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Name: epsilab
Version: 0.3.0
Summary: Python SDK for the Epsilab model evaluation and improvement platform.
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License-File: LICENSE
Keywords: ai,evaluation,fine-tuning,llm,training-data
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Typing :: Typed
Requires-Python: >=3.9
Requires-Dist: httpx>=0.27.0
Requires-Dist: python-dotenv>=1.0.1
Description-Content-Type: text/markdown

# Epsilab Python SDK

Official Python client for the [Epsilab](https://www.epsilab.com) model evaluation and improvement platform.

Epsilab evaluates AI models on real-world workflows, identifies capability gaps, and generates targeted training data to close those gaps.

## Installation

```bash
pip install epsilab
```

Or install from source:

```bash
git clone https://github.com/EpsilabAI/epsilab-python.git
cd epsilab-python
pip install -e .
```

## Quick Start

```python
from epsilab import Epsilab

client = Epsilab(api_key="sk-...")

# Compare multiple models in one evaluation
eval_result = client.create_evaluation(
    ["openai/gpt-4o", "anthropic/claude-sonnet-4-20250514", "google/gemini-2.5-flash"],
    name="Frontier comparison",
    max_tasks=25,
)
print(f"Evaluation started: {eval_result.evaluation_id}")

# Wait for completion
run = client.wait_for_completion(eval_result.runs[0].run_id)
print(f"Completed: {run.task_count} tasks, {run.gap_count} gaps found")

# View capability gaps
for gap in client.get_gaps(run.run_id):
    print(f"  {gap.capability}: alpha={gap.alpha_score:.3f}")

# Export targeted training data
client.export_run(run.run_id, format="dpo", path="output/dpo_pairs.jsonl")
```

## Configuration

| Environment Variable    | Constructor Param  | Description                              |
|-------------------------|--------------------|------------------------------------------|
| `EPSILAB_API_KEY`       | `api_key`          | Your API key                             |
| `EPSILAB_API_BASE`      | `api_base`         | API base URL (default: production)       |
| `EPSILAB_HTTP_TIMEOUT`  | `timeout_seconds`  | Request timeout in seconds (default: 120)|
| —                       | `max_retries`      | Auto-retry count for 429/5xx (default: 3)|
| —                       | `backoff_base`     | Initial retry backoff in seconds (default: 1.0) |
| —                       | `load_dotenv`      | Also read a local `.env` file (default: false) |

The SDK reads process environment variables automatically. To also read a
local `.env` file, opt in explicitly:

```python
client = Epsilab(load_dotenv=True)
```

## Multi-Model Evaluations

Compare multiple models side-by-side on the same task set:

```python
# Simple: just pass model IDs
eval_result = client.create_evaluation(
    ["openai/gpt-4o", "google/gemini-2.5-flash", "deepseek/deepseek-v4-flash"],
    name="Three-way comparison",
)

# Advanced: per-model harness overrides
eval_result = client.create_evaluation(
    [
        {"model_id": "openai/gpt-4o", "harness": "codex"},
        {"model_id": "anthropic/claude-sonnet-4-20250514", "harness": "openhands"},
        "google/gemini-2.5-flash",  # uses default_harness
    ],
    default_harness="codex",
    max_tasks=50,
    domains=["coding", "math"],
)

# Check cost before running
estimate = client.estimate_evaluation_cost(
    ["openai/gpt-4o", "anthropic/claude-sonnet-4-20250514"],
    max_tasks=25,
)
print(f"Cost: {estimate.total_credits} credits (balance: {estimate.balance})")
print(f"Sufficient: {estimate.sufficient}")
for m in estimate.per_model:
    print(f"  {m.model_id}: {m.credits} credits, {m.task_count} tasks")
```

## Bring Your Own Model

Evaluate any OpenAI-compatible endpoint:

```python
run = client.create_run(
    "internal-llm-v3",
    base_url="https://my-company.example.com/v1",
    api_key="sk-model-key",
)
```

Your model credentials are used only during the evaluation and are never stored.

## Client Methods

### Models

| Method | Description |
|--------|-------------|
| `list_models(search, provider, limit)` | Browse available models with live pricing |

### Evaluations

| Method | Description |
|--------|-------------|
| `create_evaluation(models, ...)` | Compare multiple models in one evaluation |
| `estimate_evaluation_cost(models, ...)` | Estimate credit cost before running |
| `suggest_scope(instructions)` | AI-generated scope suggestions from a description |

### Runs

| Method | Description |
|--------|-------------|
| `create_run(model_name, ...)` | Submit a single model for evaluation |
| `get_run(run_id)` | Get run status and summary |
| `list_runs(status, limit, offset)` | List your evaluation runs (single page) |
| `iter_runs(status, page_size)` | Auto-paginating iterator over all runs |
| `wait_for_completion(run_id, ...)` | Block until run completes or fails |
| `cancel_run(run_id)` | Cancel a queued or running evaluation |
| `retry_run(run_id)` | Retry a failed run, reusing completed results |
| `resume_run(run_id, ...)` | Resume a failed run with optional new credentials |
| `delete_run(run_id)` | Delete a run |

### Results & Insights

| Method | Description |
|--------|-------------|
| `get_gaps(run_id)` | Get capability gaps from a completed run |
| `get_artifacts(run_id, ...)` | Get generated artifacts (single page) |
| `iter_artifacts(run_id, ...)` | Auto-paginating iterator over all artifacts |
| `get_insights(run_id)` | Get model rankings, J1/J2/J3 metrics, and analytics |
| `request_review(run_id, gap_ids)` | Request human review for specific gaps |
| `forge(run_id)` | Generate new tasks targeting run gaps |
| `export_run(run_id, format, path)` | Export training data or reports |

### Cross-Run Analytics

| Method | Description |
|--------|-------------|
| `get_leaderboard()` | Cross-run model leaderboard |
| `get_domain_leaderboard()` | Per-domain model scores across runs |
| `get_cost_analysis()` | Cost-efficiency rankings with live pricing |
| `get_precomputed_insights()` | Per-domain best-model recommendations |

### Tasks

| Method | Description |
|--------|-------------|
| `get_task(task_id)` | Get details for a specific task |
| `create_task(task)` | Create a single custom evaluation task |
| `upload_custom_tasks(tasks)` | Batch upload custom evaluation tasks |
| `get_task_upload_limits()` | Get max file size and task count per batch |
| `classify_tasks(tasks)` | Auto-classify tasks by domain and capability |
| `list_tasks(...)` | List available tasks (single page) |
| `iter_tasks(...)` | Auto-paginating iterator over all tasks |
| `delete_task(task_id)` | Delete a custom task |

### API Keys

| Method | Description |
|--------|-------------|
| `list_api_keys()` | List your API keys |
| `create_api_key(label)` | Create a new API key |
| `revoke_api_key(key_id)` | Revoke an API key |

### Billing

| Method | Description |
|--------|-------------|
| `get_credit_balance()` | Get current credit balance |
| `get_credit_ledger(...)` | Get credit transaction history |
| `get_usage(period)` | Get monthly usage summary |

## Export Formats

| Format | Use Case |
|--------|----------|
| `dpo` | Direct Preference Optimization (chosen/rejected pairs) |
| `quality_dpo` | DPO pairs enriched with quality scores and feedback |
| `sft` | Supervised Fine-Tuning (prompt/completion pairs) |
| `kto` | Kahneman-Tversky Optimization (binary desirability) |
| `grpo` | Group Relative Policy Optimization (grouped completions) |
| `sharegpt` | Multi-turn conversation format |
| `jsonl` | Raw artifacts as NDJSON |
| `report` | Human-readable evaluation report |
| `yaml` | YAML configuration for reproduction |
| `pytest` | Pytest test cases from capability gaps |

Training data exports use anonymized model labels (e.g. `target_model`, `reference_A`) rather than real model identifiers. Evaluation prompts are included for enterprise accounts; standard accounts receive task ID references.

## Automatic Retries

The SDK automatically retries on rate-limit (429), transient server errors (500, 502, 503, 504), and transient network failures with exponential backoff and jitter. For 429 responses, the `Retry-After` header is respected when valid.

```python
# Default: 3 retries with 1s base backoff
client = Epsilab(api_key="sk-...")

# Customize retry behaviour
client = Epsilab(api_key="sk-...", max_retries=5, backoff_base=2.0)

# Disable retries entirely
client = Epsilab(api_key="sk-...", max_retries=0)
```

## Pagination

List endpoints return a single page by default. Use the `iter_*` methods to auto-paginate:

```python
# Iterate over all runs without manual offset management
for run in client.iter_runs(status="completed"):
    print(run.run_id, run.gap_count)

# Same for artifacts and tasks
for artifact in client.iter_artifacts(run_id):
    print(artifact.artifact_type)

for task in client.iter_tasks(domain="coding"):
    print(task["task_id"])
```

## Error Handling

```python
from epsilab import Epsilab, AuthError, InsufficientCreditsError, RateLimitError, ApiError

client = Epsilab(api_key="sk-...")

try:
    eval_result = client.create_evaluation(["openai/gpt-4o", "google/gemini-2.5-flash"])
except AuthError:
    print("Invalid API key")
except InsufficientCreditsError as e:
    print(f"Not enough credits: {e}")
except RateLimitError as e:
    print(f"Rate limited. Retry after {e.retry_after}s")
except ApiError as e:
    print(f"API error: {e.status_code}")
```

## Examples

See [`examples/example.py`](examples/example.py) for a complete workflow.

## License

Apache 2.0 — see [LICENSE](LICENSE).
