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fix(self-improving-mastra): use gemini-2.5-flash on GCP (broadly available) #652

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raphaeltm merged 1 commit into from
Jul 21, 2026
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fix(self-improving-mastra): use gemini-2.5-flash on GCP (broadly available) #652

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2 changes: 1 addition & 1 deletion samples/self-improving-mastra/.env.gcp
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Original file line number Diff line number Diff line change
@@ -1,5 +1,5 @@
# Non-secret Compose interpolation values selected by cli for gcp provider.
CHAT_MODEL=gemini-3.5-flash
CHAT_MODEL=gemini-2.5-flash
PUBLISH_ENABLED=true
PUBLISH_POLICY=roles/owner
PUBLISH_PROVIDER=gcp
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11 changes: 5 additions & 6 deletions samples/self-improving-mastra/README.md
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Expand Up @@ -24,7 +24,7 @@ environment:
- `db` is shared PostgreSQL for auth, todos, and feedback.
- `chat` is the managed model used by the coding agent, declared as a top-level
`models:` entry; each cloud's env file selects an explicit model — z.ai GLM 5
on AWS Bedrock, Gemini 3.5 Flash on GCP Vertex AI.
on AWS Bedrock, Gemini 2.5 Flash on GCP Vertex AI.

The **admin console is served by the agent server, not the Next.js app** — it
lives outside the source tree the coding agent edits. Caddy routes `/admin` to
Expand Down Expand Up @@ -88,7 +88,7 @@ defang config set ADMIN_TOKEN --random
## Deployment

The same Compose project deploys to either cloud; each cloud's env file selects
an explicit managed model (z.ai GLM 5 on AWS, Gemini 3.5 Flash on GCP). The
an explicit managed model (z.ai GLM 5 on AWS, Gemini 2.5 Flash on GCP). The
first deployment creates managed PostgreSQL and can take about 20 minutes.
Defang reports separate URLs for `app` and `dev`:

Expand Down Expand Up @@ -117,10 +117,9 @@ this demo for production.

### GCP (Vertex AI)

The `gcp` env file selects `gemini-3.5-flash` on Vertex AI. Gemini 3.x is
reachable through Vertex's **global** endpoint and is not offered on-demand in
every region, so deploy to a location that serves it. Provide the target project
when deploying:
The `gcp` env file selects `gemini-2.5-flash` on Vertex AI — a GA model that is
broadly available on-demand across regions with no special configuration.
Provide the target project when deploying:

```bash
export GCP_PROJECT_ID=your-gcp-project
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