nage-ai 0.1.1

Official Python SDK for Nage — source-attributed AI on SEDIM

nage-ai — Python SDK

Official Python client for Nage — source-attributed AI on the SEDIM architecture.

pip install nage-ai

(Package is published as nage-ai; import stays as nage.)

Why Nage

Every response ships with a STEMMA — a real-time attribution map showing which source contributed how much. For regulated teams (legal, compliance, finance, healthcare) where "the model said so" isn't a good answer, STEMMA is how you defend every claim.

Quickstart

from nage import Nage

client = Nage(api_key="nk_live_...")     # or set NAGE_API_KEY env var

r = client.think(
    query="Explain the GDPR right to erasure",
    platform="nm/fehm",
    inference_mode="top_k",
)

print(r.text)
print(r.stemma.weights)       # {'fehm-en': 0.71, 'cortex-reasoning': 0.21, ...}
print(r.stemma.dominant_varve) # 'fehm-en'
print(r.stemma.entropy)        # 0.52
print(r.latency_ms)            # 847

VARVE management

for v in client.varves.list():
    print(v.name, v.status, v.rank)

health = client.varves.health("<varve-id>")
# health.status ∈ {'optimal', 'weak', 'collapsed', 'overtrained', 'unknown'}
# health.rho — orbital ρ (want [0.05, 0.30])

# GDPR / KVKK Article 17 surgical removal
client.varves.privacy_delete("<varve-id>")

Upload + train a VARVE

varve = client.ingest.upload_and_train(
    file_path="./contracts/q1-2026.pdf",
    name="contracts-q1-2026",
    layer="FEHM",
    varve_type="flash",     # 'ephemeral' | 'flash' | 'full'
    wait_for_ready=True,    # poll until training completes
)
print(varve.id, varve.status)

Streaming conversation context

# Multi-turn conversation — pass prior messages as context
context = []
for turn in ["Hello", "What's STEMMA?"]:
    r = client.think(query=turn, context=context)
    context.append({"role": "user", "content": turn})
    context.append({"role": "assistant", "content": r.text})
    print(f"> {turn}\n{r.text}\n")

Error handling

All errors inherit from NageError:

from nage import Nage, RateLimitError, AuthError, NageError

try:
    r = client.think(query="...")
except RateLimitError as e:
    print(f"Rate-limited. Retry after {e.retry_after}s.")
except AuthError as e:
    print(f"Auth: {e}")
except NageError as e:
    print(f"Other: {e.status} — {e.body}")

The client retries 429 and 5xx automatically (up to max_retries=3, exponential backoff with jitter). You only see these if the retries exhaust.

Configuration

client = Nage(
    api_key="nk_live_...",         # or NAGE_API_KEY env var
    base_url="https://api.sedim.ai", # or NAGE_BASE_URL env var
    timeout_s=90,                   # cold-start can hit ~30s
    max_retries=3,
)

You can also authenticate with a user JWT (for platform-scoped operations like Canvas editing):

client = Nage(bearer_token="<jwt>")

Context manager

with Nage(api_key="nk_live_...") as client:
    r = client.think(query="...")
# Connection closed automatically

Links

• Platform: https://nage.ai
• Docs: https://nage.ai/docs
• Dashboard + keys: https://nage.ai/dashboard
• Status: https://nage.ai/status
• GitHub: https://github.com/NageAI

License

Apache-2.0