Generate AI chat responses
🤖/ai/chat generates AI chat responses from prompts using various LLM providers like Anthropic, OpenAI, and Google.

Usage example
Generate an AI chat response using Claude:
{
"steps": {
"reply": {
"robot": "/ai/chat",
"model": "auto",
"messages": "Summarize this in one sentence: Transloadit handles uploads and media processing."
}
}
}Parameters
interpolateboolean | Record<string, boolean>Controls whether Assembly Variables are interpolated for individual instruction fields.
By default, most Robot instruction fields interpolate Assembly Variables. Set this to
falseto treat every instruction field as literal text, or set an individual field path tofalseto treat only that field as literal text. For Robot-specific fields that are literal by default, set this totrueor set that field path totrueto opt back into interpolation.Use field names such as
path, or dotted paths such asffmpeg.vffor nested objects.output_metaRecord<string, boolean> | boolean | Array<string>Allows you to specify a set of metadata that is more expensive on CPU power to calculate, and thus is disabled by default to keep your Assemblies processing fast.
For images, you can add
"has_transparency": truein this object to extract if the image contains transparent parts and"dominant_colors": trueto extract an array of hexadecimal color codes from the image.For images, you can also add
"blurhash": trueto extract a BlurHash string — a compact representation of a placeholder for the image, useful for showing a blurred preview while the full image loads.For videos, you can add the
"colorspace": trueparameter to extract the colorspace of the output video.For videos, you can also add
"interlaced": trueto detect whether the video is interlaced. This combines the cheap ffprobefield_orderflag with a boundedidetsampling pass over the first frames of the source, exposinginterlaced,field_order, and a diagnosticinterlace_detectionobject underfile.meta. This is computationally expensive and billed accordingly.For audio, you can add
"mean_volume": trueto get a single value representing the mean average volume of the audio file.You can also set this to
falseto skip metadata extraction and speed up transcoding.user_metaRecord<string, any>(default:{})Adds custom metadata to each file emitted by this Robot without modifying the file’s contents.
The values are merged with any existing
user_metacarried by the input file. If both objects contain the same key, this Robot’s value takes precedence. Assembly Variables are supported, for example{ "internal_file_id": "${file.id}" }.resultboolean(default:false)Whether the results of this Step should be present in the Assembly Status JSON
queuebatchSetting the queue to 'batch', manually downgrades the priority of jobs for this step to avoid consuming Priority job slots for jobs that don't need zero queue waiting times
force_acceptboolean(default:false)Force a Robot to accept a file type it would have ignored.
By default, Robots ignore files they are not familiar with. 🤖/video/encode, for example, will happily ignore input images.
With the
force_acceptparameter set totrue, you can force Robots to accept all files thrown at them. This will typically lead to errors and should only be used for debugging or combatting edge cases.ignore_errorsboolean | Array<meta | execute>(default:[])Ignore errors during specific phases of processing.
Setting this to
["meta"]will cause the Robot to ignore errors during metadata extraction.Setting this to
["execute"]will cause the Robot to ignore errors during the main execution phase.Setting this to
trueis equivalent to["meta", "execute"]and will ignore errors in both phases.usestring | Array<string> | Array<object> | objectSpecifies which Step(s) to use as input.
- You can pick any names for Steps except
":original"(reserved for user uploads handled by Transloadit) - You can provide several Steps as input with arrays:
{ "use": [ ":original", "encoded", "resized" ] } - You can also tag input Steps with
asto pass semantic intent to robots:{ "use": [ { "name": ":original", "as": "image" }, { "name": ":original", "as": "mask" } ] }
TipThat's likely all you need to know about
use, but you can view Advanced use cases.- You can pick any names for Steps except
modelstring | "auto"(default:"auto")The model to use. Transloadit can pick the best model for the job if you set this to "auto".
schemastringThe JSON Schema that the LLM should output
messages— requiredstring | Array<object>The prompt, or message history to send to the LLM.
system_messagestringSet the system/developer prompt, if the model allows it. If this prompt contains literal documentation or code examples with
${...}syntax, setinterpolate.system_messagetofalse.reasoning_effortxhigh | high | medium | lowControls how much effort the model spends on reasoning. Higher values produce more thorough responses but cost more tokens. Applies to models that support extended thinking (OpenAI o-series, GPT-5.x, GPT-6, Anthropic Claude with thinking). If omitted, the model default is used.
credentialsstring | Array<string>Names of template credentials to make available to the robot. When using your own AI provider keys, Transloadit charges a 30% markup (minimum $0.0005 per request).
test_credentialsbooleanUse Transloadit-provided credentials for testing. Usage is billed at provider cost plus a 30% markup (minimum $0.0005 per request).
mcp_serversArray<object>The MCP servers to use for tool calling. You can use any MCP server reachable from your environment. Use
headersto pass server-specific auth (for exampleAuthorization: Bearer <token>). For Transloadit's MCP server: Bearer tokens minted via/tokensatisfy Signature Authentication (signature checks apply only to key/secret requests).auth: "transloadit"is reserved for API2-managed auth to Transloadit-hosted MCP servers.