Install Jentic One Beta

Connect the Black Forest Labs FLUX API to your agent

Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Black Forest Labs FLUX 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%2Fblackforestlabs.ai%2Fblackforestlabs" | 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%2Fblackforestlabs.ai%2Fblackforestlabs" | 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 Black Forest Labs FLUX API.

Generate images from text prompts with FLUX models

Edit existing images with FLUX Kontext models

Inpaint a masked region and outpaint to expand an image

Poll for asynchronous generation results

Check the remaining API credits on an account

GET STARTED

Start building with Black Forest Labs FLUX API

Explore with Jentic One

View OpenAPI Document

Use Cases

Patterns agents use Black Forest Labs FLUX API for, with concrete tasks.

★ AI agent image generation

An AI agent producing visual content can generate images from prompts and retrieve them once ready. Through Jentic the agent searches by intent and the API key never enters its prompt context, so a content assistant can call FLUX directly and hand back finished images.

Use Jentic to search 'generate an image with FLUX', call POST /v1/flux-2-pro with a prompt, then poll GET /v1/get_result until the image is ready.

Prompt-based image editing

A creative tool edits an existing image from a natural-language instruction using the Kontext models. The agent submits the source image with the instruction and polls for the edited output, keeping the edit loop fully programmatic.

Submit an edit via POST /v1/flux-kontext-pro with a source image and instruction, then poll GET /v1/get_result for the edited image.

Inpaint and outpaint

An image pipeline fills a masked region or extends the canvas beyond the original frame. The agent calls the fill or expand model and retrieves the result, automating retouching and canvas extension without a manual editor.

Fill a masked area via POST /v1/flux-pro-1.0-fill, or extend the canvas via POST /v1/flux-pro-1.0-expand, then retrieve the output with GET /v1/get_result.

Credit and fine-tune management

An operations agent monitors an account's remaining credits and the fine-tuned models available to it. This keeps generation workflows from stalling on an empty balance and tracks which custom models are in use.

Call GET /v1/credits to check the remaining balance and GET /v1/my_finetunes to list the account's fine-tuned models.

Key Endpoints

17 endpoints — the black forest labs flux api generates and edits images with the flux.

METHOD

PATH

DESCRIPTION

POST

/v1/flux-2-pro

Generate an image with FLUX.2 Pro

POST

/v1/flux-kontext-pro

Edit an image with FLUX Kontext Pro

POST

/v1/flux-pro-1.0-fill

Inpaint a masked region

POST

/v1/flux-pro-1.0-expand

Outpaint to expand an image

GET

/v1/get_result

Poll for a generation result

GET

/v1/credits

Check remaining credits

GET

/v1/my_finetunes

List fine-tuned models

POST

/v1/delete_finetune

Delete a fine-tune

POST

/v1/flux-2-pro

Generate an image with FLUX.2 Pro

POST

/v1/flux-kontext-pro

Edit an image with FLUX Kontext Pro

POST

/v1/flux-pro-1.0-fill

Inpaint a masked region

POST

/v1/flux-pro-1.0-expand

Outpaint to expand an image

GET

/v1/get_result

Poll for a generation result

GET

/v1/credits

Check remaining credits

GET

/v1/my_finetunes

List fine-tuned models

POST

/v1/delete_finetune

Delete a fine-tune

Why Jentic?

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

Setup

Wiring the Black Forest Labs FLUX API by hand means sending your x-key header to api.bfl.ai, submitting a generation task, and polling for the result yourself. Through Jentic you install once, import FLUX from the API Directory, store the key once, and your agent calls it.

Permission scoping

FLUX addresses each model as its own operation and carries the prompt in the request body, so a rule bounds which operations your agent may call. You can allow only the generation and result-polling operations, so deleting a fine-tune is not included unless you add it.

Credential isolation

Your Black Forest Labs 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 'generate an image' or 'edit an image', and Jentic returns the matching FLUX operation with its input schema so the agent calls the right endpoint without browsing the reference docs.

FAQs

Specific to using Black Forest Labs FLUX API through Jentic.

Is there a Black Forest Labs FLUX MCP server?

You don't need an MCP server to give your agent FLUX. Jentic connects it directly from the API Directory: import it, store your key once, and your agent calls the generation, edit, and result operations. That keeps your agent's context free of an extra server's tool definitions.

What authentication does the Black Forest Labs FLUX API use?

It authenticates with an API key sent in the x-key header, per its OpenAPI spec. Through Jentic the key is stored encrypted by your own instance and injected at call time, so it never reaches the agent's context.

Can I generate images asynchronously with the Black Forest Labs FLUX API?

Yes. Generation is asynchronous: POST /v1/flux-2-pro (or another FLUX model) submits a task, and GET /v1/get_result polls until the image is ready to download.

What are the rate limits for the Black Forest Labs FLUX API?

The OpenAPI spec does not specify rate limits. Check the Black Forest Labs documentation at https://docs.bfl.ml for current limits and any active-task caps.

How do I generate an image through Jentic?

Search Jentic for 'generate an image with FLUX', load the input schema, and execute POST /v1/flux-2-pro, then poll GET /v1/get_result. To run it on your own infrastructure, install Jentic One from its GitHub repo.

Can I limit what my agent is allowed to do with the Black Forest Labs FLUX API?

Yes. Write a rule that allows only the generation and result-polling operations, so the agent can create and fetch images and nothing else, and every call it makes is logged. Deleting a fine-tune stays out unless you add that operation.

Black Forest Labs FLUX API

AI ReadinessAI-Aware (B-)62/100

See full scorecard

Official vendor OpenAPI document · agent-readyAI/MLVisionapiKey17 EndpointsREST

For Agents

Generate images from text, edit images with FLUX Kontext, inpaint and outpaint, poll for asynchronous results, check credits, and list fine-tunes with the Black Forest Labs FLUX API.

Use for: Generate an image from a text prompt, Edit an existing image with a prompt, Inpaint a masked region of an image, Expand an image beyond its original borders

Not supported: Does not host generated images long term, generate video, or manage billing. Use for FLUX image generation, editing, and result retrieval only.

The Black Forest Labs FLUX API generates and edits images with the FLUX.2, FLUX.1, and Kontext model families. It runs text-to-image generation, prompt-based image editing, inpainting, and outpainting, all as asynchronous tasks that you submit and then poll for a result. It also reports remaining credits and lists the fine-tuned models on an account.

Jentic One on Github View OpenAPI Document

Show dimension breakdown

Foundational Compliance

Base layer of spec validity and structural soundness.

Grade: A-Signals: 4

92%

Lint Results

Aggregated quality score from linter diagnostics, weighted by severity.

100%

Resolution Completeness

Percentage of `$ref` references that resolve successfully.

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.

Developer Experience & Jentic Compatibility

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

Grade: B-Signals: 4

Example Density

How richly the API is illustrated with examples.

100%

Example Validity

Percentage of examples that conform to their schemas.

47%

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: D+Signals: 4

43%

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.

50%

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 Black Forest Labs FLUX API to your agent

Capabilities

Start building with Black Forest Labs FLUX API

Use Cases

Key Endpoints

Why Jentic?

Related APIs

FAQs

Black Forest Labs FLUX API

Jentic AI Readiness Score

Black Forest Labs FLUX 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