mirror of
https://github.com/agent0ai/agent-zero.git
synced 2026-05-19 07:59:34 +00:00
model api_base, litellm finalizing
This commit is contained in:
frdel
parent
e2e43c4ac1
commit
dfc7de0514
5 changed files with 147 additions and 169 deletions
13
agent.py
13
agent.py
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@ -206,6 +206,7 @@ class AgentContext:
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class ModelConfig:
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provider: models.ModelProvider
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name: str
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api_base: str = ""
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ctx_length: int = 0
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limit_requests: int = 0
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limit_input: int = 0
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@ -581,23 +582,29 @@ class Agent:
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return models.get_chat_model(
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self.config.chat_model.provider,
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self.config.chat_model.name,
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**self.config.chat_model.kwargs,
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**self._get_model_kwargs(self.config.chat_model),
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)
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def get_utility_model(self):
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return models.get_chat_model(
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self.config.utility_model.provider,
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self.config.utility_model.name,
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**self.config.utility_model.kwargs,
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**self._get_model_kwargs(self.config.utility_model),
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)
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def get_embedding_model(self):
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return models.get_embedding_model(
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self.config.embeddings_model.provider,
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self.config.embeddings_model.name,
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**self.config.embeddings_model.kwargs,
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**self._get_model_kwargs(self.config.embeddings_model),
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)
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def _get_model_kwargs(self, model_config: ModelConfig):
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kwargs = model_config.kwargs.copy() or {}
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if model_config.api_base and "api_base" not in kwargs:
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kwargs["api_base"] = model_config.api_base
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return kwargs
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async def call_utility_model(
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self,
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system: str,
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@ -29,6 +29,7 @@ def initialize_agent():
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chat_llm = ModelConfig(
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provider=models.ModelProvider[current_settings["chat_model_provider"]],
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name=current_settings["chat_model_name"],
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api_base=current_settings["chat_model_api_base"],
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ctx_length=current_settings["chat_model_ctx_length"],
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vision=current_settings["chat_model_vision"],
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limit_requests=current_settings["chat_model_rl_requests"],
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@ -41,6 +42,7 @@ def initialize_agent():
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utility_llm = ModelConfig(
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provider=models.ModelProvider[current_settings["util_model_provider"]],
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name=current_settings["util_model_name"],
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api_base=current_settings["util_model_api_base"],
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ctx_length=current_settings["util_model_ctx_length"],
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limit_requests=current_settings["util_model_rl_requests"],
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limit_input=current_settings["util_model_rl_input"],
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@ -51,6 +53,7 @@ def initialize_agent():
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embedding_llm = ModelConfig(
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provider=models.ModelProvider[current_settings["embed_model_provider"]],
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name=current_settings["embed_model_name"],
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api_base=current_settings["embed_model_api_base"],
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limit_requests=current_settings["embed_model_rl_requests"],
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kwargs=_normalize_model_kwargs(current_settings["embed_model_kwargs"]),
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)
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@ -58,6 +61,7 @@ def initialize_agent():
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browser_llm = ModelConfig(
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provider=models.ModelProvider[current_settings["browser_model_provider"]],
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name=current_settings["browser_model_name"],
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api_base=current_settings["browser_model_api_base"],
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vision=current_settings["browser_model_vision"],
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kwargs=_normalize_model_kwargs(current_settings["browser_model_kwargs"]),
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)
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198
models.py
198
models.py
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@ -59,19 +59,18 @@ class ModelType(Enum):
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class ModelProvider(Enum):
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ANTHROPIC = "Anthropic"
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CHUTES = "Chutes"
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DEEPSEEK = "DeepSeek"
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GOOGLE = "Google"
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GEMINI = "Google"
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GROQ = "Groq"
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HUGGINGFACE = "HuggingFace"
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LMSTUDIO = "LM Studio"
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MISTRALAI = "Mistral AI"
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LM_STUDIO = "LM Studio"
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MISTRAL = "Mistral AI"
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OLLAMA = "Ollama"
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OPENAI = "OpenAI"
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AZURE = "OpenAI Azure"
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OPENROUTER = "OpenRouter"
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SAMBANOVA = "Sambanova"
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OTHER = "Other"
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OTHER = "Other OpenAI compatible"
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class ChatChunk(TypedDict):
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@ -84,42 +83,6 @@ class ChatChunk(TypedDict):
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rate_limiters: dict[str, RateLimiter] = {}
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def configure_litellm_environment():
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env_mappings = {
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"API_KEY_OPENAI": "OPENAI_API_KEY",
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"API_KEY_ANTHROPIC": "ANTHROPIC_API_KEY",
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"API_KEY_GROQ": "GROQ_API_KEY",
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"API_KEY_GOOGLE": "GOOGLE_API_KEY",
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"API_KEY_MISTRAL": "MISTRAL_API_KEY",
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"API_KEY_OLLAMA": "OLLAMA_API_KEY",
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"API_KEY_HUGGINGFACE": "HUGGINGFACE_API_KEY",
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"API_KEY_OPENAI_AZURE": "AZURE_AI_API_KEY",
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"API_KEY_DEEPSEEK": "DEEPSEEK_API_KEY",
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"API_KEY_SAMBANOVA": "SAMBANOVA_API_KEY",
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"API_KEY_GOOGLE": "GEMINI_API_KEY",
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}
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base_url_mappings = {
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"OPENAI_BASE_URL": "OPENAI_API_BASE",
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"ANTHROPIC_BASE_URL": "ANTHROPIC_API_BASE",
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"GROQ_BASE_URL": "GROQ_API_BASE",
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"GOOGLE_BASE_URL": "GOOGLE_API_BASE",
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"MISTRAL_BASE_URL": "MISTRAL_API_BASE",
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"OLLAMA_BASE_URL": "OLLAMA_API_BASE",
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"HUGGINGFACE_BASE_URL": "HUGGINGFACE_API_BASE",
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"AZURE_BASE_URL": "AZURE_AI_API_BASE",
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"DEEPSEEK_BASE_URL": "DEEPSEEK_API_BASE",
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"SAMBANOVA_BASE_URL": "SAMBANOVA_API_BASE",
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}
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for a0, llm in env_mappings.items():
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val = dotenv.get_dotenv_value(a0)
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if val and not os.getenv(llm):
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os.environ[llm] = val
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for a0_base, llm_base in base_url_mappings.items():
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val = dotenv.get_dotenv_value(a0_base)
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if val and not os.getenv(llm_base):
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os.environ[llm_base] = val
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def get_api_key(service: str) -> str:
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return (
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dotenv.get_dotenv_value(f"API_KEY_{service.upper()}")
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@ -140,26 +103,6 @@ def get_rate_limiter(
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return limiter
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def _parse_chunk(chunk: Any) -> ChatChunk:
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delta = chunk["choices"][0].get("delta", {})
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message = chunk["choices"][0].get("model_extra", {}).get("message", {})
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response_delta = (
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delta.get("content", "")
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if isinstance(delta, dict)
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else getattr(delta, "content", "")
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) or (
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message.get("content", "")
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if isinstance(message, dict)
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else getattr(message, "content", "")
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)
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reasoning_delta = (
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delta.get("reasoning_content", "")
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if isinstance(delta, dict)
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else getattr(delta, "reasoning_content", "")
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)
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return ChatChunk(reasoning_delta=reasoning_delta, response_delta=response_delta)
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class LiteLLMChatWrapper(SimpleChatModel):
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model_name: str
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provider: str
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@ -284,7 +227,7 @@ class LiteLLMChatWrapper(SimpleChatModel):
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self,
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system_message="",
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user_message="",
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messages: List[BaseMessage]|None = None,
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messages: List[BaseMessage] | None = None,
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response_callback: Callable[[str, str], Awaitable[None]] | None = None,
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reasoning_callback: Callable[[str, str], Awaitable[None]] | None = None,
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tokens_callback: Callable[[str, int], Awaitable[None]] | None = None,
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@ -424,24 +367,66 @@ def _get_litellm_chat(
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provider_name: str = "",
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**kwargs: Any,
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):
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provider_name = provider_name.lower()
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configure_litellm_environment()
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# Use original provider name for API key lookup, fallback to mapped provider name
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# use api key from kwargs or env
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api_key = kwargs.pop("api_key", None) or get_api_key(provider_name)
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# litellm will pick up base_url from env. We just need to control the api_key.
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# base_url = dotenv.get_dotenv_value(f"{provider_name.upper()}_BASE_URL")
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# If a base_url is set, ensure api_key is not passed to litellm
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# > remove, this can be handled by api_key=None
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# if base_url:
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# if "api_key" in kwargs:
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# del kwargs["api_key"]
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# Only pass API key if no base_url is set and key is not a placeholder
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# Only pass API key if key is not a placeholder
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if api_key and api_key not in ("None", "NA"):
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kwargs["api_key"] = api_key
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provider_name, model_name, kwargs = _adjust_call_args(
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provider_name, model_name, kwargs
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)
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return cls(provider=provider_name, model=model_name, **kwargs)
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def _get_litellm_embedding(model_name: str, provider_name: str, **kwargs: Any):
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# Check if this is a local sentence-transformers model
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if provider_name == "huggingface" and model_name.startswith(
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"sentence-transformers/"
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):
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# Use local sentence-transformers instead of LiteLLM for local models
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provider_name, model_name, kwargs = _adjust_call_args(
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provider_name, model_name, kwargs
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)
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return LocalSentenceTransformerWrapper(
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provider=provider_name, model=model_name, **kwargs
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)
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# use api key from kwargs or env
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api_key = kwargs.pop("api_key", None) or get_api_key(provider_name)
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# Only pass API key if key is not a placeholder
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if api_key and api_key not in ("None", "NA"):
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kwargs["api_key"] = api_key
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provider_name, model_name, kwargs = _adjust_call_args(
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provider_name, model_name, kwargs
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)
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return LiteLLMEmbeddingWrapper(model=model_name, provider=provider_name, **kwargs)
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def _parse_chunk(chunk: Any) -> ChatChunk:
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delta = chunk["choices"][0].get("delta", {})
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message = chunk["choices"][0].get("model_extra", {}).get("message", {})
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response_delta = (
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delta.get("content", "")
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if isinstance(delta, dict)
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else getattr(delta, "content", "")
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) or (
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message.get("content", "")
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if isinstance(message, dict)
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else getattr(message, "content", "")
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)
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reasoning_delta = (
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delta.get("reasoning_content", "")
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if isinstance(delta, dict)
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else getattr(delta, "reasoning_content", "")
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)
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return ChatChunk(reasoning_delta=reasoning_delta, response_delta=response_delta)
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def _adjust_call_args(provider_name: str, model_name: str, kwargs: dict):
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# for openrouter add app reference
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if provider_name == "openrouter":
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kwargs["extra_headers"] = {
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@ -449,41 +434,19 @@ def _get_litellm_chat(
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"X-Title": "Agent Zero",
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}
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return cls(model=model_name, provider=provider_name, **kwargs)
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# remap other to openai for litellm
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if provider_name == "other":
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provider_name = "openai"
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def get_litellm_embedding(model_name: str, provider: str, **kwargs: Any):
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# Check if this is a local sentence-transformers model
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if provider == "huggingface" and model_name.startswith("sentence-transformers/"):
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# Use local sentence-transformers instead of LiteLLM for local models
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return LocalSentenceTransformerWrapper(provider=provider, model=model_name, **kwargs)
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configure_litellm_environment()
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# Use original provider name for API key lookup, fallback to mapped provider name
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api_key = kwargs.pop("api_key", None) or get_api_key(provider)
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# litellm will pick up base_url from env. We just need to control the api_key.
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# base_url = dotenv.get_dotenv_value(f"{provider.upper()}_BASE_URL")
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# If a base_url is set, ensure api_key is not passed to litellm
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# > remove, this can be handled by api_key=None
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# if base_url:
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# if "api_key" in kwargs:
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# del kwargs["api_key"]
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# Only pass API key if no base_url is set and key is not a placeholder
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if api_key and api_key not in ("None", "NA"):
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kwargs["api_key"] = api_key
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return LiteLLMEmbeddingWrapper(model=model_name, provider=provider, **kwargs)
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return provider_name, model_name, kwargs
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def get_model(type: ModelType, provider: ModelProvider, name: str, **kwargs: Any):
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provider_name = provider.name.lower()
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kwargs = _normalize_chat_kwargs(provider, kwargs)
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if type == ModelType.CHAT:
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return _get_litellm_chat(LiteLLMChatWrapper, name, provider_name, **kwargs)
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elif type == ModelType.EMBEDDING:
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return get_litellm_embedding(name, provider_name, **kwargs)
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return _get_litellm_embedding(name, provider_name, **kwargs)
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else:
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raise ValueError(f"Unsupported model type: {type}")
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@ -491,8 +454,7 @@ def get_model(type: ModelType, provider: ModelProvider, name: str, **kwargs: Any
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def get_chat_model(
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provider: ModelProvider, name: str, **kwargs: Any
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) -> LiteLLMChatWrapper:
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provider_name = _get_litellm_provider(provider)
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kwargs = _normalize_chat_kwargs(provider, kwargs)
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provider_name = provider.name.lower()
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model = _get_litellm_chat(LiteLLMChatWrapper, name, provider_name, **kwargs)
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return model
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@ -501,7 +463,6 @@ def get_browser_model(
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provider: ModelProvider, name: str, **kwargs: Any
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) -> BrowserCompatibleChatWrapper:
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provider_name = provider.name.lower()
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kwargs = _normalize_chat_kwargs(provider, kwargs)
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model = _get_litellm_chat(
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BrowserCompatibleChatWrapper, name, provider_name, **kwargs
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)
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@ -512,30 +473,5 @@ def get_embedding_model(
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provider: ModelProvider, name: str, **kwargs: Any
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) -> LiteLLMEmbeddingWrapper | LocalSentenceTransformerWrapper:
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provider_name = provider.name.lower()
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kwargs = _normalize_embedding_kwargs(kwargs)
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model = get_litellm_embedding(name, provider_name, **kwargs)
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model = _get_litellm_embedding(name, provider_name, **kwargs)
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return model
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def _normalize_chat_kwargs(provider: ModelProvider, kwargs: Any) -> Any:
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# this prevents using openai api key for other providers
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if provider == ModelProvider.OTHER:
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if "api_key" not in kwargs:
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kwargs["api_key"] = "None"
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return kwargs
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def _normalize_embedding_kwargs(kwargs: Any) -> Any:
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return kwargs
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def _get_litellm_provider(provider: ModelProvider) -> str:
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name = provider.name.lower()
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# exceptions
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if name == "google":
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name = "gemini"
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elif name == "other":
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name = "openai"
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return name
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|
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@ -17,6 +17,7 @@ class Settings(TypedDict):
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chat_model_provider: str
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chat_model_name: str
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chat_model_api_base: str
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chat_model_kwargs: dict[str, str]
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chat_model_ctx_length: int
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chat_model_ctx_history: float
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@ -27,6 +28,7 @@ class Settings(TypedDict):
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util_model_provider: str
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util_model_name: str
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util_model_api_base: str
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util_model_kwargs: dict[str, str]
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util_model_ctx_length: int
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util_model_ctx_input: float
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@ -36,12 +38,14 @@ class Settings(TypedDict):
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embed_model_provider: str
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embed_model_name: str
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embed_model_api_base: str
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embed_model_kwargs: dict[str, str]
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embed_model_rl_requests: int
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embed_model_rl_input: int
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browser_model_provider: str
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browser_model_name: str
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browser_model_api_base: str
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browser_model_vision: bool
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browser_model_kwargs: dict[str, str]
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@ -141,6 +145,16 @@ def convert_out(settings: Settings) -> SettingsOutput:
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}
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)
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chat_model_fields.append(
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{
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"id": "chat_model_api_base",
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"title": "Chat model API base URL",
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"description": "API base URL for main chat model. Leave empty for default. Only relevant for Azure, local and custom (other) providers.",
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"type": "text",
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"value": settings["chat_model_api_base"],
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}
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)
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chat_model_fields.append(
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{
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"id": "chat_model_ctx_length",
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@ -208,8 +222,7 @@ def convert_out(settings: Settings) -> SettingsOutput:
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{
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"id": "chat_model_kwargs",
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"title": "Chat model additional parameters",
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"description": """Any other parameters supported by the model. Format is KEY=VALUE on individual lines, just like .env file.
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For OpenAI compatible providers not listed here, select 'other' and specify api_base=https://... and api_key=... as additional parameters.""",
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"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.",
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"type": "textarea",
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||||
"value": _dict_to_env(settings["chat_model_kwargs"]),
|
||||
}
|
||||
|
|
@ -245,6 +258,16 @@ def convert_out(settings: Settings) -> SettingsOutput:
|
|||
}
|
||||
)
|
||||
|
||||
util_model_fields.append(
|
||||
{
|
||||
"id": "util_model_api_base",
|
||||
"title": "Utility model API base URL",
|
||||
"description": "API base URL for utility model. Leave empty for default. Only relevant for Azure, local and custom (other) providers.",
|
||||
"type": "text",
|
||||
"value": settings["util_model_api_base"],
|
||||
}
|
||||
)
|
||||
|
||||
util_model_fields.append(
|
||||
{
|
||||
"id": "util_model_rl_requests",
|
||||
|
|
@ -279,8 +302,7 @@ def convert_out(settings: Settings) -> SettingsOutput:
|
|||
{
|
||||
"id": "util_model_kwargs",
|
||||
"title": "Utility model additional parameters",
|
||||
"description": """Any other parameters supported by the model. Format is KEY=VALUE on individual lines, just like .env file.
|
||||
For OpenAI compatible providers not listed here, select 'other' and specify api_base=https://... and api_key=... as additional parameters.""",
|
||||
"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.",
|
||||
"type": "textarea",
|
||||
"value": _dict_to_env(settings["util_model_kwargs"]),
|
||||
}
|
||||
|
|
@ -316,6 +338,16 @@ def convert_out(settings: Settings) -> SettingsOutput:
|
|||
}
|
||||
)
|
||||
|
||||
embed_model_fields.append(
|
||||
{
|
||||
"id": "embed_model_api_base",
|
||||
"title": "Embedding model API base URL",
|
||||
"description": "API base URL for embedding model. Leave empty for default. Only relevant for Azure, local and custom (other) providers.",
|
||||
"type": "text",
|
||||
"value": settings["embed_model_api_base"],
|
||||
}
|
||||
)
|
||||
|
||||
embed_model_fields.append(
|
||||
{
|
||||
"id": "embed_model_rl_requests",
|
||||
|
|
@ -340,8 +372,7 @@ def convert_out(settings: Settings) -> SettingsOutput:
|
|||
{
|
||||
"id": "embed_model_kwargs",
|
||||
"title": "Embedding model additional parameters",
|
||||
"description": """Any other parameters supported by the model. Format is KEY=VALUE on individual lines, just like .env file.
|
||||
For OpenAI compatible providers not listed here, select 'other' and specify api_base=https://... and api_key=... as additional parameters.""",
|
||||
"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.",
|
||||
"type": "textarea",
|
||||
"value": _dict_to_env(settings["embed_model_kwargs"]),
|
||||
}
|
||||
|
|
@ -391,7 +422,7 @@ def convert_out(settings: Settings) -> SettingsOutput:
|
|||
{
|
||||
"id": "browser_model_kwargs",
|
||||
"title": "Web Browser model additional parameters",
|
||||
"description": "Any other parameters supported by the model. Format is KEY=VALUE on individual lines, just like .env file.",
|
||||
"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.",
|
||||
"type": "textarea",
|
||||
"value": _dict_to_env(settings["browser_model_kwargs"]),
|
||||
}
|
||||
|
|
@ -472,26 +503,9 @@ def convert_out(settings: Settings) -> SettingsOutput:
|
|||
|
||||
# api keys model section
|
||||
api_keys_fields: list[SettingsField] = []
|
||||
api_keys_fields.append(_get_api_key_field(settings, "openai", "OpenAI API Key"))
|
||||
api_keys_fields.append(
|
||||
_get_api_key_field(settings, "anthropic", "Anthropic API Key")
|
||||
)
|
||||
api_keys_fields.append(_get_api_key_field(settings, "chutes", "Chutes API Key"))
|
||||
api_keys_fields.append(_get_api_key_field(settings, "deepseek", "DeepSeek API Key"))
|
||||
api_keys_fields.append(_get_api_key_field(settings, "google", "Google API Key"))
|
||||
api_keys_fields.append(_get_api_key_field(settings, "groq", "Groq API Key"))
|
||||
api_keys_fields.append(
|
||||
_get_api_key_field(settings, "huggingface", "HuggingFace API Key")
|
||||
)
|
||||
api_keys_fields.append(
|
||||
_get_api_key_field(settings, "mistralai", "MistralAI API Key")
|
||||
)
|
||||
api_keys_fields.append(
|
||||
_get_api_key_field(settings, "openrouter", "OpenRouter API Key")
|
||||
)
|
||||
api_keys_fields.append(
|
||||
_get_api_key_field(settings, "sambanova", "Sambanova API Key")
|
||||
)
|
||||
|
||||
for provider in ModelProvider:
|
||||
api_keys_fields.append(_get_api_key_field(settings, provider.name.lower(), provider.value))
|
||||
|
||||
api_keys_section: SettingsSection = {
|
||||
"id": "api_keys",
|
||||
|
|
@ -965,6 +979,7 @@ def get_default_settings() -> Settings:
|
|||
version=_get_version(),
|
||||
chat_model_provider=ModelProvider.OPENROUTER.name,
|
||||
chat_model_name="openai/gpt-4.1",
|
||||
chat_model_api_base="",
|
||||
chat_model_kwargs={"temperature": "0"},
|
||||
chat_model_ctx_length=100000,
|
||||
chat_model_ctx_history=0.7,
|
||||
|
|
@ -974,6 +989,7 @@ def get_default_settings() -> Settings:
|
|||
chat_model_rl_output=0,
|
||||
util_model_provider=ModelProvider.OPENROUTER.name,
|
||||
util_model_name="openai/gpt-4.1-nano",
|
||||
util_model_api_base="",
|
||||
util_model_ctx_length=100000,
|
||||
util_model_ctx_input=0.7,
|
||||
util_model_kwargs={"temperature": "0"},
|
||||
|
|
@ -982,11 +998,13 @@ def get_default_settings() -> Settings:
|
|||
util_model_rl_output=0,
|
||||
embed_model_provider=ModelProvider.HUGGINGFACE.name,
|
||||
embed_model_name="sentence-transformers/all-MiniLM-L6-v2",
|
||||
embed_model_api_base="",
|
||||
embed_model_kwargs={},
|
||||
embed_model_rl_requests=0,
|
||||
embed_model_rl_input=0,
|
||||
browser_model_provider=ModelProvider.OPENROUTER.name,
|
||||
browser_model_name="openai/gpt-4.1",
|
||||
browser_model_api_base="",
|
||||
browser_model_vision=True,
|
||||
browser_model_kwargs={"temperature": "0"},
|
||||
api_keys={},
|
||||
|
|
|
|||
|
|
@ -57,7 +57,13 @@ class State:
|
|||
viewport={"width": 1024, "height": 2048},
|
||||
args=["--headless=new"],
|
||||
# Use a unique user data directory to avoid conflicts
|
||||
user_data_dir=str(Path.home() / ".config" / "browseruse" / "profiles" / f"agent_{self.agent.context.id}"),
|
||||
user_data_dir=str(
|
||||
Path.home()
|
||||
/ ".config"
|
||||
/ "browseruse"
|
||||
/ "profiles"
|
||||
/ f"agent_{self.agent.context.id}"
|
||||
),
|
||||
)
|
||||
)
|
||||
|
||||
|
|
@ -119,11 +125,10 @@ class State:
|
|||
)
|
||||
return result
|
||||
|
||||
|
||||
model = models.get_browser_model(
|
||||
provider=self.agent.config.browser_model.provider,
|
||||
name=self.agent.config.browser_model.name,
|
||||
**self.agent.config.browser_model.kwargs,
|
||||
**self.agent._get_model_kwargs(self.agent.config.browser_model),
|
||||
)
|
||||
|
||||
try:
|
||||
|
|
@ -140,7 +145,9 @@ class State:
|
|||
# available_file_paths=[],
|
||||
)
|
||||
except Exception as e:
|
||||
raise Exception(f"Browser agent initialization failed. This might be due to model compatibility issues. Error: {e}") from e
|
||||
raise Exception(
|
||||
f"Browser agent initialization failed. This might be due to model compatibility issues. Error: {e}"
|
||||
) from e
|
||||
|
||||
self.iter_no = get_iter_no(self.agent)
|
||||
|
||||
|
|
@ -298,13 +305,17 @@ class BrowserAgent(Tool):
|
|||
f"Task reached step limit without completion. Last page: {current_url}. "
|
||||
f"The browser agent may need clearer instructions on when to finish."
|
||||
)
|
||||
|
||||
|
||||
# update the log (without screenshot path here, user can click)
|
||||
self.log.update(answer=answer_text)
|
||||
|
||||
# add screenshot to the answer if we have it
|
||||
if self.log.kvps and "screenshot" in self.log.kvps and self.log.kvps['screenshot']:
|
||||
path = self.log.kvps['screenshot'].split('//', 1)[-1].split('&', 1)[0]
|
||||
if (
|
||||
self.log.kvps
|
||||
and "screenshot" in self.log.kvps
|
||||
and self.log.kvps["screenshot"]
|
||||
):
|
||||
path = self.log.kvps["screenshot"].split("//", 1)[-1].split("&", 1)[0]
|
||||
answer_text += f"\n\nScreenshot: {path}"
|
||||
|
||||
# respond (with screenshot path)
|
||||
|
|
@ -416,7 +427,9 @@ def get_use_agent_log(use_agent: browser_use.Agent | None):
|
|||
if item.success:
|
||||
short_log.append(f"✅ Done")
|
||||
else:
|
||||
short_log.append(f"❌ Error: {item.error or item.extracted_content or 'Unknown error'}")
|
||||
short_log.append(
|
||||
f"❌ Error: {item.error or item.extracted_content or 'Unknown error'}"
|
||||
)
|
||||
|
||||
# progress messages
|
||||
else:
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue