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.

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