OpenAI /responses validates tool function schemas strictly:
- object types must have "properties" (at minimum {})
- "additionalProperties": false is required
/chat/completions is lenient and accepts schemas without these fields,
but /responses rejects them with "object schema missing properties" /
"invalid_function_parameters".
Add normalize_object_schema() which recursively walks the JSON Schema
tree and fills in missing "properties"/{} and "additionalProperties":false
on every object-type node. Existing values are not overwritten.
Call it in openai_tool_definition() before building the request payload
so both /chat/completions and /responses receive strict-validator-safe
schemas.
Add unit tests covering:
- bare object schema gets both fields injected
- nested object schemas are normalised recursively
- existing additionalProperties is not overwritten
Fixes the live repro where gpt-5.4 via OpenAI compat accepted connection
and routing but rejected every tool call with schema validation errors.
Closes ROADMAP #33.
ProviderClient::from_model_with_anthropic_auth was dispatching every
ProviderKind::OpenAi match to OpenAiCompatConfig::openai(), which reads
OPENAI_API_KEY and points at api.openai.com. But DashScope models
(qwen-plus, qwen/qwen3-coder, etc.) also return ProviderKind::OpenAi
from detect_provider_kind because DashScope speaks the OpenAI wire
format. The metadata layer correctly identifies them as needing
DASHSCOPE_API_KEY and the DashScope compatible-mode endpoint, but that
metadata was being ignored at dispatch time.
Result: users running `claw --model qwen-plus` with DASHSCOPE_API_KEY
set would get a "missing OPENAI_API_KEY" error instead of being routed
to DashScope.
Fix: consult providers::metadata_for_model in the OpenAi dispatch arm
and pick dashscope() vs openai() based on metadata.auth_env.
Adds a regression test asserting ProviderClient::from_model("qwen-plus")
builds with the DashScope base URL. Exposes a pub base_url() accessor
on OpenAiCompatClient so the test can verify the routing.
Authored by droid (Kimi K2.5 Turbo) via acpx, cleaned up by Jobdori
(removed unsafe blocks unnecessary under edition 2021, imported
ProviderClient from super, adopted EnvVarGuard pattern from
providers/mod.rs tests).
Co-Authored-By: Droid <noreply@factory.ai>
Users in Discord #clawcode-get-help (web3g) asked for Qwen 3.6 Plus via
native Alibaba DashScope API instead of OpenRouter, which has stricter
rate limits. This commit adds first-class routing for qwen/ and bare
qwen- prefixed model names.
Changes:
- DEFAULT_DASHSCOPE_BASE_URL constant: /compatible-mode/v1 endpoint
- OpenAiCompatConfig::dashscope() factory mirroring openai()/xai()
- DASHSCOPE_ENV_VARS + credential_env_vars() wiring
- metadata_for_model: qwen/ and qwen- prefix routes to DashScope with
auth_env=DASHSCOPE_API_KEY, reuses ProviderKind::OpenAi because
DashScope speaks the OpenAI REST shape
- is_reasoning_model: detect qwen-qwq, qwq-*, and *-thinking variants
so tuning params (temperature, top_p, etc.) get stripped before
payload assembly (same pattern as o1/o3/grok-3-mini)
Tests added:
- providers::tests::qwen_prefix_routes_to_dashscope_not_anthropic
- openai_compat::tests::qwen_reasoning_variants_are_detected
89 api lib tests passing, 0 failing. cargo fmt --check: clean.
Closes the user-reported gap: 'use Qwen 3.6 Plus via Alibaba API
directly, not OpenRouter' without needing OPENAI_BASE_URL override
or unsetting ANTHROPIC_API_KEY.
Reasoning models reject temperature, top_p, frequency_penalty, and
presence_penalty with 400 errors. Instead of letting these flow through
and returning cryptic provider errors, strip them silently at the
request-builder boundary.
is_reasoning_model() classifies: o1*, o3*, o4*, grok-3-mini.
stop sequences are preserved (safe for all providers).
Tests added:
- reasoning_model_strips_tuning_params: o1-mini strips all 4 params, keeps stop
- grok_3_mini_is_reasoning_model: classification coverage for grok-3-mini, o1,
o3-mini, and negative cases (gpt-4o, grok-3, claude)
85 api lib tests passing, 0 failing.
MessageRequest was missing standard OpenAI-compatible generation tuning
parameters. Callers had no way to control temperature, top_p,
frequency_penalty, presence_penalty, or stop sequences.
Changes:
- Added 5 optional fields to MessageRequest (all Option, None by default)
- Wired into build_chat_completion_request: only included in payload when set
- All existing construction sites updated with ..Default::default()
- MessageRequest now derives Default for ergonomic partial construction
Tests added:
- tuning_params_included_in_payload_when_set: all 5 params flow into JSON
- tuning_params_omitted_from_payload_when_none: absent params stay absent
83 api lib tests passing, 0 failing.
cargo check --workspace: 0 warnings.
Issue #22 was triggered by generic upstream fatal wrappers that only surfaced 'Something went wrong', which left repeated Jobdori-style failures opaque in the CLI. Capture provider request ids on error responses, classify the known generic wrapper as provider_internal, and prefix the user-visible runtime error with the failure class plus session/trace identifiers so operators can correlate the failure quickly.
Constraint: Keep the fix small and user-safe without redesigning the broader runtime error taxonomy
Constraint: Preserve existing non-generic error text unless the wrapper is the known opaque fatal surface
Rejected: Broadly rewriting every runtime error into classified envelopes | unnecessary scope expansion for issue #22
Confidence: high
Scope-risk: narrow
Reversibility: clean
Directive: If more opaque wrappers appear, extend the marker list and classification helper rather than reintroducing raw wrapper text alone
Tested: cargo test -p api detects_generic_fatal_wrapper_and_classifies_it_as_provider_internal -- --nocapture; cargo test -p api retries_exhausted_preserves_nested_request_id_and_failure_class -- --nocapture; cargo test -p rusty-claude-cli opaque_provider_wrapper_surfaces_failure_class_session_and_trace -- --nocapture; cargo test -p rusty-claude-cli retry_exhaustion_preserves_internal_failure_class_for_generic_provider_wrapper -- --nocapture; cargo test --workspace
Not-tested: Live upstream reproduction of the Jobdori failure against a real provider session
The runtime already tracked rough token estimates for compaction, but provider-bound
requests still relied on naive model output limits and could be sent upstream even
when the selected model could not fit the estimated prompt plus requested output.
This adds a small model token/context registry in the API layer, estimates request
size from the serialized prompt payload, and fails locally with a dedicated
context-window error before Anthropic or xAI calls are made. Focused integration
coverage asserts the preflight fires before any HTTP request leaves the process.
Constraint: Keep the first pass minimal and reusable across both Anthropic and OpenAI-compatible providers
Rejected: Auto-compact-and-retry in the same patch | broader control-flow change than the requested minimal preflight
Confidence: medium
Scope-risk: narrow
Reversibility: clean
Directive: Expand the model registry before enabling preflight for additional providers or aliases
Tested: cargo build -p api -p tools -p rusty-claude-cli; cargo test -p api
Not-tested: End-to-end CLI auto-compaction or retry behavior after a local context_window_blocked failure
OpenAI chat-completions streams can emit a final usage chunk when the\nclient opts in, but the Rust transport was not requesting it. This\nkeeps provider config on the client and adds stream_options.include_usage\nonly for OpenAI streams so normalized message_delta usage reflects the\ntransport without changing xAI request bodies.\n\nConstraint: Keep xAI request bodies unchanged because provider-specific streaming knobs may differ\nRejected: Enable stream_options for every OpenAI-compatible provider | risks sending unsupported params to xAI-style endpoints\nConfidence: high\nScope-risk: narrow\nDirective: Keep provider-specific streaming flags tied to OpenAiCompatConfig instead of inferring provider behavior from URLs\nTested: cargo clippy -p api --tests -- -D warnings\nTested: cargo test -p api openai_streaming_requests -- --nocapture\nTested: cargo test -p api xai_streaming_requests_skip_openai_specific_usage_opt_in -- --nocapture\nTested: cargo test -p api request_translation_uses_openai_compatible_shape -- --nocapture\nTested: cargo test -p api stream_message_normalizes_text_and_multiple_tool_calls -- --exact --nocapture\nNot-tested: Live OpenAI or xAI network calls
Claw already had the core slash-command and git primitives, but the UX
still made users work to discover them, understand current workspace
state, and trust what `/commit` was about to do. This change tightens
that flow in the same places Codex-style CLIs do: command discovery,
live status, typo recovery, and commit preflight/output.
The REPL banner and `/help` now surface a clearer starter path, unknown
slash commands suggest likely matches, `/status` includes actionable git
state, and `/commit` explains what it is staging and committing before
and after the model writes the Lore message. I also cleared the
workspace's existing clippy blockers so the verification lane can stay
fully green.
Constraint: Improve UX inside the existing Rust CLI surfaces without adding new dependencies
Rejected: Add more slash commands first | discoverability and feedback were the bigger friction points
Rejected: Split verification lint fixes into a second commit | user requested one solid commit
Confidence: high
Scope-risk: moderate
Directive: Keep slash discoverability, status reporting, and commit reporting aligned so `/help`, `/status`, and `/commit` tell the same workflow story
Tested: cargo fmt --all; cargo clippy --workspace --all-targets -- -D warnings; cargo test --workspace
Not-tested: Manual interactive REPL session against live Anthropic/xAI endpoints
