Skip to main content
Outrider’s coding runtime speaks the Anthropic Messages protocol, so it can be pointed at any compatible endpoint. That makes the model backend a first-class choice per agent — the loop, guardrails, and refinement chain are identical on every backend; what changes is cost, latency, and the models on offer: The higher timeout for Z.ai and Moonshot exists because their flagship models’ thinking modes add per-turn latency; a GLM session routinely takes 15–20 minutes. The provider and model inputs are independent: provider names the endpoint, model picks the model from it (e.g. glm-5.2 / glm-4.6 on Z.ai; kimi-k3 / kimi-k2.7-code on Moonshot; claude-opus-4-8 / claude-sonnet-4-6 / claude-haiku-4-5 on Anthropic). In the app, the backend is a radio in the agent setup wizard and editable later under the agent’s Configuration. Connect each provider’s key once under Connectors; Remyx pushes it to the repo as a secret at provisioning time.

​
The provider input

In workflow YAML, one input wires the whole backend — auth env var, base URL, and mutual exclusion between the two auth styles:
Pass every backend’s secret in the step’s env: block — the action reads only the one matching provider, so a single workflow can switch backends per dispatch. This input replaced the fork-side “Configure provider auth” case-switch step older workflows carried; an empty provider preserves the old behavior exactly, so existing installs need no migration. The action fails clean with a specific error if the selected provider’s secret is missing (e.g. provider=zai requires ZAI_API_KEY in the caller's env block).
Don’t set both ANTHROPIC_API_KEY and ANTHROPIC_AUTH_TOKEN yourself: the runtime prefers x-api-key, which Bearer-auth backends reject with a 401. The provider input clears the non-selected variable for you — this is most of why it exists.
Use model-base-url directly (instead of provider) only for custom or on-prem endpoints — Bedrock, Vertex, or a proxy of your own.

​
The two-tier drafter-refiner pattern

The recommended default for a repo you want worked continuously. It splits exploration and commitment into two workflows: Workflow 1: Drafter — daily, budget tier, publish: branch
  • Pushes draft branches only: no PRs, zero maintainer-facing noise. ~$0.20–0.40/run.
  • Each run accumulates repo intel — landing zones that worked, mechanism shapes that got rejected.
Workflow 2: Refiner — weekly, strong tier, publish: pr
  • Picks the best drafter branch and generates a mechanism-level gap analysis against its source paper.
  • Re-runs from that branch (start-from-ref) with the gap analysis as its brief (lead-content), through the full refinement chain, to a draft PR. ~$5–10/week including the gap generation.
Why the split works: the drafter’s branch anchors an extension point and test scaffolding, so the refiner commits to PR-shape on borderline cases; a budget tier makes daily exploration of the arXiv frontier (~200 ML papers/day) rational; even failed branches contribute negative-space signal to the next pick. Set it up from the wizard’s Advanced — models per phase → Two-tier option (writes three workflows: the dispatch-capable main one, the drafter, the refiner), or from the CLI. Tier pairings are cross-provider and switchable — the app defaults to a GLM drafter with an Opus refiner:
Runs report their tier (main / drafter / refiner) in fleet telemetry, so Reports → Costs prices each tier separately.

​
Cost telemetry

Every run reports cost_usd, input_tokens, and output_tokens as action outputs and into fleet telemetry. How the dollar figure is computed depends on the backend:
  • Anthropic: the runtime’s own total_cost_usd figure is authoritative.
  • Z.ai / Moonshot: cost is computed from a per-model rate table keyed by the model actually used (glm-5.2, glm-4.6, kimi-k3, kimi-k2.7-code, kimi-k2.7-code-highspeed, …), overriding the CLI’s Anthropic-rate estimate. An unknown model on a known host is priced at that host’s flagship rate — expect a 3–4× error across tier pairs, so set model explicitly.
  • Custom endpoints: the reported figure is an estimate.
The Costs report turns this into unit economics: runs per day by backend against $ per code artifact.

​
Choosing a backend

There’s no house pick. Every backend runs the same loop behind the same guardrails; the practical variables are per-run cost, session latency, and the draft quality you observe on your repo:
  • Anthropic (Claude) — Claude tiers over x-api-key auth; a full PR run with the refinement chain is roughly $5–6.
  • Z.ai (GLM) — GLM tiers at roughly 20× lower per-run cost; budget the longer timeout for thinking-mode latency.
  • Moonshot (Kimi) — Kimi tiers, same Bearer-auth pattern and timeout guidance as Z.ai.
  • Custom / on-prem — anything that speaks the Anthropic Messages protocol, via model-base-url.
The cheap way to decide is evidence, not defaults: per-dispatch routing (remyxai outrider trigger --provider … --model …) makes A/B-ing providers on the same paper nearly free, and Reports → Costs shows each backend’s cost per code artifact against what its runs actually produced. The two-tier pattern sidesteps the choice entirely: explore on a budget tier daily, promote on the tier your merges say you trust.

Actions

All configuration inputs, guardrails, and run outcomes

Connectors

Connect Anthropic, Z.ai, and Moonshot keys

CLI: provider routing

Per-dispatch provider/model overrides

Backends guide (repo)

The action’s own backends reference