From 5b3348d500df2988e0cdb2e1a9ed23d8a146f43c Mon Sep 17 00:00:00 2001
From: DavidKoleczek <45405824+DavidKoleczek@users.noreply.github.com>
Date: Sat, 17 Jan 2026 14:57:32 -0500
Subject: [PATCH] Updates to interfaces and docs
---
.claude/commands/draft-release-description.md | 5 -
.claude/commands/prepare-release.md | 19 +
.vscode/settings.json | 1 +
README.md | 78 +-
...rison-all.yaml => comparison-default.yaml} | 0
data/eval-setups/score-default.yaml | 132 ++
docs/BENCHMARKING.md | 474 ++++--
eval_recipes/benchmarking/base.dockerfile | 1 -
eval_recipes/benchmarking/filters.py | 150 --
eval_recipes/benchmarking/harness.py | 320 ++--
.../benchmarking/harness_comparison.py | 39 +-
eval_recipes/benchmarking/reporting.py | 8 +-
eval_recipes/benchmarking/run_trial.py | 2 +-
eval_recipes/benchmarking/schemas.py | 28 +
pyproject.toml | 2 +-
scripts/run_benchmarks.py | 46 +-
scripts/run_comparison_benchmarks.py | 5 +-
uv.lock | 1452 ++++++++++-------
18 files changed, 1563 insertions(+), 1199 deletions(-)
delete mode 100644 .claude/commands/draft-release-description.md
create mode 100644 .claude/commands/prepare-release.md
rename data/eval-setups/{comparison-all.yaml => comparison-default.yaml} (100%)
create mode 100644 data/eval-setups/score-default.yaml
delete mode 100644 eval_recipes/benchmarking/filters.py
diff --git a/.claude/commands/draft-release-description.md b/.claude/commands/draft-release-description.md
deleted file mode 100644
index f298d32..0000000
--- a/.claude/commands/draft-release-description.md
+++ /dev/null
@@ -1,5 +0,0 @@
-Can you help me draft a release description for my current changes.
-
-1. Look at the previous release logs at https://github.com/microsoft/eval-recipes/releases your draft release MUST follow the same style and structure.
-2. Look at my currently staged changes to identify what is changing.
-3. Create a draft release description based on the recent code changes and place it in `media/draft_release_{version}.md`
diff --git a/.claude/commands/prepare-release.md b/.claude/commands/prepare-release.md
new file mode 100644
index 0000000..4f56736
--- /dev/null
+++ b/.claude/commands/prepare-release.md
@@ -0,0 +1,19 @@
+I am about to release a new version of this package. Please take the following steps to make sure it is successful:
+
+Understand changes
+1. Look at my currently staged changes to identify what changes were made.
+
+Checks
+1. Make sure the pyproject.toml was updated with a new version number.
+2. Ensure there are no spelling and grammar mistakes.
+3. Run all formatting, linting, and type checking: `make check`
+4. Run `uv build` to make sure the package builds correctly.
+5. If any of these checks fail, please stop and inform me about the issues so we can fix them before proceeding.
+
+Draft release notes
+1. Look at the previous release logs at https://github.com/microsoft/eval-recipes/releases your draft release MUST follow the same style and structure.
+2. Create a draft release description based on the recent code changes and place it in `media/draft_release_{version}.md`
+3. At the end of the release notes be sure to include:
+```
+**Full Changelog**: https://github.com/microsoft/eval-recipes/compare/v0.x1.y1...v0.x2.y2
+```
\ No newline at end of file
diff --git a/.vscode/settings.json b/.vscode/settings.json
index 70f46ff..d307cf0 100644
--- a/.vscode/settings.json
+++ b/.vscode/settings.json
@@ -9,4 +9,5 @@
},
"python.testing.unittestEnabled": false,
"python.testing.pytestEnabled": true,
+ "markdown.extension.orderedList.marker": "one"
}
\ No newline at end of file
diff --git a/README.md b/README.md
index eeb19d3..c264f96 100644
--- a/README.md
+++ b/README.md
@@ -1,38 +1,69 @@
-# Eval Recipes
+
+ Eval Recipes
+
+
+
Evaluate AI agents with benchmarking harnesses and online evaluation recipes.
+
+
+
+ 
+ 
+ 
+
Eval Recipes is a library dedicated to make it easier to keep up with the state-of-the-art in evaluating AI agents.
It currently has two main components: a **benchmarking** harness for evaluating CLI agents (GitHub Copilot CLI, Claude Code, etc) on real-world tasks via containers and an **online evaluation** framework for LLM chat assistants.
-The common thread between these components is the concept of [recipes](https://sundaylettersfromsam.substack.com/p/what-is-an-ai-recipe)
+The common thread between these components is the concept of [recipes](https://sundaylettersfromsam.substack.com/p/what-is-an-ai-recipe)
which are a mix of code and LLM calls to achieve a desired tradeoff between flexibility and quality.
-# Benchmarking
+## Installation
-Eval Recipes provides a benchmarking harness for evaluating AI agents on real-world tasks in isolated Docker containers.
-We have a few sample tasks ranging from creating CLI applications to automations. Agents are automatically scored based on deterministic and semantic tests using a specialized auditing agent.
+```bash
+# Benchmarking requires certain prerequisites, see the full documentation for more details.
+# With uv (add to project dependencies, pinned to a release tag)
+uv add "eval-recipes @ git+https://github.com/microsoft/eval-recipes@v0.29"
-Additional features include agent continuation (automatically providing follow-up prompts when needed), multi-trial evaluation for consistency measurement, and reporting with HTML dashboards.
+# With pip
+pip install "git+https://github.com/microsoft/eval-recipes@v0.29"
+```
+
+> [!WARNING]
+> This library is very early and everything is subject to change. Consider pinning the dependency to a commit with the command like: `uv pip install "git+https://github.com/microsoft/eval-recipes@v0.0.20"`
-## Run Benchmarks Quickly
-Check [BENCHMARKING.md](./docs/BENCHMARKING.md), currently running benchmarks requires some additional setup.
+# Benchmarking
-## Running Benchmarks
+Eval Recipes provides a benchmarking harness for evaluating AI agents on real-world tasks in isolated Docker containers. It supports score based, comparison based, and third party benchmarks.
+We include tasks ranging from creating CLI applications to automations. Agents are automatically scored based on deterministic and semantic tests using a specialized auditing agent.
+Additional features include agent continuation (automatically providing follow-up prompts when needed), multi-trial evaluation for consistency measurement, and reporting with HTML dashboards.
-```bash
-# The default agents/tasks require these environment variables. Check the agent definitions for others.
-export ANTHROPIC_API_KEY=your_anthropic_key
-export OPENAI_API_KEY=your_openai_key
+## Usage
-uv run scripts/run_benchmarks.py --num-trials 2
+1. Create agent definition(s). Examples are provided in [data/agents](./data/agents).
+1. Create task definition(s). Examples are provided in [data/tasks](./data/tasks).
+1. Create a run configuration. Examples are provided in [data/eval-setups](./data/eval-setups).
-# Get more info about available arguments
-uv run scripts/run_benchmarks.py --help
+```python
+import yaml
+from eval_recipes.benchmarking.harness import Harness
+from eval_recipes.benchmarking.schemas import ScoreRunSpec
+
+with Path("score-default.yaml").open(encoding="utf-8") as f:
+ run_definition = ScoreRunSpec(**yaml.safe_load(f))
+
+harness = Harness(
+ agents_dir=Path("data/agents"),
+ tasks_dir=Path("data/tasks"),
+ run_definition=run_definition,
+)
+asyncio.run(harness.run())
```
-Results are saved to timestamped directories in `data/benchmarking/runs/` containing agent logs, test outputs, timing data, and structured results.
-Any of these files may contain secrets that were used during the evaluation run. **NEVER** commit these files to source control without first checking for secrets.
-For detailed documentation on creating custom agents and tasks, see [BENCHMARKING.md](./docs/BENCHMARKING.md).
+See [docs/BENCHMARKING.md](./docs/BENCHMARKING.md) for full details, including installation prerequisites.
+
+
+---
# Online Evaluations
@@ -55,15 +86,6 @@ uv run marimo edit demos/1_evaluate.py
# Select Y to run in a sandboxed venv
```
-### 3. Start using the package
-
-```bash
-uv pip install "git+https://github.com/microsoft/eval-recipes"
-```
-
-> [!WARNING]
-> This library is very early and everything is subject to change. Consider pinning the dependency to a commit with the command like: `uv pip install "git+https://github.com/microsoft/eval-recipes@v0.0.20"`
-
## High Level API
diff --git a/data/eval-setups/comparison-all.yaml b/data/eval-setups/comparison-default.yaml
similarity index 100%
rename from data/eval-setups/comparison-all.yaml
rename to data/eval-setups/comparison-default.yaml
diff --git a/data/eval-setups/score-default.yaml b/data/eval-setups/score-default.yaml
new file mode 100644
index 0000000..9e788be
--- /dev/null
+++ b/data/eval-setups/score-default.yaml
@@ -0,0 +1,132 @@
+type: score
+definitions:
+ - agent: amplifier_v1
+ trials: 2
+ tasks:
+ - task: arxiv_conclusion_extraction
+ - task: arxiv_paper_summarizer
+ - task: code-discrepancy-docs-knack
+ - task: code-discrepancy-docstrings-grasp
+ - task: code-discrepancy-tutorials-grasp
+ - task: cpsc_recall_monitor
+ - task: cross_repo_improvement_tool
+ - task: email_drafting
+ - task: frontier-science-079657b3-e215-4944-8a67-8bb5347e4f15
+ - task: frontier-science-85b4f862-d881-4a79-8c5d-3e927b486b71
+ - task: frontier-science-cb11faa6-e12a-4621-9fa7-f6f4b11a9300
+ - task: frontier-science-eec8840a-2d00-4e70-b043-0da51bd1b288
+ - task: frontier-science-f3ba1aae-2fc3-4d9b-a5a3-42bb91de4d7d
+ - task: gdpval_extraction
+ - task: github_docs_extractor
+ - task: image_tagging
+ - task: linkedin_drafting
+ - task: markdown_deck_converter
+ - task: news_research_tool
+ - task: pdf-hr-q4
+ - task: product_review_finder
+ - task: repo_embedding_server
+ - task: style_blender
+ - agent: amplifier_foundation
+ trials: 2
+ tasks:
+ - task: arxiv_conclusion_extraction
+ - task: arxiv_paper_summarizer
+ - task: code-discrepancy-docs-knack
+ - task: code-discrepancy-docstrings-grasp
+ - task: code-discrepancy-tutorials-grasp
+ - task: cpsc_recall_monitor
+ - task: cross_repo_improvement_tool
+ - task: email_drafting
+ - task: frontier-science-079657b3-e215-4944-8a67-8bb5347e4f15
+ - task: frontier-science-85b4f862-d881-4a79-8c5d-3e927b486b71
+ - task: frontier-science-cb11faa6-e12a-4621-9fa7-f6f4b11a9300
+ - task: frontier-science-eec8840a-2d00-4e70-b043-0da51bd1b288
+ - task: frontier-science-f3ba1aae-2fc3-4d9b-a5a3-42bb91de4d7d
+ - task: gdpval_extraction
+ - task: github_docs_extractor
+ - task: image_tagging
+ - task: linkedin_drafting
+ - task: markdown_deck_converter
+ - task: news_research_tool
+ - task: pdf-hr-q4
+ - task: product_review_finder
+ - task: repo_embedding_server
+ - task: style_blender
+ - agent: claude_code
+ trials: 2
+ tasks:
+ - task: arxiv_conclusion_extraction
+ - task: arxiv_paper_summarizer
+ - task: code-discrepancy-docs-knack
+ - task: code-discrepancy-docstrings-grasp
+ - task: code-discrepancy-tutorials-grasp
+ - task: cpsc_recall_monitor
+ - task: cross_repo_improvement_tool
+ - task: email_drafting
+ - task: frontier-science-079657b3-e215-4944-8a67-8bb5347e4f15
+ - task: frontier-science-85b4f862-d881-4a79-8c5d-3e927b486b71
+ - task: frontier-science-cb11faa6-e12a-4621-9fa7-f6f4b11a9300
+ - task: frontier-science-eec8840a-2d00-4e70-b043-0da51bd1b288
+ - task: frontier-science-f3ba1aae-2fc3-4d9b-a5a3-42bb91de4d7d
+ - task: gdpval_extraction
+ - task: github_docs_extractor
+ - task: image_tagging
+ - task: linkedin_drafting
+ - task: markdown_deck_converter
+ - task: news_research_tool
+ - task: pdf-hr-q4
+ - task: product_review_finder
+ - task: repo_embedding_server
+ - task: style_blender
+ - agent: gh_cli
+ trials: 2
+ tasks:
+ - task: arxiv_conclusion_extraction
+ - task: arxiv_paper_summarizer
+ - task: code-discrepancy-docs-knack
+ - task: code-discrepancy-docstrings-grasp
+ - task: code-discrepancy-tutorials-grasp
+ - task: cpsc_recall_monitor
+ - task: cross_repo_improvement_tool
+ - task: email_drafting
+ - task: frontier-science-079657b3-e215-4944-8a67-8bb5347e4f15
+ - task: frontier-science-85b4f862-d881-4a79-8c5d-3e927b486b71
+ - task: frontier-science-cb11faa6-e12a-4621-9fa7-f6f4b11a9300
+ - task: frontier-science-eec8840a-2d00-4e70-b043-0da51bd1b288
+ - task: frontier-science-f3ba1aae-2fc3-4d9b-a5a3-42bb91de4d7d
+ - task: gdpval_extraction
+ - task: github_docs_extractor
+ - task: image_tagging
+ - task: linkedin_drafting
+ - task: markdown_deck_converter
+ - task: news_research_tool
+ - task: pdf-hr-q4
+ - task: product_review_finder
+ - task: repo_embedding_server
+ - task: style_blender
+ - agent: openai_codex
+ trials: 2
+ tasks:
+ - task: arxiv_conclusion_extraction
+ - task: arxiv_paper_summarizer
+ - task: code-discrepancy-docs-knack
+ - task: code-discrepancy-docstrings-grasp
+ - task: code-discrepancy-tutorials-grasp
+ - task: cpsc_recall_monitor
+ - task: cross_repo_improvement_tool
+ - task: email_drafting
+ - task: frontier-science-079657b3-e215-4944-8a67-8bb5347e4f15
+ - task: frontier-science-85b4f862-d881-4a79-8c5d-3e927b486b71
+ - task: frontier-science-cb11faa6-e12a-4621-9fa7-f6f4b11a9300
+ - task: frontier-science-eec8840a-2d00-4e70-b043-0da51bd1b288
+ - task: frontier-science-f3ba1aae-2fc3-4d9b-a5a3-42bb91de4d7d
+ - task: gdpval_extraction
+ - task: github_docs_extractor
+ - task: image_tagging
+ - task: linkedin_drafting
+ - task: markdown_deck_converter
+ - task: news_research_tool
+ - task: pdf-hr-q4
+ - task: product_review_finder
+ - task: repo_embedding_server
+ - task: style_blender
diff --git a/docs/BENCHMARKING.md b/docs/BENCHMARKING.md
index b565a98..0458759 100644
--- a/docs/BENCHMARKING.md
+++ b/docs/BENCHMARKING.md
@@ -1,279 +1,417 @@
# Benchmarking
-This module provides a benchmarking harness for evaluating AI agents on real-world tasks within isolated Docker containers.
+This module provides a benchmarking harness for evaluating AI agents within isolated Docker containers.
-The goal of the module is to produce a final report that details how well each agent performed on a variety of custom tasks.
-We support two evaluation modes:
-- **Score-based**: Each agent's output is scored by a `test.py` script (0-100)
-- **Comparison-based**: An LLM compares outputs from multiple agents and ranks them
+## Installation
+
+```bash
+# Install prerequisites below first.
+# With uv (add to project dependencies, pinned to a release tag)
+uv add "eval-recipes @ git+https://github.com/microsoft/eval-recipes@v0.29"
-The core of the module uses agent definitions (defaults in `data/agents/`) and task definitions (defaults in `data/tasks/`) to run agents on tasks in isolated Docker containers.
+# With pip
+pip install "git+https://github.com/microsoft/eval-recipes@v0.29"
+```
## Prerequisites
- [uv](https://docs.astral.sh/uv/getting-started/installation/)
-- Install [Docker Desktop](https://docs.docker.com/desktop/) for work on systems running Windows or [Docker Engine](https://docs.docker.com/engine/install/ubuntu/) on setups like WSL 2.
+- Install [Docker Desktop](https://docs.docker.com/desktop/) for work on systems running Windows* or [Docker Engine](https://docs.docker.com/engine/install/ubuntu/) on setups like WSL 2.
- After installing Docker Engine on WSL 2, ensure your user has docker permissions by running:
- `sudo usermod -aG docker $USER`
- `newgrp docker`
- The Claude Agent SDK which requires setting up [Claude Code](https://docs.claude.com/en/docs/claude-code/overview)
- [`ANTHROPIC_API_KEY`](https://platform.claude.com/docs/en/get-started) for the Claude Agent SDK.
-- [`OPENAI_API_KEY`](hhttps://platform.openai.com/api-keys) if using agent continuation (see parameters below, or running tasks that requires it as a dependency).
+- [`OPENAI_API_KEY`](https://platform.openai.com/api-keys) if using agent continuation (see parameters below, or running tasks that requires it as a dependency).
-## Installation
+* All features may not currently work on Windows due to Claude Agent SDK limitations.
-```bash
-# If you have make installed:
-make install
-# If not, you can manually setup the uv environment:
-uv lock --upgrade && uv sync --all-extras --group dev
+## Usage
+
+1. Create agent definition(s). Examples are provided in [data/agents](../data/agents) and described further [below](#agent-definitions).
+1. Create task definition(s). Examples are provided in [data/tasks](../data/tasks) and described further [below](#task-definitions).
+1. Create a run configuration. Examples are provided in [data/eval-setups](../data/eval-setups) and described further [below](#run-configurations).
+
+```python
+import yaml
+from eval_recipes.benchmarking.harness import Harness
+from eval_recipes.benchmarking.schemas import ScoreRunSpec
+
+with Path("score-default.yaml").open(encoding="utf-8") as f:
+ run_definition = ScoreRunSpec(**yaml.safe_load(f))
+
+harness = Harness(
+ agents_dir=Path("data/agents"),
+ tasks_dir=Path("data/tasks"),
+ run_definition=run_definition,
+)
+asyncio.run(harness.run())
```
-## Running Benchmarks
+Comparison based evaluations currently are separate from score based evaluations.
-The benchmarking harness is available via the CLI script `scripts/run_benchmarks.py`.
+```python
+import yaml
+from eval_recipes.benchmarking.harness_comparison import ComparisonHarness
+from eval_recipes.benchmarking.schemas import ComparisonTaskSpec
-### Basic Usage
+with Path("comparison-default.yaml").open(encoding="utf-8") as f:
+ data = yaml.safe_load(f)
+ specs = [ComparisonTaskSpec(task_name=c["task"], agent_names=c["agents"]) for c in data["comparisons"]]
-```bash
-# Make sure your .env file is setup according to .env.sample
-uv run scripts/run_benchmarks.py --agent-filter name=claude_code --task-filter name=cpsc_recall_monitor,arxiv_conclusion_extraction,email_drafting --max-parallel-trials 6 --num-trials 2 --continuation-provider openai
+harness = ComparisonHarness(
+ agents_dir=Path("data/agents"),
+ tasks_dir=Path("data/tasks"),
+)
-# Command for a typical full benchmark run
-# sec_10q_extractor is excluded due to most all agents failing at it.
-uv run scripts/run_benchmarks.py --agent-filter name=amplifier_v1,amplifier_v2_toolkit,claude_code,gh_cli,openai_codex --task-filter name!=sec_10q_extractor --max-parallel-trials 20 --num-trials 5 --continuation-provider openai
+asyncio.run(harness.run(specs))
```
-## Running Comparison Benchmarks
+## Agent Definitions
-Comparison benchmarks evaluate agents by having an LLM compare their outputs and rank them, rather than scoring each output independently.
+Each agent is a subdirectory containing the files needed to install and run the agent:
-### Config File Format
+```
+agent_id/ # Agent directory
+ agent.yaml # Agent configuration
+ install.dockerfile # Docker commands to install the agent
+ command_template.txt # Liquid template for the command to start a task with the agent
+ command_template_continue.txt # (Optional) Template for agent continuation when follow-up is needed. This command must continue from the previous session/conversation.
+ data/ # (Optional) Agent-specific data files used during installation or runtime
+```
-Comparison benchmarks use a YAML config file that specifies which agents to compare on each task. Can you can specify any combination of tasks and agents.
+### `agent.yaml`
+
+Configuration file for the agent. All fields are optional.
```yaml
-comparisons:
- - task: task_name
- agents:
- - agent_1
- - agent_2
- - task: another_task
- agents:
- - agent_1
- - agent_2
- - agent_3
+# Environment variables passed into the container.
+# Sourced from the harness `environment` parameter.
+required_env_vars:
+ - ANTHROPIC_API_KEY
+ - OPENAI_API_KEY
+
+# Absolute path to local source code for development.
+local_source_path: /path/to/source
```
-### Basic Usage
+### `install.dockerfile`
-```bash
-# Run with default config (data/eval-setups/comparison-all.yaml)
-uv run scripts/run_comparison_benchmarks.py
+Docker commands to install the agent. These are injected into the [base image](../eval_recipes/benchmarking/base.dockerfile).
-# Run with custom config
-uv run scripts/run_comparison_benchmarks.py --config path/to/config.yaml --comparison-runs 5
+```dockerfile
+# Example: Install GitHub Copilot CLI
+RUN npm install -g @github/copilot
+RUN copilot --version
```
-Results are saved to timestamped directories in `.comparison_results/` with HTML reports.
+### `command_template.txt`
+
+[Python Liquid](https://github.com/jg-rp/liquid) template for the command to run the agent. The `{{task_instructions}}` variable contains the task instructions from `instructions.txt`.
+```
+copilot -p "{{task_instructions}}" --allow-all-tools
+```
-## Creating a New Agent
+### `command_template_continue.txt`
-Agents are defined in the `data/agents/` directory.
-Each agent is a subdirectory containing the files needed to install and run the agent.
-Included agents are located in [data/agents/](../data/agents/).
+Optional. Liquid template for continuing an agent session when follow-up is needed. The `{{task_instructions}}` variable contains the continuation prompt. This command must resume the previous conversation.
```
-data/agents/your_agent_name/
-agent.yaml # Agent configuration
-install.dockerfile # Docker commands to install the agent
-command_template.txt # Liquid template for the command to run the agent
-command_template_continue.txt # (Optional) Template for agent continuation when follow-up is needed. This command must continue from the previous session/conversation.
-data/ # (Optional) Agent-specific data files
+copilot -p "{{task_instructions}}" --continue --allow-all-tools
```
-See [data/agents/gh_cli/](../data/agents/gh_cli/) for an example agent definition.
-
+### `data/`
-## Using Local Agent Versions
+Optional. Directory containing agent-specific data files. Contents are copied to `/project` in the container before the agent runs.
-For development and testing, create agent variants that use local source code instead of remote repositories. This enables rapid iteration on agent improvements while testing against benchmarks.
+### Defining Local Agents
-### Creating a Local Agent Variant
+For development and testing, you can create agent variants that use local source code instead of remote repositories.
-1. Create a new agent directory (e.g., `data/agents/your_agent_local/`)
-2. Add `agent.yaml` with `local_source_path` pointing to your local source:
+1. Add `agent.yaml` with `local_source_path` pointing to your local source:
```yaml
local_source_path: /absolute/path/to/your/agent/source
- required_env_vars:
- - API_KEY
```
-3. Create `install.dockerfile` that installs from `/tmp/agent_source/` (where source is automatically copied)
-4. Copy or create `command_template.txt`
+1. Create `install.dockerfile` that installs from `/tmp/agent_source/` (where source is automatically copied)
+1. By default it ignores files in `.gitignore` if present (otherwise excludes `.git`, `.venv`, `__pycache__`, etc.)
-### How It Works
+## Task Definitions
-1. Harness validates `local_source_path` exists
-2. Collects files, respecting `.gitignore` if present (otherwise excludes `.git`, `.venv`, `__pycache__`, etc.)
-3. Adds files to Docker build context as `agent_source/`
-4. Copies `agent_source` to `/tmp/agent_source/` in container
-5. Your `install.dockerfile` installs from there. This dockerfile should install the agent so that it is globally available. The commands will run in `/project/`, not where the agent's files are.
-6. Image is rebuilt each run, capturing your latest changes
+Each task is a subdirectory containing the files needed to define the task and test the agent's solution.
-### Usage
+```
+task_id/
+ task.yaml # Task configuration (required)
+ instructions.txt # Instructions given to the agent (required)
+ test.py # Python script to test the agent's solution (required for score-based tasks)
+ setup.dockerfile # (Optional) Docker commands to set up the task environment
+ task_time_data/ # (Optional) Data files copied before agent runs
+ test_time_data/ # (Optional) Data files copied before tests run
+```
-```bash
-# Run with local version
-uv run scripts/run_benchmarks.py --agent-filter name=your_agent_local
+### `task.yaml`
-# Compare local vs production
-uv run scripts/run_benchmarks.py --agent-filter name=your_agent,your_agent_local
-```
+Configuration file for the task.
-**Notes**: `local_source_path` must be absolute. Build time includes copying all source files. Images rebuild automatically to capture code changes.
+```yaml
+# Required
+task_info:
+ difficulty: medium # "easy", "medium", or "hard"
+ non_deterministic_evals: true # Whether evals use LLMs or not (default: false)
+ categories: # Optional category tags
+ - cli_tool
+ - writing
+
+# Optional
+timeout: 5400 # Timeout in seconds (default: 1800)
+required_env_vars: # Environment variables needed by the task
+ - ANTHROPIC_API_KEY
+test_command: uv run --no-project /project/test.py # Command to run tests (default shown)
+
+# For comparison-based tasks
+eval_type: comparison # "score", "comparison", or "both" (default: "score")
+comparison_eval:
+ guidelines: | # Evaluation guidelines for the comparison judge
+ Focus on clarity and completeness of the response.
+```
+### `instructions.txt`
-## Creating a New Task
+The task prompt given to the agent. This is passed to the agent via the `{{task_instructions}}` template variable in `command_template.txt`.
-Tasks are defined in the `data/tasks/` directory.
-Each task is a subdirectory containing the files needed to define the task and test the agent's solution.
-Included tasks are available at [data/tasks/](../data/tasks/).
+### `test.py`
+Python script that evaluates the agent's work. The script runs inside the container after the agent completes.
+Uses `semantic_test()` for LLM-based evaluation (eval-recipes is by default available when the script runs) or direct file checks for deterministic tests.
-### Task Directory Structure
+The script must output a JSON to following path `{output_dir}/.eval_recipes_test_results_{test_id}.json` and the following format:
-A template task is available at **[data/_template_task/](../data/_template_task/)** that you can copy as a starting point. The `/create-benchmark-test` command can automate this for you.
+```json
+{"score": 85.0, "metadata": {"field": "value"}}
+```
+The `write_test_result()` utility handles writing this file in the correct format.
+See the following complete example:
+
+```python
+import asyncio
+import sys
+from pathlib import Path
+
+import click
+from eval_recipes.benchmarking.semantic_test import semantic_test
+from eval_recipes.benchmarking.test_utils import (
+ get_instructions_from_file_or_default,
+ get_test_id_from_env_or_default,
+ write_test_result,
+)
+
+STEPS = """1. Explore the /project directory to understand what the agent created
+2. Verify the required files exist
+3. Check that the implementation meets the requirements"""
+
+RUBRIC = {
+ "files_exist": "bool - Do all required files exist?",
+ "implementation_correct": "str - Assessment of the implementation",
+ "score": "float - Overall score from 0-100",
+}
+
+@click.command()
+@click.option("--test-id", default=lambda: get_test_id_from_env_or_default("dev"))
+@click.option("--output-dir", type=click.Path(path_type=Path), default=lambda: Path(__file__).parents[0])
+@click.option("--instructions-file", type=click.Path(path_type=Path), default=None)
+def main(test_id: str, output_dir: Path, instructions_file: Path | None) -> int:
+ return asyncio.run(run_test(test_id, output_dir, instructions_file))
+
+async def run_test(test_id: str, output_dir: Path, instructions_file: Path | None) -> int:
+ instructions = get_instructions_from_file_or_default(instructions_file)
+
+ result = await semantic_test(
+ steps=STEPS,
+ rubric=RUBRIC,
+ context=instructions,
+ working_dir=Path("/project"),
+ )
+
+ write_test_result(output_dir, test_id, result.score, result.metadata)
+ return 0
+
+if __name__ == "__main__":
+ sys.exit(main())
```
-data/tasks/your_task_name/
-task.yaml # Task configuration (required)
-instructions.txt # Instructions given to the agent (required)
-test.py # Python script to test the agent's solution (required for score-based tasks)
-setup.dockerfile # (Optional) Docker commands to set up the task environment
-task_time_data/ # (Optional) Data files copied before agent runs
-test_time_data/ # (Optional) Data files copied before tests run
+
+### `setup.dockerfile`
+
+Optional. Docker commands to set up task-specific dependencies. Injected into the [base image](../eval_recipes/benchmarking/base.dockerfile) after the agent installation.
+
+```dockerfile
+# Example: Install Ollama for embedding tasks
+RUN curl -fsSL https://ollama.com/install.sh | sh
+RUN nohup ollama serve > /dev/null 2>&1 & sleep 5 && ollama pull embeddinggemma:300m-qat-q8_0
```
-### File Descriptions
+### `task_time_data/`
+
+Optional. Directory containing files the agent needs to complete the task (e.g., PDFs, source code archives). Contents are copied to `/project` before the agent runs.
+
+### `test_time_data/`
+
+Optional. Directory containing files needed only for testing (e.g., expected outputs, test inputs). Contents are copied to `/project` before tests run, but after the agent completes.
-#### `task.yaml` (Required)
-Required fields:
-- `eval_type`: One of `score`, `comparison`, or `both`
-- `task_info`: Object containing:
- - `difficulty`: One of `easy`, `medium`, or `hard`
- - `non_deterministic_evals`: Boolean indicating if test evaluations are non-deterministic (e.g., semantic tests using LLMs)
+## Run Configurations
-Optional task_info fields:
-- `categories`: List of strings categorizing the task (e.g., `["cli", "automation"]`)
+YAML files that define which agents to run on which tasks. Agent and task names must match directory names in `agents_dir` and `tasks_dir` passed to the harness.
-Optional fields:
-- `required_env_vars`: List of environment variables required for the task (e.g., API keys for evaluation)
-- `test_command`: Command to run the test script (score-based tasks only)
-- `timeout`: Timeout in seconds for agent execution
-- `comparison_eval.guidelines`: Task-specific evaluation guidelines for comparison tasks
+### Score-Based Configuration
-#### `setup.dockerfile` (Optional)
+```yaml
+type: score
+definitions:
+ - agent: claude_code
+ trials: 3 # Default trials for this agent's tasks
+ tasks:
+ - task: email_drafting
+ - task: arxiv_paper_summarizer
+ trials: 5 # Override trials for this specific task
+ - agent: gh_cli
+ trials: 3
+ tasks:
+ - task: email_drafting
+```
+
+### Comparison-Based Configuration
-Contains Docker `RUN` commands to install any dependencies needed for the task or tests. This should be kept minimal, only include resources the agent would not be able to configure themselves.
+Each comparison can include any number of agents (minimum 2). The harness runs each agent on the task and uses an agent judge to rank their outputs.
-#### `task_time_data/` (Optional)
+```yaml
+comparisons:
+ - task: ppt-1
+ agents:
+ - claude_code
+ - gh_cli
+ - openai_codex
+ - task: ppt-2
+ agents:
+ - claude_code
+ - gh_cli
+```
-Optional directory containing data files that will be copied into the container **before the agent runs**. This is useful for:
-- Providing input files that the agent needs to work with
-- Setting up initial state that the agent's task depends on
-Files are copied to `/project` in the container, so they are accessible at relative paths like `./myfile.txt`.
+## Harnesses
-#### `test_time_data/` (Optional)
+Harnesses orchestrate the benchmark run: they build Docker images for each agent-task combination, run agents in isolated containers, execute test scripts to evaluate results, and generate HTML reports with scores and analysis.
-Optional directory containing data files that will be copied into the container **before tests run** (after the agent finishes). This is useful for:
-- Supplying reference data for semantic tests to validate against
-- Providing expected outputs or golden files for comparison
-- Making tests more deterministic and reproducible
+### Score Harness
-Files are copied to `/project` in the container. Examples: See `data/tasks/style_blender/test_time_data/` and `data/tasks/email_drafting/test_time_data/`
+[Source](../eval_recipes/benchmarking/harness.py)
-#### `instructions.txt` (Required)
-Plain text instructions that will be passed to the agent. This describes what the agent should build or solve.
+```python
+from eval_recipes.benchmarking.harness import Harness
-#### `test.py` (Required)
-A Python test script that validates the agent's solution and outputs a score.
+harness = Harness(
+ agents_dir=Path, # Directory containing agent definitions
+ tasks_dir=Path, # Directory containing task definitions
+ run_definition=ScoreRunSpec, # Parsed run configuration
+ runs_dir=Path | None, # Output directory (default: .benchmark_results)
+ environment=dict | None, # Environment variables for containers
+ max_parallel_trials=5, # Maximum concurrent trials
+ continuation_provider="none", # "openai", "azure_openai", or "none"
+ continuation_model="gpt-5", # Model for continuation prompts
+ eval_recipes_version="...", # Version of eval-recipes in containers
+ report_score_threshold=85.0, # Generate failure reports below this score
+)
-All test scripts must follow a standardized contract for integration with the harness. See **[data/_template_task/test.py](../data/_template_task/test.py)** for the complete contract specification, detailed documentation, and a reference implementation you can copy when creating new tasks.
+await harness.run()
+```
+### Comparison Harness
-### Semantic Tests
+[Source](../eval_recipes/benchmarking/harness_comparison.py)
-Many tasks use semantic tests. They are tests where an LLM "auditor" follows specific steps and evaluates outputs against a rubric. This is useful for complex tasks where deterministic validation is difficult.
-See **[data/tasks/style_blender/test.py](../data/tasks/style_blender/test.py)** and **[data/tasks/email_drafting/test.py](../data/tasks/email_drafting/test.py)** for examples of these.
+```python
+from eval_recipes.benchmarking.harness_comparison import ComparisonHarness
+harness = ComparisonHarness(
+ agents_dir=Path, # Directory containing agent definitions
+ tasks_dir=Path, # Directory containing task definitions
+ runs_dir=Path | None, # Output directory (default: .comparison_results)
+ environment=dict | None, # Environment variables for containers
+ max_parallel=5, # Maximum concurrent jobs
+ comparison_runs=3, # Number of comparison runs per task
+ continuation_provider="none", # "openai", "azure_openai", or "none"
+ continuation_model="gpt-5", # Model for continuation prompts
+ eval_recipes_version="...", # Version of eval-recipes in containers
+ report_score_threshold=85.0, # Score threshold for reports
+)
-### Helper Commands for Task Creation
+await harness.run(comparison_specs) # Pass list of ComparisonTaskSpec
+```
-Two slash commands are available to help create high-quality benchmark tasks:
-- [`/create-benchmark-test`](../.claude/commands/create-benchmark-test.md) - Guides you through creating a complete new benchmark task
-- [`/create-semantic-tests`](../.claude/commands/create-semantic-tests.md) - Helps design semantic tests for a task
+## Semantic Test
-## Multi-Trial Evaluation
+LLM-based evaluation functions that use Claude to audit agent work. Used within `test.py` scripts.
-The harness supports running multiple trials of the same agent-task pair to measure consistency and reliability. Use `--num-trials N` to run each task N times.
-Results are aggregated with statistics including mean, median, standard deviation, minimum, and maximum scores.
-Each trial is stored in a separate subdirectory (`trial_1`, `trial_2`, etc.) within the results directory.
+### `semantic_test`
+[Source](../eval_recipes/benchmarking/semantic_test.py)
-## Command-Line Options
+Audits a single agent's work against a rubric.
-### Score-Based Benchmarks
+```python
+from eval_recipes.benchmarking.semantic_test import semantic_test
-The `scripts/run_benchmarks.py` script accepts the following options:
+result = await semantic_test(
+ steps="1. Explore /project\n2. Check if README exists\n3. Run the tests",
+ rubric={
+ "readme_exists": "bool - Does README.md exist?",
+ "tests_pass": "bool - Do the tests pass?",
+ "score": "float - Overall score 0-100", # Required field
+ },
+ context="The agent was asked to create a CLI tool...",
+ working_dir=Path("/project"),
+)
-- `--agents-dir`: Path to agents directory
-- `--tasks-dir`: Path to tasks directory
-- `--runs-dir`: Output directory for results. If not provided, creates a new timestamped directory. If provided and exists with prior state, resumes the run.
-- `--agent-filter`: Filter agents by field (format: `field=value`, `field!=value`, or `field=val1,val2` for multiple)
-- `--task-filter`: Filter tasks by field (same format as agent-filter)
-- `--max-parallel-trials`: Maximum number of trials to run in parallel
-- `--num-trials`: Number of trials per agent-task pair
-- `--continuation-provider`: LLM provider for agent continuation - `openai`, `azure_openai`, or `none` to disable
-- `--continuation-model`: Model to use for agent continuation decisions - `gpt-5` or `gpt-5.1`
-- `--report-score-threshold`: Score threshold for generating failure reports
+# result.score: float (0-100)
+# result.metadata: dict (other rubric fields)
+```
-### Comparison Benchmarks
+### `semantic_test_comparison`
-The `scripts/run_comparison_benchmarks.py` script accepts the following options:
+[Source](../eval_recipes/benchmarking/semantic_test_comparison.py)
-- `--config`: Path to YAML config file with comparison specs (default: `data/eval-setups/comparison-all.yaml`)
-- `--agents-dir`: Path to agents directory
-- `--tasks-dir`: Path to tasks directory
-- `--runs-dir`: Output directory for results
-- `--max-parallel`: Maximum number of trials to run in parallel
-- `--comparison-runs`: Number of times to run the comparison evaluation per task (for uncertainty measurement)
-- `--continuation-provider`: LLM provider for agent continuation
-- `--continuation-model`: Model to use for agent continuation decisions
+Compares multiple agents' work on the same task using blind evaluation. Directories are anonymized before comparison.
+```python
+from eval_recipes.benchmarking.semantic_test_comparison import semantic_test_comparison
-## Results
+result = await semantic_test_comparison(
+ original_task="Create a CLI tool that...",
+ directories=[
+ Path("/outputs/agent_1/project"),
+ Path("/outputs/agent_2/project"),
+ Path("/outputs/agent_3/project"),
+ ],
+ guidelines="Focus on code quality and completeness", # Optional
+ log_file=Path("comparison.log"), # Optional
+)
-Results include detailed metrics:
-- **Scores**: Task-specific scores from test scripts
-- **Timing**: Agent execution duration and test execution duration
-- **Reports**: Three types of reports are generated:
- - **Trial failure reports**: Individual analysis for each trial scoring below the threshold
- - **Consolidated reports**: Per-agent summary of all failures
- - **HTML reports**: Interactive dashboards with tabbed interface showing overview, task catalog, and per-agent detailed results
+# result.reasoning: str (bullet-point analysis)
+# result.rankings: list[int] (indices ordered best to worst)
+# result.anonymous_to_index: dict (maps anonymized names to indices)
+```
## Third-Party Benchmarks
-Scripts for integrating third-party benchmarks are located in `scripts/third_party_benchmarks/`. These scripts download benchmark data from external sources and convert them into eval-recipes task format.
+Scripts for integrating third-party benchmarks are located in `scripts/third_party_benchmarks/`. These scripts download benchmark data from external sources and convert them into eval-recipes score-based task format.
### ARC-AGI-2
diff --git a/eval_recipes/benchmarking/base.dockerfile b/eval_recipes/benchmarking/base.dockerfile
index 02e8e38..8f89c4c 100644
--- a/eval_recipes/benchmarking/base.dockerfile
+++ b/eval_recipes/benchmarking/base.dockerfile
@@ -8,7 +8,6 @@ RUN apt-get update && \
# Install uv: https://docs.astral.sh/uv/getting-started/installation/
COPY --from=ghcr.io/astral-sh/uv:latest /uv /usr/local/bin/uv
-ENV UV_SYSTEM_PYTHON=1
WORKDIR /project
diff --git a/eval_recipes/benchmarking/filters.py b/eval_recipes/benchmarking/filters.py
deleted file mode 100644
index f13ade1..0000000
--- a/eval_recipes/benchmarking/filters.py
+++ /dev/null
@@ -1,150 +0,0 @@
-# Copyright (c) Microsoft. All rights reserved.
-
-from typing import Any, TypeVar
-
-from pydantic import BaseModel
-
-T = TypeVar("T", bound=BaseModel)
-
-
-def parse_filter(filter_str: str) -> tuple[str, list[str], bool]:
- """
- Parse a filter string like 'field=value1,value2' or 'field!=value' into field path, values, and negation flag.
-
- Args:
- filter_str: Filter string in format 'field=value', 'field=value1,value2', or 'field!=value'
-
- Returns:
- Tuple of (field_path, list_of_values, is_negation)
-
- Examples:
- >>> parse_filter('name=claude_code')
- ('name', ['claude_code'], False)
- >>> parse_filter('task_info.difficulty=easy,medium')
- ('task_info.difficulty', ['easy', 'medium'], False)
- >>> parse_filter('name!=sec_10q_extractor')
- ('name', ['sec_10q_extractor'], True)
- """
- # Check for negation operator first
- if "!=" in filter_str:
- field_path, values_str = filter_str.split("!=", 1)
- values = [v.strip() for v in values_str.split(",")]
- return field_path.strip(), values, True
- elif "=" in filter_str:
- field_path, values_str = filter_str.split("=", 1)
- values = [v.strip() for v in values_str.split(",")]
- return field_path.strip(), values, False
- else:
- raise ValueError(f"Invalid filter format: '{filter_str}'. Expected 'field=value' or 'field!=value'")
-
-
-def get_nested_field(obj: BaseModel, field_path: str) -> Any:
- """
- Get a nested field value from a Pydantic model using dot notation.
-
- Args:
- obj: Pydantic model instance
- field_path: Field path using dot notation (e.g., 'name' or 'task_info.difficulty')
-
- Returns:
- The value of the field
-
- Raises:
- AttributeError: If the field path is invalid
-
- Examples:
- >>> get_nested_field(task_config, 'name')
- 'blog_writing'
- >>> get_nested_field(task_config, 'task_info.difficulty')
- 'medium'
- """
- parts = field_path.split(".")
- value: Any = obj
-
- for part in parts:
- if hasattr(value, part):
- value = getattr(value, part)
- else:
- raise AttributeError(f"Field '{part}' not found in path '{field_path}'")
-
- return value
-
-
-def apply_filters(items: list[T], filters: list[str]) -> list[T]:
- """
- Apply multiple filters to a list of Pydantic models.
-
- Multiple filters use AND logic (all must match).
- Comma-separated values within a filter use OR logic (any can match).
- Negation filters (using !=) exclude items matching the specified values.
-
- Args:
- items: List of Pydantic model instances
- filters: List of filter strings like ['name=value', 'field=value1,value2', 'name!=excluded_value']
-
- Returns:
- Filtered list where each item matches ALL filters
-
- Examples:
- >>> # Single filter
- >>> apply_filters(tasks, ['difficulty=medium'])
- >>> # Multiple values (OR logic)
- >>> apply_filters(tasks, ['name=task1,task2'])
- >>> # Multiple filters (AND logic)
- >>> apply_filters(tasks, ['difficulty=medium', 'task_info.non_deterministic_evals=true'])
- >>> # Negation filter (exclude items)
- >>> apply_filters(tasks, ['name!=sec_10q_extractor'])
- """
- if not filters:
- return items
-
- filtered_items = items.copy()
- for filter_str in filters:
- field_path, allowed_values, is_negation = parse_filter(filter_str)
- if is_negation:
- # Exclusion: keep items that DO NOT match any of the values
- filtered_items = [item for item in filtered_items if not _matches_filter(item, field_path, allowed_values)]
- else:
- # Inclusion: keep items that DO match any of the values
- filtered_items = [item for item in filtered_items if _matches_filter(item, field_path, allowed_values)]
-
- return filtered_items
-
-
-def _matches_filter(item: BaseModel, field_path: str, allowed_values: list[str]) -> bool:
- """
- Check if an item matches a filter.
-
- Args:
- item: Pydantic model instance
- field_path: Field path to check
- allowed_values: List of allowed values (OR logic)
-
- Returns:
- True if the item's field value matches any of the allowed values
- """
- try:
- field_value = get_nested_field(item, field_path)
-
- # Convert field value to string for comparison
- field_value_str = str(field_value).lower()
-
- # Check if field value matches any of the allowed values
- for allowed_value in allowed_values:
- allowed_value_lower = allowed_value.lower()
-
- # Handle boolean values
- if isinstance(field_value, bool):
- if allowed_value_lower in ("true", "1", "yes") and field_value:
- return True
- if allowed_value_lower in ("false", "0", "no") and not field_value:
- return True
- # String comparison
- elif field_value_str == allowed_value_lower:
- return True
-
- return False
-
- except AttributeError:
- # Field doesn't exist on this item, so it doesn't match
- return False
diff --git a/eval_recipes/benchmarking/harness.py b/eval_recipes/benchmarking/harness.py
index 0a06f6c..594d2f5 100644
--- a/eval_recipes/benchmarking/harness.py
+++ b/eval_recipes/benchmarking/harness.py
@@ -7,12 +7,11 @@
import yaml
from eval_recipes.benchmarking.create_html_report import create_html_report
-from eval_recipes.benchmarking.filters import apply_filters
from eval_recipes.benchmarking.jobs.base import Job, JobContext, JobResult, JobStatus
from eval_recipes.benchmarking.jobs.runner import JobRunner
from eval_recipes.benchmarking.reporting import generate_agent_consolidated_report, generate_trial_report
from eval_recipes.benchmarking.run_trial import DEFAULT_EVAL_RECIPES_VERSION, TrialConfig, run_trial
-from eval_recipes.benchmarking.schemas import AgentConfig, ScoreEvalConfig, TaskConfig, TaskInfo
+from eval_recipes.benchmarking.schemas import AgentConfig, ScoreEvalConfig, ScoreRunSpec, TaskConfig, TaskInfo
class TrialJob(Job):
@@ -118,13 +117,11 @@ class ConsolidatedReportJob(Job):
def __init__(
self,
agent: AgentConfig,
- task_names: list[str],
- num_trials: int,
+ task_trials: dict[str, int],
runs_dir: Path,
):
self._agent = agent
- self._task_names = task_names
- self._num_trials = num_trials
+ self._task_trials = task_trials # Maps task_name -> num_trials
self._runs_dir = runs_dir
@property
@@ -135,8 +132,8 @@ def job_id(self) -> str:
def dependencies(self) -> list[str]:
# Depend on ALL report jobs for this agent across all tasks
deps = []
- for task_name in self._task_names:
- for trial_num in range(1, self._num_trials + 1):
+ for task_name, num_trials in self._task_trials.items():
+ for trial_num in range(1, num_trials + 1):
deps.append(f"report:{self._agent.name}:{task_name}:{trial_num}")
return deps
@@ -145,8 +142,7 @@ async def run(self, context: JobContext) -> JobResult:
report_generated = await generate_agent_consolidated_report(
agent_name=self._agent.name,
runs_dir=self._runs_dir,
- task_names=self._task_names,
- num_trials=self._num_trials,
+ task_trials=self._task_trials,
)
return JobResult(
status=JobStatus.COMPLETED,
@@ -164,13 +160,13 @@ def __init__(
self,
agent_names: list[str],
task_names: list[str],
- num_trials: int,
+ max_trials: int,
runs_dir: Path,
tasks_dir: Path,
):
self._agent_names = agent_names
self._task_names = task_names
- self._num_trials = num_trials
+ self._max_trials = max_trials
self._runs_dir = runs_dir
self._tasks_dir = tasks_dir
@@ -190,7 +186,7 @@ async def run(self, context: JobContext) -> JobResult:
tasks_directory=self._tasks_dir,
agent_names=self._agent_names,
task_names=self._task_names,
- num_trials=self._num_trials,
+ num_trials=self._max_trials,
)
return JobResult(
status=JobStatus.COMPLETED,
@@ -202,46 +198,55 @@ async def run(self, context: JobContext) -> JobResult:
class Harness:
- continuation_provider: Literal["openai", "azure_openai", "none"]
- continuation_model: Literal["gpt-5", "gpt-5.1"]
-
def __init__(
self,
- runs_dir: Path,
- agents_dir: Path | None = None,
- tasks_dir: Path | None = None,
+ agents_dir: Path,
+ tasks_dir: Path,
+ run_definition: ScoreRunSpec,
+ runs_dir: Path | None = None,
environment: dict[str, str] | None = None,
- agent_filters: list[str] | None = None,
- task_filters: list[str] | None = None,
max_parallel_trials: int = 5,
- num_trials: int = 1,
continuation_provider: Literal["openai", "azure_openai", "none"] = "none",
continuation_model: Literal["gpt-5", "gpt-5.1"] = "gpt-5",
eval_recipes_version: str = DEFAULT_EVAL_RECIPES_VERSION,
report_score_threshold: float = 85.0,
) -> None:
- repo_root = Path(__file__).parents[2]
- self.agents_dir = agents_dir or repo_root / "data" / "agents"
- self.tasks_dir = tasks_dir or repo_root / "data" / "tasks"
- self.runs_dir = runs_dir
+ self.agents_dir = agents_dir
+ self.tasks_dir = tasks_dir
+ self.run_definition = run_definition
+ self.runs_dir = runs_dir or Path.cwd() / ".benchmark_results"
self.runs_dir.mkdir(parents=True, exist_ok=True)
self.environment = environment or {}
- self.agent_filters = agent_filters
- self.task_filters = task_filters
self.max_parallel_trials = max_parallel_trials
- self.num_trials = num_trials
- self.continuation_provider = continuation_provider
- self.continuation_model = continuation_model
+ self.continuation_provider: Literal["openai", "azure_openai", "none"] = continuation_provider
+ self.continuation_model: Literal["gpt-5", "gpt-5.1"] = continuation_model
self.eval_recipes_version = eval_recipes_version
self.report_score_threshold = report_score_threshold
async def run(self) -> None:
- agents = _load_agents(self.agents_dir, self.agent_filters)
- tasks = _load_tasks(self.tasks_dir, self.task_filters)
-
- if not agents or not tasks:
- logger.error("No agents or tasks to run. Exiting.")
+ # Load all agents and tasks
+ agents_by_name = self._load_agents_by_name()
+ tasks_by_name = self._load_tasks_by_name()
+
+ # Resolve run definition to (agent, task, num_trials) tuples
+ agent_task_trials: list[tuple[AgentConfig, TaskConfig, int]] = []
+ for agent_spec in self.run_definition.definitions:
+ agent = agents_by_name.get(agent_spec.agent)
+ if agent is None:
+ raise ValueError(f"Agent '{agent_spec.agent}' not found in {self.agents_dir}")
+
+ for task_spec in agent_spec.tasks:
+ task = tasks_by_name.get(task_spec.task)
+ if task is None:
+ raise ValueError(f"Task '{task_spec.task}' not found in {self.tasks_dir}")
+
+ # Determine trial count: task-level > agent-level > 1
+ num_trials = task_spec.trials or agent_spec.trials or 1
+ agent_task_trials.append((agent, task, num_trials))
+
+ if not agent_task_trials:
+ logger.error("No agent-task combinations to run. Exiting.")
return
trial_config = TrialConfig(
@@ -251,55 +256,68 @@ async def run(self) -> None:
eval_recipes_version=self.eval_recipes_version,
)
+ # Build jobs from agent_task_trials
jobs: list[Job] = []
- for agent in agents:
- for task in tasks:
- base_run_dir = self.runs_dir / f"{agent.name}_{task.name}"
- base_run_dir.mkdir(parents=True, exist_ok=True)
-
- for trial_num in range(1, self.num_trials + 1):
- # Trial job
- jobs.append(
- TrialJob(
- agent=agent,
- task=task,
- trial_num=trial_num,
- base_run_dir=base_run_dir,
- config=trial_config,
- )
+
+ # Track task_trials per agent for consolidated reports
+ agent_task_trials_map: dict[str, dict[str, int]] = {}
+ all_task_names: set[str] = set()
+ all_agent_names: set[str] = set()
+ max_trials = 0
+
+ for agent, task, num_trials in agent_task_trials:
+ all_agent_names.add(agent.name)
+ all_task_names.add(task.name)
+ max_trials = max(max_trials, num_trials)
+
+ if agent.name not in agent_task_trials_map:
+ agent_task_trials_map[agent.name] = {}
+ agent_task_trials_map[agent.name][task.name] = num_trials
+
+ base_run_dir = self.runs_dir / f"{agent.name}_{task.name}"
+ base_run_dir.mkdir(parents=True, exist_ok=True)
+
+ for trial_num in range(1, num_trials + 1):
+ # Trial job
+ jobs.append(
+ TrialJob(
+ agent=agent,
+ task=task,
+ trial_num=trial_num,
+ base_run_dir=base_run_dir,
+ config=trial_config,
)
+ )
- # Report job (depends on trial)
- jobs.append(
- TrialReportJob(
- agent=agent,
- task=task,
- trial_num=trial_num,
- base_run_dir=base_run_dir,
- tasks_dir=self.tasks_dir,
- report_score_threshold=self.report_score_threshold,
- )
+ # Report job (depends on trial)
+ jobs.append(
+ TrialReportJob(
+ agent=agent,
+ task=task,
+ trial_num=trial_num,
+ base_run_dir=base_run_dir,
+ tasks_dir=self.tasks_dir,
+ report_score_threshold=self.report_score_threshold,
)
+ )
- # Create consolidated report jobs (one per agent, depends on all reports for that agent)
- task_names = [task.name for task in tasks]
- for agent in agents:
+ # Create consolidated report jobs (one per agent)
+ for agent_name in all_agent_names:
+ agent = agents_by_name[agent_name]
jobs.append(
ConsolidatedReportJob(
agent=agent,
- task_names=task_names,
- num_trials=self.num_trials,
+ task_trials=agent_task_trials_map[agent_name],
runs_dir=self.runs_dir,
)
)
# Create HTML report job (depends on all consolidated reports)
- agent_names = [agent.name for agent in agents]
jobs.append(
HtmlReportJob(
- agent_names=agent_names,
- task_names=task_names,
- num_trials=self.num_trials,
+ agent_names=list(all_agent_names),
+ task_names=list(all_task_names),
+ max_trials=max_trials,
runs_dir=self.runs_dir,
tasks_dir=self.tasks_dir,
)
@@ -316,109 +334,104 @@ async def run(self) -> None:
await runner.run()
logger.info("Job execution complete")
+ def _load_agents_by_name(self) -> dict[str, AgentConfig]:
+ """Load agent configurations from the agents directory."""
+ agents: dict[str, AgentConfig] = {}
+ if not self.agents_dir.exists():
+ logger.warning(f"Agents directory {self.agents_dir} does not exist.")
+ return agents
-def _load_agents(agents_dir: Path, agent_filters: list[str] | None) -> list[AgentConfig]:
- """Load agent configurations from the agents directory."""
- agents = []
- if not agents_dir.exists():
- logger.warning(f"Agents directory {agents_dir} does not exist.")
- return agents
-
- for agent_dir in agents_dir.iterdir():
- if not agent_dir.is_dir():
- continue
-
- install_file = agent_dir / "install.dockerfile"
- command_template_file = agent_dir / "command_template.txt"
- command_template_continue_file = agent_dir / "command_template_continue.txt"
- agent_yaml_file = agent_dir / "agent.yaml"
- data_dir = agent_dir / "data"
-
- if not install_file.exists() or not command_template_file.exists() or not agent_yaml_file.exists():
- continue
-
- with agent_yaml_file.open(encoding="utf-8") as f:
- agent_yaml = yaml.safe_load(f) or {}
-
- local_source_path = None
- if "local_source_path" in agent_yaml:
- local_source_path_str = agent_yaml["local_source_path"]
- local_source_path = Path(local_source_path_str).resolve()
- if not local_source_path.exists():
- logger.warning(
- f"Agent '{agent_dir.name}' specifies local_source_path='{local_source_path}' "
- f"but path does not exist. Skipping this agent."
- )
+ for agent_dir in self.agents_dir.iterdir():
+ if not agent_dir.is_dir():
continue
- if not local_source_path.is_dir():
- logger.warning(
- f"Agent '{agent_dir.name}' specifies local_source_path='{local_source_path}' "
- f"but path is not a directory. Skipping this agent."
- )
+
+ install_file = agent_dir / "install.dockerfile"
+ command_template_file = agent_dir / "command_template.txt"
+ command_template_continue_file = agent_dir / "command_template_continue.txt"
+ agent_yaml_file = agent_dir / "agent.yaml"
+ data_dir = agent_dir / "data"
+
+ if not install_file.exists() or not command_template_file.exists() or not agent_yaml_file.exists():
continue
- logger.info(f"Agent '{agent_dir.name}' will use local source from: {local_source_path}")
- agents.append(
- AgentConfig(
+ with agent_yaml_file.open(encoding="utf-8") as f:
+ agent_yaml = yaml.safe_load(f) or {}
+
+ local_source_path = None
+ if "local_source_path" in agent_yaml:
+ local_source_path_str = agent_yaml["local_source_path"]
+ local_source_path = Path(local_source_path_str).resolve()
+ if not local_source_path.exists():
+ logger.warning(
+ f"Agent '{agent_dir.name}' specifies local_source_path='{local_source_path}' "
+ f"but path does not exist. Skipping this agent."
+ )
+ continue
+ if not local_source_path.is_dir():
+ logger.warning(
+ f"Agent '{agent_dir.name}' specifies local_source_path='{local_source_path}' "
+ f"but path is not a directory. Skipping this agent."
+ )
+ continue
+ logger.info(f"Agent '{agent_dir.name}' will use local source from: {local_source_path}")
+
+ agents[agent_dir.name] = AgentConfig(
name=agent_dir.name,
required_env_vars=agent_yaml.get("required_env_vars", []),
- agent_installation=install_file.read_text(),
- command_template=command_template_file.read_text(),
+ agent_installation=install_file.read_text(encoding="utf-8"),
+ command_template=command_template_file.read_text(encoding="utf-8"),
command_template_continue=(
- command_template_continue_file.read_text() if command_template_continue_file.exists() else None
+ command_template_continue_file.read_text(encoding="utf-8")
+ if command_template_continue_file.exists()
+ else None
),
data_dir=data_dir if data_dir.exists() and data_dir.is_dir() else None,
local_source_path=local_source_path,
)
- )
-
- if agent_filters:
- agents = apply_filters(agents, agent_filters)
- logger.info(f"Loaded {len(agents)} agent(s)")
- return agents
+ logger.info(f"Loaded {len(agents)} agent(s)")
+ return agents
-def _load_tasks(tasks_dir: Path, task_filters: list[str] | None) -> list[TaskConfig]:
- """Load task configurations from the tasks directory."""
- tasks = []
- if not tasks_dir.exists():
- logger.warning(f"Tasks directory {tasks_dir} does not exist.")
- return tasks
+ def _load_tasks_by_name(self) -> dict[str, TaskConfig]:
+ """Load task configurations from the tasks directory."""
+ tasks: dict[str, TaskConfig] = {}
+ if not self.tasks_dir.exists():
+ logger.warning(f"Tasks directory {self.tasks_dir} does not exist.")
+ return tasks
- for task_dir in tasks_dir.iterdir():
- if not task_dir.is_dir():
- continue
+ for task_dir in self.tasks_dir.iterdir():
+ if not task_dir.is_dir():
+ continue
- setup_file = task_dir / "setup.dockerfile"
- instructions_file = task_dir / "instructions.txt"
- test_script = task_dir / "test.py"
- task_yaml_file = task_dir / "task.yaml"
- task_time_data_dir = task_dir / "task_time_data"
- test_time_data_dir = task_dir / "test_time_data"
+ setup_file = task_dir / "setup.dockerfile"
+ instructions_file = task_dir / "instructions.txt"
+ test_script = task_dir / "test.py"
+ task_yaml_file = task_dir / "task.yaml"
+ task_time_data_dir = task_dir / "task_time_data"
+ test_time_data_dir = task_dir / "test_time_data"
- if not instructions_file.exists() or not test_script.exists() or not task_yaml_file.exists():
- continue
+ if not instructions_file.exists() or not test_script.exists() or not task_yaml_file.exists():
+ continue
- with task_yaml_file.open(encoding="utf-8") as f:
- task_yaml = yaml.safe_load(f) or {}
+ with task_yaml_file.open(encoding="utf-8") as f:
+ task_yaml = yaml.safe_load(f) or {}
- task_info_data = task_yaml.get("task_info")
- if not task_info_data:
- logger.warning(f"Skipping task '{task_dir.name}', missing required 'task_info' field in task.yaml")
- continue
+ task_info_data = task_yaml.get("task_info")
+ if not task_info_data:
+ logger.warning(f"Skipping task '{task_dir.name}', missing required 'task_info' field in task.yaml")
+ continue
- task_info = TaskInfo(
- difficulty=task_info_data["difficulty"],
- non_deterministic_evals=task_info_data["non_deterministic_evals"],
- )
+ task_info = TaskInfo(
+ difficulty=task_info_data["difficulty"],
+ non_deterministic_evals=task_info_data.get("non_deterministic_evals", False),
+ )
- tasks.append(
- TaskConfig(
+ tasks[task_dir.name] = TaskConfig(
name=task_dir.name,
eval_type="score",
required_env_vars=task_yaml.get("required_env_vars", []),
- task_installation=setup_file.read_text() if setup_file.exists() else "",
- instructions=instructions_file.read_text(),
+ task_installation=setup_file.read_text(encoding="utf-8") if setup_file.exists() else "",
+ instructions=instructions_file.read_text(encoding="utf-8"),
score_eval=ScoreEvalConfig(
test_script=test_script,
test_command=task_yaml.get("test_command", "uv run --no-project /project/test.py"),
@@ -432,9 +445,6 @@ def _load_tasks(tasks_dir: Path, task_filters: list[str] | None) -> list[TaskCon
timeout=task_yaml.get("timeout", 1800),
task_info=task_info,
)
- )
- if task_filters:
- tasks = apply_filters(tasks, task_filters)
- logger.info(f"Loaded {len(tasks)} task(s)")
- return tasks
+ logger.info(f"Loaded {len(tasks)} task(s)")
+ return tasks
diff --git a/eval_recipes/benchmarking/harness_comparison.py b/eval_recipes/benchmarking/harness_comparison.py
index 9378296..eca24b2 100644
--- a/eval_recipes/benchmarking/harness_comparison.py
+++ b/eval_recipes/benchmarking/harness_comparison.py
@@ -1,6 +1,7 @@
# Copyright (c) Microsoft. All rights reserved.
from collections import defaultdict
+import json
from pathlib import Path
from typing import Literal
@@ -291,8 +292,6 @@ async def run(self, context: JobContext) -> JobResult:
error="raw_results.json not found",
)
- import json
-
raw_data = json.loads(raw_results_path.read_text(encoding="utf-8"))
benchmark_results = ComparisonBenchmarkResults.model_validate(raw_data)
@@ -407,20 +406,11 @@ class ComparisonHarness:
comparisons evaluated via semantic_test_comparison.
"""
- runs_dir: Path
- agents_dir: Path
- environment: dict[str, str]
- max_parallel: int
- comparison_runs: int
- continuation_provider: Literal["openai", "azure_openai", "none"]
- continuation_model: Literal["gpt-5", "gpt-5.1"]
- eval_recipes_version: str
- report_score_threshold: float
-
def __init__(
self,
- runs_dir: Path,
- agents_dir: Path | None = None,
+ agents_dir: Path,
+ tasks_dir: Path,
+ runs_dir: Path | None = None,
environment: dict[str, str] | None = None,
max_parallel: int = 5,
comparison_runs: int = 3,
@@ -432,8 +422,9 @@ def __init__(
"""Initialize comparison harness.
Args:
- runs_dir: Directory to store run outputs.
- agents_dir: Directory containing agent configs (default: data/agents/).
+ agents_dir: Directory containing agent configs.
+ tasks_dir: Directory containing task configs.
+ runs_dir: Directory to store run outputs (default: .comparison_results/).
environment: Environment variables to pass to containers.
max_parallel: Maximum parallel job executions.
comparison_runs: Number of semantic_test_comparison runs.
@@ -442,13 +433,14 @@ def __init__(
eval_recipes_version: Version of eval-recipes to use.
report_score_threshold: Score threshold for reports.
"""
- self.runs_dir = runs_dir
- self.agents_dir = agents_dir or Path("data/agents")
+ self.runs_dir = runs_dir or Path.cwd() / ".comparison_results"
+ self.agents_dir = agents_dir
+ self.tasks_dir = tasks_dir
self.environment = environment or {}
self.max_parallel = max_parallel
self.comparison_runs = comparison_runs
- self.continuation_provider = continuation_provider
- self.continuation_model = continuation_model
+ self.continuation_provider: Literal["openai", "azure_openai", "none"] = continuation_provider
+ self.continuation_model: Literal["gpt-5", "gpt-5.1"] = continuation_model
self.eval_recipes_version = eval_recipes_version
self.report_score_threshold = report_score_threshold
@@ -457,25 +449,22 @@ def __init__(
async def run(
self,
comparison_specs: list[ComparisonTaskSpec],
- tasks_dir: Path | None = None,
) -> Path:
"""Run comparison benchmark.
Args:
comparison_specs: List of comparison task specifications.
- tasks_dir: Directory containing task configs (default: data/tasks/).
Returns:
Path to the results JSON file.
"""
- tasks_dir = tasks_dir or Path("data/tasks")
# Phase 1: Resolve specs to configs
logger.info(f"Resolving {len(comparison_specs)} comparison spec(s)")
- configs = self._resolve_comparison_configs(comparison_specs, tasks_dir)
+ configs = self._resolve_comparison_configs(comparison_specs, self.tasks_dir)
# Phase 2: Build job DAG
- jobs = self._build_jobs(configs, tasks_dir)
+ jobs = self._build_jobs(configs, self.tasks_dir)
logger.info(f"Created {len(jobs)} jobs")
# Phase 3: Run jobs with JobRunner
diff --git a/eval_recipes/benchmarking/reporting.py b/eval_recipes/benchmarking/reporting.py
index 21ff4ac..98470c3 100644
--- a/eval_recipes/benchmarking/reporting.py
+++ b/eval_recipes/benchmarking/reporting.py
@@ -221,8 +221,7 @@ async def generate_trial_report(
async def generate_agent_consolidated_report(
agent_name: str,
runs_dir: Path,
- task_names: list[str],
- num_trials: int,
+ task_trials: dict[str, int],
) -> bool:
"""
Generate a consolidated report for a single agent synthesizing all failure reports.
@@ -230,15 +229,14 @@ async def generate_agent_consolidated_report(
Args:
agent_name: Name of the agent
runs_dir: Base directory containing all run outputs
- task_names: List of task names this agent ran
- num_trials: Number of trials per task
+ task_trials: Maps task name to number of trials for that task
Returns:
True if report was generated, False if no failure reports found
"""
# Collect failure reports from filesystem
failure_reports = []
- for task_name in task_names:
+ for task_name, num_trials in task_trials.items():
base_run_dir = runs_dir / f"{agent_name}_{task_name}"
for trial_num in range(1, num_trials + 1):
report_path = base_run_dir / f"trial_{trial_num}" / f"FAILURE_REPORT_trial_{trial_num}.md"
diff --git a/eval_recipes/benchmarking/run_trial.py b/eval_recipes/benchmarking/run_trial.py
index 50c24ef..429f994 100644
--- a/eval_recipes/benchmarking/run_trial.py
+++ b/eval_recipes/benchmarking/run_trial.py
@@ -16,7 +16,7 @@
from eval_recipes.benchmarking.docker_manager import DockerManager
from eval_recipes.benchmarking.schemas import AgentConfig, TaskConfig, TrialResult
-DEFAULT_EVAL_RECIPES_VERSION = "0.0.28"
+DEFAULT_EVAL_RECIPES_VERSION = "0.29"
@dataclass
diff --git a/eval_recipes/benchmarking/schemas.py b/eval_recipes/benchmarking/schemas.py
index 4fa315c..49ebad1 100644
--- a/eval_recipes/benchmarking/schemas.py
+++ b/eval_recipes/benchmarking/schemas.py
@@ -209,3 +209,31 @@ class FinalAggregateReport(BaseModel):
# endregion
+
+
+# region Score Run Definition Schemas
+
+
+class ScoreTaskSpec(BaseModel):
+ """Specification for a task within a score run definition."""
+
+ task: str # Task name (maps to task directory)
+ trials: int | None = None # Optional trial count override
+
+
+class ScoreAgentSpec(BaseModel):
+ """Specification for an agent and its tasks in a score run definition."""
+
+ agent: str # Agent name (maps to agent directory)
+ trials: int | None = None # Default trial count for this agent's tasks
+ tasks: list[ScoreTaskSpec]
+
+
+class ScoreRunSpec(BaseModel):
+ """Run definition for score-based benchmarking."""
+
+ type: Literal["score"] = "score"
+ definitions: list[ScoreAgentSpec]
+
+
+# endregion
diff --git a/pyproject.toml b/pyproject.toml
index 542bb43..9a7095e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
[project]
name = "eval_recipes"
-version = "0.0.28"
+version = "0.0.29"
description = "Eval Recipes"
authors = [{ name = "Semantic Workbench Team" }]
readme = "README.md"
diff --git a/scripts/run_benchmarks.py b/scripts/run_benchmarks.py
index 5bf7e03..3829fdf 100644
--- a/scripts/run_benchmarks.py
+++ b/scripts/run_benchmarks.py
@@ -9,13 +9,22 @@
import click
from dotenv import load_dotenv
from loguru import logger
+import yaml
from eval_recipes.benchmarking.harness import Harness
+from eval_recipes.benchmarking.schemas import ScoreRunSpec
load_dotenv()
@click.command()
+@click.option(
+ "--config",
+ "config_file",
+ type=click.Path(exists=True, dir_okay=False, path_type=Path),
+ default=lambda: Path(__file__).parents[1] / "data" / "eval-setups" / "score-default.yaml",
+ help="Path to YAML config file with run definition",
+)
@click.option(
"--agents-dir",
type=click.Path(exists=True, file_okay=False, path_type=Path),
@@ -34,32 +43,12 @@
default=None,
help="Run directory. If not provided, creates a new timestamped dir. If provided and exists, resumes.",
)
-@click.option(
- "--agent-filter",
- "agent_filters",
- multiple=True,
- default=("name=amplifier_v1,amplifier_foundation,claude_code,gh_cli,openai_codex",),
- help="Filter agents by field. Format: field=value or field!=value. Can specify multiple times.",
-)
-@click.option(
- "--task-filter",
- "task_filters",
- multiple=True,
- default=("name!=sec_10q_extractor,pdf-hr-q1,pdf-hr-q2,pdf-hr-q3,pdf-hr-q5",),
- help="Filter tasks by field. Format: field=value or field!=value. Can specify multiple times.",
-)
@click.option(
"--max-parallel-trials",
type=int,
default=20,
help="Maximum number of trials to run in parallel",
)
-@click.option(
- "--num-trials",
- type=int,
- default=3,
- help="Number of times to run each task",
-)
@click.option(
"--continuation-provider",
type=click.Choice(["openai", "azure_openai", "none"], case_sensitive=False),
@@ -79,17 +68,22 @@
help="Minimum score threshold to skip report generation (reports generated for scores below this)",
)
def main(
+ config_file: Path,
agents_dir: Path,
tasks_dir: Path,
runs_dir: Path | None,
- agent_filters: tuple[str, ...],
- task_filters: tuple[str, ...],
max_parallel_trials: int,
- num_trials: int,
continuation_provider: str,
continuation_model: str,
report_score_threshold: float,
) -> None:
+ # Load run definition from config file
+ with config_file.open(encoding="utf-8") as f:
+ config = yaml.safe_load(f)
+
+ run_definition = ScoreRunSpec.model_validate(config)
+ logger.info(f"Loaded run definition from {config_file}")
+
if runs_dir is None:
base_dir = Path(__file__).parents[1] / ".benchmark_results"
timestamp = datetime.now(UTC).strftime("%Y-%m-%d_%H-%M-%S-%f")[:-3]
@@ -107,9 +101,10 @@ def main(
logger.info(f"Logging to {log_file}")
harness = Harness(
- runs_dir=runs_dir,
agents_dir=agents_dir,
tasks_dir=tasks_dir,
+ run_definition=run_definition,
+ runs_dir=runs_dir,
environment={
"ANTHROPIC_API_KEY": os.environ.get("ANTHROPIC_API_KEY", ""),
"OPENAI_API_KEY": os.environ.get("OPENAI_API_KEY", ""),
@@ -117,10 +112,7 @@ def main(
"AZURE_OPENAI_ENDPOINT": os.environ.get("AZURE_OPENAI_ENDPOINT", ""),
"AZURE_OPENAI_VERSION": os.environ.get("AZURE_OPENAI_VERSION", ""),
},
- agent_filters=list(agent_filters) if agent_filters else None,
- task_filters=list(task_filters) if task_filters else None,
max_parallel_trials=max_parallel_trials,
- num_trials=num_trials,
continuation_provider=cast(Literal["openai", "azure_openai", "none"], continuation_provider),
continuation_model=cast(Literal["gpt-5", "gpt-5.1"], continuation_model),
report_score_threshold=report_score_threshold,
diff --git a/scripts/run_comparison_benchmarks.py b/scripts/run_comparison_benchmarks.py
index a480cc0..fb290c3 100644
--- a/scripts/run_comparison_benchmarks.py
+++ b/scripts/run_comparison_benchmarks.py
@@ -23,7 +23,7 @@
"--config",
"config_file",
type=click.Path(exists=True, dir_okay=False, path_type=Path),
- default=lambda: Path(__file__).parents[1] / "data" / "eval-setups" / "comparison-all.yaml",
+ default=lambda: Path(__file__).parents[1] / "data" / "eval-setups" / "comparison-default.yaml",
help="Path to YAML config file with comparison specs",
)
@click.option(
@@ -133,6 +133,7 @@ def main(
harness = ComparisonHarness(
runs_dir=runs_dir,
agents_dir=agents_dir,
+ tasks_dir=tasks_dir,
environment={
"ANTHROPIC_API_KEY": os.environ.get("ANTHROPIC_API_KEY", ""),
"OPENAI_API_KEY": os.environ.get("OPENAI_API_KEY", ""),
@@ -146,7 +147,7 @@ def main(
continuation_model=cast(Literal["gpt-5", "gpt-5.1"], continuation_model),
)
- results_path = asyncio.run(harness.run(comparison_specs=comparison_specs, tasks_dir=tasks_dir))
+ results_path = asyncio.run(harness.run(comparison_specs=comparison_specs))
logger.info(f"Comparison benchmark complete. Results written to: {results_path}")
# Generate HTML report
diff --git a/uv.lock b/uv.lock
index a2a7017..e222a1a 100644
--- a/uv.lock
+++ b/uv.lock
@@ -17,7 +17,7 @@ wheels = [
[[package]]
name = "aiohttp"
-version = "3.13.2"
+version = "3.13.3"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "aiohappyeyeballs" },
@@ -28,93 +28,93 @@ dependencies = [
{ name = "propcache" },
{ name = "yarl" },
]
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