Check Model Deprecation in Kiln

Audit libs/core/kiln_ai/adapters/ml_model_list.py to find models that have been deprecated, removed, or are approaching end-of-life across all providers. All checks are free — they use model-listing endpoints, not inference.

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Global Rules

• Sandbox: All curl, uv run, and python3 commands that hit the network MUST use required_permissions: ["all"]. The sandbox blocks network access.
• Env vars: Source .env before running check scripts: export $(grep -v '^#' .env | xargs)
• Vertex AI auth: The Vertex check requires gcloud CLI authentication. Before running, ask the user to run gcloud auth login if they haven't recently. If gcloud auth print-access-token fails, prompt the user to authenticate.
• Non-destructive: This skill only sets deprecated=True on individual provider entries. It never removes models or providers from the list.
• Skip already-deprecated: Only check providers where deprecated=False (the default). Don't re-check entries already marked deprecated.

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Phase 1 – Extract Current Model-Provider Pairs

Run the extraction script from the repo root:

python3 .agents/skills/kiln-check-deprecation/scripts/extract_models.py > /tmp/kiln_extracted.json

This fetches the published model list from https://remote-config.getkiln.ai/kiln_config_v2.json and extracts:

• The provider name
• The model_id
• The parent model's enum name

It skips entries already marked deprecated=True.

Output: JSON to stdout (pipe to file), human summary to stderr.

The JSON contains:

• deprecated_count: number of already-deprecated entries
• providers: dict of provider_name → sorted unique list of model_ids
• entries: list of {enum, provider, model_id} for each non-deprecated entry

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Phase 2 – Check Each Provider

Option A: Check all providers at once

export $(grep -v '^#' .env | xargs)
python3 .agents/skills/kiln-check-deprecation/scripts/check_provider.py all > /tmp/kiln_check_results.json

Option B: Check a single provider

export $(grep -v '^#' .env | xargs)
python3 .agents/skills/kiln-check-deprecation/scripts/check_provider.py openrouter > /tmp/kiln_check_openrouter.json

The script handles all provider API quirks automatically:

• Together AI returns a flat JSON array (not {data: [...]})
• Gemini API needs both v1 and v1beta endpoints (preview/Gemma models only on v1beta)
• Gemini model names are prefixed with models/ — stripped automatically
• Anthropic uses x-api-key header, not Authorization: Bearer
• OpenRouter is public (no auth) and includes expiration_date fields
• Fireworks AI /v1/models only lists serverless models; the script instead checks each model individually via the model detail API (GET /v1/{model_id}), which covers all tiers (serverless, on-demand, fine-tune). A model is only flagged as missing if it returns HTTP 404 from the detail API.
• OpenRouter :exacto is a virtual routing suffix (quality-first provider sorting) that never appears in model listings. The script strips it before checking. :free and :thinking are real model entries that appear in the listing when available — if they're missing, it's a genuine removal.
• Cloudflare uses the authenticated models-search API (task=Text Generation, include_deprecated=true) when CLOUDFLARE_API_KEY and CLOUDFLARE_ACCOUNT_ID are both set, and otherwise Cloudflare's public catalog JSON (https://ai-cloudflare-com.pages.dev/api/models), so it is never skipped for a missing key. Every model with a planned_deprecation_date is reported under expiring. Once that date has passed, the model also counts as missing even if the listing still shows it. Never use LiteLLM's catalog or models.dev to decide a Cloudflare model still exists: LiteLLM's is stale and models.dev no longer deletes retired models.
• Vertex AI uses the v1beta1 publisher models endpoint with x-goog-user-project header. Requires gcloud CLI auth. Kiln entries may use meta/ prefix for LiteLLM routing — stripped automatically. Versioned aliases (e.g. gemini-2.0-flash-001 → gemini-2.0-flash) are handled by stripping 3-digit version suffixes.

Output: JSON to stdout with per-provider results, human summary to stderr.

Each provider result contains:

• missing: model_ids not found in the provider's listing
• expiring: model_ids with upcoming expiration dates (OpenRouter and Cloudflare)
• entries_to_deprecate: full enum/provider/model_id entries for each missing model
• skipped / error: if credentials missing or API call failed

Supported providers

Provider	Env Var	Auth
OpenRouter	(none — public API)	None
OpenAI	OPENAI_API_KEY	Bearer
Anthropic	ANTHROPIC_API_KEY	x-api-key
Gemini API	GEMINI_API_KEY	Query param
Fireworks AI	FIREWORKS_API_KEY	Bearer
Together AI	TOGETHER_API_KEY	Bearer
SiliconFlow CN	SILICONFLOW_CN_API_KEY	Bearer
Cerebras	CEREBRAS_API_KEY	Bearer
Groq	GROQ_API_KEY	Bearer
Vertex AI	VERTEX_PROJECT_ID + gcloud CLI auth	OAuth (gcloud)
Cloudflare	CLOUDFLARE_API_KEY + CLOUDFLARE_ACCOUNT_ID (optional — falls back to the public catalog)	Bearer

Providers NOT covered by the script (check manually if needed)

Amazon Bedrock

Requires aws CLI configured. If not installed, skip.

aws bedrock list-foundation-models --output json | \
  jq '[.modelSummaries[] | {modelId, status: .modelLifecycle.status, endOfLife: .modelLifecycle.endOfLifeTime}]'

Flag any model where status == "LEGACY" or endOfLifeTime is approaching.

LiteLLM Model DB (supplementary signal)

Cross-reference against LiteLLM's public model database for deprecation_date fields. This mostly covers Azure/OpenAI date-stamped models but is a useful secondary signal.

curl -s https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json | \
  jq 'to_entries[] | select(.value.deprecation_date != null) | {model: .key, deprecation_date: .value.deprecation_date}'

Providers to skip

These don't have meaningful deprecation to check:

• ollama — local models, user-managed
• kiln_fine_tune — internal
• kiln_custom_registry — internal
• openai_compatible — user-configured
• docker_model_runner — local
• azure_openai — typically mirrors OpenAI, covered by OpenAI check
• huggingface — serverless inference, model availability varies
• typesafe — adapter-backed, not LiteLLM; its /v1/models needs a key, so Jev entries can only be checked with one

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Phase 3 – Report Findings

Read the JSON output from check_provider.py and present findings in a clear table to the user:

Deprecation Audit Results
=========================

❌ REMOVED (model not found in provider's model list):
  - ModelName.gemini_1_5_pro → gemini_api (model_id: gemini-1.5-pro)
  - ModelName.gemini_1_5_pro → openrouter (model_id: google/gemini-pro-1.5)

⚠️ EXPIRING SOON (within 90 days):
  - ModelName.claude_3_7_sonnet → openrouter (model_id: anthropic/claude-3.7-sonnet, expires: 2026-05-05)

⚠️ LEGACY (Bedrock lifecycle status):
  - ModelName.mistral_large → amazon_bedrock (model_id: mistral.mistral-large-2407-v1:0, status: LEGACY)

✅ ALL CLEAR:
  - openai: 20/20 models found
  - anthropic: 8/11 models found (3 deprecated above)

⏭️ SKIPPED (no credentials):
  - groq
  - amazon_bedrock

Group by provider within each category. Use the entries_to_deprecate field from the JSON to get the ModelName enum for each entry.

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Phase 4 – Mark Deprecated

For each confirmed-removed model, set deprecated=True on the affected KilnModelProvider entry.

To find the line to edit, grep for the model_id in ml_model_list.py:

grep -n 'model_id="<model_id>"' libs/core/kiln_ai/adapters/ml_model_list.py

Then add deprecated=True, after the model_id= line in the matching KilnModelProvider block.

Rules:

• Only mark a provider deprecated if its model_id is confirmed missing from that provider's model list
• If ALL providers for a KilnModel are deprecated, note this to the user — they may want to consider removing the model entirely
• For "expiring soon" models, inform the user but don't mark deprecated yet — let them decide. Exception: Cloudflare. When Cloudflare retires a model it may silently alias the old ID to a different model, and the response still echoes the requested ID, so a stale entry keeps "working" while running something else. Propose marking every Cloudflare entry with a planned_deprecation_date deprecated now, before that date, and flag it as urgent in the report
• For Bedrock LEGACY status, mark deprecated (the model still works but is on its way out)

Ask the user to confirm before making changes. Present the list of changes and wait for approval.

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Phase 5 – Verify

After marking providers deprecated, run a quick sanity check:

grep -c "deprecated=True" libs/core/kiln_ai/adapters/ml_model_list.py

Ensure the count matches what you expect (previous count + newly marked).

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Checklist

• Extraction script run, JSON saved to /tmp/kiln_extracted.json
• Provider checks run (all or individually)
• Vertex auth confirmed (gcloud auth print-access-token works, or skip Vertex)
• Bedrock checked manually (skip if aws CLI unavailable)
• LiteLLM DB checked for supplementary deprecation_date signals
• Findings reported to user with clear table
• User confirmed changes before marking deprecated
• deprecated=True set on confirmed entries
• Deprecation count verified