Files
optimclaw/providers.json
T
8cd9b4bcfd chore: sync main into staging (#855)
* fix(ci): secrets can't be used in step if conditions [skip-regression-check] (#787)

GitHub Actions step-level `if:` doesn't have access to `secrets` context.
Replace `if: secrets.X != ''` with `continue-on-error: true` and let
the Set token step handle the fallback.

Co-authored-by: Claude Sonnet 4.6 <[email protected]>

* fix(ci): clean up staging pipeline — remove hacks, skip redundant checks [skip-regression-check] (#794)

- Remove continue-on-error from staging-ci.yml app token steps (secrets are configured)
- Skip test.yml and code_style.yml on PRs targeting staging (staging-ci.yml
  already runs tests before promoting, promotion PR gets full CI on main)
- Allow ironclaw-ci[bot] in Claude Code review for bot-created promotion PRs

Co-authored-by: Claude Opus 4.6 <[email protected]>

* fix(ci): run fmt + clippy on staging PRs, skip Windows clippy [skip-regression-check] (#802)

- Remove branches:[main] filter from code_style.yml so it runs on all PRs
- Gate clippy-windows with `if: github.base_ref == 'main'` (skip on staging PRs)
- Update rollup job to allow skipped clippy-windows
- Simplify claude-review.yml to only trigger on labeled event (avoids duplicate runs)

Co-authored-by: Claude Opus 4.6 <[email protected]>

* feat: persist user_id in save_job and expose job_id on routine runs (#709)

* feat: persist worker events to DB and fix activity tab rendering

In-process Worker (used by Scheduler::dispatch_job) now persists events
via save_job_event at key execution points: plan creation, LLM
responses, tool_use, tool_result, and job completion/failure/stuck.
Event data shapes match the container worker format so the gateway
activity tab renders them correctly.

Frontend: tool_result errors now show a red X icon with danger styling
instead of a silent empty output. The result event falls back to the
error field when message is absent.

Co-Authored-By: Claude Opus 4.6 <[email protected]>

* feat: wire RoutineEngine into gateway for direct manual trigger firing

Replace the message-channel hack in routines_trigger_handler with a
direct call to RoutineEngine::fire_manual(), ensuring FullJob routines
dispatch correctly when triggered from the web UI. Inject the engine
into GatewayState from Agent::run after construction.

Also persists user_id in save_job for both PG and libSQL backends,
removes the source='sandbox' filter so all jobs are visible, and
exposes job_id on RoutineRunInfo for the frontend job link.

Co-Authored-By: Claude Opus 4.6 <[email protected]>

* fix: remove stale gateway_state argument from Agent::new test call sites

The gateway_state parameter was removed from Agent::new during rebase
(replaced by post-construction set_routine_engine_slot), but three test
call sites still passed the extra None argument.

Co-Authored-By: Claude Opus 4.6 <[email protected]>

* fix: address PR review — restore sandbox source filter, remove blank lines

- Revert removal of `source = 'sandbox'` filter in all SandboxStore
  queries (8 sites across PG and libSQL). Sandbox-specific APIs should
  stay scoped to sandbox jobs; unified job listing for the Jobs tab
  should use a separate query path.
- Remove extra blank lines in agent_loop.rs and worker.rs that caused
  formatting CI failure.

[skip-regression-check]

Co-Authored-By: Claude Opus 4.6 <[email protected]>

* fix: address review — regenerate Cargo.lock, add user_id regression test

- Regenerate Cargo.lock from main's lockfile to eliminate dependency
  version downgrades (anyhow, syn, etc.) that were churn from rebase.
- Add regression test verifying user_id round-trips through save_job
  and get_job in the libSQL backend.

Co-Authored-By: Claude Opus 4.6 <[email protected]>

* style: remove trailing blank line in libsql jobs.rs

[skip-regression-check]

Co-Authored-By: Claude Opus 4.6 <[email protected]>

* test: add Postgres-side regression test for user_id persistence in save_job

Mirrors the existing libSQL test (test_save_job_persists_user_id) for the
Postgres backend. Gated behind #[cfg(feature = "postgres")] + #[ignore]
since it requires a running PostgreSQL instance (integration tier).

Co-Authored-By: Claude Opus 4.6 <[email protected]>

---------

Co-authored-by: Claude Opus 4.6 <[email protected]>

* feat(llm): per-provider unsupported parameter filtering (#749, #728) (#809)

Add declarative `unsupported_params` field to provider definitions in
providers.json. Parameters listed are stripped from requests before
sending, preventing 400 errors from providers that reject them (e.g.
gpt-5 family and kimi-k2.5 rejecting custom temperature values).

- Add `unsupported_params` to ProviderDefinition and RegistryProviderConfig
- Propagate from registry through config resolution
- Generic strip helpers handle temperature, max_tokens, stop_sequences
- Apply filtering in RigAdapter and AnthropicOAuthProvider
- Mark openai and tinfoil providers as unsupporting temperature
- Update openai default model to gpt-5-mini

Co-authored-by: Claude Opus 4.6 <[email protected]>

---------

Co-authored-by: Claude Sonnet 4.6 <[email protected]>
Co-authored-by: Illia Polosukhin <[email protected]>
2026-03-10 08:14:27 -07:00

385 lines
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[
{
"id": "openai",
"aliases": [
"open_ai"
],
"protocol": "open_ai_completions",
"api_key_env": "OPENAI_API_KEY",
"api_key_required": true,
"base_url_env": "OPENAI_BASE_URL",
"model_env": "OPENAI_MODEL",
"default_model": "gpt-5-mini",
"description": "OpenAI GPT models (direct API)",
"unsupported_params": ["temperature"],
"setup": {
"kind": "api_key",
"secret_name": "llm_openai_api_key",
"key_url": "https://platform.openai.com/api-keys",
"display_name": "OpenAI",
"can_list_models": true
}
},
{
"id": "anthropic",
"aliases": [
"claude"
],
"protocol": "anthropic",
"api_key_env": "ANTHROPIC_API_KEY",
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"base_url_env": "ANTHROPIC_BASE_URL",
"model_env": "ANTHROPIC_MODEL",
"default_model": "claude-sonnet-4-20250514",
"description": "Anthropic Claude models (direct API)",
"setup": {
"kind": "api_key",
"secret_name": "llm_anthropic_api_key",
"key_url": "https://console.anthropic.com/settings/keys",
"display_name": "Anthropic",
"can_list_models": true
}
},
{
"id": "ollama",
"aliases": [],
"protocol": "ollama",
"default_base_url": "http://localhost:11434",
"base_url_env": "OLLAMA_BASE_URL",
"model_env": "OLLAMA_MODEL",
"default_model": "llama3",
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"setup": {
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"display_name": "Ollama",
"can_list_models": true
}
},
{
"id": "openai_compatible",
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"openai-compatible",
"compatible"
],
"protocol": "open_ai_completions",
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"api_key_env": "LLM_API_KEY",
"api_key_required": false,
"model_env": "LLM_MODEL",
"default_model": "default",
"extra_headers_env": "LLM_EXTRA_HEADERS",
"description": "Custom OpenAI-compatible endpoint (vLLM, LiteLLM, etc.)",
"setup": {
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"display_name": "OpenAI-compatible",
"can_list_models": false
}
},
{
"id": "tinfoil",
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"api_key_env": "TINFOIL_API_KEY",
"api_key_required": true,
"model_env": "TINFOIL_MODEL",
"default_model": "kimi-k2-5",
"description": "Tinfoil private inference (hardware-attested TEE)",
"unsupported_params": ["temperature"],
"setup": {
"kind": "api_key",
"secret_name": "llm_tinfoil_api_key",
"key_url": "https://tinfoil.sh",
"display_name": "Tinfoil",
"can_list_models": false
}
},
{
"id": "openrouter",
"aliases": [
"open_router"
],
"protocol": "open_ai_completions",
"default_base_url": "https://openrouter.ai/api/v1",
"api_key_env": "OPENROUTER_API_KEY",
"api_key_required": true,
"model_env": "OPENROUTER_MODEL",
"default_model": "openai/gpt-4o",
"description": "OpenRouter multi-provider gateway (200+ models)",
"setup": {
"kind": "api_key",
"secret_name": "llm_openrouter_api_key",
"key_url": "https://openrouter.ai/settings/keys",
"display_name": "OpenRouter",
"can_list_models": false
}
},
{
"id": "groq",
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"default_base_url": "https://api.groq.com/openai/v1",
"api_key_env": "GROQ_API_KEY",
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"model_env": "GROQ_MODEL",
"default_model": "llama-3.3-70b-versatile",
"description": "Groq LPU inference (ultra-fast)",
"setup": {
"kind": "api_key",
"secret_name": "llm_groq_api_key",
"key_url": "https://console.groq.com/keys",
"display_name": "Groq",
"can_list_models": true,
"models_filter": "chat"
}
},
{
"id": "nvidia",
"aliases": [
"nvidia_nim",
"nim"
],
"protocol": "open_ai_completions",
"default_base_url": "https://integrate.api.nvidia.com/v1",
"api_key_env": "NVIDIA_API_KEY",
"api_key_required": true,
"model_env": "NVIDIA_MODEL",
"default_model": "meta/llama-3.3-70b-instruct",
"description": "NVIDIA NIM API (high-performance inference)",
"setup": {
"kind": "api_key",
"secret_name": "llm_nvidia_api_key",
"key_url": "https://build.nvidia.com",
"display_name": "NVIDIA NIM",
"can_list_models": true
}
},
{
"id": "venice",
"aliases": [
"venice_ai",
"veniceai"
],
"protocol": "open_ai_completions",
"default_base_url": "https://api.venice.ai/api/v1",
"api_key_env": "VENICE_API_KEY",
"api_key_required": true,
"model_env": "VENICE_MODEL",
"default_model": "llama-3.3-70b",
"description": "Venice.ai privacy-focused inference",
"setup": {
"kind": "api_key",
"secret_name": "llm_venice_api_key",
"key_url": "https://venice.ai/settings/api",
"display_name": "Venice.ai",
"can_list_models": false
}
},
{
"id": "together",
"aliases": [
"together_ai",
"togetherai"
],
"protocol": "open_ai_completions",
"default_base_url": "https://api.together.xyz/v1",
"api_key_env": "TOGETHER_API_KEY",
"api_key_required": true,
"model_env": "TOGETHER_MODEL",
"default_model": "meta-llama/Llama-3-70b-chat-hf",
"description": "Together AI inference",
"setup": {
"kind": "api_key",
"secret_name": "llm_together_api_key",
"key_url": "https://api.together.ai/settings/api-keys",
"display_name": "Together AI",
"can_list_models": false
}
},
{
"id": "fireworks",
"aliases": [
"fireworks_ai"
],
"protocol": "open_ai_completions",
"default_base_url": "https://api.fireworks.ai/inference/v1",
"api_key_env": "FIREWORKS_API_KEY",
"api_key_required": true,
"model_env": "FIREWORKS_MODEL",
"default_model": "accounts/fireworks/models/llama-v3p1-70b-instruct",
"description": "Fireworks AI inference",
"setup": {
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"key_url": "https://fireworks.ai/api-keys",
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"can_list_models": false
}
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{
"id": "deepseek",
"aliases": [
"deep_seek"
],
"protocol": "open_ai_completions",
"default_base_url": "https://api.deepseek.com/v1",
"api_key_env": "DEEPSEEK_API_KEY",
"api_key_required": true,
"model_env": "DEEPSEEK_MODEL",
"default_model": "deepseek-chat",
"description": "DeepSeek inference API",
"setup": {
"kind": "api_key",
"secret_name": "llm_deepseek_api_key",
"key_url": "https://platform.deepseek.com/api_keys",
"display_name": "DeepSeek",
"can_list_models": false
}
},
{
"id": "cerebras",
"aliases": [],
"protocol": "open_ai_completions",
"default_base_url": "https://api.cerebras.ai/v1",
"api_key_env": "CEREBRAS_API_KEY",
"api_key_required": true,
"model_env": "CEREBRAS_MODEL",
"default_model": "llama-3.3-70b",
"description": "Cerebras wafer-scale inference",
"setup": {
"kind": "api_key",
"secret_name": "llm_cerebras_api_key",
"key_url": "https://cloud.cerebras.ai",
"display_name": "Cerebras",
"can_list_models": false
}
},
{
"id": "sambanova",
"aliases": [
"samba_nova"
],
"protocol": "open_ai_completions",
"default_base_url": "https://api.sambanova.ai/v1",
"api_key_env": "SAMBANOVA_API_KEY",
"api_key_required": true,
"model_env": "SAMBANOVA_MODEL",
"default_model": "Meta-Llama-3.1-70B-Instruct",
"description": "SambaNova Cloud inference",
"setup": {
"kind": "api_key",
"secret_name": "llm_sambanova_api_key",
"key_url": "https://cloud.sambanova.ai/apis",
"display_name": "SambaNova",
"can_list_models": false
}
},
{
"id": "gemini",
"aliases": [
"google_gemini",
"google"
],
"protocol": "open_ai_completions",
"default_base_url": "https://generativelanguage.googleapis.com/v1beta/openai",
"api_key_env": "GEMINI_API_KEY",
"api_key_required": true,
"model_env": "GEMINI_MODEL",
"default_model": "gemini-2.5-flash",
"description": "Google Gemini (via OpenAI-compatible endpoint)",
"setup": {
"kind": "api_key",
"secret_name": "llm_gemini_api_key",
"key_url": "https://aistudio.google.com/app/apikey",
"display_name": "Google Gemini",
"can_list_models": true
}
},
{
"id": "ionet",
"aliases": [
"io_net",
"io.net"
],
"protocol": "open_ai_completions",
"default_base_url": "https://api.intelligence.io.solutions/api/v1",
"api_key_env": "IONET_API_KEY",
"api_key_required": true,
"model_env": "IONET_MODEL",
"default_model": "deepseek-coder-v2-instruct",
"description": "io.net Intelligence API",
"setup": {
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"display_name": "io.net",
"can_list_models": true
}
},
{
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"mistral_ai",
"mistralai"
],
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"api_key_env": "MISTRAL_API_KEY",
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"model_env": "MISTRAL_MODEL",
"default_model": "mistral-large-latest",
"description": "Mistral AI API",
"setup": {
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"secret_name": "llm_mistral_api_key",
"key_url": "https://console.mistral.ai/api-keys",
"display_name": "Mistral",
"can_list_models": true
}
},
{
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"aliases": [
"yandex_ai_studio",
"yandexgpt",
"yandex_gpt"
],
"protocol": "open_ai_completions",
"default_base_url": "https://ai.api.cloud.yandex.net/v1",
"api_key_env": "YANDEX_API_KEY",
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"model_env": "YANDEX_MODEL",
"extra_headers_env": "YANDEX_EXTRA_HEADERS",
"default_model": "yandexgpt-lite",
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"setup": {
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"display_name": "Yandex AI Studio",
"can_list_models": true
}
},
{
"id": "cloudflare",
"aliases": [
"cloudflare_ai",
"cf_ai"
],
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"api_key_required": true,
"base_url_env": "CLOUDFLARE_BASE_URL",
"model_env": "CLOUDFLARE_MODEL",
"default_model": "@cf/meta/llama-3.3-70b-instruct-fp8-fast",
"description": "Cloudflare Workers AI",
"setup": {
"kind": "open_ai_compatible",
"secret_name": "llm_cloudflare_api_key",
"display_name": "Cloudflare Workers AI",
"can_list_models": false
}
}
]