Can I insert new test data dynamically tracking specific limits?

Yes. Utilizing the `insert_dataset_row` method, you can effortlessly inject exact JSON tracking payload mapping strings directly inside the text corpus evaluating the final results.

Does it pull out original Prompt definitions stored securely?

Certainly. The `get_prompt` command isolates and returns perfectly version-controlled bounding parameters slicing literal templates natively hosted under the Braintrust database.

How deeply can it inspect test regressions or scoring limits?

Using the robust `list_experiments` call, you can branch full arrays separating LLM version behaviors over massive iterations tracking the performance anomalies accurately.

Braintrust MCP Server for LangChain 10 tools — connect in under 2 minutes

Name: Braintrust
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 10 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Braintrust through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

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

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "braintrust": {
            "transport": "streamable_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,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using Braintrust, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Braintrust MCP Server

Connect your Braintrust AI observation platform to any agent and maintain intense logic evaluation capabilities directly over conversation.

LangChain's ecosystem of 500+ components combines seamlessly with Braintrust through native MCP adapters. Connect 10 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

What you can do

Project Analytics — Retrieve logic banks and branch isolated AI test sets
Experiments — Create real trace regression tests appending unique LLM scoring iterations
Datasets — Query accurate Ground Truth sets and insert new prompt templates mapping your system accuracy
Prompt Versioning — Grab perfectly frozen semantic prompts without editing core code boundaries

The Braintrust MCP Server exposes 10 tools through the Vinkius. Connect it to LangChain in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Braintrust to LangChain via MCP

Follow these steps to integrate the Braintrust MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 10 tools from Braintrust via MCP

Why Use LangChain with the Braintrust MCP Server

LangChain provides unique advantages when paired with Braintrust through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents. combine Braintrust MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across Braintrust queries for multi-turn workflows

Braintrust + LangChain Use Cases

Practical scenarios where LangChain combined with the Braintrust MCP Server delivers measurable value.

RAG with live data: combine Braintrust tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query Braintrust, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain Braintrust tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every Braintrust tool call, measure latency, and optimize your agent's performance

Braintrust MCP Tools for LangChain (10)

These 10 tools become available when you connect Braintrust to LangChain via MCP:

create_experiment

Establish a new historical experiment trace to record LLM pipeline tests

create_project

Create a new project environment for tracking AI evaluations and datasets

get_dataset

Retrieve a specific dataset containing exact schemas bounding LLM outputs

get_prompt

Retrieve exact variable contexts and literal text templates for a prompt

insert_dataset_row

Append new test cases into a dataset matrix targeting specific evaluations

list_datasets

List isolated Ground Truth text banks used for automated evaluation scoring

list_env_vars

Probe the Braintrust AI Gateway configurations managing model API keys securely

list_experiments

Retrieve all evaluation experiments mapping model test scores and metrics

list_projects

Retrieve the list of all AI evaluation projects in Braintrust

list_prompts

Retrieve explicitly version-controlled system prompts isolated in Braintrust

Example Prompts for Braintrust in LangChain

Ready-to-use prompts you can give your LangChain agent to start working with Braintrust immediately.

"List all active test datasets configured under Braintrust."

"Look up prompt template using specific ID XYZ."

"Analyze recent experiments across multiple models testing behavior."

Troubleshooting Braintrust MCP Server with LangChain

Common issues when connecting Braintrust to LangChain through the Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Braintrust + LangChain FAQ

Common questions about integrating Braintrust MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.