docs/content/docs/tutorials/gemini-cli-runtime.md
rabbitblood 40bd0cfdde fix: restore build infrastructure deleted by bad PR #59 merge
[Molecule-Platform-Evolvement-Manager]

PR #59 (commit dae42e2) was merged ~2 weeks ago with a bad diff that
deleted all Next.js/Fumadocs build files (package.json, app/, lib/,
source.config.ts, tsconfig.json, etc.) and most MDX content pages.
This broke the Vercel build, taking doc.moleculesai.app offline.

Root cause: the PR branch was likely rebased or reset to a state that
only contained the marketing/ subtree, so the merge diff showed
deletions for every other file.

This commit:
1. Restores all build infrastructure from the last good commit (86fa0e9)
2. Restores 25 deleted MDX content pages (concepts, quickstart, etc.)
3. Adds frontmatter (title) to 55 .md files added post-bad-merge that
   were missing the required YAML frontmatter for Fumadocs
4. Removes duplicate quickstart.mdx (superseded by quickstart.md)
5. Adds CI workflow (.github/workflows/ci.yml) to catch build failures
   on PRs before merge — this would have prevented the outage

Build verified: 99 static pages generated successfully.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-22 14:03:24 -07:00

2.9 KiB

title
Running a Gemini CLI Workspace on Molecule AI

Running a Gemini CLI Workspace on Molecule AI

Molecule AI now ships a gemini-cli runtime adapter alongside the existing claude-code adapter. This tutorial walks you from zero to a running Gemini agent workspace in under five minutes.

What you'll need

  • A Molecule AI account with at least one provisioned tenant
  • A Google GEMINI_API_KEY (get one at aistudio.google.com)
  • The Molecule AI CLI (pip install molecule-ai)

Setup (10 steps)

# 1. Install / upgrade the CLI
pip install --upgrade molecule-ai

# 2. Authenticate
molecule auth login

# 3. Store your Gemini API key as a global secret
molecule secrets set GEMINI_API_KEY="YOUR_KEY_HERE" --global

# 4. Create a gemini-cli workspace
molecule workspace create my-gemini-agent --runtime gemini-cli

# 5. Confirm it's running (status → "ready" within ~30 s)
molecule workspace status my-gemini-agent

# 6. Send your first task
molecule workspace run my-gemini-agent "Summarise the last 5 git commits in this repo"

# 7. View the streamed response
molecule workspace logs my-gemini-agent --follow

# 8. Check the agent's memory file (GEMINI.md)
molecule workspace exec my-gemini-agent cat GEMINI.md

# 9. Delegate a cross-workspace task to your new Gemini peer
molecule workspace run orchestrator "delegate_task my-gemini-agent 'Draft release notes for v1.4'"

# 10. Tear down when done
molecule workspace delete my-gemini-agent

Expected output

After step 5 you should see:

my-gemini-agent  gemini-cli  ready   ord   2026-04-16T06:30:00Z

After step 6, Gemini CLI streams its reasoning and final answer directly to stdout. The agent uses GEMINI.md (seeded from your workspace's system-prompt.md) as persistent context — equivalent to CLAUDE.md for Claude Code workspaces.

How it works

Molecule AI's gemini-cli adapter mirrors the battle-tested claude-code pattern: a Docker image installs @google/gemini-cli globally, and CLIAgentExecutor drives the subprocess. Because Gemini CLI reads MCP config from ~/.gemini/settings.json rather than accepting a --mcp-config flag, the adapter's setup() method merges the A2A MCP server definition into that file at boot — preserving any user-defined tools.

Multi-provider teams

The real power surfaces when you mix runtimes on the same Molecule AI tenant. Your orchestrator workspace can delegate tasks to both claude-code and gemini-cli workers simultaneously using delegate_task_async, then synthesize results — all through the same A2A protocol. This is provider diversity at the infrastructure layer, not at the application layer.