The 'auto' model on OpenRouter dynamically routes to any underlying model
OpenRouter selects, including reasoning-required endpoints. Previously,
OpenClaw would unconditionally inject `reasoning.effort: "none"` into
every request when the thinking level was "off", which causes a 400 error
on models where reasoning is mandatory and cannot be disabled.
Root cause:
- openrouter/auto has reasoning: false in the built-in catalog
- With thinking level "off", createOpenRouterWrapper injects
`reasoning: { effort: "none" }` via mapThinkingLevelToOpenRouterReasoningEffort
- For any OpenRouter-routed model that requires reasoning this results in:
"400 Reasoning is mandatory for this endpoint and cannot be disabled"
- The reasoning: false is then persisted back to models.json on every
ensureOpenClawModelsJson call, so manually removing it has no lasting effect
Fix:
- In applyExtraParamsToAgent, when provider is "openrouter" and the model
id is "auto", pass undefined as thinkingLevel to createOpenRouterWrapper
so no reasoning.effort is injected at all, letting OpenRouter's upstream
model handle it natively
- Add an explanatory comment in buildOpenrouterProvider clarifying that the
reasoning: false catalog value does NOT cause effort injection for "auto"
Users who need explicit reasoning control should target a specific model
id (e.g. openrouter/deepseek/deepseek-r1) rather than the auto router.
Fixes#24851
(cherry picked from commit aa55439798)
* feat: Add Kilo Gateway provider
Add support for Kilo Gateway as a model provider, similar to OpenRouter.
Kilo Gateway provides a unified API that routes requests to many models
behind a single endpoint and API key.
Changes:
- Add kilocode provider option to auth-choice and onboarding flows
- Add KILOCODE_API_KEY environment variable support
- Add kilocode/ model prefix handling in model-auth and extra-params
- Add provider documentation in docs/providers/kilocode.md
- Update model-providers.md with Kilo Gateway section
- Add design doc for the integration
* kilocode: add provider tests and normalize onboard auth-choice registration
* kilocode: register in resolveImplicitProviders so models appear in provider filter
* kilocode: update base URL from /api/openrouter/ to /api/gateway/
* docs: fix formatting in kilocode docs
* fix: address PR review — remove kilocode from cacheRetention, fix stale model refs and CLI name in docs, fix TS2742
* docs: fix stale refs in design doc — Moltbot to OpenClaw, MoltbotConfig to OpenClawConfig, remove extra-params section, fix doc path
* fix: use resolveAgentModelPrimaryValue for AgentModelConfig union type
---------
Co-authored-by: Mark IJbema <mark@kilocode.ai>
* OpenRouter: allow any model ID instead of restricting to static catalog
OpenRouter models were restricted to a hardcoded prefix list in the internal model catalog, preventing use of newly added or less common models. This change makes OpenRouter work as the pass-through proxy it is -- any valid OpenRouter model ID now resolves dynamically.
Fixes https://github.com/openclaw/openclaw/issues/5241
Changes:
- Add OpenRouter as an implicit provider in resolveImplicitProviders so models.json is populated when an API key is detected (models-config.providers.ts)
- Add a pass-through fallback in resolveModel that creates OpenRouter models on-the-fly when they aren't pre-registered in the local catalog (
model.ts
)
- Remove the static prefix filter for OpenRouter/opencode in isModernModelRef (live-model-filter.ts)
* Apply requested change for maxTokens
* Agents: remove dead helper in live model filter
* Changelog: note Joly0/main OpenRouter fix
* Changelog: fix OpenRouter entry text
---------
Co-authored-by: Vincent Koc <vincentkoc@ieee.org>
Add support for NVIDIA's API (https://integrate.api.nvidia.com/v1) with three models:
- nvidia/llama-3.1-nemotron-70b-instruct (default)
- nvidia/llama-3.3-70b-instruct
- nvidia/mistral-nemo-minitron-8b-8k-instruct
Users can configure via NVIDIA_API_KEY environment variable or auth profiles.
Co-authored-by: thesomewhatyou <162917831+thesomewhatyou@users.noreply.github.com>
* initial commit
* removes assesment from docs
* resolves automated review comments
* resolves lint , type , tests , refactors , and submits
* solves : why do we have to lint the tests xD
* adds greptile fixes
* solves a type error
* solves a ci error
* refactors auths
* solves a failing test after i pulled from main lol
* solves a failing test after i pulled from main lol
* resolves token naming issue to comply with better practices when using hf / huggingface
* fixes curly lints !
* fixes failing tests for google api from main
* solve merge conflicts
* solve failing tests with a defensive check 'undefined' openrouterapi key
* fix: preserve Hugging Face auth-choice intent and token behavior (#13472) (thanks @Josephrp)
* test: resolve auth-choice cherry-pick conflict cleanup (#13472)
---------
Co-authored-by: Cursor <cursoragent@cursor.com>
Co-authored-by: Peter Steinberger <steipete@gmail.com>
* fix(ollama): add streaming config and fix OLLAMA_API_KEY env var support
Adds configurable streaming parameter to model configuration and sets streaming
to false by default for Ollama models. This addresses the corrupted response
issue caused by upstream SDK bug badlogic/pi-mono#1205 where interleaved
content/reasoning deltas in streaming responses cause garbled output.
Changes:
- Add streaming param to AgentModelEntryConfig type
- Set streaming: false default for Ollama models
- Add OLLAMA_API_KEY to envMap (was missing, preventing env var auth)
- Document streaming configuration in Ollama provider docs
- Add tests for Ollama model configuration
Users can now configure streaming per-model and Ollama authentication
via OLLAMA_API_KEY environment variable works correctly.
Fixes#8839
Related: badlogic/pi-mono#1205
* docs(ollama): use gpt-oss:20b as primary example
Updates documentation to use gpt-oss:20b as the primary example model
since it supports tool calling. The model examples now show:
- gpt-oss:20b as the primary recommended model (tool-capable)
- llama3.3 and qwen2.5-coder:32b as additional options
This provides users with a clear, working example that supports
OpenClaw's tool calling features.
* chore: remove unused vi import from ollama test
MiniMax has updated their API. The previous configuration used an
incorrect endpoint (api.minimax.io/anthropic) with anthropic-messages
format, which no longer works.
Changes:
- Update MINIMAX_API_BASE_URL to https://api.minimax.chat/v1
- Change API format from anthropic-messages to openai-completions
- Remove minimax from isAnthropicApi check in transcript-policy
This fixes the issue where MiniMax API calls return no results.
Venice AI is a privacy-focused AI inference provider with support for
uncensored models and access to major proprietary models via their
anonymized proxy.
This integration adds:
- Complete model catalog with 25 models:
- 15 private models (Llama, Qwen, DeepSeek, Venice Uncensored, etc.)
- 10 anonymized models (Claude, GPT-5.2, Gemini, Grok, Kimi, MiniMax)
- Auto-discovery from Venice API with fallback to static catalog
- VENICE_API_KEY environment variable support
- Interactive onboarding via 'venice-api-key' auth choice
- Model selection prompt showing all available Venice models
- Provider auto-registration when API key is detected
- Comprehensive documentation covering:
- Privacy modes (private vs anonymized)
- All 25 models with context windows and features
- Streaming, function calling, and vision support
- Model selection recommendations
Privacy modes:
- Private: Fully private, no logging (open-source models)
- Anonymized: Proxied through Venice (proprietary models)
Default model: venice/llama-3.3-70b (good balance of capability + privacy)
Venice API: https://api.venice.ai/api/v1 (OpenAI-compatible)
* feat: Add Ollama provider with automatic model discovery
- Add Ollama provider builder with automatic model detection
- Discover available models from local Ollama instance via /api/tags API
- Make resolveImplicitProviders async to support dynamic model discovery
- Add comprehensive Ollama documentation with setup and usage guide
- Add tests for Ollama provider integration
- Update provider index and model providers documentation
Closes#1531
* fix: Correct Ollama provider type definitions and error handling
- Fix input property type to match ModelDefinitionConfig
- Import ModelDefinitionConfig type properly
- Fix error template literal to use String() for type safety
- Simplify return type signature of discoverOllamaModels
* fix: Suppress unhandled promise warnings from ensureClawdbotModelsJson in tests
- Cast unused promise returns to 'unknown' to suppress TypeScript warnings
- Tests that don't await the promise are intentionally not awaiting it
- This fixes the failing test suite caused by unawaited async calls
* fix: Skip Ollama model discovery during tests
- Check for VITEST or NODE_ENV=test before making HTTP requests
- Prevents test timeouts and hangs from network calls
- Ollama discovery will still work in production/normal usage
* fix: Set VITEST environment variable in test setup
- Ensures Ollama discovery is skipped in all test runs
- Prevents network calls during tests that could cause timeouts
* test: Temporarily skip Ollama provider tests to diagnose CI failures
* fix: Make Ollama provider opt-in to avoid breaking existing tests
**Root Cause:**
The Ollama provider was being added to ALL configurations by default
(with a fallback API key of 'ollama-local'), which broke tests that
expected NO providers when no API keys were configured.
**Solution:**
- Removed the default fallback API key for Ollama
- Ollama provider now requires explicit configuration via:
- OLLAMA_API_KEY environment variable, OR
- Ollama profile in auth store
- Updated documentation to reflect the explicit configuration requirement
- Added a test to verify Ollama is not added by default
This fixes all 4 failing test suites:
- checks (node, test, pnpm test)
- checks (bun, test, bunx vitest run)
- checks-windows (node, test, pnpm test)
- checks-macos (test, pnpm test)
Closes#1531
Add automatic discovery of AWS Bedrock models using ListFoundationModels API.
When AWS credentials are detected, models that support streaming and text output
are automatically discovered and made available.
- Add @aws-sdk/client-bedrock dependency
- Add discoverBedrockModels() with caching (default 1 hour)
- Add resolveImplicitBedrockProvider() for auto-registration
- Add BedrockDiscoveryConfig for optional filtering by provider/region
- Filter to active, streaming, text-output models only
- Update docs/bedrock.md with auto-discovery documentation
