Install Jentic One Beta

Connect the PromptLayer API to your agent

Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the PromptLayer 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://jentic.com/install.sh?src=apis&api=%2Fapis%2Fpromptlayer.com%2Fpromptlayer" | sh

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%2Fpromptlayer.com%2Fpromptlayer" | 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 PromptLayer API.

Publish a new prompt template version with a prompt_name, prompt_template body, and tags

Fetch a prompt template by identifier at a specific version or release label

Create a release label on a prompt version and move it to another version

Attach metadata, a score with an optional score_name, a group_id, or prompt input variables to a logged request

GET STARTED

Start building with PromptLayer API

Explore with Jentic One

View OpenAPI Document

Use Cases

Patterns agents use PromptLayer API for, with concrete tasks.

★ Agent-Managed Prompt Releases

An AI agent connected through Jentic pulls the prompt template it should use by label, so it always runs the version tagged for production. When a prompt engineer approves a change, the agent publishes the new template version and moves the release label onto it, keeping prompt rollout in PromptLayer instead of in application code.

Fetch the 'support-reply' prompt template with label 'prod', publish a new version with an updated prompt_template, then move the prod label to that version

Request Scoring and Tagging

After each LLM call is logged, a pipeline attaches context so the request can be filtered and reviewed later. It records metadata such as customer tier, links related calls with a group id, and writes a numeric score from a user rating or automated grader. Scores and metadata both require only the request_id plus the value, so the step is cheap to add to an existing logging flow.

Attach metadata {"customer_tier": "pro"} and a score of 0.9 with score_name 'helpfulness' to request_id 12345

Prompt Regression Evaluation

Before a prompt change ships, an evaluation pipeline runs it against a dataset and scores the output. The PromptLayer API creates the pipeline, adds columns that define each evaluation step, triggers a full run, and returns the overall score plus per-row results, so a CI job or an agent can block a release whose score drops.

Create an evaluation pipeline named 'classifier-v2-check', add a column to it, run the full evaluation, and report the score

Trace Debugging

When an agent run goes wrong, a developer or a debugging assistant reads the full trace by its trace_id and the individual logged request by its request_id to see the inputs, outputs, and steps involved. This keeps the investigation in one place without exporting logs by hand.

Retrieve trace abc-123 and the logged request 12345, and summarize where the run diverged from the expected output

Key Endpoints

27 endpoints — jentic publishes the only available openapi specification for promptlayer api, keeping it validated and agent-ready.

METHOD

PATH

DESCRIPTION

POST

/prompt-templates/{identifier}

Get a prompt template by version or label

POST

/rest/prompt-templates

Publish a prompt template version

POST

/prompts/{prompt_id}/label

Create a release label on a prompt

POST

/rest/track-score

Attach a score to a logged request

POST

/rest/track-metadata

Attach metadata to a logged request

GET

/api/public/v2/traces/{trace_id}

Get a trace

POST

/reports/{report_id}/run

Run a full evaluation

GET

/reports/{report_id}/score

Get an evaluation score

POST

/prompt-templates/{identifier}

Get a prompt template by version or label

POST

/rest/prompt-templates

Publish a prompt template version

POST

/prompts/{prompt_id}/label

Create a release label on a prompt

POST

/rest/track-score

Attach a score to a logged request

POST

/rest/track-metadata

Attach metadata to a logged request

GET

/api/public/v2/traces/{trace_id}

Get a trace

POST

/reports/{report_id}/run

Run a full evaluation

GET

/reports/{report_id}/score

Get an evaluation score

Why Jentic?

What agents get from Jentic-routed access to this vendor.

Setup

Wiring PromptLayer by hand means sending the `X-API-KEY` header on every call, keeping the integer request_id from each logged call so you can attach metadata and scores, and mapping prompt identifiers to labels and versions. Through Jentic you install once, import PromptLayer from the API Directory, store the key once, and your agent calls it.

Permission scoping

PromptLayer puts the template identifier in the URL path for template reads (/prompt-templates/{identifier}), so a rule can limit your agent to reading one prompt template. You choose the operations it may call, so destructive ones like deleting an evaluation pipeline or a prompt label are not included unless you add them.

Credential isolation

Your PromptLayer 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 'get a prompt template by label' or 'score a logged request', and Jentic returns the matching PromptLayer operation with its input schema so the agent calls the right endpoint without browsing the reference docs.

FAQs

Specific to using PromptLayer API through Jentic.

Why is there no official OpenAPI spec for PromptLayer API?

PromptLayer does not publish an OpenAPI specification. Jentic generates and maintains this spec so that AI agents and developers can call PromptLayer API via structured tooling. It is validated against the live API and kept up to date. To run it on your own infrastructure, install Jentic One from its GitHub repo.

What authentication does the PromptLayer API use?

The PromptLayer API authenticates with an API key sent in the `X-API-KEY` header, per its OpenAPI spec. Through Jentic the key is stored encrypted by your own Jentic One instance and injected at call time, so it never reaches the agent's prompt, logs, or context.

Can I fetch a specific prompt version with the PromptLayer API?

Yes. The get prompt template operation takes the template identifier in the path and accepts an optional version number or label in the body, so you can pin a call to version 7 or to whatever version currently carries the prod label. A separate operation returns the raw template data, and another lists the labels on a template.

What are the rate limits for the PromptLayer API?

The OpenAPI spec does not specify rate limits. Check https://docs.promptlayer.com for the current limits that apply to your plan.

Is there a PromptLayer MCP server?

You don't need an MCP server to give your agent PromptLayer. Jentic connects it directly from the API Directory: import it, store your credential once, and your agent calls it, with operations found on demand rather than loaded into the agent's context up front.

Can I limit what my agent is allowed to do with the PromptLayer API?

Yes. For example, write a rule that allows only the get prompt template, track-score, and track-metadata operations, so the agent can read its prompts and annotate its own requests, while publishing templates, moving labels, and deleting evaluation pipelines stay out of reach. Every call it makes is logged.

PromptLayer API

AI ReadinessAI-Aware (B-)60/100

See full scorecard

Agent-ready OpenAPI document · curated by JenticDeveloper ToolsMonitoring ObservabilityapiKey27 EndpointsREST

Know of an official OpenAPI document? Contribute it →

For Agents

Fetch and publish versioned prompt templates, move release labels, attach metadata and scores to logged LLM requests, read traces, and run evaluation pipelines in PromptLayer.

Use for: Get the production version of the 'support-reply' prompt template, Publish a new version of my summarization prompt, Move the prod label to version 7 of this prompt, Attach a quality score of 0.8 to request 12345

Not supported: Does not run model completions, host models, or fine-tune them. Use for prompt template versioning, request tracking, and evaluation in PromptLayer only.

Jentic publishes the only available OpenAPI specification for PromptLayer API, keeping it validated and agent-ready. The PromptLayer API versions and serves prompt templates and records what happens to LLM requests after they run. It publishes new prompt template versions, moves release labels between versions, and fetches a template by version or label. It attaches metadata, scores, group ids, and the prompt used to a logged request, reads back requests and traces, and builds evaluation pipelines whose columns, runs, and scores are all reachable over the API, alongside read access to datasets and their rows.

Jentic One on Github View OpenAPI Document

Show dimension breakdown

Foundational Compliance

Base layer of spec validity and structural soundness.

Grade: A-Signals: 4

97%

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).

Structural Integrity

Structural correctness score based on schema issues using logarithmic dampening.

Developer Experience & Jentic Compatibility

Clarity, completeness, and ingestion readiness for developers and tooling.

Grade: CSignals: 4

Example Density

How richly the API is illustrated with examples.

100%

Example Validity

Percentage of examples that conform to their schemas.

26%

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.

AI-Readiness & Agent Experience

Semantic breadth, depth, and agent comprehension for AI systems.

Grade: DSignals: 4

33%

Description Coverage

Coverage of descriptions across API elements.

Error Standardization

Coverage of RFC 9457 Problem Details for error responses.

100%

OperationId Quality

Coverage, uniqueness, and casing consistency of operationIds for AI inference.

54%

Summary Coverage

Coverage of summaries across operations/tags/info.

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.

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).

AI Discoverability

Findability, semantic richness, and reasoning readiness.

Grade: BSignals: 1

64%

Descriptive Richness

Clarity and depth of descriptions across API elements.

Connect the PromptLayer API to your agent

Capabilities

Start building with PromptLayer API

Use Cases

Key Endpoints

Why Jentic?

Related APIs

FAQs

PromptLayer API

Jentic AI Readiness Score

PromptLayer API

Foundational Compliance

Lint Results

Resolution Completeness

Specification Validity

Structural Integrity

Developer Experience & Jentic Compatibility

Example Density

Example Validity

Response Coverage

Tooling Readiness

AI-Readiness & Agent Experience

Description Coverage

Error Standardization

OperationId Quality

Summary Coverage

Agent Usability

Complexity Comfort

Security

Authentication Strength

AI Discoverability

Descriptive Richness