An agent run represents a single execution of your agent, from the moment it receives a user input to the final response.
All setter methods return self, so you can chain calls:
r.prompt("What is the weather in London?").metadata(userId="user-123").response("15°C and cloudy.").end()
Required
Initialize run
| Parameter | Required | Description |
|---|
run_id | No | Custom run ID. If not provided, a UUID is automatically generated. |
session_id | No | Group multiple runs into an agent session. See Track agent sessions. |
conversational | No | Set to True to indicate the run was initiated by a user. See Conversational Runs. |
import contextcompany as tcc
r = tcc.run(run_id="your-custom-uuid", session_id="session-123", conversational=True)
.run_id property you can use to retrieve its ID (auto-generated or custom):
Set prompt
The prompt is required before calling .end().
r.prompt(user_prompt, system_prompt=None)
| Parameter | Required | Description |
|---|
user_prompt | Yes | The user input to the agent |
system_prompt | No | A system message to include alongside the user prompt |
# User prompt only
r.prompt("What is the weather in London?")
# User prompt with system prompt
r.prompt("What is the weather in London?", system_prompt="You are a helpful weather agent.")
End run
End the run and export it. A run must have a prompt set before calling .end(). The run cannot be modified after calling .end().
import contextcompany as tcc
r = tcc.run()
r.prompt("What is the weather in London?")
# --- Your agent loop ---
# ...
# --- End of agent loop ---
r.response("The weather in London is 15°C and cloudy.")
r.end()
Optional run data
Set response
| Parameter | Required | Description |
|---|
text | Yes | The final response from the agent |
r.response("The weather in London is 15°C and cloudy.")
Mark run as failed
Sets the status code to error and exports the payload. The run cannot be modified after calling .error(). Unlike .end(), calling .error() does not require a prompt to be set.
| Parameter | Required | Description |
|---|
status_message | No | A description of the error |
import contextcompany as tcc
r = tcc.run()
r.prompt("What is the weather in London?")
try:
result = call_weather_api()
r.response(result)
r.end()
except Exception as e:
r.error(status_message=str(e))
Custom metadata allows you to add additional properties to your agent runs. This is particularly useful for tying agent runs to your own specific business logic, letting you filter and analyze agent runs by user, organization, feature, or some other dimension.
r.metadata(data, **kwargs)
| Parameter | Required | Description |
|---|
data | No | A dictionary of key-value pairs |
**kwargs | No | Key-value pairs as keyword arguments |
# Pass metadata as keyword arguments
r.metadata(userId="4a6b111c-b53a-4d00-a877-67185022ab9e", orgId="org-123")
# Or pass metadata as a dictionary
r.metadata({
"userId": "4a6b111c-b53a-4d00-a877-67185022ab9e",
"orgId": "org-123",
})
Identifying the agent
If your product ships more than one named agent, set the reserved tcc.agent metadata key to scope the run to a specific agent. The dashboard’s top-level agent selector, per-agent patterns and recaps, and the agent filter on the REST API and MCP tools all read from this key.
r.metadata({
"tcc.agent": "support-agent",
})
Agent names that collide with reserved dashboard routes (for example runs, sessions, patterns, recaps, overview, search, failures, feedback, tools, topics, views, settings, mcp-and-api) are dropped.
Identifying users and organizations
Attach the end user and their organization to a run as first-class identity using the reserved tcc.userId, tcc.userName, tcc.orgId, and tcc.orgName metadata keys. This is not the same as adding a userId field to custom metadata — these keys promote user and org identity to dedicated dashboard filters and unlock native user/org search, per-user views, and per-org analytics. See User and organization identity for the full concept.
Set these whenever you have a stable identifier for the end user or their organization in your product.
r.metadata({
"tcc.userId": "user-123",
"tcc.userName": "Jane Doe",
"tcc.orgId": "org-456",
"tcc.orgName": "Acme Inc.",
})
tcc.userName and tcc.orgName require the corresponding ID (tcc.userId / tcc.orgId) to also be set. Names without IDs are dropped.
Add user feedback
User feedback allows you to collect score (thumbs up & thumbs down) and text feedback (up to 2000 characters) from end users on your agent runs.
This is useful for tracking user satisfaction, identifying problematic responses, and filtering agent runs in the dashboard to focus on positive or negative feedback.
Both score and text are optional individually, but each request must include at least one of them.
score is the thumbs rating. Use only "thumbs_up" or "thumbs_down".
text is written feedback from your user, up to 2000 characters.
| Parameter | Required | Description |
|---|
score | No | Thumbs rating. Must be "thumbs_up" or "thumbs_down" |
text | No | Written user feedback, up to 2000 characters |
Option 1: On the run object
If you have access to the run object, call .feedback() directly. No run ID needed.
import contextcompany as tcc
r = tcc.run()
r.prompt("What is the weather in London?")
r.response("The weather in London is 15°C and cloudy.")
r.end()
r.feedback(
score="thumbs_up", # Optional thumbs rating: "thumbs_up" or "thumbs_down"
text="Very helpful!", # Optional written user feedback, up to 2000 characters
)
Option 2: With a run ID
If you need to submit feedback separately (e.g. from a client), pass the run’s ID to tcc.submit_feedback().
import contextcompany as tcc
# Server side: pass the run ID to your client
r = tcc.run()
run_id = r.run_id
r.prompt("What is the weather in London?")
r.response("The weather in London is 15°C and cloudy.")
r.end()
# Client side: submit score and/or text feedback using the run ID
tcc.submit_feedback(
run_id=run_id,
score="thumbs_down", # Optional thumbs rating: "thumbs_up" or "thumbs_down"
text="The temperature was wrong.", # Optional written user feedback, up to 2000 characters
)
Track agent sessions
Agent sessions represent multiple agent runs that are grouped together. The most common use case is tracking entire conversations between a human user and an AI agent in chatbot interfaces.
Agent sessions can be tracked by passing a session_id when creating a run:
import contextcompany as tcc
session_id = "unique-session-identifier"
# First message in the conversation
r1 = tcc.run(session_id=session_id)
r1.prompt("What is the weather in London?")
r1.response("The weather in London is 15°C and cloudy.")
r1.end()
# Second message in the same conversation
r2 = tcc.run(session_id=session_id)
r2.prompt("What about Paris?")
r2.response("The weather in Paris is 18°C and sunny.")
r2.end()
session_id should be a unique string identifier. We recommend using a UUID.
Agent sessions are automatically indexed and can be filtered directly in the dashboard. 