Export agents into endpoints so you can use it directly in your product
OpenSesame Agent Endpoint Documentation
The OpenSesame agent endpoint allows users to execute AI-driven workflows involving multiple components, such as LLMs (Large Language Models) and integrated tools (e.g., Gmail). This documentation covers how to use the endpoint via cURL and Python.
Click on Generate Endpoint to generate the endpoint for your agent
Copy the endpoint
Base URL:
https://opensesame--%7Bname%7D-modal-endpoint.modal.run/
HTTP Method:
POST
Headers:
accept: application/json
Content-Type: application/json
Request Body Parameters:
agent_config
: Defines the agents and tools used in the workflow.
Key Parameters:
id
: Unique identifier for each agent/tool.
type
: Specifies if the component is an LLM or a tool.
name
: Name of the agent or tool.
provider_name
: For LLMs, it specifies the AI provider (e.g., OpenAI).
model_name
: Specifies the AI model to use (e.g., gpt-4o-mini
).
tool_name
and url
: For tools, provides integration information and authentication URLs.
children
: Defines the execution order by specifying downstream agents/tools.
user_query
: A natural-language instruction for what the workflow should accomplish.
user_id
: Identifier for the user making the request.
conversation_id
: Unique conversation or workflow identifier.
A successful response will return a JSON object containing the results of the workflow execution, including any outputs from the AI agents and tools used. Check the response.json()
for specifics.
Export agents into endpoints so you can use it directly in your product
OpenSesame Agent Endpoint Documentation
The OpenSesame agent endpoint allows users to execute AI-driven workflows involving multiple components, such as LLMs (Large Language Models) and integrated tools (e.g., Gmail). This documentation covers how to use the endpoint via cURL and Python.
Click on Generate Endpoint to generate the endpoint for your agent
Copy the endpoint
Base URL:
https://opensesame--%7Bname%7D-modal-endpoint.modal.run/
HTTP Method:
POST
Headers:
accept: application/json
Content-Type: application/json
Request Body Parameters:
agent_config
: Defines the agents and tools used in the workflow.
Key Parameters:
id
: Unique identifier for each agent/tool.
type
: Specifies if the component is an LLM or a tool.
name
: Name of the agent or tool.
provider_name
: For LLMs, it specifies the AI provider (e.g., OpenAI).
model_name
: Specifies the AI model to use (e.g., gpt-4o-mini
).
tool_name
and url
: For tools, provides integration information and authentication URLs.
children
: Defines the execution order by specifying downstream agents/tools.
user_query
: A natural-language instruction for what the workflow should accomplish.
user_id
: Identifier for the user making the request.
conversation_id
: Unique conversation or workflow identifier.
A successful response will return a JSON object containing the results of the workflow execution, including any outputs from the AI agents and tools used. Check the response.json()
for specifics.