How to Use the BlaBlaCar MCP in LangChain

Q: How does LangChain handle a failed BlaBlaCar trip search?

When searchtripsbycity returns zero results, your LangChain agent catches the empty list and automatically triggers searchbustrips. You don't write custom error handlers; the chain's ReAct loop naturally tries the backup tool to find a route.

Q: Can I trace BlaBlaCar tool calls in LangChain using LangSmith?

Yes, every time your LangChain agent calls getdriverprofile or searchtrips, the input parameters and raw JSON payloads are logged. You can see the exact driver ratings and trip IDs passing through your chain in real-time.

Get your LangChain agents hunting down the cheapest BlaBlaCar rides and building multi-leg transit chains.

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Connect BlaBlaCar MCP to LangChain

Create your Vinkius account to connect BlaBlaCar to LangChain and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup BlaBlaCar with LangChain

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Perplexity

Chain BlaBlaCar searches with LangChain

Stop writing manual loops to find BlaBlaCar rides in LangChain. Use `search_trips_by_city` to grab initial carpool options, then pipe that output directly into `get_driver_profile` to filter out drivers with low ratings. Your agent handles the sequence, checking driver credibility without you writing a single line of glue code. If a direct carpool isn't there, the chain doesn't break. The agent automatically switches to `search_bus_trips` to find a backup route. LangSmith traces the entire flow, showing you exactly how the agent decided to pivot from a carpool to a bus.

Multi-day route planning via MCP Server tools

When exact dates don't line up, your LangChain agent uses `search_flexible_dates` to pull up to 50 options across a range of days. It handles the parsing, grouping the cheapest rides first so you don't have to scroll through endless listings manually. For cross-border trips, the agent feeds those flexible dates into `search_international_trips`. It checks the required coordinates and country codes, building a complete itinerary that spans multiple countries while verifying vehicle comfort using `get_trip_details`.

Spatial coordinate mapping for precise pickups

Your LangChain agent can map exact pickup spots by feeding coordinates into `search_trips` and `search_trips_with_radius`. This MCP Server lets you search a wider area when direct city matches are too restrictive, finding drivers who pass near your actual location. Once the agent narrows down the coordinate-based options, it invokes `get_trip_details` to verify the exact pickup spot and vehicle model. You get a clean, filtered list of rides that actually match your geographic constraints instead of generic city centers.

Setup guide

Set up BlaBlaCar MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes BlaBlaCar tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "blablacar-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent BlaBlaCar transactions"
    })
    print(result["messages"][-1].content)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by BlaBlaCar. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about BlaBlaCar MCP in LangChain

When `search_trips_by_city` returns zero results, your LangChain agent catches the empty list and automatically triggers `search_bus_trips`. You don't write custom error handlers; the chain's ReAct loop naturally tries the backup tool to find a route.

Yes, every time your LangChain agent calls `get_driver_profile` or `search_trips`, the input parameters and raw JSON payloads are logged. You can see the exact driver ratings and trip IDs passing through your chain in real-time.

Your agent takes the latitude and longitude from your user prompt and feeds them directly into `search_trips_with_radius`. The MCP Server handles the formatting, so your LangChain chain gets clean JSON back instantly.

You don't have to write authentication code or build complex schemas for tools like `search_international_trips`. The Vinkius MCP Server exposes these tools directly to your agent, letting you focus on the logical chains instead of API maintenance.

Your coordinates, city names, and driver IDs are processed inside an ephemeral V8 sandbox. Vinkius never stores your search parameters or BlaBlaCar profile queries, keeping your travel history completely private.

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