LLM
Blocks for interacting with Large Language Models including AI conversations, image generation, video creation, and intelligent condition evaluation.
AI Ad Maker Video Creator
What it is
Creates an AI‑generated 30‑second advert (text + images)
How it works
This block generates video advertisements by combining AI-generated visuals with narrated scripts. Line breaks in the script create scene transitions. Choose from various voices and background music options.
Optionally provide your own images via input_media_urls, or let the AI generate visuals. The finished video is returned as a URL for download or embedding.
Inputs
script
Short advertising copy. Line breaks create new scenes.
str
Yes
ratio
Aspect ratio
str
No
target_duration
Desired length of the ad in seconds.
int
No
voice
Narration voice
"Lily" | "Daniel" | "Brian" | "Jessica" | "Charlotte" | "Callum" | "Eva"
No
background_music
Background track
"Observer" | "Futuristic Beat" | "Science Documentary" | "Hotline" | "Bladerunner 2049" | "A Future" | "Elysian Embers" | "Inspiring Cinematic" | "Bladerunner Remix" | "Izzamuzzic" | "Nas" | "Paris - Else" | "Snowfall" | "Burlesque" | "Corny Candy" | "Highway Nocturne" | "I Don't Think So" | "Losing Your Marbles" | "Refresher" | "Tourist" | "Twin Tyches" | "Dont Stop Me Abstract Future Bass"
No
input_media_urls
List of image URLs to feature in the advert.
List[str]
No
use_only_provided_media
Restrict visuals to supplied images only.
bool
No
Outputs
error
Error message if the operation failed
str
video_url
URL of the finished advert
str
Possible use case
Product Marketing: Create quick promotional videos for products or services.
Social Media Ads: Generate short video ads for social media advertising campaigns.
Content Automation: Automatically create video ads from product descriptions or scripts.
AI Condition
What it is
Uses AI to evaluate natural language conditions and provide conditional outputs
How it works
This block uses an LLM to evaluate natural language conditions that can't be expressed with simple comparisons. Describe the condition in plain English, and the AI determines if it's true or false for the given input.
The result routes data to yes_output or no_output, enabling intelligent branching based on meaning, sentiment, or complex criteria.
Inputs
input_value
The input value to evaluate with the AI condition
Input Value
Yes
condition
A plaintext English description of the condition to evaluate
str
Yes
yes_value
(Optional) Value to output if the condition is true. If not provided, input_value will be used.
Yes Value
No
no_value
(Optional) Value to output if the condition is false. If not provided, input_value will be used.
No Value
No
model
The language model to use for evaluating the condition.
"o3-mini" | "o3-2025-04-16" | "o1" | "o1-mini" | "gpt-5.2-2025-12-11" | "gpt-5.1-2025-11-13" | "gpt-5-2025-08-07" | "gpt-5-mini-2025-08-07" | "gpt-5-nano-2025-08-07" | "gpt-5-chat-latest" | "gpt-4.1-2025-04-14" | "gpt-4.1-mini-2025-04-14" | "gpt-4o-mini" | "gpt-4o" | "gpt-4-turbo" | "gpt-3.5-turbo" | "claude-opus-4-1-20250805" | "claude-opus-4-20250514" | "claude-sonnet-4-20250514" | "claude-opus-4-5-20251101" | "claude-sonnet-4-5-20250929" | "claude-haiku-4-5-20251001" | "claude-3-7-sonnet-20250219" | "claude-3-haiku-20240307" | "Qwen/Qwen2.5-72B-Instruct-Turbo" | "nvidia/llama-3.1-nemotron-70b-instruct" | "meta-llama/Llama-3.3-70B-Instruct-Turbo" | "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" | "meta-llama/Llama-3.2-3B-Instruct-Turbo" | "llama-3.3-70b-versatile" | "llama-3.1-8b-instant" | "llama3.3" | "llama3.2" | "llama3" | "llama3.1:405b" | "dolphin-mistral:latest" | "openai/gpt-oss-120b" | "openai/gpt-oss-20b" | "google/gemini-2.5-pro-preview-03-25" | "google/gemini-3-pro-preview" | "google/gemini-2.5-flash" | "google/gemini-2.0-flash-001" | "google/gemini-2.5-flash-lite-preview-06-17" | "google/gemini-2.0-flash-lite-001" | "mistralai/mistral-nemo" | "cohere/command-r-08-2024" | "cohere/command-r-plus-08-2024" | "deepseek/deepseek-chat" | "deepseek/deepseek-r1-0528" | "perplexity/sonar" | "perplexity/sonar-pro" | "perplexity/sonar-deep-research" | "nousresearch/hermes-3-llama-3.1-405b" | "nousresearch/hermes-3-llama-3.1-70b" | "amazon/nova-lite-v1" | "amazon/nova-micro-v1" | "amazon/nova-pro-v1" | "microsoft/wizardlm-2-8x22b" | "gryphe/mythomax-l2-13b" | "meta-llama/llama-4-scout" | "meta-llama/llama-4-maverick" | "x-ai/grok-4" | "x-ai/grok-4-fast" | "x-ai/grok-4.1-fast" | "x-ai/grok-code-fast-1" | "moonshotai/kimi-k2" | "qwen/qwen3-235b-a22b-thinking-2507" | "qwen/qwen3-coder" | "Llama-4-Scout-17B-16E-Instruct-FP8" | "Llama-4-Maverick-17B-128E-Instruct-FP8" | "Llama-3.3-8B-Instruct" | "Llama-3.3-70B-Instruct" | "v0-1.5-md" | "v0-1.5-lg" | "v0-1.0-md"
No
Outputs
error
Error message if the AI evaluation is uncertain or fails
str
result
The result of the AI condition evaluation (True or False)
bool
yes_output
The output value if the condition is true
Yes Output
no_output
The output value if the condition is false
No Output
Possible use case
Sentiment Routing: Route messages differently based on whether they express frustration or satisfaction.
Content Moderation: Check if content contains inappropriate material or policy violations.
Intent Detection: Determine if a user message is a question, complaint, or request.
AI Conversation
What it is
A block that facilitates multi-turn conversations with a Large Language Model (LLM), maintaining context across message exchanges.
How it works
The block sends the entire conversation history to the chosen LLM, including system messages, user inputs, and previous responses. It then returns the LLM's response as the next part of the conversation.
Inputs
prompt
The prompt to send to the language model.
str
No
messages
List of messages in the conversation.
List[Any]
Yes
model
The language model to use for the conversation.
"o3-mini" | "o3-2025-04-16" | "o1" | "o1-mini" | "gpt-5.2-2025-12-11" | "gpt-5.1-2025-11-13" | "gpt-5-2025-08-07" | "gpt-5-mini-2025-08-07" | "gpt-5-nano-2025-08-07" | "gpt-5-chat-latest" | "gpt-4.1-2025-04-14" | "gpt-4.1-mini-2025-04-14" | "gpt-4o-mini" | "gpt-4o" | "gpt-4-turbo" | "gpt-3.5-turbo" | "claude-opus-4-1-20250805" | "claude-opus-4-20250514" | "claude-sonnet-4-20250514" | "claude-opus-4-5-20251101" | "claude-sonnet-4-5-20250929" | "claude-haiku-4-5-20251001" | "claude-3-7-sonnet-20250219" | "claude-3-haiku-20240307" | "Qwen/Qwen2.5-72B-Instruct-Turbo" | "nvidia/llama-3.1-nemotron-70b-instruct" | "meta-llama/Llama-3.3-70B-Instruct-Turbo" | "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" | "meta-llama/Llama-3.2-3B-Instruct-Turbo" | "llama-3.3-70b-versatile" | "llama-3.1-8b-instant" | "llama3.3" | "llama3.2" | "llama3" | "llama3.1:405b" | "dolphin-mistral:latest" | "openai/gpt-oss-120b" | "openai/gpt-oss-20b" | "google/gemini-2.5-pro-preview-03-25" | "google/gemini-3-pro-preview" | "google/gemini-2.5-flash" | "google/gemini-2.0-flash-001" | "google/gemini-2.5-flash-lite-preview-06-17" | "google/gemini-2.0-flash-lite-001" | "mistralai/mistral-nemo" | "cohere/command-r-08-2024" | "cohere/command-r-plus-08-2024" | "deepseek/deepseek-chat" | "deepseek/deepseek-r1-0528" | "perplexity/sonar" | "perplexity/sonar-pro" | "perplexity/sonar-deep-research" | "nousresearch/hermes-3-llama-3.1-405b" | "nousresearch/hermes-3-llama-3.1-70b" | "amazon/nova-lite-v1" | "amazon/nova-micro-v1" | "amazon/nova-pro-v1" | "microsoft/wizardlm-2-8x22b" | "gryphe/mythomax-l2-13b" | "meta-llama/llama-4-scout" | "meta-llama/llama-4-maverick" | "x-ai/grok-4" | "x-ai/grok-4-fast" | "x-ai/grok-4.1-fast" | "x-ai/grok-code-fast-1" | "moonshotai/kimi-k2" | "qwen/qwen3-235b-a22b-thinking-2507" | "qwen/qwen3-coder" | "Llama-4-Scout-17B-16E-Instruct-FP8" | "Llama-4-Maverick-17B-128E-Instruct-FP8" | "Llama-3.3-8B-Instruct" | "Llama-3.3-70B-Instruct" | "v0-1.5-md" | "v0-1.5-lg" | "v0-1.0-md"
No
max_tokens
The maximum number of tokens to generate in the chat completion.
int
No
ollama_host
Ollama host for local models
str
No
Outputs
error
Error message if the operation failed
str
response
The model's response to the conversation.
str
prompt
The prompt sent to the language model.
List[Any]
Possible use case
Creating an interactive chatbot that can maintain context over multiple exchanges, such as a customer service assistant or a language learning companion.
AI Image Customizer
What it is
Generate and edit custom images using Google's Nano-Banana model from Gemini 2.5. Provide a prompt and optional reference images to create or modify images.
How it works
This block uses Google's Gemini Nano-Banana models for image generation and editing. Provide a text prompt describing the desired image, and optionally include reference images for style guidance or modification.
Configure aspect ratio to match your needs and choose between JPG or PNG output format. The generated image is returned as a URL.
Inputs
prompt
A text description of the image you want to generate
str
Yes
model
The AI model to use for image generation and editing
"google/nano-banana" | "google/nano-banana-pro"
No
images
Optional list of input images to reference or modify
List[str (file)]
No
aspect_ratio
Aspect ratio of the generated image
"match_input_image" | "1:1" | "2:3" | "3:2" | "3:4" | "4:3" | "4:5" | "5:4" | "9:16" | "16:9" | "21:9"
No
output_format
Format of the output image
"jpg" | "png"
No
Outputs
error
Error message if the operation failed
str
image_url
URL of the generated image
str (file)
Possible use case
Product Visualization: Generate product images with different backgrounds or settings.
Creative Content: Create unique images for marketing, social media, or presentations.
Image Modification: Edit existing images by providing them as references with modification prompts.
AI Image Editor
What it is
Edit images using BlackForest Labs' Flux Kontext models. Provide a prompt and optional reference image to generate a modified image.
How it works
This block uses BlackForest Labs' Flux Kontext models for context-aware image editing. Describe the desired edit in the prompt, and optionally provide an input image to modify.
Choose between Flux Kontext Pro or Max for different quality/speed tradeoffs. Set a seed for reproducible results across multiple runs.
Inputs
prompt
Text instruction describing the desired edit
str
Yes
input_image
Reference image URI (jpeg, png, gif, webp)
str (file)
No
aspect_ratio
Aspect ratio of the generated image
"match_input_image" | "1:1" | "16:9" | "9:16" | "4:3" | "3:4" | "3:2" | "2:3" | "4:5" | "5:4" | "21:9" | "9:21" | "2:1" | "1:2"
No
seed
Random seed. Set for reproducible generation
int
No
model
Model variant to use
"Flux Kontext Pro" | "Flux Kontext Max"
No
Outputs
error
Error message if the operation failed
str
output_image
URL of the transformed image
str (file)
Possible use case
Style Transfer: Transform images to match different artistic styles or moods.
Object Editing: Add, remove, or modify specific elements in existing images.
Background Changes: Replace or modify image backgrounds while preserving subjects.
AI Image Generator
What it is
Generate images using various AI models through a unified interface
How it works
This block generates images from text prompts using your choice of AI models including Flux and Recraft. Select the image size (square, landscape, portrait, wide, or tall) and visual style to match your needs.
The unified interface allows switching between models without changing your workflow, making it easy to compare results or adapt to different use cases.
Inputs
prompt
Text prompt for image generation
str
Yes
model
The AI model to use for image generation
"Flux 1.1 Pro" | "Flux 1.1 Pro Ultra" | "Recraft v3" | "Stable Diffusion 3.5 Medium" | "Nano Banana Pro"
No
size
Format of the generated image: - Square: Perfect for profile pictures, icons - Landscape: Traditional photo format - Portrait: Vertical photos, portraits - Wide: Cinematic format, desktop wallpapers - Tall: Mobile wallpapers, social media stories
"square" | "landscape" | "portrait" | "wide" | "tall"
No
style
Visual style for the generated image
"any" | "realistic_image" | "realistic_image/b_and_w" | "realistic_image/hdr" | "realistic_image/natural_light" | "realistic_image/studio_portrait" | "realistic_image/enterprise" | "realistic_image/hard_flash" | "realistic_image/motion_blur" | "digital_illustration" | "digital_illustration/pixel_art" | "digital_illustration/hand_drawn" | "digital_illustration/grain" | "digital_illustration/infantile_sketch" | "digital_illustration/2d_art_poster" | "digital_illustration/2d_art_poster_2" | "digital_illustration/handmade_3d" | "digital_illustration/hand_drawn_outline" | "digital_illustration/engraving_color"
No
Outputs
error
Error message if the operation failed
str
image_url
URL of the generated image
str
Possible use case
Content Creation: Generate images for blog posts, articles, or social media.
Marketing Visuals: Create product images, banners, or promotional graphics.
Illustration: Generate custom illustrations for presentations or documents.
AI List Generator
What it is
A block that creates lists of items based on prompts using a Large Language Model (LLM), with optional source data for context.
How it works
The block formulates a prompt based on the given focus or source data, sends it to the chosen LLM, and then processes the response to ensure it's a valid Python list. It can retry multiple times if the initial attempts fail.
Inputs
focus
The focus of the list to generate.
str
No
source_data
The data to generate the list from.
str
No
model
The language model to use for generating the list.
"o3-mini" | "o3-2025-04-16" | "o1" | "o1-mini" | "gpt-5.2-2025-12-11" | "gpt-5.1-2025-11-13" | "gpt-5-2025-08-07" | "gpt-5-mini-2025-08-07" | "gpt-5-nano-2025-08-07" | "gpt-5-chat-latest" | "gpt-4.1-2025-04-14" | "gpt-4.1-mini-2025-04-14" | "gpt-4o-mini" | "gpt-4o" | "gpt-4-turbo" | "gpt-3.5-turbo" | "claude-opus-4-1-20250805" | "claude-opus-4-20250514" | "claude-sonnet-4-20250514" | "claude-opus-4-5-20251101" | "claude-sonnet-4-5-20250929" | "claude-haiku-4-5-20251001" | "claude-3-7-sonnet-20250219" | "claude-3-haiku-20240307" | "Qwen/Qwen2.5-72B-Instruct-Turbo" | "nvidia/llama-3.1-nemotron-70b-instruct" | "meta-llama/Llama-3.3-70B-Instruct-Turbo" | "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" | "meta-llama/Llama-3.2-3B-Instruct-Turbo" | "llama-3.3-70b-versatile" | "llama-3.1-8b-instant" | "llama3.3" | "llama3.2" | "llama3" | "llama3.1:405b" | "dolphin-mistral:latest" | "openai/gpt-oss-120b" | "openai/gpt-oss-20b" | "google/gemini-2.5-pro-preview-03-25" | "google/gemini-3-pro-preview" | "google/gemini-2.5-flash" | "google/gemini-2.0-flash-001" | "google/gemini-2.5-flash-lite-preview-06-17" | "google/gemini-2.0-flash-lite-001" | "mistralai/mistral-nemo" | "cohere/command-r-08-2024" | "cohere/command-r-plus-08-2024" | "deepseek/deepseek-chat" | "deepseek/deepseek-r1-0528" | "perplexity/sonar" | "perplexity/sonar-pro" | "perplexity/sonar-deep-research" | "nousresearch/hermes-3-llama-3.1-405b" | "nousresearch/hermes-3-llama-3.1-70b" | "amazon/nova-lite-v1" | "amazon/nova-micro-v1" | "amazon/nova-pro-v1" | "microsoft/wizardlm-2-8x22b" | "gryphe/mythomax-l2-13b" | "meta-llama/llama-4-scout" | "meta-llama/llama-4-maverick" | "x-ai/grok-4" | "x-ai/grok-4-fast" | "x-ai/grok-4.1-fast" | "x-ai/grok-code-fast-1" | "moonshotai/kimi-k2" | "qwen/qwen3-235b-a22b-thinking-2507" | "qwen/qwen3-coder" | "Llama-4-Scout-17B-16E-Instruct-FP8" | "Llama-4-Maverick-17B-128E-Instruct-FP8" | "Llama-3.3-8B-Instruct" | "Llama-3.3-70B-Instruct" | "v0-1.5-md" | "v0-1.5-lg" | "v0-1.0-md"
No
max_retries
Maximum number of retries for generating a valid list.
int
No
force_json_output
Whether to force the LLM to produce a JSON-only response. This can increase the block's reliability, but may also reduce the quality of the response because it prohibits the LLM from reasoning before providing its JSON response.
bool
No
max_tokens
The maximum number of tokens to generate in the chat completion.
int
No
ollama_host
Ollama host for local models
str
No
Outputs
error
Error message if the operation failed
str
generated_list
The generated list.
List[str]
list_item
Each individual item in the list.
str
prompt
The prompt sent to the language model.
List[Any]
Possible use case
Automatically generating a list of key points or action items from a long meeting transcript or summarizing the main topics discussed in a series of documents.
AI Music Generator
What it is
This block generates music using Meta's MusicGen model on Replicate.
How it works
This block uses Meta's MusicGen model to generate original music from text descriptions. Describe the desired music style, mood, and instruments in the prompt, and the AI creates a matching audio track.
Configure duration, temperature (for variety), and output format. Higher temperature produces more diverse results, while lower values stay closer to typical patterns.
Inputs
prompt
A description of the music you want to generate
str
Yes
music_gen_model_version
Model to use for generation
"stereo-large" | "melody-large" | "large"
No
duration
Duration of the generated audio in seconds
int
No
temperature
Controls the 'conservativeness' of the sampling process. Higher temperature means more diversity
float
No
top_k
Reduces sampling to the k most likely tokens
int
No
top_p
Reduces sampling to tokens with cumulative probability of p. When set to 0 (default), top_k sampling is used
float
No
classifier_free_guidance
Increases the influence of inputs on the output. Higher values produce lower-variance outputs that adhere more closely to inputs
int
No
output_format
Output format for generated audio
"wav" | "mp3"
No
normalization_strategy
Strategy for normalizing audio
"loudness" | "clip" | "peak" | "rms"
No
Outputs
error
Error message if the operation failed
str
result
URL of the generated audio file
str
Possible use case
Video Soundtracks: Generate background music for videos, podcasts, or presentations.
Content Creation: Create original music for social media or marketing content.
Prototyping: Quickly generate music concepts for creative projects.
AI Screenshot To Video Ad
What it is
Turns a screenshot into an engaging, avatar‑narrated video advert.
How it works
This block creates video advertisements featuring a screenshot with AI-generated narration. Provide the screenshot URL and narration script, and the block generates a video with voice and background music.
Choose from various voices and music tracks. The video showcases the screenshot while the AI narrator reads your script.
Inputs
script
Narration that will accompany the screenshot.
str
Yes
screenshot_url
Screenshot or image URL to showcase.
str
Yes
ratio
-
str
No
target_duration
-
int
No
voice
-
"Lily" | "Daniel" | "Brian" | "Jessica" | "Charlotte" | "Callum" | "Eva"
No
background_music
-
"Observer" | "Futuristic Beat" | "Science Documentary" | "Hotline" | "Bladerunner 2049" | "A Future" | "Elysian Embers" | "Inspiring Cinematic" | "Bladerunner Remix" | "Izzamuzzic" | "Nas" | "Paris - Else" | "Snowfall" | "Burlesque" | "Corny Candy" | "Highway Nocturne" | "I Don't Think So" | "Losing Your Marbles" | "Refresher" | "Tourist" | "Twin Tyches" | "Dont Stop Me Abstract Future Bass"
No
Outputs
error
Error message if the operation failed
str
video_url
Rendered video URL
str
Possible use case
App Demos: Create narrated demonstrations of software features from screenshots.
Product Tours: Turn product screenshots into engaging video walkthroughs.
Tutorial Videos: Generate instructional videos from step-by-step screenshots.
AI Shortform Video Creator
What it is
Creates a shortform video using revid.ai
How it works
This block creates short-form videos from scripts using revid.ai. Format scripts with line breaks for scene changes and use [brackets] to guide visual generation. Text outside brackets becomes narration.
Choose video style (stock video, moving images, or AI-generated), voice, background music, and generation presets. The finished video URL is returned for download or sharing.
Inputs
script
1. Use short and punctuated sentences 2. Use linebreaks to create a new clip 3. Text outside of brackets is spoken by the AI, and [text between brackets] will be used to guide the visual generation. For example, [close-up of a cat] will show a close-up of a cat.
str
Yes
ratio
Aspect ratio of the video
str
No
resolution
Resolution of the video
str
No
frame_rate
Frame rate of the video
int
No
generation_preset
Generation preset for visual style - only affects AI-generated visuals
"Default" | "Anime" | "Realist" | "Illustration" | "Sketch Color" | "Sketch B&W" | "Pixar" | "Japanese Ink" | "3D Render" | "Lego" | "Sci-Fi" | "Retro Cartoon" | "Pixel Art" | "Creative" | "Photography" | "Raytraced" | "Environment" | "Fantasy" | "Anime Realism" | "Movie" | "Stylized Illustration" | "Manga" | "DEFAULT"
No
background_music
Background music track
"Observer" | "Futuristic Beat" | "Science Documentary" | "Hotline" | "Bladerunner 2049" | "A Future" | "Elysian Embers" | "Inspiring Cinematic" | "Bladerunner Remix" | "Izzamuzzic" | "Nas" | "Paris - Else" | "Snowfall" | "Burlesque" | "Corny Candy" | "Highway Nocturne" | "I Don't Think So" | "Losing Your Marbles" | "Refresher" | "Tourist" | "Twin Tyches" | "Dont Stop Me Abstract Future Bass"
No
voice
AI voice to use for narration
"Lily" | "Daniel" | "Brian" | "Jessica" | "Charlotte" | "Callum" | "Eva"
No
video_style
Type of visual media to use for the video
"stockVideo" | "movingImage" | "aiVideo"
No
Outputs
error
Error message if the operation failed
str
video_url
The URL of the created video
str
Possible use case
Social Media Content: Create TikTok, Reels, or Shorts content automatically.
Explainer Videos: Generate short educational or promotional videos.
Content Repurposing: Convert written content into engaging short-form video.
AI Structured Response Generator
What it is
A block that generates structured JSON responses using a Large Language Model (LLM), with schema validation and format enforcement.
How it works
The block sends the input prompt to a chosen LLM, along with any system prompts and expected response format. It then processes the LLM's response, ensuring it matches the expected format, and returns the structured data.
Inputs
prompt
The prompt to send to the language model.
str
Yes
expected_format
Expected format of the response. If provided, the response will be validated against this format. The keys should be the expected fields in the response, and the values should be the description of the field.
Dict[str, str]
Yes
list_result
Whether the response should be a list of objects in the expected format.
bool
No
model
The language model to use for answering the prompt.
"o3-mini" | "o3-2025-04-16" | "o1" | "o1-mini" | "gpt-5.2-2025-12-11" | "gpt-5.1-2025-11-13" | "gpt-5-2025-08-07" | "gpt-5-mini-2025-08-07" | "gpt-5-nano-2025-08-07" | "gpt-5-chat-latest" | "gpt-4.1-2025-04-14" | "gpt-4.1-mini-2025-04-14" | "gpt-4o-mini" | "gpt-4o" | "gpt-4-turbo" | "gpt-3.5-turbo" | "claude-opus-4-1-20250805" | "claude-opus-4-20250514" | "claude-sonnet-4-20250514" | "claude-opus-4-5-20251101" | "claude-sonnet-4-5-20250929" | "claude-haiku-4-5-20251001" | "claude-3-7-sonnet-20250219" | "claude-3-haiku-20240307" | "Qwen/Qwen2.5-72B-Instruct-Turbo" | "nvidia/llama-3.1-nemotron-70b-instruct" | "meta-llama/Llama-3.3-70B-Instruct-Turbo" | "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" | "meta-llama/Llama-3.2-3B-Instruct-Turbo" | "llama-3.3-70b-versatile" | "llama-3.1-8b-instant" | "llama3.3" | "llama3.2" | "llama3" | "llama3.1:405b" | "dolphin-mistral:latest" | "openai/gpt-oss-120b" | "openai/gpt-oss-20b" | "google/gemini-2.5-pro-preview-03-25" | "google/gemini-3-pro-preview" | "google/gemini-2.5-flash" | "google/gemini-2.0-flash-001" | "google/gemini-2.5-flash-lite-preview-06-17" | "google/gemini-2.0-flash-lite-001" | "mistralai/mistral-nemo" | "cohere/command-r-08-2024" | "cohere/command-r-plus-08-2024" | "deepseek/deepseek-chat" | "deepseek/deepseek-r1-0528" | "perplexity/sonar" | "perplexity/sonar-pro" | "perplexity/sonar-deep-research" | "nousresearch/hermes-3-llama-3.1-405b" | "nousresearch/hermes-3-llama-3.1-70b" | "amazon/nova-lite-v1" | "amazon/nova-micro-v1" | "amazon/nova-pro-v1" | "microsoft/wizardlm-2-8x22b" | "gryphe/mythomax-l2-13b" | "meta-llama/llama-4-scout" | "meta-llama/llama-4-maverick" | "x-ai/grok-4" | "x-ai/grok-4-fast" | "x-ai/grok-4.1-fast" | "x-ai/grok-code-fast-1" | "moonshotai/kimi-k2" | "qwen/qwen3-235b-a22b-thinking-2507" | "qwen/qwen3-coder" | "Llama-4-Scout-17B-16E-Instruct-FP8" | "Llama-4-Maverick-17B-128E-Instruct-FP8" | "Llama-3.3-8B-Instruct" | "Llama-3.3-70B-Instruct" | "v0-1.5-md" | "v0-1.5-lg" | "v0-1.0-md"
No
force_json_output
Whether to force the LLM to produce a JSON-only response. This can increase the block's reliability, but may also reduce the quality of the response because it prohibits the LLM from reasoning before providing its JSON response.
bool
No
sys_prompt
The system prompt to provide additional context to the model.
str
No
conversation_history
The conversation history to provide context for the prompt.
List[Dict[str, Any]]
No
retry
Number of times to retry the LLM call if the response does not match the expected format.
int
No
prompt_values
Values used to fill in the prompt. The values can be used in the prompt by putting them in a double curly braces, e.g. {{variable_name}}.
Dict[str, str]
No
max_tokens
The maximum number of tokens to generate in the chat completion.
int
No
compress_prompt_to_fit
Whether to compress the prompt to fit within the model's context window.
bool
No
ollama_host
Ollama host for local models
str
No
Outputs
error
Error message if the operation failed
str
response
The response object generated by the language model.
Dict[str, Any] | List[Dict[str, Any]]
prompt
The prompt sent to the language model.
List[Any]
Possible use case
Extracting specific information from unstructured text, such as generating a product description with predefined fields (name, features, price) from a lengthy product review.
AI Text Generator
What it is
A block that produces text responses using a Large Language Model (LLM) based on customizable prompts and system instructions.
How it works
The block sends the input prompt to a chosen LLM, processes the response, and returns the generated text.
Inputs
prompt
The prompt to send to the language model. You can use any of the {keys} from Prompt Values to fill in the prompt with values from the prompt values dictionary by putting them in curly braces.
str
Yes
model
The language model to use for answering the prompt.
"o3-mini" | "o3-2025-04-16" | "o1" | "o1-mini" | "gpt-5.2-2025-12-11" | "gpt-5.1-2025-11-13" | "gpt-5-2025-08-07" | "gpt-5-mini-2025-08-07" | "gpt-5-nano-2025-08-07" | "gpt-5-chat-latest" | "gpt-4.1-2025-04-14" | "gpt-4.1-mini-2025-04-14" | "gpt-4o-mini" | "gpt-4o" | "gpt-4-turbo" | "gpt-3.5-turbo" | "claude-opus-4-1-20250805" | "claude-opus-4-20250514" | "claude-sonnet-4-20250514" | "claude-opus-4-5-20251101" | "claude-sonnet-4-5-20250929" | "claude-haiku-4-5-20251001" | "claude-3-7-sonnet-20250219" | "claude-3-haiku-20240307" | "Qwen/Qwen2.5-72B-Instruct-Turbo" | "nvidia/llama-3.1-nemotron-70b-instruct" | "meta-llama/Llama-3.3-70B-Instruct-Turbo" | "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" | "meta-llama/Llama-3.2-3B-Instruct-Turbo" | "llama-3.3-70b-versatile" | "llama-3.1-8b-instant" | "llama3.3" | "llama3.2" | "llama3" | "llama3.1:405b" | "dolphin-mistral:latest" | "openai/gpt-oss-120b" | "openai/gpt-oss-20b" | "google/gemini-2.5-pro-preview-03-25" | "google/gemini-3-pro-preview" | "google/gemini-2.5-flash" | "google/gemini-2.0-flash-001" | "google/gemini-2.5-flash-lite-preview-06-17" | "google/gemini-2.0-flash-lite-001" | "mistralai/mistral-nemo" | "cohere/command-r-08-2024" | "cohere/command-r-plus-08-2024" | "deepseek/deepseek-chat" | "deepseek/deepseek-r1-0528" | "perplexity/sonar" | "perplexity/sonar-pro" | "perplexity/sonar-deep-research" | "nousresearch/hermes-3-llama-3.1-405b" | "nousresearch/hermes-3-llama-3.1-70b" | "amazon/nova-lite-v1" | "amazon/nova-micro-v1" | "amazon/nova-pro-v1" | "microsoft/wizardlm-2-8x22b" | "gryphe/mythomax-l2-13b" | "meta-llama/llama-4-scout" | "meta-llama/llama-4-maverick" | "x-ai/grok-4" | "x-ai/grok-4-fast" | "x-ai/grok-4.1-fast" | "x-ai/grok-code-fast-1" | "moonshotai/kimi-k2" | "qwen/qwen3-235b-a22b-thinking-2507" | "qwen/qwen3-coder" | "Llama-4-Scout-17B-16E-Instruct-FP8" | "Llama-4-Maverick-17B-128E-Instruct-FP8" | "Llama-3.3-8B-Instruct" | "Llama-3.3-70B-Instruct" | "v0-1.5-md" | "v0-1.5-lg" | "v0-1.0-md"
No
sys_prompt
The system prompt to provide additional context to the model.
str
No
retry
Number of times to retry the LLM call if the response does not match the expected format.
int
No
prompt_values
Values used to fill in the prompt. The values can be used in the prompt by putting them in a double curly braces, e.g. {{variable_name}}.
Dict[str, str]
No
ollama_host
Ollama host for local models
str
No
max_tokens
The maximum number of tokens to generate in the chat completion.
int
No
Outputs
error
Error message if the operation failed
str
response
The response generated by the language model.
str
prompt
The prompt sent to the language model.
List[Any]
Possible use case
Generating creative writing, such as short stories or poetry, based on a given theme or starting sentence.
AI Text Summarizer
What it is
A block that summarizes long texts using a Large Language Model (LLM), with configurable focus topics and summary styles.
How it works
The block splits the input text into smaller chunks, sends each chunk to an LLM for summarization, and then combines these summaries. If the combined summary is still too long, it repeats the process until a concise summary is achieved.
Inputs
text
The text to summarize.
str
Yes
model
The language model to use for summarizing the text.
"o3-mini" | "o3-2025-04-16" | "o1" | "o1-mini" | "gpt-5.2-2025-12-11" | "gpt-5.1-2025-11-13" | "gpt-5-2025-08-07" | "gpt-5-mini-2025-08-07" | "gpt-5-nano-2025-08-07" | "gpt-5-chat-latest" | "gpt-4.1-2025-04-14" | "gpt-4.1-mini-2025-04-14" | "gpt-4o-mini" | "gpt-4o" | "gpt-4-turbo" | "gpt-3.5-turbo" | "claude-opus-4-1-20250805" | "claude-opus-4-20250514" | "claude-sonnet-4-20250514" | "claude-opus-4-5-20251101" | "claude-sonnet-4-5-20250929" | "claude-haiku-4-5-20251001" | "claude-3-7-sonnet-20250219" | "claude-3-haiku-20240307" | "Qwen/Qwen2.5-72B-Instruct-Turbo" | "nvidia/llama-3.1-nemotron-70b-instruct" | "meta-llama/Llama-3.3-70B-Instruct-Turbo" | "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" | "meta-llama/Llama-3.2-3B-Instruct-Turbo" | "llama-3.3-70b-versatile" | "llama-3.1-8b-instant" | "llama3.3" | "llama3.2" | "llama3" | "llama3.1:405b" | "dolphin-mistral:latest" | "openai/gpt-oss-120b" | "openai/gpt-oss-20b" | "google/gemini-2.5-pro-preview-03-25" | "google/gemini-3-pro-preview" | "google/gemini-2.5-flash" | "google/gemini-2.0-flash-001" | "google/gemini-2.5-flash-lite-preview-06-17" | "google/gemini-2.0-flash-lite-001" | "mistralai/mistral-nemo" | "cohere/command-r-08-2024" | "cohere/command-r-plus-08-2024" | "deepseek/deepseek-chat" | "deepseek/deepseek-r1-0528" | "perplexity/sonar" | "perplexity/sonar-pro" | "perplexity/sonar-deep-research" | "nousresearch/hermes-3-llama-3.1-405b" | "nousresearch/hermes-3-llama-3.1-70b" | "amazon/nova-lite-v1" | "amazon/nova-micro-v1" | "amazon/nova-pro-v1" | "microsoft/wizardlm-2-8x22b" | "gryphe/mythomax-l2-13b" | "meta-llama/llama-4-scout" | "meta-llama/llama-4-maverick" | "x-ai/grok-4" | "x-ai/grok-4-fast" | "x-ai/grok-4.1-fast" | "x-ai/grok-code-fast-1" | "moonshotai/kimi-k2" | "qwen/qwen3-235b-a22b-thinking-2507" | "qwen/qwen3-coder" | "Llama-4-Scout-17B-16E-Instruct-FP8" | "Llama-4-Maverick-17B-128E-Instruct-FP8" | "Llama-3.3-8B-Instruct" | "Llama-3.3-70B-Instruct" | "v0-1.5-md" | "v0-1.5-lg" | "v0-1.0-md"
No
focus
The topic to focus on in the summary
str
No
style
The style of the summary to generate.
"concise" | "detailed" | "bullet points" | "numbered list"
No
max_tokens
The maximum number of tokens to generate in the chat completion.
int
No
chunk_overlap
The number of overlapping tokens between chunks to maintain context.
int
No
ollama_host
Ollama host for local models
str
No
Outputs
error
Error message if the operation failed
str
summary
The final summary of the text.
str
prompt
The prompt sent to the language model.
List[Any]
Possible use case
Summarizing lengthy research papers or articles to quickly grasp the main points and key findings.
Code Generation
What it is
Generate or refactor code using OpenAI's Codex (Responses API).
How it works
This block uses OpenAI's Codex model optimized for code generation and refactoring. Provide a prompt describing the code you need, and optionally a system prompt with coding guidelines or context.
Configure reasoning_effort to control how much the model "thinks" before responding. The block returns generated code along with any reasoning the model produced.
Inputs
prompt
Primary coding request passed to the Codex model.
str
Yes
system_prompt
Optional instructions injected via the Responses API instructions field.
str
No
model
Codex-optimized model served via the Responses API.
"gpt-5.1-codex"
No
reasoning_effort
Controls the Responses API reasoning budget. Select 'none' to skip reasoning configs.
"none" | "low" | "medium" | "high"
No
max_output_tokens
Upper bound for generated tokens (hard limit 128,000). Leave blank to let OpenAI decide.
int
No
Outputs
error
Error message if the operation failed
str
response
Code-focused response returned by the Codex model.
str
reasoning
Reasoning summary returned by the model, if available.
str
response_id
ID of the Responses API call for auditing/debugging.
str
Possible use case
Code Automation: Generate boilerplate code, functions, or entire modules from descriptions.
Refactoring: Transform existing code to follow different patterns or conventions.
Code Completion: Fill in missing implementation details based on signatures or comments.
Create Talking Avatar Video
What it is
This block integrates with D-ID to create video clips and retrieve their URLs.
How it works
The block sends a request to the D-ID API with your specified parameters. It then regularly checks the status of the video creation process until it's complete or an error occurs.
Inputs
script_input
The text input for the script
str
Yes
provider
The voice provider to use
"microsoft" | "elevenlabs" | "amazon"
No
presenter_id
The presenter ID to use
str
No
driver_id
The driver ID to use
str
No
result_format
The desired result format
"mp4" | "gif" | "wav"
No
crop_type
The crop type for the presenter
"wide" | "square" | "vertical"
No
subtitles
Whether to include subtitles
bool
No
ssml
Whether the input is SSML
bool
No
max_polling_attempts
Maximum number of polling attempts
int
No
polling_interval
Interval between polling attempts in seconds
int
No
Outputs
error
Error message if the operation failed
str
video_url
The URL of the created video
str
Possible use case
A marketing team could use this block to create engaging video content for social media. They could input a script promoting a new product, select a friendly-looking avatar, and generate a video that explains the product's features in an appealing way.
Ideogram Model
What it is
This block runs Ideogram models with both simple and advanced settings.
How it works
This block generates images using Ideogram's models (V1, V2, V3) which excel at rendering text within images. Configure aspect ratio, style type, and optionally enable MagicPrompt for enhanced results.
Advanced options include upscaling, custom color palettes, and negative prompts to exclude unwanted elements. Set a seed for reproducible generation.
Inputs
prompt
Text prompt for image generation
str
Yes
ideogram_model_name
The name of the Image Generation Model, e.g., V_3
"V_3" | "V_2" | "V_1" | "V_1_TURBO" | "V_2_TURBO"
No
aspect_ratio
Aspect ratio for the generated image
"ASPECT_10_16" | "ASPECT_16_10" | "ASPECT_9_16" | "ASPECT_16_9" | "ASPECT_3_2" | "ASPECT_2_3" | "ASPECT_4_3" | "ASPECT_3_4" | "ASPECT_1_1" | "ASPECT_1_3" | "ASPECT_3_1"
No
upscale
Upscale the generated image
"AI Upscale" | "No Upscale"
No
magic_prompt_option
Whether to use MagicPrompt for enhancing the request
"AUTO" | "ON" | "OFF"
No
seed
Random seed. Set for reproducible generation
int
No
style_type
Style type to apply, applicable for V_2 and above
"AUTO" | "GENERAL" | "REALISTIC" | "DESIGN" | "RENDER_3D" | "ANIME"
No
negative_prompt
Description of what to exclude from the image
str
No
color_palette_name
Color palette preset name, choose 'None' to skip
"NONE" | "EMBER" | "FRESH" | "JUNGLE" | "MAGIC" | "MELON" | "MOSAIC" | "PASTEL" | "ULTRAMARINE"
No
custom_color_palette
Only available for model version V_2 or V_2_TURBO. Provide one or more color hex codes (e.g., ['#000030', '#1C0C47', '#9900FF', '#4285F4', '#FFFFFF']) to define a custom color palette. Only used if 'color_palette_name' is 'NONE'.
List[str]
No
Outputs
error
Error message if the operation failed
str
result
Generated image URL
str
Possible use case
Text-Heavy Graphics: Create images with logos, signs, or text overlays.
Marketing Materials: Generate promotional images with clear, readable text.
Social Media Graphics: Create quote images, announcements, or branded content with text.
Perplexity
What it is
Query Perplexity's sonar models with real-time web search capabilities and receive annotated responses with source citations.
How it works
This block queries Perplexity's sonar models which combine LLM capabilities with real-time web search. Responses include source citations as annotations, providing verifiable references for the information.
Choose from different sonar model variants including deep-research for comprehensive analysis. The block returns both the response text and structured citation data.
Inputs
prompt
The query to send to the Perplexity model.
str
Yes
model
The Perplexity sonar model to use.
"perplexity/sonar" | "perplexity/sonar-pro" | "perplexity/sonar-deep-research"
No
system_prompt
Optional system prompt to provide context to the model.
str
No
max_tokens
The maximum number of tokens to generate.
int
No
Outputs
error
Error message if the operation failed
str
response
The response from the Perplexity model.
str
annotations
List of URL citations and annotations from the response.
List[Dict[str, Any]]
Possible use case
Research Automation: Get answers to questions with verifiable sources for fact-checking.
Current Events: Query real-time information that LLMs with static training data can't provide.
Competitive Intelligence: Research companies, products, or markets with cited sources.
Smart Decision Maker
What it is
Uses AI to intelligently decide what tool to use.
How it works
This block enables agentic behavior by letting an LLM decide which tools to use based on the prompt. Connect tool outputs to feed back results, creating autonomous reasoning loops.
Configure agent_mode_max_iterations to control loop behavior: 0 for single decisions, -1 for infinite looping, or a positive number for max iterations. The block outputs tool calls or a finished message.
Inputs
prompt
The prompt to send to the language model.
str
Yes
model
The language model to use for answering the prompt.
"o3-mini" | "o3-2025-04-16" | "o1" | "o1-mini" | "gpt-5.2-2025-12-11" | "gpt-5.1-2025-11-13" | "gpt-5-2025-08-07" | "gpt-5-mini-2025-08-07" | "gpt-5-nano-2025-08-07" | "gpt-5-chat-latest" | "gpt-4.1-2025-04-14" | "gpt-4.1-mini-2025-04-14" | "gpt-4o-mini" | "gpt-4o" | "gpt-4-turbo" | "gpt-3.5-turbo" | "claude-opus-4-1-20250805" | "claude-opus-4-20250514" | "claude-sonnet-4-20250514" | "claude-opus-4-5-20251101" | "claude-sonnet-4-5-20250929" | "claude-haiku-4-5-20251001" | "claude-3-7-sonnet-20250219" | "claude-3-haiku-20240307" | "Qwen/Qwen2.5-72B-Instruct-Turbo" | "nvidia/llama-3.1-nemotron-70b-instruct" | "meta-llama/Llama-3.3-70B-Instruct-Turbo" | "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" | "meta-llama/Llama-3.2-3B-Instruct-Turbo" | "llama-3.3-70b-versatile" | "llama-3.1-8b-instant" | "llama3.3" | "llama3.2" | "llama3" | "llama3.1:405b" | "dolphin-mistral:latest" | "openai/gpt-oss-120b" | "openai/gpt-oss-20b" | "google/gemini-2.5-pro-preview-03-25" | "google/gemini-3-pro-preview" | "google/gemini-2.5-flash" | "google/gemini-2.0-flash-001" | "google/gemini-2.5-flash-lite-preview-06-17" | "google/gemini-2.0-flash-lite-001" | "mistralai/mistral-nemo" | "cohere/command-r-08-2024" | "cohere/command-r-plus-08-2024" | "deepseek/deepseek-chat" | "deepseek/deepseek-r1-0528" | "perplexity/sonar" | "perplexity/sonar-pro" | "perplexity/sonar-deep-research" | "nousresearch/hermes-3-llama-3.1-405b" | "nousresearch/hermes-3-llama-3.1-70b" | "amazon/nova-lite-v1" | "amazon/nova-micro-v1" | "amazon/nova-pro-v1" | "microsoft/wizardlm-2-8x22b" | "gryphe/mythomax-l2-13b" | "meta-llama/llama-4-scout" | "meta-llama/llama-4-maverick" | "x-ai/grok-4" | "x-ai/grok-4-fast" | "x-ai/grok-4.1-fast" | "x-ai/grok-code-fast-1" | "moonshotai/kimi-k2" | "qwen/qwen3-235b-a22b-thinking-2507" | "qwen/qwen3-coder" | "Llama-4-Scout-17B-16E-Instruct-FP8" | "Llama-4-Maverick-17B-128E-Instruct-FP8" | "Llama-3.3-8B-Instruct" | "Llama-3.3-70B-Instruct" | "v0-1.5-md" | "v0-1.5-lg" | "v0-1.0-md"
No
multiple_tool_calls
Whether to allow multiple tool calls in a single response.
bool
No
sys_prompt
The system prompt to provide additional context to the model.
str
No
conversation_history
The conversation history to provide context for the prompt.
List[Dict[str, Any]]
No
last_tool_output
The output of the last tool that was called.
Last Tool Output
No
retry
Number of times to retry the LLM call if the response does not match the expected format.
int
No
prompt_values
Values used to fill in the prompt. The values can be used in the prompt by putting them in a double curly braces, e.g. {{variable_name}}.
Dict[str, str]
No
max_tokens
The maximum number of tokens to generate in the chat completion.
int
No
ollama_host
Ollama host for local models
str
No
agent_mode_max_iterations
Maximum iterations for agent mode. 0 = traditional mode (single LLM call, yield tool calls for external execution), -1 = infinite agent mode (loop until finished), 1+ = agent mode with max iterations limit.
int
No
conversation_compaction
Automatically compact the context window once it hits the limit
bool
No
Outputs
error
Error message if the operation failed
str
tools
The tools that are available to use.
Tools
finished
The finished message to display to the user.
str
conversations
The conversation history to provide context for the prompt.
List[Any]
Possible use case
Autonomous Agents: Build agents that can independently decide which tools to use for tasks.
Dynamic Workflows: Create workflows that adapt their execution path based on AI decisions.
Multi-Tool Orchestration: Let AI coordinate multiple tools to accomplish complex goals.
Unreal Text To Speech
What it is
Converts text to speech using the Unreal Speech API
How it works
This block converts text into natural-sounding speech using Unreal Speech API. Provide the text content and optionally select a specific voice ID to customize the audio output.
The generated audio is returned as an MP3 URL that can be downloaded, played, or used as input for video creation blocks.
Inputs
text
The text to be converted to speech
str
Yes
voice_id
The voice ID to use for text-to-speech conversion
str
No
Outputs
error
Error message if the operation failed
str
mp3_url
The URL of the generated MP3 file
str
Possible use case
Voiceover Generation: Create narration audio for videos, presentations, or tutorials.
Accessibility: Convert written content to audio for visually impaired users.
Audio Content: Generate podcast intros, announcements, or automated phone messages.
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