langchain-sibfly 0.1.0

An integration package connecting SibFly (measured ground motion per US address, from NASA InSAR) and LangChain

langchain-sibfly

LangChain integration for SibFly — measured ground motion for any US address.

Give it a US address (or lat/lon) and it tells your agent how fast the ground is sinking or rising, in mm/year, measured from NASA OPERA Sentinel-1 satellite radar (InSAR) — measured, not modeled. Negative mm/year = sinking.

Agent-friendly pricing: $0.40 per covered report, and misses are free (out-of-coverage, no-data, too-stale, and low-confidence results all cost $0). Use dry_run=True for a free coverage + price check first.

Install

pip install -U langchain-sibfly
export SIBFLY_API_KEY="sf_live_..."

Get a key with free starter credits at sibfly.com. Agents can self-register with no human step: POST https://sibfly.com/api/v1/autonomous/register.

Usage

from langchain_sibfly import SibflyGroundMotion

motion = SibflyGroundMotion()  # reads SIBFLY_API_KEY

# By address
print(motion.invoke({"address": "1100 Congress Ave, Austin, TX"}))

# By coordinates, free preview (no charge)
print(motion.invoke({"lat": 30.3244, "lon": -97.8102, "dry_run": True}))

Example result:

{
    "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 logic on assessment_code — one of rapid_subsidence, notable_subsidence, stable, mild_uplift, strong_uplift — not on the human-readable assessment string.

Free gates (avoid paying for data you'd reject)

• dry_run=True — free coverage + would_cost_usd, no reading, no charge.
• max_age_days=N — if the newest data is older than N days, returns a free stale_data result instead of billing.
• min_confidence=0.0..1.0 — below this pixel confidence, returns a free low_confidence result instead of billing.

Use it in an agent

from langchain_sibfly import SibflyGroundMotion
from langgraph.prebuilt import create_react_agent
from langchain_anthropic import ChatAnthropic

agent = create_react_agent(
    ChatAnthropic(model="claude-sonnet-5"),
    tools=[SibflyGroundMotion()],
)
agent.invoke({"messages": [("user", "Is the ground sinking at 1100 Congress Ave, Austin TX?")]})

Links

• API: https://sibfly.com · Docs: https://sibfly.com/docs · Agent docs: https://sibfly.com/llms.txt
• OpenAPI: https://sibfly.com/openapi.json · Machine schema: https://sibfly.com/api/v1/schema
• MCP server: https://sibfly.com/mcp (also in the official MCP registry as com.sibfly/ground-motion)

License

MIT