Use Cline with GammaInfra

Cline drives multi-step agentic loops: every "build me X" can fan out to 10–30 LLM calls including tool use. Two pains kick in fast — token spend is opaque until the bill arrives, and one provider rate-limit halts a 20-tool-call session. GammaInfra fixes both.

The pain Cline users actually hit

Cline is great because it's autonomous. That's also where the pain lives:

Cost goes from "fine" to "ouch" in one session. A non-trivial Cline task can chain 20+ LLM calls (plan → tool call → observation → next plan → ...). Each tool result feeds back as context, so input tokens compound. You only notice when the monthly OpenAI invoice arrives.
Provider rate limits kill loops mid-flight. Cline can saturate a provider's per-minute rate limit on a single complex task. When that happens, your "deploy this feature" agent stops at step 14 of 20 and you start over.
Switching providers mid-task isn't a real option. You can change Cline's provider config and restart, but the partial work is gone.

What changes with GammaInfra

Drop GammaInfra's smart router between Cline and the underlying LLM providers — same OpenAI SDK shape Cline already speaks. Three things flip:

Per-tool-call cost visibility. Every response carries X-GammaInfra-Cost-USD, X-GammaInfra-Input-Cost-USD, and X-GammaInfra-Output-Cost-USD headers. Sum across a Cline session and you know exactly what each agentic task cost. The dashboard groups requests by your API key so you can attribute spend per project.
Rate-limit fallback that doesn't break the loop. When OpenAI throttles, GammaInfra's fallback chain cascades to Anthropic, then Google, then Mistral — all returning a 200 to Cline. Your agentic loop sees a successful response and keeps going. The cascade is recorded in X-GammaInfra-Fallback-Chain so you can see which provider actually served each call.
Model pinning across providers. Cline uses one model name for the whole loop, but GammaInfra lets you pin different models per request type. Use anthropic/claude-opus-4-7 when you want deeper reasoning, openai/gpt-5-mini when you want cheap fast tool calls — all through one Cline config.

Setup

1. Get a GammaInfra API key

Sign up at gammainfra.com and verify your email. The $3 free trial credit covers roughly one or two Cline agentic tasks end-to-end — enough to feel the difference before topping up.

2. Open Cline's settings

In VS Code:

Open the Cline panel (sidebar)
Click the settings gear icon in the top-right of the Cline panel
Under API Provider, select OpenAI Compatible

3. Set base URL, key, and model

Three fields under "OpenAI Compatible":

Base URL:    https://api.gammainfra.com/v1
API Key:     sk-gammainfra-...   (paste your GammaInfra key)
Model ID:    gammainfra/auto     (or any specific model below)

4. Pick the right model for your workload

Cline benefits from quality-tier reasoning models for the planning steps. Some patterns:

gammainfra/auto — task-aware routing. GammaInfra classifies each prompt and picks an appropriate model. Reasoning prompts get routed to higher-tier models; tool-call returns get routed to cheaper models. Good default.
anthropic/claude-opus-4-7 — Anthropic's flagship. Best for Cline's planning and reflection steps. More expensive per call but fewer failed loops.
anthropic/claude-sonnet-4-6 — Sonnet. Balanced. A reasonable default for most Cline workloads.
openai/gpt-5 — OpenAI's flagship. Strong tool-use support.
deepseek/deepseek-v4-pro — Cheaper reasoning tier with thinking mode enabled by default. Trade quality for cost on long-running loops.

Cline's tool-use semantics work as-is. GammaInfra passes tools and tool_choice through unchanged to the underlying provider, and translates tool_call.id across providers (toolu_* ↔ call_*) so Cline's agent loop round-trips correctly whether the call lands on Anthropic or OpenAI.

Verify it's working

Start a Cline task. Then in the dashboard:

Recent requests appear with timestamps, the resolved provider/model, latency, and exact cost.
If the request fell back through the chain, you'll see the cascade in the request detail view.
Top-level metrics show daily spend by model — drill into a heavy Cline day and you can see the full cost shape.

Cost-conscious patterns

A few things experienced Cline + GammaInfra users do:

Use the cost-quality dial for long loops. Set X-GammaInfra-Cost-Quality: 0.7 (closer to cost) for the bulk of tool calls, then increase to 0.3 (closer to quality) for known-hard planning steps. The dial is a header Cline passes through if you add it to the custom-headers config.
Set a max-latency budget. Add X-GammaInfra-Max-Latency-Ms: 30000 as a custom header. GammaInfra will return a 504 if any single request exceeds 30 seconds — instead of a Cline session quietly stalling for 90+ seconds on a slow provider response.
BYOK for high-volume. If you already pay for direct OpenAI/Anthropic accounts, add those provider keys via the dashboard's Provider Keys tab. GammaInfra routes through your keys (BYOK) at 1–2% per-request fee instead of the standard pass-through model.

Trade-offs to know about

Latency overhead. ~10–50 ms per request vs going direct. For Cline (which has thinking gaps between calls anyway), this is invisible.
Cost overhead. 3% top-up fee during the launch window (5% standard) on managed credits. Pass-through provider rates on tokens — no markup. Cline workloads where fallback prevents one session restart usually pay for the fee instantly.
Privacy. Prompts and responses aren't logged by default. Cost/latency metadata is logged for the dashboard and routing improvements. See privacy policy.

Roo Code users

Roo Code is a Cline fork and uses the same OpenAI-Compatible provider config. The setup above works identically — set Base URL to https://api.gammainfra.com/v1, paste your GammaInfra API key, pick a model.

Troubleshoot

Cline hangs on first call. Check the Cline output panel — usually a config-paste issue (extra whitespace in the Base URL or API key field).
Tool calls fail mid-loop with a 4xx. Some Anthropic-shape tool calls don't translate to OpenAI-shape cleanly when GammaInfra cascades. Pin the model (anthropic/claude-opus-4-7) instead of using gammainfra/auto if you hit this often.
Cost higher than expected. Check the dashboard for the request breakdown — long Cline loops can fan out 30+ calls. If the breakdown surprises you, switch to gammainfra/cheap for less critical tool calls.

Detailed error codes in the docs. Stuck? Discord — usually a quick fix.

Ready to try it?

Get a GammaInfra API key →

$3 free trial credit on signup, $10 minimum top-up. Pass-through provider token rates plus 3% top-up fee during the launch window (5% after 2026-06-23).

Frequently asked questions

How do I configure Cline to use GammaInfra?

In VS Code, open Cline Settings, choose API Provider: OpenAI Compatible, set Base URL to https://api.gammainfra.com/v1, paste your sk-gammainfra-... key, and set Model ID to gammainfra/auto (or any specific model). No extension swap needed — Cline's existing OpenAI-Compatible mode handles everything.

Does GammaInfra work with Cline's agent mode and tool calls?

Yes. Cline relies on tool calling for its file-read, file-write, and command-execution steps. GammaInfra's tool_call.id translation across providers (toolu_* on Anthropic, call_* on OpenAI) keeps Cline's tool-call flow working regardless of which provider serves the request. The router classifies tool-heavy prompts and routes them to tool-capable models specifically.

Can I see per-step cost in Cline's task log?

The cost appears in GammaInfra's dashboard, not Cline's task log directly. Each request_logs row in your account shows the dispatched provider, model, latency, and cost. For long agent runs, sum the cost across the run's request IDs. Cline doesn't currently surface response headers in its UI.

What model should I pin for Cline's coding-heavy agent loops?

anthropic/claude-sonnet-4-6 is the strongest practical choice for Cline as of mid-2026 — best real-world code quality, tool calling works, reasonable cost. For cost-sensitive runs, gammainfra/auto routes per-step (cheap models for file reads, stronger ones for actual code generation). Avoid claude-opus-4-7 unless you're paying for quality on hard problems — it's ~5× more expensive per token than Sonnet.

Does the smart router handle Cline's long-context tasks well?

Yes. When Cline sends a large prompt (e.g. multi-file context), the router still classifies and routes appropriately. For prompts over 272K tokens, gpt-5.5 family applies a long-context surcharge (2× input, 1.5× output) which GammaInfra reflects in X-GammaInfra-Cost-USD. The cost split header shows input vs output separately so you can see which direction dominated.