feat(routing): support json_object output for custom classifiers

[chethanuk]

What

Custom-mode llm_classifier routes can now set response_format_type = "json_object". On main the runner rejects that combination at config load. Capability and escalation routes already support it.

It works the same way as for the packaged classifiers. Switchyard appends the configured response_schema to the judge prompt, sends {"type": "json_object"}, and checks the verdict against the schema locally. A verdict that fails the check falls back to default_target. JSON Schema stays the default, so existing routes don't change.

• ClassifierContract::from_inner_schema takes the response format. from_config and the custom path share one helper, schema_in_prompt.
• In JSON Object mode, a response_schema whose root type doesn't allow object (a string other than "object", or an array without it) is rejected at load. The provider only returns objects, so such a schema would send every turn to default_target. A schema with no type at all is not checked.
• CustomClassifierConfig gets a response_format_type field (default JsonSchema). The runner passes it for the top-level route in build_algorithm and for the nested subagents classifier in build_subagent_router_config.
• The Python CustomClassifierConfig takes response_format_type="json_schema", parsed the same way as the other classifier configs. The switchyard_rust/libsy.py stub is updated.
• Docs: the response_format_type row in toml_schema.md and the custom section of llm_classifier_routing.md.

Why

For NVIDIA-NeMo#429. Some providers support JSON Object mode but not JSON Schema, and custom classifiers couldn't use them. The contract follows the issue's conservative option: response_schema is still required in both modes and is the source of truth, so a prompt never needs its own copy. Provider wrappers such as {"json_schema": {...}} are still rejected as an inner schema.

Before / After

Same config and request against a stub OpenAI upstream. The config (custom-json-object.toml) and stub (mock.py) are in pr-evidence/6. Both binaries were built with cargo build --locked -p switchyard-server --bin switchyard-server, the stub was started with python3 mock.py 18431, and every output line below is copied verbatim from that run.

$ grep -E '^(mode|response_format_type) ' custom-json-object.toml
mode = "custom"
response_format_type = "json_object"

Before, base a601a9a3f9db149a1ad430fa43b1463a170c8a82 (fork main, before it was synced to upstream). The server exits with status 1:

$ switchyard-server --config custom-json-object.toml --host 127.0.0.1 --port 18432
invalid server config custom-json-object.toml: llm_classifier route custom mode custom cannot use capability or escalation fields and response_format_type must be 'json_schema': llm_classifier route custom mode custom cannot use capability or escalation fields and response_format_type must be 'json_schema': llm_classifier route custom mode custom cannot use capability or escalation fields and response_format_type must be 'json_schema'

After, PR head d9c819f599322108d8debd0df16608a7e7cb3547 (re-run at this head):

$ switchyard-server --config custom-json-object.toml --host 127.0.0.1 --port 18432 &
$ curl -s http://127.0.0.1:18432/v1/chat/completions -H 'Content-Type: application/json' -d '{"model":"switchyard/custom","messages":[{"role":"user","content":"Refactor the scheduler."}]}' | jq -r '.model, .choices[0].message.content'
strong/model
hello from strong/model
$ cat mock.log   # what the stub upstream received
[mock] judge response_format = {"type": "json_object"}
[mock] schema in judge prompt: True
[mock] completion served by strong/model

Notes for reviewers

Start with crates/libsy/src/algorithms/util/classifier_contract.rs. Both constructors run validate_prompt on the template, never on the prompt with the schema appended. The appended text is never empty, and a schema may itself contain the literal {{RESPONSE_SCHEMA}}.

This adds a public field to CustomClassifierConfig, so Rust callers that build it with a struct literal instead of new() must add response_format_type. It follows how other public config fields were added before 1.0.

build_subagent_router_config is easy to miss. Without the field there, a nested custom classifier would quietly stay on JSON Schema; subagent_custom_classifier_can_request_json_object_output covers it.

The server test mock used to recognize custom judge calls only by response_format.json_schema. It now also recognizes a json_object request whose prompt contains the custom schema.

Verification, at the head of this PR rebased on fbabf51c (upstream main):

cargo fmt --all --check                                                                      # clean
cargo clippy -p switchyard-libsy -p switchyard-runner -p switchyard-server -p switchyard-py \
  --all-targets -- -D warnings                                                               # clean
cargo test -p switchyard-libsy -p switchyard-runner -p switchyard-server                     # all pass (330 libsy unit tests, 64 runner, 58 server)

The new runner and server tests fail on main with the old config error. The Python tests (tests/test_libsy_minimal_bindings.py) pass locally against an extension built with maturin develop (22 passed, including the two new custom-classifier tests). They have not run on the fork's CI.

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🤖 CodeAnt AI — Review Status

Status	Commit	Started (UTC)	Finished (UTC)
✅ Incremental review completed	723f761	Sep 30, 2026 · 14:21	14:22
✅ Incremental review completed	0739a18	Sep 30, 2026 · 12:40	12:41
✅ Incremental review completed	0d2fe44	Sep 30, 2026 · 06:04	06:04
✅ Incremental review completed	10ac672	Sep 29, 2026 · 21:11	21:11
✅ Incremental review completed	70af01c	Sep 29, 2026 · 19:49	19:50

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User description

What

Custom-mode llm_classifier routes can now set response_format_type = "json_object". Before this, the runner rejected that combination when it loaded the config. Capability and escalation routes already supported it.

JSON Object mode works the same way it does for the packaged classifiers. Switchyard appends the configured response_schema to the judge prompt and sends {"type": "json_object"}. It then checks the verdict against the schema locally. A verdict that fails the check falls back to default_target. JSON Schema is still the default, so existing routes don't change.

The change covers the whole custom surface:

• ClassifierContract::from_inner_schema takes the response format. from_config and the new custom path share one helper, schema_in_prompt.
• CustomClassifierConfig gets a response_format_type field (default JsonSchema).
• The runner no longer rejects json_object in custom mode. It passes the field to both places that build a CustomClassifierConfig: the top-level route in build_algorithm and the nested subagents classifier in build_subagent_router_config.
• The Python CustomClassifierConfig takes a response_format_type="json_schema" keyword argument, so the Python API matches the TOML surface. It uses the same parser as TaskClassifierConfig and EscalationClassifierConfig, and the stub in switchyard_rust/libsy.py is updated.
• Docs: the response_format_type row in toml_schema.md and the custom routing section of llm_classifier_routing.md. CHANGELOG entry added.

Why

Some providers support JSON Object mode but not JSON Schema. Packaged classifiers could already use those providers, but custom classifiers could not. The configured response_schema stays required and remains the source of truth in both modes. A prompt never needs its own copy of the schema. Provider wrappers such as {"json_schema": {...}} are still rejected as an inner schema in both modes.

Notes for reviewers

Start with crates/libsy/src/algorithms/util/classifier_contract.rs. schema_in_prompt runs validate_prompt on the template before it appends the schema. Without that, an empty prompt would get past the check, because the appended text is never empty. an_empty_prompt_is_rejected_in_json_object_mode covers this.

build_subagent_router_config is easy to miss. If the field weren't passed there, a nested custom classifier would quietly fall back to JSON Schema. subagent_custom_classifier_can_request_json_object_output catches that case.

The server mock in crates/switchyard-server/tests/server.rs used to recognize custom judge calls only by response_format.json_schema. It now also recognizes a json_object request whose prompt contains the custom schema. No packaged prompt or schema contains "decision", and stage_classifier_can_request_json_object_output still passes.

Tests:

• cargo test --workspace --locked: 834 passed, 0 failed. The new runner and server tests fail on main with the old config error.
• cargo fmt --all --check and cargo clippy --workspace --all-targets --locked -- -D warnings on the pinned 1.96.1: clean.
• uv run pytest tests/test_libsy_minimal_bindings.py -m "not integration": 20 passed.
• mkdocs build --strict: clean.

---

CodeAnt-AI Description

Support JSON Object output for custom classifiers

What Changed

• Custom classifier routes and subagent classifiers can use response_format_type = "json_object" when their provider does not support JSON Schema.
• The configured response schema is included in the judge prompt, while the provider is asked only for a JSON object.
• Verdicts are still checked against the schema locally; invalid verdicts fall back to the configured default target.
• The Python custom classifier configuration accepts the same output-format option, with JSON Schema remaining the default.

Impact

✅ Custom routing works with JSON-only providers
✅ Invalid classifier verdicts safely use the default target
✅ Consistent JSON output settings across TOML and Python

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[coderabbitai]

Walkthrough

Custom classifier routes now support json_object and json_schema response formats. The selected format propagates through Rust and Python configuration, changes contract construction, validates JSON Object verdicts locally, and is covered by deployment and integration tests.

Changes

Custom classifier response formats

Layer / File(s)	Summary
Classifier contract format handling crates/libsy/src/algorithms/llm_class.rs, crates/libsy/src/algorithms/util/classifier_contract.rs	Custom classifier contracts accept the selected response format. JSON Object mode appends the configured schema to the prompt and validates verdicts locally.
Configuration and runtime propagation crates/switchyard-py/src/libsy_bindings.rs, switchyard_rust/libsy.py, crates/switchyard-runner/src/algorithm.rs	Rust routes and Python bindings accept response_format_type, default it to json_schema, reject unknown values, and pass the setting to runtime custom classifiers.
Deployment and integration validation crates/switchyard-runner/src/config.rs, crates/switchyard-server/tests/server.rs, tests/test_libsy_minimal_bindings.py	Tests cover top-level and subagent JSON Object routes, schema prompts, local verdict validation, fallback routing, and invalid format values.
Configuration documentation CHANGELOG.md, docs/reference/toml_schema.md, docs/routing_algorithms/llm_classifier_routing.md	Documentation describes JSON Object configuration, schema placement, local validation, defaults, and fallback behavior.

Priority: ⬇️ Low

Estimated code review effort: 3 (Moderate) | ~25 minutes

Merge Risk: 🔵 Low · up to ebdbf

The configuration reference can mislead custom-classifier users about automatic schema insertion. Correct the wording before merge or ensure owners explicitly accept this narrow documentation risk.

🚥 Pre-merge checks | ✅ 4 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 51.61% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 31 functions across 8 files. (3 skipped: …	Write docstrings for the functions missing them to satisfy the coverage threshold.

✅ Passed checks (4 passed)

Check name	Status	Explanation
Linked Issues check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Description Check	✅ Passed	Check skipped - CodeRabbit’s high-level summary is enabled.
Title check	✅ Passed	The title clearly and concisely describes the main change: adding JSON Object output support for custom classifiers.

Full details: Docstring Coverage

Explanation

Docstring coverage is 51.61% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 31 functions across 8 files. (3 skipped: 3 unsupported.)

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JSON carrots arrive in a tidy state
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Actionable comments posted: 1

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Inline comments:
In `@docs/reference/toml_schema.md`:
- Line 219: Update the response_format_type documentation to clarify that in
custom JSON Object mode, the configured response_schema is the source of truth,
Switchyard inserts it into the judge prompt automatically, and users must not
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Reviewing files that changed from the base of the PR and between 0a32f56 and ebdbf0e.

📒 Files selected for processing (11)

• CHANGELOG.md
• crates/libsy/src/algorithms/llm_class.rs
• crates/libsy/src/algorithms/util/classifier_contract.rs
• crates/switchyard-py/src/libsy_bindings.rs
• crates/switchyard-runner/src/algorithm.rs
• crates/switchyard-runner/src/config.rs
• crates/switchyard-server/tests/server.rs
• docs/reference/toml_schema.md
• docs/routing_algorithms/llm_classifier_routing.md
• switchyard_rust/libsy.py
• tests/test_libsy_minimal_bindings.py

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[codeant-ai]

User description

What

Custom-mode llm_classifier routes can now set response_format_type = "json_object". Before this, the runner rejected that combination when it loaded the config. Capability and escalation routes already supported it.

JSON Object mode works the same way it does for the packaged classifiers. Switchyard appends the configured response_schema to the judge prompt and sends {"type": "json_object"}. It then checks the verdict against the schema locally. A verdict that fails the check falls back to default_target. JSON Schema is still the default, so existing routes don't change.

The change covers the whole custom surface:

• ClassifierContract::from_inner_schema takes the response format. from_config and the new custom path share one helper, schema_in_prompt.
• CustomClassifierConfig gets a response_format_type field (default JsonSchema).
• The runner no longer rejects json_object in custom mode. It passes the field to both places that build a CustomClassifierConfig: the top-level route in build_algorithm and the nested subagents classifier in build_subagent_router_config.
• The Python CustomClassifierConfig takes a response_format_type="json_schema" keyword argument, so the Python API matches the TOML surface. It uses the same parser as TaskClassifierConfig and EscalationClassifierConfig, and the stub in switchyard_rust/libsy.py is updated.
• Docs: the response_format_type row in toml_schema.md and the custom routing section of llm_classifier_routing.md. CHANGELOG entry added.

Why

Some providers support JSON Object mode but not JSON Schema. Packaged classifiers could already use those providers, but custom classifiers could not. The configured response_schema stays required and remains the source of truth in both modes. A prompt never needs its own copy of the schema. Provider wrappers such as {"json_schema": {...}} are still rejected as an inner schema in both modes.

Notes for reviewers

Start with crates/libsy/src/algorithms/util/classifier_contract.rs. schema_in_prompt runs validate_prompt on the template before it appends the schema. Without that, an empty prompt would get past the check, because the appended text is never empty. an_empty_prompt_is_rejected_in_json_object_mode covers this.

build_subagent_router_config is easy to miss. If the field weren't passed there, a nested custom classifier would quietly fall back to JSON Schema. subagent_custom_classifier_can_request_json_object_output catches that case.

The server mock in crates/switchyard-server/tests/server.rs used to recognize custom judge calls only by response_format.json_schema. It now also recognizes a json_object request whose prompt contains the custom schema. No packaged prompt or schema contains "decision", and stage_classifier_can_request_json_object_output still passes.

Tests:

• cargo test --workspace --locked: 834 passed, 0 failed. The new runner and server tests fail on main with the old config error.
• cargo fmt --all --check and cargo clippy --workspace --all-targets --locked -- -D warnings on the pinned 1.96.1: clean.
• uv run pytest tests/test_libsy_minimal_bindings.py -m "not integration": 20 passed.
• mkdocs build --strict: clean.

Summary by CodeRabbit

• New Features

  • Custom classifiers now support JSON Object responses through response_format_type = "json_object".
  • Configure JSON Object output from Python while continuing to use JSON Schema by default.
  • Custom classifier schemas are included in judge prompts and validated locally; invalid results use the configured default target.

• Documentation

  • Updated routing and configuration guidance to cover structured output across all classifier modes.

---

CodeAnt-AI Description

Allow custom classifiers to use JSON Object output

What Changed

• Custom classifier routes and subagent classifiers now accept response_format_type = "json_object", while JSON Schema remains the default.
• Switchyard automatically adds the configured response schema to the judge prompt, requests a JSON object, and validates the returned verdict locally.
• Invalid verdicts continue to fall back to the route’s default_target.
• The Python custom classifier configuration supports the same option and rejects unsupported values.

Impact

✅ Custom routes work with providers that lack JSON Schema support
✅ Invalid classifier verdicts safely use the default target
✅ No duplicate schema text is needed in custom prompts

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User description

What

Custom-mode llm_classifier routes can now set response_format_type = "json_object". Before this, the runner rejected that combination when it loaded the config. Capability and escalation routes already supported it.

JSON Object mode works the same way it does for the packaged classifiers. Switchyard appends the configured response_schema to the judge prompt and sends {"type": "json_object"}. It then checks the verdict against the schema locally. A verdict that fails the check falls back to default_target. JSON Schema is still the default, so existing routes don't change.

The change covers the whole custom surface:

• ClassifierContract::from_inner_schema takes the response format. from_config and the new custom path share one helper, schema_in_prompt.
• CustomClassifierConfig gets a response_format_type field (default JsonSchema).
• The runner no longer rejects json_object in custom mode. It passes the field to both places that build a CustomClassifierConfig: the top-level route in build_algorithm and the nested subagents classifier in build_subagent_router_config.
• The Python CustomClassifierConfig takes a response_format_type="json_schema" keyword argument, so the Python API matches the TOML surface. It uses the same parser as TaskClassifierConfig and EscalationClassifierConfig, and the stub in switchyard_rust/libsy.py is updated.
• Docs: the response_format_type row in toml_schema.md and the custom routing section of llm_classifier_routing.md. CHANGELOG entry added.

Why

Some providers support JSON Object mode but not JSON Schema. Packaged classifiers could already use those providers, but custom classifiers could not. The configured response_schema stays required and remains the source of truth in both modes. A prompt never needs its own copy of the schema. Provider wrappers such as {"json_schema": {...}} are still rejected as an inner schema in both modes.

Notes for reviewers

Start with crates/libsy/src/algorithms/util/classifier_contract.rs. schema_in_prompt runs validate_prompt on the template before it appends the schema. Without that, an empty prompt would get past the check, because the appended text is never empty. an_empty_prompt_is_rejected_in_json_object_mode covers this.

CustomClassifierConfig gets a new public field. Rust code that builds it with a struct literal instead of new() must add the field. This matches how the other classifier configs gained response_format_type.

build_subagent_router_config is easy to miss. If the field weren't passed there, a nested custom classifier would quietly fall back to JSON Schema. subagent_custom_classifier_can_request_json_object_output catches that case.

The server mock in crates/switchyard-server/tests/server.rs used to recognize custom judge calls only by response_format.json_schema. It now also recognizes a json_object request whose prompt contains the custom schema. No packaged prompt or schema contains "decision", and stage_classifier_can_request_json_object_output still passes.

Tests:

• cargo test --workspace --locked: 834 passed, 0 failed. The new runner and server tests fail on main with the old config error.
• cargo fmt --all --check and cargo clippy --workspace --all-targets --locked -- -D warnings on the pinned 1.96.1: clean.
• uv run pytest tests/test_libsy_minimal_bindings.py -m "not integration": 20 passed.
• mkdocs build --strict: clean.

---

CodeAnt-AI Description

Support JSON Object custom classifiers and preserve streamed response continuity

What Changed

• Custom classifier routes can use response_format_type = "json_object" in both top-level and subagent configurations.
• Custom classifier schemas are added to the judge prompt and checked locally; invalid verdicts fall back to default_target, and non-object schemas are rejected during configuration.
• Streamed Responses keep the upstream response ID, allowing follow-up requests to continue the correct conversation.
• Reasoning summaries that arrive before their provider ID are held until the ID or later output determines how to emit them, preserving encrypted reasoning and output order.
• Python configuration types, validation, documentation, and coverage now include custom JSON Object mode and streaming edge cases.

Impact

✅ Custom classifiers work with providers that support JSON Object but not JSON Schema
✅ Invalid classifier verdicts route safely to the configured default
✅ Follow-up requests preserve streamed conversation history

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Every time you make a pull request, our system automatically looks through it. We check for security issues, mistakes in how you're setting up your infrastructure, and common code problems. We do this to make sure your changes are solid and won't cause any trouble later.

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[codeant-ai]

User description

What

Custom-mode llm_classifier routes can now set response_format_type = "json_object". On main the runner rejects that combination at config load. Capability and escalation routes already support it.

It works the same way as for the packaged classifiers. Switchyard appends the configured response_schema to the judge prompt, sends {"type": "json_object"}, and checks the verdict against the schema locally. A verdict that fails the check falls back to default_target. JSON Schema stays the default, so existing routes don't change.

• ClassifierContract::from_inner_schema takes the response format. from_config and the custom path share one helper, schema_in_prompt.
• In JSON Object mode, a response_schema whose root type doesn't allow object (a string other than "object", or an array without it) is rejected at load. The provider only returns objects, so such a schema would send every turn to default_target.
• CustomClassifierConfig gets a response_format_type field (default JsonSchema). The runner passes it for the top-level route in build_algorithm and for the nested subagents classifier in build_subagent_router_config.
• The Python CustomClassifierConfig takes response_format_type="json_schema", parsed the same way as the other classifier configs. The switchyard_rust/libsy.py stub is updated.
• Docs: the response_format_type row in toml_schema.md and the custom section of llm_classifier_routing.md.

Why

For NVIDIA-NeMo#429. Some providers support JSON Object mode but not JSON Schema, and custom classifiers couldn't use them. The contract follows the issue's conservative option: response_schema is still required in both modes and is the source of truth, so a prompt never needs its own copy. Provider wrappers such as {"json_schema": {...}} are still rejected as an inner schema.

Same config and request against a stub OpenAI upstream (script):

Before (upstream main @ c7fee3e; the branch is now rebased on fork main @ a601a9a, which differs only in translation code):

After:

Notes for reviewers

Start with crates/libsy/src/algorithms/util/classifier_contract.rs. schema_in_prompt runs validate_prompt on the template before appending the schema; otherwise an empty prompt would pass, since the appended text is never empty.

CustomClassifierConfig gets a new public field, so Rust code that builds it with a struct literal instead of new() must add it. The other classifier configs gained response_format_type the same way in NVIDIA-NeMo#411.

build_subagent_router_config is easy to miss. Without the field there, a nested custom classifier would quietly stay on JSON Schema; subagent_custom_classifier_can_request_json_object_output covers it.

The server test mock used to recognize custom judge calls only by response_format.json_schema. It now also recognizes a json_object request whose prompt contains the custom schema.

Verification:

• cargo test --workspace --locked: 887 passed, 0 failed. The new runner and server tests fail on main with the old config error.
• cargo fmt --all --check and cargo +1.96.1 clippy --workspace --all-targets --locked -- -D warnings: clean.

---

CodeAnt-AI Description

Enable JSON Object output for custom classifier routes

What Changed

• Custom classifier routes can now use response_format_type = "json_object" in server, subagent, and Python configurations.
• The configured response schema is added to the judge prompt, while the provider is asked for a JSON object.
• Returned objects are still checked against the schema locally; invalid results use default_target.
• Configurations reject schemas that cannot produce an object and document the new behavior.

Impact

✅ Custom classifiers work with providers that lack JSON Schema support
✅ Invalid classifier verdicts fall back safely
✅ Consistent JSON Object support for top-level and subagent routes

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[chethanuk]

Thanks for the review. Changes are in 10ac672.

• Unrelated translation diff: fixed. I rebased the branch onto fork main (a601a9a), so the PR now shows only the 10 feature files. The two upstream commits (fix(translation): keep the provider reasoning id when a summary streams before it NVIDIA-NeMo/Switchyard#861, fix(responses): preserve streamed response IDs for conversation continuation NVIDIA-NeMo/Switchyard#869) don't touch any of them. The body now says the "before" screenshot came from upstream c7fee3e.
• type union rejected in json_object mode: real bug, fixed. The guard now rejects only a string type other than "object" or an array that doesn't contain it. I added ["string", "null"] to the rejection test and a new test that ["object"] and ["object", "null"] are accepted. I left out schemas like {"const": "x"} on purpose: the check is there to catch the obvious mistake at load, and local validation still sends anything else to default_target.
• Stale docs: fixed. The line-126 table row now matches toml_schema.md. The custom-section paragraph and the CustomClassifierConfig::response_schema doc comment now describe both modes.
• No positive Python test: added test_custom_classifier_config_accepts_json_object_output. It checks that the judge request has {"type": "json_object"} and that the schema appears in the prompt.
• from __future__ removal, skill_distillation snapshot, configure flags, STATS_TEST_LOCK, switchyard-skill-distillation validation, negative-flag test: these don't apply here. None of those files are in this PR. They're from the skill-distillation branch (the review worktree at 4759b61).

Verification: cargo test -p switchyard-libsy classifier_contract passed 10/10, and cargo fmt --check and cargo clippy -p switchyard-libsy --all-targets -D warnings are clean. ruff check on the test file passes. I couldn't run the new Python test because the disk filled while maturin develop was building. CI will run it.

[codeant-ai]

User description

What

Custom-mode llm_classifier routes can now set response_format_type = "json_object". On main the runner rejects that combination at config load. Capability and escalation routes already support it.

It works the same way as for the packaged classifiers. Switchyard appends the configured response_schema to the judge prompt, sends {"type": "json_object"}, and checks the verdict against the schema locally. A verdict that fails the check falls back to default_target. JSON Schema stays the default, so existing routes don't change.

• ClassifierContract::from_inner_schema takes the response format. from_config and the custom path share one helper, schema_in_prompt.
• In JSON Object mode, a response_schema whose root type doesn't allow object (a string other than "object", or an array without it) is rejected at load. The provider only returns objects, so such a schema would send every turn to default_target.
• CustomClassifierConfig gets a response_format_type field (default JsonSchema). The runner passes it for the top-level route in build_algorithm and for the nested subagents classifier in build_subagent_router_config.
• The Python CustomClassifierConfig takes response_format_type="json_schema", parsed the same way as the other classifier configs. The switchyard_rust/libsy.py stub is updated.
• Docs: the response_format_type row in toml_schema.md and the custom section of llm_classifier_routing.md.

Why

For NVIDIA-NeMo#429. Some providers support JSON Object mode but not JSON Schema, and custom classifiers couldn't use them. The contract follows the issue's conservative option: response_schema is still required in both modes and is the source of truth, so a prompt never needs its own copy. Provider wrappers such as {"json_schema": {...}} are still rejected as an inner schema.

Before / After

Same config and request against a stub OpenAI upstream. The config (custom-json-object.toml) and stub (mock.py) are in pr-evidence/6. Both binaries were built with cargo build --locked -p switchyard-server --bin switchyard-server, the stub was started with python3 mock.py 18431, and every output line below is copied verbatim from that run.

$ grep -E '^(mode|response_format_type) ' custom-json-object.toml
mode = "custom"
response_format_type = "json_object"

Before, base a601a9a3f9db149a1ad430fa43b1463a170c8a82 (fork main). The server exits with status 1:

$ switchyard-server --config custom-json-object.toml --host 127.0.0.1 --port 18432
invalid server config custom-json-object.toml: llm_classifier route custom mode custom cannot use capability or escalation fields and response_format_type must be 'json_schema': llm_classifier route custom mode custom cannot use capability or escalation fields and response_format_type must be 'json_schema': llm_classifier route custom mode custom cannot use capability or escalation fields and response_format_type must be 'json_schema'

After, PR head 10ac6723e49a4ef4b59b5e58e8167bfb7610e567 (server starts and /v1/models answers):

$ switchyard-server --config custom-json-object.toml --host 127.0.0.1 --port 18432 &
$ curl -s http://127.0.0.1:18432/v1/chat/completions -H 'Content-Type: application/json' -d '{"model":"switchyard/custom","messages":[{"role":"user","content":"Refactor the scheduler."}]}' | jq -r '.model, .choices[0].message.content'
strong/model
hello from strong/model
$ cat mock.log   # what the stub upstream received
[mock] judge response_format = {"type": "json_object"}
[mock] schema in judge prompt: True
[mock] completion served by strong/model

Notes for reviewers

Start with crates/libsy/src/algorithms/util/classifier_contract.rs. schema_in_prompt runs validate_prompt on the template before appending the schema; otherwise an empty prompt would pass, since the appended text is never empty.

CustomClassifierConfig gets a new public field, so Rust code that builds it with a struct literal instead of new() must add it. The other classifier configs gained response_format_type the same way in NVIDIA-NeMo#411.

build_subagent_router_config is easy to miss. Without the field there, a nested custom classifier would quietly stay on JSON Schema; subagent_custom_classifier_can_request_json_object_output covers it.

The server test mock used to recognize custom judge calls only by response_format.json_schema. It now also recognizes a json_object request whose prompt contains the custom schema.

Verification:

• cargo test --workspace --locked: 887 passed, 0 failed. The new runner and server tests fail on main with the old config error.
• cargo fmt --all --check and cargo +1.96.1 clippy --workspace --all-targets --locked -- -D warnings: clean.

---

CodeAnt-AI Description

Enable JSON Object output for custom classifier routes

What Changed

• Custom classifier routes, including subagent classifiers and Python configurations, can now request json_object output.
• The configured response schema is added to the judge prompt and still validated locally; invalid verdicts fall back to default_target.
• Configuration rejects schemas that cannot produce an object and rejects empty prompts before adding the schema.
• Existing JSON Schema behavior remains the default.

Impact

✅ Custom classifiers work with providers that lack JSON Schema support
✅ Invalid classifier verdicts use the configured fallback target
✅ Clearer configuration errors for incompatible schemas

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[codeant-ai]

User description

What

Custom-mode llm_classifier routes can now set response_format_type = "json_object". On main the runner rejects that combination at config load. Capability and escalation routes already support it.

It works the same way as for the packaged classifiers. Switchyard appends the configured response_schema to the judge prompt, sends {"type": "json_object"}, and checks the verdict against the schema locally. A verdict that fails the check falls back to default_target. JSON Schema stays the default, so existing routes don't change.

• ClassifierContract::from_inner_schema takes the response format. from_config and the custom path share one helper, schema_in_prompt.
• In JSON Object mode, a response_schema whose root type doesn't allow object (a string other than "object", or an array without it) is rejected at load. The provider only returns objects, so such a schema would send every turn to default_target.
• CustomClassifierConfig gets a response_format_type field (default JsonSchema). The runner passes it for the top-level route in build_algorithm and for the nested subagents classifier in build_subagent_router_config.
• The Python CustomClassifierConfig takes response_format_type="json_schema", parsed the same way as the other classifier configs. The switchyard_rust/libsy.py stub is updated.
• Docs: the response_format_type row in toml_schema.md and the custom section of llm_classifier_routing.md.

Why

For NVIDIA-NeMo#429. Some providers support JSON Object mode but not JSON Schema, and custom classifiers couldn't use them. The contract follows the issue's conservative option: response_schema is still required in both modes and is the source of truth, so a prompt never needs its own copy. Provider wrappers such as {"json_schema": {...}} are still rejected as an inner schema.

Before / After

Same config and request against a stub OpenAI upstream. The config (custom-json-object.toml) and stub (mock.py) are in pr-evidence/6. Both binaries were built with cargo build --locked -p switchyard-server --bin switchyard-server, the stub was started with python3 mock.py 18431, and every output line below is copied verbatim from that run.

$ grep -E '^(mode|response_format_type) ' custom-json-object.toml
mode = "custom"
response_format_type = "json_object"

Before, base a601a9a3f9db149a1ad430fa43b1463a170c8a82 (fork main). The server exits with status 1:

$ switchyard-server --config custom-json-object.toml --host 127.0.0.1 --port 18432
invalid server config custom-json-object.toml: llm_classifier route custom mode custom cannot use capability or escalation fields and response_format_type must be 'json_schema': llm_classifier route custom mode custom cannot use capability or escalation fields and response_format_type must be 'json_schema': llm_classifier route custom mode custom cannot use capability or escalation fields and response_format_type must be 'json_schema'

After, PR head 10ac6723e49a4ef4b59b5e58e8167bfb7610e567 (captured before the final rebase and squash; server starts and /v1/models answers):

$ switchyard-server --config custom-json-object.toml --host 127.0.0.1 --port 18432 &
$ curl -s http://127.0.0.1:18432/v1/chat/completions -H 'Content-Type: application/json' -d '{"model":"switchyard/custom","messages":[{"role":"user","content":"Refactor the scheduler."}]}' | jq -r '.model, .choices[0].message.content'
strong/model
hello from strong/model
$ cat mock.log   # what the stub upstream received
[mock] judge response_format = {"type": "json_object"}
[mock] schema in judge prompt: True
[mock] completion served by strong/model

Notes for reviewers

Start with crates/libsy/src/algorithms/util/classifier_contract.rs. Both constructors run validate_prompt on the template, never on the prompt with the schema appended. The appended text is never empty, and a schema may itself contain the literal {{RESPONSE_SCHEMA}}.

CustomClassifierConfig gets a new public field, so Rust code that builds it with a struct literal instead of new() must add it. The other classifier configs gained response_format_type the same way in NVIDIA-NeMo#411.

build_subagent_router_config is easy to miss. Without the field there, a nested custom classifier would quietly stay on JSON Schema; subagent_custom_classifier_can_request_json_object_output covers it.

The server test mock used to recognize custom judge calls only by response_format.json_schema. It now also recognizes a json_object request whose prompt contains the custom schema.

Verification, at the head of this PR rebased on fbabf51c:

cargo fmt --all --check                                                                      # clean
cargo clippy -p switchyard-libsy -p switchyard-runner -p switchyard-server -p switchyard-py \
  --all-targets -- -D warnings                                                               # clean
cargo test -p switchyard-libsy -p switchyard-runner -p switchyard-server                     # all pass (331 libsy unit tests, 64 runner, 58 server)

The new runner and server tests fail on main with the old config error. I did not run the Python tests (tests/test_libsy_minimal_bindings.py) locally because the extension build needs more disk than I had; CI covers them.

---

CodeAnt-AI Description

Add reversible escalation routing, JSON Object classifiers, and streaming reliability fixes

What Changed

• Escalation routes can optionally return sessions to the efficient model after the strong model resolves the triggering issue, with confirmation thresholds, strong-tier limits, and cooldowns.
• Custom classifiers now support json_object output in TOML and Python configurations. Schemas are included in the judge prompt and still validated locally, falling back to the default target when invalid.
• Streaming responses preserve upstream response IDs and keep reasoning summaries attached to the correct provider item, including when IDs arrive after summary text.
• Stream and buffered responses now accept explicit error: null fields, while incomplete Responses streams are recognized as valid terminal events.
• Added configuration guidance and recipes for routing Codex and Claude Code through a single provider login.

Impact

✅ Sessions can return to efficient models after recovery
✅ Custom classifiers work with providers that lack JSON Schema support
✅ Preserved conversation history across streamed follow-up requests
✅ Reliable reasoning replay when streamed IDs arrive late

💡 Usage Guide

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[codeant-ai]

User description

What

Custom-mode llm_classifier routes can now set response_format_type = "json_object". On main the runner rejects that combination at config load. Capability and escalation routes already support it.

It works the same way as for the packaged classifiers. Switchyard appends the configured response_schema to the judge prompt, sends {"type": "json_object"}, and checks the verdict against the schema locally. A verdict that fails the check falls back to default_target. JSON Schema stays the default, so existing routes don't change.

• ClassifierContract::from_inner_schema takes the response format. from_config and the custom path share one helper, schema_in_prompt.
• In JSON Object mode, a response_schema whose root type doesn't allow object (a string other than "object", or an array without it) is rejected at load. The provider only returns objects, so such a schema would send every turn to default_target.
• CustomClassifierConfig gets a response_format_type field (default JsonSchema). The runner passes it for the top-level route in build_algorithm and for the nested subagents classifier in build_subagent_router_config.
• The Python CustomClassifierConfig takes response_format_type="json_schema", parsed the same way as the other classifier configs. The switchyard_rust/libsy.py stub is updated.
• Docs: the response_format_type row in toml_schema.md and the custom section of llm_classifier_routing.md.

Why

For NVIDIA-NeMo#429. Some providers support JSON Object mode but not JSON Schema, and custom classifiers couldn't use them. The contract follows the issue's conservative option: response_schema is still required in both modes and is the source of truth, so a prompt never needs its own copy. Provider wrappers such as {"json_schema": {...}} are still rejected as an inner schema.

Before / After

Same config and request against a stub OpenAI upstream. The config (custom-json-object.toml) and stub (mock.py) are in pr-evidence/6. Both binaries were built with cargo build --locked -p switchyard-server --bin switchyard-server, the stub was started with python3 mock.py 18431, and every output line below is copied verbatim from that run.

$ grep -E '^(mode|response_format_type) ' custom-json-object.toml
mode = "custom"
response_format_type = "json_object"

Before, base a601a9a3f9db149a1ad430fa43b1463a170c8a82 (fork main). The server exits with status 1:

$ switchyard-server --config custom-json-object.toml --host 127.0.0.1 --port 18432
invalid server config custom-json-object.toml: llm_classifier route custom mode custom cannot use capability or escalation fields and response_format_type must be 'json_schema': llm_classifier route custom mode custom cannot use capability or escalation fields and response_format_type must be 'json_schema': llm_classifier route custom mode custom cannot use capability or escalation fields and response_format_type must be 'json_schema'

After, PR head 10ac6723e49a4ef4b59b5e58e8167bfb7610e567 (captured before the object-root check and the validate_prompt move, and before the final rebase and squash; the unit and server tests cover those changes at the current head):

$ switchyard-server --config custom-json-object.toml --host 127.0.0.1 --port 18432 &
$ curl -s http://127.0.0.1:18432/v1/chat/completions -H 'Content-Type: application/json' -d '{"model":"switchyard/custom","messages":[{"role":"user","content":"Refactor the scheduler."}]}' | jq -r '.model, .choices[0].message.content'
strong/model
hello from strong/model
$ cat mock.log   # what the stub upstream received
[mock] judge response_format = {"type": "json_object"}
[mock] schema in judge prompt: True
[mock] completion served by strong/model

Notes for reviewers

Start with crates/libsy/src/algorithms/util/classifier_contract.rs. Both constructors run validate_prompt on the template, never on the prompt with the schema appended. The appended text is never empty, and a schema may itself contain the literal {{RESPONSE_SCHEMA}}.

CustomClassifierConfig gets a new public field, so Rust code that builds it with a struct literal instead of new() must add it. This adds a public field to CustomClassifierConfig, so Rust callers that build it with a struct literal instead of new() must add response_format_type. It follows how other public config fields were added before 1.0.

build_subagent_router_config is easy to miss. Without the field there, a nested custom classifier would quietly stay on JSON Schema; subagent_custom_classifier_can_request_json_object_output covers it.

The server test mock used to recognize custom judge calls only by response_format.json_schema. It now also recognizes a json_object request whose prompt contains the custom schema.

Verification, at the head of this PR rebased on fbabf51c:

cargo fmt --all --check                                                                      # clean
cargo clippy -p switchyard-libsy -p switchyard-runner -p switchyard-server -p switchyard-py \
  --all-targets -- -D warnings                                                               # clean
cargo test -p switchyard-libsy -p switchyard-runner -p switchyard-server                     # all pass (331 libsy unit tests, 64 runner, 58 server)

The new runner and server tests fail on main with the old config error. I did not run the Python tests (tests/test_libsy_minimal_bindings.py) locally because the extension build needs more disk than I had; CI covers them.

---

CodeAnt-AI Description

Add reversible model escalation and JSON Object output support across classifier routes

What Changed

• Escalation routes can optionally return sessions from the capable tier to the efficient tier after configurable review confirmations, strong-tier limits, and cooldown calls.
• Custom classifiers can request json_object output in TOML and Python configurations; the schema is included in the judge prompt and still validated locally, with invalid verdicts falling back to the default target.
• Streaming Responses preserve upstream response and reasoning IDs, including summaries that arrive before encrypted reasoning payloads, so conversation continuation and reasoning replay remain intact.
• Stream validation accepts explicit null error fields and incomplete Responses terminal events without treating them as failures.
• Added configuration validation, Python bindings, integration coverage, and setup guidance for single-provider coding agents.

Impact

✅ Sessions can return to efficient models after work is resolved
✅ Custom classifiers work with providers that support JSON Object but not JSON Schema
✅ Reliable conversation continuation and reasoning replay

💡 Usage Guide

Checking Your Pull Request

Every time you make a pull request, our system automatically looks through it. We check for security issues, mistakes in how you're setting up your infrastructure, and common code problems. We do this to make sure your changes are solid and won't cause any trouble later.

Talking to CodeAnt AI

Got a question or need a hand with something in your pull request? You can easily get in touch with CodeAnt AI right here. Just type the following in a comment on your pull request, and replace "Your question here" with whatever you want to ask:

@codeant-ai ask: Your question here

This lets you have a chat with CodeAnt AI about your pull request, making it easier to understand and improve your code.

Example

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Example

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