tensorfeed

Python SDK for the TensorFeed.ai API.

Free endpoints (news, status, models, benchmarks, history, routing preview) need no auth. The premium tier (top-N routing, plus more endpoints landing later) is paid via USDC on Base. No accounts, no API keys, no traditional payment processors.

Featured: Route Verdict

The single best model to use right now, as one signed call. Route Verdict fuses live pricing, contamination-discounted benchmark capability, real production usage, measured p95 latency probes, live incident state, and deprecation flags into one ranked decision, with an AFTA-signed receipt over the exact inputs. Instead of stitching together pricing pages, benchmark leaderboards, status dashboards, and your own latency tests, you get a current, defensible routing answer in one request.

Zero install, no key, one command (works today)

curl -s -A "tensorfeed-cc-quickstart" "https://tensorfeed.ai/api/preview/route-verdict?task=code"

Swap task for reasoning, creative, or general, or pass ?model=<id-or-name> to score a specific model. The free preview is 10 calls per day per IP, no token. Abridged real response:

{
  "verdict": {
    "rank": 1,
    "model": { "name": "Gemini 2.5 Pro", "provider": "google" },
    "pricing": { "blended": 5.625, "unit": "per 1M tokens" },
    "latency": { "measured_p95_ms": 1223, "source": "measured_probe" },
    "operational": { "ok": true, "status": "operational" },
    "composite_score": 0.8449,
    "why": "code quality 0.6498 after trust discount; corroborated by real usage (rank 5, 6.5% share, flat); measured p95 1223 ms; operational; blended $5.625 / 1M"
  },
  "rate_limit": { "limit": 10, "remaining": 9, "scope": "per IP per UTC day" },
  "upgrade": { "premium_endpoint": "/api/premium/route-verdict", "adds": ["runners_up", "AFTA-signed receipt", "filter params", "no rate limit"] }
}

The premium version (/api/premium/route-verdict, 1 credit) adds ranked runners-up, the constraint filters (max_latency_p95_ms, budget, min_quality, require_operational, exclude_deprecated), the AFTA-signed receipt, and no rate limit. It needs a token; credits come from tensorfeed.ai/developers/agent-payments.

Native SDK call

from tensorfeed import TensorFeed

tf = TensorFeed()

# Free preview: the top verdict only, no token. 10 calls/day per IP.
preview = tf.route_verdict_preview(task="code")
print(preview["verdict"]["model"]["name"], preview["verdict"]["why"])

# Premium: runners-up, constraint filters, and the AFTA-signed receipt.
# Needs a token (see the payment quickstart below).
tf_paid = TensorFeed(token="YOUR_TOKEN")
verdict = tf_paid.route_verdict(task="code", max_latency_p95_ms=1500)
print(verdict["verdict"], verdict["runners_up"])

Pass model="<id-or-name>" instead of task= to score one model. Both methods accept exactly one of the two selectors.

The agent path (MCP)

With the @tensorfeed/mcp-server MCP server installed, an agent gets two tools: route_verdict_preview (free, the pick plus reasoning) and route_verdict (1 credit, ranked runners-up, constraint filters, and the AFTA-signed receipt it can audit).

Why it matters

Models, prices, and latency move week to week. Route Verdict is one signed call an agent can act on now and later prove why it routed the way it did, without rebuilding the comparison from scratch each time.

Install

pip install tensorfeed

Stdlib-only. No external dependencies.

Free Tier

from tensorfeed import TensorFeed

tf = TensorFeed()

# News
for article in tf.news(category="research", limit=10)["articles"]:
    print(article["title"])

# Live AI service status
for svc in tf.status()["services"]:
    print(f'{svc["name"]}: {svc["status"]}')

# Is a service down?
print(tf.is_down("claude"))

# Model pricing and benchmarks
print(tf.models())
print(tf.benchmarks())

# Daily history snapshots (the moat)
print(tf.history())  # list of available dates
print(tf.history_snapshot("2026-04-27", "pricing"))

# Free routing preview (top-1 model, 5 calls/day per IP)
preview = tf.routing_preview(task="code")
print(preview["recommendation"])

Premium Tier (paid, USDC on Base)

from tensorfeed import TensorFeed, PaymentRequired

tf = TensorFeed()

# Step 1: get a 30-minute quote. sender_wallet is the Base wallet you will
# send the USDC from (required); quote['wallet'] below is where you send it.
quote = tf.buy_credits(amount_usd=1.00, sender_wallet="0xYourBaseWallet")
print(f"Send {quote['amount_usd']} USDC on Base to {quote['wallet']}")
print(f"Memo: {quote['memo']} (expires in {quote['ttl_seconds']}s)")
print(f"Will get: {quote['credits']} credits")

# Step 2: send the USDC tx with your wallet
# (auto-send via web3 is on the roadmap; for v1.1 you sign and send manually)

# Step 3: confirm with the tx hash
result = tf.confirm(tx_hash="0xYOUR_TX_HASH", nonce=quote["memo"])
print(f"Got {result['credits']} credits, token: {result['token']}")
# The token is also stored on `tf` automatically; routing() will use it.

# Step 4: call premium endpoints
rec = tf.routing(task="code", budget=5.0, top_n=5)
for r in rec["recommendations"]:
    print(f'#{r["rank"]}: {r["model"]["name"]} (score: {r["composite_score"]:.2f})')

# Custom routing weights
rec = tf.routing(
    task="general",
    weights={"quality": 0.6, "cost": 0.3, "availability": 0.1, "latency": 0.0},
)

# Premium history series (1 credit each, range default = last 30 days, max 90)
prices = tf.pricing_series(model="Claude Opus 4.7")
print(f'Price changed {prices["summary"]["delta_pct_blended"]}% over the window')

scores = tf.benchmark_series(model="Claude Opus 4.7", benchmark="swe_bench")
print(f'SWE-bench moved {scores["summary"]["delta_pp"]} pp')

uptime = tf.status_uptime(provider="anthropic")
print(f'Anthropic uptime: {uptime["uptime_pct"]}% over {uptime["days_with_data"]} days')

# Premium webhook watches (1 credit per registration, free reads)
created = tf.create_watch(
    spec={
        "type": "price",
        "model": "Claude Opus 4.7",
        "field": "blended",
        "op": "lt",
        "threshold": 30,
    },
    callback_url="https://agent.example.com/hook",
    secret="any-shared-secret",  # used to sign deliveries
)
watch_id = created["watch"]["id"]
print(f"Watch {watch_id} active until {created['watch']['expires_at']}")

# When a delivery arrives, verify it:
#   sig = request.headers["X-TensorFeed-Signature"]  # "sha256=<hex>"
#   import hmac, hashlib
#   expected = "sha256=" + hmac.new(b"any-shared-secret", body, hashlib.sha256).hexdigest()
#   assert hmac.compare_digest(sig, expected)

print(tf.list_watches())          # see all your active watches
print(tf.get_watch(watch_id))     # check fire_count, last_fired_at
tf.delete_watch(watch_id)         # remove when done

# Check remaining credits
print(tf.balance())

Auto-Send (optional, web3 extra)

The base SDK keeps you dependency-free, but if you want to skip the manual tx step, install with the web3 extra:

pip install 'tensorfeed[web3]'

Then tf.purchase_credits() quotes, signs, broadcasts, and confirms in one call:

import os
from tensorfeed import TensorFeed

tf = TensorFeed()

result = tf.purchase_credits(
    amount_usd=1.00,
    private_key=os.environ["TENSORFEED_PRIVATE_KEY"],  # NEVER hardcode
    # rpc_url="https://base-mainnet.g.alchemy.com/v2/<key>",  # optional
)
print(result["token"])      # auto-stored on tf
print(result["tx_hash"])    # for your records
print(result["credits"])    # how many credits were minted

# Token already on tf, so:
rec = tf.routing(task="code")

Security: read the key from a secret manager or env var. Never commit it. The raw key is held in memory only for the duration of one signing call.

Reusing a Token Across Sessions

Save the token after confirm(). Reuse it next time:

# Save once
token = result["token"]

# Reuse in another process / job
tf = TensorFeed(token=token)
print(tf.balance())
rec = tf.routing(task="code")

Framework Tools (LangChain, CrewAI)

Drop TensorFeed into an agent framework as ready-made tools.

pip install 'tensorfeed[langchain]'   # or 'tensorfeed[crewai]'

from tensorfeed.langchain import tensorfeed_tools, tensorfeed_premium_tools

# Five free tools (news, status, attention, harnesses, routing preview)
free = tensorfeed_tools()

# Free plus the paid catalog (whats_new, full routing, compare models,
# cost projection, news search, provider deep dive, status leaderboard)
all_tools = tensorfeed_tools(token="YOUR_TOKEN", include_premium=True)

# Just the paid catalog
paid = tensorfeed_premium_tools(token="YOUR_TOKEN")

CrewAI is identical: from tensorfeed.crewai import tensorfeed_tools, tensorfeed_premium_tools.

The default tensorfeed_tools() stays free-only and unchanged, so existing code keeps working. Premium is opt-in via include_premium=True.

Payment posture. The premium tools never move funds. Attach them with or without a token: called without spendable credits they return a short, actionable guidance string (explaining that an operator provisions credits out of band) instead of paying or raising. There is no code path from a tool call to the wallet or the web3 signer. Credit purchases stay an explicit, per-action operator decision, by design. This is enforced in one shared module and covered by tests, including a static guard that fails if a future change introduces an autonomous-payment path.

Error Handling

from tensorfeed import TensorFeed, PaymentRequired, RateLimited, TensorFeedError

tf = TensorFeed(token="bad_token")
try:
    tf.routing(task="code")
except PaymentRequired as e:
    # 402: token invalid, expired, or out of credits
    # e.payload contains wallet, credits required, top_up_at, etc.
    print("Need to top up:", e.payload)
except RateLimited as e:
    # 429: free preview tier hit its 5/day per-IP limit
    print("Hit the rate limit:", e.payload)
except TensorFeedError as e:
    # Other API errors
    print("API error", e.status_code, e.payload)

API Reference

Free

Token-required

Auto-send (requires tensorfeed[web3])

Wallet & Trust

The TensorFeed payment wallet is 0x549c82e6bfc54bdae9a2073744cbc2af5d1fc6d1 on Base mainnet. USDC contract: 0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913.

Cross-check this address before sending funds at:

• https://tensorfeed.ai/llms.txt
• https://tensorfeed.ai/api/payment/info
• https://github.com/RipperMercs/tensorfeed (README)
• https://x.com/tensorfeed (bio)

If any source disagrees, do not send.

Premium Data Terms

Premium API responses are licensed for inference use only. Use of TensorFeed premium data for training, fine-tuning, evaluation, or distillation of ML models is prohibited (Section 17.1 of the Terms).

No refunds; credits do not expire

All credit purchases are final and non-refundable per Section 17.5 of the Terms. Credits never expire and are jointly redeemable on tensorfeed.ai and terminalfeed.io. The recommended pattern is to buy in small increments (for example, $1 USDC for 50 credits) until call volume is calibrated, then top up as needed.

Sanctions

Premium API access is unavailable to persons or entities subject to OFAC, EU, UK, or UN sanctions, and to residents of comprehensively sanctioned jurisdictions (Cuba, Iran, North Korea, Syria, Crimea, Donetsk, Luhansk). Inbound credit-purchase transactions are screened against the Chainalysis public sanctions API. See Section 17.9 of the Terms.

Links

• API Docs
• TensorFeed.ai
• GitHub
• Changelog

License

MIT