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Bfl Api

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calesthio
bfl-api

BFL FLUX API integration guide covering endpoints, async polling patterns, rate limiting, error handling, webhooks, and regional endpoints with Python and TypeScript code examples.

Overview

Publishercalesthio
RepositoryOpenMontage
Skill namebfl-api
Stars
67K
Forks
8.5K
Bundled files
9
LicenseAGPL-3.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 9 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by calesthio on GitHub. Read the source before you install it.

Installation

Install the Bfl Api AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/calesthio/OpenMontage/tree/main/.agents/skills/bfl-api
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/calesthio/OpenMontage.git /tmp/OpenMontage
mkdir -p .claude/skills
cp -r /tmp/OpenMontage/.agents/skills/bfl-api .claude/skills/bfl-api
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Bfl Api in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Bfl Api on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Bfl Api is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

BFL API Integration Guide

Use this skill when integrating BFL FLUX APIs into applications for image generation, editing, and processing.

First: Check API Key

Before generating images, verify your API key is set:

bash
echo $BFL_API_KEY

If empty or you see "Not authenticated" errors, see API Key Setup below.

Important: Image URLs Expire in 10 Minutes

Result URLs from the API are temporary. Download images immediately after generation completes - do not store or cache the URLs themselves.

When to Use

  • Setting up BFL API client
  • Implementing async polling patterns
  • Handling rate limits and errors
  • Configuring webhooks for production
  • Selecting regional endpoints
  • Building production-ready integrations

Quick Reference

Base Endpoints

RegionEndpointUse Case
Globalhttps://api.bfl.aiDefault, automatic failover
EUhttps://api.eu.bfl.aiGDPR compliance
UShttps://api.us.bfl.aiUS data residency

Model Endpoints & Pricing

Credit pricing: 1 credit = $0.01 USD. FLUX.2 uses megapixel-based pricing (cost scales with resolution).

FLUX.2 Models
ModelPath1st MP+MP1MP T2I1MP I2IBest For
FLUX.2 [klein] 4B/v1/flux-2-klein-4b1.4c0.1c$0.014$0.015Real-time, high volume
FLUX.2 [klein] 9B/v1/flux-2-klein-9b1.5c0.2c$0.015$0.017Balanced quality/speed
FLUX.2 [pro]/v1/flux-2-pro3c1.5c$0.03$0.045Production, fast turnaround
FLUX.2 [max]/v1/flux-2-max7c3c$0.07$0.10Maximum quality
FLUX.2 [flex]/v1/flux-2-flex5c5c$0.05$0.10Typography, adjustable controls
FLUX.2 [dev]---FreeFreeLocal development (non-commercial)

Pricing formula: (firstMP + (outputMP-1) * mpPrice) + (inputMP * mpPrice) in cents

FLUX.1 Models
ModelPathPrice/ImageBest For
FLUX.1 Kontext [pro]/v1/flux-kontext$0.04Image editing with context
FLUX.1 Kontext [max]/v1/flux-kontext-max$0.08Max quality editing
FLUX1.1 [pro]/v1/flux-pro-1.1$0.04Standard T2I, fast & reliable
FLUX1.1 [pro] Ultra/v1/flux-pro-1.1-ultra$0.06Ultra high-resolution
FLUX1.1 [pro] Raw/v1/flux-pro-1.1-raw$0.06Candid photography feel
FLUX.1 Fill [pro]/v1/flux-pro-1.0-fill$0.05Inpainting

Tip: All FLUX.2 models support image editing via the input_image parameter - no separate editing endpoint needed. Use bfl.ai/pricing calculator for exact costs at different resolutions.

Image Input for Editing

Preferred: Use URLs directly - simpler and more convenient than base64.

Single image editing:

bash
curl -X POST "https://api.bfl.ai/v1/flux-2-pro" \
  -H "x-key: $BFL_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "Change the background to a sunset",
    "input_image": "https://example.com/photo.jpg"
  }'

Multi-reference editing:

bash
curl -X POST "https://api.bfl.ai/v1/flux-2-pro" \
  -H "x-key: $BFL_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "The person from image 1 in the environment from image 2",
    "input_image": "https://example.com/person.jpg",
    "input_image_2": "https://example.com/background.jpg"
  }'

The API fetches URLs automatically. Both URL and base64 work, but URLs are recommended when available.

Multi-Reference I2I

FLUX.2 models support multiple input images for combining elements, style transfer, and character consistency:

ModelMax References
FLUX.2 [klein]4 images
FLUX.2 [pro/max/flex]8 images

Parameters: input_image, input_image_2, input_image_3, ... input_image_8

Prompt pattern: Reference images by number in your prompt:

  • "The subject from image 1 in the environment from image 2"
  • "Apply the style of image 2 to the scene in image 1"
  • "The person from image 1 wearing the outfit from image 2, in the pose from image 3"

For detailed multi-reference patterns (character consistency, style transfer, pose guidance), see flux-best-practices/rules/multi-reference-editing.md

Rate Limits

TierConcurrent Requests
Standard (most endpoints)24

Polling vs Webhooks

ApproachUse When
PollingScripts, CLI tools, local development, single requests, simple integrations
WebhooksProduction apps, high volume, server-to-server, when you need immediate notification

Start with polling - it's simpler and works everywhere. Switch to webhooks when you need to scale or want event-driven architecture.

Key Behaviors

  • Polling: Response includes polling_url for async results
  • URL Expiration: Result URLs expire after 10 minutes
  • Webhook Support: Configure webhook_url for production workloads

API Key Setup

Required: The BFL_API_KEY environment variable must be set before using the API.

Quick Check

bash
echo $BFL_API_KEY

If Not Set

  1. Get a key: Go to https://dashboard.bfl.ai/get-started → Click "Create Key" → Select organization
  2. Save to .env (recommended for persistence):
    bash
    echo 'BFL_API_KEY=bfl_your_key_here' >> .env
    echo '.env' >> .gitignore  # Don't commit secrets

See references/api-key-setup.md for detailed setup instructions.

Authentication

bash
x-key: YOUR_API_KEY

Basic Request Flow

1. POST request to model endpoint
   └─> Response: { "polling_url": "..." }

2. GET polling_url (repeat until complete)
   └─> Response: { "status": "Pending" | "Ready" | "Error", ... }

3. When Ready, download result URL
   └─> URL expires in 10 minutes - download immediately

Related

  • Prompting best practices (T2I, I2I, typography, colors): see the flux-best-practices skill
  • Multi-reference patterns (character consistency, style transfer, pose guidance): see flux-best-practices/rules/multi-reference-editing.md

References

Code Examples

Note: cURL examples are preferred by default as they work universally without requiring Python or Node.js. Use language-specific clients when building production applications.

Quick Start Example

1. Submit Generation Request

bash
curl -s -X POST "https://api.bfl.ai/v1/flux-2-pro" \
  -H "x-key: $BFL_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"prompt": "A serene mountain landscape at sunset", "width": 1024, "height": 1024}'

Response:

json
{ "id": "abc123", "polling_url": "https://api.bfl.ai/v1/get_result?id=abc123" }

2. Poll for Result

bash
curl -s "POLLING_URL" -H "x-key: $BFL_API_KEY"

Response when ready:

json
{ "status": "Ready", "result": { "sample": "https://...", "seed": 1234 } }

3. Download Image

bash
curl -s -o output.png "IMAGE_URL"

Tip: Result URLs expire in 10 minutes. Download immediately after status becomes Ready.

4. Multi-Reference Example

Combine elements from multiple images:

bash
curl -s -X POST "https://api.bfl.ai/v1/flux-2-pro" \
  -H "x-key: $BFL_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "The cat from image 1 sitting in the cozy room from image 2",
    "input_image": "https://example.com/cat.jpg",
    "input_image_2": "https://example.com/room.jpg",
    "width": 1024,
    "height": 1024
  }'

Reference images by number in your prompt. See Multi-Reference I2I for limits and patterns.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Bfl Api AI skill do?

BFL FLUX API integration guide covering endpoints, async polling patterns, rate limiting, error handling, webhooks, and regional endpoints with Python and TypeScript code examples.

Why use Bfl Api on TypingMind?

Because you install it once and use it with any model. Bfl Api is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Bfl Api in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/calesthio/OpenMontage/tree/main/.agents/skills/bfl-api. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Bfl Api?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Bfl Api?

As many as you like. As long as a model supports skills, you can use Bfl Api with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Bfl Api AI skill free?

Yes. It is published on GitHub by calesthio under the AGPL-3.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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