Flatland in LangChain
and LangGraph.

pip install langchain-mcp-adapters langgraph langchain-anthropic

from langchain_mcp_adapters.client import MultiServerMCPClient

client = MultiServerMCPClient({
    "flatland": {
        "transport": "streamable_http",
        "url": "https://api.flatlandfi.com/mcp",
        "headers": {"X-API-Key": "fl_live_YOUR_KEY_HERE"},
    }
})

tools = await client.get_tools()
# flatland_init, flatland_create_model, flatland_bulk_add,
# flatland_compile, flatland_create_scenario, flatland_diff_scenarios,
# flatland_sensitivity, flatland_save_model, flatland_load_model, ...

import asyncio
from langchain_anthropic import ChatAnthropic
from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent

async def build_model(description: str):
    client = MultiServerMCPClient({
        "flatland": {
            "transport": "streamable_http",
            "url": "https://api.flatlandfi.com/mcp",
            "headers": {"X-API-Key": "fl_live_YOUR_KEY_HERE"},
        }
    })
    tools = await client.get_tools()
    agent = create_react_agent(
        ChatAnthropic(model="claude-opus-4-5"),
        tools,
    )
    return await agent.ainvoke({
        "messages": [{
            "role": "user",
            "content": f"""
                Call flatland_init first.

                Build a financial model for this business:
                {description}

                1. flatland_create_model
                2. flatland_bulk_add — all typed drivers, with
                   assertions on key metrics
                3. flatland_compile
                4. flatland_sensitivity on the primary output driver
                5. Return compiled results and sensitivity ranking
            """,
        }]
    })

result = asyncio.run(build_model(
    "SaaS. $18K MRR, 12% monthly growth, 6% monthly churn. "
    "$900 CAC, 18-month payback target. 4 engineers at $160K/yr. "
    "Build a 12-month P&L with unit economics."
))

async def run_scenario(base: str, question: str):
    client = MultiServerMCPClient({
        "flatland": {
            "transport": "streamable_http",
            "url": "https://api.flatlandfi.com/mcp",
            "headers": {"X-API-Key": "fl_live_YOUR_KEY_HERE"},
        }
    })
    tools = await client.get_tools()
    agent = create_react_agent(
        ChatAnthropic(model="claude-opus-4-5"),
        tools,
    )
    return await agent.ainvoke({
        "messages": [{
            "role": "user",
            "content": f"""
                flatland_init, then build this base model: {base}

                Then answer: {question}

                1. flatland_create_model + flatland_bulk_add + flatland_compile
                2. flatland_create_scenario — override only the
                   assumptions that change to answer the question
                3. flatland_compile_scenario
                4. flatland_diff_scenarios — return which drivers
                   changed and by how much each output moved
            """,
        }]
    })

result = asyncio.run(run_scenario(
    base="E-commerce. $50K/mo ad spend, 2.8% CVR, $85 AOV, 28% repeat rate.",
    question="What happens if CVR drops to 1.4% and we cut ad spend 30%?",
))

# Session 1 — build and save
await agent.ainvoke({"messages": [{"role": "user", "content":
    "flatland_init, build a headcount model for a 12-person eng team "
    "with loaded costs and runway assertion, then "
    "flatland_save_model name='headcount-q3'"
}]})

# Session 2 — load and update
await agent.ainvoke({"messages": [{"role": "user", "content":
    "flatland_init, flatland_load_model name='headcount-q3', "
    "add 2 engineers at $175K/yr, recompile, "
    "run sensitivity on total_headcount_cost"
}]})