Metadata-Version: 2.4
Name: veriops-sdk
Version: 0.1.1
Summary: Python SDK for agent observability event ingestion and runbook validation.
Project-URL: Homepage, https://github.com/Ashking07/agent-observability-runbooks
Project-URL: Repository, https://github.com/Ashking07/agent-observability-runbooks
Project-URL: Documentation, https://github.com/Ashking07/agent-observability-runbooks/tree/main/sdk-python
Project-URL: Issues, https://github.com/Ashking07/agent-observability-runbooks/issues
Author-email: Ashwin Kapile <ashwinkapile2002@gmail.com>
License: MIT
Keywords: agents,ai-agents,fastapi,llm,observability,runbooks,telemetry
Requires-Python: >=3.9
Requires-Dist: httpx>=0.27.0
Provides-Extra: dev
Requires-Dist: mypy>=1.8.0; extra == 'dev'
Requires-Dist: pytest>=8.0.0; extra == 'dev'
Requires-Dist: ruff>=0.5.0; extra == 'dev'
Description-Content-Type: text/markdown

# veriops-sdk (Python)

Python SDK for **VeriOps Agent Observability** - ingest agent execution events and validate runs against YAML runbook specs.

## Features

- **Event ingestion**: Buffered, batched event emission to `POST /v1/events` with automatic retries
- **Context managers**: `client.run()` and `run.step()` for clean instrumentation
- **Runbook validation**: Validate runs against YAML specs (allowed tools, required steps, budgets)
- **Policy support**: Validate using stored policies by ID
- **Thread-safe**: Safe for concurrent use
- **Production-ready**: Exponential backoff, error handling, hooks for monitoring

## Installation

```bash
pip install veriops-sdk
```

## Quick Start

### Basic Usage

```python
from veriops_sdk import ObsClient

# Initialize client
client = ObsClient(
    base_url="http://localhost:8000",
    api_key="dev-key",
    project_id="my-project",
)

# Instrument a run with steps
with client.run(runbook="my_runbook_v1") as run:
    with run.step(name="fetch_data", tool="httpx.get", input={"url": "https://api.example.com"}) as step:
        # Your actual work here
        result = httpx.get("https://api.example.com")
        step.set_output({"status_code": result.status_code})
        step.set_tokens_cost(tokens=100, cost_usd=0.001)
    
    run.set_totals(tokens=100, cost_usd=0.001)

# Validate the run
validation = client.validate_run(
    run.run_id,
    runbook_yaml="""
    allowed_tools:
      - httpx.get
    required_steps:
      - name: fetch_data
    budgets:
      max_total_tokens: 1000
      max_total_cost_usd: 1.0
    """
)

print(f"Validation status: {validation['status']}")
print(f"Reasons: {validation['reasons']}")

client.close()
```

### Using Policies

If you've created a policy in the backend, validate using its ID:

```python
validation = client.validate_run(
    run.run_id,
    policy_id="123e4567-e89b-12d3-a456-426614174000"
)
```

### Environment Variables

```bash
export OBS_BASE_URL=http://localhost:8000
export OBS_API_KEY=dev-key
export OBS_PROJECT_ID=my-project
```

```python
import os
from veriops_sdk import ObsClient

client = ObsClient(
    base_url=os.getenv("OBS_BASE_URL", "http://localhost:8000"),
    api_key=os.getenv("OBS_API_KEY", "dev-key"),
    project_id=os.getenv("OBS_PROJECT_ID", "default"),
)
```

## API Reference

### `ObsClient`

Main client for event ingestion and validation.

#### Constructor

```python
ObsClient(
    base_url: str,
    api_key: str,
    project_id: str,
    *,
    max_batch_events: int = 100,
    flush_interval_events: int = 50,
    timeout_s: float = 10.0,
    max_retries: int = 5,
    backoff_base_s: float = 0.3,
    backoff_cap_s: float = 5.0,
    backoff_jitter: float = 0.2,
    raise_on_flush_error: bool = False,
    on_result: Optional[Callable[[Dict[str, Any]], None]] = None,
    on_error: Optional[Callable[[BaseException], None]] = None,
    http_client: Optional[httpx.Client] = None,
)
```

**Parameters:**
- `base_url`: Backend API base URL (e.g., `"http://localhost:8000"`)
- `api_key`: API key for authentication
- `project_id`: Project identifier for grouping runs
- `max_batch_events`: Maximum events per batch (default: 100)
- `flush_interval_events`: Auto-flush when buffer reaches this size (default: 50)
- `timeout_s`: HTTP request timeout in seconds (default: 10.0)
- `max_retries`: Maximum retry attempts for failed requests (default: 5)
- `backoff_base_s`: Base backoff delay in seconds (default: 0.3)
- `backoff_cap_s`: Maximum backoff delay in seconds (default: 5.0)
- `backoff_jitter`: Jitter factor for backoff (default: 0.2)
- `raise_on_flush_error`: Raise exception on flush failure (default: False)
- `on_result`: Optional callback for successful flush results
- `on_error`: Optional callback for flush errors
- `http_client`: Optional custom httpx.Client instance

#### Methods

##### `run(runbook: str) -> RunContext`

Create a run context manager. Emits `run.start` on enter and `run.end` on exit.

```python
with client.run(runbook="my_runbook") as run:
    # Your agent code here
    run.set_totals(tokens=100, cost_usd=0.01)
```

##### `step(name: str, tool: str, input: Optional[Dict[str, Any]] = None) -> StepContext`

Create a step context manager within a run. Emits `step.start` on enter and `step.end` on exit.

```python
with run.step(name="process", tool="my_tool", input={"data": "..."}) as step:
    result = do_work()
    step.set_output({"result": result})
    step.set_tokens_cost(tokens=50, cost_usd=0.005)
```

##### `validate_run(run_id: str, *, runbook_yaml: Optional[str] = None, policy_id: Optional[str] = None) -> Dict[str, Any]`

Validate a run against a runbook spec or policy.

**Parameters:**
- `run_id`: UUID of the run to validate
- `runbook_yaml`: Optional YAML string for the runbook spec
- `policy_id`: Optional UUID of a policy (takes precedence over `runbook_yaml`)

**Returns:** Validation result dict with `status`, `reasons`, `summary`, etc.

##### `flush() -> FlushResult`

Manually flush buffered events. Returns a `FlushResult` with ingestion stats.

##### `close()`

Close the HTTP client. Call when done with the client.

### `RunContext`

Context manager for a single agent run.

#### Attributes

- `run_id: str` - UUID of the run

#### Methods

##### `set_totals(*, tokens: Optional[int] = None, cost_usd: Optional[float] = None, **extra: Any)`

Set run-level totals (tokens, cost, etc.).

##### `step(name: str, tool: str, input: Optional[Dict[str, Any]] = None) -> StepContext`

Create a step within this run.

### `StepContext`

Context manager for a single step within a run.

#### Methods

##### `set_output(output: Dict[str, Any])`

Set the step's output data.

##### `set_tokens_cost(*, tokens: Optional[int] = None, cost_usd: Optional[float] = None)`

Set token count and cost for this step.

##### `set_status(status: str)`

Set step status (typically `"ok"` or `"error"`).

## Advanced Usage

### Error Handling

By default, the SDK logs errors but doesn't raise exceptions. To fail fast:

```python
client = ObsClient(
    base_url="...",
    api_key="...",
    project_id="...",
    raise_on_flush_error=True,  # Raise on flush failures
)
```

### Hooks

Monitor ingestion results and errors:

```python
def on_result(data: Dict[str, Any]) -> None:
    print(f"Ingested: {data.get('ingested')}, Failed: {data.get('failed')}")

def on_error(exc: BaseException) -> None:
    print(f"Error: {exc}")

client = ObsClient(
    base_url="...",
    api_key="...",
    project_id="...",
    on_result=on_result,
    on_error=on_error,
)
```

### Manual Event Emission

For advanced use cases, you can emit events manually:

```python
from veriops_sdk.types import run_start, step_start, step_end, run_end

client.enqueue(run_start(
    run_id="...",
    project_id="...",
    runbook="my_runbook"
))

client.enqueue(step_start(
    run_id="...",
    step_id="...",
    index=0,
    name="my_step",
    tool="my_tool",
    input={"key": "value"}
))

client.flush()
```

### Exception Handling in Steps

Steps automatically capture exceptions:

```python
with run.step(name="risky_operation", tool="my_tool") as step:
    # If this raises, step.end will have status="error"
    # and output will include error details
    risky_function()
```

## Runbook YAML Format

```yaml
allowed_tools:
  - httpx.get
  - httpx.post
  - openai.chat.completions

required_steps:
  - name: fetch_data
  - name: process

budgets:
  max_total_tokens: 1000
  max_total_cost_usd: 1.0
```

## Examples

See the `examples/` directory for complete examples:
- `local_demo.py` - Basic usage with context managers
- `shortify_integration_test.py` - Integration with a real API

## Requirements

- Python >= 3.9
- httpx >= 0.27.0

## License

MIT

## Links

- **Repository**: https://github.com/Ashking07/agent-observability-runbooks
- **Backend API**: See the main repository for API documentation
- **Issues**: https://github.com/Ashking07/agent-observability-runbooks/issues
