diff options
Diffstat (limited to 'docs')
| -rw-r--r-- | docs/api-keys-setup.md | 11 |
1 files changed, 7 insertions, 4 deletions
diff --git a/docs/api-keys-setup.md b/docs/api-keys-setup.md index 731a711..1a9daf9 100644 --- a/docs/api-keys-setup.md +++ b/docs/api-keys-setup.md @@ -9,7 +9,7 @@ Providers, in escalation-ladder order (cheapest/free first): | Groq | to add (works once configured — Phase 1's `openaicompat` adapter, no dedicated phase needed) | free tier (real, no card required historically) | OpenAI-compatible | | OpenRouter | to add (same as Groq) | free tier (rate-limited) + pay-as-you-go | OpenAI-compatible | | OpenAI | to add (same as Groq) | prepaid credit, no ongoing free tier | OpenAI-compatible (native) | -| Google (Gemini) | to add — native adapter lands Phase 4 | paid (already have account) | native (Gemini API) | +| Google (Gemini) | **wired up (Phase 4)** — `agent.type: "google"` works as soon as a key is in `config.toml` | paid (already have account) | native (Gemini API) | | Anthropic (Claude) | **wired up (Phase 2)** — `agent.type: "anthropic"` works as soon as a key is in `config.toml` | paid (already have account) | native (Messages API) | > Note on freshness: signup flows and free-tier limits change without much notice. Treat the specific numbers below (rate limits, credit thresholds) as "true as of when this was written" and double-check the provider's own pricing/limits page before relying on them for capacity planning. @@ -87,14 +87,17 @@ No meaningful ongoing free tier at this point — expect to prepay. Included in --- -## Google (Gemini) — you already have a paid account +## Google (Gemini) — you already have a paid account, and this one's live too -The existing `credentials/gemini/` directory holds an OAuth session for the `gemini` CLI (used by `ContainerRunner`). The native-API path this harness adds is separate: a plain API key from Google AI Studio, used directly against the Gemini API rather than through the CLI. +Same situation as Anthropic below: the existing `credentials/gemini/` directory holds an OAuth session for the `gemini` CLI (used by `ContainerRunner`). The native-API path this harness adds is separate: a plain API key from Google AI Studio, used directly against the Gemini `generateContent` API rather than through the CLI. 1. Go to `aistudio.google.com/apikey` (Google AI Studio), signed in with the same Google account as your paid plan. 2. **Create API key** — choose the Google Cloud project tied to your paid billing if prompted, so usage bills correctly rather than falling back to any free-tier project quota. 3. Copy the key (`AIza...`). -4. `[providers.google]` needs no `endpoint` override (the adapter defaults to the native Gemini API base) — just `api_key` and `default_model`. +4. `[providers.google]` needs no `endpoint` override (the adapter defaults to the native Gemini API base) — just `api_key` and `default_model` (e.g. `gemini-2.5-flash` for a cheap/fast rung, `gemini-2.5-pro` for a more capable one). +5. Since this is a separate credential from the CLI's OAuth session, keep an eye on AI Studio/Cloud Console usage separately from whatever the `gemini` CLI itself reports — they draw from the same account but are tracked as distinct API consumers. This is intentional: `executions.agent = "google"` (native path) and `"gemini"` (CLI-subprocess `ContainerRunner` path) are tracked as **separate budget buckets** even though both bill the same account — set `[budget].provider_5h_usd.google` independently of `.gemini` if you want different caps on each. +6. As soon as the key is set, `claudomator serve`/`run` registers `agent.type: "google"` as a usable runner — no restart-time flag needed beyond having the key present. To turn it off without removing the key, set `[runners] google = false`. +7. Verify it's live: submit any task with `agent.type: "google"`, then check `GET /api/budget` for a distinct `"google"` row once the execution completes. If you'd rather use Vertex AI (GCP's enterprise entry point to Gemini, with IAM-based auth instead of a bare API key) that's a heavier setup (service account, GCP project, `gcloud` auth) — not necessary for this harness unless you specifically want Vertex's quota/governance features. AI Studio's simple API key is sufficient for what's being built here. |