# Honeyhive AI integration on Definable

> HoneyHive is a modern AI observability and evaluation platform that enables developers and domain experts to collaboratively build reliable AI applications faster.

## What this connects

HoneyHive is a modern AI observability and evaluation platform that enables developers and domain experts to collaboratively build reliable AI applications faster.

Vendor: https://www.honeyhive.ai/

## Tools available

**42** tools available. First 12:

- `HONEYHIVE_ADD_DATAPOINTS_TO_DATASET` — Add datapoints to dataset — Tool to add datapoints to a dataset. Use when you need to append multiple entries with specified input, ground truth, and history mappings.
- `HONEYHIVE_COMPARE_RUNS` — Compare Experiment Runs — Tool to retrieve experiment comparison between two evaluation runs. Use when you need to analyze the differences in metrics, datapoints, and events between two runs.
- `HONEYHIVE_COMPARE_RUNS_EVENTS` — Compare Runs Events — Tool to compare events between two experiment runs side-by-side. Use when analyzing differences in model behavior, performance metrics, or outputs between evaluation runs. Returns matched event pairs with their respective data from both runs for comparison.
- `HONEYHIVE_CREATE_BATCH_DATAPOINTS` — Batch Create Datapoints — Tool to create multiple datapoints in a single batch operation. Use when you need to bulk-import events into a dataset or create many datapoints at once. Supports filtering by date range, event IDs, or custom criteria. Efficient for migrating large numbers of events to evaluation datasets.
- `HONEYHIVE_CREATE_BATCH_MODEL_EVENTS` — Create Batch Model Events — Tool to create multiple model events in a single request. Use when you need to log a batch of event interactions to HoneyHive.
- `HONEYHIVE_CREATE_BATCH_TOOL_EVENTS` — Create Batch Tool Events — Tool to log a batch of external API calls as tool events. Use when you need to record multiple tool events in one request—use after gathering all event data.
- `HONEYHIVE_CREATE_CONFIGURATION` — Create Configuration — Creates a new configuration in HoneyHive for managing LLM or pipeline settings. Use this to define reusable configurations with specific models, prompts, and parameters that can be deployed across different environments (dev, staging, prod). Configurations enable version control and environment-specific management of your AI application settings.
- `HONEYHIVE_CREATE_DATAPOINT` — Create Datapoint — Tool to create a new datapoint with input-output pairs. Use when you need to add a single datapoint with inputs, ground truth, conversation history, and metadata.
- `HONEYHIVE_CREATE_DATASET` — Create Dataset — Tool to create a dataset. Use when you need to initialize a new dataset within a project.
- `HONEYHIVE_CREATE_EVENT` — Create Event — Tool to create a new event in HoneyHive to track execution of different parts of your application. Use when you need to log a model call, tool execution, or chain step. Events can be grouped into sessions and nested hierarchically using parent_id and children_ids.
- `HONEYHIVE_CREATE_METRIC` — Create Metric — Tool to create a new metric in HoneyHive. Use when you need to define how to evaluate model outputs, whether through code (PYTHON), AI evaluation (LLM), human review (HUMAN), or combining multiple metrics (COMPOSITE). Important: LLM metrics require both model_provider and model_name to be specified.
- `HONEYHIVE_CREATE_MODEL_EVENT` — Create Model Event — Tool to create a new model event to log LLM call data. Use when you need to track a single model interaction including messages, responses, usage, and metadata.

## Auth

Auth schemes: `API_KEY`.

## How agents use Honeyhive

Inside a Definable workflow, Honeyhive is one of the tools the **Distributor specialist** can call. Example coordination patterns:

- **Researcher → Honeyhive** — the Researcher (GPT-5.5) pulls context from Honeyhive (records, threads, documents), synthesises findings, and briefs the rest of the team.
- **Writer → Distributor → Honeyhive** — the Writer (Claude Opus 4.7) drafts copy in brand voice, the Verifier passes it, then the Distributor writes the result into Honeyhive (create record, post message, draft email).
- **Designer / Engineer → Distributor → Honeyhive** — the Designer ships an asset or the Engineer ships a code change, the Distributor delivers it via Honeyhive (attach file, open PR comment, post status).

The Verifier checks every Honeyhive call. On rate limit, schema drift, or auth refresh it self-heals and retries — the workflow completes without manual intervention.

## Categories

- artificial intelligence — https://definable.ai/apps/category/artificial-intelligence/
- developer tools — https://definable.ai/apps/category/developer-tools/

## Related

- HTML page: https://definable.ai/apps/honeyhive/
- Same category (artificial intelligence): https://definable.ai/apps/category/artificial-intelligence/
- All integrations: https://definable.ai/apps/
- Workflow (multi-agent loop): https://definable.ai/workflow/
- Apps llms.txt index: https://definable.ai/llms-apps.txt