lumify-sdk 0.1.0

Official Python SDK for the Lumify agent-ready sports intelligence API (schedules, live scores, odds, betting splits, and AI bet intelligence).

lumify-sdk

Official Python client for Lumify, the agent-ready sports intelligence API — schedules, live scores, odds, line movement, public betting splits, and AI bet intelligence across MLB, NFL, NCAAF, NBA, NHL, tennis, and soccer.

Status: v0.1.0 — the data-plane slice (sports/seasons/events/teams/players, SSE streaming, webhooks, agent onboarding). Same data as the REST API, the TypeScript SDK, and the MCP server — this SDK is the typed REST path, not a third implementation. Synchronous today; an async client (AsyncLumify) is the planned 0.2.0 follow-up.

Install

pip install lumify-sdk

Requires Python 3.8+. Zero runtime dependencies — the transport is stdlib urllib and signatures use stdlib hmac/hashlib.

Quick start

import os
from lumify import Lumify

client = Lumify(api_key=os.environ["LUMIFY_API_KEY"])

sports = client.sports.list()

event = client.events.get(12345, include_odds=True, include_intelligence=True)
print(event["status"], event.get("intelligence"))

Create a key at https://lumify.ai/api-keys or programmatically via client.agent.keys.create().

Resources

client.sports.list()  # client.seasons.list()
client.events.list(**filters)  # .get(id, include_odds=?, include_intelligence=?, bookmaker=?)
client.events.batch_get(event_ids)  # up to 25 ids in one round-trip; see below
client.events.query(text, limit=None)  # natural-language search, e.g. "live nfl games today"
client.events.odds(id)  # .odds_history(id) / .score(id) / .intelligence(id) / .splits(id)
client.events.stream(id)              # SSE iterator of live score updates
client.events.paginate(**filters)     # .iterate(**filters) — cursor pagination helpers
client.teams.list(**filters)  # .get(id)
client.players.list(**filters)  # .get(id) / .events(id, **filters)
client.webhooks.create(url=...)  # .list() / .delete(id) / .verify(secret, header, raw_body)
client.agent.keys.create()  # .list() / .revoke(id)
client.agent.credits.get()  # .list_packs() / .topup(pack_id)

Every method maps 1:1 to a REST endpoint and returns the same JSON shape you'd get from curl (a plain dict, typed as the matching model in lumify.models) — see https://lumify.ai/docs/reference for full field docs.

Pagination

List endpoints are cursor-paginated (after_id/limit, max 100). Use the iterator helpers instead of tracking cursors by hand:

for event in client.events.iterate(sport="nfl", status="scheduled"):
    print(event["id"], event["starts_at"])

# Or page-by-page (events pages use "events", not "data"):
for page in client.events.paginate(sport="nfl", limit=50):
    print(len(page.get("events", [])), "events, next cursor:", page.get("next_after_id"))

Standalone helpers paginate() / iterate_items() are also exported for custom fetchers.

Batch event lookup

Already have a list of event ids (e.g. from client.events.list()) and want full detail for each? batch_get fetches up to 25 in a single round-trip instead of one GET per event:

result = client.events.batch_get([101, 102, 999999999], include_odds=True)
print(result["total"], "found;", result["not_found"], "missing")
for event in result["events"]:
    print(event["id"], event["status"])

Duplicate ids are billed once; ids that don't exist are returned under not_found and cost nothing — credits are the sum of each event's normal GET /v1/events/{id} cost, with the same billing-fairness rules (unavailable odds/intelligence stay free).

Natural-language search

query maps free text to the same filters client.events.list() accepts — sport, status, and date/date-range — using a small, deterministic, rule-based mapper (not an LLM call). Costs 1 credit, same as list():

result = client.events.query("live nfl games today", limit=5)
print(result["interpreted"])          # {"sport": "nfl", "status": "inprogress", "date": "2026-07-15", ...}
print(result["equivalent_request"])   # "GET /v1/events?sport=nfl&status=inprogress&date=2026-07-15"
print(result["unrecognized_terms"])   # words that didn't map to a filter
for event in result["events"]:
    print(event["id"], event["status"])

Live score streaming (SSE)

for evt in client.events.stream(event_id):
    if evt.event == "score":
        print(evt.data["status"], evt.data.get("clock"))
    if evt.event == "done":
        break

Cheaper than polling client.events.score(id) — the server only emits on change, plus periodic keep-alives, and closes when the event finishes.

Webhooks

sub = client.webhooks.create(url="https://you.example.com/hooks/lumify")
# sub["signing_secret"] ("whsec_...") is returned once — store it.

# In your webhook handler, verify against the *raw* request body:
client.webhooks.verify(signing_secret, request.headers["Lumify-Signature"], raw_body)

verify() raises WebhookSignatureError (bad format, signature mismatch, or a stale/replayed timestamp) — treat that as "reject with 4xx", not a crash.

Errors

Every non-2xx response raises a typed subclass of LumifyError — switch on err.code (the stable machine-readable slug), not str(err):

from lumify import NotFoundError, RateLimitError, ValidationError

try:
    client.events.get(999999999)
except NotFoundError:
    ...
except RateLimitError as err:
    print("retry after", err.retry_after, "s")
except ValidationError as err:
    print(err.field_errors)

AuthenticationError (401), PaymentError (402), PermissionError (403, has .upgrade_url on sport-scope denials), NotFoundError (404), ValidationError (422, has .field_errors), RateLimitError (429, has .retry_after from the envelope or Retry-After header), APIError (5xx), and ConnectionError (network/timeout, never reached the server) all extend LumifyError (.code, .status, .doc_url, .request_id).

GET requests are automatically retried (default: 2 attempts) with exponential backoff on 429/5xx/network failures, honoring Retry-After. Non-idempotent requests (POST/webhook & key creation) are never auto-retried. Configure with max_retries / timeout on the Lumify constructor.

Credits and rate limits

Every successful response carries X-Credits-Used, X-Credits-Remaining, and X-RateLimit-* headers. Read them via get_meta() (attached out-of-band on the returned object, so it never pollutes json.dumps or iteration):

from lumify import get_meta

odds = client.events.odds(event_id)
meta = get_meta(odds)
print(meta.credits_used, meta.rate_limit_remaining)

Queries for data that isn't available yet (e.g. odds not yet posted) return credits_used == 0 — you're never charged for a "not available" read.

Sync with REST, MCP, and the OpenAPI contract

This SDK's response types (lumify/models.py) are generated from the same filtered OpenAPI slice the TypeScript SDK uses (clients/lumify-sdk/openapi/openapi.sdk.json, produced by scripts/export_openapi_sdk.py at the repo root), not hand-maintained. CI fails if the models are stale, so the two SDKs can't disagree with each other or drift from what the API returns. A shared cross-surface probe matrix (tests/fixtures/agent_contract.json) additionally asserts this SDK builds the exact REST request that REST and MCP agree on. The client ergonomics (this README's resource shape, pagination/SSE/webhook helpers) are hand-written.

python scripts/export_openapi_sdk.py                       # regenerate the schema slice
python clients/lumify-sdk-python/scripts/gen_models.py      # regenerate the Python models

Development

python -m venv .venv && . .venv/bin/activate
pip install -e ".[dev]"
pytest

License

MIT © 2026 Lumify AI

Related

• Docs: https://lumify.ai/docs
• TypeScript SDK: @lumifyai/sdk
• MCP server (for AI agent runtimes): @lumifyai/mcp