Work with teams

Every resource in SeekrFlow (agents, fine-tuning jobs, deployments, files, and vector databases) belongs to a team. SDK requests are scoped to a single team. Resources you create are owned by that team, and list and retrieve calls return only that team's resources. For the underlying access model, see Role-based access control.

ℹ️

Threads are user-owned

Threads, and the messages and runs in them, are always user-owned, so selecting a team doesn't affect them. Agents are team-owned, but the threads you run them on are not.

Set the active team

Setting a team is optional. If you don't, requests use your personal workspace, the private, single-member team that every user has by default.

The Teams Settings page in the SeekrFlow web interface lists every team you belong to and its ID. Resources returned by the SDK also include the team_id of their owning team.

The SDK sends the team you select as the x-team-id request header. You can set it in two ways.

Environment variable (recommended). Set SEEKR_TEAM_ID and the SDK applies it automatically to every request:

export SEEKR_TEAM_ID=124cb1bb-d8d3-4e9b-9c96-2039fba43e78

set SEEKR_TEAM_ID=124cb1bb-d8d3-4e9b-9c96-2039fba43e78

import os
from seekrai import SeekrFlow

# Picks up SEEKR_API_KEY and SEEKR_TEAM_ID from the environment.
client = SeekrFlow()

Per-client header. Set supplied_headers when you construct the client. This takes precedence over SEEKR_TEAM_ID, which is useful when one process works across several teams:

import os
from seekrai import SeekrFlow

client = SeekrFlow(
    api_key=os.environ.get("SEEKR_API_KEY"),
    supplied_headers={"x-team-id": "124cb1bb-d8d3-4e9b-9c96-2039fba43e78"},
)

Both approaches work the same way on the asynchronous client:

from seekrai import AsyncSeekrFlow

client = AsyncSeekrFlow(supplied_headers={"x-team-id": "124cb1bb-d8d3-4e9b-9c96-2039fba43e78"})

ℹ️

One team per client

The team applies to the whole client. Every call made through a client is scoped to that client's team. To act on more than one team in the same process, create a separate client per team.

Create resources in a team

Once a client is scoped to a team, every resource you create through it belongs to that team. No extra argument is needed on individual calls:

from seekrai import SeekrFlow

client = SeekrFlow(supplied_headers={"x-team-id": "124cb1bb-d8d3-4e9b-9c96-2039fba43e78"})

# Owned by the client's team.
uploaded = client.files.upload(file="training_data.jsonl", purpose="fine-tune")

Read resources from a team

List and retrieve calls return only the resources owned by the client's team. Requests for a resource that belongs to a team you aren't a member of are rejected.

from seekrai import SeekrFlow

client = SeekrFlow(supplied_headers={"x-team-id": "124cb1bb-d8d3-4e9b-9c96-2039fba43e78"})

# Only files owned by the client's team.
for f in client.files.list().data:
    print(f.id, f.filename)

Returned resources carry the team_id of their owning team alongside the user_id of the creator, so you can confirm where a resource lives:

agent = client.agents.retrieve(agent_id)
print(agent.team_id)  # e.g. "124cb1bb-d8d3-4e9b-9c96-2039fba43e78"
print(agent.user_id)  # the user who created it

Work across multiple teams

To operate on several teams in one program, create one client per team:

from seekrai import SeekrFlow

research = SeekrFlow(supplied_headers={"x-team-id": "124cb1bb-d8d3-4e9b-9c96-2039fba43e78"})
production = SeekrFlow(supplied_headers={"x-team-id": "7d9f2a10-4c6b-4f23-8a1e-5b0c9d3e4f21"})

research_files = research.files.list().data
production_files = production.files.list().data

A resource created in one team is not visible to another. To use a resource across teams, recreate it in each team.