Bring Ci Cd
to LangChain
Learn how to connect Dagger (Programmable CI) to LangChain and start using 10 AI agent tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code.
GDPR Free for SubscribersCompatible with every major AI agent and IDE
Gemini
What is the Dagger (Programmable CI) MCP Server?
Connect to the Dagger Engine to orchestrate your delivery pipelines using a powerful, programmable GraphQL API. This server allows your AI agent to interact directly with Dagger's Directed Acyclic Graph (DAG) of operations.
What you can do
- Container Orchestration — Initialize scratch containers, pull images, and manage OCI-compatible states.
- GraphQL Workflows — Execute raw GraphQL queries to compose complex build and test logic dynamically.
- Source Control — Query Git repositories and host environments to pull source code into your pipelines.
- Resource Management — Handle secrets securely, manage persistent cache volumes, and fetch remote files via HTTP.
- Module Inspection — Query the current module state and engine version to ensure environment consistency.
How it works
- Ensure a Dagger Engine is running in your environment.
- Provide the session port and token generated by the Dagger CLI.
- Start building and deploying through natural language commands.
Who is this for?
- DevOps Engineers — Automate pipeline debugging and execution without leaving the chat interface.
- Software Developers — Run builds, tests, and container operations directly from the code editor.
- SREs — Inspect engine states and orchestrate infrastructure tasks using programmable CI logic.
Built-in capabilities (10)
You can chain fields to create a Directed Acyclic Graph (DAG) of operations. Execute a raw GraphQL query against the Dagger engine
Constructs a cache volume
Creates a scratch container and returns its ID
Queries the current module
Creates an empty directory and returns its ID
Queries a Git repository
Queries the host environment
Returns a file from a URL
g., env://VAR_NAME, file://PATH, cmd://COMMAND). Creates a new secret
Get the Dagger Engine version
Why LangChain?
LangChain's ecosystem of 500+ components combines seamlessly with Dagger (Programmable CI) 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.
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The largest ecosystem of integrations, chains, and agents. combine Dagger (Programmable CI) MCP tools with 500+ LangChain components
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Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
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LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
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Memory and conversation persistence let agents maintain context across Dagger (Programmable CI) queries for multi-turn workflows
Dagger (Programmable CI) in LangChain
Dagger (Programmable CI) and 4,000+ other MCP servers. One platform. One governance layer.
Teams that connect Dagger (Programmable CI) to LangChain through Vinkius don't need to source, host, or maintain individual MCP servers. Every tool call runs inside a hardened runtime with credential isolation, DLP, and a signed audit chain.
Raw MCP | Vinkius | |
|---|---|---|
| Server catalog | Find and host yourself | 4,000+ managed |
| Infrastructure | Self-hosted | Sandboxed V8 isolates |
| Credential handling | Plaintext in config | Vault + runtime injection |
| Data loss prevention | None | Configurable DLP policies |
| Kill switch | None | Global instant shutdown |
| Financial circuit breakers | None | Per-server limits + alerts |
| Audit trail | None | Ed25519 signed logs |
| SIEM log streaming | None | Splunk, Datadog, Webhook |
| Honeytokens | None | Canary alerts on leak |
| Custom domains | Not applicable | DNS challenge verified |
| GDPR compliance | Manual effort | Automated purge + export |
Why teams choose Vinkius for Dagger (Programmable CI) in LangChain
The Dagger (Programmable CI) 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. All 10 tools execute in hardened sandboxes optimized for native MCP execution.
Your AI agents in LangChain only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure, zero maintenance.
* 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
How Vinkius secures
Dagger (Programmable CI) for LangChain
Every tool call from LangChain to the Dagger (Programmable CI) MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.
Frequently asked questions
How do I run a specific build command using Dagger?
You can use the execute_graphql_query tool to send a raw GraphQL query to the Dagger engine, allowing you to chain container operations like from, withExec, and stdout.
Can I access files from my local machine in the pipeline?
Yes, the query_host tool allows you to retrieve the state ID for your host environment, which can then be used to mount local directories into your Dagger containers.
How are secrets handled in this integration?
Use the query_secret tool to load secrets from URIs (like env:// or file://). This returns a secret ID that can be safely passed to other Dagger operations without exposing the plaintext value.
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.
Can I trace MCP tool calls in LangSmith?
Yes. All MCP tool invocations appear as traced steps in LangSmith, showing input parameters, response payloads, latency, and token usage.
MultiServerMCPClient not found
Install: pip install langchain-mcp-adapters
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