@samitouri / QOSami_Agents_Zero / commits / dfc7de05

model api_base, litellm finalizing

frdel committed Jul 6, 2025 at 22:25 UTC dfc7de05145c8d5e2fabeaa6e89d5515d4a481da
5 files changed +146 -168
agent.py
+10 -3
@@ -206,6 +206,7 @@ class AgentContext:
206 class ModelConfig:
207 provider: models.ModelProvider
208 name: str
209 + api_base: str = ""
210 ctx_length: int = 0
211 limit_requests: int = 0
212 limit_input: int = 0
@@ -581,23 +582,29 @@ class Agent:
582 return models.get_chat_model(
583 self.config.chat_model.provider,
584 self.config.chat_model.name,
584 - **self.config.chat_model.kwargs,
585 + **self._get_model_kwargs(self.config.chat_model),
586 )
587
588 def get_utility_model(self):
589 return models.get_chat_model(
590 self.config.utility_model.provider,
591 self.config.utility_model.name,
591 - **self.config.utility_model.kwargs,
592 + **self._get_model_kwargs(self.config.utility_model),
593 )
594
595 def get_embedding_model(self):
596 return models.get_embedding_model(
597 self.config.embeddings_model.provider,
598 self.config.embeddings_model.name,
598 - **self.config.embeddings_model.kwargs,
599 + **self._get_model_kwargs(self.config.embeddings_model),
600 )
601
602 + def _get_model_kwargs(self, model_config: ModelConfig):
603 + kwargs = model_config.kwargs.copy() or {}
604 + if model_config.api_base and "api_base" not in kwargs:
605 + kwargs["api_base"] = model_config.api_base
606 + return kwargs
607 +
608 async def call_utility_model(
609 self,
610 system: str,
initialize.py
+4
@@ -29,6 +29,7 @@ def initialize_agent():
29 chat_llm = ModelConfig(
30 provider=models.ModelProvider[current_settings["chat_model_provider"]],
31 name=current_settings["chat_model_name"],
32 + api_base=current_settings["chat_model_api_base"],
33 ctx_length=current_settings["chat_model_ctx_length"],
34 vision=current_settings["chat_model_vision"],
35 limit_requests=current_settings["chat_model_rl_requests"],
@@ -41,6 +42,7 @@ def initialize_agent():
42 utility_llm = ModelConfig(
43 provider=models.ModelProvider[current_settings["util_model_provider"]],
44 name=current_settings["util_model_name"],
45 + api_base=current_settings["util_model_api_base"],
46 ctx_length=current_settings["util_model_ctx_length"],
47 limit_requests=current_settings["util_model_rl_requests"],
48 limit_input=current_settings["util_model_rl_input"],
@@ -51,6 +53,7 @@ def initialize_agent():
53 embedding_llm = ModelConfig(
54 provider=models.ModelProvider[current_settings["embed_model_provider"]],
55 name=current_settings["embed_model_name"],
56 + api_base=current_settings["embed_model_api_base"],
57 limit_requests=current_settings["embed_model_rl_requests"],
58 kwargs=_normalize_model_kwargs(current_settings["embed_model_kwargs"]),
59 )
@@ -58,6 +61,7 @@ def initialize_agent():
61 browser_llm = ModelConfig(
62 provider=models.ModelProvider[current_settings["browser_model_provider"]],
63 name=current_settings["browser_model_name"],
64 + api_base=current_settings["browser_model_api_base"],
65 vision=current_settings["browser_model_vision"],
66 kwargs=_normalize_model_kwargs(current_settings["browser_model_kwargs"]),
67 )
models.py
+66 -130
@@ -59,19 +59,18 @@ class ModelType(Enum):
59
60 class ModelProvider(Enum):
61 ANTHROPIC = "Anthropic"
62 - CHUTES = "Chutes"
62 DEEPSEEK = "DeepSeek"
64 - GOOGLE = "Google"
63 + GEMINI = "Google"
64 GROQ = "Groq"
65 HUGGINGFACE = "HuggingFace"
67 - LMSTUDIO = "LM Studio"
68 - MISTRALAI = "Mistral AI"
66 + LM_STUDIO = "LM Studio"
67 + MISTRAL = "Mistral AI"
68 OLLAMA = "Ollama"
69 OPENAI = "OpenAI"
70 AZURE = "OpenAI Azure"
71 OPENROUTER = "OpenRouter"
72 SAMBANOVA = "Sambanova"
74 - OTHER = "Other"
73 + OTHER = "Other OpenAI compatible"
74
75
76 class ChatChunk(TypedDict):
@@ -84,42 +83,6 @@ class ChatChunk(TypedDict):
83 rate_limiters: dict[str, RateLimiter] = {}
84
85
87 -def configure_litellm_environment():
88 - env_mappings = {
89 - "API_KEY_OPENAI": "OPENAI_API_KEY",
90 - "API_KEY_ANTHROPIC": "ANTHROPIC_API_KEY",
91 - "API_KEY_GROQ": "GROQ_API_KEY",
92 - "API_KEY_GOOGLE": "GOOGLE_API_KEY",
93 - "API_KEY_MISTRAL": "MISTRAL_API_KEY",
94 - "API_KEY_OLLAMA": "OLLAMA_API_KEY",
95 - "API_KEY_HUGGINGFACE": "HUGGINGFACE_API_KEY",
96 - "API_KEY_OPENAI_AZURE": "AZURE_AI_API_KEY",
97 - "API_KEY_DEEPSEEK": "DEEPSEEK_API_KEY",
98 - "API_KEY_SAMBANOVA": "SAMBANOVA_API_KEY",
99 - "API_KEY_GOOGLE": "GEMINI_API_KEY",
100 - }
101 - base_url_mappings = {
102 - "OPENAI_BASE_URL": "OPENAI_API_BASE",
103 - "ANTHROPIC_BASE_URL": "ANTHROPIC_API_BASE",
104 - "GROQ_BASE_URL": "GROQ_API_BASE",
105 - "GOOGLE_BASE_URL": "GOOGLE_API_BASE",
106 - "MISTRAL_BASE_URL": "MISTRAL_API_BASE",
107 - "OLLAMA_BASE_URL": "OLLAMA_API_BASE",
108 - "HUGGINGFACE_BASE_URL": "HUGGINGFACE_API_BASE",
109 - "AZURE_BASE_URL": "AZURE_AI_API_BASE",
110 - "DEEPSEEK_BASE_URL": "DEEPSEEK_API_BASE",
111 - "SAMBANOVA_BASE_URL": "SAMBANOVA_API_BASE",
112 - }
113 - for a0, llm in env_mappings.items():
114 - val = dotenv.get_dotenv_value(a0)
115 - if val and not os.getenv(llm):
116 - os.environ[llm] = val
117 - for a0_base, llm_base in base_url_mappings.items():
118 - val = dotenv.get_dotenv_value(a0_base)
119 - if val and not os.getenv(llm_base):
120 - os.environ[llm_base] = val
121 -
122 -
86 def get_api_key(service: str) -> str:
87 return (
88 dotenv.get_dotenv_value(f"API_KEY_{service.upper()}")
@@ -140,26 +103,6 @@ def get_rate_limiter(
103 return limiter
104
105
143 -def _parse_chunk(chunk: Any) -> ChatChunk:
144 - delta = chunk["choices"][0].get("delta", {})
145 - message = chunk["choices"][0].get("model_extra", {}).get("message", {})
146 - response_delta = (
147 - delta.get("content", "")
148 - if isinstance(delta, dict)
149 - else getattr(delta, "content", "")
150 - ) or (
151 - message.get("content", "")
152 - if isinstance(message, dict)
153 - else getattr(message, "content", "")
154 - )
155 - reasoning_delta = (
156 - delta.get("reasoning_content", "")
157 - if isinstance(delta, dict)
158 - else getattr(delta, "reasoning_content", "")
159 - )
160 - return ChatChunk(reasoning_delta=reasoning_delta, response_delta=response_delta)
161 -
162 -
106 class LiteLLMChatWrapper(SimpleChatModel):
107 model_name: str
108 provider: str
@@ -284,7 +227,7 @@ class LiteLLMChatWrapper(SimpleChatModel):
227 self,
228 system_message="",
229 user_message="",
287 - messages: List[BaseMessage]|None = None,
230 + messages: List[BaseMessage] | None = None,
231 response_callback: Callable[[str, str], Awaitable[None]] | None = None,
232 reasoning_callback: Callable[[str, str], Awaitable[None]] | None = None,
233 tokens_callback: Callable[[str, int], Awaitable[None]] | None = None,
@@ -424,66 +367,86 @@ def _get_litellm_chat(
367 provider_name: str = "",
368 **kwargs: Any,
369 ):
427 - provider_name = provider_name.lower()
428 -
429 - configure_litellm_environment()
430 - # Use original provider name for API key lookup, fallback to mapped provider name
370 + # use api key from kwargs or env
371 api_key = kwargs.pop("api_key", None) or get_api_key(provider_name)
372
433 - # litellm will pick up base_url from env. We just need to control the api_key.
434 - # base_url = dotenv.get_dotenv_value(f"{provider_name.upper()}_BASE_URL")
435 -
436 - # If a base_url is set, ensure api_key is not passed to litellm
437 - # > remove, this can be handled by api_key=None
438 - # if base_url:
439 - # if "api_key" in kwargs:
440 - # del kwargs["api_key"]
441 - # Only pass API key if no base_url is set and key is not a placeholder
373 + # Only pass API key if key is not a placeholder
374 if api_key and api_key not in ("None", "NA"):
375 kwargs["api_key"] = api_key
376
445 - # for openrouter add app reference
446 - if provider_name == "openrouter":
447 - kwargs["extra_headers"] = {
448 - "HTTP-Referer": "https://agent-zero.ai",
449 - "X-Title": "Agent Zero",
450 - }
451 -
452 - return cls(model=model_name, provider=provider_name, **kwargs)
377 + provider_name, model_name, kwargs = _adjust_call_args(
378 + provider_name, model_name, kwargs
379 + )
380 + return cls(provider=provider_name, model=model_name, **kwargs)
381
382
455 -def get_litellm_embedding(model_name: str, provider: str, **kwargs: Any):
383 +def _get_litellm_embedding(model_name: str, provider_name: str, **kwargs: Any):
384 # Check if this is a local sentence-transformers model
457 - if provider == "huggingface" and model_name.startswith("sentence-transformers/"):
385 + if provider_name == "huggingface" and model_name.startswith(
386 + "sentence-transformers/"
387 + ):
388 # Use local sentence-transformers instead of LiteLLM for local models
459 - return LocalSentenceTransformerWrapper(provider=provider, model=model_name, **kwargs)
460 -
461 - configure_litellm_environment()
462 - # Use original provider name for API key lookup, fallback to mapped provider name
463 - api_key = kwargs.pop("api_key", None) or get_api_key(provider)
389 + provider_name, model_name, kwargs = _adjust_call_args(
390 + provider_name, model_name, kwargs
391 + )
392 + return LocalSentenceTransformerWrapper(
393 + provider=provider_name, model=model_name, **kwargs
394 + )
395
465 - # litellm will pick up base_url from env. We just need to control the api_key.
466 - # base_url = dotenv.get_dotenv_value(f"{provider.upper()}_BASE_URL")
396 + # use api key from kwargs or env
397 + api_key = kwargs.pop("api_key", None) or get_api_key(provider_name)
398
468 - # If a base_url is set, ensure api_key is not passed to litellm
469 - # > remove, this can be handled by api_key=None
470 - # if base_url:
471 - # if "api_key" in kwargs:
472 - # del kwargs["api_key"]
473 - # Only pass API key if no base_url is set and key is not a placeholder
399 + # Only pass API key if key is not a placeholder
400 if api_key and api_key not in ("None", "NA"):
401 kwargs["api_key"] = api_key
402
477 - return LiteLLMEmbeddingWrapper(model=model_name, provider=provider, **kwargs)
403 + provider_name, model_name, kwargs = _adjust_call_args(
404 + provider_name, model_name, kwargs
405 + )
406 + return LiteLLMEmbeddingWrapper(model=model_name, provider=provider_name, **kwargs)
407 +
408 +
409 +def _parse_chunk(chunk: Any) -> ChatChunk:
410 + delta = chunk["choices"][0].get("delta", {})
411 + message = chunk["choices"][0].get("model_extra", {}).get("message", {})
412 + response_delta = (
413 + delta.get("content", "")
414 + if isinstance(delta, dict)
415 + else getattr(delta, "content", "")
416 + ) or (
417 + message.get("content", "")
418 + if isinstance(message, dict)
419 + else getattr(message, "content", "")
420 + )
421 + reasoning_delta = (
422 + delta.get("reasoning_content", "")
423 + if isinstance(delta, dict)
424 + else getattr(delta, "reasoning_content", "")
425 + )
426 + return ChatChunk(reasoning_delta=reasoning_delta, response_delta=response_delta)
427 +
428 +
429 +def _adjust_call_args(provider_name: str, model_name: str, kwargs: dict):
430 + # for openrouter add app reference
431 + if provider_name == "openrouter":
432 + kwargs["extra_headers"] = {
433 + "HTTP-Referer": "https://agent-zero.ai",
434 + "X-Title": "Agent Zero",
435 + }
436 +
437 + # remap other to openai for litellm
438 + if provider_name == "other":
439 + provider_name = "openai"
440 +
441 + return provider_name, model_name, kwargs
442
443
444 def get_model(type: ModelType, provider: ModelProvider, name: str, **kwargs: Any):
445 provider_name = provider.name.lower()
482 - kwargs = _normalize_chat_kwargs(provider, kwargs)
446 if type == ModelType.CHAT:
447 return _get_litellm_chat(LiteLLMChatWrapper, name, provider_name, **kwargs)
448 elif type == ModelType.EMBEDDING:
486 - return get_litellm_embedding(name, provider_name, **kwargs)
449 + return _get_litellm_embedding(name, provider_name, **kwargs)
450 else:
451 raise ValueError(f"Unsupported model type: {type}")
452
@@ -491,8 +454,7 @@ def get_model(type: ModelType, provider: ModelProvider, name: str, **kwargs: Any
454 def get_chat_model(
455 provider: ModelProvider, name: str, **kwargs: Any
456 ) -> LiteLLMChatWrapper:
494 - provider_name = _get_litellm_provider(provider)
495 - kwargs = _normalize_chat_kwargs(provider, kwargs)
457 + provider_name = provider.name.lower()
458 model = _get_litellm_chat(LiteLLMChatWrapper, name, provider_name, **kwargs)
459 return model
460
@@ -501,7 +463,6 @@ def get_browser_model(
463 provider: ModelProvider, name: str, **kwargs: Any
464 ) -> BrowserCompatibleChatWrapper:
465 provider_name = provider.name.lower()
504 - kwargs = _normalize_chat_kwargs(provider, kwargs)
466 model = _get_litellm_chat(
467 BrowserCompatibleChatWrapper, name, provider_name, **kwargs
468 )
@@ -512,30 +473,5 @@ def get_embedding_model(
473 provider: ModelProvider, name: str, **kwargs: Any
474 ) -> LiteLLMEmbeddingWrapper | LocalSentenceTransformerWrapper:
475 provider_name = provider.name.lower()
515 - kwargs = _normalize_embedding_kwargs(kwargs)
516 - model = get_litellm_embedding(name, provider_name, **kwargs)
476 + model = _get_litellm_embedding(name, provider_name, **kwargs)
477 return model
518 -
519 -
520 -def _normalize_chat_kwargs(provider: ModelProvider, kwargs: Any) -> Any:
521 - # this prevents using openai api key for other providers
522 - if provider == ModelProvider.OTHER:
523 - if "api_key" not in kwargs:
524 - kwargs["api_key"] = "None"
525 - return kwargs
526 -
527 -
528 -def _normalize_embedding_kwargs(kwargs: Any) -> Any:
529 - return kwargs
530 -
531 -
532 -def _get_litellm_provider(provider: ModelProvider) -> str:
533 - name = provider.name.lower()
534 -
535 - # exceptions
536 - if name == "google":
537 - name = "gemini"
538 - elif name == "other":
539 - name = "openai"
540 -
541 - return name
python/helpers/settings.py
+45 -27
@@ -17,6 +17,7 @@ class Settings(TypedDict):
17
18 chat_model_provider: str
19 chat_model_name: str
20 + chat_model_api_base: str
21 chat_model_kwargs: dict[str, str]
22 chat_model_ctx_length: int
23 chat_model_ctx_history: float
@@ -27,6 +28,7 @@ class Settings(TypedDict):
28
29 util_model_provider: str
30 util_model_name: str
31 + util_model_api_base: str
32 util_model_kwargs: dict[str, str]
33 util_model_ctx_length: int
34 util_model_ctx_input: float
@@ -36,12 +38,14 @@ class Settings(TypedDict):
38
39 embed_model_provider: str
40 embed_model_name: str
41 + embed_model_api_base: str
42 embed_model_kwargs: dict[str, str]
43 embed_model_rl_requests: int
44 embed_model_rl_input: int
45
46 browser_model_provider: str
47 browser_model_name: str
48 + browser_model_api_base: str
49 browser_model_vision: bool
50 browser_model_kwargs: dict[str, str]
51
@@ -141,6 +145,16 @@ def convert_out(settings: Settings) -> SettingsOutput:
145 }
146 )
147
148 + chat_model_fields.append(
149 + {
150 + "id": "chat_model_api_base",
151 + "title": "Chat model API base URL",
152 + "description": "API base URL for main chat model. Leave empty for default. Only relevant for Azure, local and custom (other) providers.",
153 + "type": "text",
154 + "value": settings["chat_model_api_base"],
155 + }
156 + )
157 +
158 chat_model_fields.append(
159 {
160 "id": "chat_model_ctx_length",
@@ -208,8 +222,7 @@ def convert_out(settings: Settings) -> SettingsOutput:
222 {
223 "id": "chat_model_kwargs",
224 "title": "Chat model additional parameters",
211 - "description": """Any other parameters supported by the model. Format is KEY=VALUE on individual lines, just like .env file.
212 - For OpenAI compatible providers not listed here, select 'other' and specify api_base=https://... and api_key=... as additional parameters.""",
225 + "description": "Any other parameters supported by <a href='https://docs.litellm.ai/docs/set_keys' target='_blank'>LiteLLM</a>. Format is KEY=VALUE on individual lines, just like .env file.",
226 "type": "textarea",
227 "value": _dict_to_env(settings["chat_model_kwargs"]),
228 }
@@ -245,6 +258,16 @@ def convert_out(settings: Settings) -> SettingsOutput:
258 }
259 )
260
261 + util_model_fields.append(
262 + {
263 + "id": "util_model_api_base",
264 + "title": "Utility model API base URL",
265 + "description": "API base URL for utility model. Leave empty for default. Only relevant for Azure, local and custom (other) providers.",
266 + "type": "text",
267 + "value": settings["util_model_api_base"],
268 + }
269 + )
270 +
271 util_model_fields.append(
272 {
273 "id": "util_model_rl_requests",
@@ -279,8 +302,7 @@ def convert_out(settings: Settings) -> SettingsOutput:
302 {
303 "id": "util_model_kwargs",
304 "title": "Utility model additional parameters",
282 - "description": """Any other parameters supported by the model. Format is KEY=VALUE on individual lines, just like .env file.
283 - For OpenAI compatible providers not listed here, select 'other' and specify api_base=https://... and api_key=... as additional parameters.""",
305 + "description": "Any other parameters supported by <a href='https://docs.litellm.ai/docs/set_keys' target='_blank'>LiteLLM</a>. Format is KEY=VALUE on individual lines, just like .env file.",
306 "type": "textarea",
307 "value": _dict_to_env(settings["util_model_kwargs"]),
308 }
@@ -316,6 +338,16 @@ def convert_out(settings: Settings) -> SettingsOutput:
338 }
339 )
340
341 + embed_model_fields.append(
342 + {
343 + "id": "embed_model_api_base",
344 + "title": "Embedding model API base URL",
345 + "description": "API base URL for embedding model. Leave empty for default. Only relevant for Azure, local and custom (other) providers.",
346 + "type": "text",
347 + "value": settings["embed_model_api_base"],
348 + }
349 + )
350 +
351 embed_model_fields.append(
352 {
353 "id": "embed_model_rl_requests",
@@ -340,8 +372,7 @@ def convert_out(settings: Settings) -> SettingsOutput:
372 {
373 "id": "embed_model_kwargs",
374 "title": "Embedding model additional parameters",
343 - "description": """Any other parameters supported by the model. Format is KEY=VALUE on individual lines, just like .env file.
344 - For OpenAI compatible providers not listed here, select 'other' and specify api_base=https://... and api_key=... as additional parameters.""",
375 + "description": "Any other parameters supported by <a href='https://docs.litellm.ai/docs/set_keys' target='_blank'>LiteLLM</a>. Format is KEY=VALUE on individual lines, just like .env file.",
376 "type": "textarea",
377 "value": _dict_to_env(settings["embed_model_kwargs"]),
378 }
@@ -391,7 +422,7 @@ def convert_out(settings: Settings) -> SettingsOutput:
422 {
423 "id": "browser_model_kwargs",
424 "title": "Web Browser model additional parameters",
394 - "description": "Any other parameters supported by the model. Format is KEY=VALUE on individual lines, just like .env file.",
425 + "description": "Any other parameters supported by <a href='https://docs.litellm.ai/docs/set_keys' target='_blank'>LiteLLM</a>. Format is KEY=VALUE on individual lines, just like .env file.",
426 "type": "textarea",
427 "value": _dict_to_env(settings["browser_model_kwargs"]),
428 }
@@ -472,26 +503,9 @@ def convert_out(settings: Settings) -> SettingsOutput:
503
504 # api keys model section
505 api_keys_fields: list[SettingsField] = []
475 - api_keys_fields.append(_get_api_key_field(settings, "openai", "OpenAI API Key"))
476 - api_keys_fields.append(
477 - _get_api_key_field(settings, "anthropic", "Anthropic API Key")
478 - )
479 - api_keys_fields.append(_get_api_key_field(settings, "chutes", "Chutes API Key"))
480 - api_keys_fields.append(_get_api_key_field(settings, "deepseek", "DeepSeek API Key"))
481 - api_keys_fields.append(_get_api_key_field(settings, "google", "Google API Key"))
482 - api_keys_fields.append(_get_api_key_field(settings, "groq", "Groq API Key"))
483 - api_keys_fields.append(
484 - _get_api_key_field(settings, "huggingface", "HuggingFace API Key")
485 - )
486 - api_keys_fields.append(
487 - _get_api_key_field(settings, "mistralai", "MistralAI API Key")
488 - )
489 - api_keys_fields.append(
490 - _get_api_key_field(settings, "openrouter", "OpenRouter API Key")
491 - )
492 - api_keys_fields.append(
493 - _get_api_key_field(settings, "sambanova", "Sambanova API Key")
494 - )
506 +
507 + for provider in ModelProvider:
508 + api_keys_fields.append(_get_api_key_field(settings, provider.name.lower(), provider.value))
509
510 api_keys_section: SettingsSection = {
511 "id": "api_keys",
@@ -965,6 +979,7 @@ def get_default_settings() -> Settings:
979 version=_get_version(),
980 chat_model_provider=ModelProvider.OPENROUTER.name,
981 chat_model_name="openai/gpt-4.1",
982 + chat_model_api_base="",
983 chat_model_kwargs={"temperature": "0"},
984 chat_model_ctx_length=100000,
985 chat_model_ctx_history=0.7,
@@ -974,6 +989,7 @@ def get_default_settings() -> Settings:
989 chat_model_rl_output=0,
990 util_model_provider=ModelProvider.OPENROUTER.name,
991 util_model_name="openai/gpt-4.1-nano",
992 + util_model_api_base="",
993 util_model_ctx_length=100000,
994 util_model_ctx_input=0.7,
995 util_model_kwargs={"temperature": "0"},
@@ -982,11 +998,13 @@ def get_default_settings() -> Settings:
998 util_model_rl_output=0,
999 embed_model_provider=ModelProvider.HUGGINGFACE.name,
1000 embed_model_name="sentence-transformers/all-MiniLM-L6-v2",
1001 + embed_model_api_base="",
1002 embed_model_kwargs={},
1003 embed_model_rl_requests=0,
1004 embed_model_rl_input=0,
1005 browser_model_provider=ModelProvider.OPENROUTER.name,
1006 browser_model_name="openai/gpt-4.1",
1007 + browser_model_api_base="",
1008 browser_model_vision=True,
1009 browser_model_kwargs={"temperature": "0"},
1010 api_keys={},
python/tools/browser_agent.py
+21 -8
@@ -57,7 +57,13 @@ class State:
57 viewport={"width": 1024, "height": 2048},
58 args=["--headless=new"],
59 # Use a unique user data directory to avoid conflicts
60 - user_data_dir=str(Path.home() / ".config" / "browseruse" / "profiles" / f"agent_{self.agent.context.id}"),
60 + user_data_dir=str(
61 + Path.home()
62 + / ".config"
63 + / "browseruse"
64 + / "profiles"
65 + / f"agent_{self.agent.context.id}"
66 + ),
67 )
68 )
69
@@ -119,11 +125,10 @@ class State:
125 )
126 return result
127
122 -
128 model = models.get_browser_model(
129 provider=self.agent.config.browser_model.provider,
130 name=self.agent.config.browser_model.name,
126 - **self.agent.config.browser_model.kwargs,
131 + **self.agent._get_model_kwargs(self.agent.config.browser_model),
132 )
133
134 try:
@@ -140,7 +145,9 @@ class State:
145 # available_file_paths=[],
146 )
147 except Exception as e:
143 - raise Exception(f"Browser agent initialization failed. This might be due to model compatibility issues. Error: {e}") from e
148 + raise Exception(
149 + f"Browser agent initialization failed. This might be due to model compatibility issues. Error: {e}"
150 + ) from e
151
152 self.iter_no = get_iter_no(self.agent)
153
@@ -298,13 +305,17 @@ class BrowserAgent(Tool):
305 f"Task reached step limit without completion. Last page: {current_url}. "
306 f"The browser agent may need clearer instructions on when to finish."
307 )
301 -
308 +
309 # update the log (without screenshot path here, user can click)
310 self.log.update(answer=answer_text)
311
312 # add screenshot to the answer if we have it
306 - if self.log.kvps and "screenshot" in self.log.kvps and self.log.kvps['screenshot']:
307 - path = self.log.kvps['screenshot'].split('//', 1)[-1].split('&', 1)[0]
313 + if (
314 + self.log.kvps
315 + and "screenshot" in self.log.kvps
316 + and self.log.kvps["screenshot"]
317 + ):
318 + path = self.log.kvps["screenshot"].split("//", 1)[-1].split("&", 1)[0]
319 answer_text += f"\n\nScreenshot: {path}"
320
321 # respond (with screenshot path)
@@ -416,7 +427,9 @@ def get_use_agent_log(use_agent: browser_use.Agent | None):
427 if item.success:
428 short_log.append(f"✅ Done")
429 else:
419 - short_log.append(f"❌ Error: {item.error or item.extracted_content or 'Unknown error'}")
430 + short_log.append(
431 + f"❌ Error: {item.error or item.extracted_content or 'Unknown error'}"
432 + )
433
434 # progress messages
435 else: