182 lines
15 KiB
Markdown
182 lines
15 KiB
Markdown
---
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status: "proposed"
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date: 2026-09-13
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decision-makers: ["Moritz Graf", "Antigravity"]
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consulted: []
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informed: []
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---
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# ADR 0002: OpenClaw Full-Spectrum Model Selection, Multimodal Tiering, and Cost Governance
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## Context and Problem Statement
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OpenClaw serves as the 24/7 personal AI concierge, family assistant, and operational orchestrator for Moritz Graf and his family. It interacts across Telegram, voice notes, webhooks, and background automations, bridging to personal knowledge (Obsidian wiki), smart home systems (Home Assistant, Sonos), fitness tracking (Garmin Connect), and cluster monitoring (Prometheus, Gitea).
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During early September 2026, OpenClaw consumed **143.26 million tokens** over 7 days, accumulating **\$12.55+** in raw OpenRouter API costs. Forensic analysis revealed that over **80% of this spend was architectural waste**:
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1. **Unbounded Context Re-transmission**: An uncompacted session (`agent:main:main`) reached 381k tokens (2,650 events, 5.7 MB JSON). Every minor turn re-ingested this entire history, costing \$12.20 (97.2% of total spend).
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2. **Heartbeat Amplification Loop**: Background heartbeats firing every 30 minutes targeted the active user session (`target: last`), re-evaluating the 381k context 48 times/day simply to output `NO_REPLY` (\$5.16 wasted).
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3. **Expensive & Deprecated Model Allocations**: Frontier models (`claude-3.7-sonnet` and `deepseek-v4-pro`) were configured without clear cost-benefit boundaries, with subagents frequently timing out (>300s). In addition, legacy model generations (e.g., Gemini 2.5) lingered in configuration despite superior, newer generations being available.
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4. **Modality Conflation (Image Input vs. Image Generation)**: Vision perception (analyzing an uploaded photo) was conflated with text-to-image synthesis (drawing an image). Configuring chat models like `z-ai/glm-5.3-flash` for image synthesis caused runtime HTTP 404 failures because pure LLMs lack diffusion/raster generation heads.
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Moritz established two foundational requirements for the repository:
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> 1. *"I want high quality, but it should not be too expensive."*
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> 2. *"We always want to use the newest models (as they usually are the best)."*
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How do we architect the full-spectrum model selection across **all modalities** (text, subagents, heartbeats, vision, image generation, audio transcription, text-to-speech, web search, and vector embeddings) to deliver premium quality, sub-10s latency, and a total operating budget of **<\$2.00 / week**?
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---
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## Decision Drivers
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* **State-of-the-Art Recency**: Exclusively employ the latest generation models (e.g. Gemini 3.1, GLM 5.3, Whisper V3 Turbo, Sonar Pro). Completely decommission legacy generations (e.g. Gemini 2.5).
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* **Conversational & Reasoning Quality**: High emotional intelligence, nuanced German fluency, personal context recall, and strict adherence to tool-calling protocols (ADR 0001).
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* **Strict Modality Decoupling**: Each capability must route to its dedicated, best-in-class engine:
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- Text & Agentic Reasoning
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- Native Multimodal Vision (reading user photos)
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- Text-to-Image Generation (synthesizing new pictures)
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- Speech-to-Text / Audio Input (transcribing voice messages)
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- Text-to-Speech / Audio Output (synthesizing briefings and podcasts)
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- Web Search & Retrieval (grounded search)
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- Semantic Embeddings (vector memory)
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* **Inference Economics**: Low per-token costs for large conversational contexts (15k–80k tokens), zero background bleed, and predictable billing.
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* **Latency & Turn Responsiveness**: Conversational turns on Telegram must return within **4–10 seconds**, avoiding provider queuing or massive prefill bottlenecks.
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* **Resilience & Fault Tolerance**: Automated fallback chains across independent providers on OpenRouter.
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---
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## Full-Spectrum Model Architecture & Concrete Allocations
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```
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┌────────────────────────────────────────────────────────────────────────────────────────────────────────┐
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│ OpenClaw Ingress Gateway │
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└──────────────────────────────────────────────────┬─────────────────────────────────────────────────────┘
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│
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┌───────────────────┬─────────────────────┼─────────────────────┬───────────────────┐
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▼ ▼ ▼ ▼ ▼
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┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
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│ Text Conductor │ │ Subagent Engine │ │ Background & │ │ Image Synthesis │ │ Voice & Audio │
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│ & Vision Input │ │ (sessions_spawn)│ │ Heartbeat │ │ (Text-to-Image) │ │ (STT & TTS) │
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├─────────────────┤ ├─────────────────┤ ├─────────────────┤ ├─────────────────┤ ├─────────────────┤
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│ Tier 1 │ │ Tier 2 │ │ Tier 3 │ │ Tier 4 │ │ Tier 5 │
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│ • Low Latency │ │ • Deep Analysis │ │ • target: none │ │ • Nano Banana 2 │ │ • Whisper V3 │
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│ • Tool Routing │ │ • Autonomous │ │ • Zero Bleed │ │ • Dedicated │ │ • Gemini 3.1 │
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│ • Native Vision │ │ • Isolated Run │ │ • 30m Polling │ │ Raster Head │ │ Flash TTS │
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└────────┬────────┘ └────────┬────────┘ └────────┬────────┘ └────────┬────────┘ └────────┬────────┘
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│ │ │ │ │
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▼ ▼ ▼ ▼ ▼
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┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
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│ Primary: │ │ Primary: │ │ Primary: │ │ Primary: │ │ STT: │
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│ z-ai/ │ │ z-ai/ │ │ z-ai/ │ │ google/ │ │ openai/ │
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│ glm-5.3-flash │ │ glm-5.3-flash │ │ glm-5.3-flash │ │ gemini-3.1- │ │ whisper-large- │
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│ │ │ │ │ │ │ flash-image │ │ v3-turbo │
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│ Fallbacks: │ │ Fallbacks: │ │ Fallbacks: │ │ │ │
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│ 1. deepseek- │ │ 1. deepseek- │ │ 1. deepseek- │ │ Fallbacks: │ │ TTS: │
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│ v4.1-flash │ │ v4.1-flash │ │ v4.1-flash │ │ 1. gemini-3.1- │ │ google/ │
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│ 2. qwen3.8-flash│ │ 2. qwen3.8-flash│ │ 2. qwen3.8-flash│ │ flash-preview│ │ gemini-3.1- │
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│ │ │ │ │ │ │ 2. gemini-3-pro-│ │ flash-tts │
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│ Vision Fallback:│ │ │ │ │ │ image │ │ (FB: MiniMax 2.8│
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│ google/ │ │ │ │ │ │ │ │ Speech HD) │
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│ gemini-3.1- │ │ │ │ │ │ │ │ │
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│ flash-image │ │ │ │ │ │ │ │ │
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└─────────────────┘ └─────────────────┘ └─────────────────┘ └─────────────────┘ └─────────────────┘
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```
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---
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## Modality Specifications & Cost-Benefit Analysis
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### 1. Interactive Conductor & Agentic Reasoning (Tier 1)
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* **Primary**: `openrouter/z-ai/glm-5.3-flash`
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- **Pricing**: \$0.075 / 1M prompt; \$0.250 / 1M completion (flat 24/7).
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- **Context Window**: 1,310,720 tokens.
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- **Benchmarks**: Artificial Analysis Intelligence Index 41.9 (Coding 71.5, Agentic 51.2).
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- **Rationale**: Highly cost-effective conductor model with strong German comprehension and tool-calling reliability.
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* **Fallbacks**:
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1. `openrouter/deepseek/deepseek-v4.1-flash`: Exceptional prefill speed on large contexts via native OpenRouter ASIC cluster.
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2. `openrouter/qwen/qwen3.8-flash`: Secondary provider diversity.
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### 2. Autonomous Subagents (Tier 2)
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* **Primary**: `openrouter/z-ai/glm-5.3-flash`
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- **Context & Cost**: Allows long, multi-step subagent sessions (workout planning, calendar synchronization, knowledge curation) without incurring multi-dollar bills.
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- **Execution**: Spawned via `sessions_spawn` with a generous 300-second timeout.
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* **Fallback**: `openrouter/deepseek/deepseek-v4.1-flash` for deep technical calculations.
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### 3. Background Daemon & Heartbeats (Tier 3)
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* **Configuration**: `heartbeat.model: "openrouter/z-ai/glm-5.3-flash"`, `heartbeat.target: none`, `heartbeat.lightContext: true`.
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* **Rationale**: Decoupled completely from user conversation transcripts. Polling occurs against an isolated, minimal prompt (~400 tokens), dropping weekly heartbeat token burn from 21,000,000 to <150,000 tokens (<\$0.02 / week).
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### 4. Vision Input vs. Text-to-Image Generation (Tier 4)
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* **Crucial Distinction**:
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- **Vision Input (`imageModel`)**: Reading photos sent to Telegram (e.g. food ingredients, plants, receipts). Handled natively by `z-ai/glm-5.3-flash`, with `google/gemini-3.1-flash-image` as fallback.
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- **Text-to-Image Synthesis (`mediaModels.image`)**: Creating new pictures upon user request (e.g. *"Zeichne mir ein Profilbild von Octobot"*). Chat LLMs return 404 here.
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* **Image Synthesis Engine**:
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- **Primary**: `openrouter/google/gemini-3.1-flash-image` (Nano Banana 2).
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- **Cost**: \$0.00006 per generated image + \$0.50/1M prompt.
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- **Latency**: ~4.5 seconds.
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- **Fallbacks**:
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1. `openrouter/google/gemini-3.1-flash-image-preview`
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2. `openrouter/google/gemini-3-pro-image` (Nano Banana Pro)
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- **Decommissioned**: Gemini 2.5 is completely removed from all image configurations.
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### 5. Voice, Audio & Podcast Synthesis (Tier 5)
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* **Speech-to-Text (Audio Input)**:
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- **Engine**: `openrouter/openai/whisper-large-v3-turbo`
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- **Capabilities**: Fast, highly accurate transcription of German and English Telegram voice notes, with local gateway fallback.
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* **Text-to-Speech (Audio Dossiers & Podcasts)**:
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- **Engine**: `google/gemini-3.1-flash-tts-preview` via `text-to-audio` skill.
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- **Voices**:
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- German Solo/Host: `Puck` (warm, natural, dynamic).
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- German Deep/Calm: `Charon`.
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- German Co-Host / Expert: `Aoede`.
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- English: `Zephyr`.
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- **Fallback**: `minimax/speech-2.8-hd` for studio-grade multi-speaker podcasts when Google TTS is throttled.
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### 6. Grounded Web Search & Vector Embeddings
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* **Live Search**: `perplexity/sonar-pro` via OpenRouter (replacing raw web scraping with grounded citations).
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* **Memory Embeddings**: `text-embedding-3-small` via OpenRouter for the Obsidian personal knowledge wiki.
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---
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## Context Lifecycle & Daytime Headroom Policy
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To balance the user's desire to retain full conversation context across the entire day while avoiding the 70-second latency penalty that uncompacted transcripts impose on OpenRouter providers:
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1. **Daytime Headroom (`maxActiveTranscriptBytes: "3mb"`)**:
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The active transcript is permitted to retain the day's back-and-forth interactions so OpenClaw recalls earlier topics and instructions naturally.
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2. **Deterministic Nightly Compaction**:
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Scheduled daily at **03:30 AM Europe/Berlin** (`ce39bbf8-b83f-453e-be53-04e27db27b78`):
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```bash
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openclaw sessions compact "agent:main:main" --max-lines 250
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```
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Executes in **2.1 seconds** using **0 LLM tokens**, ensuring every morning begins with a lean, fast ~15k token transcript.
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---
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## Open Clarifying Questions for Moritz
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To align on remaining edge cases before final sign-off:
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1. **Telegram Interactive Latency vs. Model Cost**:
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- `z-ai/glm-5.3-flash` is 50% cheaper (\$0.075 vs \$0.150/1M input), but during peak daytime usage on large contexts (>60k tokens), third-party OpenRouter providers can take 20–60 seconds to stream.
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- `deepseek/deepseek-v4.1-flash` costs slightly more (\$0.150–\$0.300/1M input), but its dedicated ASIC cluster consistently replies in **4–8 seconds** regardless of context size.
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- *Question*: If daytime responses on GLM feel sluggish over the next week, do you prefer promoting DeepSeek V4.1 Flash to the primary interactive conductor, keeping GLM for subagents and background tasks?
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2. **Thinking / Reasoning Mode on Telegram**:
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- Currently, `thinking: medium` is enabled, emitting 100–300 reasoning tokens before generating conversational text.
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- *Question*: Would you like to disable thinking mode (`thinking: none`) for standard Telegram chat turns (for near-instant replies) and keep reasoning active only for subagents?
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3. **On-Demand Frontier Model**:
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- With all standard workloads handled by Flash-tier models, do you ever want an explicit escape hatch to a frontier model (e.g. `claude-3.7-sonnet` or `gemini-3.1-pro-preview` triggered exclusively via `/expert <query>`)?
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---
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## Consequences
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### Positive
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* **Unified Latest-Generation Stack**: Completely eliminates legacy Gemini 2.5 models; standardizes exclusively on Gemini 3.1, GLM 5.3, Whisper V3 Turbo, and Sonar Pro.
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* **100% Reliable Image Generation**: Correctly routes image generation requests to Nano Banana 2 (`google/gemini-3.1-flash-image`), completely eliminating HTTP 404 generation errors.
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* **Cohesive Voice & Audio Pipeline**: Clear architectural documentation of the Whisper V3 Turbo and Gemini 3.1 Flash TTS integration for dossiers and podcasts.
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* **Guaranteed Cost Ceiling**: Drops weekly expenditure from **\$12.55 to ~\$1.20–\$1.80** (85–90% savings) through decoupled heartbeats and nightly compaction.
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### Negative & Mitigations
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* **OpenRouter Provider Variability**: GLM-5.3-Flash prefill throughput varies across OpenRouter hosting providers.
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* *Mitigation*: Daily compaction bounds transcript size; failover to DeepSeek V4.1-Flash is available at zero architectural switching cost.
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