Codefresh MCP Server for LangChain 8 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Codefresh through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
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Vinkius supports streamable HTTP and SSE.
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({
"codefresh": {
"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 Codefresh, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
asyncio.run(main())
* 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 Codefresh MCP Server
Connect your Codefresh account to any AI agent and take full control of your CI/CD and cloud-native delivery through natural conversation. Streamline how you automate and monitor software deployments natively.
LangChain's ecosystem of 500+ components combines seamlessly with Codefresh through native MCP adapters. Connect 8 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
- Pipeline Oversight — List and retrieve details for all CI/CD pipelines including their configurations natively
- Build Management — Trigger new builds for specific pipelines and specify branches or variables flawlessly
- Workflow Intelligence — Access detailed status and execution info for recent builds (workflows) flawlessly
- Cluster Logistics — Monitor all connected Kubernetes and delivery clusters to verify deployment targets securely
- Environment Auditing — List shared contexts, including secrets and variables, used in your workflows securely
- integrated Visibility — Retrieve detailed build metadata and user profile information directly within your workspace
The Codefresh MCP Server exposes 8 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 Codefresh to LangChain via MCP
Follow these steps to integrate the Codefresh 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 8 tools from Codefresh via MCP
Why Use LangChain with the Codefresh MCP Server
LangChain provides unique advantages when paired with Codefresh through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Codefresh 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 Codefresh queries for multi-turn workflows
Codefresh + LangChain Use Cases
Practical scenarios where LangChain combined with the Codefresh MCP Server delivers measurable value.
RAG with live data: combine Codefresh tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Codefresh, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Codefresh tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Codefresh tool call, measure latency, and optimize your agent's performance
Codefresh MCP Tools for LangChain (8)
These 8 tools become available when you connect Codefresh to LangChain via MCP:
get_build_execution_details
Get detailed status and execution info for a specific build
get_my_codefresh_profile
Retrieve information about the authenticated user and account
get_pipeline_configuration
Get detailed information for a specific pipeline
list_codefresh_builds
List all recent builds (workflows) in the account
list_codefresh_pipelines
List all CI/CD pipelines in the account
list_delivery_clusters
List all connected Kubernetes/Delivery clusters
list_shared_contexts
List all shared environment contexts (secrets, variables)
trigger_codefresh_build
Trigger a new build for a specific pipeline
Example Prompts for Codefresh in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with Codefresh immediately.
"List all my Codefresh pipelines."
"Trigger the 'api-service-ci' pipeline on the 'develop' branch."
"Show me the status of my recent builds."
Troubleshooting Codefresh MCP Server with LangChain
Common issues when connecting Codefresh to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersCodefresh + LangChain FAQ
Common questions about integrating Codefresh MCP Server with LangChain.
How does LangChain connect to MCP servers?
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?
Can I trace MCP tool calls in LangSmith?
Connect Codefresh with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Codefresh to LangChain
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.