Both tutorials cited misattributed PRs and claimed shipped runtimes that didn't exist (RFC internal#730 finding): - google-adk-runtime.md: cited 'PR #550' (actually a MemoryTab test suite) + 'already first-class'. Rewritten to the REAL implementation — ADK engine-only (google-adk[mcp]==2.1.0, no [a2a]), Vertex AI via ADC (keyless), a2a-1.x bridge — with correct PR refs (template PR #1, core #2003, ci #26) + a landing-status banner. - gemini-cli-runtime.md: cited 'PR #379' (actually CI cleanup); no gemini-cli runtime exists in manifest/knownRuntimes. Added a correction banner pointing to the real google-adk runtime. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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Running a Gemini CLI Workspace on Molecule AI
⚠️ Accuracy correction (2026-05-29): this page is aspirational, not shipped. There is no
gemini-cliruntime inmanifest.jsonor the provisioner'sknownRuntimes, and the "PR #379" cited below is unrelated (a CI-workflow-cleanup PR, not a gemini-cli adapter). Do not follow this as-is.For Gemini on Molecule, use the real
google-adkruntime instead — seegoogle-adk-runtime.md(ADK engine + Gemini on Vertex AI/AI Studio), implemented in PRmolecule-ai-workspace-template-google-adk#1per RFCinternal#730. This gemini-cli page is retained only until it's either implemented for real or removed.
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.