-        eval_rec[dimension] = {'question': question, 'answer': answer, 'score': score}
-
-    eval_rec['context'] = context_score
-    eval_rec['accuracy_score'] = (
-        1.0
-        * eval_rec['context']['score']
-        * eval_rec['var']['score']['f1']
-        * eval_rec['rel']['score']
-    )
-
-    return eval_rec
-
-
-def run_eval_gold_vs_gen_NL_hypo_workflow(
-    query,
-    gold_hypo,
-    gold_workflow,
-    gen_hypo,
-    gen_workflow,
-    dataset_meta,
-    llm_used,
-    dataset_type,
-    use_column_metadata=True,
-):
-    # Input: Dataset Metadata, Query, Gold {Hg, Wg}, Predicted {Hp, Wp}
-    # Output: eval_rec json includes final_score
-
-    # Procedure:
-    # Dataset Metadata, Query, Gold {Hg, Wg}, Pred {Hg, Wg}
-    # Gold: [Hg1, Hg2] (compute on the fly) Hg1 is a NL form of subhypothesis
-    # Predicted: [Hp1, Hp2] (compute on the fly)
-
-    # Compute Intersection: [(Hg_i, Hp_j), …]  # tuples of (gold,pred) that matched with context (do this w/o explicit extraction)
-    # # filter so that a gold context and a predicted context are only attached to one tuple
-    # Compute recall_context (programmatically)
-
-    # r_v_list = []
-    # For (Hg_i, Hp_j) in the intersection:
-    #             With Hg_i, Hp_j in NL, ask GPT4 → #variables and #intersection and a paragraph explanation and programmatically calculate f1_v
-    # Hg_i, Hp_j in NL, ask GPT4 → matching score (0, 0.5 or 1) : A) very similar B) similar but general than HypoA C) different + explanation
-    # 	r_v_list ← f1_v * score_r
-    # accuracy_score = mean(r_v_list)
-    # score =   [ recall_context * mean over predicted context(context_score * var_score *rel_score )]
-
-    # recall_context = 1.0  # COMMENT: never used
-    eval_rec = {
-        'query': query,
-        'HypoA': gold_hypo,
-        'WorkflowA': gold_workflow,
-        'HypoB': gen_hypo,
-        'WorkflowB': gen_workflow,
-    }
-
-    gold_sub_hypo_json = get_sub_hypotheses(
-        query=query,
-        hypo=gold_hypo,
-        workflow=gold_workflow,
-        dataset_meta=dataset_meta,
-        llm_used=llm_used,
-        dataset_type=dataset_type,
-        use_column_metadata=use_column_metadata,
-    )
-    if len(gold_sub_hypo_json['sub_hypo']) == 0:
-        gold_sub_hypo_json['sub_hypo'] = [
-            {
-                'text': gold_hypo,
-                'context': 'None',
-                'variables': [],
-                'relations': '',
-                'explanation': 'unable to segment',
-            }
-        ]
-    print(f'gold_sub_hypo_json: {gold_sub_hypo_json}')
-
-    gen_sub_hypo_json = get_sub_hypotheses(
-        query=query,
-        hypo=gen_hypo,
-        workflow=gen_workflow,
-        dataset_meta=dataset_meta,
-        llm_used=llm_used,
-        dataset_type=dataset_type,
-        use_column_metadata=use_column_metadata,
-    )
-    if len(gen_sub_hypo_json['sub_hypo']) == 0:
-        gen_sub_hypo_json['sub_hypo'] = [
-            {
-                'text': gen_hypo,
-                'context': 'None',
-                'variables': [],
-                'relations': '',
-                'explanation': 'unable to segment',
-            }
-        ]
-    print(f'gen_sub_hypo_json: {gen_sub_hypo_json}')
-
-    eval_rec['gold_sub_hypo'] = gold_sub_hypo_json
-    eval_rec['gen_sub_hypo'] = gen_sub_hypo_json
-
-    gold_subh_covered = []
-    gen_subh_to_gold_subh = dict()
-    gen_gold_subh_to_context = dict()
-
-    for p_id, gen_subh in enumerate(gen_sub_hypo_json['sub_hypo']):
-        gen_subh_to_gold_subh[p_id] = -1
-
-        for g_id, gold_subh in enumerate(gold_sub_hypo_json['sub_hypo']):
-            if g_id in gold_subh_covered:
-                continue
-
-            # match context
-            context_bool = is_matching_context(
-                gold_subh['text'],
-                gold_subh.get('context', ''),
-                gen_subh['text'],
-                gen_subh.get('context', ''),
-                llm_used,
-            )
-            if context_bool:
-                context_score = 1.0
-            else:
-                context_score = 0.0
-
-            if context_score == 1.0:  # match only when context_score = 1.0
-                gen_subh_to_gold_subh[p_id] = g_id
-                gold_subh_covered.append(g_id)
-                gen_gold_subh_to_context[f'P{p_id}||G{g_id}'] = {
-                    'question': f"""Comapring: GoldH: {gold_subh["text"]}, GoldC: {gold_subh['context']}\nGenH: {gen_subh['text']}, GenC: {gen_subh['context']}""",
-                    'answer': context_bool,
-                    'score': context_score,
-                }
-                break
-
-    print(f'gen_subh_to_gold_subh: {gen_subh_to_gold_subh}')
-    eval_rec['gen_subh_to_gold_subh'] = gen_subh_to_gold_subh
-    eval_rec['gold_subh_covered'] = gold_subh_covered
-    matched_gold_gen_subh_evals = dict()
-    sum_accuracy_score = 0.0
-    for p_id, g_id in gen_subh_to_gold_subh.items():
-        if g_id >= 0:
-            key = f'P{p_id}||G{g_id}'
-            context_score = gen_gold_subh_to_context[key]
-            subh_eval_rec = run_eval_gold_vs_gen_NL_subhypo(
-                query,
-                gold_hypo,
-                gold_workflow,
-                gen_hypo,
-                gen_workflow,
-                dataset_meta,
-                llm_used,
-                context_score,
-                dataset_type=dataset_type,
-                use_column_metadata=use_column_metadata,
-            )
-            sum_accuracy_score += subh_eval_rec['accuracy_score']
-            matched_gold_gen_subh_evals[key] = subh_eval_rec
-
-    eval_rec['matched_gold_gen_subh_evals'] = matched_gold_gen_subh_evals
-    eval_rec['recall_context'] = (
-        len(gold_subh_covered) / len(gold_sub_hypo_json['sub_hypo'])
-        if len(gold_sub_hypo_json['sub_hypo'])
-        else 0.0
-    )
-    mean_accuracy_score = (
-        sum_accuracy_score / len(gen_subh_to_gold_subh)
-        if len(gen_subh_to_gold_subh)
-        else 0.0
-    )
-    eval_rec['mean_accuracy_score'] = mean_accuracy_score
-    final_score = eval_rec['recall_context'] * mean_accuracy_score
-    eval_rec['final_score'] = final_score
-    print(f'eval_rec: {json.dumps(eval_rec, indent=2)}')
-
-    return eval_rec

evaluation/discoverybench/eval_utils/lm_utils.py

@@ -1,64 +0,0 @@
-import os
-import sys
-import time
-
-from openai import OpenAI
-from tenacity import (
-    retry,
-    stop_after_attempt,  # type: ignore
-    wait_random_exponential,  # type: ignore
-)
-
-if sys.version_info >= (3, 8):
-    from typing import Literal
-else:
-    from typing_extensions import Literal
-
-
-Model = Literal['gpt-4', 'gpt-3.5-turbo', 'text-davinci-003']
-
-OpenAI.api_key = os.getenv('OPENAI_API_KEY')
-OPENAI_GEN_HYP = {
-    'temperature': 0,
-    'max_tokens': 250,
-    'top_p': 1.0,
-    'frequency_penalty': 0,
-    'presence_penalty': 0,
-}
-
-
-@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
-def run_chatgpt_query_multi_turn(
-    messages,
-    model_name='gpt-4-turbo',  # pass "gpt4" for more recent model output
-    max_tokens=256,
-    temperature=0.0,
-    json_response=False,
-):
-    response = None
-    num_retries = 3
-    retry = 0
-    while retry < num_retries:
-        retry += 1
-        try:
-            client = OpenAI()
-
-            if json_response:
-                response = client.chat.completions.create(
-                    model=model_name,
-                    response_format={'type': 'json_object'},
-                    messages=messages,
-                    **OPENAI_GEN_HYP,
-                )
-            else:
-                response = client.chat.completions.create(
-                    model=model_name, messages=messages, **OPENAI_GEN_HYP
-                )
-            break
-
-        except Exception as e:
-            print(e)
-            print('GPT error. Retrying in 2 seconds...')
-            time.sleep(2)
-
-    return response

evaluation/discoverybench/eval_utils/openai_helpers.py

@@ -1,190 +0,0 @@
-import json
-
-
-def OPENAI_TOPIC_GEN_MESSAGES(n=10):
-    return [
-        {
-            'role': 'system',
-            'content': 'You are a helpful assistant who is not talkative. You only respond with the exact answer to a query without additional conversation.',
-        },
-        {
-            'role': 'user',
-            'content': f'Given `n`, come up with a list of `n` distinct topics and their descriptions. The topics can be absolutely anything. Be as creative as possible. Return your answer as a JSON object. \n\nFor example, for `n`=3, a valid answer might be:\n```json\n{{"topics": [\n  {{"id": 1, "topic": "cooking", "description": "Related to recipes, ingredients, chefs, etc."}},\n  {{"id": 2, "topic": "sports", "description": "Related to players, stadiums, trophies, etc."}},\n  {{"id": 3, "topic": "antiquing", "description": "Related to unique items, history, etc."}}\n]}}```\n\nNow, give me a list for `n`={n}. Remember, pick diverse topics from everything possible. No consecutive topics should be broadly similar. Directly respond with the answer JSON object.',
-        },
-    ]
-
-
-OPENAI_GEN_HYP = {
-    'temperature': 1.0,
-    'max_tokens': 4096,
-    'top_p': 1.0,
-    'frequency_penalty': 0,
-    'presence_penalty': 0,
-}
-
-
-def OPENAI_SEMANTICS_GEN_MESSAGES(dependent, relationship, domain, domain_desc):
-    return [
-        {
-            'role': 'system',
-            'content': 'You are a helpful assistant who is not talkative. You only respond with the exact answer to a query without additional conversation.',
-        },
-        {
-            'role': 'user',
-            'content': f'Given the true relationship in a dataset and a given domain, your task is to come up with an interpretation of some real-world concepts that the relationship could be modeling from the provided domain. It\'s okay to be wrong, but suggest something reasonable. Try as much as possible to make sure that the TARGET is actually derivable from the other variables. Give your answer as a JSON object. Here\'s an example:\n\nRelationship for x2 = "(96.4 * x1 ** 3) + (88.72 * x5 ** 2) + (81.96 * x6 ** -2) + (28.13 * x3)  + (97.0) + (0 * x4)"\nDomain="Sales"\nDomain description="Related to product distribution, revenues, marketing, etc."\n\nBased on this, the following real-world concepts might be applicable:\n```json\n{{\n  "dependent": "x2",\n  "relationship": "(96.4 * x1 ** 3) + (88.72 * x5 ** 2) + (81.96 * x6 ** -2) + (28.13 * x3)  + (97.0) + (0 * x4)",\n  "domain": "Sales",\n  "trends": {{\n    "x1": "Positive, cubic factor",\n    "x2": "TARGET",\n    "x3": "Positive, linear factor",\n    "x4": "No relation",\n    "x5": "Positive quadratic factor",\n    "x6": "Positive, inverse quadratic factor"\n  }},\n  "interpretation": {{\n    "x2": {{"description": "Volume of product sales by area", "name": "sales_area", "is_target": true}},\n    "x1": {{"description": "Population by area", "name": "pop_area"}},\n    "x3": {{"description": "Advertising spending", "name": "ad_spend"}},\n    "x4": {{"description": "Gender ratio of marketing team", "name": "gdr_ratio_mkt_team"}},\n    "x5": {{"description": "Intensity of marketing campaign", "name": "mkt_intensity"}}\n  }},\n    "x6": {{"description": "Distance to distribution center", "name": "dist_to_distr_ctr"}}\n}}```\n\nHere\'s a new test question:\nRelationship for {dependent} = "{relationship}"\nDomain = "{domain}"\nDomain description="{domain_desc}"\n\nRespond only with the answer JSON. Make sure that you do not forget to include the TARGET variable in the interpretation object.',
-        },
-    ]
-
-
-def OPENAI_SEMANTICS_GEN_W_MAP_MESSAGES(
-    dependent, relationship, domain, domain_desc, mapping
-):
-    return [
-        {
-            'role': 'system',
-            'content': 'You are a helpful assistant who is not talkative. You only respond with the exact answer to a query without additional conversation.',
-        },
-        {
-            'role': 'user',
-            'content': f'Given a partial mapping from variables to real-world concepts and a true relationship in a dataset, your task is to come up with an interpretation of real-world concepts for the variables without any assigned mapping (those starting with x). Suggest something reasonable. The dependent variable must be derivable only from the other variables in the dependent relationship. Give your answer as a JSON object. Here\'s an example:\n\nExample partial mapping and relationship:\n```json\n{{\n  "domain": "Sales",\n  "domain_description": "Related to product distribution, revenues, marketing, etc.",\n  "variable_mapping": {{\n    "x1": {{"description": "Population by area", "name": "pop_area"}},\n    "x2": {{"description": "Volume of product sales by area", "name": "sales_area"}},\n    "x4": {{"description": "Gender ratio of marketing team", "name": "gdr_ratio_mkt_team"}},\n    "x6": {{"description": "Distance to distribution center", "name": "dist_to_distr_ctr"}}\n  }},\n  "dependent_variable": "sales_area",\n  "dependent_relationship": "(96.4 * pop_area ** 3) + (88.72 * x5 ** 2) + (81.96 * dist_to_distr_ctr ** -2) + (28.13 * x3)  + (97.0)"\n}}```\nBased on this, an example answer would be:\n```json\n{{\n  "dependent_variable": "sales_area",\n  "missing_mapping": ["x3", "x5"],\n  "trends": {{\n    "x3": "Positive, linear factor",\n    "x5": "Positive quadratic factor"\n  }},\n  "interpretation": {{\n    "x3": {{"description": "Advertising spending", "name": "ad_spend"}},\n    "x5": {{"description": "Intensity of marketing campaign", "name": "mkt_intensity"}}\n  }}\n}}```\n\nHere\'s a new test question:\n```json\n{{\n  "domain": "{domain}",\n  "domain_description": "{domain_desc}",\n  "variable_mapping": {json.dumps(mapping, indent=2)},\n  "dependent_variable": "{dependent}",\n  "dependent_relationship": "{relationship}"\n}}```\nRespond only with the answer JSON.',
-        },
-    ]
-
-
-def OPENAI_SEMANTICS_GEN_SUMMARY_MESSAGES(dataset):
-    return [
-        {
-            'role': 'system',
-            'content': 'You are a helpful assistant who is not talkative. You only respond with the exact answer to a query without additional conversation.',
-        },
-        {
-            'role': 'user',
-            'content': f'Given the following descriptions of the columns of a dataset, your task is to come up with a natural language overview of the dataset, which should include (1) what the dataset is about, (2) how the data was collected, (3) when the data was collected, and (3) for what purpose the data was collected. Be specific and creative.\n\nExample dataset:\n```json\n{{  \n  "dataset": {{                                                                                                                                                                                       \n    "x6": {{"description": "Ancient artifact significance score", "name": "artifact_significance_score", "is_target": true}},\n    "x1": {{"description": "Distance to ancient city center", "name": "dist_to_ancient_city_ctr"}},\n    "x2": {{"description": "Quantity of discovered relics", "name": "relic_discovery_qty"}},\n    "x3": {{"description": "Years since last archaeological expedition", "name": "years_since_exp"}},\n    "x4": {{"description": "Number of artifacts in excavation site", "name": "artifact_qty"}},\n    "x5": {{"description": "Soil fertility coefficient", "name": "soil_fertility_coef"}},\n    "x7": {{"description": "Distance to ancient burial grounds", "name": "dist_to_burial_grounds"}},\n    "x8": {{"description": "Population estimate of ancient civilization", "name": "ancient_civilization_pop_estimate"}},\n    "x9": {{"description": "Temperature variation in excavation region", "name": "temp_variation"}}\n  }}\n}}```\nExample description:\nThis dataset is about archaeological explorations and findings linked to ancient civilizations. The data was collected in the form of field metrics during various archaeological expeditions during the late mid-20th century. The purpose of the data collection is to evaluate the significance of ancient artifacts discovered during excavations.\n\nHere is a new test dataset.\n{json.dumps(dataset, indent=2)}\nProvide only the description.',
-        },
-    ]
-
-
-def OPENAI_GEN_HYPO_MESSAGES(dataset):
-    return [
-        {
-            'role': 'system',
-            'content': 'You are a helpful assistant who is not talkative. You only respond with the exact answer to a query without additional conversation.',
-        },
-        {
-            'role': 'user',
-            'content': f'Given a dataset with its descriptions and the true functional relationship between its variables, your task is to generate 3 levels of hypotheses for the stated relationship in plain English. The three levels are "broad", "medium" and "narrow". Make sure that the hypotheses sound natural. *Only include concepts for variables that are present in the provided functional relationship.* Give your answer as a JSON.\n\nFor example, an example dataset might be the following:\n```json\n{{\n  "domain": "cybersecurity",\n  "summary": "This dataset is about measuring cybersecurity threats in a system. The data was collected by monitoring various cybersecurity metrics in a network environment. The purpose of the data collection is to assess and predict potential cybersecurity risks and vulnerabilities.",\n  "variables": [\n    {{\n      "description": "Level of cybersecurity threat",\n      "name": "cybersecurity_threat",\n      "is_target": true\n    }},\n    {{\n      "description": "Number of failed login attempts",\n      "name": "failed_login_attempts"\n    }},\n    {{\n      "description": "Amount of encrypted data",\n      "name": "encrypted_data"\n    }},\n    {{\n      "description": "Frequency of software updates",\n      "name": "software_updates"\n    }},\n    {{\n      "description": "Number of antivirus software installed",\n      "name": "antivirus_software"\n    }},\n    {{\n      "description": "Quality of firewall protection",\n      "name": "firewall_quality"\n    }}\n  ],\n  "relationship": {{\n    "dependent": "cybersecurity_threat",\n    "relation": "-53.5*encrypted_data**2 - 53.85*failed_login_attempts**2 + 67.75*firewall_quality - 92.16 - 36.68/software_updates**3"\n  }}\n}}```\nGiven this dataset, the following is a valid answer:\n```json\n{{\n  "broad": {{\n    "instruction": "Be vague. Only indicate which concepts might be related but not how they are related",\n    "hypothesis": "Threat to cybersecurity is influenced by several factors including the amount of encrypted data, the number of failed login attempts, the quality of the firewall, as well as how often the software is updated."\n  }},\n  "medium": {{\n    "instruction": "Be slightly more specific. For each factor, indicate carefully whether it positively or negatively affects the relationship, but do not indicate what the exponent is.",\n    "hypothesis": "Cybersecurity threat tends to decrease with the amount of data encryption, the number of failed login attempts, as well as the frequency of software updates to some extent, while improvement in the firewall quality has a positive effect."\n  }},\n  "narrow": {{\n    "instruction": "Be specific. Communicate the concepts, whether there is a positive or negative effect (be careful), and the meaning of the exponent",\n    "hypothesis": "The threat to cybersecurity interacts in a complex manner with various factors. As the amount of encrypted data increases, there is a quadratic decrease in threat. Similarly for the number of failed login attempts, there is a negative quadratic relationship. The quality of the firewall protection on the other hand demonstrates a positive and linear relationship. Finally, the frequency of software updates has an inverse cubic relationship to the threat."\n  }},\n}}\n```\n\nBased on this, provide an answer for the following test dataset:\n```json\n{dataset}```\nRespond only with a JSON.',
-        },
-    ]
-
-
-def create_prompt(usr_msg):
-    return [
-        {
-            'role': 'system',
-            'content': 'You are a helpful assistant who is not talkative. You only respond with the exact answer to a query without additional conversation.',
-        },
-        {'role': 'user', 'content': usr_msg},
-    ]
-
-
-def get_response(client, prompt, max_retry=5, model='gpt-3.5-turbo', verbose=False):
-    n_try = 0
-    while n_try < max_retry:
-        response = client.chat.completions.create(
-            model=model, messages=create_prompt(prompt), **OPENAI_GEN_HYP
-        )
-
-        # COMMENT: changed from
-        # response.choices[0].message.content.strip().strip('```json').strip('```')
-        content = response.choices[0].message.content
-        cleaned_content = content.split('```json')[1].split('```')[0].strip()
-        output = cleaned_content
-        try:
-            response_json = json.loads(output)
-            return response_json
-        except ValueError:
-            if verbose:
-                print(f'Bad JSON output:\n\n{output}')
-            n_try += 1
-            if n_try < max_retry:
-                if verbose:
-                    print('Retrying...')
-            else:
-                if verbose:
-                    print('Retry limit reached')
-    return None
-
-
-def get_code_fix(
-    client, code, error, max_retry=5, model='gpt-3.5-turbo', verbose=False
-):
-    prompt = f"""\
-Given the following code snippet and error message, provide a single-line fix for the error. \
-Note that the code is going to be executed using python `eval`. \
-The code should be executable and should not produce the error message. Be as specific as possible.
-
-Here's the code and the error:
-{{
-    "code": "{code}",
-    "error": "{error}"
-}}
-
-Return only a JSON object with the fixed code in the following format:
-```json
-{{
-    "fixed_code": "..."
-}}"""
-    response = get_response(
-        client, prompt, max_retry=max_retry, model=model, verbose=verbose
-    )
-    return response
-
-
-def get_new_hypothesis(
-    client, target, old, expr, cols, model='gpt-3.5-turbo', verbose=False
-):
-    prompt = f"""\
-Given a target column from a dataset, a pandas expression to derive the column from existing columns, a list of \
-existing columns, and a previously written hypothesis text, carefully check if the hypothesis text is consistent with \
-the pandas expression or not. If it is consistent, simply return the hypothesis as it is. If it is not consistent, \
-provide a new natural language hypothesis that is consistent with the pandas expression using only the provided \
-information. Be specific.
-
-Here's the information:
-```json
-{{
-    "target_column": "{target}",
-    "pandas_expression": "{expr}",
-    "existing_columns": {json.dumps(cols, indent=4)}
-    "old_hypothesis": "{old}",
-}}```
-
-Give your answer as a new JSON with the following format:
-```json
-{{
-    "hypothesis": "..."
-}}"""
-    response = get_response(client, prompt, model=model, verbose=verbose)
-    return response
-
-
-def replace_variable(client, expr, old, new, model='gpt-3.5-turbo', verbose=False):
-    prompt = f"""\
-Given a pandas "expression", replace mentions of the "old" column with its "new" value such that the resultant \
-expression is equivalent to the original expression.
-
-Here's the information:
-```json
-{{
-    "expression": "{expr}",
-    "old": "{old}",
-    "new": "{new}"
-}}```
-
-Give your answer as a new JSON with the following format:
-```json
-{{
-    "new_expression": "..."
-}}"""
-    response = get_response(client, prompt, model=model, verbose=verbose)
-    return response

evaluation/discoverybench/eval_utils/openai_semantic_gen_prompts.py

@@ -1,151 +0,0 @@
-common_hypothesis_features = [
-    '1-2 sentences',
-    'surprising finding',
-    'includes numeric concepts',
-    'includes categorical concepts',
-    'includes binary concepts',
-]
-hypothesis_features = [
-    ['requires within-cluster analysis'],
-    ['requires across-cluster analysis'],
-    ['corresponds to a polynomial relationship of some columns'],
-    ['corresponds to a ratio between some columns'],
-    ['requires temporal analysis'],
-    ['relationship is based on descriptive statistics of some columns'],
-    ['requires concepts based on percentage or percentiles'],
-    ['relationship is only applicable to one cluster in the data and not the others'],
-]
-
-column_features = [
-    [
-        'must have one target column',
-        'must have quantifiable columns',
-        'must have a few categorical columns',
-        'make sure the categorical column values do not contain special characters',
-        'include a few distractor columns',
-    ]
-]
-
-common_pandas_features = [
-    'must be executable using python `eval` to create the target column in variable `df` (pandas dataframe)',
-    "for e.g., df['A']**2 + 3*df['B'] + 9, np.where(df['A'] > 3, 'Yes', 'No'), etc.",
-    'variables in pandas_expression must be from the existing columns listed above',
-    'variables in pandas_expression must NOT contain the target column itself',
-]
-pandas_features = [
-    ['expression is a quadratic polynomial'],
-    ['expression is a cubic polynomial'],
-    ['expression is a ratio of existing columns'],
-    ['expression is derived through logical combination of existing columns'],
-    # workflow
-]
-pandas_features = [common_pandas_features + p for p in pandas_features]
-
-common_derived_features = [
-    '1-2 sentences',
-    'includes numeric concepts',
-    'includes categorical concepts',
-    'includes binary concepts',
-]
-derived_features = [common_derived_features + h for h in hypothesis_features]
-hypothesis_features = [common_hypothesis_features + h for h in hypothesis_features]
-
-PROMPT_HYP = """\
-Given a dataset topic and description, generate an interesting hypothesis based on \
-the provided instructions. Be creative and come up with an unusual finding.
-
-```json
-{
-    "topic": "%s",
-    "description": "%s",
-    "hypothesis_features": %s,
-    "hypothesis": "..."
-}```
-
-Give your answer as a new JSON with the following format:
-```json
-{
-    "hypothesis": "..."
-}
-```"""
-
-PROMPT_COL = """\
-Given a dataset topic, its description, and a true hypothesis that can be determined from it, \
-generate a list of valid columns based on the provided instructions.
-
-```json
-{
-    "topic": "%s",
-    "description": "%s",
-    "hypothesis": "%s",
-    "column_instructions": %s,
-    "columns": [
-        {
-            "col_name": "...",  # should be an "_"-separated string
-            "description": "...",
-            "data_type": "...",  # should be executable using python's `eval` function. E.g., str, float, int, bool
-            "data_range": {...},  # should be either {"min": ..., "max": ...} or {"values": [...]}
-            "is_distractor": true/false,  # boolean indicating whether this is a distractor that could cause confusion during data analysis
-            "is_target": true/false  # boolean indicating whether this is the target variable for the hypothesis; at least one column should be the target
-        },
-        ...
-    ],
-    "pandas_instructions": %s,
-    "pandas_equation_for_hypothesis": {
-        "target_col": "...",
-        "target_col_type": "...",
-        "target_col_range": {...},
-        "independent_cols_in_pandas_expression": [],  # list of column names that will be used to derive the target column
-        "pandas_expression": "..."  # expression to derive df[target_col] using df[ind_col1], df[ind_col2], etc.
-    }
-}```
-
-Give your answer as a new JSON with the "columns" and "pandas_equation_for_hypothesis" keys filled using the following format:
-```json
-{
-    "columns": [...],
-    "pandas_equation_for_hypothesis": {...}
-}
-```"""
-
-PROMPT_DER = """\
-Given a dataset topic, description, a true hypothesis that can be determined from the data, \
-and a target column from the dataset, generate a hypothesis for the target column using new independent columns not present in the existing columns.
-
-```json
-{
-    "topic": "%s",
-    "description": "%s",
-    "hypothesis": "%s",
-    "existing_columns": %s,
-    "target_column": "%s",
-    "new_to_target_instructions": %s,
-    "new_to_target_hypothesis": "...",  # describe a relationship between new columns that explains the target column
-    "new_columns_for_target": [  # do not repeat any of the existing columns in the dataset
-        {
-            "col_name": "...",  # should be an "_"-separated string
-            "description": "...",
-            "data_type": "...",  # should be executable using python's `eval` function. E.g., str, float, int, bool
-            "data_range": {...},  # should be either {"min": ..., "max": ...} or {"values": [...]}
-        },
-        ...
-    ],
-    "pandas_instructions": %s,
-    "pandas_equation_for_new_to_target_hypothesis": {
-        "target_col": "...",
-        "target_col_type": "...",
-        "target_col_range": {...},
-        "independent_cols_in_pandas_expression": [],  # list of column names from new_columns_for_target that will be used to derive target_col
-        "pandas_expression": "..."  # expression to derive df[target_col] using df[ind_col1], df[ind_col2], etc.
-    }
-}```
-
-Give your answer as a new JSON with the "new_to_target_hypothesis", "new_columns_for_target", and \
-"pandas_equation_for_new_to_target_hypothesis" keys filled using the following format:
-```json
-{
-    "new_to_target_hypothesis": "...",
-    "new_columns_for_target": [...],
-    "pandas_equation_for_new_to_target_hypothesis": {...}
-}
-```"""

evaluation/discoverybench/eval_utils/response_parser.py

@@ -1,52 +0,0 @@
-workflow_summary_markers = [
-    'WORKFLOW SUMMARY',
-    'WORKFLOW_SUMMARY',
-    'WORKFLOW-SUMMARY',
-    'Workflow Summary',
-]
-
-final_answer_markers = [
-    'FINAL ANSWER',
-    'FINAL_ANSWER',
-    'FINAL-ANSWER',
-    'Final Answer',
-    'Scientific Hypothesis',
-    'Hypothesis',
-]
-
-next_agent_markers = [
-    'NEXT AGENT',
-    'NEXT-AGENT',
-    'NEXT_AGENT',
-    'FEEDBACK',
-]
-
-
-def extract_between(content, start_markers, end_markers=None):
-    for marker in start_markers:
-        if marker in content:
-            result = content.split(marker, 1)[1]
-            if end_markers:
-                for end_marker in end_markers:
-                    if end_marker in result:
-                        result = result.split(end_marker, 1)[0]
-            return result
-    return ''
-
-
-def extract_gen_hypo_from_logs(content: str):
-    error = ''
-
-    gen_workflow = extract_between(
-        content, workflow_summary_markers, final_answer_markers
-    )
-
-    if not gen_workflow:
-        error += 'No Workflow Summary found in the line. | '
-
-    gen_hypothesis = extract_between(content, final_answer_markers, next_agent_markers)
-
-    if not gen_hypothesis:
-        error += 'No Final Answer in the line.'
-
-    return gen_hypothesis, gen_workflow, error

evaluation/discoverybench/run_infer.py

@@ -1,492 +0,0 @@
-import asyncio
-import json
-import os
-
-import git
-import pandas as pd
-
-from evaluation.discoverybench.eval_utils.eval_w_subhypo_gen import (
-    run_eval_gold_vs_gen_NL_hypo_workflow,
-)
-from evaluation.discoverybench.eval_utils.response_parser import (
-    extract_gen_hypo_from_logs,
-)
-from evaluation.utils.shared import (
-    EvalMetadata,
-    EvalOutput,
-    codeact_user_response,
-    compatibility_for_eval_history_pairs,
-    make_metadata,
-    prepare_dataset,
-    reset_logger_for_multiprocessing,
-    run_evaluation,
-)
-from openhands.controller.state.state import State
-from openhands.core.config import (
-    AgentConfig,
-    AppConfig,
-    SandboxConfig,
-    get_llm_config_arg,
-    parse_arguments,
-)
-from openhands.core.logger import openhands_logger as logger
-from openhands.core.main import create_runtime, run_controller
-from openhands.events.action import AgentFinishAction, CmdRunAction, MessageAction
-from openhands.events.observation import CmdOutputObservation
-from openhands.runtime.base import Runtime
-from openhands.utils.async_utils import call_async_from_sync
-
-EVALUATION_LLM = 'gpt-4-1106-preview'
-
-DATA_FILES = {}
-
-LIBRARIES = [
-    'pandas',
-    'numpy',
-    'scipy',
-    'matplotlib',
-    'seaborn',
-    'scikit-learn',
-    'statsmodels',
-]
-
-AGENT_CLS_TO_FAKE_USER_RESPONSE_FN = {
-    'CodeActAgent': codeact_user_response,
-}
-
-AGENT_CLS_TO_INST_SUFFIX = {
-    'CodeActAgent': 'When you think you have fixed the issue through code changes, please run the following command: <execute_bash> exit </execute_bash>.\n'
-}
-
-
-def get_config(
-    metadata: EvalMetadata,
-) -> AppConfig:
-    config = AppConfig(
-        default_agent=metadata.agent_class,
-        run_as_openhands=False,
-        runtime='eventstream',
-        max_iterations=metadata.max_iterations,
-        sandbox=SandboxConfig(
-            base_container_image='python:3.12-bookworm',
-            enable_auto_lint=True,
-            use_host_network=False,
-        ),
-        # do not mount workspace
-        workspace_base=None,
-        workspace_mount_path=None,
-    )
-    config.set_llm_config(metadata.llm_config)
-    agent_config = AgentConfig(
-        function_calling=False,
-        codeact_enable_jupyter=True,
-        codeact_enable_browsing_delegate=True,
-    )
-    config.set_agent_config(agent_config)
-    return config
-
-
-def get_dv_query_for_real(
-    datasets, question, domain_knowledge=None, workflow_tags=None
-):
-    """
-    Prepare a structured query for the agent to execute on the specified datasets.
-
-    This function constructs a query by compiling metadata from the provided datasets, along with any relevant domain knowledge and workflow tags.
-
-    Args:
-        datasets: List of datasets
-        question: Query to be answered
-        domain_knowledge: Domain knowledge if any
-        workflow_tags: Workflow tags if any
-
-    Returns:
-        query_to_dv: Query to be run on the dataset
-        dataset_meta: Metadata of the dataset
-    """
-
-    dataset_meta = ''
-    for dataset_metadata in datasets:
-        dataset_meta += 'Dataset name: ' + dataset_metadata['name']
-        dataset_meta += 'Dataset description: ' + dataset_metadata['description']
-        dataset_meta += '\nBrief description of columns: '
-        for col in dataset_metadata['columns']['raw']:
-            dataset_meta += col['name'] + ': ' + col['description'] + ', '
-
-    query_to_dv = dataset_meta
-
-    query_to_dv += f'\nQuery: {question}'
-
-    if domain_knowledge:
-        query_to_dv += (
-            '\nAdditionally, we provide some hints that might be useful to solve the task. Domain Knowledge: \n'
-            + domain_knowledge
-            + '.\n'
-        )
-
-    if workflow_tags:
-        query_to_dv += 'The meta tags are: ' + workflow_tags + '.\n'
-
-    query_to_dv += (
-        'In the final answer, please write down a scientific hypothesis in '
-        'natural language, derived from the provided dataset, clearly stating the '
-        'context of hypothesis (if any), variables chosen (if any) and '
-        'relationship between those variables (if any) including any statistical significance.'
-        'Also generate a summary of the full workflow starting from data loading that led to the final answer as WORKFLOW SUMMARY:'
-    )
-
-    # Run the NL query through datavoyager
-    return query_to_dv, dataset_meta
-
-
-def initialize_runtime(runtime: Runtime, data_files: list[str]):
-    """
-    Initialize the runtime for the agent.
-
-    This function is called before the runtime is used to run the agent.
-    """
-    logger.info(f"{'-' * 50} BEGIN Runtime Initialization Fn {'-' * 50}")
-    obs: CmdOutputObservation
-
-    action = CmdRunAction(command='mkdir -p /workspace')
-    logger.info(action, extra={'msg_type': 'ACTION'})
-    obs = runtime.run_action(action)
-    assert obs.exit_code == 0
-
-    action = CmdRunAction(command='cd /workspace')
-    logger.info(action, extra={'msg_type': 'ACTION'})
-    obs = runtime.run_action(action)
-    assert obs.exit_code == 0
-
-    for file in data_files:
-        runtime.copy_to(
-            file,
-            '/workspace',
-        )
-
-    for lib in LIBRARIES:
-        action = CmdRunAction(command=f'pip install {lib}')
-        logger.info(action, extra={'msg_type': 'ACTION'})
-        obs = runtime.run_action(action)
-        assert obs.exit_code == 0
-
-    logger.info(f"{'-' * 50} END Runtime Initialization Fn {'-' * 50}")
-
-
-def get_last_agent_finish_action(state: State) -> AgentFinishAction:
-    for event in reversed(state.history):
-        if isinstance(event, AgentFinishAction):
-            return event
-    return None
-
-
-def get_last_message_action(state: State) -> MessageAction:
-    for event in reversed(state.history):
-        if isinstance(event, MessageAction):
-            return event
-    return None
-
-
-def complete_runtime(state: State):
-    last_agent_finish_action = get_last_agent_finish_action(state)
-    last_agent_message_action = get_last_message_action(state)
-
-    if last_agent_finish_action is not None:
-        final_message_1 = last_agent_finish_action.thought
-        gen_hypo_1, gen_workflow_1, error_1 = extract_gen_hypo_from_logs(
-            final_message_1
-        )
-    else:
-        gen_hypo_1, gen_workflow_1, error_1 = '', '', ''
-
-    if last_agent_message_action is not None:
-        final_message_2 = last_agent_message_action.content
-        gen_hypo_2, gen_workflow_2, error_2 = extract_gen_hypo_from_logs(
-            final_message_2
-        )
-    else:
-        gen_hypo_2, gen_workflow_2, error_2 = '', '', ''
-
-    if gen_hypo_1 and gen_hypo_2:
-        test_result = {
-            'gen_hypo': last_agent_finish_action.thought
-            if last_agent_finish_action
-            else last_agent_message_action.content,
-            'gen_workflow': '',
-            'error': '',
-        }
-        return test_result
-
-    test_result = {
-        'gen_hypo': gen_hypo_1 if gen_hypo_1 else gen_hypo_2,
-        'gen_workflow': gen_workflow_1 if gen_workflow_1 else gen_workflow_2,
-        'error': error_1 if error_1 else error_2,
-    }
-
-    return test_result
-
-
-def process_instance(
-    instance: pd.Series,
-    metadata: EvalMetadata,
-    reset_logger: bool = True,
-):
-    """
-    Process and evaluate a single instance of the dataset.
-
-    This function executes the OpenHands agent
-    for a specific instance of the dataset. It retrieves
-    the agent's results and evaluates them against the gold
-    hypothesis.
-
-    Args:
-        instance: A single row of the dataset
-        metadata: Metadata for the evaluation
-        reset_logger: Whether to reset the logger
-
-    Returns:
-        output: EvalOutput object
-    """
-
-    config = get_config(metadata)
-
-    # use a session id for concurrent evaluation
-    sid = 'ID_' + str(instance.instance_id)
-
-    # Setup the logger properly, so you can run
-    # multi-processing to parallelize the evaluation
-    if reset_logger:
-        log_dir = os.path.join(metadata.eval_output_dir, 'infer_logs')
-        reset_logger_for_multiprocessing(logger, instance.instance_id, log_dir)
-    else:
-        logger.info(f'Starting evaluation for instance {instance.instance_id}.')
-
-    problem_statement, dataset_metadata = get_dv_query_for_real(
-        datasets=instance.datasets,
-        question=instance.query,
-        domain_knowledge=instance.domain_knowledge,
-        workflow_tags=instance.workflow_tags,
-    )
-
-    # Prepare instruction
-    instruction = (
-        f'You are a discovery agent who can execute a python code only once to answer a query based on one or more datasets. The datasets will be present in the current directory.\n\n'
-        'Environment has been set up for you to start working. You may assume all necessary tools and datasets are installed.\n\n'
-        '# Problem Statement\n'
-        f'{problem_statement}\n\n'
-    )
-    instruction += (
-        'IMPORTANT: You should ONLY interact with the environment provided to you AND NEVER ASK FOR HUMAN HELP.\n'
-        'You should NOT modify any existing test case files. If needed, you can add new test cases in a NEW file to reproduce the issue.\n'
-        'You SHOULD INCLUDE PROPER INDENTATION in your edit commands.\n'
-    )
-    # NOTE: You can actually set slightly different instruction for different agents
-    instruction += AGENT_CLS_TO_INST_SUFFIX[metadata.agent_class]
-
-    # Here's how you can run the agent (similar to the `main` function) and get the final task state
-    runtime = create_runtime(config, sid=sid)
-    call_async_from_sync(runtime.connect)
-    initialize_runtime(runtime, instance.data_files)
-
-    state: State | None = asyncio.run(
-        run_controller(
-            config=config,
-            initial_user_action=MessageAction(content=instruction),
-            runtime=runtime,
-            fake_user_response_fn=AGENT_CLS_TO_FAKE_USER_RESPONSE_FN.get(
-                metadata.agent_class
-            ),
-        )
-    )
-
-    if state is None:
-        raise ValueError('State should not be None.')
-
-    metrics = state.metrics.get() if state.metrics else None
-    test_result = complete_runtime(state)
-
-    # history is now available as a stream of events, rather than list of pairs of (Action, Observation)
-    # for compatibility with the existing output format, we can remake the pairs here
-    # remove when it becomes unnecessary
-    histories = compatibility_for_eval_history_pairs(state.history)
-
-    # DiscoveryBench Evaluation
-    eval_rec = run_eval_gold_vs_gen_NL_hypo_workflow(
-        query=instance.query,
-        gold_hypo=instance.gold_hypo,
-        gold_workflow='',
-        gen_hypo=test_result['gen_hypo'],
-        gen_workflow='',
-        dataset_meta=instance.dataset_metadata,
-        llm_used=EVALUATION_LLM,
-        dataset_type='real',
-    )
-
-    test_result['eval_rec'] = eval_rec
-
-    output = EvalOutput(
-        instance_id=str(instance.instance_id),
-        instruction=instruction,
-        metadata=metadata,
-        history=histories,
-        metrics=metrics,
-        error=state.last_error if state and state.last_error else None,
-        test_result=test_result,
-    )
-
-    return output
-
-
-def update_csv_name(name):
-    name = name.replace('-', '_')
-
-    if 'meta_regression' in name:
-        name = name.replace('meta_regression', 'meta-regression')
-    if 'ML_enabled' in name:
-        name = name.replace('ML_enabled', 'ML-enabled')
-
-    return name
-
-
-def list_csv_files(list_of_datasets):
-    res = []
-    for ele in list_of_datasets:
-        for key, value in ele.items():
-            if key == 'name':
-                csv_file_name = update_csv_name(value)
-                res.append(DATA_FILES[csv_file_name])
-    return res
-
-
-def create_dataset(repo_location: str, split: str = 'test'):
-    """
-    Create a dataset from the discoverybench repository
-    by walking through the repository and extracting metadata
-    from the metadata_{}.json files
-
-    Args:
-        repo_location: Location of the repository
-        split: Split of the dataset to use
-
-    Returns:
-        df: DataFrame containing the dataset instances
-    """
-
-    data_dict = {}
-
-    data_location = os.path.join(repo_location, 'discoverybench', 'real', split)
-    answer_key_location = os.path.join(repo_location, 'eval', 'answer_key_real.csv')
-
-    idx = 0
-
-    for root, dirs, files in os.walk(data_location):
-        for file in files:
-            if file.endswith('.json'):
-                if 'metadata' in file:
-                    metadata = json.load(open(os.path.join(root, file)))
-
-                    dataset = root.split('/')[-1]
-                    metadata_id = file.split('_')[-1].split('.')[0]
-                    domain = metadata.get('domain', '')
-                    domain_knowledge = metadata.get('domain_knowledge', '')
-                    workflow_tags = metadata.get('workflow_tags', '')
-                    datasets = metadata.get('datasets', [])
-                    queries = metadata.get('queries', [])
-                    gold_workflow = metadata.get('workflow')
-
-                    # loop through queries list to get queries
-                    # and each query has qid; add that to dictionary
-                    for query in queries[0]:
-                        qid = query.get('qid', '')
-
-                        data = {
-                            'dataset': dataset,
-                            'metadata_id': metadata_id,
-                            'qid': qid,
-                            'domain': domain,
-                            'domain_knowledge': domain_knowledge,
-                            'workflow_tags': workflow_tags,
-                            'datasets': datasets,
-                            'question_type': query['question_type'],
-                            'query': query['question'],
-                            'gold_workflow': gold_workflow,
-                            'dataset_metadata': metadata,
-                        }
-
-                        data_dict[idx] = data
-                        idx += 1
-
-            if file.endswith('.csv'):
-                DATA_FILES[file] = os.path.join(root, file)
-            if file.endswith('.txt'):
-                DATA_FILES[file] = os.path.join(root, file)
-
-    df = pd.DataFrame.from_dict(data_dict, orient='index')
-
-    df['instance_id'] = df.index
-
-    df['data_files'] = df['datasets'].apply(lambda x: list_csv_files(x))
-
-    answer_key = pd.read_csv(answer_key_location)
-
-    answer_key = answer_key.rename(
-        columns={
-            'metadataid': 'metadata_id',
-            'query_id': 'qid',
-            'gold_hypothesis': 'gold_hypothesis',
-        }
-    )
-
-    df['qid'] = df['qid'].astype(int)
-    df['metadata_id'] = df['metadata_id'].astype(int)
-
-    answer_key['qid'] = answer_key['qid'].astype(int)
-    answer_key['metadata_id'] = answer_key['metadata_id'].astype(int)
-
-    df = pd.merge(df, answer_key, on=['dataset', 'metadata_id', 'qid'], how='left')
-
-    return df
-
-
-if __name__ == '__main__':
-    args = parse_arguments()
-
-    # clone git repositor for csv files
-    repo_url = 'https://github.com/allenai/discoverybench.git'
-    repo_location = 'git-discoverybench-allenai'
-
-    try:
-        git.Repo.clone_from(repo_url, repo_location)
-    except git.exc.GitCommandError:
-        print('Repository already exists')
-
-    dataset = create_dataset(repo_location)
-
-    # check if there is any empty csv_file
-    if dataset['data_files'].isnull().any():
-        raise ValueError('Some csv files are missing.')
-
-    llm_config = None
-    if args.llm_config:
-        llm_config = get_llm_config_arg(args.llm_config)
-    if llm_config is None:
-        raise ValueError(f'Could not find LLM config: --llm_config {args.llm_config}')
-
-    metadata = make_metadata(
-        llm_config,
-        'discoverybench-python',
-        args.agent_cls,
-        args.max_iterations,
-        args.eval_note,
-        args.eval_output_dir,
-    )
-    output_file = os.path.join(metadata.eval_output_dir, 'output.jsonl')
-    instances = prepare_dataset(dataset, output_file, args.eval_n_limit)
-
-    run_evaluation(
-        instances,
-        metadata,
-        output_file,
-        args.eval_num_workers,
-        process_instance,
-    )

evaluation/discoverybench/scripts/run_infer.sh

@@ -1,46 +0,0 @@
-#!/bin/bash
-set -eo pipefail
-
-source "evaluation/utils/version_control.sh"
-
-MODEL_CONFIG=$1
-COMMIT_HASH=$2
-AGENT=$3
-EVAL_LIMIT=$4
-NUM_WORKERS=$5
-
-if [ -z "$NUM_WORKERS" ]; then
-  NUM_WORKERS=1
-  echo "Number of workers not specified, use default $NUM_WORKERS"
-fi
-
-# ################################################################################
-
-checkout_eval_branch
-
-if [ -z "$AGENT" ]; then
-  echo "Agent not specified, use default CodeActAgent"
-  AGENT="CodeActAgent"
-fi
-
-get_agent_version
-
-echo "AGENT: $AGENT"
-echo "AGENT_VERSION: $AGENT_VERSION"
-echo "MODEL_CONFIG: $MODEL_CONFIG"
-
-COMMAND="poetry run python evaluation/discoverybench/run_infer.py \
-  --agent-cls $AGENT \
-  --llm-config $MODEL_CONFIG \
-  --max-iterations 10 \
-  --max-chars 10000000 \
-  --eval-num-workers $NUM_WORKERS \
-  --eval-note $AGENT_VERSION"
-
-if [ -n "$EVAL_LIMIT" ]; then
-  echo "EVAL_LIMIT: $EVAL_LIMIT"
-  COMMAND="$COMMAND --eval-n-limit $EVAL_LIMIT"
-fi
-
-# Run the command
-eval $COMMAND

evaluation/gaia/run_infer.py

@@ -12,7 +12,6 @@ from evaluation.utils.shared import (
     EvalMetadata,
     EvalOutput,
     codeact_user_response,
-    compatibility_for_eval_history_pairs,
     make_metadata,
     prepare_dataset,
     reset_logger_for_multiprocessing,
@@ -167,7 +166,7 @@ def process_instance(
 
     model_answer_raw = ''
     # get the last message or thought from the agent
-    for event in reversed(state.history):
+    for event in state.history.get_events(reverse=True):
         if event.source == 'agent':
             if isinstance(event, AgentFinishAction):
                 model_answer_raw = event.thought
@@ -204,7 +203,7 @@ def process_instance(
     # history is now available as a stream of events, rather than list of pairs of (Action, Observation)
     # for compatibility with the existing output format, we can remake the pairs here
     # remove when it becomes unnecessary
-    histories = compatibility_for_eval_history_pairs(state.history)
+    histories = state.history.compatibility_for_eval_history_pairs()
 
     # Save the output
     output = EvalOutput(

evaluation/gorilla/run_infer.py

@@ -10,7 +10,6 @@ from evaluation.utils.shared import (
     EvalMetadata,
     EvalOutput,
     codeact_user_response,
-    compatibility_for_eval_history_pairs,
     make_metadata,
     prepare_dataset,
     reset_logger_for_multiprocessing,
@@ -102,8 +101,7 @@ def process_instance(
         raise ValueError('State should not be None.')
 
     # retrieve the last message from the agent
-    last_agent_message = state.get_last_agent_message()
-    model_answer_raw = last_agent_message.content if last_agent_message else ''
+    model_answer_raw = state.history.get_last_agent_message()
 
     # attempt to parse model_answer
     ast_eval_fn = instance['ast_eval']
@@ -116,7 +114,7 @@ def process_instance(
     # history is now available as a stream of events, rather than list of pairs of (Action, Observation)
     # for compatibility with the existing output format, we can remake the pairs here
     # remove when it becomes unnecessary
-    histories = compatibility_for_eval_history_pairs(state.history)
+    histories = state.history.compatibility_for_eval_history_pairs()
 
     output = EvalOutput(
         instance_id=instance_id,

evaluation/gpqa/run_infer.py

@@ -28,7 +28,6 @@ from datasets import load_dataset
 from evaluation.utils.shared import (
     EvalMetadata,
     EvalOutput,
-    compatibility_for_eval_history_pairs,
     make_metadata,
     prepare_dataset,
     reset_logger_for_multiprocessing,
@@ -245,7 +244,7 @@ Ok now its time to start solving the question. Good luck!
         'C': False,
         'D': False,
     }
-    for event in reversed(state.history):
+    for event in state.history.get_events(reverse=True):
         if (
             isinstance(event, AgentFinishAction)
             and event.source != 'user'
@@ -301,7 +300,7 @@ Ok now its time to start solving the question. Good luck!
         instance_id=str(instance.instance_id),
         instruction=instruction,
         metadata=metadata,
-        history=compatibility_for_eval_history_pairs(state.history),
+        history=state.history.compatibility_for_eval_history_pairs(),
         metrics=metrics,
         error=state.last_error if state and state.last_error else None,
         test_result={

evaluation/humanevalfix/run_infer.py

@@ -21,7 +21,6 @@ from evaluation.utils.shared import (
     EvalMetadata,
     EvalOutput,
     codeact_user_response,
-    compatibility_for_eval_history_pairs,
     make_metadata,
     prepare_dataset,
     reset_logger_for_multiprocessing,
@@ -256,7 +255,7 @@ def process_instance(
     # history is now available as a stream of events, rather than list of pairs of (Action, Observation)
     # for compatibility with the existing output format, we can remake the pairs here
     # remove when it becomes unnecessary
-    histories = compatibility_for_eval_history_pairs(state.history)
+    histories = state.history.compatibility_for_eval_history_pairs()
 
     # Save the output
     output = EvalOutput(

evaluation/integration_tests/run_infer.py

@@ -13,7 +13,6 @@ from evaluation.utils.shared import (
     prepare_dataset,
     reset_logger_for_multiprocessing,
     run_evaluation,
-    update_llm_config_for_completions_logging,
 )
 from openhands.controller.state.state import State
 from openhands.core.config import (
@@ -56,14 +55,18 @@ def get_config(
         workspace_base=None,
         workspace_mount_path=None,
     )
-    config.set_llm_config(
-        update_llm_config_for_completions_logging(
-            metadata.llm_config, metadata.eval_output_dir, instance_id
+    if metadata.llm_config.log_completions:
+        metadata.llm_config.log_completions_folder = os.path.join(
+            metadata.eval_output_dir, 'llm_completions', instance_id
         )
-    )
+        logger.info(
+            f'Logging LLM completions for instance {instance_id} to '
+            f'{metadata.llm_config.log_completions_folder}'
+        )
+    config.set_llm_config(metadata.llm_config)
     agent_config = AgentConfig(
         codeact_enable_jupyter=True,
-        codeact_enable_browsing=True,
+        codeact_enable_browsing_delegate=True,
         codeact_enable_llm_editor=False,
     )
     config.set_agent_config(agent_config)
@@ -129,7 +132,7 @@ def process_instance(
     # # result evaluation
     # # =============================================
 
-    histories = [event_to_dict(event) for event in state.history]
+    histories = [event_to_dict(event) for event in state.history.get_events()]
     test_result: TestResult = test_class.verify_result(runtime, histories)
     metrics = state.metrics.get() if state.metrics else None
 

evaluation/integration_tests/tests/t06_github_pr_browsing.py

@@ -1,44 +0,0 @@
-from evaluation.integration_tests.tests.base import BaseIntegrationTest, TestResult
-from openhands.events.action import AgentFinishAction, MessageAction
-from openhands.events.event import Event
-from openhands.events.observation import AgentDelegateObservation
-from openhands.runtime.base import Runtime
-
-
-class Test(BaseIntegrationTest):
-    INSTRUCTION = 'Look at https://github.com/All-Hands-AI/OpenHands/pull/8, and tell me what is happening there and what did @asadm suggest.'
-
-    @classmethod
-    def initialize_runtime(cls, runtime: Runtime) -> None:
-        pass
-
-    @classmethod
-    def verify_result(cls, runtime: Runtime, histories: list[Event]) -> TestResult:
-        # check if the "The answer is OpenHands is all you need!" is in any message
-        message_actions = [
-            event
-            for event in histories
-            if isinstance(
-                event, (MessageAction, AgentFinishAction, AgentDelegateObservation)
-            )
-        ]
-        for event in message_actions:
-            if isinstance(event, AgentDelegateObservation):
-                content = event.content
-            elif isinstance(event, AgentFinishAction):
-                content = event.outputs.get('content', '')
-            elif isinstance(event, MessageAction):
-                content = event.content
-            else:
-                raise ValueError(f'Unknown event type: {type(event)}')
-
-            if (
-                'non-commercial' in content
-                or 'MIT' in content
-                or 'Apache 2.0' in content
-            ):
-                return TestResult(success=True)
-        return TestResult(
-            success=False,
-            reason=f'The answer is not found in any message. Total messages: {len(message_actions)}. Messages: {message_actions}',
-        )

evaluation/logic_reasoning/run_infer.py

@@ -8,7 +8,6 @@ from evaluation.utils.shared import (
     EvalMetadata,
     EvalOutput,
     codeact_user_response,
-    compatibility_for_eval_history_pairs,
     make_metadata,
     prepare_dataset,
     reset_logger_for_multiprocessing,
@@ -226,7 +225,7 @@ def process_instance(
         raise ValueError('State should not be None.')
 
     final_message = ''
-    for event in reversed(state.history):
+    for event in state.history.get_events(reverse=True):
         if isinstance(event, AgentFinishAction):
             final_message = event.thought
             break
@@ -248,7 +247,7 @@ def process_instance(
     # history is now available as a stream of events, rather than list of pairs of (Action, Observation)
     # for compatibility with the existing output format, we can remake the pairs here
     # remove when it becomes unnecessary
-    histories = compatibility_for_eval_history_pairs(state.history)
+    histories = state.history.compatibility_for_eval_history_pairs()
 
     # Save the output
     output = EvalOutput(

evaluation/miniwob/run_infer.py

@@ -10,13 +10,10 @@ import pandas as pd
 from evaluation.utils.shared import (
     EvalMetadata,
     EvalOutput,
-    codeact_user_response,
-    compatibility_for_eval_history_pairs,
     make_metadata,
     prepare_dataset,
     reset_logger_for_multiprocessing,
     run_evaluation,
-    update_llm_config_for_completions_logging,
 )
 from openhands.controller.state.state import State
 from openhands.core.config import (
@@ -32,10 +29,7 @@ from openhands.events.action import (
     CmdRunAction,
     MessageAction,
 )
-from openhands.events.observation import (
-    BrowserOutputObservation,
-    CmdOutputObservation,
-)
+from openhands.events.observation import CmdOutputObservation
 from openhands.runtime.base import Runtime
 from openhands.runtime.browser.browser_env import (
     BROWSER_EVAL_GET_GOAL_ACTION,
@@ -43,11 +37,7 @@ from openhands.runtime.browser.browser_env import (
 )
 from openhands.utils.async_utils import call_async_from_sync
 
-SUPPORTED_AGENT_CLS = {'BrowsingAgent', 'CodeActAgent'}
-
-AGENT_CLS_TO_FAKE_USER_RESPONSE_FN = {
-    'CodeActAgent': codeact_user_response,
-}
+SUPPORTED_AGENT_CLS = {'BrowsingAgent'}
 
 
 def get_config(
@@ -57,32 +47,25 @@ def get_config(
     config = AppConfig(
         default_agent=metadata.agent_class,
         run_as_openhands=False,
-        runtime=os.environ.get('RUNTIME', 'eventstream'),
+        runtime='eventstream',
         max_iterations=metadata.max_iterations,
         sandbox=SandboxConfig(
             base_container_image='xingyaoww/od-eval-miniwob:v1.0',
             enable_auto_lint=True,
             use_host_network=False,
             browsergym_eval_env=env_id,
-            api_key=os.environ.get('ALLHANDS_API_KEY', None),
-            remote_runtime_api_url=os.environ.get('SANDBOX_REMOTE_RUNTIME_API_URL'),
-            keep_runtime_alive=False,
         ),
         # do not mount workspace
         workspace_base=None,
         workspace_mount_path=None,
     )
-    config.set_llm_config(
-        update_llm_config_for_completions_logging(
-            metadata.llm_config, metadata.eval_output_dir, env_id
-        )
-    )
+    config.set_llm_config(metadata.llm_config)
     return config
 
 
 def initialize_runtime(
     runtime: Runtime,
-) -> tuple[str, BrowserOutputObservation]:
+) -> str:
     """Initialize the runtime for the agent.
 
     This function is called before the runtime is used to run the agent.
@@ -102,14 +85,8 @@ def initialize_runtime(
     logger.info(obs, extra={'msg_type': 'OBSERVATION'})
     goal = obs.content
 
-    # Run noop to get the initial browser observation (e.g., the page URL & content)
-    action = BrowseInteractiveAction(browser_actions='noop(1000)')
-    logger.info(action, extra={'msg_type': 'ACTION'})
-    obs = runtime.run_action(action)
-    logger.info(obs, extra={'msg_type': 'OBSERVATION'})
-
     logger.info(f"{'-' * 50} END Runtime Initialization Fn {'-' * 50}")
-    return goal, obs
+    return goal
 
 
 def complete_runtime(
@@ -140,7 +117,7 @@ def process_instance(
     metadata: EvalMetadata,
     reset_logger: bool = True,
 ) -> EvalOutput:
-    env_id = instance.instance_id
+    env_id = instance.id
     config = get_config(metadata, env_id)
 
     # Setup the logger properly, so you can run multi-processing to parallelize the evaluation
@@ -152,12 +129,7 @@ def process_instance(
 
     runtime = create_runtime(config)
     call_async_from_sync(runtime.connect)
-    task_str, obs = initialize_runtime(runtime)
-
-    task_str += (
-        f'\nInitial browser state (output of `noop(1000)`):\n{obs.get_agent_obs_text()}'
-    )
-
+    task_str = initialize_runtime(runtime)
     state: State | None = asyncio.run(
         run_controller(
             config=config,
@@ -165,9 +137,6 @@ def process_instance(
                 content=task_str
             ),  # take output from initialize_runtime
             runtime=runtime,
-            fake_user_response_fn=AGENT_CLS_TO_FAKE_USER_RESPONSE_FN[
-                metadata.agent_class
-            ],
         )
     )
 
@@ -183,19 +152,19 @@ def process_instance(
 
     # Instruction is the first message from the USER
     instruction = ''
-    for event in state.history:
+    for event in state.history.get_events():
         if isinstance(event, MessageAction):
             instruction = event.content
             break
 
     return_val = complete_runtime(runtime)
     logger.info(f'Return value from complete_runtime: {return_val}')
-    reward = max(return_val['rewards'], default=0)
+    reward = max(return_val['rewards'])
 
     # history is now available as a stream of events, rather than list of pairs of (Action, Observation)
     # for compatibility with the existing output format, we can remake the pairs here
     # remove when it becomes unnecessary
-    histories = compatibility_for_eval_history_pairs(state.history)
+    histories = state.history.compatibility_for_eval_history_pairs()
 
     # Save the output
     output = EvalOutput(

evaluation/mint/run_infer.py

@@ -13,7 +13,6 @@ from evaluation.mint.tasks import Task
 from evaluation.utils.shared import (
     EvalMetadata,
     EvalOutput,
-    compatibility_for_eval_history_pairs,
     make_metadata,
     prepare_dataset,
     reset_logger_for_multiprocessing,
@@ -29,7 +28,6 @@ from openhands.core.config import (
 from openhands.core.logger import openhands_logger as logger
 from openhands.core.main import create_runtime, run_controller
 from openhands.events.action import (
-    Action,
     CmdRunAction,
     MessageAction,
 )
@@ -47,10 +45,7 @@ def codeact_user_response_mint(state: State, task: Task, task_config: dict[str,
         task=task,
         task_config=task_config,
     )
-    last_action = next(
-        (event for event in reversed(state.history) if isinstance(event, Action)),
-        None,
-    )
+    last_action = state.history.get_last_action()
     result_state: TaskState = env.step(last_action.message or '')
 
     state.extra_data['task_state'] = result_state
@@ -207,7 +202,7 @@ def process_instance(
     # history is now available as a stream of events, rather than list of pairs of (Action, Observation)
     # for compatibility with the existing output format, we can remake the pairs here
     # remove when it becomes unnecessary
-    histories = compatibility_for_eval_history_pairs(state.history)
+    histories = state.history.compatibility_for_eval_history_pairs()
 
     # Save the output
     output = EvalOutput(

evaluation/ml_bench/run_infer.py

@@ -24,7 +24,6 @@ from evaluation.utils.shared import (
     EvalMetadata,
     EvalOutput,
     codeact_user_response,
-    compatibility_for_eval_history_pairs,
     make_metadata,
     prepare_dataset,
     reset_logger_for_multiprocessing,
@@ -257,7 +256,7 @@ def process_instance(instance: Any, metadata: EvalMetadata, reset_logger: bool =
     # history is now available as a stream of events, rather than list of pairs of (Action, Observation)
     # for compatibility with the existing output format, we can remake the pairs here
     # remove when it becomes unnecessary
-    histories = compatibility_for_eval_history_pairs(state.history)
+    histories = state.history.compatibility_for_eval_history_pairs()
 
     # Save the output
     output = EvalOutput(

evaluation/scienceagentbench/run_infer.py

@@ -10,12 +10,10 @@ from evaluation.utils.shared import (
     EvalMetadata,
     EvalOutput,
     codeact_user_response,
-    compatibility_for_eval_history_pairs,
     make_metadata,
     prepare_dataset,
     reset_logger_for_multiprocessing,
     run_evaluation,
-    update_llm_config_for_completions_logging,
 )
 from openhands.controller.state.state import State
 from openhands.core.config import (
@@ -72,19 +70,21 @@ def get_config(
             timeout=300,
             api_key=os.environ.get('ALLHANDS_API_KEY', None),
             remote_runtime_api_url=os.environ.get('SANDBOX_REMOTE_RUNTIME_API_URL'),
-            keep_runtime_alive=False,
+            keep_remote_runtime_alive=False,
         ),
         # do not mount workspace
         workspace_base=None,
         workspace_mount_path=None,
     )
-    config.set_llm_config(
-        update_llm_config_for_completions_logging(
-            metadata.llm_config,
-            metadata.eval_output_dir,
-            instance_id,
+    config.set_llm_config(metadata.llm_config)
+    if metadata.llm_config.log_completions:
+        metadata.llm_config.log_completions_folder = os.path.join(
+            metadata.eval_output_dir, 'llm_completions', instance_id
+        )
+        logger.info(
+            f'Logging LLM completions for instance {instance_id} to '
+            f'{metadata.llm_config.log_completions_folder}'
         )
-    )
     return config
 
 
@@ -233,7 +233,7 @@ If the program uses some packages that are incompatible, please figure out alter
     # history is now available as a stream of events, rather than list of pairs of (Action, Observation)
     # for compatibility with the existing output format, we can remake the pairs here
     # remove when it becomes unnecessary
-    histories = compatibility_for_eval_history_pairs(state.history)
+    histories = state.history.compatibility_for_eval_history_pairs()
 
     # Save the output
     output = EvalOutput(

evaluation/swe_bench/eval_infer.py

@@ -1,7 +1,6 @@
 import os
 import tempfile
 import time
-from functools import partial
 
 import pandas as pd
 from swebench.harness.grading import get_eval_report
@@ -84,7 +83,6 @@ def get_config(instance: pd.Series) -> AppConfig:
             timeout=1800,
             api_key=os.environ.get('ALLHANDS_API_KEY', None),
             remote_runtime_api_url=os.environ.get('SANDBOX_REMOTE_RUNTIME_API_URL'),
-            remote_runtime_init_timeout=1800,
         ),
         # do not mount workspace
         workspace_base=None,
@@ -95,28 +93,13 @@ def get_config(instance: pd.Series) -> AppConfig:
 
 def process_instance(
     instance: pd.Series,
-    metadata: EvalMetadata,
+    metadata: EvalMetadata | None = None,
     reset_logger: bool = True,
-    log_dir: str | None = None,
 ) -> EvalOutput:
-    """
-    Evaluate agent performance on a SWE-bench problem instance.
-
-    Note that this signature differs from the expected input to `run_evaluation`. Use
-    `functools.partial` to provide optional arguments before passing to the evaluation harness.
-
-    Args:
-        log_dir (str | None, default=None): Path to directory where log files will be written. Must
-        be provided if `reset_logger` is set.
-
-    Raises:
-        AssertionError: if the `reset_logger` flag is set without a provided log directory.
-    """
     # Setup the logger properly, so you can run multi-processing to parallelize the evaluation
     if reset_logger:
-        assert (
-            log_dir is not None
-        ), "Can't reset logger without a provided log directory."
+        global output_file
+        log_dir = output_file.replace('.jsonl', '.logs')
         os.makedirs(log_dir, exist_ok=True)
         reset_logger_for_multiprocessing(logger, instance.instance_id, log_dir)
     else:
@@ -143,7 +126,6 @@ def process_instance(
         return EvalOutput(
             instance_id=instance_id,
             test_result=instance['test_result'],
-            metadata=metadata,
         )
 
     runtime = create_runtime(config)
@@ -193,7 +175,6 @@ def process_instance(
             return EvalOutput(
                 instance_id=instance_id,
                 test_result=instance['test_result'],
-                metadata=metadata,
             )
         elif 'APPLY_PATCH_PASS' in apply_patch_output:
             logger.info(f'[{instance_id}] {APPLY_PATCH_PASS}:\n{apply_patch_output}')
@@ -287,7 +268,6 @@ def process_instance(
             return EvalOutput(
                 instance_id=instance_id,
                 test_result=instance['test_result'],
-                metadata=metadata,
             )
         else:
             logger.info(
@@ -374,26 +354,12 @@ if __name__ == '__main__':
     output_file = args.input_file.replace('.jsonl', '.swebench_eval.jsonl')
     instances = prepare_dataset(predictions, output_file, args.eval_n_limit)
 
-    # If possible, load the relevant metadata to avoid issues with `run_evaluation`.
-    metadata: EvalMetadata | None = None
-    metadata_filepath = os.path.join(os.path.dirname(args.input_file), 'metadata.json')
-    if os.path.exists(metadata_filepath):
-        with open(metadata_filepath, 'r') as metadata_file:
-            data = metadata_file.read()
-            metadata = EvalMetadata.model_validate_json(data)
-
-    # The evaluation harness constrains the signature of `process_instance_func` but we need to
-    # pass extra information. Build a new function object to avoid issues with multiprocessing.
-    process_instance_func = partial(
-        process_instance, log_dir=output_file.replace('.jsonl', '.logs')
-    )
-
     run_evaluation(
         instances,
-        metadata=metadata,
+        metadata=None,
         output_file=output_file,
         num_workers=args.eval_num_workers,
-        process_instance_func=process_instance_func,
+        process_instance_func=process_instance,
     )
 
     # Load evaluated predictions & print number of resolved predictions

evaluation/swe_bench/run_infer.py

@@ -20,7 +20,6 @@ from evaluation.utils.shared import (
     prepare_dataset,
     reset_logger_for_multiprocessing,
     run_evaluation,
-    update_llm_config_for_completions_logging,
 )
 from openhands.controller.state.state import State
 from openhands.core.config import (
@@ -36,12 +35,11 @@ from openhands.events.action import CmdRunAction, MessageAction
 from openhands.events.observation import CmdOutputObservation, ErrorObservation
 from openhands.events.serialization.event import event_to_dict
 from openhands.runtime.base import Runtime
+from openhands.runtime.utils.shutdown_listener import sleep_if_should_continue
 from openhands.utils.async_utils import call_async_from_sync
-from openhands.utils.shutdown_listener import sleep_if_should_continue
 
 USE_HINT_TEXT = os.environ.get('USE_HINT_TEXT', 'false').lower() == 'true'
 USE_INSTANCE_IMAGE = os.environ.get('USE_INSTANCE_IMAGE', 'false').lower() == 'true'
-RUN_WITH_BROWSING = os.environ.get('RUN_WITH_BROWSING', 'false').lower() == 'true'
 
 AGENT_CLS_TO_FAKE_USER_RESPONSE_FN = {
     'CodeActAgent': codeact_user_response,
@@ -81,7 +79,7 @@ def get_instruction(instance: pd.Series, metadata: EvalMetadata):
             '</pr_description>\n\n'
             'Can you help me implement the necessary changes to the repository so that the requirements specified in the <pr_description> are met?\n'
             "I've already taken care of all changes to any of the test files described in the <pr_description>. This means you DON'T have to modify the testing logic or any of the tests in any way!\n"
-            'Your task is to make the minimal changes to non-tests files in the /workspace directory to ensure the <pr_description> is satisfied.\n'
+            'Your task is to make the minimal changes to non-tests files in the /repo directory to ensure the <pr_description> is satisfied.\n'
             'Follow these steps to resolve the issue:\n'
             '1. As a first step, it might be a good idea to explore the repo to familiarize yourself with its structure.\n'
             '2. Create a script to reproduce the error and execute it with `python <filename.py>` using the BashTool, to confirm the error\n'
@@ -90,13 +88,6 @@ def get_instruction(instance: pd.Series, metadata: EvalMetadata):
             '5. Think about edgecases and make sure your fix handles them as well\n'
             "Your thinking should be thorough and so it's fine if it's very long.\n"
         )
-
-    if RUN_WITH_BROWSING:
-        instruction += (
-            '<IMPORTANT!>\n'
-            'You SHOULD NEVER attempt to browse the web. '
-            '</IMPORTANT!>\n'
-        )
     return instruction
 
 
@@ -145,21 +136,24 @@ def get_config(
             platform='linux/amd64',
             api_key=os.environ.get('ALLHANDS_API_KEY', None),
             remote_runtime_api_url=os.environ.get('SANDBOX_REMOTE_RUNTIME_API_URL'),
-            keep_runtime_alive=False,
-            remote_runtime_init_timeout=1800,
+            keep_remote_runtime_alive=False,
         ),
         # do not mount workspace
         workspace_base=None,
         workspace_mount_path=None,
     )
-    config.set_llm_config(
-        update_llm_config_for_completions_logging(
-            metadata.llm_config, metadata.eval_output_dir, instance['instance_id']
+    if metadata.llm_config.log_completions:
+        metadata.llm_config.log_completions_folder = os.path.join(
+            metadata.eval_output_dir, 'llm_completions', instance['instance_id']
         )
-    )
+        logger.info(
+            f'Logging LLM completions for instance {instance["instance_id"]} to '
+            f'{metadata.llm_config.log_completions_folder}'
+        )
+    config.set_llm_config(metadata.llm_config)
     agent_config = AgentConfig(
         codeact_enable_jupyter=False,
-        codeact_enable_browsing=RUN_WITH_BROWSING,
+        codeact_enable_browsing_delegate=False,
         codeact_enable_llm_editor=False,
     )
     config.set_agent_config(agent_config)
@@ -444,8 +438,7 @@ def process_instance(
     if state is None:
         raise ValueError('State should not be None.')
 
-    # NOTE: this is NO LONGER the event stream, but an agent history that includes delegate agent's events
-    histories = [event_to_dict(event) for event in state.history]
+    histories = [event_to_dict(event) for event in state.history.get_events()]
     metrics = state.metrics.get() if state.metrics else None
 
     # Save the output

evaluation/swe_bench/scripts/run_infer.sh

@@ -34,11 +34,6 @@ if [ -z "$USE_INSTANCE_IMAGE" ]; then
   USE_INSTANCE_IMAGE=true
 fi
 
-if [ -z "$RUN_WITH_BROWSING" ]; then
-  echo "RUN_WITH_BROWSING not specified, use default false"
-  RUN_WITH_BROWSING=false
-fi
-
 
 if [ -z "$DATASET" ]; then
   echo "DATASET not specified, use default princeton-nlp/SWE-bench_Lite"
@@ -52,8 +47,6 @@ fi
 
 export USE_INSTANCE_IMAGE=$USE_INSTANCE_IMAGE
 echo "USE_INSTANCE_IMAGE: $USE_INSTANCE_IMAGE"
-export RUN_WITH_BROWSING=$RUN_WITH_BROWSING
-echo "RUN_WITH_BROWSING: $RUN_WITH_BROWSING"
 
 get_agent_version
 
@@ -74,10 +67,6 @@ if [ "$USE_HINT_TEXT" = false ]; then
   EVAL_NOTE="$EVAL_NOTE-no-hint"
 fi
 
-if [ "$RUN_WITH_BROWSING" = true ]; then
-  EVAL_NOTE="$EVAL_NOTE-with-browsing"
-fi
-
 if [ -n "$EXP_NAME" ]; then
   EVAL_NOTE="$EVAL_NOTE-$EXP_NAME"
 fi

evaluation/toolqa/run_infer.py

@@ -9,7 +9,6 @@ from evaluation.utils.shared import (
     EvalMetadata,
     EvalOutput,
     codeact_user_response,
-    compatibility_for_eval_history_pairs,
     make_metadata,
     prepare_dataset,
     reset_logger_for_multiprocessing,
@@ -127,8 +126,7 @@ def process_instance(instance: Any, metadata: EvalMetadata, reset_logger: bool =
         raise ValueError('State should not be None.')
 
     # retrieve the last message from the agent
-    last_agent_message = state.get_last_agent_message()
-    model_answer_raw = last_agent_message.content if last_agent_message else ''
+    model_answer_raw = state.history.get_last_agent_message()
 
     # attempt to parse model_answer
     correct = eval_answer(str(model_answer_raw), str(answer))
@@ -139,7 +137,7 @@ def process_instance(instance: Any, metadata: EvalMetadata, reset_logger: bool =
     # history is now available as a stream of events, rather than list of pairs of (Action, Observation)
     # for compatibility with the existing output format, we can remake the pairs here
     # remove when it becomes unnecessary
-    histories = compatibility_for_eval_history_pairs(state.history)
+    histories = state.history.compatibility_for_eval_history_pairs()
 
     # Save the output
     output = EvalOutput(

evaluation/utils/shared.py

@@ -18,9 +18,6 @@ from openhands.core.logger import get_console_handler
 from openhands.core.logger import openhands_logger as logger
 from openhands.events.action import Action
 from openhands.events.action.message import MessageAction
-from openhands.events.event import Event
-from openhands.events.serialization.event import event_to_dict
-from openhands.events.utils import get_pairs_from_events
 
 
 class EvalMetadata(BaseModel):
@@ -115,14 +112,7 @@ def codeact_user_response(
     if state.history:
         # check if the last action has an answer, if so, early exit
         if try_parse is not None:
-            last_action = next(
-                (
-                    event
-                    for event in reversed(state.history)
-                    if isinstance(event, Action)
-                ),
-                None,
-            )
+            last_action = state.history.get_last_action()
             ans = try_parse(last_action)
             if ans is not None:
                 return '/exit'
@@ -130,7 +120,7 @@ def codeact_user_response(
         # check if the agent has tried to talk to the user 3 times, if so, let the agent know it can give up
         user_msgs = [
             event
-            for event in state.history
+            for event in state.history.get_events()
             if isinstance(event, MessageAction) and event.source == 'user'
         ]
         if len(user_msgs) >= 2:
@@ -346,7 +336,6 @@ def run_evaluation(
             f'model {metadata.llm_config.model}, max iterations {metadata.max_iterations}.\n'
         )
     else:
-        logger.warning('Running evaluation without metadata.')
         logger.info(f'Evaluation started with {num_workers} workers.')
 
     total_instances = len(dataset)
@@ -422,35 +411,3 @@ def reset_logger_for_multiprocessing(
     )
     file_handler.setLevel(logging.INFO)
     logger.addHandler(file_handler)
-
-
-def update_llm_config_for_completions_logging(
-    llm_config: LLMConfig,
-    eval_output_dir: str,
-    instance_id: str,
-) -> LLMConfig:
-    """Update the LLM config for logging completions."""
-    if llm_config.log_completions:
-        llm_config.log_completions_folder = os.path.join(
-            eval_output_dir, 'llm_completions', instance_id
-        )
-        logger.info(
-            f'Logging LLM completions for instance {instance_id} to '
-            f'{llm_config.log_completions_folder}'
-        )
-    return llm_config
-
-
-# history is now available as a filtered stream of events, rather than list of pairs of (Action, Observation)
-# we rebuild the pairs here
-# for compatibility with the existing output format in evaluations
-# remove this when it's no longer necessary
-def compatibility_for_eval_history_pairs(
-    history: list[Event],
-) -> list[tuple[dict, dict]]:
-    history_pairs = []
-
-    for action, observation in get_pairs_from_events(history):
-        history_pairs.append((event_to_dict(action), event_to_dict(observation)))
-
-    return history_pairs

evaluation/webarena/run_infer.py

@@ -10,7 +10,6 @@ import pandas as pd
 from evaluation.utils.shared import (
     EvalMetadata,
     EvalOutput,
-    compatibility_for_eval_history_pairs,
     make_metadata,
     prepare_dataset,
     reset_logger_for_multiprocessing,
@@ -167,7 +166,7 @@ def process_instance(
 
     # Instruction is the first message from the USER
     instruction = ''
-    for event in state.history:
+    for event in state.history.get_events():
         if isinstance(event, MessageAction):
             instruction = event.content
             break
@@ -179,7 +178,7 @@ def process_instance(
     # history is now available as a stream of events, rather than list of pairs of (Action, Observation)
     # for compatibility with the existing output format, we can remake the pairs here
     # remove when it becomes unnecessary
-    histories = compatibility_for_eval_history_pairs(state.history)
+    histories = state.history.compatibility_for_eval_history_pairs()
 
     # Save the output
     output = EvalOutput(

frontend/.gitignore

@@ -2,8 +2,3 @@
 public/locales/**/*
 src/i18n/declaration.ts
 .env
-node_modules/
-/test-results/
-/playwright-report/
-/blob-report/
-/playwright/.cache/

frontend/__tests__/clear-session.test.ts

@@ -1,40 +0,0 @@
-import { describe, it, expect, beforeEach, vi } from "vitest";
-import { clearSession } from "../src/utils/clear-session";
-import store from "../src/store";
-import { initialState as browserInitialState } from "../src/state/browserSlice";
-
-describe("clearSession", () => {
-  beforeEach(() => {
-    // Mock localStorage
-    const localStorageMock = {
-      getItem: vi.fn(),
-      setItem: vi.fn(),
-      removeItem: vi.fn(),
-      clear: vi.fn(),
-    };
-    vi.stubGlobal("localStorage", localStorageMock);
-
-    // Set initial browser state to non-default values
-    store.dispatch({
-      type: "browser/setUrl",
-      payload: "https://example.com",
-    });
-    store.dispatch({
-      type: "browser/setScreenshotSrc",
-      payload: "base64screenshot",
-    });
-  });
-
-  it("should clear localStorage and reset browser state", () => {
-    clearSession();
-
-    // Verify localStorage items were removed
-    expect(localStorage.removeItem).toHaveBeenCalledWith("token");
-    expect(localStorage.removeItem).toHaveBeenCalledWith("repo");
-
-    // Verify browser state was reset
-    const state = store.getState();
-    expect(state.browser.url).toBe(browserInitialState.url);
-    expect(state.browser.screenshotSrc).toBe(browserInitialState.screenshotSrc);
-  });
-});

frontend/__tests__/components/chat/chat-input.test.tsx

@@ -1,5 +1,5 @@
 import userEvent from "@testing-library/user-event";
-import { fireEvent, render, screen } from "@testing-library/react";
+import { render, screen } from "@testing-library/react";
 import { describe, afterEach, vi, it, expect } from "vitest";
 import { ChatInput } from "#/components/chat-input";
 
@@ -158,46 +158,4 @@ describe("ChatInput", () => {
     await user.tab();
     expect(onBlurMock).toHaveBeenCalledOnce();
   });
-
-  it("should handle text paste correctly", () => {
-    const onSubmit = vi.fn();
-    const onChange = vi.fn();
-
-    render(<ChatInput onSubmit={onSubmit} onChange={onChange} />);
-
-    const input = screen.getByTestId("chat-input").querySelector("textarea");
-    expect(input).toBeTruthy();
-
-    // Fire paste event with text data
-    fireEvent.paste(input!, {
-      clipboardData: {
-        getData: (type: string) => type === 'text/plain' ? 'test paste' : '',
-        files: []
-      }
-    });
-  });
-
-  it("should handle image paste correctly", () => {
-    const onSubmit = vi.fn();
-    const onImagePaste = vi.fn();
-
-    render(<ChatInput onSubmit={onSubmit} onImagePaste={onImagePaste} />);
-
-    const input = screen.getByTestId("chat-input").querySelector("textarea");
-    expect(input).toBeTruthy();
-
-    // Create a paste event with an image file
-    const file = new File(["dummy content"], "image.png", { type: "image/png" });
-
-    // Fire paste event with image data
-    fireEvent.paste(input!, {
-      clipboardData: {
-        getData: () => '',
-        files: [file]
-      }
-    });
-
-    // Verify image paste was handled
-    expect(onImagePaste).toHaveBeenCalledWith([file]);
-  });
 });

frontend/__tests__/components/chat/chat-interface.test.tsx

@@ -1,157 +1,20 @@
-import { afterEach, beforeAll, describe, expect, it, vi } from "vitest";
-import { act, screen, waitFor, within } from "@testing-library/react";
+import { afterEach, describe, expect, it, vi } from "vitest";
+import { render, screen, within } from "@testing-library/react";
 import userEvent from "@testing-library/user-event";
-import { renderWithProviders } from "test-utils";
 import { ChatInterface } from "#/components/chat-interface";
-import { addUserMessage } from "#/state/chatSlice";
-import { SUGGESTIONS } from "#/utils/suggestions";
-import * as ChatSlice from "#/state/chatSlice";
+import { SocketProvider } from "#/context/socket";
 
 // eslint-disable-next-line @typescript-eslint/no-unused-vars
 const renderChatInterface = (messages: (Message | ErrorMessage)[]) =>
-  renderWithProviders(<ChatInterface />);
-
-describe("Empty state", () => {
-  const { send: sendMock } = vi.hoisted(() => ({
-    send: vi.fn(),
-  }));
-
-  const { useWsClient: useWsClientMock } = vi.hoisted(() => ({
-    useWsClient: vi.fn(() => ({ send: sendMock, runtimeActive: true })),
-  }));
-
-  beforeAll(() => {
-    vi.mock("#/context/socket", async (importActual) => ({
-      ...(await importActual<typeof import("#/context/ws-client-provider")>()),
-      useWsClient: useWsClientMock,
-    }));
-  });
-
-  afterEach(() => {
-    vi.clearAllMocks();
-  });
-
-  it("should render suggestions if empty", () => {
-    const { store } = renderWithProviders(<ChatInterface />, {
-      preloadedState: {
-        chat: { messages: [] },
-      },
-    });
-
-    expect(screen.getByTestId("suggestions")).toBeInTheDocument();
-
-    act(() => {
-      store.dispatch(
-        addUserMessage({
-          content: "Hello",
-          imageUrls: [],
-          timestamp: new Date().toISOString(),
-        }),
-      );
-    });
-
-    expect(screen.queryByTestId("suggestions")).not.toBeInTheDocument();
-  });
-
-  it("should render the default suggestions", () => {
-    renderWithProviders(<ChatInterface />, {
-      preloadedState: {
-        chat: { messages: [] },
-      },
-    });
-
-    const suggestions = screen.getByTestId("suggestions");
-    const repoSuggestions = Object.keys(SUGGESTIONS.repo);
-
-    // check that there are at most 4 suggestions displayed
-    const displayedSuggestions = within(suggestions).getAllByRole("button");
-    expect(displayedSuggestions.length).toBeLessThanOrEqual(4);
-
-    // Check that each displayed suggestion is one of the repo suggestions
-    displayedSuggestions.forEach((suggestion) => {
-      expect(repoSuggestions).toContain(suggestion.textContent);
-    });
-  });
-
-  it.fails(
-    "should load the a user message to the input when selecting",
-    async () => {
-      // this is to test that the message is in the UI before the socket is called
-      useWsClientMock.mockImplementation(() => ({
-        send: sendMock,
-        runtimeActive: false, // mock an inactive runtime setup
-      }));
-      const addUserMessageSpy = vi.spyOn(ChatSlice, "addUserMessage");
-      const user = userEvent.setup();
-      const { store } = renderWithProviders(<ChatInterface />, {
-        preloadedState: {
-          chat: { messages: [] },
-        },
-      });
-
-      const suggestions = screen.getByTestId("suggestions");
-      const displayedSuggestions = within(suggestions).getAllByRole("button");
-      const input = screen.getByTestId("chat-input");
-
-      await user.click(displayedSuggestions[0]);
-
-      // user message loaded to input
-      expect(addUserMessageSpy).not.toHaveBeenCalled();
-      expect(screen.queryByTestId("suggestions")).toBeInTheDocument();
-      expect(store.getState().chat.messages).toHaveLength(0);
-      expect(input).toHaveValue(displayedSuggestions[0].textContent);
-    },
-  );
-
-  it.fails(
-    "should send the message to the socket only if the runtime is active",
-    async () => {
-      useWsClientMock.mockImplementation(() => ({
-        send: sendMock,
-        runtimeActive: false, // mock an inactive runtime setup
-      }));
-      const user = userEvent.setup();
-      const { rerender } = renderWithProviders(<ChatInterface />, {
-        preloadedState: {
-          chat: { messages: [] },
-        },
-      });
-
-      const suggestions = screen.getByTestId("suggestions");
-      const displayedSuggestions = within(suggestions).getAllByRole("button");
-
-      await user.click(displayedSuggestions[0]);
-      expect(sendMock).not.toHaveBeenCalled();
-
-      useWsClientMock.mockImplementation(() => ({
-        send: sendMock,
-        runtimeActive: true, // mock an active runtime setup
-      }));
-      rerender(<ChatInterface />);
-
-      await waitFor(() =>
-        expect(sendMock).toHaveBeenCalledWith(expect.any(String)),
-      );
-    },
-  );
-});
+  render(<ChatInterface />, { wrapper: SocketProvider });
 
 describe.skip("ChatInterface", () => {
-  beforeAll(() => {
-    // mock useScrollToBottom hook
-    vi.mock("#/hooks/useScrollToBottom", () => ({
-      useScrollToBottom: vi.fn(() => ({
-        scrollDomToBottom: vi.fn(),
-        onChatBodyScroll: vi.fn(),
-        hitBottom: vi.fn(),
-      })),
-    }));
-  });
-
   afterEach(() => {
     vi.clearAllMocks();
   });
 
+  it.todo("should render suggestions if empty");
+
   it("should render messages", () => {
     const messages: Message[] = [
       {
@@ -265,14 +128,14 @@ describe.skip("ChatInterface", () => {
         timestamp: new Date().toISOString(),
       },
       {
-        error: true,
-        id: "",
+        error: "Woops!",
         message: "Something went wrong",
       },
     ];
     renderChatInterface(messages);
 
     const error = screen.getByTestId("error-message");
+    expect(within(error).getByText("Woops!")).toBeInTheDocument();
     expect(within(error).getByText("Something went wrong")).toBeInTheDocument();
   });
 

frontend/__tests__/components/interactive-chat-box.test.tsx

@@ -25,21 +25,6 @@ describe("InteractiveChatBox", () => {
     within(chatBox).getByTestId("upload-image-input");
   });
 
-  it.fails("should set custom values", () => {
-    render(
-      <InteractiveChatBox
-        onSubmit={onSubmitMock}
-        onStop={onStopMock}
-        value="Hello, world!"
-      />,
-    );
-
-    const chatBox = screen.getByTestId("interactive-chat-box");
-    const chatInput = within(chatBox).getByTestId("chat-input");
-
-    expect(chatInput).toHaveValue("Hello, world!");
-  });
-
   it("should display the image previews when images are uploaded", async () => {
     const user = userEvent.setup();
     render(<InteractiveChatBox onSubmit={onSubmitMock} onStop={onStopMock} />);

frontend/__tests__/components/suggestion-item.test.tsx

@@ -1,30 +0,0 @@
-import { render, screen } from "@testing-library/react";
-import userEvent from "@testing-library/user-event";
-import { afterEach, describe, expect, it, vi } from "vitest";
-import { SuggestionItem } from "#/components/suggestion-item";
-
-describe("SuggestionItem", () => {
-  const suggestionItem = { label: "suggestion1", value: "a long text value" };
-  const onClick = vi.fn();
-
-  afterEach(() => {
-    vi.clearAllMocks();
-  });
-
-  it("should render a suggestion", () => {
-    render(<SuggestionItem suggestion={suggestionItem} onClick={onClick} />);
-
-    expect(screen.getByTestId("suggestion")).toBeInTheDocument();
-    expect(screen.getByText(/suggestion1/i)).toBeInTheDocument();
-  });
-
-  it("should call onClick when clicking a suggestion", async () => {
-    const user = userEvent.setup();
-    render(<SuggestionItem suggestion={suggestionItem} onClick={onClick} />);
-
-    const suggestion = screen.getByTestId("suggestion");
-    await user.click(suggestion);
-
-    expect(onClick).toHaveBeenCalledWith("a long text value");
-  });
-});

frontend/__tests__/components/suggestions.test.tsx

@@ -1,60 +0,0 @@
-import { render, screen } from "@testing-library/react";
-import userEvent from "@testing-library/user-event";
-import { afterEach, describe, expect, it, vi } from "vitest";
-import { Suggestions } from "#/components/suggestions";
-
-describe("Suggestions", () => {
-  const firstSuggestion = {
-    label: "first-suggestion",
-    value: "value-of-first-suggestion",
-  };
-  const secondSuggestion = {
-    label: "second-suggestion",
-    value: "value-of-second-suggestion",
-  };
-  const suggestions = [firstSuggestion, secondSuggestion];
-
-  const onSuggestionClickMock = vi.fn();
-
-  afterEach(() => {
-    vi.clearAllMocks();
-  });
-
-  it("should render suggestions", () => {
-    render(
-      <Suggestions
-        suggestions={suggestions}
-        onSuggestionClick={onSuggestionClickMock}
-      />,
-    );
-
-    expect(screen.getByTestId("suggestions")).toBeInTheDocument();
-    const suggestionElements = screen.getAllByTestId("suggestion");
-
-    expect(suggestionElements).toHaveLength(2);
-    expect(suggestionElements[0]).toHaveTextContent("first-suggestion");
-    expect(suggestionElements[1]).toHaveTextContent("second-suggestion");
-  });
-
-  it("should call onSuggestionClick when clicking a suggestion", async () => {
-    const user = userEvent.setup();
-    render(
-      <Suggestions
-        suggestions={suggestions}
-        onSuggestionClick={onSuggestionClickMock}
-      />,
-    );
-
-    const suggestionElements = screen.getAllByTestId("suggestion");
-
-    await user.click(suggestionElements[0]);
-    expect(onSuggestionClickMock).toHaveBeenCalledWith(
-      "value-of-first-suggestion",
-    );
-
-    await user.click(suggestionElements[1]);
-    expect(onSuggestionClickMock).toHaveBeenCalledWith(
-      "value-of-second-suggestion",
-    );
-  });
-});

frontend/__tests__/hooks/use-terminal.test.tsx

@@ -2,9 +2,8 @@ import { beforeAll, describe, expect, it, vi } from "vitest";
 import { render } from "@testing-library/react";
 import { afterEach } from "node:test";
 import { useTerminal } from "#/hooks/useTerminal";
+import { SocketProvider } from "#/context/socket";
 import { Command } from "#/state/commandSlice";
-import { WsClientProvider } from "#/context/ws-client-provider";
-import { ReactNode } from "react";
 
 interface TestTerminalComponentProps {
   commands: Command[];
@@ -19,17 +18,6 @@ function TestTerminalComponent({
   return <div ref={ref} />;
 }
 
-interface WrapperProps {
-  children: ReactNode;
-}
-
-
-function Wrapper({children}: WrapperProps) {
-  return (
-    <WsClientProvider enabled={true} token="NO_JWT" ghToken="NO_GITHUB" settings={null}>{children}</WsClientProvider>
-  )
-}
-
 describe("useTerminal", () => {
   const mockTerminal = vi.hoisted(() => ({
     loadAddon: vi.fn(),
@@ -62,7 +50,7 @@ describe("useTerminal", () => {
 
   it("should render", () => {
     render(<TestTerminalComponent commands={[]} secrets={[]} />, {
-      wrapper: Wrapper,
+      wrapper: SocketProvider,
     });
   });
 
@@ -73,7 +61,7 @@ describe("useTerminal", () => {
     ];
 
     render(<TestTerminalComponent commands={commands} secrets={[]} />, {
-      wrapper: Wrapper,
+      wrapper: SocketProvider,
     });
 
     expect(mockTerminal.writeln).toHaveBeenNthCalledWith(1, "echo hello");
@@ -97,7 +85,7 @@ describe("useTerminal", () => {
         secrets={[secret, anotherSecret]}
       />,
       {
-        wrapper: Wrapper,
+        wrapper: SocketProvider,
       },
     );
 

frontend/__tests__/initial-query.test.tsx

@@ -1,17 +0,0 @@
-import { describe, it, expect } from "vitest";
-import store from "../src/store";
-import { setInitialQuery, clearInitialQuery } from "../src/state/initial-query-slice";
-
-describe("Initial Query Behavior", () => {
-  it("should clear initial query when clearInitialQuery is dispatched", () => {
-    // Set up initial query in the store
-    store.dispatch(setInitialQuery("test query"));
-    expect(store.getState().initalQuery.initialQuery).toBe("test query");
-
-    // Clear the initial query
-    store.dispatch(clearInitialQuery());
-
-    // Verify initial query is cleared
-    expect(store.getState().initalQuery.initialQuery).toBeNull();
-  });
-});

frontend/__tests__/routes/_oh.app.test.tsx

@@ -1,5 +0,0 @@
-import { describe, it } from "vitest";
-
-describe("App", () => {
-  it.todo("should render");
-});

frontend/__tests__/utils/cache.test.ts

@@ -1,53 +0,0 @@
-import { afterEach } from "node:test";
-import { beforeEach, describe, expect, it, vi } from "vitest";
-import { cache } from "#/utils/cache";
-
-describe("Cache", () => {
-  const testKey = "key";
-  const testData = { message: "Hello, world!" };
-  const testTTL = 1000; // 1 second
-
-  beforeEach(() => {
-    vi.useFakeTimers();
-  });
-
-  afterEach(() => {
-    vi.useRealTimers();
-  });
-
-  it("gets data from memory if not expired", () => {
-    cache.set(testKey, testData, testTTL);
-
-    expect(cache.get(testKey)).toEqual(testData);
-  });
-
-  it("should expire after 5 minutes by default", () => {
-    cache.set(testKey, testData);
-    expect(cache.get(testKey)).not.toBeNull();
-
-    vi.advanceTimersByTime(5 * 60 * 1000 + 1);
-
-    expect(cache.get(testKey)).toBeNull();
-  });
-
-  it("returns null if cached data is expired", () => {
-    cache.set(testKey, testData, testTTL);
-
-    vi.advanceTimersByTime(testTTL + 1);
-    expect(cache.get(testKey)).toBeNull();
-  });
-
-  it("deletes data from memory", () => {
-    cache.set(testKey, testData, testTTL);
-    cache.delete(testKey);
-    expect(cache.get(testKey)).toBeNull();
-  });
-
-  it("clears all data with the app prefix from memory", () => {
-    cache.set(testKey, testData, testTTL);
-    cache.set("anotherKey", { data: "More data" }, testTTL);
-    cache.clearAll();
-    expect(cache.get(testKey)).toBeNull();
-    expect(cache.get("anotherKey")).toBeNull();
-  });
-});

frontend/__tests__/utils/extractModelAndProvider.test.ts

@@ -59,9 +59,9 @@ describe("extractModelAndProvider", () => {
       separator: "/",
     });
 
-    expect(extractModelAndProvider("claude-3-5-sonnet-20240620")).toEqual({
+    expect(extractModelAndProvider("claude-3-5-sonnet-20241022")).toEqual({
       provider: "anthropic",
-      model: "claude-3-5-sonnet-20240620",
+      model: "claude-3-5-sonnet-20241022",
       separator: "/",
     });
 

frontend/__tests__/utils/organizeModelsAndProviders.test.ts

@@ -15,7 +15,7 @@ test("organizeModelsAndProviders", () => {
     "gpt-4o",
     "together-ai-21.1b-41b",
     "gpt-4o-mini",
-    "anthropic/claude-3-5-sonnet-20241022",
+    "claude-3-5-sonnet-20241022",
     "claude-3-haiku-20240307",
     "claude-2",
     "claude-2.1",

frontend/package-lock.json

@@ -1,12 +1,12 @@
 {
   "name": "openhands-frontend",
-  "version": "0.13.1",
+  "version": "0.12.0",
   "lockfileVersion": 3,
   "requires": true,
   "packages": {
     "": {
       "name": "openhands-frontend",
-      "version": "0.13.1",
+      "version": "0.12.0",
       "dependencies": {
         "@monaco-editor/react": "^4.6.0",
         "@nextui-org/react": "^2.4.8",
@@ -26,7 +26,6 @@
         "isbot": "^5.1.17",
         "jose": "^5.9.4",
         "monaco-editor": "^0.52.0",
-        "posthog-js": "^1.184.1",
         "react": "^18.3.1",
         "react-dom": "^18.3.1",
         "react-highlight": "^0.15.0",
@@ -46,7 +45,6 @@
         "ws": "^8.18.0"
       },
       "devDependencies": {
-        "@playwright/test": "^1.48.2",
         "@remix-run/dev": "^2.11.2",
         "@remix-run/testing": "^2.11.2",
         "@tailwindcss/typography": "^0.5.15",
@@ -63,7 +61,6 @@
         "@typescript-eslint/parser": "^7.18.0",
         "@vitest/coverage-v8": "^1.6.0",
         "autoprefixer": "^10.4.20",
-        "cross-env": "^7.0.3",
         "eslint": "^8.57.0",
         "eslint-config-airbnb": "^19.0.4",
         "eslint-config-airbnb-typescript": "^18.0.0",
@@ -3381,21 +3378,6 @@
         "url": "https://opencollective.com/unts"
       }
     },
-    "node_modules/@playwright/test": {
-      "version": "1.48.2",
-      "resolved": "https://registry.npmjs.org/@playwright/test/-/test-1.48.2.tgz",
-      "integrity": "sha512-54w1xCWfXuax7dz4W2M9uw0gDyh+ti/0K/MxcCUxChFh37kkdxPdfZDw5QBbuPUJHr1CiHJ1hXgSs+GgeQc5Zw==",
-      "dev": true,
-      "dependencies": {
-        "playwright": "1.48.2"
-      },
-      "bin": {
-        "playwright": "cli.js"
-      },
-      "engines": {
-        "node": ">=18"
-      }
-    },
     "node_modules/@polka/url": {
       "version": "1.0.0-next.28",
       "resolved": "https://registry.npmjs.org/@polka/url/-/url-1.0.0-next.28.tgz",
@@ -7882,16 +7864,6 @@
         "node": ">=6.6.0"
       }
     },
-    "node_modules/core-js": {
-      "version": "3.38.1",
-      "resolved": "https://registry.npmjs.org/core-js/-/core-js-3.38.1.tgz",
-      "integrity": "sha512-OP35aUorbU3Zvlx7pjsFdu1rGNnD4pgw/CWoYzRY3t2EzoVT7shKHY1dlAy3f41cGIO7ZDPQimhGFTlEYkG/Hw==",
-      "hasInstallScript": true,
-      "funding": {
-        "type": "opencollective",
-        "url": "https://opencollective.com/core-js"
-      }
-    },
     "node_modules/core-util-is": {
       "version": "1.0.3",
       "resolved": "https://registry.npmjs.org/core-util-is/-/core-util-is-1.0.3.tgz",
@@ -7924,24 +7896,6 @@
         }
       }
     },
-    "node_modules/cross-env": {
-      "version": "7.0.3",
-      "resolved": "https://registry.npmjs.org/cross-env/-/cross-env-7.0.3.tgz",
-      "integrity": "sha512-+/HKd6EgcQCJGh2PSjZuUitQBQynKor4wrFbRg4DtAgS1aWO+gU52xpH7M9ScGgXSYmAVS9bIJ8EzuaGw0oNAw==",
-      "dev": true,
-      "dependencies": {
-        "cross-spawn": "^7.0.1"
-      },
-      "bin": {
-        "cross-env": "src/bin/cross-env.js",
-        "cross-env-shell": "src/bin/cross-env-shell.js"
-      },
-      "engines": {
-        "node": ">=10.14",
-        "npm": ">=6",
-        "yarn": ">=1"
-      }
-    },
     "node_modules/cross-fetch": {
       "version": "4.0.0",
       "resolved": "https://registry.npmjs.org/cross-fetch/-/cross-fetch-4.0.0.tgz",
@@ -9712,11 +9666,6 @@
         "url": "https://github.com/sponsors/wooorm"
       }
     },
-    "node_modules/fflate": {
-      "version": "0.4.8",
-      "resolved": "https://registry.npmjs.org/fflate/-/fflate-0.4.8.tgz",
-      "integrity": "sha512-FJqqoDBR00Mdj9ppamLa/Y7vxm+PRmNWA67N846RvsoYVMKB4q3y/de5PA7gUmRMYK/8CMz2GDZQmCRN1wBcWA=="
-    },
     "node_modules/file-entry-cache": {
       "version": "6.0.1",
       "resolved": "https://registry.npmjs.org/file-entry-cache/-/file-entry-cache-6.0.1.tgz",
@@ -19457,50 +19406,6 @@
         "pathe": "^1.1.2"
       }
     },
-    "node_modules/playwright": {
-      "version": "1.48.2",
-      "resolved": "https://registry.npmjs.org/playwright/-/playwright-1.48.2.tgz",
-      "integrity": "sha512-NjYvYgp4BPmiwfe31j4gHLa3J7bD2WiBz8Lk2RoSsmX38SVIARZ18VYjxLjAcDsAhA+F4iSEXTSGgjua0rrlgQ==",
-      "dev": true,
-      "dependencies": {
-        "playwright-core": "1.48.2"
-      },
-      "bin": {
-        "playwright": "cli.js"
-      },
-      "engines": {
-        "node": ">=18"
-      },
-      "optionalDependencies": {
-        "fsevents": "2.3.2"
-      }
-    },
-    "node_modules/playwright-core": {
-      "version": "1.48.2",
-      "resolved": "https://registry.npmjs.org/playwright-core/-/playwright-core-1.48.2.tgz",
-      "integrity": "sha512-sjjw+qrLFlriJo64du+EK0kJgZzoQPsabGF4lBvsid+3CNIZIYLgnMj9V6JY5VhM2Peh20DJWIVpVljLLnlawA==",
-      "dev": true,
-      "bin": {
-        "playwright-core": "cli.js"
-      },
-      "engines": {
-        "node": ">=18"
-      }
-    },
-    "node_modules/playwright/node_modules/fsevents": {
-      "version": "2.3.2",
-      "resolved": "https://registry.npmjs.org/fsevents/-/fsevents-2.3.2.tgz",
-      "integrity": "sha512-xiqMQR4xAeHTuB9uWm+fFRcIOgKBMiOBP+eXiyT7jsgVCq1bkVygt00oASowB7EdtpOHaaPgKt812P9ab+DDKA==",
-      "dev": true,
-      "hasInstallScript": true,
-      "optional": true,
-      "os": [
-        "darwin"
-      ],
-      "engines": {
-        "node": "^8.16.0 || ^10.6.0 || >=11.0.0"
-      }
-    },
     "node_modules/possible-typed-array-names": {
       "version": "1.0.0",
       "resolved": "https://registry.npmjs.org/possible-typed-array-names/-/possible-typed-array-names-1.0.0.tgz",
@@ -19748,31 +19653,6 @@
       "resolved": "https://registry.npmjs.org/postcss-value-parser/-/postcss-value-parser-4.2.0.tgz",
       "integrity": "sha512-1NNCs6uurfkVbeXG4S8JFT9t19m45ICnif8zWLd5oPSZ50QnwMfK+H3jv408d4jw/7Bttv5axS5IiHoLaVNHeQ=="
     },
-    "node_modules/posthog-js": {
-      "version": "1.184.1",
-      "resolved": "https://registry.npmjs.org/posthog-js/-/posthog-js-1.184.1.tgz",
-      "integrity": "sha512-q/1Kdard5SZnL2smrzeKcD+RuUi2PnbidiN4D3ThK20bNrhy5Z2heIy9SnRMvEiARY5lcQ7zxmDCAKPBKGSOtQ==",
-      "dependencies": {
-        "core-js": "^3.38.1",
-        "fflate": "^0.4.8",
-        "preact": "^10.19.3",
-        "web-vitals": "^4.2.0"
-      }
-    },
-    "node_modules/posthog-js/node_modules/web-vitals": {
-      "version": "4.2.4",
-      "resolved": "https://registry.npmjs.org/web-vitals/-/web-vitals-4.2.4.tgz",
-      "integrity": "sha512-r4DIlprAGwJ7YM11VZp4R884m0Vmgr6EAKe3P+kO0PPj3Unqyvv59rczf6UiGcb9Z8QxZVcqKNwv/g0WNdWwsw=="
-    },
-    "node_modules/preact": {
-      "version": "10.24.3",
-      "resolved": "https://registry.npmjs.org/preact/-/preact-10.24.3.tgz",
-      "integrity": "sha512-Z2dPnBnMUfyQfSQ+GBdsGa16hz35YmLmtTLhM169uW944hYL6xzTYkJjC07j+Wosz733pMWx0fgON3JNw1jJQA==",
-      "funding": {
-        "type": "opencollective",
-        "url": "https://opencollective.com/preact"
-      }
-    },
     "node_modules/prelude-ls": {
       "version": "1.2.1",
       "resolved": "https://registry.npmjs.org/prelude-ls/-/prelude-ls-1.2.1.tgz",

frontend/package.json

@@ -1,6 +1,6 @@
 {
   "name": "openhands-frontend",
-  "version": "0.13.1",
+  "version": "0.12.0",
   "private": true,
   "type": "module",
   "engines": {
@@ -25,7 +25,6 @@
     "isbot": "^5.1.17",
     "jose": "^5.9.4",
     "monaco-editor": "^0.52.0",
-    "posthog-js": "^1.184.1",
     "react": "^18.3.1",
     "react-dom": "^18.3.1",
     "react-highlight": "^0.15.0",
@@ -45,12 +44,11 @@
     "ws": "^8.18.0"
   },
   "scripts": {
-    "dev": "npm run make-i18n && cross-env VITE_MOCK_API=false remix vite:dev",
-    "dev:mock": "npm run make-i18n && cross-env VITE_MOCK_API=true remix vite:dev",
+    "dev": "npm run make-i18n && VITE_MOCK_API=false remix vite:dev",
+    "dev:mock": "npm run make-i18n && VITE_MOCK_API=true remix vite:dev",
     "build": "npm run make-i18n && tsc && remix vite:build",
     "start": "npx sirv-cli build/ --single",
     "test": "vitest run",
-    "test:e2e": "playwright test",
     "test:coverage": "npm run make-i18n && vitest run --coverage",
     "dev_wsl": "VITE_WATCH_USE_POLLING=true vite",
     "preview": "vite preview",
@@ -72,7 +70,6 @@
     ]
   },
   "devDependencies": {
-    "@playwright/test": "^1.48.2",
     "@remix-run/dev": "^2.11.2",
     "@remix-run/testing": "^2.11.2",
     "@tailwindcss/typography": "^0.5.15",
@@ -89,7 +86,6 @@
     "@typescript-eslint/parser": "^7.18.0",
     "@vitest/coverage-v8": "^1.6.0",
     "autoprefixer": "^10.4.20",
-    "cross-env": "^7.0.3",
     "eslint": "^8.57.0",
     "eslint-config-airbnb": "^19.0.4",
     "eslint-config-airbnb-typescript": "^18.0.0",
@@ -120,4 +116,4 @@
       "public"
     ]
   }
-}
+}

frontend/playwright.config.ts

@@ -1,79 +0,0 @@
-import { defineConfig, devices } from "@playwright/test";
-
-/**
- * Read environment variables from file.
- * https://github.com/motdotla/dotenv
- */
-// import dotenv from 'dotenv';
-// import path from 'path';
-// dotenv.config({ path: path.resolve(__dirname, '.env') });
-
-/**
- * See https://playwright.dev/docs/test-configuration.
- */
-export default defineConfig({
-  testDir: "./tests",
-  /* Run tests in files in parallel */
-  fullyParallel: true,
-  /* Fail the build on CI if you accidentally left test.only in the source code. */
-  forbidOnly: !!process.env.CI,
-  /* Retry on CI only */
-  retries: process.env.CI ? 2 : 0,
-  /* Opt out of parallel tests on CI. */
-  workers: process.env.CI ? 1 : undefined,
-  /* Reporter to use. See https://playwright.dev/docs/test-reporters */
-  reporter: "html",
-  /* Shared settings for all the projects below. See https://playwright.dev/docs/api/class-testoptions. */
-  use: {
-    /* Base URL to use in actions like `await page.goto('/')`. */
-    baseURL: "http://127.0.0.1:3000",
-
-    /* Collect trace when retrying the failed test. See https://playwright.dev/docs/trace-viewer */
-    trace: "on-first-retry",
-  },
-
-  /* Configure projects for major browsers */
-  projects: [
-    {
-      name: "chromium",
-      use: { ...devices["Desktop Chrome"] },
-    },
-
-    {
-      name: "firefox",
-      use: { ...devices["Desktop Firefox"] },
-    },
-
-    {
-      name: "webkit",
-      use: { ...devices["Desktop Safari"] },
-    },
-
-    /* Test against mobile viewports. */
-    // {
-    //   name: 'Mobile Chrome',
-    //   use: { ...devices['Pixel 5'] },
-    // },
-    // {
-    //   name: 'Mobile Safari',
-    //   use: { ...devices['iPhone 12'] },
-    // },
-
-    /* Test against branded browsers. */
-    // {
-    //   name: 'Microsoft Edge',
-    //   use: { ...devices['Desktop Edge'], channel: 'msedge' },
-    // },
-    // {
-    //   name: 'Google Chrome',
-    //   use: { ...devices['Desktop Chrome'], channel: 'chrome' },
-    // },
-  ],
-
-  /* Run your local dev server before starting the tests */
-  webServer: {
-    command: "npm run dev:mock -- --port 3000",
-    url: "http://127.0.0.1:3000",
-    reuseExistingServer: !process.env.CI,
-  },
-});

frontend/public/config.json

@@ -1,5 +1,4 @@
 {
   "APP_MODE": "oss",
-  "GITHUB_CLIENT_ID": "",
-  "POSTHOG_CLIENT_KEY": "phc_3ESMmY9SgqEAGBB6sMGK5ayYHkeUuknH2vP6FmWH9RA"
-}
+  "GITHUB_CLIENT_ID": ""
+}

frontend/src/api/github.ts

@@ -122,9 +122,6 @@ export const retrieveGitHubUser = async (
       id: data.id,
       login: data.login,
       avatar_url: data.avatar_url,
-      company: data.company,
-      name: data.name,
-      email: data.email,
     };
 
     return user;
@@ -139,6 +136,33 @@ export const retrieveGitHubUser = async (
   return error;
 };
 
+/**
+ * Given a GitHub token and a repository name, creates a repository for the authenticated user
+ * @param token The GitHub token
+ * @param repositoryName Name of the repository to create
+ * @param description Description of the repository
+ * @param isPrivate Boolean indicating if the repository should be private
+ * @returns The created repository or an error response
+ */
+export const createGitHubRepository = async (
+  token: string,
+  repositoryName: string,
+  description?: string,
+  isPrivate = true,
+): Promise<GitHubRepository | GitHubErrorReponse> => {
+  const response = await fetch("https://api.github.com/user/repos", {
+    method: "POST",
+    headers: generateGitHubAPIHeaders(token),
+    body: JSON.stringify({
+      name: repositoryName,
+      description,
+      private: isPrivate,
+    }),
+  });
+
+  return response.json();
+};
+
 export const retrieveLatestGitHubCommit = async (
   token: string,
   repository: string,

frontend/src/api/open-hands.ts

@@ -1,5 +1,4 @@
 import { request } from "#/services/api";
-import { cache } from "#/utils/cache";
 import {
   SaveFileSuccessResponse,
   FileUploadSuccessResponse,
@@ -8,7 +7,6 @@ import {
   GitHubAccessTokenResponse,
   ErrorResponse,
   GetConfigResponse,
-  GetVSCodeUrlResponse,
 } from "./open-hands.types";
 
 class OpenHands {
@@ -17,13 +15,7 @@ class OpenHands {
    * @returns List of models available
    */
   static async getModels(): Promise<string[]> {
-    const cachedData = cache.get<string[]>("models");
-    if (cachedData) return cachedData;
-
-    const data = await request("/api/options/models");
-    cache.set("models", data);
-
-    return data;
+    return request("/api/options/models");
   }
 
   /**
@@ -31,13 +23,7 @@ class OpenHands {
    * @returns List of agents available
    */
   static async getAgents(): Promise<string[]> {
-    const cachedData = cache.get<string[]>("agents");
-    if (cachedData) return cachedData;
-
-    const data = await request(`/api/options/agents`);
-    cache.set("agents", data);
-
-    return data;
+    return request(`/api/options/agents`);
   }
 
   /**
@@ -45,23 +31,11 @@ class OpenHands {
    * @returns List of security analyzers available
    */
   static async getSecurityAnalyzers(): Promise<string[]> {
-    const cachedData = cache.get<string[]>("agents");
-    if (cachedData) return cachedData;
-
-    const data = await request(`/api/options/security-analyzers`);
-    cache.set("security-analyzers", data);
-
-    return data;
+    return request(`/api/options/security-analyzers`);
   }
 
   static async getConfig(): Promise<GetConfigResponse> {
-    const cachedData = cache.get<GetConfigResponse>("config");
-    if (cachedData) return cachedData;
-
-    const data = await request("/config.json");
-    cache.set("config", data);
-
-    return data;
+    return request("/config.json");
   }
 
   /**
@@ -175,14 +149,6 @@ class OpenHands {
       true,
     );
   }
-
-  /**
-   * Get the VSCode URL
-   * @returns VSCode URL
-   */
-  static async getVSCodeUrl(): Promise<GetVSCodeUrlResponse> {
-    return request(`/api/vscode-url`, {}, false, false, 1);
-  }
 }
 
 export default OpenHands;

frontend/src/api/open-hands.types.ts

@@ -43,11 +43,5 @@ export interface Feedback {
 
 export interface GetConfigResponse {
   APP_MODE: "saas" | "oss";
-  GITHUB_CLIENT_ID: string;
-  POSTHOG_CLIENT_KEY: string;
-}
-
-export interface GetVSCodeUrlResponse {
-  vscode_url: string | null;
-  error?: string;
+  GITHUB_CLIENT_ID: string | null;
 }

frontend/src/assets/vscode-alt.svg

@@ -1,57 +0,0 @@
-<svg width="100" height="100" viewBox="0 0 100 100" fill="none" xmlns="http://www.w3.org/2000/svg">
-<g clip-path="url(#clip0)">
-<g filter="url(#filter0_d)">
-<mask id="mask0" mask-type="alpha" maskUnits="userSpaceOnUse" x="0" y="0" width="100" height="100">
-<path fill-rule="evenodd" clip-rule="evenodd" d="M70.9119 99.5723C72.4869 100.189 74.2828 100.15 75.8725 99.3807L96.4604 89.4231C98.624 88.3771 100 86.1762 100 83.7616V16.2392C100 13.8247 98.624 11.6238 96.4604 10.5774L75.8725 0.619067C73.7862 -0.389991 71.3446 -0.142885 69.5135 1.19527C69.252 1.38636 69.0028 1.59985 68.769 1.83502L29.3551 37.9795L12.1872 24.88C10.5891 23.6607 8.35365 23.7606 6.86938 25.1178L1.36302 30.1525C-0.452603 31.8127 -0.454583 34.6837 1.35854 36.3466L16.2471 50.0001L1.35854 63.6536C-0.454583 65.3164 -0.452603 68.1876 1.36302 69.8477L6.86938 74.8824C8.35365 76.2395 10.5891 76.34 12.1872 75.1201L29.3551 62.0207L68.769 98.1651C69.3925 98.7923 70.1246 99.2645 70.9119 99.5723ZM75.0152 27.1813L45.1092 50.0001L75.0152 72.8189V27.1813Z" fill="white"/>
-</mask>
-<g mask="url(#mask0)">
-<path d="M96.4614 10.593L75.8567 0.62085C73.4717 -0.533437 70.6215 -0.0465506 68.7498 1.83492L1.29834 63.6535C-0.515935 65.3164 -0.513852 68.1875 1.30281 69.8476L6.8125 74.8823C8.29771 76.2395 10.5345 76.339 12.1335 75.1201L93.3604 13.18C96.0854 11.102 100 13.0557 100 16.4939V16.2535C100 13.84 98.6239 11.64 96.4614 10.593Z" fill="#D9D9D9"/>
-<g filter="url(#filter1_d)">
-<path d="M96.4614 89.4074L75.8567 99.3797C73.4717 100.534 70.6215 100.047 68.7498 98.1651L1.29834 36.3464C-0.515935 34.6837 -0.513852 31.8125 1.30281 30.1524L6.8125 25.1177C8.29771 23.7605 10.5345 23.6606 12.1335 24.88L93.3604 86.8201C96.0854 88.8985 100 86.9447 100 83.5061V83.747C100 86.1604 98.6239 88.3603 96.4614 89.4074Z" fill="#E6E6E6"/>
-</g>
-<g filter="url(#filter2_d)">
-<path d="M75.8578 99.3807C73.4721 100.535 70.6219 100.047 68.75 98.1651C71.0564 100.483 75 98.8415 75 95.5631V4.43709C75 1.15852 71.0565 -0.483493 68.75 1.83492C70.6219 -0.0467614 73.4721 -0.534276 75.8578 0.618963L96.4583 10.5773C98.6229 11.6237 100 13.8246 100 16.2391V83.7616C100 86.1762 98.6229 88.3761 96.4583 89.4231L75.8578 99.3807Z" fill="white"/>
-</g>
-<g style="mix-blend-mode:overlay" opacity="0.25">
-<path style="mix-blend-mode:overlay" opacity="0.25" fill-rule="evenodd" clip-rule="evenodd" d="M70.8508 99.5723C72.4258 100.189 74.2218 100.15 75.8115 99.3807L96.4 89.4231C98.5635 88.3771 99.9386 86.1762 99.9386 83.7616V16.2391C99.9386 13.8247 98.5635 11.6239 96.4 10.5774L75.8115 0.618974C73.7252 -0.390085 71.2835 -0.142871 69.4525 1.19518C69.1909 1.38637 68.9418 1.59976 68.7079 1.83493L29.2941 37.9795L12.1261 24.88C10.528 23.6606 8.2926 23.7605 6.80833 25.1177L1.30198 30.1524C-0.51354 31.8126 -0.515625 34.6837 1.2975 36.3465L16.186 50L1.2975 63.6536C-0.515625 65.3164 -0.51354 68.1875 1.30198 69.8476L6.80833 74.8824C8.2926 76.2395 10.528 76.339 12.1261 75.1201L29.2941 62.0207L68.7079 98.1651C69.3315 98.7923 70.0635 99.2645 70.8508 99.5723ZM74.9542 27.1812L45.0481 50L74.9542 72.8188V27.1812Z" fill="url(#paint0_linear)"/>
-</g>
-</g>
-</g>
-</g>
-<defs>
-<filter id="filter0_d" x="-6.25" y="-4.16667" width="112.5" height="112.5" filterUnits="userSpaceOnUse" color-interpolation-filters="sRGB">
-<feFlood flood-opacity="0" result="BackgroundImageFix"/>
-<feColorMatrix in="SourceAlpha" type="matrix" values="0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 127 0"/>
-<feOffset dy="2.08333"/>
-<feGaussianBlur stdDeviation="3.125"/>
-<feColorMatrix type="matrix" values="0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.15 0"/>
-<feBlend mode="normal" in2="BackgroundImageFix" result="effect1_dropShadow"/>
-<feBlend mode="normal" in="SourceGraphic" in2="effect1_dropShadow" result="shape"/>
-</filter>
-<filter id="filter1_d" x="-8.39436" y="15.6951" width="116.728" height="92.6376" filterUnits="userSpaceOnUse" color-interpolation-filters="sRGB">
-<feFlood flood-opacity="0" result="BackgroundImageFix"/>
-<feColorMatrix in="SourceAlpha" type="matrix" values="0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 127 0"/>
-<feOffset/>
-<feGaussianBlur stdDeviation="4.16667"/>
-<feColorMatrix type="matrix" values="0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.25 0"/>
-<feBlend mode="overlay" in2="BackgroundImageFix" result="effect1_dropShadow"/>
-<feBlend mode="normal" in="SourceGraphic" in2="effect1_dropShadow" result="shape"/>
-</filter>
-<filter id="filter2_d" x="60.4167" y="-8.33346" width="47.9167" height="116.667" filterUnits="userSpaceOnUse" color-interpolation-filters="sRGB">
-<feFlood flood-opacity="0" result="BackgroundImageFix"/>
-<feColorMatrix in="SourceAlpha" type="matrix" values="0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 127 0"/>
-<feOffset/>
-<feGaussianBlur stdDeviation="4.16667"/>
-<feColorMatrix type="matrix" values="0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.25 0"/>
-<feBlend mode="overlay" in2="BackgroundImageFix" result="effect1_dropShadow"/>
-<feBlend mode="normal" in="SourceGraphic" in2="effect1_dropShadow" result="shape"/>
-</filter>
-<linearGradient id="paint0_linear" x1="49.939" y1="-5.19792e-05" x2="49.939" y2="100.001" gradientUnits="userSpaceOnUse">
-<stop stop-color="white"/>
-<stop offset="1" stop-color="white" stop-opacity="0"/>
-</linearGradient>
-<clipPath id="clip0">
-<rect width="100" height="100" fill="white"/>
-</clipPath>
-</defs>
-</svg>

frontend/src/components/AgentControlBar.tsx

@@ -6,7 +6,7 @@ import PlayIcon from "#/assets/play";
 import { generateAgentStateChangeEvent } from "#/services/agentStateService";
 import { RootState } from "#/store";
 import AgentState from "#/types/AgentState";
-import { useWsClient } from "#/context/ws-client-provider";
+import { useSocket } from "#/context/socket";
 
 const IgnoreTaskStateMap: Record<string, AgentState[]> = {
   [AgentState.PAUSED]: [
@@ -72,7 +72,7 @@ function ActionButton({
 }
 
 function AgentControlBar() {
-  const { send } = useWsClient();
+  const { send } = useSocket();
   const { curAgentState } = useSelector((state: RootState) => state.agent);
 
   const handleAction = (action: AgentState) => {

frontend/src/components/AgentStatusBar.tsx

@@ -1,7 +1,6 @@
 import React, { useEffect } from "react";
 import { useTranslation } from "react-i18next";
 import { useSelector } from "react-redux";
-import toast from "react-hot-toast";
 import { I18nKey } from "#/i18n/declaration";
 import { RootState } from "#/store";
 import AgentState from "#/types/AgentState";
@@ -17,7 +16,7 @@ enum IndicatorColor {
 }
 
 function AgentStatusBar() {
-  const { t, i18n } = useTranslation();
+  const { t } = useTranslation();
   const { curAgentState } = useSelector((state: RootState) => state.agent);
   const { curStatusMessage } = useSelector((state: RootState) => state.status);
 
@@ -95,27 +94,15 @@ function AgentStatusBar() {
   const [statusMessage, setStatusMessage] = React.useState<string>("");
 
   React.useEffect(() => {
-    let message = curStatusMessage.message || "";
-    if (curStatusMessage?.id) {
-      const id = curStatusMessage.id.trim();
-      if (i18n.exists(id)) {
-        message = t(curStatusMessage.id.trim()) || message;
+    if (curAgentState === AgentState.LOADING) {
+      const trimmedCustomMessage = curStatusMessage.status.trim();
+      if (trimmedCustomMessage) {
+        setStatusMessage(t(trimmedCustomMessage));
+        return;
       }
     }
-    if (curStatusMessage?.type === "error") {
-      toast.error(message);
-      return;
-    }
-    if (curAgentState === AgentState.LOADING && message.trim()) {
-      setStatusMessage(message);
-    } else {
-      setStatusMessage(AgentStatusMap[curAgentState].message);
-    }
-  }, [curStatusMessage.id]);
-
-  React.useEffect(() => {
     setStatusMessage(AgentStatusMap[curAgentState].message);
-  }, [curAgentState]);
+  }, [curAgentState, curStatusMessage.status]);
 
   return (
     <div className="flex flex-col items-center">

frontend/src/components/Jupyter.tsx

@@ -25,11 +25,7 @@ function JupyterCell({ cell }: IJupyterCell): JSX.Element {
           className="scrollbar-custom scrollbar-thumb-gray-500 hover:scrollbar-thumb-gray-400 dark:scrollbar-thumb-white/10 dark:hover:scrollbar-thumb-white/20 overflow-auto px-5"
           style={{ padding: 0, marginBottom: 0, fontSize: "0.75rem" }}
         >
-          <SyntaxHighlighter
-            language="python"
-            style={atomOneDark}
-            wrapLongLines
-          >
+          <SyntaxHighlighter language="python" style={atomOneDark}>
             {code}
           </SyntaxHighlighter>
         </pre>
@@ -82,11 +78,7 @@ function JupyterCell({ cell }: IJupyterCell): JSX.Element {
   );
 }
 
-interface JupyterEditorProps {
-  maxWidth: number;
-}
-
-function JupyterEditor({ maxWidth }: JupyterEditorProps) {
+function JupyterEditor(): JSX.Element {
   const { t } = useTranslation();
 
   const { cells } = useSelector((state: RootState) => state.jupyter);
@@ -96,7 +88,7 @@ function JupyterEditor({ maxWidth }: JupyterEditorProps) {
     useScrollToBottom(jupyterRef);
 
   return (
-    <div className="flex-1" style={{ maxWidth }}>
+    <div className="flex-1">
       <div
         className="overflow-y-auto h-full"
         ref={jupyterRef}