Most "is this property risky?" data your agent can reach is modeled — a prediction from soil maps and flood zones. There's a different, underused signal: measured ground motion. Satellites have been tracking how far the ground has physically moved, to the millimeter, for years.
SibFly packages that into one call: give it a US address, get back how fast the ground is sinking or rising, in mm/year, derived from NASA's OPERA Sentinel-1 InSAR dataset. Measured, not modeled. Negative = sinking.
This post wires it into a LangChain agent in about 30 lines.
Ground subsidence (sinking) quietly wrecks foundations, and it's invisible on a normal map. It shows up in radar interferometry (InSAR): compare two satellite passes over the same spot and you can measure sub-centimeter vertical change. NASA processes this for all of North America and publishes it for free — but the raw data is 400 MB HDF5 granules in radar geometry, not something an agent can query per address.
SibFly does the ETL and serves the pixel: GET /api/v1/motion?address=...
→ a JSON verdict.
pip install -U langchain-sibfly
export SIBFLY_API_KEY="sf_live_..."
Grab a key with free starter credits at sibfly.com. Agents can also self-register with no human in the loop:
curl -X POST https://sibfly.com/api/v1/autonomous/register \
-H "Content-Type: application/json" -d '{"email":"you@example.com"}'
python
from langchain_sibfly import SibflyGroundMotion
motion = SibflyGroundMotion() # reads SIBFLY_API_KEY
print(motion.invoke({"address": "1100 Congress Ave, Austin, TX"}))
{
"status": "ok",
"velocity_vertical_mm_yr": -6.0,
"velocity_uncertainty_mm_yr": 1.5,
"assessment_code": "notable_subsidence",
"confidence": 0.86,
"data_age_days": 73,
"cost_usd": 0.4,
"credits_remaining_usd": 0.6,
}
Route your logic on assessment_code
— a stable enum (rapid_subsidence
, notable_subsidence
, stable
, mild_uplift
, strong_uplift
) — not on the human-readable string.
The thing I appreciate as someone who builds agents: misses are free. Out-of-coverage, no-data, too-stale, and low-confidence responses all come back HTTP 200 with cost_usd: 0
. You only pay $0.40 when you get a real covered reading. And there's a free preview:
motion.invoke({"lat": 30.3244, "lon": -97.8102, "dry_run": True})
You can also refuse to pay for data you'd reject, server-side:
motion.invoke({
"address": "...",
"max_age_days": 400, # too old -> free "stale_data"
"min_confidence": 0.7, # too noisy -> free "low_confidence"
})
python
from langchain_sibfly import SibflyGroundMotion
from langchain_anthropic import ChatAnthropic
from langgraph.prebuilt import create_react_agent
agent = create_react_agent(
ChatAnthropic(model="claude-sonnet-5"),
tools=[SibflyGroundMotion()],
)
out = agent.invoke({"messages": [
("user", "Is the ground sinking under 1100 Congress Ave, Austin TX? "
"Check coverage first so we don't waste a call.")
]})
print(out["messages"][-1].content)
The agent will typically dry_run
first (free), see it's covered, then pull the real reading.
SibFly also runs a hosted MCP server, so any MCP client can use it with no SDK:
https://sibfly.com/mcp (Streamable HTTP, Bearer auth)
It's in the official MCP registry as com.sibfly/ground-motion
. Tools: check_ground_motion
, check_coverage
, check_portfolio
, get_motion_history
, get_account
.
This is a screening signal, not an engineering survey. It tells you a parcel is moving and roughly how fast; it doesn't replace a geotechnical report. SibFly returns an uncertainty (±mm/yr
), a confidence score, and a neighbor_consistent
flag so your agent can tell a real trend from a single noisy pixel. Treat it like a credit-score check: fast, cheap, directional.
llama-index-tools-sibfly