Paste what your AI told you. See if it's still true.
Models answer with confidence and a frozen brain. Paste any claim and we check it against the live present, and hand back the current truth with an Ed25519 signature you can verify yourself.
An agent can't tell "I remember" from "it's true."
Before an autonomous agent acts, answers a user, files a report, makes a call, it can run the claim past live data and get back a verdict it can prove. That's the difference between a model that sounds right and a system that checked. One call, keyless:
POST https://dynamicfeed.ai/v1/batch with {"calls":[{"tool":"reality_check","args":{"claim":"…"}}]}, returns the verdict, the live value, a confidence, and a signature. Wire it into LangChain, CrewAI, the OpenAI Agents SDK & more as a pre-answer guard. Verdicts are signed but not gospel, we prove the live value, not the wisdom of the claim.
The whole guard is ~8 lines, keyless, stdlib-only, drop it in front of any answer your agent gives about the present:
import json, urllib.request
def guard(claim, base="https://dynamicfeed.ai"):
"""True = safe to assert; False = stale/wrong (answer from the live note instead)."""
req = urllib.request.Request(base + "/v1/batch",
data=json.dumps({"calls":[{"tool":"reality_check","args":{"claim":claim}}]}).encode(),
headers={"Content-Type":"application/json"})
d = json.load(urllib.request.urlopen(req, timeout=25))["results"][0]["data"]
return d.get("verdict") != "stale_or_wrong", d.get("note","")
ok, live = guard("the latest stable Python is 3.11")
# ok == False · live == "You said python 3.11; the latest stable is 3.14.6. Out of date."
Give your agent a reality check.
Keyless, signed, one line. Connect it, or see what your model's already wrong about.