update README and format code
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liangxinbing
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@ -143,6 +143,9 @@ Join our networking group on Feishu and share your experience with other develop
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Thanks to [anthropic-computer-use](https://github.com/anthropics/anthropic-quickstarts/tree/main/computer-use-demo)
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and [browser-use](https://github.com/browser-use/browser-use) for providing basic support for this project!
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Additionally, we are grateful to [AAAJ](https://github.com/metauto-ai/agent-as-a-judge)
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and [MetaGPT](https://github.com/mannaandpoem/MetaGPT).
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OpenManus is built by contributors from MetaGPT. Huge thanks to this agent community!
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## Cite
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@ -145,4 +145,7 @@ python run_flow.py
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特别感谢 [anthropic-computer-use](https://github.com/anthropics/anthropic-quickstarts/tree/main/computer-use-demo)
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和 [browser-use](https://github.com/browser-use/browser-use) 为本项目提供的基础支持！
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此外，我们感谢 [AAAJ](https://github.com/metauto-ai/agent-as-a-judge)
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和 [MetaGPT](https://github.com/mannaandpoem/MetaGPT).
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OpenManus 由 MetaGPT 社区的贡献者共同构建，感谢这个充满活力的智能体开发者社区！
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@ -47,4 +47,4 @@ class Cost:
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logs = ""
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for key, value in cost.items():
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logs += f"{key}: {value}\n"
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return logs
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return logs
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@ -1,6 +1,6 @@
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from typing import Dict, List, Literal, Optional, Union, Tuple, Any
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import os
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import base64
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import os
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from typing import Any, Dict, List, Literal, Optional, Tuple, Union
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import litellm
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from litellm import completion, completion_cost
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@ -9,12 +9,17 @@ from litellm.exceptions import (
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RateLimitError,
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ServiceUnavailableError,
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)
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from tenacity import retry, stop_after_attempt, wait_random_exponential, retry_if_exception_type
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from tenacity import (
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retry,
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retry_if_exception_type,
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stop_after_attempt,
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wait_random_exponential,
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)
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from app.config import LLMSettings, config
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from app.llm.cost import Cost
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from app.logger import logger
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from app.schema import Message
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from app.llm.cost import Cost
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class LLM:
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@ -32,16 +37,20 @@ class LLM:
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def __init__(
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self, config_name: str = "default", llm_config: Optional[LLMSettings] = None
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):
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if not hasattr(self, "initialized"): # Only initialize if not already initialized
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if not hasattr(
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self, "initialized"
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): # Only initialize if not already initialized
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llm_config = llm_config or config.llm
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llm_config = llm_config.get(config_name, llm_config["default"])
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self.model = getattr(llm_config, "model", "gpt-3.5-turbo")
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self.max_tokens = getattr(llm_config, "max_tokens", 4096)
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self.temperature = getattr(llm_config, "temperature", 0.7)
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self.top_p = getattr(llm_config, "top_p", 0.9)
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self.api_type = getattr(llm_config, "api_type", "openai")
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self.api_key = getattr(llm_config, "api_key", os.environ.get("OPENAI_API_KEY", ""))
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self.api_key = getattr(
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llm_config, "api_key", os.environ.get("OPENAI_API_KEY", "")
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)
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self.api_version = getattr(llm_config, "api_version", "")
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self.base_url = getattr(llm_config, "base_url", "https://api.openai.com/v1")
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self.timeout = getattr(llm_config, "timeout", 60)
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@ -49,14 +58,14 @@ class LLM:
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self.retry_min_wait = getattr(llm_config, "retry_min_wait", 1)
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self.retry_max_wait = getattr(llm_config, "retry_max_wait", 10)
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self.custom_llm_provider = getattr(llm_config, "custom_llm_provider", None)
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# Get model info if available
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self.model_info = None
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try:
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self.model_info = litellm.get_model_info(self.model)
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except Exception as e:
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logger.warning(f"Could not get model info for {self.model}: {e}")
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# Configure litellm
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if self.api_type == "azure":
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litellm.api_base = self.base_url
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@ -66,17 +75,17 @@ class LLM:
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litellm.api_key = self.api_key
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if self.base_url:
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litellm.api_base = self.base_url
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# Initialize cost tracker
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self.cost_tracker = Cost()
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self.initialized = True
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# Initialize completion function
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self._initialize_completion_function()
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def _initialize_completion_function(self):
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"""Initialize the completion function with retry logic"""
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def attempt_on_error(retry_state):
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logger.error(
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f"{retry_state.outcome.exception()}. Attempt #{retry_state.attempt_number}"
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@ -98,7 +107,7 @@ class LLM:
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model_name = self.model
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if self.api_type == "azure":
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model_name = f"azure/{self.model}"
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# Set default parameters if not provided
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if "max_tokens" not in kwargs:
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kwargs["max_tokens"] = self.max_tokens
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@ -108,9 +117,9 @@ class LLM:
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kwargs["top_p"] = self.top_p
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if "timeout" not in kwargs:
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kwargs["timeout"] = self.timeout
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kwargs["model"] = model_name
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# Add API credentials if not in kwargs
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if "api_key" not in kwargs:
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kwargs["api_key"] = self.api_key
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@ -120,10 +129,10 @@ class LLM:
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kwargs["api_version"] = self.api_version
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if "custom_llm_provider" not in kwargs and self.custom_llm_provider:
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kwargs["custom_llm_provider"] = self.custom_llm_provider
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resp = completion(**kwargs)
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return resp
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self._completion = wrapper
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@staticmethod
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@ -169,22 +178,24 @@ class LLM:
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def _calculate_and_track_cost(self, response) -> float:
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"""
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Calculate and track the cost of an LLM API call.
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Args:
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response: The response from litellm
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Returns:
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float: The calculated cost
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"""
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try:
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# Use litellm's completion_cost function
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cost = completion_cost(completion_response=response)
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# Add the cost to our tracker
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if cost > 0:
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self.cost_tracker.add_cost(cost)
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logger.info(f"Added cost: ${cost:.6f}, Total: ${self.cost_tracker.accumulated_cost:.6f}")
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logger.info(
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f"Added cost: ${cost:.6f}, Total: ${self.cost_tracker.accumulated_cost:.6f}"
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)
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return cost
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except Exception as e:
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logger.warning(f"Cost calculation failed: {e}")
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@ -193,7 +204,7 @@ class LLM:
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def is_local(self) -> bool:
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"""
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Check if the model is running locally.
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Returns:
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bool: True if the model is running locally, False otherwise
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"""
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@ -202,46 +213,50 @@ class LLM:
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substring in self.base_url
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for substring in ["localhost", "127.0.0.1", "0.0.0.0"]
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)
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if self.model and (self.model.startswith("ollama") or "local" in self.model.lower()):
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if self.model and (
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self.model.startswith("ollama") or "local" in self.model.lower()
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):
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return True
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return False
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def do_completion(self, *args, **kwargs) -> Tuple[Any, float, float]:
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"""
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Perform a completion request and track cost.
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Returns:
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Tuple[Any, float, float]: (response, current_cost, accumulated_cost)
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"""
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response = self._completion(*args, **kwargs)
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# Calculate and track cost
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current_cost = self._calculate_and_track_cost(response)
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return response, current_cost, self.cost_tracker.accumulated_cost
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@staticmethod
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def encode_image(image_path: str) -> str:
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"""
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Encode an image to base64.
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Args:
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image_path: Path to the image file
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Returns:
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str: Base64-encoded image
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"""
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with open(image_path, "rb") as image_file:
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return base64.b64encode(image_file.read()).decode("utf-8")
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def prepare_messages(self, text: str, image_path: Optional[str] = None) -> List[dict]:
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def prepare_messages(
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self, text: str, image_path: Optional[str] = None
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) -> List[dict]:
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"""
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Prepare messages for completion, including multimodal content if needed.
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Args:
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text: Text content
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image_path: Optional path to an image file
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Returns:
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List[dict]: Formatted messages
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"""
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@ -257,14 +272,16 @@ class LLM:
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]
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return messages
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def do_multimodal_completion(self, text: str, image_path: str) -> Tuple[Any, float, float]:
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def do_multimodal_completion(
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self, text: str, image_path: str
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) -> Tuple[Any, float, float]:
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"""
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Perform a multimodal completion with text and image.
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Args:
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text: Text prompt
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image_path: Path to the image file
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Returns:
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Tuple[Any, float, float]: (response, current_cost, accumulated_cost)
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"""
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@ -320,10 +337,10 @@ class LLM:
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temperature=temperature or self.temperature,
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stream=False,
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)
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# Calculate and track cost
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self._calculate_and_track_cost(response)
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if not response.choices or not response.choices[0].message.content:
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raise ValueError("Empty or invalid response from LLM")
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return response.choices[0].message.content
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@ -340,9 +357,9 @@ class LLM:
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chunk_message = chunk.choices[0].delta.content or ""
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collected_messages.append(chunk_message)
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print(chunk_message, end="", flush=True)
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# For streaming responses, cost is calculated on the last chunk
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if hasattr(chunk, 'usage') and chunk.usage:
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if hasattr(chunk, "usage") and chunk.usage:
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self._calculate_and_track_cost(chunk)
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print() # Newline after streaming
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@ -425,7 +442,7 @@ class LLM:
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timeout=timeout,
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**kwargs,
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)
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# Calculate and track cost
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self._calculate_and_track_cost(response)
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@ -442,37 +459,37 @@ class LLM:
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except Exception as e:
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logger.error(f"Unexpected error in ask_tool: {e}")
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raise
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def get_cost(self):
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"""
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Get the current cost information.
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Returns:
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dict: Dictionary containing accumulated cost and individual costs
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"""
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return self.cost_tracker.get()
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def log_cost(self):
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"""
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Log the current cost information.
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Returns:
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str: Formatted string of cost information
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"""
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return self.cost_tracker.log()
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def get_token_count(self, messages):
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"""
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Get the token count for a list of messages.
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Args:
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messages: List of messages
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Returns:
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int: Token count
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"""
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return litellm.token_counter(model=self.model, messages=messages)
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def __str__(self):
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return f"LLM(model={self.model}, base_url={self.base_url})"
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@ -484,22 +501,25 @@ class LLM:
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if __name__ == "__main__":
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# Load environment variables if needed
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from dotenv import load_dotenv
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load_dotenv()
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# Create LLM instance
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llm = LLM()
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# Test text completion
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messages = llm.prepare_messages("Hello, how are you?")
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response, cost, total_cost = llm.do_completion(messages=messages)
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print(f"Response: {response['choices'][0]['message']['content']}")
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print(f"Cost: ${cost:.6f}, Total cost: ${total_cost:.6f}")
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# Test multimodal if image path is available
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image_path = os.getenv("TEST_IMAGE_PATH")
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if image_path and os.path.exists(image_path):
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multimodal_response, mm_cost, mm_total_cost = llm.do_multimodal_completion(
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"What's in this image?", image_path
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)
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print(f"Multimodal response: {multimodal_response['choices'][0]['message']['content']}")
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print(
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f"Multimodal response: {multimodal_response['choices'][0]['message']['content']}"
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)
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print(f"Cost: ${mm_cost:.6f}, Total cost: ${mm_total_cost:.6f}")
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@ -15,4 +15,4 @@ Terminate: End the current interaction when the task is complete or when you nee
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Based on user needs, proactively select the most appropriate tool or combination of tools. For complex tasks, you can break down the problem and use different tools step by step to solve it. After using each tool, clearly explain the execution results and suggest the next steps.
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Always maintain a helpful, informative tone throughout the interaction. If you encounter any limitations or need more details, clearly communicate this to the user before terminating.
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"""
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"""
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@ -11,6 +11,7 @@ from pydantic_core.core_schema import ValidationInfo
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from app.tool.base import BaseTool, ToolResult
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MAX_LENGTH = 2000
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_BROWSER_DESCRIPTION = """
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@ -181,7 +182,9 @@ class BrowserUseTool(BaseTool):
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elif action == "get_html":
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html = await context.get_page_html()
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truncated = html[:MAX_LENGTH] + "..." if len(html) > MAX_LENGTH else html
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truncated = (
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html[:MAX_LENGTH] + "..." if len(html) > MAX_LENGTH else html
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)
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return ToolResult(output=truncated)
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elif action == "get_text":
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@ -20,4 +20,4 @@ aiofiles~=24.1.0
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pydantic_core~=2.27.2
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colorama~=0.4.6
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playwright~=1.49.1
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litellm~=1.63.6
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litellm~=1.63.6
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