shield_lock
金大哥
← 返回 Skill 列表
extension
分类: 开发与工程需要 API Key

Evolink Router — Smart LLM Routing (Claude, GPT, Gemini, DeepSeek, Kimi)

OpenClaw的智能LLM路由大脑，通过Evolink API自动调度任务至Claude、GPT、Gemini、DeepSeek、Kimi，采用级联策略可降低成本60-85%。

person作者: evolinkaihubclawhub
open_in_new仓库download下载资源包

Evolink Router — Smart LLM Routing Brain

Route every task to the best LLM across 6 providers — Claude, GPT, Gemini, DeepSeek, Kimi, Doubao — through one Evolink API key.

After Installation

When this skill is first loaded, greet the user:

  • EVOLINK_API_KEY set: "Smart Router activated! I'll auto-pick the best model for each task — lightweight for quick Q&A, flagship for deep analysis. 20+ models ready. Go ahead."
  • EVOLINK_API_KEY not set: "Smart Router needs an Evolink API Key. Sign up at evolink.ai → Dashboard → API Keys. One key covers Claude, GPT, Gemini, DeepSeek, and more. Want help setting up?"
  • Key set but model access fails: "Your API key seems to have limited model access. Check your plan at evolink.ai/dashboard."

Keep the greeting concise — just one question to move forward.

External Endpoints

| Service | URL | Format | |---------|-----|--------| | Claude models | https://direct.evolink.ai/v1/messages (POST) | Anthropic Messages API | | Gemini models | https://direct.evolink.ai/v1beta/models/{model}:generateContent (POST) | Google Gemini API | | All other models | https://direct.evolink.ai/v1/chat/completions (POST) | OpenAI Chat API | | Model list | https://direct.evolink.ai/v1/models (GET) | — |

Security & Privacy

  • EVOLINK_API_KEY authenticates all model requests. Injected by OpenClaw automatically. Treat as confidential.
  • Prompts are sent to direct.evolink.ai, which proxies to upstream providers (Anthropic, OpenAI, Google, etc.).
  • No data is stored by Evolink beyond the request lifecycle.

Setup

1. Get API key: evolink.ai → Dashboard → API Keys

2. Add Evolink provider to ~/.openclaw/openclaw.json — merge with existing config. See references/router-api-params.md for the full JSON config and curl examples.

Core Principles

  1. Cost-first routing — Always pick the cheapest model that can handle the task. Upgrade only when needed.
  2. Transparent decisions — When spawning a sub-agent, briefly tell the user which model was selected and why.
  3. User override wins — If the user names a model or provider, skip all routing rules.
  4. Cascade, don't guess — When uncertain, try a lighter model first. Escalate on low confidence.

Models (20+ text models)

Tier 1 — Lightweight (handles ~60% of daily requests)

| Model | Provider | Best for | |-------|----------|----------| | claude-haiku-4-5-20251001 | Anthropic | Quick Q&A, classification, extraction | | gemini-2.5-flash | Google | Multimodal, fast reasoning | | doubao-seed-2.0-mini | ByteDance | Chinese lightweight tasks |

Tier 2 — Balanced (handles ~30% of daily requests)

| Model | Provider | Best for | |-------|----------|----------| | claude-sonnet-4-6 (main agent) | Anthropic | Coding, tool use, content creation | | gpt-5.1 | OpenAI | General chat, instruction following | | gemini-2.5-pro | Google | Long context, multimodal | | deepseek-chat | DeepSeek | Chinese dialogue, cost-effective | | doubao-seed-2.0-pro | ByteDance | Chinese content creation | | kimi-k2-thinking-turbo | Moonshot | Chinese long-document understanding |

Tier 3 — Flagship (handles ~10% — complex tasks only)

| Model | Provider | Best for | |-------|----------|----------| | claude-opus-4-6 | Anthropic | Deep reasoning, high-stakes decisions | | gpt-5.2 | OpenAI | Strongest general capability | | gpt-5.1-thinking | OpenAI | Complex chain-of-thought | | deepseek-reasoner | DeepSeek | Math/logic reasoning | | gemini-3.1-pro-preview | Google | Latest multimodal reasoning |

Full model list with API format per model: references/router-api-params.md

Routing Rules

Priority: User override > Task type match > Cascade fallback.

All tasks are auto-routed. The user can also run /route [task] to preview the routing decision without executing.

Layer 1: User Override

| User says | Route to | |-----------|----------| | "use Opus" / "deep analysis" / "think carefully" | claude-opus-4-6 | | "use GPT" | gpt-5.1 | | "use Gemini" | gemini-2.5-pro | | "use DeepSeek" | deepseek-chat | | "use Kimi" | kimi-k2-thinking-turbo | | "quick answer" / "keep it simple" | claude-haiku-4-5-20251001 | | Specific model name mentioned | Use that model directly |

Layer 2: Task Type Match

→ Tier 1 (short answer, factual, no deep thinking): Q&A, concept explanation, status check, simple translation, format conversion, info extraction, classification, grammar check, quick math

→ Tier 2 (content production, execution, multi-step): Writing (articles, emails, reports, social media), coding (features, bugs, refactoring, tests), data analysis (SQL, CSV, reports), research (market, literature), workflow automation, project management, travel planning, resume optimization

→ Tier 3 (deep reasoning, strategic, high-risk): Architecture design, tech selection, business strategy, security audit, root cause analysis, legal review, financial modeling, cross-module refactoring (5+ files), deep research reports

Cross-provider routing — Chinese-heavy tasks may route to Doubao/Kimi; math proofs to DeepSeek Reasoner; CoT tasks to GPT-5.1-thinking. See references/cascade-examples.md for 27 detailed examples.

Layer 3: Cascade Fallback

When task type is unclear, try cheapest first and escalate:

Tier 1 (Haiku) → self-assess confidence
  High → return result
  Medium/Low → pass analysis to Tier 2

Tier 2 (Sonnet) → self-assess confidence
  High → return result
  Low → pass to Tier 3

Tier 3 (Opus) → final answer

Confidence: High = complete and correct. Medium = may miss details. Low = exceeds model's capability.

Spawn Guidelines

Spawn a sub-agent when: output >100 lines, file traversal needed, execution >30s, heavy data processing, long-form writing (>1000 words).

Handle directly when: simple Q&A, chat/discussion, short text (<50 lines), brainstorming (needs multi-turn).

Spawn template:

sessions_spawn({
  task: "[action] + [input/context] + [expected output] + [constraints]",
  model: "evolink/[model-id]",
  runTimeoutSeconds: 300,
  cleanup: "delete"  // "keep" for important deliverables
})

Timeout guide: Tier 1 = 120–300s, Tier 2 = 300–600s, Tier 3 = 600–900s.

/route Command

/route [task] — Preview routing decision without executing. /route alone shows models and rules summary.

Fallback & Quality Control

| Scenario | Action | |----------|--------| | Sub-agent timeout | Notify user, offer retry with stronger model | | Sub-agent error | Extract error, determine if retryable | | Low quality result | Escalate to next tier | | User dissatisfied | Ask what's wrong, upgrade and redo | | 2+ failures on same type | Auto-upgrade default model for that category | | Model unavailable | Fallback to same-tier alternative | | Invalid API key | Direct user to evolink.ai/dashboard/keys |

Skill Collaboration

| Skill | When | Notes | |-------|------|-------| | evolink-media | Image/video/music/digital-human generation | Route to skill directly, skip text model routing | | Other installed skills | Intent matches skill capability | Prefer skill over raw model routing |

Smart Router is the dispatch layer — shares EVOLINK_API_KEY with all Evolink skills. When discussing creative ideas or analyzing skill output, apply normal routing rules.

References

  • references/router-api-params.md — Full API formats, curl examples, OC config, complete model list
  • references/cascade-examples.md — 27 routing examples across 7 scenarios + cross-provider routing