The SDK can open Docent web pages directly in your default browser — useful when
working in notebooks or scripts.
Open an Agent Run
from docent import Docent
client = Docent()
url = client.open_agent_run("my-collection-id", "run-id-123")
# Opens browser to the agent run detail page
print(url)
Parameters
ID of the collection containing the run.
ID of the agent run to open.
Returns
Open a Rubric
Open a rubric page, optionally focused on a specific agent run or judge result.
# Rubric overview
client.open_rubric("my-collection-id", "rubric-123")
# Specific agent run within the rubric
client.open_rubric("my-collection-id", "rubric-123", agent_run_id="run-456")
# Specific judge result
client.open_rubric(
"my-collection-id",
"rubric-123",
agent_run_id="run-456",
judge_result_id="result-789",
)
Parameters
Optional agent run to focus on within the rubric view.
Optional judge result to focus on. Requires agent_run_id.
Open a Result Set
url = client.open_result_set("my-collection-id", "analysis/experiment_1")
Parameters
Name or UUID of the result set.
Start a Chat
Create an interactive chat session with agent runs or transcripts as context, and open it
in the browser.
run1 = client.get_agent_run("my-collection-id", "run-1")
run2 = client.get_agent_run("my-collection-id", "run-2")
session_id = client.start_chat([run1, run2])
# Opens browser to chat UI with both runs as context
Parameters
context
LLMContext | list[AgentRun | Transcript]
required
Objects to include as chat context. Can be a list of AgentRun or Transcript
instances, or a pre-built LLMContext.
Optional model to use, in "provider/model_name" format.
reasoning_effort
Literal['minimal', 'low', 'medium', 'high'] | None
Optional reasoning effort hint passed to the LLM provider.
Returns
The session ID of the created chat session.
