improvement: centralise model config and deduplicate message-conversi…

agent/architect/graph.py

@@ -1,4 +1,3 @@
-import json
 from typing import List, TypedDict, Optional
 
 from langchain_anthropic import ChatAnthropic
@@ -15,6 +14,7 @@
 from agent.tools.write import get_files_structure
 from helpers.prompts import markdown_to_prompt_template
 from agent.common.entities import ImplementationPlan
+from agent.common.utils import DEFAULT_MODEL, convert_tools_messages_to_ai_and_human
 
 
 class ResearchStep(BaseModel):
@@ -33,10 +33,10 @@ class ResearchEvaluation(BaseModel):
 extract_implementation_prompt = markdown_to_prompt_template("agent/architect/prompts/extract_implementation_plan.md")
 
 # runnable
-plan_next_step_runnable = plan_next_step_prompt | ChatAnthropic(model="claude-sonnet-4-20250514").with_structured_output(ResearchStep)
-check_research_runnable = check_research_prompt | ChatAnthropic(model="claude-sonnet-4-20250514").with_structured_output(ResearchEvaluation)
-conduct_research_runnable = conduct_research_prompt | ChatAnthropic(model="claude-sonnet-4-20250514").bind_tools(search_tools+codemap_tools)
-extract_implementation_runnable = extract_implementation_prompt | ChatAnthropic(model="claude-sonnet-4-20250514") | JsonOutputParser(pydantic_object=ImplementationPlan)
+plan_next_step_runnable = plan_next_step_prompt | ChatAnthropic(model=DEFAULT_MODEL).with_structured_output(ResearchStep)
+check_research_runnable = check_research_prompt | ChatAnthropic(model=DEFAULT_MODEL).with_structured_output(ResearchEvaluation)
+conduct_research_runnable = conduct_research_prompt | ChatAnthropic(model=DEFAULT_MODEL).bind_tools(search_tools+codemap_tools)
+extract_implementation_runnable = extract_implementation_prompt | ChatAnthropic(model=DEFAULT_MODEL) | JsonOutputParser(pydantic_object=ImplementationPlan)
 
 tool_node = ToolNode(codemap_tools+search_tools, messages_key="implementation_research_scratchpad")
 
@@ -85,22 +85,6 @@ def conduct_research(state: SoftwareArchitectState):
     })
     return {"implementation_research_scratchpad": [response]}
 
-def convert_tools_messages_to_ai_and_human(implementation_research_scratchpad: List[AnyMessage]):
-    messages = []
-    for message in implementation_research_scratchpad:
-        if message.type == "ai":
-            if message.tool_calls:
-                tool_name = message.tool_calls[0]["name"]
-                tool_args = json.dumps(message.tool_calls[0]["args"])
-                messages.append(AIMessage(content=f"I want to call the tool {tool_name} with the following arguments: {tool_args}"))
-            else:
-                messages.append(message)
-        elif message.type == "tool":
-            messages.append(HumanMessage(content=f"When executing Tool {message.name} \n The result was {message.content} was called"))
-        else:
-            messages.append(message)
-    return messages
-
 def extract_implementation_plan(state: SoftwareArchitectState):
     """Extract implementation plan from research findings"""
     response = extract_implementation_runnable.invoke({

agent/common/utils.py

@@ -0,0 +1,44 @@
+"""Shared utilities used across architect and developer agents."""
+
+import json
+from typing import List
+
+from langchain_core.messages import AnyMessage, AIMessage, HumanMessage
+
+# Single source of truth for the Claude model used by all runnables.
+# Change this one constant to upgrade the model across the entire agent.
+DEFAULT_MODEL = "claude-sonnet-4-20250514"
+
+
+def convert_tools_messages_to_ai_and_human(messages: List[AnyMessage]) -> List[AnyMessage]:
+    """Convert tool call / tool result messages into plain AI and Human messages.
+
+    LangChain tool messages are not always accepted by every runnable directly.
+    This helper normalises a mixed message list so every message is either an
+    AIMessage or a HumanMessage, which any Claude runnable can consume.
+
+    Args:
+        messages: Raw message list that may contain AIMessages with tool_calls
+                  and ToolMessages with tool results.
+
+    Returns:
+        List of AIMessage / HumanMessage with the same semantic content.
+    """
+    result = []
+    for message in messages:
+        if message.type == "ai":
+            if message.tool_calls:
+                tool_name = message.tool_calls[0]["name"]
+                tool_args = json.dumps(message.tool_calls[0]["args"])
+                result.append(
+                    AIMessage(content=f"I want to call the tool {tool_name} with the following arguments: {tool_args}")
+                )
+            else:
+                result.append(message)
+        elif message.type == "tool":
+            result.append(
+                HumanMessage(content=f"When executing Tool {message.name} \n The result was {message.content} was called")
+            )
+        else:
+            result.append(message)
+    return result

agent/developer/graph.py

@@ -1,10 +1,9 @@
-import json
 import os
 import re
 from typing import List
 from diff_match_patch import diff_match_patch
 from langchain_anthropic import ChatAnthropic
-from langchain_core.messages import AnyMessage, AIMessage, HumanMessage
+from langchain_core.messages import AnyMessage
 from langchain_core.output_parsers import StrOutputParser, JsonOutputParser
 from langgraph.constants import END, START
 from langgraph.graph import  StateGraph
@@ -14,21 +13,22 @@
 from agent.tools.search import search_tools
 from agent.tools.codemap import codemap_tools
 from agent.tools.write import get_files_structure
+from agent.common.utils import DEFAULT_MODEL, convert_tools_messages_to_ai_and_human
 # Load the extract diff prompt
 extract_diffs_tasks_prompt = markdown_to_prompt_template("agent/developer/prompts/create_diff_prompt.md")
 implement_diffs_prompt = markdown_to_prompt_template("agent/developer/prompts/implement_diff.md")
 implement_new_file_prompt = markdown_to_prompt_template("agent/developer/prompts/implement_new_file.md")
 
 # Create the runnable with the prompt and model
-extract_diff_runnable = extract_diffs_tasks_prompt | ChatAnthropic(model="claude-sonnet-4-20250514") | StrOutputParser()
-edit_according_to_diff_runnable = implement_diffs_prompt | ChatAnthropic(model="claude-sonnet-4-20250514") | StrOutputParser()
-create_new_file_runnable = implement_new_file_prompt | ChatAnthropic(model="claude-sonnet-4-20250514") | StrOutputParser()
+extract_diff_runnable = extract_diffs_tasks_prompt | ChatAnthropic(model=DEFAULT_MODEL) | StrOutputParser()
+edit_according_to_diff_runnable = implement_diffs_prompt | ChatAnthropic(model=DEFAULT_MODEL) | StrOutputParser()
+create_new_file_runnable = implement_new_file_prompt | ChatAnthropic(model=DEFAULT_MODEL) | StrOutputParser()
 
 # Load the get clear implementation plan prompt
 get_clear_implementation_plan_prompt = markdown_to_prompt_template("agent/developer/prompts/get_clear_implementation_plan.md")
 
 # Create the runnable with the prompt and model
-get_clear_implementation_plan_runnable = get_clear_implementation_plan_prompt | ChatAnthropic(model="claude-sonnet-4-20250514").bind_tools(search_tools+codemap_tools)
+get_clear_implementation_plan_runnable = get_clear_implementation_plan_prompt | ChatAnthropic(model=DEFAULT_MODEL).bind_tools(search_tools+codemap_tools)
 dmp = diff_match_patch()
 def start_implementing(state: SoftwareDeveloperState):
     return {
@@ -108,22 +108,6 @@ def is_implementation_complete(state: SoftwareDeveloperState):
     plan = state.implementation_plan
     return END if current_task_idx >= len(plan.tasks) else "continue"
 
-def convert_tools_messages_to_ai_and_human(implementation_research_scratchpad: List[AnyMessage]):
-    messages = []
-    for message in implementation_research_scratchpad:
-        if message.type == "ai":
-            if message.tool_calls:
-                tool_name = message.tool_calls[0]["name"]
-                tool_args = json.dumps(message.tool_calls[0]["args"])
-                messages.append(AIMessage(content=f"I want to call the tool {tool_name} with the following arguments: {tool_args}"))
-            else:
-                messages.append(message)
-        elif message.type == "tool":
-            messages.append(HumanMessage(content=f"When executing Tool {message.name} \n The result was {message.content} was called"))
-        else:
-            messages.append(message)
-    return messages
-
 def creating_diffs_for_task(state: SoftwareDeveloperState):
     # Get current task information
     current_task = state.implementation_plan.tasks[state.current_task_idx]