fmxexpress/pasclaw
AI agent built in Object Pascal
Run fmxexpress/pasclaw with an API
Use one of our client libraries to get started quickly. Clicking on a library will take you to the Playground tab where you can tweak different inputs, see the results, and copy the corresponding code to use in your own project.
Input schema
The fields you can use to run this model with an API. If you don't give a value for a field its default value will be used.
| Field | Type | Default value | Description |
|---|---|---|---|
| message |
string
|
Message or prompt to send to the PasClaw agent.
|
|
| openai_api_key |
string
|
|
Optional OpenAI API Key.
|
| anthropic_api_key |
string
|
|
Optional Anthropic API Key.
|
| gemini_api_key |
string
|
|
Optional Google Gemini API Key.
|
| groq_api_key |
string
|
|
Optional Groq API Key.
|
| openrouter_api_key |
string
|
|
Optional OpenRouter API Key.
|
| deepseek_api_key |
string
|
|
Optional DeepSeek API Key.
|
| provider |
string
|
|
LLM provider to run on (openai, anthropic, gemini, groq, openrouter, deepseek). "
"If left empty, defaults to the first configured API key.
|
| model |
string
|
|
Optional model override to use (e.g. gpt-4o, claude-3-5-sonnet, gemini-1.5-pro, deepseek-chat). "
"If empty, defaults to the selected provider's standard catalog default model.
|
{
"type": "object",
"title": "Input",
"required": [
"message"
],
"properties": {
"model": {
"type": "string",
"title": "Model",
"default": "",
"x-order": 8,
"description": "Optional model override to use (e.g. gpt-4o, claude-3-5-sonnet, gemini-1.5-pro, deepseek-chat). \"\n \"If empty, defaults to the selected provider's standard catalog default model."
},
"message": {
"type": "string",
"title": "Message",
"x-order": 0,
"description": "Message or prompt to send to the PasClaw agent."
},
"provider": {
"type": "string",
"title": "Provider",
"default": "",
"x-order": 7,
"description": "LLM provider to run on (openai, anthropic, gemini, groq, openrouter, deepseek). \"\n \"If left empty, defaults to the first configured API key."
},
"groq_api_key": {
"type": "string",
"title": "Groq Api Key",
"default": "",
"x-order": 4,
"description": "Optional Groq API Key."
},
"gemini_api_key": {
"type": "string",
"title": "Gemini Api Key",
"default": "",
"x-order": 3,
"description": "Optional Google Gemini API Key."
},
"openai_api_key": {
"type": "string",
"title": "Openai Api Key",
"default": "",
"x-order": 1,
"description": "Optional OpenAI API Key."
},
"deepseek_api_key": {
"type": "string",
"title": "Deepseek Api Key",
"default": "",
"x-order": 6,
"description": "Optional DeepSeek API Key."
},
"anthropic_api_key": {
"type": "string",
"title": "Anthropic Api Key",
"default": "",
"x-order": 2,
"description": "Optional Anthropic API Key."
},
"openrouter_api_key": {
"type": "string",
"title": "Openrouter Api Key",
"default": "",
"x-order": 5,
"description": "Optional OpenRouter API Key."
}
}
}
Output schema
The shape of the response you’ll get when you run this model with an API.
Schema
{
"type": "string",
"title": "Output"
}