Install Jentic One Beta

Connect the Morph API to your agent

Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Morph 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%2Fmorphllm.com%2Fmorphllm" | 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%2Fmorphllm.com%2Fmorphllm" | 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 Morph API.

Merge an edit snippet into an original file with the morph-v3-fast or morph-v3-large Apply models

Run an agentic repository search turn with the morph-warp-grep-v1 model

Compress chat history or code context with Compact, tuning compression_ratio and preserve_recent

Classify text with a Reflex model in realtime, as an inline batch, or as an asynchronous batch

GET STARTED

Start building with Morph API

Explore with Jentic One

View OpenAPI Document

Use Cases

Patterns agents use Morph API for, with concrete tasks.

★ Coding agent that applies edits through Morph

A coding agent that drafts changes with a large model needs those changes merged into the real file without rewriting it by hand. Through Jentic the agent searches for 'apply a code edit to a file', loads the Morph chat completions operation with its input schema, and sends the original file and edit snippet with model morph-v3-fast. Your own Jentic One instance attaches the sk- API key at call time, and the merged file comes back in the completion.

Call POST /v1/chat/completions with model morph-v3-fast and messages containing the original file and the edit snippet, then write the returned content back to the file

Keeping long agent sessions inside the context window

Long-running agents accumulate chat history and code that crowd the context window. Compact takes string input or a message array plus a query, and removes lines irrelevant to that query while every line it keeps stays identical to the original. Options such as compression_ratio, preserve_recent, and include_line_ranges control how aggressively it prunes.

Call POST /v1/compact with the session messages, the current task as query, and a compression_ratio, then continue the session with the compacted messages

Low-latency text classification with Reflex

Routing support tickets or moderating messages needs a label fast. Reflex predict runs a default or trained classifier over text and returns a label, with several classifiers runnable over one shared prefill by passing a models array. For larger jobs, rows can be sent as an inline batch or uploaded as an asynchronous batch and polled by batch_id.

Upload rows with POST /v1/reflex/asynchronous_batches/upload using an Idempotency-Key header, poll GET /v1/reflex/asynchronous_batches/{batch_id}, then fetch GET /v1/reflex/asynchronous_batches/{batch_id}/results

Typed decisions without generated text

Agent loops often need a yes/no or multiple-choice decision rather than prose. Singleshot reads a state once and answers named questions of type noul, choice, or score, returning a probability distribution for each, with a confidence for choice and score questions. Code can branch directly on the result.

Call POST /v1/singleshot with the agent's latest state, a model, and questions including a noul question 'is the task complete' and a choice question for the next step

Key Endpoints

19 endpoints — the morph api runs fast, specialised models for coding agents through openai- and anthropic-compatible endpoints.

METHOD

PATH

DESCRIPTION

POST

/v1/chat/completions

Apply an edit or run Warp Grep

POST

/v1/compact

Compact context

POST

/v1/reflex/predict

Classify text in realtime

POST

/v1/singleshot

Answer typed questions about a state

POST

/v1/messages

Create an Anthropic-shaped message

POST

/v1/fine_tuning/jobs

Train a Reflex

GET

/v1/models

List models

POST

/v1/chat/completions

Apply an edit or run Warp Grep

POST

/v1/compact

Compact context

POST

/v1/reflex/predict

Classify text in realtime

POST

/v1/singleshot

Answer typed questions about a state

POST

/v1/messages

Create an Anthropic-shaped message

POST

/v1/fine_tuning/jobs

Train a Reflex

GET

/v1/models

List models

Why Jentic?

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

Setup

Wiring Morph by hand means sending an sk- key as a bearer token, picking the right model id per request shape on the shared chat completions endpoint, and adding an Idempotency-Key header for async batch uploads. Through Jentic you install once, import Morph from the API Directory, store the key once, and your agent calls it.

Permission scoping

Fine-tuning jobs and models are addressed by id in the URL path, as in /v1/fine_tuning/jobs/{job_id}, so a rule can hold your agent to one job, while inference calls carry their inputs in the body and are bounded by operation. You choose the operations it may call, so destructive ones like deleting a model or a job are not included unless you add them.

Credential isolation

Your Morph 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 'apply a code edit to a file' or 'classify text', and Jentic returns the matching Morph operation with its input schema, including the model enum and required fields.

FAQs

Specific to using Morph API through Jentic.

What authentication does the Morph API use?

Per its OpenAPI spec, the Morph API uses HTTP bearer authentication with a Morph API key passed as Authorization: Bearer sk-..., created in the Morph dashboard. Through Jentic the key is stored by your own Jentic One instance and attached at call time, so the agent never handles it.

Can I use the Morph API as a drop-in for OpenAI or Anthropic clients?

The spec describes POST /v1/chat/completions as OpenAI-compatible and POST /v1/messages as Anthropic-shaped, so existing client code can point at https://api.morphllm.com with a Morph model id. The chat completions endpoint selects the request shape by model: Apply models such as morph-v3-fast and morph-v3-large, or morph-warp-grep-v1 for repository search.

What are the rate limits for the Morph API?

The OpenAPI spec does not specify rate limits. Check the Morph site at https://www.morphllm.com for current limits on your plan.

Is there a Morph MCP server?

You don't need an MCP server to give your agent the Morph API. Jentic connects it directly from the API Directory: import it, store your API key once, and your agent calls Apply, Compact, and Reflex on demand without another server's tool definitions in its context.

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

Yes. Write a rule that allows only POST /v1/chat/completions and POST /v1/compact, so a coding agent can apply edits and compress context but cannot start fine-tuning jobs or delete models. Every call it makes is logged.

How do I compress agent context through Jentic?

Search Jentic for 'compress agent context', load the POST /v1/compact operation, and execute it with input or messages and a query describing the current task. The response keeps only relevant lines, each identical to the original. To run it on your own infrastructure, install Jentic One from its GitHub repo.

Morphllm Morph API

AI ReadinessAI-Aware (A-)74/100

See full scorecard

Official vendor OpenAPI document · agent-readyAI/MLLanguage Modelsbearer19 EndpointsREST

For Agents

Apply code edits to files with Fast Apply, compress agent context with Compact, classify text with Reflex, answer typed questions with Singleshot, and train custom classifiers through Morph.

Use for: Apply this edit snippet to my source file, I need to compress my agent's conversation history before the next turn, Classify this support message with a Reflex model, Search the repository for where authentication is handled

Not supported: Does not host repositories, run code, or generate images. Use for Morph code-edit merging, context compression, classification, and typed decisions only.

The Morph API runs fast, specialised models for coding agents through OpenAI- and Anthropic-compatible endpoints. Fast Apply models such as morph-v3-fast and morph-v3-large merge an edit snippet into an original file, and the morph-warp-grep-v1 model runs an agentic repository search, both on the chat completions endpoint. Compact prunes irrelevant lines from chat history and code context while keeping every surviving line byte-for-byte identical. Reflex classifies text in realtime, inline, or as asynchronous batches, Singleshot returns probability distributions for typed yes/no, choice, and score questions about a state, and fine-tuning jobs train custom Reflex classifiers.

Jentic One on Github View OpenAPI Document

Show dimension breakdown

Foundational Compliance

Base layer of spec validity and structural soundness.

Grade: ASignals: 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).

50%

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: ASignals: 4

62%

Example Density

How richly the API is illustrated with examples.

100%

Example Validity

Percentage of examples that conform to their schemas.

75%

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: C+Signals: 4

85%

Description Coverage

Coverage of descriptions across API elements.

Error Standardization

Coverage of RFC 9457 Problem Details for error responses.

95%

OperationId Quality

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

53%

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: B-Signals: 1

60%

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: ASignals: 1

82%

Descriptive Richness

Clarity and depth of descriptions across API elements.

Connect the Morph API to your agent

Capabilities

Start building with Morph API

Use Cases

Key Endpoints

Why Jentic?

Related APIs

FAQs

Morphllm Morph API

Jentic AI Readiness Score

Morph 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