opencode-kimchi

Automatic model routing for OpenCode using Cast AI's Kimchi models.

The plugin analyzes each message and routes it to the most cost-effective model tier:

Tier	When
Reasoning	Planning, architecture, debugging, code review, security audit
Coding	Implementation, refactoring, writing tests
Quick	Simple questions, lookups, summaries, codebase exploration

The model used for each tier is selected dynamically from whatever models you have configured in your Kimchi provider. The plugin ranks known models by benchmark performance and cost, then picks the best available one per tier. A single model can serve multiple tiers — for example, kimi-k2.5 is ranked across all three, and minimax-m2.5 covers both coding and quick.

Reasoning tier (ranked by SWE-bench, GPQA, AIME): Claude Opus family → o3 → o4-mini → Kimi K2.5 → o3-mini → Gemini 2.5 Pro

Coding tier (ranked by performance/cost): Kimi K2.5 → Claude Sonnet family → GPT-4.1 → Gemini 2.5 Flash → Minimax M2.5 → GPT-4.1 Mini

Quick tier (ranked by cost-effectiveness): Kimi K2.5 → Minimax M2.5 → GPT-4.1 Nano → Gemini 2.0 Flash → Claude Haiku

Unknown models are auto-assigned to a tier based on their reasoning flag and cost.

Install

opencode plugin @kimchi-dev/opencode-kimchi

That's it. The plugin auto-configures itself — no manual provider setup needed.

How it works

The plugin registers as a Kimchi provider and exposes virtual models:

• kimchi/auto — Default. Routes each message to the best model automatically.
• kimchi/reasoning — Lock to the reasoning model for the session.
• kimchi/coding — Lock to the coding model for the session.
• kimchi/quick — Lock to the quick/cheap model for the session.

You can also select any specific Kimchi model directly (e.g., kimchi/kimi-k2.5) to lock that model for the session. Use /auto in chat to return to automatic routing.

Auto-routing in practice

When you use kimchi/auto, the plugin classifies every message independently and picks the best tier for it. Here's what that looks like in a real session:

• You ask "Plan the architecture for auth" → reasoning tier (kimi-k2.5)
• You say "Now implement the auth module" → coding tier (claude-sonnet-4)
• You ask "What does this function do?" → coding tier (quick tier floored to coding for primary agents)
• You say "Debug the TypeError in the handler" → reasoning tier (kimi-k2.5)
• You say "Fix it" → coding tier (claude-sonnet-4)

What you should expect:

• Most coding sessions will spend the majority of time on the coding tier. This is by design — implementation, refactoring, and follow-up prompts are all classified as coding tasks.
• The model switches to reasoning when the prompt clearly involves planning, debugging, architecture, or code review.
• The quick tier is reserved for subagents (explore, general). Your primary chat agent is floored to the coding tier minimum, so you won't see the quick model used for your direct messages.
• There is intentional stickiness within a tier — if the classifier is unsure and you're already in a tier, it stays put rather than flip-flopping. A clear cross-tier signal (e.g., switching from implementation to debugging) will still trigger a change.
• The TUI model display stays on "auto" throughout. The actual model used for each message is shown in the routing toast.

Overriding auto-routing

Method	Effect	Duration
Select kimchi/reasoning (or coding/quick) in TUI	Locks to that tier's best model	Until you switch or /auto
Select a specific model (e.g., kimchi/kimi-k2.5)	Locks to that exact model	Until you switch or /auto
/plan, /debug, /review, /code, /refactor	One-shot override for the next message	Single message
/lock debug	Locks to a profile until cleared	Until /auto
/auto	Clears all locks, returns to auto-routing	Immediate

Classification cascade

The auto-router uses a multi-stage approach to classify each message:

1. Heuristic classifier (instant, free) — keyword signals and structural cues handle ~70% of messages with high confidence
2. LLM classifier (fast, cheap) — when heuristics are ambiguous, the cheapest Kimchi model classifies the message in ~100 tokens
3. Conversation phase detection — after 3+ messages, tool usage patterns, message lengths, and structural signals refine routing
4. Live tool tracking — file edits, reads, and error patterns in tool output feed routing decisions in real-time
5. Mode stickiness — within the same tier, stays put when confidence is low to avoid noisy flip-flopping
6. LLM self-routing — the model itself can suggest switching tiers for the next message

Agent profiles

Each model tier maps to a specialized agent profile with a tailored system prompt:

Profile	Tier	Behavior
planner	Reasoning	Step-by-step thinking, trade-off analysis, phase breakdown
debugger	Reasoning	Scientific method: observe → hypothesize → test → verify
reviewer	Reasoning	Constructive criticism: bugs, security (OWASP), performance, edge cases
coder	Coding	Complete implementation, no stubs, follows existing conventions
refactorer	Coding	Behavior-preserving transformations, one change type at a time
assistant	Quick	Concise and direct, terse answers, file lookups

Profiles are activated automatically based on routing.

Orchestration

When running as kimchi/auto, the agent operates as an orchestrator — it plans, delegates, and verifies rather than doing all work directly:

• Codebase exploration is delegated to @explore subagents
• Multi-step research is delegated to @general subagents
• Implementation work is delegated via task() with detailed prompts
• Direct tool use is reserved for trivial single-file changes (< 20 lines)

The plugin tracks direct vs. delegated tool calls and injects a reminder when the agent starts doing too much work itself instead of delegating.

Context-aware model upgrades

When the conversation context exceeds 85% of the current model's context window, the plugin automatically upgrades to a larger model in the same tier. For example, if you're on minimax-m2.7 (196K context) and the conversation grows past 166K tokens, it switches to gpt-4.1-nano (1M context) or kimi-k2.5 (262K context) — whichever is available in the same tier.

Proactive context compaction

When the conversation context exceeds 78% of the model's context window, the plugin automatically triggers a compaction — summarizing the conversation while preserving critical routing state:

• Active mode and routing history
• Files modified and read this session
• Tool activity counts (edits, reads, errors)
• Recent errors and user overrides

This prevents context overflow without losing important session context.

Model fallback

If a model request fails (rate limit, unavailability, etc.), the plugin automatically falls back to the next best model in the same tier, then across tiers if needed. Fallback state is tracked per session and cleared on success.

Configuration

{
  "plugin": [
    ["@kimchi-dev/opencode-kimchi", {
      "provider": "kimchi",
      "verbose": true,
      "models": {
        "reasoning": "claude-opus-4-6",
        "coding": "claude-sonnet-4-6",
        "quick": "minimax-m2.5"
      },
      "telemetry": true
    }]
  ]
}

The models override is optional — omit it and the plugin will automatically select the best available model per tier from your provider config.

Option	Default	Description
provider	"kimchi"	Provider ID to route (matches your Kimchi provider config)
verbose	false	Log model selection decisions
models	(auto)	Override model IDs per tier
apiKey	(from provider config)	CastAI API key (auto-read from your Kimchi provider config)
llmBaseUrl	https://llm.cast.ai/openai/v1	LLM endpoint for classifier
llmClassifier	true	Enable LLM fallback classifier for ambiguous messages
llmClassifierThreshold	0.5	Confidence threshold below which LLM classifier is invoked
telemetry	false	Enable usage telemetry (can also be enabled via env var)

Telemetry

The plugin can send usage telemetry and productivity metrics to the Kimchi service. This is opt-in — telemetry is only active when explicitly enabled.

Enabling telemetry

Either set the plugin option:

{
  "plugin": [
    ["@kimchi-dev/opencode-kimchi", { "telemetry": true }]
  ]
}

Or set the environment variable:

export OPENCODE_ENABLE_TELEMETRY=1

Environment variables

Variable	Required	Description
OPENCODE_ENABLE_TELEMETRY	No	Set to 1 to enable telemetry (alternative to plugin option)
OPENCODE_OTLP_ENDPOINT	Yes*	Kimchi logs ingest endpoint URL
OPENCODE_OTLP_METRICS_ENDPOINT	Yes*	Kimchi metrics ingest endpoint URL
OPENCODE_OTLP_HEADERS	Yes*	Authorization header (Authorization=Bearer <token>)

* Required when telemetry is enabled.

export OPENCODE_OTLP_ENDPOINT=https://api.cast.ai/ai-optimizer/v1beta/logs:ingest
export OPENCODE_OTLP_METRICS_ENDPOINT=https://api.cast.ai/ai-optimizer/v1beta/metrics:ingest
export OPENCODE_OTLP_HEADERS="Authorization=Bearer YOUR_API_KEY_HERE"

Data sent

API Request Logs — sent per completed assistant message:

• Model, provider, input/output tokens, cache tokens, cost, duration

Productivity Metrics — cumulative, flushed every 30 seconds:

• Token usage and cost by model
• Git commits and pull requests (detected from bash tool)
• Lines of code added/removed (from edit/write/patch tools)
• Edit decisions by tool name and language

Requirements

• OpenCode with @opencode-ai/plugin SDK ≥1.3.0
• Kimchi configured with your Cast AI API key

Development

npm install
npm run build
npm run dev    # watch mode
npm test       # run tests

License

MIT