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PATCH
/
agents
/
{agent_id}
Python
from datagrid_ai import Datagrid

client = Datagrid(
    api_key="My API Key",
)
agent = client.agents.update(
    agent_id="agent_id",
)
print(agent.id)
{
  "object": "agent",
  "id": "<string>",
  "name": "<string>",
  "created_at": "2023-11-07T05:31:56Z",
  "agent_model": "magpie-1.1",
  "llm_model": "gemini-2.5-pro",
  "knowledge_ids": [
    "<string>"
  ],
  "tools": [
    {
      "name": "data_analysis",
      "connection_id": "<string>"
    }
  ],
  "system_prompt": "<string>",
  "custom_prompt": "<string>",
  "planning_prompt": "<string>"
}

Authorizations

​
Authorization
string
header
required

Bearer authentication header of the form Bearer <token>, where <token> is your auth token.

Path Parameters

​
agent_id
string
required

The ID of the agent to update

Body

application/json
​
name
string | null

The name of the agent

Maximum length: 255
​
agent_model
enum<string> | null
default:magpie-1.1

The version of Datagrid's agent brain.

  • magpie-1.1 is the default and most powerful model.
  • magpie-1.1-flash is a faster model useful for RAG usecases, it currently only supports semantic_search tool. Structured outputs are not supported with this model.
Available options:
magpie-1.1,
magpie-1.1-flash,
magpie-1
​
llm_model
default:gemini-2.5-flash

The LLM used to generate responses.

Available options:
gemini-2.5-pro,
gemini-2.5-pro-preview-05-06,
gemini-2.5-flash,
gemini-2.5-flash-preview-04-17,
gemini-2.5-flash-lite,
gpt-5,
gemini-2.0-flash-001,
gemini-2.0-flash,
gemini-1.5-pro-001,
gemini-1.5-pro-002,
gemini-1.5-flash-002,
gemini-1.5-flash-001,
chatgpt-4o-latest,
gpt-4o,
gpt-4,
gpt-4-turbo,
gpt-4o-mini
​
knowledge_ids
string[] | null

Array of Knowledge IDs the agent should use during the converse. When ommited, all knowledge is used.

​
tools
object[]

Tools that this agent can use. Array of the agent tools to enable. If not provided - default tools of the agent are used. If empty list provided - none of the tools are used. If null provided - all tools are used. When connection_id is set for a tool, it will use that specific connection instead of the default one.

Knowledge management tools:

  • data_analysis: Answer statistical or analytical questions like "Show my quarterly revenue growth"
  • semantic_search: Search knowledge through natural language queries.
  • agent_memory: Agents can remember experiences, conversations and user preferences.
  • schema_info: Helps the Agent understand column names and dataset purpose. Avoid disabling
  • table_info: Allow the AI Agent to get information about datasets and schemas
  • create_dataset: Agents respond with data tables

Actions:

  • calendar: Allow the Agent to access and make changes to your Google Calendar
  • schedule_recurring_message_tool: Eliminate busywork such as: "Send a summary of today's meetings at 5pm on workdays"

Data processing tools:

  • data_classification: Agents handle queries like "Label these emails as high, medium, or low priority"
  • data_extraction: Helps the agent understand data from other tools. Avoid disabling
  • image_detection: Extract information from images using AI
  • pdf_extraction: Extraction of information from PDFs using AI

Enhanced response tools:

  • connect_data: Agents provide buttons to import data in response to queries like "Connect Hubspot"
  • download_data: Agents handle queries like "download the table as CSV"

Web tools:

  • web_search: Agents search the internet, and provide links to their sources
  • fetch_url: Fetch URL content
  • company_prospect_researcher: Agents provide information about companies
  • people_prospect_researcher: Agents provide information about people
​
system_prompt
string | null

Directs your AI Agent's operational behavior.

​
custom_prompt
string | null

Use custom prompt to instruct the style and formatting of the agent's response

​
planning_prompt
string | null

Define the planning strategy your AI Agent should use when breaking down tasks and solving problems

​
disabled_tools
array | null

Array of the agent tools to disable. Disabling is performed after the 'agent_tools' rules are applied. For example, agent_tools: null and disabled_tools: [data_analysis] will enable everything but the data_analysis tool. If nothing or [] is provided, nothing is disabled and therefore only the agent_tools setting is relevant.

Response

Updated agent

​
object
enum<string>
required

The object type, always 'agent'

Available options:
agent
​
id
string
required

Unique identifier for the agent

​
name
string
required

The name of the agent

​
created_at
string<date-time>
required

The ISO string for when the agent was created

​
agent_model
enum<string> | null
default:magpie-1.1
required

The version of Datagrid's agent brain.

  • magpie-1.1 is the default and most powerful model.
  • magpie-1.1-flash is a faster model useful for RAG usecases, it currently only supports semantic_search tool. Structured outputs are not supported with this model.
Available options:
magpie-1.1,
magpie-1.1-flash,
magpie-1
​
llm_model
default:gemini-2.5-flash
required

The LLM used to generate responses.

Available options:
gemini-2.5-pro,
gemini-2.5-pro-preview-05-06,
gemini-2.5-flash,
gemini-2.5-flash-preview-04-17,
gemini-2.5-flash-lite,
gpt-5,
gemini-2.0-flash-001,
gemini-2.0-flash,
gemini-1.5-pro-001,
gemini-1.5-pro-002,
gemini-1.5-flash-002,
gemini-1.5-flash-001,
chatgpt-4o-latest,
gpt-4o,
gpt-4,
gpt-4-turbo,
gpt-4o-mini
​
knowledge_ids
string[] | null
required

Array of Knowledge IDs the agent should use during the converse. When ommited, all knowledge is used.

​
tools
object[]
required

Tools that this agent can use.

​
system_prompt
string | null
required

Directs your AI Agent's operational behavior.

​
custom_prompt
string | null
required

Use custom prompt to instruct the style and formatting of the agent's response

​
planning_prompt
string | null
required

Define the planning strategy your AI Agent should use when breaking down tasks and solving problems

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