Merge pull request #573 from nezhazheng/main

Use the max_input_tokens configuration to constrain the agent’s token usage.

app/agent/toolcall.py

@@ -4,6 +4,7 @@ from typing import Any, List, Optional, Union
 from pydantic import Field
 
 from app.agent.react import ReActAgent
+from app.exceptions import TokenLimitExceeded
 from app.logger import logger
 from app.prompt.toolcall import NEXT_STEP_PROMPT, SYSTEM_PROMPT
 from app.schema import TOOL_CHOICE_TYPE, AgentState, Message, ToolCall, ToolChoice
@@ -32,6 +33,7 @@ class ToolCallAgent(ReActAgent):
 
     max_steps: int = 30
     max_observe: Optional[Union[int, bool]] = None
+    max_input_tokens: Optional[int] = None
 
     async def think(self) -> bool:
         """Process current state and decide next actions using tools"""
@@ -39,6 +41,7 @@ class ToolCallAgent(ReActAgent):
             user_msg = Message.user_message(self.next_step_prompt)
             self.messages += [user_msg]
 
+        try:
             # Get response with tool options
             response = await self.llm.ask_tool(
                 messages=self.messages,
@@ -48,6 +51,22 @@ class ToolCallAgent(ReActAgent):
                 tools=self.available_tools.to_params(),
                 tool_choice=self.tool_choices,
             )
+        except ValueError as e:
+            raise
+        except Exception as e:
+            # Check if this is a RetryError containing TokenLimitExceeded
+            if hasattr(e, "__cause__") and isinstance(e.__cause__, TokenLimitExceeded):
+                token_limit_error = e.__cause__
+                logger.error(f"🚨 Token limit error (from RetryError): {token_limit_error}")
+                self.memory.add_message(
+                    Message.assistant_message(
+                        f"Maximum token limit reached, cannot continue execution: {str(token_limit_error)}"
+                    )
+                )
+                self.state = AgentState.FINISHED
+                return False
+            raise
+
         self.tool_calls = response.tool_calls
 
         # Log response info

app/config.py

@@ -20,6 +20,7 @@ class LLMSettings(BaseModel):
     base_url: str = Field(..., description="API base URL")
     api_key: str = Field(..., description="API key")
     max_tokens: int = Field(4096, description="Maximum number of tokens per request")
+    max_input_tokens: Optional[int] = Field(None, description="Maximum input tokens to use across all requests (None for unlimited)")
     temperature: float = Field(1.0, description="Sampling temperature")
     api_type: str = Field(..., description="AzureOpenai or Openai")
     api_version: str = Field(..., description="Azure Openai version if AzureOpenai")
@@ -118,6 +119,7 @@ class Config:
             "base_url": base_llm.get("base_url"),
             "api_key": base_llm.get("api_key"),
             "max_tokens": base_llm.get("max_tokens", 4096),
+            "max_input_tokens": base_llm.get("max_input_tokens"),
             "temperature": base_llm.get("temperature", 1.0),
             "api_type": base_llm.get("api_type", ""),
             "api_version": base_llm.get("api_version", ""),

app/exceptions.py

@@ -3,3 +3,11 @@ class ToolError(Exception):
 
     def __init__(self, message):
         self.message = message
+
+class OpenManusError(Exception):
+    """Base exception for all OpenManus errors"""
+    pass
+
+class TokenLimitExceeded(OpenManusError):
+    """Exception raised when the token limit is exceeded"""
+    pass

app/llm.py

@@ -8,9 +8,11 @@ from openai import (
     OpenAIError,
     RateLimitError,
 )
-from tenacity import retry, stop_after_attempt, wait_random_exponential
+import tiktoken
+from tenacity import retry, stop_after_attempt, wait_random_exponential, retry_if_exception_type
 
 from app.config import LLMSettings, config
+from app.exceptions import TokenLimitExceeded
 from app.logger import logger  # Assuming a logger is set up in your app
 from app.schema import (
     ROLE_VALUES,
@@ -49,6 +51,18 @@ class LLM:
             self.api_key = llm_config.api_key
             self.api_version = llm_config.api_version
             self.base_url = llm_config.base_url
+            
+            # Add token counting related attributes
+            self.total_input_tokens = 0
+            self.max_input_tokens = llm_config.max_input_tokens if hasattr(llm_config, "max_input_tokens") else None
+            
+            # Initialize tokenizer
+            try:
+                self.tokenizer = tiktoken.encoding_for_model(self.model)
+            except KeyError:
+                # If the model is not in tiktoken's presets, use cl100k_base as default
+                self.tokenizer = tiktoken.get_encoding("cl100k_base")
+            
             if self.api_type == "azure":
                 self.client = AsyncAzureOpenAI(
                     base_url=self.base_url,
@@ -58,6 +72,70 @@ class LLM:
             else:
                 self.client = AsyncOpenAI(api_key=self.api_key, base_url=self.base_url)
 
+    def count_tokens(self, text: str) -> int:
+        """Calculate the number of tokens in a text"""
+        if not text:
+            return 0
+        return len(self.tokenizer.encode(text))
+    
+    def count_message_tokens(self, messages: List[dict]) -> int:
+        """Calculate the number of tokens in a message list"""
+        token_count = 0
+        for message in messages:
+            # Base token count for each message (according to OpenAI's calculation method)
+            token_count += 4  # Base token count for each message
+            
+            # Calculate tokens for the role
+            if "role" in message:
+                token_count += self.count_tokens(message["role"])
+            
+            # Calculate tokens for the content
+            if "content" in message and message["content"]:
+                token_count += self.count_tokens(message["content"])
+            
+            # Calculate tokens for tool calls
+            if "tool_calls" in message and message["tool_calls"]:
+                for tool_call in message["tool_calls"]:
+                    if "function" in tool_call:
+                        # Function name
+                        if "name" in tool_call["function"]:
+                            token_count += self.count_tokens(tool_call["function"]["name"])
+                        # Function arguments
+                        if "arguments" in tool_call["function"]:
+                            token_count += self.count_tokens(tool_call["function"]["arguments"])
+            
+            # Calculate tokens for tool responses
+            if "name" in message and message["name"]:
+                token_count += self.count_tokens(message["name"])
+            
+            if "tool_call_id" in message and message["tool_call_id"]:
+                token_count += self.count_tokens(message["tool_call_id"])
+        
+        # Add extra tokens for message format
+        token_count += 2  # Extra tokens for message format
+        
+        return token_count
+    
+    def update_token_count(self, input_tokens: int) -> None:
+        """Update token counts"""
+        # Only track tokens if max_input_tokens is set
+        self.total_input_tokens += input_tokens
+        logger.info(f"Token usage: Input={input_tokens}, Cumulative Input={self.total_input_tokens}")
+    
+    def check_token_limit(self, input_tokens: int) -> bool:
+        """Check if token limits are exceeded"""
+        if self.max_input_tokens is not None:
+            return (self.total_input_tokens + input_tokens) <= self.max_input_tokens
+        # If max_input_tokens is not set, always return True
+        return True
+
+    def get_limit_error_message(self, input_tokens: int) -> str:
+        """Generate error message for token limit exceeded"""
+        if self.max_input_tokens is not None and (self.total_input_tokens + input_tokens) > self.max_input_tokens:
+            return f"Request may exceed input token limit (Current: {self.total_input_tokens}, Needed: {input_tokens}, Max: {self.max_input_tokens})"
+        
+        return "Token limit exceeded"
+
     @staticmethod
     def format_messages(messages: List[Union[dict, Message]]) -> List[dict]:
         """
@@ -109,6 +187,7 @@ class LLM:
     @retry(
         wait=wait_random_exponential(min=1, max=60),
         stop=stop_after_attempt(6),
+        retry=retry_if_exception_type((OpenAIError, Exception, ValueError)),  # Don't retry TokenLimitExceeded
     )
     async def ask(
         self,
@@ -130,6 +209,7 @@ class LLM:
             str: The generated response
 
         Raises:
+            TokenLimitExceeded: If token limits are exceeded
             ValueError: If messages are invalid or response is empty
             OpenAIError: If API call fails after retries
             Exception: For unexpected errors
@@ -142,6 +222,15 @@ class LLM:
             else:
                 messages = self.format_messages(messages)
             
+            # Calculate input token count
+            input_tokens = self.count_message_tokens(messages)
+            
+            # Check if token limits are exceeded
+            if not self.check_token_limit(input_tokens):
+                error_message = self.get_limit_error_message(input_tokens)
+                # Raise a special exception that won't be retried
+                raise TokenLimitExceeded(error_message)
+
             params = {
                 "model": self.model,
                 "messages": messages,
@@ -161,9 +250,15 @@ class LLM:
 
                 if not response.choices or not response.choices[0].message.content:
                     raise ValueError("Empty or invalid response from LLM")
+                
+                # Update token counts
+                self.update_token_count(response.usage.prompt_tokens)
+                
                 return response.choices[0].message.content
 
-            # Streaming request
+            # Streaming request, For streaming, update estimated token count before making the request
+            self.update_token_count(input_tokens)
+                
             params["stream"] = True
             response = await self.client.chat.completions.create(**params)
 
@@ -177,13 +272,23 @@ class LLM:
             full_response = "".join(collected_messages).strip()
             if not full_response:
                 raise ValueError("Empty response from streaming LLM")
+            
             return full_response
 
+        except TokenLimitExceeded:
+            # Re-raise token limit errors without logging
+            raise
         except ValueError as ve:
             logger.error(f"Validation error: {ve}")
             raise
         except OpenAIError as oe:
             logger.error(f"OpenAI API error: {oe}")
+            if isinstance(oe, AuthenticationError):
+                logger.error("Authentication failed. Check API key.")
+            elif isinstance(oe, RateLimitError):
+                logger.error("Rate limit exceeded. Consider increasing retry attempts.")
+            elif isinstance(oe, APIError):
+                logger.error(f"API error: {oe}")
             raise
         except Exception as e:
             logger.error(f"Unexpected error in ask: {e}")
@@ -192,6 +297,7 @@ class LLM:
     @retry(
         wait=wait_random_exponential(min=1, max=60),
         stop=stop_after_attempt(6),
+        retry=retry_if_exception_type((OpenAIError, Exception, ValueError)),  # Don't retry TokenLimitExceeded
     )
     async def ask_tool(
         self,
@@ -219,6 +325,7 @@ class LLM:
             ChatCompletionMessage: The model's response
 
         Raises:
+            TokenLimitExceeded: If token limits are exceeded
             ValueError: If tools, tool_choice, or messages are invalid
             OpenAIError: If API call fails after retries
             Exception: For unexpected errors
@@ -235,6 +342,23 @@ class LLM:
             else:
                 messages = self.format_messages(messages)
 
+            # Calculate input token count
+            input_tokens = self.count_message_tokens(messages)
+            
+            # If there are tools, calculate token count for tool descriptions
+            tools_tokens = 0
+            if tools:
+                for tool in tools:
+                    tools_tokens += self.count_tokens(str(tool))
+            
+            input_tokens += tools_tokens
+            
+            # Check if token limits are exceeded
+            if not self.check_token_limit(input_tokens):
+                error_message = self.get_limit_error_message(input_tokens)
+                # Raise a special exception that won't be retried
+                raise TokenLimitExceeded(error_message)
+            
             # Validate tools if provided
             if tools:
                 for tool in tools:
@@ -264,12 +388,19 @@ class LLM:
                 print(response)
                 raise ValueError("Invalid or empty response from LLM")
             
+            # Update token counts
+            self.update_token_count(response.usage.prompt_tokens)
+
             return response.choices[0].message
 
+        except TokenLimitExceeded:
+            # Re-raise token limit errors without logging
+            raise
         except ValueError as ve:
             logger.error(f"Validation error in ask_tool: {ve}")
             raise
         except OpenAIError as oe:
+            logger.error(f"OpenAI API error: {oe}")
             if isinstance(oe, AuthenticationError):
                 logger.error("Authentication failed. Check API key.")
             elif isinstance(oe, RateLimitError):

config/config.example.toml

@@ -5,6 +5,7 @@ base_url = "https://api.openai.com/v1"    # API endpoint URL
 api_key = "sk-..."    # Your API key
 max_tokens = 8192     # Maximum number of tokens in the response
 temperature = 0.0     # Controls randomness
+#max_input_tokens = 100000  # Maximum input tokens to use across all requests (set to null or delete this line for unlimited)
 
 # [llm] #AZURE OPENAI:
 # api_type= 'azure'

requirements.txt

@@ -6,6 +6,7 @@ loguru~=0.7.3
 numpy
 datasets~=3.2.0
 fastapi~=0.115.11
+tiktoken~=0.9.0
 
 html2text~=2024.2.26
 gymnasium~=1.0.0