Build Agents for GitLab CI/CD

Build and run Flue agents in GitLab CI/CD pipelines. This guide walks you through creating your first agent, running it locally with the CLI, and wiring it into a pipeline.

By the end, you will have a Flue agent running inside GitLab CI/CD, and you will know how to use local sandbox context, external CLIs, subagents, skills, and typed results to build CI workflows.

Hello World

A minimal agent that runs in CI whenever an issue is opened.

1. Set up your project

mkdir my-flue-project && cd my-flue-project
npm init -y
npm install @flue/runtime valibot
npm install -D @flue/cli

2. Create your first agent

.flue/workflows/hello.ts:

import { createAgent, type FlueContext } from '@flue/runtime';
import { local } from '@flue/runtime/node';
import * as v from 'valibot';

const agent = createAgent(() => ({ sandbox: local(), model: 'anthropic/claude-sonnet-4-6' }));

export async function run ({ init, payload }: FlueContext) {
  const harness = await init(agent);
  const session = await harness.session();

  const { data } = await session.prompt(
    `Say hello to ${payload.name ?? 'the user'} and share an interesting fact.`,
    {
      result: v.object({
        greeting: v.string(),
        fact: v.string(),
      }),
    },
  );

  return data;
}

A few things to note:

• This workflow omits public route and websocket middleware, so it is internal-only and designed to be run from the CLI, which is perfect for CI.
• model — init(agent) fails unless the created agent config provides a model, sets model: false, or supplies a profile with a model.
• local() — The local() sandbox runs the agent directly against the host filesystem and shell. In CI, that’s the checked-out repo plus whatever binaries are on $PATH (glab, git, npm, etc.). Skills and AGENTS.md are discovered automatically from the project root. By default only shell-essential env vars (PATH, HOME, locale, etc.) are inherited from process.env — pass local({ env: { GITLAB_TOKEN: process.env.GITLAB_TOKEN } }) to expose more. Use local() only when the runner itself provides the isolation boundary.
• Schemas — The Valibot schema defines the expected output shape. Flue parses the agent’s response and returns it on response.data, fully typed.

3. Test it locally

npx flue run hello --target node \
  --payload '{"name": "World"}'

flue run builds the project, invokes the workflow through a private local child-process communication, streams progress to stderr, and prints the final result as JSON to stdout. The workflow does not need public transport exposure for this local command.

4. Wire it into GitLab CI/CD

.gitlab-ci.yml:

hello:
  image: node:22
  rules:
    - if: $CI_PIPELINE_SOURCE == "trigger" && $ISSUE_ACTION == "open"
  before_script:
    - npm ci
  script:
    - |
      npx flue run hello --target node \
        --payload "{\"name\": \"$ISSUE_AUTHOR\"}"

Triggering pipelines from issue events

GitLab doesn’t pass issue data into CI variables automatically. You need a pipeline trigger to bridge the gap:

1. Create a pipeline trigger token: Settings > CI/CD > Pipeline trigger tokens
2. Add a project webhook (Settings > Webhooks) that fires on Issue events, pointing at a small relay that calls the trigger API with the right variables:

// Deploy as a serverless function or lightweight server
async function handleGitLabWebhook(event) {
  const { object_kind, object_attributes, issue } = event;
  let variables: Record<string, string> = {};

  if (object_kind === 'issue') {
    variables = {
      ISSUE_ACTION: object_attributes.action,
      ISSUE_IID: String(object_attributes.iid),
      ISSUE_AUTHOR: object_attributes.author?.username ?? '',
    };
  } else if (object_kind === 'note' && issue) {
    variables = {
      ISSUE_ACTION: 'note',
      ISSUE_IID: String(issue.iid),
    };
  } else {
    return;
  }

  await fetch(`${GITLAB_URL}/api/v4/projects/${PROJECT_ID}/trigger/pipeline`, {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({ token: TRIGGER_TOKEN, ref: 'main', variables }),
  });
}

Once wired up, open an issue and you’ll see a passing pipeline with the agent’s greeting in the logs.

Building a real agent

Now let’s build something useful — an issue triage agent that analyzes an issue and reports back. This is where Flue’s agent features start to shine.

The agent handler

The agent handler is where orchestration lives. The FlueContext gives you everything you need: init() to create a session, payload for input data, and env for environment bindings.

Once you have a session, you have three core methods:

• session.shell(cmd) — Run a shell command in the sandbox. Returns { stdout, stderr, exitCode }.
• session.prompt(text, opts) — Send a prompt to the agent and get back a result.
• session.skill(name, opts) — Run a named skill — a reusable agent task defined by a markdown instruction file.

Both prompt() and skill() accept a result option — a Valibot schema that defines the expected output shape. Flue parses the agent’s response and returns it on response.data, fully typed:

import * as v from 'valibot';

// summary: string
const { data: summary } = await session.prompt(`Summarize this diff:\n${diff}`, {
  result: v.string(),
});

// diagnosis: { reproducible: boolean, skipped: boolean }
const { data: diagnosis } = await session.skill('triage', {
  args: { issueIid, issue },
  result: v.object({
    reproducible: v.boolean(),
    skipped: v.boolean(),
  }),
});

Connecting external CLIs

Your agent often needs to interact with external tools. With local(), the agent’s bash tool runs against the host shell directly — anything on $PATH is reachable. Host env vars are opt-in: only shell essentials (PATH, HOME, locale, etc.) are inherited by default, so you pass the specific vars your CLIs need via local({ env: { ... } }).

In GitLab CI, this means you set the secrets you want the agent’s CLIs to see in the job’s variables: block (or as masked CI/CD variables), then forward them explicitly into the sandbox. The runner is your isolation boundary; flue makes the inner boundary (host → spawned shell) explicit.

.flue/workflows/triage.ts:

import { createAgent, type FlueContext } from '@flue/runtime';
import { local } from '@flue/runtime/node';
import * as v from 'valibot';

const agent = createAgent(() => ({
  sandbox: local({
    env: { GITLAB_TOKEN: process.env.GITLAB_TOKEN },
  }),
  model: 'anthropic/claude-opus-4-7',
}));

export async function run ({ init, payload }: FlueContext) {
  const harness = await init(agent);
  const session = await harness.session();

  // The agent's bash tool can run `glab` directly — only the env vars
  // forwarded above are visible to it. Prefer purpose-built CLIs like
  // `glab` over general-purpose HTTP clients so the token's blast radius
  // is scoped to that one provider.
  const { data } = await session.skill('triage', {
    args: {
      issueIid: payload.issueIid,
      projectId: payload.projectId,
    },
    result: v.object({
      severity: v.picklist(['low', 'medium', 'high', 'critical']),
      reproducible: v.boolean(),
      summary: v.string(),
      fix_applied: v.boolean(),
    }),
  });

  return data;
}

If you want a tighter boundary — the agent can call a specific operation but never see the underlying token — return the custom tool from createAgent(...) with tools: [...]. The tool implementation reads the secret from process.env; the agent only sees the tool’s parameters and result.

Subagents

Named subagents can run focused detached tasks:

const reviewer = defineAgentProfile({
  name: 'reviewer',
  instructions: 'Focus on correctness, security, and project standards.',
});
const agent = createAgent(() => ({ model: 'anthropic/claude-sonnet-4-6', subagents: [reviewer] }));
const harness = await init(agent);
const session = await harness.session();
const { data } = await session.task(`Review this MR:\n${diff}`, {
  agent: 'reviewer',
  result: v.object({ approved: v.boolean(), comments: v.array(v.string()) }),
});

Sandbox context

The agent reads AGENTS.md and skills from its sandbox at runtime. CI agents typically use local(), which gives direct access to the runner’s checkout — so any files in your repo are visible automatically.

Skills are reusable agent tasks defined as markdown files in .agents/skills/. They give the agent a focused instruction set for a specific job:

.agents/skills/triage/SKILL.md:

---
name: triage
description: Triage a GitLab issue — reproduce, assess severity, and optionally fix.
---

Given the issue IID and project ID in the arguments:

1. Use `glab issue view <iid>` to fetch the issue details
2. Read the codebase to understand the relevant area
3. Attempt to reproduce the issue
4. Assess severity and write a summary
5. If the fix is straightforward, apply it and push a branch

AGENTS.md at your project root is the agent’s system prompt — it provides global context about the project:

You are a helpful assistant working on the my-project codebase.

## Project structure

- `src/` — Application source code
- `tests/` — Test suite

## Guidelines

- Always run tests before suggesting a fix is complete
- Use the project's existing patterns and conventions

Wiring it into GitLab CI/CD

.gitlab-ci.yml:

triage:
  image: node:22
  timeout: 30 minutes
  rules:
    - if: $CI_PIPELINE_SOURCE == "trigger" && $ISSUE_ACTION == "open"
  before_script:
    - npm ci
  script:
    - |
      npx flue run triage --target node \
        --payload "{\"issueIid\": $ISSUE_IID, \"projectId\": \"$CI_PROJECT_ID\"}"

Add these as CI/CD variables (Settings > CI/CD > Variables, masked):

Variable	Description
ANTHROPIC_API_KEY	API key for your LLM provider
GITLAB_API_TOKEN	Project or personal access token with api scope

Typed results and orchestration

Result schemas aren’t just for type safety — they’re how you orchestrate multi-step workflows. Because you get typed data back from prompt() and skill(), you can branch on results within a single agent:

import { createAgent, type FlueContext } from '@flue/runtime';
import { local } from '@flue/runtime/node';
import * as v from 'valibot';

const agent = createAgent(() => ({ sandbox: local(), model: 'anthropic/claude-sonnet-4-6' }));

export async function run ({ init, payload }: FlueContext) {
  const harness = await init(agent);
  const session = await harness.session();

  const { data } = await session.skill('triage', {
    args: { issueIid: payload.issueIid },
    result: v.object({
      severity: v.picklist(['low', 'medium', 'high', 'critical']),
      reproducible: v.boolean(),
      summary: v.string(),
    }),
  });

  if (data.severity === 'critical' && data.reproducible) {
    // Escalate: attempt an automated fix
    await session.skill('auto-fix', {
      args: { issueIid: payload.issueIid },
      result: v.object({ fix_applied: v.boolean(), branch: v.optional(v.string()) }),
    });
  }

  return data;
}

This pattern — prompt or skill call, check the result, decide what to do next — is how you build sophisticated agents that go beyond single-shot prompts.

Running workflows locally

During development, flue run is your main tool. It builds the project and runs the workflow in one step:

# Run with a payload
npx flue run triage --target node \
  --payload '{"issueIid": 42, "projectId": "123"}'

# Pipe the result to jq
npx flue run triage --target node \
  --payload '{"issueIid": 42}' | jq '.severity'

The CLI builds your project root, invokes the workflow through a private local child-process communication, streams progress to stderr, and prints the final result to stdout.