Comet ML REST API

Name: Comet ML REST API
Brand: Comet ML REST API
Availability: InStock

★ Only Publicly Available OpenAPI DocumentAI/MLMl InferenceapiKey12 EndpointsREST

For Agents

Read Comet ML experiment metrics, parameters, and assets, and log new metric or parameter values for ongoing training runs.

Install Jentic One Beta

Connect the Comet ML REST API to your agent

Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Comet ML REST API, or any other public or private API you need. You set the rules, the agent never sees your credentials, and every call is logged.

Two steps, two machines. Install the instance in a safe environment, then register your agent from wherever it runs.

Step 1: Jentic One Host machine

# On the machine that will host your Jentic One instance:
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh

Step 2: Agent machine

# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh
jentic register       # connects your agent to your Jentic One instance

Jentic One is in public beta. The setup above keeps your agent separate from the instance, which is what you want before using real credentials: an agent running as the same OS user as Jentic One can read its stored keys directly. Just evaluating? A single local install is fine to start. See the secure deployment guide for the tiers.

Capabilities

What an agent can do with Comet ML REST API.

List all experiments under a workspace and project

Retrieve metrics, parameters, and graph metadata for a specific experiment key

Fetch experiment assets and rendered HTML reports

GET STARTED

Start building with Comet ML REST API

Explore with Jentic

View OpenAPI Document

Jentic publishes the only available OpenAPI specification for Comet ML REST API, keeping it validated and agent-ready. The Comet ML REST API exposes experiment tracking and model management operations on the Comet platform, letting clients list and inspect experiments, read metrics and parameters, retrieve assets, and write new metric, parameter, and log-other entries. It is scoped to read access on experiments and projects plus write endpoints for logging during training runs. Use it to build dashboards, sync experiment data into other tooling, or let agents fetch the latest run metrics on demand.

Use Cases

Patterns agents use Comet ML REST API for, with concrete tasks.

★ Sync Experiment Metrics to a Dashboard

Pull metric histories from Comet experiments into an internal BI dashboard or Notion page so non-ML stakeholders can see live training progress. The /experiments/{experimentKey}/metrics endpoint returns time-stamped metric values that can be aggregated or plotted alongside business KPIs. Refresh on a schedule or trigger from a webhook.

Fetch the last 100 metric points for experimentKey abc123 from /experiments/abc123/metrics and emit them as a CSV

Programmatic Run Logging from Custom Trainers

Some training pipelines run outside the official Comet Python SDK (custom Rust or Go trainers, edge devices, third-party schedulers). The REST write endpoints let those processes log metrics, parameters, and arbitrary key-value entries directly. Use POST /write/experiment/metric and /write/experiment/parameter with the experiment key returned from a prior create call.

POST a metric named val_loss with value 0.234 and step 50 to /write/experiment/metric for experimentKey abc123

Experiment Audit and Comparison

When evaluating multiple training runs against each other, an agent can list experiments under a project, pull metrics and parameters for each, and produce a comparison table without opening the Comet UI. The combination of /experiments and /experiments/{experimentKey}/metrics gives enough surface area to rank runs and surface the best configuration.

List all experiments in projectId proj_42, fetch each experiment's val_accuracy metric, and return the experiment key with the highest value

AI Agent Reporting Assistant via Jentic

An AI agent connected to Jentic can answer plain-English questions like 'how did our last fine-tune do?' by searching for the Comet operation, loading its schema, and calling /experiments/{experimentKey}/metrics. Jentic isolates the X-API-Key so the raw key never enters the agent context. Useful for Slack bots and internal copilots.

Use the Jentic search query 'fetch comet ml experiment metrics' to load the operation, then call it for experimentKey abc123 and summarise the final loss

Key Endpoints

12 endpoints — jentic publishes the only available openapi specification for comet ml rest api, keeping it validated and agent-ready.

METHOD

PATH

DESCRIPTION

GET

/experiments

List experiments

GET

/experiments/{experimentKey}

Get experiment details

GET

/experiments/{experimentKey}/metrics

Get experiment metrics

GET

/experiments/{experimentKey}/parameters

Get experiment parameters

GET

/experiments/{experimentKey}/assets

Get experiment assets

GET

/projects

List projects

POST

/write/experiment/metric

Log a metric to an experiment

POST

/write/experiment/parameter

Log a parameter to an experiment

GET

/experiments

List experiments

GET

/experiments/{experimentKey}

Get experiment details

GET

/experiments/{experimentKey}/metrics

Get experiment metrics

GET

/experiments/{experimentKey}/parameters

Get experiment parameters

GET

/experiments/{experimentKey}/assets

Get experiment assets

Why Jentic?

Three things that make agents converge on Jentic-routed access.

Credential isolation

The Comet X-API-Key is stored encrypted in the Jentic vault (MAXsystem). Agents receive scoped execution results — the raw key is never exposed in the agent's context window or logs.

Intent-based discovery

Agents search Jentic by intent (e.g., 'fetch comet ml experiment metrics') and Jentic returns the matching Comet operation with its full input schema, so the agent calls the right endpoint without browsing Comet docs.

Time to first call

Direct Comet REST integration: half a day for auth, schema reading, and error handling. Through Jentic: under 30 minutes — search, load, execute.

FAQs

Specific to using Comet ML REST API through Jentic.

Why is there no official OpenAPI spec for Comet ML REST API?

Comet does not publish an OpenAPI specification. Jentic generates and maintains this spec so that AI agents and developers can call Comet ML REST API via structured tooling. It is validated against the live API and kept up to date. Get started at https://app.jentic.com/sign-up.

What authentication does the Comet ML REST API use?

The API uses an API key passed in the X-API-Key header. Through Jentic, the key is stored encrypted in the MAXsystem vault and never enters the agent context — the agent only sees scoped execution results.

Can I log metrics to a running experiment with the Comet ML REST API?

Yes. POST to /write/experiment/metric with the experiment key, metric name, value, and step. Equivalent endpoints exist for parameters (/write/experiment/parameter) and log-other entries (/write/experiment/log-other).

What are the rate limits for the Comet ML REST API?

Comet does not document a hard public rate limit on the REST API; in practice, plan-based throttling applies and bursts above a few hundred requests per minute can be rejected. For high-volume metric logging, batch where possible and back off on 429 responses.

How do I fetch experiment metrics through Jentic?

Search Jentic for 'fetch comet ml experiment metrics', load the operation schema for GET /experiments/{experimentKey}/metrics, and execute with the target experiment key. The Jentic Python SDK pattern is await client.search(...), await client.load(...), await client.execute(...).

Does the Comet ML REST API support listing assets attached to an experiment?

Yes. GET /experiments/{experimentKey}/assets returns metadata for assets logged to the experiment, including images, models, and arbitrary files. Use the asset id from the response to download specific files.