honeycomb-api 0.7.1

Python client for the Honeycomb.io API

honeycomb-api-python

A modern, async-first Python client for the Honeycomb.io API.

📚 Read the full documentation

Features

• Async-first design with full sync support
• Fluent builder pattern for queries, triggers, SLOs, and boards
• CLI tool for porting objects between environments
• Claude tool definitions exposing the full Honeycomb API for Claude-based agents
• Pydantic models for type-safe request/response handling
• Automatic retries with exponential backoff for transient failures
• Comprehensive error handling with specific exception types
• Dual authentication support (API keys and Management keys)
• Resource-oriented API for intuitive usage

Installation

# Using Poetry
poetry add honeycomb-api

# Using uv
uv add honeycomb-api

Quick Start

Async Usage (Recommended)

import asyncio
from honeycomb import HoneycombClient, QueryBuilder

async def main():
    async with HoneycombClient(api_key="your-api-key") as client:
        # List all datasets
        datasets = await client.datasets.list_async()
        for ds in datasets:
            print(f"Dataset: {ds.name} ({ds.slug})")

        # Run a query using the fluent QueryBuilder
        query, result = await client.query_results.create_and_run_async(
            QueryBuilder("Error Analysis")  # Optional name for board integration
                .dataset("my-dataset")       # Dataset scope on builder
                .last_24_hours()             # Time preset matching Honeycomb UI
                .count()
                .p99("duration_ms")
                .avg("duration_ms")
                .gte("status_code", 500)     # Filter shortcuts: gte, eq, contains, etc.
                .group_by("service", "endpoint")
                .order_by_count()
                .limit(100)
        )

        for row in result.data.rows:
            print(f"Service: {row['service']}, Count: {row['COUNT']}, P99: {row['P99']}")

asyncio.run(main())

Sync Usage

from honeycomb import HoneycombClient, QueryBuilder

with HoneycombClient(api_key="your-api-key", sync=True) as client:
    datasets = client.datasets.list()

    # Run queries with the same fluent API
    query, result = client.query_results.create_and_run(
        QueryBuilder()
            .dataset("my-dataset")
            .last_1_hour()
            .count()
            .group_by("endpoint"),
    )

Builders with resource mixins

TriggerBuilder

from honeycomb import TriggerBuilder

# Create sophisticated alert in one fluent call
trigger = await client.triggers.create_async(
    "api-logs",
    TriggerBuilder("High Error Rate")
        .dataset("api-logs")             # Or .environment_wide() for all datasets
        .last_15_minutes()                # Frequency presets
        .count()
        .gte("status_code", 500)
        .threshold_gt(100)                # Threshold shortcuts
        .email("[e-mail]")      # Multiple recipients
        .pagerduty("critical")
        .slack("#incidents")
        .tag("team", "backend")           # Tag support with validation
        .build()
)

SLOBuilder

from honeycomb import SLOBuilder

# Create SLO with derived column and burn alerts automatically
slos = await client.slos.create_from_bundle_async(
    SLOBuilder("API Availability")
        .dataset("api-logs")
        .target_nines(3)                         # 99.9% = .target_percentage(99.9)
        .time_period_days(30)
        .sli(
            alias="success_rate",
            expression="IF(LT($status_code, 400), 1, 0)",
            description="Success indicator"
        )
        # Burn alerts with integrated recipients
        .exhaustion_alert(exhaustion_minutes=15)
        .budget_rate_alert(window_minutes=60, threshold_percentage=10)
        .email("[e-mail]")
        .pagerduty("critical")
        .build()
)

BoardBuilder

from honeycomb import BoardBuilder, QueryBuilder, SLOBuilder

# Create board with inline queries and SLOs - no pre-creation needed!
board = await client.boards.create_from_bundle_async(
    BoardBuilder("Production Dashboard")
        .description("Service health monitoring")
        .auto_layout()
        .tag("team", "platform")
        # Inline QueryBuilder - creates query automatically
        .query(
            QueryBuilder("Request Count")
                .dataset("api-logs")
                .last_24_hours()
                .count()
                .group_by("service"),
            style="graph"
        )
        # Inline SLOBuilder - creates SLO automatically
        .slo(
            SLOBuilder("API Availability")
                .dataset("api-logs")
                .target_nines(3)
                .sli(alias="sli_success")
        )
        # Environment-wide query
        .query(
            QueryBuilder("P99 Latency")
                .environment_wide()  # All datasets
                .last_1_hour()
                .p99("duration_ms")
                .group_by("endpoint"),
            style="table"
        )
        .build()
)

See full documentation for more examples and advanced features.

Authentication

The client supports two authentication methods:

API Key (Single Environment)

For accessing a single Honeycomb environment:

client = HoneycombClient(api_key="your-api-key")

The API key is sent via the X-Honeycomb-Team header.

Management Key (Multi-Environment)

For management operations across multiple environments:

client = HoneycombClient(
    management_key="your-key-id",
    management_secret="your-key-secret"
)

Management credentials are sent via the Authorization: Bearer header.

CLI Tool

For quick operations without writing Python:

# Run without installing (using uvx or pipx)
export HONEYCOMB_API_KEY=your_api_key_here
uvx honeycomb-api triggers list
# or
pipx run honeycomb-api triggers list

# Or install and use the short alias
uv tool install honeycomb-api
# or
pipx install honeycomb-api

hny triggers list
hny query run --dataset my-dataset --count --last-30-minutes

See the CLI Reference for full documentation.

Usage Guide

For complete usage examples and guides, see the full documentation:

• Quick Start Guide - Common operations with examples
• Working with Queries - Saved, ephemeral, and combined query patterns
• Working with Triggers - Alert configuration
• Working with SLOs - Service level objectives
• API Reference - Complete API documentation

Error Handling

The client provides specific exception types for different error scenarios (authentication, rate limiting, validation, etc.). All exceptions include useful debugging information like HTTP status codes and request IDs for support tickets.

See the Error Handling Guide for complete documentation and best practices.

Configuration

Client Options

from honeycomb import HoneycombClient, RetryConfig

client = HoneycombClient(
    api_key="...",                              # API key for single-environment access
    management_key="...",                       # Management key ID (alternative auth)
    management_secret="...",                    # Management key secret
    base_url="https://api.honeycomb.io",        # API base URL (default)
    timeout=30.0,                               # Request timeout in seconds (default: 30)
    max_retries=3,                              # Max retry attempts (default: 3)
    retry_config=None,                          # Custom retry configuration (optional)
    sync=False,                                 # Use sync mode (default: False)
)

Retry Behavior

The client automatically retries requests on:

• HTTP 429 (Rate Limited) - respects Retry-After header
• HTTP 500, 502, 503, 504 (Server Errors)
• Connection timeouts

Retries use exponential backoff: 1s, 2s, 4s, ... up to 30s max.

Custom Retry Configuration

from honeycomb import HoneycombClient, RetryConfig

# Customize retry behavior
retry_config = RetryConfig(
    max_retries=5,                # More retry attempts
    base_delay=2.0,               # Start with 2s delay
    max_delay=60.0,               # Cap at 60s
    exponential_base=2.0,         # Double each time
    retry_statuses={429, 503},    # Only retry these status codes
)

client = HoneycombClient(api_key="...", retry_config=retry_config)

API Reference

The client provides resource-oriented access to the Honeycomb API:

Core Resources:

• client.datasets - Dataset management
• client.triggers - Alert triggers
• client.slos - Service level objectives
• client.boards - Dashboards
• client.queries - Saved queries
• client.query_results - Query execution

Data Management:

• client.columns - Column schema management
• client.markers - Event markers and annotations
• client.recipients - Notification recipients
• client.burn_alerts - SLO burn rate alerts
• client.events - Event ingestion (send data to Honeycomb)

Team Management (v2 - requires Management Key):

• client.api_keys - API key management (team-scoped)
• client.environments - Environment management (team-scoped)

All methods have both sync and async variants (list() / list_async()).

See the API Reference for complete documentation.

Development

Prerequisites

• Python 3.10+
• Poetry for dependency management
• direnv (optional, for environment management)
• Make

Setup

# Clone the repository
git clone https://github.com/irvingpop/honeycomb-api-python.git
cd honeycomb-api-python

# Install dependencies
make install-dev
# Or: poetry install

# Set up environment variables (for live API testing)
cp .envrc.example .envrc
# Edit .envrc with your API key
direnv allow

Testing Standards

This project maintains high test coverage standards:

• ≥95% coverage required for all resource modules
• Tests use Polyfactory for schema-valid mock data generation

Make Commands

All common development tasks are available via make. Run make help for a full list:

make help          # Show all available commands

Setup

Command	Description
make install	Install poetry production dependencies only
make install-dev	Install all poetry dependencies (including dev)

Code Quality

Command	Description
make lint	Run linter (ruff check)
make lint-fix	Run linter and auto-fix issues
make format	Format code with ruff
make typecheck	Run type checker (mypy)
make check	Run all checks (lint + typecheck)

Testing

Command	Description
make test	Run all tests
make test-unit	Run only unit tests
make test-cov	Run tests with coverage report
make test-live	Run live API tests (requires HONEYCOMB_API_KEY)

Build & Publish

Command	Description
make build	Build distribution packages
make publish	Publish to PyPI
make publish-test	Publish to Test PyPI

Maintenance

Command	Description
make clean	Remove build artifacts and cache files
make update-deps	Update dependencies to latest versions
make ci	Run full CI pipeline (install, check, test)

Running Tests (Manual)

# Run all tests
make test
# Or: poetry run pytest tests/ -v

# Run with coverage
make test-cov
# Or: poetry run pytest --cov=honeycomb --cov-report=html

# Run specific test file
poetry run pytest tests/unit/test_wrapper_client.py -v

Code Quality (Manual)

# Run all checks
make check

# Or run individually:
poetry run ruff check src/ tests/    # Linting
poetry run ruff format src/ tests/   # Formatting
poetry run mypy src/                 # Type checking

Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes
4. Run checks and tests (make check && make test)
5. Commit your changes (git commit -m 'Add amazing feature')
6. Push to the branch (git push origin feature/amazing-feature)
7. Open a Pull Request

Guidelines

• Follow the existing code style (enforced by Ruff)
• Add tests for new functionality
• Update documentation as needed
• Keep commits focused and atomic
• Run make ci before submitting to ensure all checks pass

License

This project is licensed under the MIT License - see the LICENSE file for details.

Related Links

• Honeycomb Documentation
• Honeycomb API Reference
• Issue Tracker

Acknowledgments

• Built with httpx for async HTTP
• Models powered by Pydantic
• API spec from Honeycomb OpenAPI