* docs: add context optimization design spec, implementation plan, and pi-layer research - Spec: 6-change design for GSD extension context optimization - Plan: 9-task TDD implementation plan with exact file paths and code - Pi-layer doc: 10 infrastructure opportunities (research only, not planned) Part of #3171, #3406, #3452, #3433. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * feat(context): add observation masking for auto-mode sessions Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * feat(context): add phase handoff anchors for auto-mode Introduces PhaseAnchor read/write utilities so downstream agents can inherit decisions, blockers, and intent written at phase boundaries without re-inferring from conversation history. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * feat(context): add capability-aware model routing and context management preferences Implement ADR-004 Phase 2 capability scoring with 7-dimension model profiles, task requirement vectors, and weighted scoring. Add ContextManagementConfig preferences for observation masking thresholds. Wire capability scoring into auto-model-selection dispatch path. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * feat(context): wire observation masking, phase anchors, and tool truncation Register observation masker in before_provider_request hook to replace old tool results with placeholders during auto-mode. Add tool result truncation (configurable via context_management.tool_result_max_chars). Inject phase handoff anchors into prompt builders so downstream phases inherit decisions from research/planning. Write anchors after successful phase completion. Update ADR-004 status to Implemented. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * chore: remove internal planning artifacts from PR Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * docs: add capability routing, observation masking, and context management Update dynamic-model-routing.md with capability-aware scoring section. Update token-optimization.md with observation masking, tool truncation, and phase handoff anchor documentation. Update configuration.md with context_management preference block and capability_routing flag. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * Merge branch 'main' into feat/gsd-context-optimization * fix: add context_management to known keys and prevent tool truncation state corruption - Add missing 'context_management' to KNOWN_PREFERENCE_KEYS set so users don't get spurious unknown-key warnings when configuring it. - Replace in-place mutation of tool result content with immutable spread to prevent corrupting shared conversation message objects. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix: add stop and backtrack to triage-ui classification labels The Classification type gained stop and backtrack variants from main but triage-ui.ts was not updated, causing a TypeScript build failure. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix: context masker and tool truncation operate on correct pi-ai message format The observation masker and tool result truncation in before_provider_request were checking m.type === "toolResult" but the actual pi-ai payload uses m.role === "toolResult" with content as TextContent[] arrays (not strings). bashExecution messages are converted to {role:"user"} by convertToLlm before the hook fires, so checking m.type === "bashExecution" was a no-op. - Fix context-masker to match on role, handle array content, detect bash results by their "Ran `" prefix - Fix register-hooks truncation to operate on role:"toolResult" with array content blocks - Update tests to use correct pi-ai LLM payload format Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
157 lines
6.3 KiB
Markdown
157 lines
6.3 KiB
Markdown
# Dynamic Model Routing
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*Introduced in v2.19.0*
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Dynamic model routing automatically selects cheaper models for simple work and reserves expensive models for complex tasks. This reduces token consumption by 20-50% on capped plans without sacrificing quality where it matters.
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## How It Works
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Each unit dispatched by auto-mode is classified into a complexity tier:
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| Tier | Typical Work | Default Model Level |
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|------|-------------|-------------------|
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| **Light** | Slice completion, UAT, hooks | Haiku-class |
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| **Standard** | Research, planning, execution, milestone completion | Sonnet-class |
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| **Heavy** | Replanning, roadmap reassessment, complex execution | Opus-class |
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The router then selects a model for that tier. The key rule: **downgrade-only semantics**. The user's configured model is always the ceiling — routing never upgrades beyond what you've configured.
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## Enabling
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Dynamic routing is off by default. Enable it in preferences:
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```yaml
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---
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version: 1
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dynamic_routing:
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enabled: true
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---
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```
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## Configuration
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```yaml
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dynamic_routing:
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enabled: true
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tier_models: # explicit model per tier (optional)
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light: claude-haiku-4-5
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standard: claude-sonnet-4-6
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heavy: claude-opus-4-6
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escalate_on_failure: true # bump tier on task failure (default: true)
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budget_pressure: true # auto-downgrade when approaching budget ceiling (default: true)
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cross_provider: true # consider models from other providers (default: true)
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hooks: true # apply routing to post-unit hooks (default: true)
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```
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### `tier_models`
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Override which model is used for each tier. When omitted, the router uses a built-in capability mapping that knows common model families:
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- **Light:** `claude-haiku-4-5`, `gpt-4o-mini`, `gemini-2.0-flash`
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- **Standard:** `claude-sonnet-4-6`, `gpt-4o`, `gemini-2.5-pro`
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- **Heavy:** `claude-opus-4-6`, `gpt-4.5-preview`, `gemini-2.5-pro`
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### `escalate_on_failure`
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When a task fails at a given tier, the router escalates to the next tier on retry. Light → Standard → Heavy. This prevents cheap models from burning retries on work that needs more reasoning.
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### `budget_pressure`
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When approaching the budget ceiling, the router progressively downgrades:
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| Budget Used | Effect |
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|------------|--------|
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| < 50% | No adjustment |
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| 50-75% | Standard → Light |
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| 75-90% | More aggressive downgrading |
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| > 90% | Nearly everything → Light; only Heavy stays at Standard |
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### `cross_provider`
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When enabled, the router may select models from providers other than your primary. This uses the built-in cost table to find the cheapest model at each tier. Requires the target provider to be configured.
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## Capability-Aware Scoring
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*Introduced in v2.59.0 (ADR-004 Phase 2)*
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When `capability_routing` is enabled, the router goes beyond tier classification and scores models against task-specific capability requirements. Each known model has a 7-dimension profile:
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| Dimension | What It Measures |
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|-----------|-----------------|
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| `coding` | Code generation, refactoring, implementation quality |
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| `debugging` | Error diagnosis, fix accuracy |
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| `research` | Information gathering, codebase exploration |
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| `reasoning` | Multi-step logic, architectural decisions |
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| `speed` | Response latency (inverse of cost) |
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| `longContext` | Performance with large context windows |
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| `instruction` | Adherence to structured instructions and templates |
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Each unit type maps to a weighted requirement vector. For example, `execute-task` weights `coding: 0.9, reasoning: 0.6, debugging: 0.5` while `research-slice` weights `research: 0.9, reasoning: 0.7, longContext: 0.5`.
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For `execute-task` units, the classifier also inspects task metadata (tags, description) to refine requirements. Documentation tasks boost `instruction` and lower `coding`; test tasks boost `debugging`.
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Enable capability routing:
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```yaml
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dynamic_routing:
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enabled: true
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capability_routing: true
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```
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When enabled, models within the target tier are ranked by capability score rather than selected arbitrarily. When disabled (the default), the existing tier-only selection applies.
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## Complexity Classification
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Units are classified using pure heuristics — no LLM calls, sub-millisecond:
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### Unit Type Defaults
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| Unit Type | Default Tier |
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|-----------|-------------|
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| `complete-slice`, `run-uat` | Light |
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| `research-*`, `plan-*`, `complete-milestone` | Standard |
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| `execute-task` | Standard (upgraded by task analysis) |
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| `replan-slice`, `reassess-roadmap` | Heavy |
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| `hook/*` | Light |
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### Task Plan Analysis
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For `execute-task` units, the classifier analyzes the task plan:
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| Signal | Simple → Light | Complex → Heavy |
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|--------|---------------|----------------|
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| Step count | ≤ 3 | ≥ 8 |
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| File count | ≤ 3 | ≥ 8 |
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| Description length | < 500 chars | > 2000 chars |
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| Code blocks | — | ≥ 5 |
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| Complexity keywords | None | Present |
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**Complexity keywords:** `research`, `investigate`, `refactor`, `migrate`, `integrate`, `complex`, `architect`, `redesign`, `security`, `performance`, `concurrent`, `parallel`, `distributed`, `backward compat`
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### Adaptive Learning
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The routing history (`.gsd/routing-history.json`) tracks success/failure per tier per unit type. If a tier's failure rate exceeds 20% for a given pattern, future classifications are bumped up. User feedback (`over`/`under`/`ok`) is weighted 2× vs automatic outcomes.
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## Interaction with Token Profiles
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Dynamic routing and token profiles are complementary:
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- **Token profiles** (`budget`/`balanced`/`quality`) control phase skipping and context compression
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- **Dynamic routing** controls per-unit model selection within the configured phase model
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When both are active, token profiles set the baseline models and dynamic routing further optimizes within those baselines. The `budget` token profile + dynamic routing provides maximum cost savings.
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## Cost Table
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The router includes a built-in cost table for common models, used for cross-provider cost comparison. Costs are per-million tokens (input/output):
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| Model | Input | Output |
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|-------|-------|--------|
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| claude-haiku-4-5 | $0.80 | $4.00 |
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| claude-sonnet-4-6 | $3.00 | $15.00 |
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| claude-opus-4-6 | $15.00 | $75.00 |
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| gpt-4o-mini | $0.15 | $0.60 |
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| gpt-4o | $2.50 | $10.00 |
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| gemini-2.0-flash | $0.10 | $0.40 |
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The cost table is used for comparison only — actual billing comes from your provider.
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