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‎docs/MODEL_COMPATIBILITY.md‎

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# Model Compatibility Guide
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This document describes model-specific handling in the OpenAI-compatible provider. When adding new models or providers, review this guide to ensure proper compatibility.
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## Table of Contents
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- [Overview](#overview)
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- [Model-Specific Handling](#model-specific-handling)
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- [Kimi Models (is_error Exclusion)](#kimi-models-is_error-exclusion)
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- [Reasoning Models (Tuning Parameter Stripping)](#reasoning-models-tuning-parameter-stripping)
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- [GPT-5 (max_completion_tokens)](#gpt-5-max_completion_tokens)
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- [Qwen Models (DashScope Routing)](#qwen-models-dashscope-routing)
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- [Implementation Details](#implementation-details)
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- [Adding New Models](#adding-new-models)
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- [Testing](#testing)
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## Overview
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The `openai_compat.rs` provider translates Claude Code's internal message format to OpenAI-compatible chat completion requests. Different models have varying requirements for:
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- Tool result message fields (`is_error`)
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- Sampling parameters (temperature, top_p, etc.)
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- Token limit fields (`max_tokens` vs `max_completion_tokens`)
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- Base URL routing
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## Model-Specific Handling
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### Kimi Models (is_error Exclusion)
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**Affected models:** `kimi-k2.5`, `kimi-k1.5`, `kimi-moonshot`, and any model with `kimi` in the name (case-insensitive)
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**Behavior:** The `is_error` field is **excluded** from tool result messages.
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**Rationale:** Kimi models (via Moonshot AI and DashScope) reject the `is_error` field with a 400 Bad Request error:
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```json
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{
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"error": {
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"type": "invalid_request_error",
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"message": "Unknown field: is_error"
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}
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}
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```
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**Detection:**
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```rust
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fn model_rejects_is_error_field(model: &str) -> bool {
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let lowered = model.to_ascii_lowercase();
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let canonical = lowered.rsplit('/').next().unwrap_or(lowered.as_str());
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canonical.starts_with("kimi-")
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}
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```
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**Testing:** See `model_rejects_is_error_field_detects_kimi_models` and related tests in `openai_compat.rs`.
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---
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### Reasoning Models (Tuning Parameter Stripping)
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**Affected models:**
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- OpenAI: `o1`, `o1-*`, `o3`, `o3-*`, `o4`, `o4-*`
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- xAI: `grok-3-mini`
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- Alibaba DashScope: `qwen-qwq-*`, `qwq-*`, `qwen3-*-thinking`
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**Behavior:** The following tuning parameters are **stripped** from requests:
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- `temperature`
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- `top_p`
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- `frequency_penalty`
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- `presence_penalty`
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**Rationale:** Reasoning/chain-of-thought models use fixed sampling strategies and reject these parameters with 400 errors.
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**Exception:** `reasoning_effort` is included for compatible models when explicitly set.
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**Detection:**
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```rust
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fn is_reasoning_model(model: &str) -> bool {
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let canonical = model.to_ascii_lowercase()
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.rsplit('/')
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.next()
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.unwrap_or(model);
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canonical.starts_with("o1")
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|| canonical.starts_with("o3")
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|| canonical.starts_with("o4")
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|| canonical == "grok-3-mini"
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|| canonical.starts_with("qwen-qwq")
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|| canonical.starts_with("qwq")
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|| (canonical.starts_with("qwen3") && canonical.contains("-thinking"))
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}
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```
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**Testing:** See `reasoning_model_strips_tuning_params`, `grok_3_mini_is_reasoning_model`, and `qwen_reasoning_variants_are_detected` tests.
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---
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### GPT-5 (max_completion_tokens)
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**Affected models:** All models starting with `gpt-5`
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**Behavior:** Uses `max_completion_tokens` instead of `max_tokens` in the request payload.
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**Rationale:** GPT-5 models require the `max_completion_tokens` field. Legacy `max_tokens` causes request validation failures:
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```json
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{
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"error": {
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"message": "Unknown field: max_tokens"
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}
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}
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```
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**Implementation:**
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```rust
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let max_tokens_key = if wire_model.starts_with("gpt-5") {
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"max_completion_tokens"
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} else {
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"max_tokens"
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};
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```
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**Testing:** See `gpt5_uses_max_completion_tokens_not_max_tokens` and `non_gpt5_uses_max_tokens` tests.
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---
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### Qwen Models (DashScope Routing)
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**Affected models:** All models with `qwen` prefix
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**Behavior:** Routed to DashScope (`https://dashscope.aliyuncs.com/compatible-mode/v1`) rather than default providers.
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**Rationale:** Qwen models are hosted by Alibaba Cloud's DashScope service, not OpenAI or Anthropic.
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**Configuration:**
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```rust
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pub const DEFAULT_DASHSCOPE_BASE_URL: &str = "https://dashscope.aliyuncs.com/compatible-mode/v1";
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```
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**Authentication:** Uses `DASHSCOPE_API_KEY` environment variable.
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**Note:** Some Qwen models are also reasoning models (see [Reasoning Models](#reasoning-models-tuning-parameter-stripping) above) and receive both treatments.
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## Implementation Details
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### File Location
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All model-specific logic is in:
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```
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rust/crates/api/src/providers/openai_compat.rs
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```
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### Key Functions
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| Function | Purpose |
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|----------|---------|
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| `model_rejects_is_error_field()` | Detects models that don't support `is_error` in tool results |
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| `is_reasoning_model()` | Detects reasoning models that need tuning param stripping |
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| `translate_message()` | Converts internal messages to OpenAI format (applies `is_error` logic) |
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| `build_chat_completion_request()` | Constructs full request payload (applies all model-specific logic) |
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### Provider Prefix Handling
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All model detection functions strip provider prefixes (e.g., `dashscope/kimi-k2.5` → `kimi-k2.5`) before matching:
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```rust
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let canonical = model.to_ascii_lowercase()
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.rsplit('/')
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.next()
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.unwrap_or(model);
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```
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This ensures consistent detection regardless of whether models are referenced with or without provider prefixes.
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## Adding New Models
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When adding support for new models:
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1. **Check if the model is a reasoning model**
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- Does it reject temperature/top_p parameters?
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- Add to `is_reasoning_model()` detection
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2. **Check tool result compatibility**
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- Does it reject the `is_error` field?
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- Add to `model_rejects_is_error_field()` detection
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3. **Check token limit field**
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- Does it require `max_completion_tokens` instead of `max_tokens`?
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- Update the `max_tokens_key` logic
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4. **Add tests**
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- Unit test for detection function
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- Integration test in `build_chat_completion_request`
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5. **Update this documentation**
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- Add the model to the affected lists
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- Document any special behavior
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## Testing
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### Running Model-Specific Tests
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```bash
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# All OpenAI compatibility tests
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cargo test --package api providers::openai_compat
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# Specific test categories
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cargo test --package api model_rejects_is_error_field
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cargo test --package api reasoning_model
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cargo test --package api gpt5
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cargo test --package api qwen
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```
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### Test Files
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- Unit tests: `rust/crates/api/src/providers/openai_compat.rs` (in `mod tests`)
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- Integration tests: `rust/crates/api/tests/openai_compat_integration.rs`
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### Verifying Model Detection
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To verify a model is detected correctly without making API calls:
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```rust
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#[test]
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fn my_new_model_is_detected() {
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// is_error handling
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assert!(model_rejects_is_error_field("my-model"));
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// Reasoning model detection
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assert!(is_reasoning_model("my-model"));
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// Provider prefix handling
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assert!(model_rejects_is_error_field("provider/my-model"));
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}
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```
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---
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*Last updated: 2026-04-16*
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For questions or updates, see the implementation in `rust/crates/api/src/providers/openai_compat.rs`.

‎prd.json‎

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],
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"passes": true,
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"priority": "P0"
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},
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{
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"id": "US-009",
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"title": "Add unit tests for kimi model compatibility fix",
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"description": "During dogfooding we discovered the existing test coverage for model-specific is_error handling is insufficient. Need to add dedicated tests for model_rejects_is_error_field function and translate_message behavior with different models.",
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"acceptanceCriteria": [
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"Test model_rejects_is_error_field identifies kimi-k2.5, kimi-k1.5, dashscope/kimi-k2.5",
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"Test translate_message includes is_error for gpt-4, grok-3, claude models",
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"Test translate_message excludes is_error for kimi models",
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"Test build_chat_completion_request produces correct payload for kimi vs non-kimi",
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"All new tests pass",
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"cargo test --package api passes"
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],
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"passes": true,
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"priority": "P1"
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},
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{
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"id": "US-010",
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"title": "Add model compatibility documentation",
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"description": "Document which models require special handling (is_error exclusion, reasoning model tuning param stripping, etc.) in a MODEL_COMPATIBILITY.md file for operators and contributors.",
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"acceptanceCriteria": [
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"MODEL_COMPATIBILITY.md created in docs/ or repo root",
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"Document kimi models is_error exclusion",
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"Document reasoning models (o1, o3, grok-3-mini) tuning param stripping",
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"Document gpt-5 max_completion_tokens requirement",
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"Document qwen model routing through dashscope",
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"Cross-reference with existing code comments"
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],
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"passes": true,
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"priority": "P2"
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},
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{
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"id": "US-011",
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"title": "Performance optimization: reduce API request serialization overhead",
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"description": "The translate_message function creates intermediate JSON Value objects that could be optimized. Profile and optimize the hot path for API request building, especially for conversations with many tool results.",
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"acceptanceCriteria": [
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"Profile current request building with criterion or similar",
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"Identify bottlenecks in translate_message and build_chat_completion_request",
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"Implement optimizations (Vec pre-allocation, reduced cloning, etc.)",
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"Benchmark before/after showing improvement",
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"No functional changes or API breakage"
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],
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"passes": true,
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"priority": "P2"
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}
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]
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}

‎progress.txt‎

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- cargo clippy --workspace: PASSED
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All 7 stories from prd.json now have passes: true
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Iteration 2: 2026-04-16
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------------------------
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US-009 COMPLETED (Add unit tests for kimi model compatibility fix)
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- Files: rust/crates/api/src/providers/openai_compat.rs
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- Added 4 comprehensive unit tests:
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1. model_rejects_is_error_field_detects_kimi_models - verifies detection of kimi-k2.5, kimi-k1.5, dashscope/kimi-k2.5, case insensitivity
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2. translate_message_includes_is_error_for_non_kimi_models - verifies gpt-4o, grok-3, claude include is_error
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3. translate_message_excludes_is_error_for_kimi_models - verifies kimi models exclude is_error (prevents 400 Bad Request)
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4. build_chat_completion_request_kimi_vs_non_kimi_tool_results - full integration test for request building
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- Tests: 4 new tests, 119 unit tests total in api crate (+4), all passing
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- Integration tests: 29 passing (no regressions)
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US-010 COMPLETED (Add model compatibility documentation)
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- Files: docs/MODEL_COMPATIBILITY.md
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- Created comprehensive documentation covering:
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1. Kimi Models (is_error Exclusion) - documents the 400 Bad Request issue and solution
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2. Reasoning Models (Tuning Parameter Stripping) - covers o1, o3, o4, grok-3-mini, qwen-qwq, qwen3-thinking
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3. GPT-5 (max_completion_tokens) - documents max_tokens vs max_completion_tokens requirement
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4. Qwen Models (DashScope Routing) - explains routing and authentication
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- Added implementation details section with key functions
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- Added "Adding New Models" guide for future contributors
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- Added testing section with example commands
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- Cross-referenced with existing code comments in openai_compat.rs
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- cargo clippy passes
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US-011 COMPLETED (Performance optimization: reduce API request serialization overhead)
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- Files:
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- rust/crates/api/Cargo.toml (added criterion dev-dependency and bench config)
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- rust/crates/api/benches/request_building.rs (new benchmark suite)
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- rust/crates/api/src/providers/openai_compat.rs (optimizations)
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- rust/crates/api/src/lib.rs (public exports for benchmarks)
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- Optimizations implemented:
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1. flatten_tool_result_content: Pre-allocate String capacity and avoid intermediate Vec
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- Before: collected to Vec<String> then joined
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- After: single String with pre-calculated capacity, push directly
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2. Made key functions public for benchmarking: translate_message, build_chat_completion_request,
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flatten_tool_result_content, is_reasoning_model, model_rejects_is_error_field
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- Benchmark results:
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- flatten_tool_result_content/single_text: ~17ns
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- flatten_tool_result_content/multi_text (10 blocks): ~46ns
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- flatten_tool_result_content/large_content (50 blocks): ~11.7µs
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- translate_message/text_only: ~200ns
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- translate_message/tool_result: ~348ns
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- build_chat_completion_request/10 messages: ~16.4µs
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- build_chat_completion_request/100 messages: ~209µs
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- is_reasoning_model detection: ~26-42ns depending on model
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- All tests pass (119 unit tests + 29 integration tests)
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- cargo clippy passes

‎rust/crates/api/Cargo.toml‎

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telemetry = { path = "../telemetry" }
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tokio = { version = "1", features = ["io-util", "macros", "net", "rt-multi-thread", "time"] }
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[dev-dependencies]
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criterion = { version = "0.5", features = ["html_reports"] }
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[lints]
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workspace = true
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[[bench]]
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name = "request_building"
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harness = false

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