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ChatObject: Create and handle LLM interactions—Wolfram Documentation

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ChatObject[]

represents an ongoing conversation with a remote service.

ChatObject[init]

creates a new chat using the initialization init.

ChatObject[…][prop]

extracts the property prop from the object.

Details and Options
Details and Options Details and Options
Examples  
Basic Examples  
Scope  
Options  
Authentication  
LLMEvaluator  
Possible Issues  
See Also
Related Guides
History
Cite this Page

ChatObject

This functionality requires LLM access »

ChatObject[]

represents an ongoing conversation with a remote service.

ChatObject[init]

creates a new chat using the initialization init.

ChatObject[…][prop]

extracts the property prop from the object.

Details and Options

  • ChatObject stores a full conversation together with the message metadata.
  • The initialization init can take the following values:
  • "text"static text
    LLMPrompt["name"]a repository prompt
    StringTemplate[…]templated text
    TemplateObject[…]template for creating a prompt
    Image[…]an image
    {prompt1,…}a list of prompts
    {msg1,…}a list of messages
  • Non-message init values are converted to messages with "Role""System".
  • Template objects are automatically converted to message content via TemplateObject[…][].
  • Prompt created with TemplateObject can contain text and images. Not every LLM supports image input.
  • When the initialization is a list of messages, each message must be an association with the following keys:
  • "Content"contentcontent of the message
    "Role"Stringrole of the participant
    "Timestamp"DateObjectmessage timestamp (optional)
  • Possible form of content are:
  • "text"a string
    Image[…]an image object
    <|"Type""Text","Data"data|>an explicit text part
    <|"Type""Image","Data"data|>an explicit image part (supports File[…] objects)
    {content1,…}multiple part content
  • Possible values for "Role" include:
  • "Assistant"LLM-generated message
    "System"system message
    "Tool"autogenerated tool response
    "User"user message
  • The following options can be specified:
  • Authentication Automaticauthentication scheme
    LLMEvaluator $LLMEvaluatorLLM configuration to use
  • LLMEvaluator can be set to an LLMConfiguration object or an association with any of the following keys:
  • "MaxTokens"maximum amount of tokens to generate
    "Model"base model
    "PromptDelimiter"string to insert between prompts
    "Prompts"initial prompts or LLMPromptGenerator objects
    "StopTokens"tokens on which to stop generation
    "Temperature"sampling temperature
    "ToolMethod"method to use for tool calling
    "Tools"list of LLMTool objects to make available
    "TopProbabilities"sampling classes cutoff
    "TotalProbabilityCutoff"sampling probability cutoff (nucleus sampling)
  • Valid forms of "Model" include:
  • namenamed model
    {service,name}named model from service
    <|"Service"service,"Name"name|>fully specified model
  • The generated text is sampled from a distribution. Details of the sampling can be specified using the following properties of the LLMEvaluator:
  • "Temperature"tAutomaticsample using a positive temperature t
    "TopProbabilities"kAutomaticsample only among the k highest-probability classes
    "TotalProbabilityCutoff"pAutomaticsample among the most probable choices with an accumulated probability of at least p (nucleus sampling)
  • The Automatic value of these parameters uses the default for the specified "Model".
  • Possible values for "ToolMethod" include:
  • "Service"rely on the tool mechanism of service
    "Textual"used prompt-based tool calling
  • Possible values for Authentication are:
  • Automaticchoose the authentication scheme automatically
    Environmentcheck for a key in the environment variables
    SystemCredentialcheck for a key in the system keychain
    ServiceObject[…]inherit the authentication from a service object
    "string"provide an explicit API key
  • With AuthenticationAutomatic, the function checks the variable ToUpperCase[service]<>"_API_KEY" in Environment and SystemCredential; otherwise, it uses ServiceConnect[service].
  • Properties of a chat object can be extracted using ChatObject[…][prop].
  • Possible values for prop include:
  • "ChatID"the unique ID of the conversation
    "FullText"string representation of the conversation
    "LLMEvaluator"the stored LLMConfiguration
    "MessageGrid"typeset grid of the conversation
    "Messages"a list of exchanged messages
    "Properties"a list of all the possible properties
    "Usage"cumulative API usage (calls, tokens, …)
    {prop1,…}a list of properties

Examples

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Basic Examples  (2)

Create a new chat:

Wolfram Language code: chat = ChatObject[]

Add a message and a response to the conversation:

Wolfram Language code: chat = ChatEvaluate[chat, "What's the tallest mountain?"]

Get a list of all the messages:

Wolfram Language code: chat["Messages"]

Create a chat object with a tool:

Wolfram Language code: toolchat = ChatObject[LLMEvaluator -> <|"Tools" -> LLMTool["countcharacter", "string", StringLength]|>]

Show the LLM answer together with the tool-calling steps:

Wolfram Language code: ChatEvaluate[toolchat, "How many letters in the word \"characters\" (use the tool) ?"]

Scope  (5)

Create an empty chat:

Wolfram Language code: ChatObject[]

Create a chat with an initial prompt:

Wolfram Language code: chat = ChatObject["always talk like a pirate. always stay in character."]
Wolfram Language code: chat//=ChatEvaluate["who are you?"]

Create a chat from a list of messages:

Wolfram Language code: ChatObject[{<|"Role" -> "User", "Content" -> "What's the tallest mountain?", "Timestamp" -> DateObject[{2023, 4, 3, 17, 55, 43.705775}, "Instant", "Gregorian", 2.]|>, <|"Role" -> "Assistant", "Content" -> "The tallest mountain is Mount Everest, which stands at 29,029 feet (8,848 meters) tall.", "Timestamp" -> DateObject[{2023, 4, 3, 17, 55, 43.}, "Instant", "Gregorian", 2.]|>}]

Extract a chat property:

Wolfram Language code: ChatObject[Association["LLMEvaluator" -> LLMConfiguration[ Association["Model" -> Association["Service" -> "OpenAI", "Name" -> "gpt-4"], "MaxTokens" -> Automatic, "Temperature" -> Automatic, "TotalProbabilityCutoff" -> Automatic, "P ... 1.}, {21.00040054321289, 21.}}}]}, FaceForm[RGBColor[0.5372549019607843, 0.5372549019607843, 0.5372549019607843, 1.]]]}, ImageSize -> {{27., 27.}, {27., 27.}}, PlotRange -> {{-0.5, 26.5}, {-0.5, 26.5}}, AspectRatio -> Automatic]]]["FullText"]

Extract a list of properties:

Wolfram Language code: ChatObject[Association["LLMEvaluator" -> LLMConfiguration[ Association["Model" -> Association["Service" -> "OpenAI", "Name" -> "gpt-4"], "MaxTokens" -> Automatic, "Temperature" -> Automatic, "TotalProbabilityCutoff" -> Automatic, "P ... 1.}, {21.00040054321289, 21.}}}]}, FaceForm[RGBColor[0.5372549019607843, 0.5372549019607843, 0.5372549019607843, 1.]]]}, ImageSize -> {{27., 27.}, {27., 27.}}, PlotRange -> {{-0.5, 26.5}, {-0.5, 26.5}}, AspectRatio -> Automatic]]][{"ChatID", "Messages"}]

List all the available properties:

Wolfram Language code: ChatObject[Association["LLMEvaluator" -> LLMConfiguration[ Association["Model" -> Association["Service" -> "OpenAI", "Name" -> "gpt-4"], "MaxTokens" -> Automatic, "Temperature" -> Automatic, "TotalProbabilityCutoff" -> Automatic, "P ... 1.}, {21.00040054321289, 21.}}}]}, FaceForm[RGBColor[0.5372549019607843, 0.5372549019607843, 0.5372549019607843, 1.]]]}, ImageSize -> {{27., 27.}, {27., 27.}}, PlotRange -> {{-0.5, 26.5}, {-0.5, 26.5}}, AspectRatio -> Automatic]]]["Properties"]

Options  (5)

Authentication  (4)

Provide an authentication key for the API:

Wolfram Language code: SystemCredential["OPENAI_API_KEY"] = "<my-key>"

Look for the key in the system keychain:

Wolfram Language code: ChatObject[LLMEvaluator -> <|"Model" -> {"OpenAI", Automatic}|>, Authentication -> SystemCredential ]

Store the API key in an environment variable:

Wolfram Language code: SetEnvironment["OPENAI_API_KEY" -> "<my-key>"]

Look for the key in the system environment:

Wolfram Language code: ChatObject[LLMEvaluator -> <|"Model" -> {"OpenAI", Automatic}|>, Authentication -> Environment ]

Authenticate via a service object:

Wolfram Language code: so = ServiceConnect["OpenAI"]
Wolfram Language code: ChatObject[LLMEvaluator -> <|"Model" -> {"OpenAI", Automatic}|>, Authentication -> so]

Provide an authentication key for the API:

Wolfram Language code: ChatObject[LLMEvaluator -> <|"Model" -> {"OpenAI", Automatic}|>, Authentication -> "<my-key>" ]

LLMEvaluator  (1)

Create a chat that uses a specific model:

Wolfram Language code: chat = ChatObject[LLMEvaluator -> <|"Model" -> {"OpenAI", "gpt-4"}|>]
Wolfram Language code: ChatEvaluate[chat, "What's the tallest mountain?"]

Possible Issues  (1)

All models do not support images in "System" message:

Wolfram Language code: ChatEvaluate[ChatObject[Entity["TaxonomicSpecies", "SusScrofa::s42j5"]["Image"]], "Can you see the image?", LLMEvaluator -> {"Model" -> {"OpenAI", "gpt-4o"}}]

Use a different model:

Wolfram Language code: ChatEvaluate[ChatObject[Entity["TaxonomicSpecies", "SusScrofa::s42j5"]["Image"]], "Can you see the image?", LLMEvaluator -> {"Model" -> {"OpenAI", "gpt-4-vision-preview"}}]

Specify a different "Role" value:

Wolfram Language code: ChatEvaluate[ChatObject[{<|"Role" -> "User", "Type" -> "Image", "Content" -> Entity["TaxonomicSpecies", "SusScrofa::s42j5"]["Image"]|>}], "Can you see the image?", LLMEvaluator -> {"Model" -> {"OpenAI", "gpt-4o"}}]
Wolfram Research (2023), ChatObject, Wolfram Language function, https://reference.wolfram.com/language/ref/ChatObject.html (updated 2025).

Text

Wolfram Research (2023), ChatObject, Wolfram Language function, https://reference.wolfram.com/language/ref/ChatObject.html (updated 2025).

CMS

Wolfram Language. 2023. "ChatObject." Wolfram Language & System Documentation Center. Wolfram Research. Last Modified 2025. https://reference.wolfram.com/language/ref/ChatObject.html.

APA

Wolfram Language. (2023). ChatObject. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/ChatObject.html

BibTeX

@misc{reference.wolfram_2026_chatobject, author="Wolfram Research", title="{ChatObject}", year="2025", howpublished="\url{https://reference.wolfram.com/language/ref/ChatObject.html}", note=[Accessed: 14-November-2026]}

BibLaTeX

@online{reference.wolfram_2026_chatobject, organization={Wolfram Research}, title={ChatObject}, year={2025}, url={https://reference.wolfram.com/language/ref/ChatObject.html}, note=[Accessed: 14-November-2026]}