Comet ML REST API
64
AI ReadinessAI-Aware (B)64/100
Agent-ready OpenAPI document · curated by JenticAI/MLMl InferenceapiKey12 EndpointsREST
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For Agents
Read Comet ML experiment metrics, parameters, and assets, and log new metric or parameter values for ongoing training runs.
Use for: I need to fetch the latest accuracy metric from a Comet experiment, List all experiments in a Comet project, Retrieve hyperparameters for a specific training run, Log a custom metric to an in-progress Comet experiment
Not supported: Does not handle model deployment, dataset hosting, or compute orchestration - use for experiment tracking and metric logging only.
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.
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.
1
Step 1: Jentic One Host machine
# On the machine that will host your Jentic One instance:
curl -fsSL "https://jentic.com/install.sh?src=apis&api=%2Fapis%2Fcomet.com%2Fcomet" | sh2
Step 2: Agent machine
# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL "https://jentic.com/install.sh?src=apis&api=%2Fapis%2Fcomet.com%2Fcomet" | sh
jentic register # connects your agent to your Jentic One instanceJentic 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
Log new metric values to a running experiment via POST /write/experiment/metric
Log hyperparameters to an experiment via POST /write/experiment/parameter
List projects available in a Comet workspace
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
GET
/projects
List projects
POST
/write/experiment/metric
Log a metric to an experiment
POST
/write/experiment/parameter
Log a parameter to an experiment
Jentic AI Readiness Score
This API is usable in Jentic One now. Its AI-readiness score against Jentic's framework shows where it stands today and where improvements would make it even easier for agents to use.
C
Comet ML REST API
- AI-Aware (B)64/100
100
Foundational Compliance63
Developer Experience & Jentic Compatibility50
AI-Readiness & Agent Experience94
Agent Usability50
Security58
AI DiscoverabilityPowered by JenticScoring Framework 1.0.0 | Scoring Engine 0.4.0
Show dimension breakdown
100
Foundational Compliance
Base layer of spec validity and structural soundness.
Grade: A+Signals: 4
98%
Lint Results
Aggregated quality score from linter diagnostics, weighted by severity.
100%
Resolution Completeness
Percentage of `$ref` references that resolve successfully.
100%
Specification Validity
Checks whether the API description parses successfully and conforms to its declared specification (e.g., OpenAPI).
100%
Structural Integrity
Structural correctness score based on schema issues using logarithmic dampening.
63
Developer Experience & Jentic Compatibility
Clarity, completeness, and ingestion readiness for developers and tooling.
Grade: B-Signals: 4
0%
Example Density
How richly the API is illustrated with examples.
100%
Example Validity
Percentage of examples that conform to their schemas.
50%
Response Coverage
Percentage of operations with complete response definitions (success, client error, server error).
100%
Tooling Readiness
Health of API ingestion, bundling, and resolution within Jentic pipelines.
50
AI-Readiness & Agent Experience
Semantic breadth, depth, and agent comprehension for AI systems.
Grade: C-Signals: 4
51%
Description Coverage
Coverage of descriptions across API elements.
0%
Error Standardization
Coverage of RFC 9457 Problem Details for error responses.
100%
OperationId Quality
Coverage, uniqueness, and casing consistency of operationIds for AI inference.
50%
Summary Coverage
Coverage of summaries across operations/tags/info.
94
Agent Usability
Functional utility, complexity comfort, and AI orchestration readiness.
Grade: A+Signals: 1
94%
Complexity Comfort
Agent comfort level based on API operational and structural complexity.
50
Security
Trust, risk posture, and security compliance.
Grade: C-Signals: 1
50%
Authentication Strength
Average quality of security schemes based on authentication method strength (weakest link for OAuth2).
58
AI Discoverability
Findability, semantic richness, and reasoning readiness.
Grade: C+Signals: 1
58%
Descriptive Richness
Clarity and depth of descriptions across API elements.
Score it yourself
Every API in the directory is allowlisted, so you can re-score it with no key required.
npx @jentic/api-scorecard-cli score <openapi-url>Why Jentic?
What agents get from Jentic-routed access to this vendor.
Setup
Wiring Comet ML by hand means setting its X-API-Key header on the comet.com REST host and handling the read and write endpoints for experiments, metrics, and parameters yourself. Through Jentic you install once, import Comet ML from the API Directory, store the key once, and your agent calls it.
Permission scoping
Comet ML puts the experiment key in the URL path (/experiments/{experimentKey}/...), so a rule can pin your agent to one experiment: it reads that experiment's metrics and parameters and nothing else. You choose the operations it may call, so write actions like logging a metric or parameter are not included unless you add them.
Credential isolation
Your Comet API key is stored once, encrypted, by your own Jentic One instance and injected at execution time. It never enters the agent's prompt, logs, or context.
Intent-based discovery
Agents search Jentic by intent such as 'fetch Comet ML experiment metrics' or 'list projects', and Jentic returns the matching Comet operation with its input schema so the agent calls the right endpoint without browsing the reference docs.
Related APIs
Alternatives and complements available in the Jentic catalogue.
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 with Jentic One, the self-hosted execution layer.
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 your Jentic One instance 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.
Can I limit what my agent is allowed to do with the Comet ML REST API?
Yes. Because you run Jentic One yourself, your own rules decide which Comet ML operations and credentials the agent may use. Comet puts the experiment key in the URL path, so you can pin the agent to a single experiment and grant only the read operations, such as GET /experiments/{experimentKey}/metrics and /parameters, while excluding write actions like POST /write/experiment/metric unless you add them. The operator chooses the exact endpoints, so the agent cannot log or change data it was not explicitly allowed to touch.
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