thinking & reasoning

backend/agent/api.py

@@ -1,4 +1,4 @@
-from fastapi import APIRouter, HTTPException, Depends, Request
+from fastapi import APIRouter, HTTPException, Depends, Request, Body
 from fastapi.responses import StreamingResponse
 import asyncio
 import json
@@ -7,6 +7,7 @@ from datetime import datetime, timezone
 import uuid
 from typing import Optional, List, Dict, Any
 import jwt
+from pydantic import BaseModel
 
 from agentpress.thread_manager import ThreadManager
 from services.supabase import DBConnection
@@ -26,6 +27,12 @@ db = None
 # In-memory storage for active agent runs and their responses
 active_agent_runs: Dict[str, List[Any]] = {}
 
+class AgentStartRequest(BaseModel):
+    model_name: Optional[str] = "anthropic/claude-3-7-sonnet-latest"
+    enable_thinking: Optional[bool] = False
+    reasoning_effort: Optional[str] = 'low'
+    stream: Optional[bool] = False # Default stream to False for API
+
 def initialize(
     _thread_manager: ThreadManager,
     _db: DBConnection,
@@ -237,9 +244,13 @@ async def _cleanup_agent_run(agent_run_id: str):
         # Non-fatal error, can continue
 
 @router.post("/thread/{thread_id}/agent/start")
-async def start_agent(thread_id: str, user_id: str = Depends(get_current_user_id)):
+async def start_agent(
+    thread_id: str,
+    body: AgentStartRequest = Body(...), # Accept request body
+    user_id: str = Depends(get_current_user_id)
+):
     """Start an agent for a specific thread in the background."""
-    logger.info(f"Starting new agent for thread: {thread_id}")
+    logger.info(f"Starting new agent for thread: {thread_id} with config: model={body.model_name}, thinking={body.enable_thinking}, effort={body.reasoning_effort}, stream={body.stream}")
     client = await db.client
     
     # Verify user has access to this thread
@@ -314,7 +325,17 @@ async def start_agent(thread_id: str, user_id: str = Depends(get_current_user_id
     
     # Run the agent in the background
     task = asyncio.create_task(
-        run_agent_background(agent_run_id, thread_id, instance_id, project_id, sandbox)
+        run_agent_background(
+            agent_run_id=agent_run_id,
+            thread_id=thread_id,
+            instance_id=instance_id,
+            project_id=project_id,
+            sandbox=sandbox,
+            model_name=body.model_name,
+            enable_thinking=body.enable_thinking,
+            reasoning_effort=body.reasoning_effort,
+            stream=body.stream # Pass stream parameter
+        )
     )
     
     # Set a callback to clean up when task is done
@@ -441,9 +462,19 @@ async def stream_agent_run(
         }
     )
 
-async def run_agent_background(agent_run_id: str, thread_id: str, instance_id: str, project_id: str, sandbox):
+async def run_agent_background(
+    agent_run_id: str,
+    thread_id: str,
+    instance_id: str,
+    project_id: str,
+    sandbox,
+    model_name: str,
+    enable_thinking: Optional[bool],
+    reasoning_effort: Optional[str],
+    stream: bool # Add stream parameter
+):
     """Run the agent in the background and handle status updates."""
-    logger.debug(f"Starting background agent run: {agent_run_id} for thread: {thread_id} (instance: {instance_id})")
+    logger.debug(f"Starting background agent run: {agent_run_id} for thread: {thread_id} (instance: {instance_id}) with model={model_name}, thinking={enable_thinking}, effort={reasoning_effort}, stream={stream}")
     client = await db.client
     
     # Tracking variables
@@ -561,9 +592,16 @@ async def run_agent_background(agent_run_id: str, thread_id: str, instance_id: s
     try:
         # Run the agent
         logger.debug(f"Initializing agent generator for thread: {thread_id} (instance: {instance_id})")
-        agent_gen = run_agent(thread_id, stream=True, 
-                      thread_manager=thread_manager, project_id=project_id, 
-                      sandbox=sandbox)
+        agent_gen = run_agent(
+            thread_id=thread_id,
+            project_id=project_id,
+            stream=stream, # Pass stream parameter from API request
+            thread_manager=thread_manager,
+            sandbox=sandbox,
+            model_name=model_name, # Pass model_name
+            enable_thinking=enable_thinking, # Pass enable_thinking
+            reasoning_effort=reasoning_effort # Pass reasoning_effort
+        )
         
         # Collect all responses to save to database
         all_responses = []

backend/agent/run.py

@@ -21,7 +21,18 @@ from utils.billing import check_billing_status, get_account_id_from_thread
 
 load_dotenv()
 
-async def run_agent(thread_id: str, project_id: str, sandbox, stream: bool = True, thread_manager: Optional[ThreadManager] = None, native_max_auto_continues: int = 25, max_iterations: int = 150):
+async def run_agent(
+    thread_id: str,
+    project_id: str,
+    sandbox,
+    stream: bool,
+    thread_manager: Optional[ThreadManager] = None,
+    native_max_auto_continues: int = 25,
+    max_iterations: int = 150,
+    model_name: str = "anthropic/claude-3-7-sonnet-latest",
+    enable_thinking: Optional[bool] = False,
+    reasoning_effort: Optional[str] = 'low'
+):
     """Run the development agent with specified configuration."""
     
     if not thread_manager:
@@ -112,7 +123,7 @@ async def run_agent(thread_id: str, project_id: str, sandbox, stream: bool = Tru
             thread_id=thread_id,
             system_prompt=system_message,
             stream=stream,
-            llm_model=os.getenv("MODEL_TO_USE", "anthropic/claude-3-7-sonnet-latest"),
+            llm_model=model_name,
             llm_temperature=0,
             llm_max_tokens=64000,
             tool_choice="auto",
@@ -128,6 +139,8 @@ async def run_agent(thread_id: str, project_id: str, sandbox, stream: bool = Tru
             ),
             native_max_auto_continues=native_max_auto_continues,
             include_xml_examples=True,
+            enable_thinking=enable_thinking,
+            reasoning_effort=reasoning_effort
         )
             
         if isinstance(response, dict) and "status" in response and response["status"] == "error":
@@ -250,7 +263,15 @@ async def test_agent():
     
     print("\n👋 Test completed. Goodbye!")
 
-async def process_agent_response(thread_id: str, project_id: str, thread_manager: ThreadManager):
+async def process_agent_response(
+    thread_id: str,
+    project_id: str,
+    thread_manager: ThreadManager,
+    stream: bool = True,
+    model_name: str = "anthropic/claude-3-7-sonnet-latest",
+    enable_thinking: Optional[bool] = False,
+    reasoning_effort: Optional[str] = 'low'
+):
     """Process the streaming response from the agent."""
     chunk_counter = 0
     current_response = ""
@@ -259,9 +280,19 @@ async def process_agent_response(thread_id: str, project_id: str, thread_manager
     # Create a test sandbox for processing
     sandbox_pass = str(uuid4())
     sandbox = create_sandbox(sandbox_pass)
-    print(f"\033[91mTest sandbox created: {sandbox.get_preview_link(6080)}/vnc_lite.html?password={sandbox_pass}\033[0m")
+    print(f"\033[91mTest sandbox created: {str(sandbox.get_preview_link(6080))}/vnc_lite.html?password={sandbox_pass}\033[0m")
     
-    async for chunk in run_agent(thread_id=thread_id, project_id=project_id, sandbox=sandbox, stream=True, thread_manager=thread_manager, native_max_auto_continues=25):
+    async for chunk in run_agent(
+        thread_id=thread_id,
+        project_id=project_id,
+        sandbox=sandbox,
+        stream=stream,
+        thread_manager=thread_manager,
+        native_max_auto_continues=25,
+        model_name=model_name,
+        enable_thinking=enable_thinking,
+        reasoning_effort=reasoning_effort
+    ):
         chunk_counter += 1
         
         if chunk.get('type') == 'content' and 'content' in chunk:

backend/agentpress/response_processor.py

@@ -98,6 +98,8 @@ class ResponseProcessor:
         llm_response: AsyncGenerator,
         thread_id: str,
         config: ProcessorConfig = ProcessorConfig(),
+        prompt_messages: Optional[List[Dict[str, Any]]] = None,
+        llm_model: Optional[str] = None
     ) -> AsyncGenerator:
         """Process a streaming LLM response, handling tool calls and execution.
         
@@ -105,6 +107,8 @@ class ResponseProcessor:
             llm_response: Streaming response from the LLM
             thread_id: ID of the conversation thread
             config: Configuration for parsing and execution
+            prompt_messages: List of messages used for cost calculation
+            llm_model: Name of the LLM model used for cost calculation
             
         Yields:
             Formatted chunks of the response including content and tool results
@@ -175,15 +179,20 @@ class ResponseProcessor:
                         accumulated_content += chunk_content
                         current_xml_content += chunk_content
 
-                        # Calculate cost using prompt and completion
-                        try:
-                            cost = completion_cost(model=chunk.model, prompt=accumulated_content, completion=chunk_content)
-                            tcount = token_counter(model=chunk.model, messages=[{"role": "user", "content": accumulated_content}])
-                            accumulated_cost += cost
-                            accumulated_token_count += tcount
-                            logger.debug(f"Cost: {cost:.6f}, Token count: {tcount}")
-                        except Exception as e:
-                            logger.error(f"Error calculating cost: {str(e)}")
+                        # Process reasoning content if present (Anthropic)
+                        if hasattr(delta, 'reasoning_content') and delta.reasoning_content:
+                            logger.info(f"[THINKING]: {delta.reasoning_content}")
+                            accumulated_content += delta.reasoning_content # Append reasoning to main content
+
+                        # Calculate cost using prompt and completion - MOVED AFTER MESSAGE SAVE
+                        # try:
+                        #     cost = completion_cost(model=chunk.model, prompt=accumulated_content, completion=chunk_content)
+                        #     tcount = token_counter(model=chunk.model, messages=[{"role": "user", "content": accumulated_content}])
+                        #     accumulated_cost += cost
+                        #     accumulated_token_count += tcount
+                        #     logger.debug(f"Cost: {cost:.6f}, Token count: {tcount}")
+                        # except Exception as e:
+                        #     logger.error(f"Error calculating cost: {str(e)}")
                         
                         # Check if we've reached the XML tool call limit before yielding content
                         if config.max_xml_tool_calls > 0 and xml_tool_call_count >= config.max_xml_tool_calls:
@@ -489,6 +498,35 @@ class ResponseProcessor:
                         "thread_run_id": thread_run_id
                     }
                 
+                # --- Cost Calculation (moved here) ---
+                if prompt_messages and llm_model and accumulated_content:
+                    try:
+                        cost = completion_cost(
+                            model=llm_model,
+                            messages=prompt_messages,
+                            completion=accumulated_content
+                        )
+                        token_count = token_counter(
+                            model=llm_model,
+                            messages=prompt_messages + [{"role": "assistant", "content": accumulated_content}]
+                        )
+                        await self.add_message(
+                            thread_id=thread_id,
+                            type="cost",
+                            content={
+                                "cost": cost,
+                                "prompt_tokens": token_count - token_counter(model=llm_model, messages=[{"role": "assistant", "content": accumulated_content}]), # Approx
+                                "completion_tokens": token_counter(model=llm_model, messages=[{"role": "assistant", "content": accumulated_content}]), # Approx
+                                "total_tokens": token_count,
+                                "model_name": llm_model
+                            },
+                            is_llm_message=False
+                        )
+                        logger.info(f"Calculated cost for streaming response: {cost:.6f} using model {llm_model}")
+                    except Exception as e:
+                        logger.error(f"Error calculating cost: {str(e)}")
+                # --- End Cost Calculation ---
+
                 # --- Process All Tool Calls Now --- 
                 if config.execute_tools:
                     final_tool_calls_to_process = []
@@ -626,23 +664,24 @@ class ResponseProcessor:
             yield {"type": "error", "message": str(e), "thread_run_id": thread_run_id if 'thread_run_id' in locals() else None}
         
         finally:
-            # Yield a finish signal including the final assistant message ID
-            if last_assistant_message_id:
-                # Yield the overall run end signal
+            # Yield the detected finish reason if one exists and wasn't suppressed
+            if finish_reason and finish_reason != "xml_tool_limit_reached":
                 yield {
-                    "type": "thread_run_end",
-                    "thread_run_id": thread_run_id
-                }
-            else:
-                # Yield the overall run end signal
-                yield {
-                    "type": "thread_run_end",
+                    "type": "finish",
+                    "finish_reason": finish_reason,
                     "thread_run_id": thread_run_id if 'thread_run_id' in locals() else None
                 }
 
+            # Yield a finish signal including the final assistant message ID
+            # Ensure thread_run_id is defined, even if an early error occurred
+            run_id = thread_run_id if 'thread_run_id' in locals() else str(uuid.uuid4()) # Fallback ID if needed
+            yield {
+                "type": "thread_run_end",
+                "thread_run_id": run_id
+            }
+
+            # Remove old cost calculation code
             pass
-            # track the cost and token count
-            # todo: there is a bug as it adds every chunk to db because finally will run every time even in yield
             # await self.add_message(
             #     thread_id=thread_id, 
             #     type="cost", 
@@ -658,7 +697,9 @@ class ResponseProcessor:
         self,
         llm_response: Any,
         thread_id: str,
-        config: ProcessorConfig = ProcessorConfig()
+        config: ProcessorConfig = ProcessorConfig(),
+        prompt_messages: Optional[List[Dict[str, Any]]] = None,
+        llm_model: Optional[str] = None
     ) -> AsyncGenerator[Dict[str, Any], None]:
         """Process a non-streaming LLM response, handling tool calls and execution.
         
@@ -666,6 +707,8 @@ class ResponseProcessor:
             llm_response: Response from the LLM
             thread_id: ID of the conversation thread
             config: Configuration for parsing and execution
+            prompt_messages: List of messages used for cost calculation
+            llm_model: Name of the LLM model used for cost calculation
             
         Yields:
             Formatted response including content and tool results
@@ -861,6 +904,50 @@ class ResponseProcessor:
                     "thread_run_id": thread_run_id
                 }
                     
+            # --- Cost Calculation (moved here) ---
+            if prompt_messages and llm_model:
+                cost = None
+                # Attempt to get cost from LiteLLM response first
+                if hasattr(llm_response, '_hidden_params') and 'response_cost' in llm_response._hidden_params:
+                    cost = llm_response._hidden_params['response_cost']
+                    logger.info(f"Using pre-calculated cost from LiteLLM: {cost:.6f}")
+
+                # If no pre-calculated cost, calculate manually
+                if cost is None:
+                    try:
+                        cost = completion_cost(
+                            model=llm_model,
+                            messages=prompt_messages,
+                            completion=content # Use extracted content
+                        )
+                        logger.info(f"Manually calculated cost for non-streaming response: {cost:.6f} using model {llm_model}")
+                    except Exception as e:
+                        logger.error(f"Error calculating cost: {str(e)}")
+
+                # Add cost message if cost was determined
+                if cost is not None:
+                    try:
+                        # Approximate token counts
+                        completion_tokens = token_counter(model=llm_model, messages=[{"role": "assistant", "content": content}])
+                        prompt_tokens = token_counter(model=llm_model, messages=prompt_messages)
+                        total_tokens = prompt_tokens + completion_tokens
+
+                        await self.add_message(
+                            thread_id=thread_id,
+                            type="cost",
+                            content={
+                                "cost": cost,
+                                "prompt_tokens": prompt_tokens,
+                                "completion_tokens": completion_tokens,
+                                "total_tokens": total_tokens,
+                                "model_name": llm_model
+                            },
+                            is_llm_message=False
+                        )
+                    except Exception as e:
+                         logger.error(f"Error saving cost message: {str(e)}")
+            # --- End Cost Calculation ---
+
         except Exception as e:
             logger.error(f"Error processing response: {str(e)}", exc_info=True)
             yield {"type": "error", "message": str(e), "thread_run_id": thread_run_id if 'thread_run_id' in locals() else None}

backend/agentpress/thread_manager.py

@@ -162,6 +162,8 @@ class ThreadManager:
         native_max_auto_continues: int = 25,
         max_xml_tool_calls: int = 0,
         include_xml_examples: bool = False,
+        enable_thinking: Optional[bool] = False,
+        reasoning_effort: Optional[str] = 'low'
     ) -> Union[Dict[str, Any], AsyncGenerator]:
         """Run a conversation thread with LLM integration and tool execution.
         
@@ -179,6 +181,8 @@ class ThreadManager:
                                       finish_reason="tool_calls" (0 disables auto-continue)
             max_xml_tool_calls: Maximum number of XML tool calls to allow (0 = no limit)
             include_xml_examples: Whether to include XML tool examples in the system prompt
+            enable_thinking: Whether to enable thinking before making a decision
+            reasoning_effort: The effort level for reasoning
             
         Returns:
             An async generator yielding response chunks or error dict
@@ -313,7 +317,9 @@ Here are the XML tools available with examples:
                         max_tokens=llm_max_tokens,
                         tools=openapi_tool_schemas,
                         tool_choice=tool_choice if processor_config.native_tool_calling else None,
-                        stream=stream
+                        stream=stream,
+                        enable_thinking=enable_thinking,
+                        reasoning_effort=reasoning_effort
                     )
                     logger.debug("Successfully received raw LLM API response stream/object")
 
@@ -327,7 +333,9 @@ Here are the XML tools available with examples:
                     response_generator = self.response_processor.process_streaming_response(
                         llm_response=llm_response,
                         thread_id=thread_id,
-                        config=processor_config
+                        config=processor_config,
+                        prompt_messages=prepared_messages,
+                        llm_model=llm_model
                     )
                     
                     return response_generator
@@ -338,7 +346,9 @@ Here are the XML tools available with examples:
                         response_generator = self.response_processor.process_non_streaming_response(
                             llm_response=llm_response,
                             thread_id=thread_id,
-                            config=processor_config
+                            config=processor_config,
+                            prompt_messages=prepared_messages,
+                            llm_model=llm_model
                         )
                         return response_generator # Return the generator
                     except Exception as e:

backend/services/llm.py

@@ -17,6 +17,8 @@ import asyncio
 from openai import OpenAIError
 import litellm
 from utils.logger import logger
+from datetime import datetime
+import traceback
 
 # litellm.set_verbose=True
 litellm.modify_params=True
@@ -82,7 +84,9 @@ def prepare_params(
     api_base: Optional[str] = None,
     stream: bool = False,
     top_p: Optional[float] = None,
-    model_id: Optional[str] = None
+    model_id: Optional[str] = None,
+    enable_thinking: Optional[bool] = False,
+    reasoning_effort: Optional[str] = 'low'
 ) -> Dict[str, Any]:
     """Prepare parameters for the API call."""
     params = {
@@ -211,6 +215,16 @@ def prepare_params(
             else:
                 logger.warning(f"Last user message (index {last_user_idx}) content is not a string or list ({type(content)}), skipping cache_control.")
 
+    # Add reasoning_effort for Anthropic models if enabled
+    use_thinking = enable_thinking if enable_thinking is not None else False
+    is_anthropic = "anthropic" in effective_model_name.lower() or "claude" in effective_model_name.lower()
+
+    if is_anthropic and use_thinking:
+        effort_level = reasoning_effort if reasoning_effort else 'low'
+        params["reasoning_effort"] = effort_level
+        params["temperature"] = 1.0 # Required by Anthropic when reasoning_effort is used
+        logger.info(f"Anthropic thinking enabled with reasoning_effort='{effort_level}'")
+
     return params
 
 async def make_llm_api_call(
@@ -225,7 +239,9 @@ async def make_llm_api_call(
     api_base: Optional[str] = None,
     stream: bool = False,
     top_p: Optional[float] = None,
-    model_id: Optional[str] = None
+    model_id: Optional[str] = None,
+    enable_thinking: Optional[bool] = False,
+    reasoning_effort: Optional[str] = 'low'
 ) -> Union[Dict[str, Any], AsyncGenerator]:
     """
     Make an API call to a language model using LiteLLM.
@@ -243,6 +259,8 @@ async def make_llm_api_call(
         stream: Whether to stream the response
         top_p: Top-p sampling parameter
         model_id: Optional ARN for Bedrock inference profiles
+        enable_thinking: Whether to enable thinking
+        reasoning_effort: Level of reasoning effort
         
     Returns:
         Union[Dict[str, Any], AsyncGenerator]: API response or stream
@@ -251,7 +269,7 @@ async def make_llm_api_call(
         LLMRetryError: If API call fails after retries
         LLMError: For other API-related errors
     """
-    logger.debug(f"Making LLM API call to model: {model_name}")
+    logger.debug(f"Making LLM API call to model: {model_name} (Thinking: {enable_thinking}, Effort: {reasoning_effort})")
     params = prepare_params(
         messages=messages,
         model_name=model_name,
@@ -264,7 +282,9 @@ async def make_llm_api_call(
         api_base=api_base,
         stream=stream,
         top_p=top_p,
-        model_id=model_id
+        model_id=model_id,
+        enable_thinking=enable_thinking,
+        reasoning_effort=reasoning_effort
     )
     
     last_error = None