gts-agency-python-sdk 0.0.1rc13

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Agency Python SDK

Python client SDK for the GTS Agency platform. Provides typed HTTP clients for datasets, datasources, files, ontologies, prompts, and rules APIs.

Installation

pip install gts-agency-python-sdk

For development:

pip install -e ".[dev]"

Optional OpenTelemetry tracing support (Langfuse backend):

pip install gts-agency-python-sdk[observability]

Configuration

The examples in this repository use the following environment variables for authentication and configuration:

• AGENCY_AUTH_URL: The OAuth2 token endpoint URL.
• AGENCY_API_URL: The base URL for the Agency API.
• AGENCY_ORG_ID: The organization ID to use for requests.
• AGENCY_CLIENT_ID: Your OAuth2 client ID.
• AGENCY_CLIENT_SECRET: Your OAuth2 client secret.

Usage

from agency_sdk.client import AgencyClient, CredentialsSupplier

credentials = CredentialsSupplier(
    auth_base_url="https://auth.example.com",
    client_id="your-client-id",
    client_secret="your-client-secret",
)
client = AgencyClient(token_supplier=credentials, base_url="https://api.example.com")

Delegate Clients

Access each API domain through the facade:

• client.dataset() — datasets CRUD, filesystem traversal, clone
• client.datasource() — datasource and table introspection
• client.files() — tenant file storage: list, upload, folder management, delete, signed URLs, gtsf:// URI resolution, streamed download
• client.ontology() — ontology export (JSON, Turtle, ISON) and entity-datasource mappings
• client.prompts() — prompt CRUD via command pattern
• client.rules() — rule listing, detail, execution, and execution history
• client.session_vault() — session-scoped key/value vault for agent state (classification-based encryption, audited reveal)
• client.work_queues() — work-queue ingestion: create items with exactly-once external refs, publish, claim by ref, org-scoped lookup by ref, unblock/retry/reprocess, delete (409 returns a typed claim-lost result, not an exception)
• client.sessions() — report progress on a dispatched control-plane session: attach to the inherited session then update its status/result/events/metrics (the agent inherits the session, never self-registers — no register)
• client.gateway(org_id=..., gateway_base_url=...) — OpenAI-compatible LLM calls routed through the org's agentgateway (shared credentials + x-org routing header)

Rules Example

from agency_sdk.delegates.rules_dto import ExecuteRequest

rules = client.rules()

# List rules
result = rules.list(organisation_id=2)
for rule in result.items:
    print(f"{rule.name} (status={rule.active_version_status})")

# Get rule detail
detail = rules.get(rule_id="rule-id", organisation_id=2)

# Execute a rule
response = rules.execute(
    rule_id="rule-id",
    request=ExecuteRequest(organisation=2, context={"key": "value"}, trace=True),
)
print(f"Result: {response.result}")

# List execution history
executions = rules.list_executions(rule_id="rule-id", organisation_id=2)

Files Example

files = client.files()

# Upload into a folder (multipart; up to 100 MiB per file and per request)
result = files.upload(organisation_id=2, file_paths=["report.pdf"], path="guidelines")
file_id = result.uploaded[0].id

# Resolve a gtsf:// reference (as found in configurations and rule annotations)
signed = files.resolve_gtsf_uri(f"gtsf://{file_id}", organisation_id=2)
print(f"Download until {signed.expires_at}: {signed.signed_url}")

# Or download directly (streamed to disk via the signed URL)
files.download(file_id=file_id, organisation_id=2, target_path="./report.pdf")

See docs/files_storage_flows.md for the full upload/download architecture.

Observability Example

Opt-in OpenTelemetry tracing + log correlation, shipped to a Langfuse backend and authenticated with the same credentials as the API client. Requires the [observability] extra.

obs = client.observability("gts-myagent")   # reuses credentials; host defaults to base_url
tracer = obs.init()                          # exporters live; stdlib logging bridged

with obs.agent_run("agent.myagent", correlation_id=cid) as span:
    logger.info("doing work")                # stamped with the span's trace id
    result = do_work()

See docs/observability.md for the full setup, the API, and migration from a per-agent bootstrap.

Agent Gateway Example

LLM calls routed through the org's deployed agentgateway — one m2m credential instead of per-provider API keys. The gateway lives on its own host (not the control-plane base_url); the SDK wires the rotating bearer, the x-org routing header, and the URL into a standard openai client and hands it back.

gateway = client.gateway(org_id="2", gateway_base_url="http://localhost:4000")

oai = gateway.openai_client()   # standard openai.OpenAI, pre-wired to the gateway
r = oai.chat.completions.create(
    model="biglambda1",         # virtual-model name from the org's gateway config
    messages=[{"role": "user", "content": "Summarize this rule ..."}],
    temperature=0.0,
)

# Streaming / tools / structured outputs — plain openai SDK
for chunk in oai.chat.completions.create(model="biglambda1", messages=[...], stream=True):
    ...

# Async (e.g. httpx-based agents)
aoai = gateway.async_openai_client()

See docs/gateway.md for prod/test URL selection, URL discovery, rotation-safe DIY openai wiring, and error semantics.

Examples

export AGENCY_AUTH_URL="http://localhost:8080/realms/agency/protocol/openid-connect/token"
export AGENCY_API_URL="http://localhost:13001"
export AGENCY_ORG_ID="2"
export AGENCY_CLIENT_ID="your-client-id"
export AGENCY_CLIENT_SECRET="your-client-secret"

python examples/quick_clone_dataset.py
python examples/quick_create_prompt.py
python examples/quick_export_ontology.py
python examples/quick_execute_rule.py
python examples/quick_files.py
python examples/quick_observability.py   # requires the [observability] extra
python examples/quick_gateway.py         # needs GATEWAY_BASE_URL (+ GATEWAY_MODEL); local gateway on :4000

To verify the SDK end to end against the local platform stack (gts-local-environment), follow docs/local_e2e.md.