import VideoPlayer from '@components/VideoPlayer';

LLM Integration

Dagger can be used as a runtime and programming environment for AI agents. Dagger provides an LLM type that enables native integration of Large Language Models (LLM) in your workflows.

Tool use

A key feature of Dagger's LLM integration is out-of-the-box support for tool use using Dagger Functions: an LLM can automatically discover and use any available Dagger Functions in the provided environment.

Environments

Environments configure any number of inputs and outputs for the LLM. For example, an environment might provide a Directory, a Container, a custom module, and a string variable. The LLM can use these objects and their functions to complete the assigned task.

The documentation for the modules are provided to the LLM via inline documentation in your Dagger Functions. The LLM can then analyze the available tools and select which ones to use.

Here's an example:

Here, an instance of a Container is attached as an input to the Env environment. The Container is a core type with a number of useful functions, such as withNewFile() and withExec(). When this environment is attached to an LLM, the LLM can call any of these Dagger Functions to change the state of the Container and complete the assigned task.

Agent loop

Consider the following Dagger Function:

This Dagger Function creates a new LLM, gives it an environment (a container with various tools) with an assignment, and prompts it to complete the assignment. The LLM then runs in a loop, calling tools and iterating on its work, until it completes the assignment. This loop happens inside the LLM object, so the value of result is the environment with the completed assignment.

MCP

Model Context Protocol (MCP) support in Dagger can be broken into two categories:

1. Exposing MCP outside Dagger
2. Connecting to external MCP servers from Dagger

Expose MCP outside Dagger

Dagger has built-in MCP support that allows you to easily expose Dagger modules as an MCP server. This allows you to configure a client (such as Claude Desktop, Cursor, Goose CLI/Desktop) to consume modules from the Daggerverse or any Git repository as native MCP servers.

flowchart TD
    A["MCP Client
e.g. Claude Desktop / Cursor / Goose"] -- "dagger -m <module> mcp (stdio)" --> C["Dagger MCP Server: tools"]
    C --> E["Dagger module: functions"]

:::warning Currently, only Dagger modules with no required constructor arguments are supported when exposing an MCP server outside Dagger. :::

Connect to external MCP servers from Dagger

To connect with MCP servers, You can use LLM.withMCPServer to attach them to LLM. You will need to supply MCP Servers you want to use as Service.

Following is an example adds MCP Language Server to LLM.

mcpServer := dag.Container().
    From("golang").
    WithExec([]string{"go", "install", "golang.org/x/tools/gopls@latest"}).
    WithExec([]string{"go", "install", "github.com/isaacphi/mcp-language-server@latest"}).
    WithWorkdir("/src").
    AsService(dagger.ContainerAsServiceOpts{Args: []string{"mcp-language-server", "--workspace", "/src", "--lsp", "gopls"}})

work := dag.LLM().
		WithEnv(environment).
		WithMCPServer("lsp", mcpServer).
		WithPrompt("yout llm prompt")

mcp_server = (
    dag.container()
    .from_("golang")
    .with_exec(["go", "install", "golang.org/x/tools/gopls@latest"])
    .with_exec(["go", "install", "github.com/isaacphi/mcp-language-server@latest"])
    .with_workdir("/src")
    .as_service(args=["mcp-language-server", "--workspace", "/src", "--lsp", "gopls"])
)

work = (
    dag.llm()
    .with_env(environment)
    .with_mcp_server("lsp", mcp_server)
    .with_prompt("your llm prompt")
)

const mcpServer = dag
  .container()
  .from("golang")
  .withExec(["go", "install", "golang.org/x/tools/gopls@latest"])
  .withExec(["go", "install", "github.com/isaacphi/mcp-language-server@latest"])
  .withWorkdir("/src")
  .asService({
    args: ["mcp-language-server", "--workspace", "/src", "--lsp", "gopls"],
  })

const work = dag
  .llm()
  .withEnv(environment)
  .withMCPServer("lsp", mcpServer)
  .withPrompt("your llm prompt")

$mcpServer = dag()
    ->container()
    ->from('golang')
    ->withExec(['go', 'install', 'golang.org/x/tools/gopls@latest'])
    ->withExec(['go', 'install', 'github.com/isaacphi/mcp-language-server@latest'])
    ->withWorkdir('/src')
    ->asService(args: ['mcp-language-server', '--workspace', '/src', '--lsp', 'gopls']);

$work = dag()
    ->llm()
    ->withEnv($environment)
    ->withMCPServer('lsp', $mcpServer)
    ->withPrompt('your llm prompt');

Service mcpServer = dag().container()
    .from("golang")
    .withExec(List.of("go", "install", "golang.org/x/tools/gopls@latest"))
    .withExec(List.of("go", "install", "github.com/isaacphi/mcp-language-server@latest"))
    .withWorkdir("/src")
    .asService(
        new Container.AsServiceArguments()
            .withArgs(List.of("mcp-language-server", "--workspace", "/src", "--lsp", "gopls")));

var work = dag()
    .llm()
    .withEnv(environment)
    .withMCPServer("lsp", mcpServer)
    .withPrompt("your llm prompt");

Prompt mode

Dagger Shell lets you interact with the attached LLM using natural language commands. Each input builds upon previous interactions, creating a prompt chain that lets you execute complex workflows without needing to know the exact syntax of the underlying Dagger API.

"Prompt mode" can be accessed at any time in the Dagger Shell by typing >. Here's an example:

source=$(container | from node | with-mounted-directory /src https://github.com/dagger/hello-dagger | with-workdir /src)

You have a container with source code in /src. Describe the source code.

Does the application have unit tests?

Find all the unit tests. Deduce what the application does. Describe it in one paragraph.

Supported models

Dagger supports a wide range of popular language models, including those from OpenAI, Anthropic and Google. Dagger can access these models either through their respective cloud-based APIs or using local providers like Docker Model Runner or Ollama.

Dagger uses your system's standard environment variables to route LLM requests. Dagger will look for these variables in your environment, or in a .env file in the current directory. Learn more about configuring LLM endpoints in Dagger.

Observability

Dagger provides end-to-end tracing of prompts, tool calls, and even low-level system operations. All agent state changes are observable in real time.