Merge remote-tracking branch 'origin/main' into feat/front_end

pmcp/agents/agent_base.py

@@ -1,7 +1,7 @@
 import logging
 from typing import List, TypeVar, Optional
 
-from langchain_core.messages import AnyMessage, BaseMessage
+from langchain_core.messages import AnyMessage, BaseMessage, SystemMessage
 from pydantic import BaseModel
 from langchain_core.tools import BaseTool
 from langchain_openai import ChatOpenAI
@@ -29,14 +29,21 @@ class AgentBlueprint:
             self.tools = {tool.name: tool for tool in tools}
         self.logger = logging.getLogger(self.__class__.__name__)
 
+    def __set_system_prompt(self, messages: List[AnyMessage]):
+        if self.system_prompt:
+            return [SystemMessage(content=self.system_prompt)] + messages
+        return messages
+
     def call_agent(self, messages: List[AnyMessage]) -> BaseMessage:
-        response = self.llm.with_retry(stop_after_attempt=2).invoke(input=messages)
+        response = self.llm.with_retry(stop_after_attempt=2).invoke(
+            input=self.__set_system_prompt(messages)
+        )
         response.name = self.agent_name
         return response
 
     async def acall_agent(self, messages: List[AnyMessage]) -> BaseMessage:
         response = await self.llm.with_retry(stop_after_attempt=2).ainvoke(
-            input=messages
+            input=self.__set_system_prompt(messages)
         )
         response.name = self.agent_name
         return response
@@ -47,7 +54,7 @@ class AgentBlueprint:
         response = (
             self.llm.with_structured_output(clazz)
             .with_retry(stop_after_attempt=2)
-            .invoke(input=messages)
+            .invoke(input=self.__set_system_prompt(messages))
         )
 
         return response
@@ -58,7 +65,7 @@ class AgentBlueprint:
         response = (
             await self.llm.with_structured_output(clazz)
             .with_retry(stop_after_attempt=2)
-            .ainvoke(input=messages)
+            .ainvoke(input=self.__set_system_prompt(messages))
         )
 
         return response

pmcp/agents/executor.py

@@ -5,8 +5,8 @@ from langchain_core.tools import BaseTool
 from langchain_openai import ChatOpenAI
 from langchain_core.messages import HumanMessage
 
-from pmcp.models.plan import PlanStep
 from pmcp.models.state import PlanningState
+from loguru import logger
 
 
 class ExecutorAgent:
@@ -18,16 +18,21 @@ class ExecutorAgent:
         )
 
     def call_executor_agent(self, state: PlanningState):
-        current_step = state.plan_step
-        plan_step: PlanStep = state.plan.steps[current_step]
-        return {
-            "plan_step": current_step + 1,
-            "messages": [
+        plan_step_index = state.plan_step
+        current_step = None
+        messages = []
+        if len(state.plan.steps) > plan_step_index:
+            current_step = state.plan.steps[plan_step_index]
+            messages = [
                 HumanMessage(
-                    content=f"The {plan_step.agent} agent should perform the following action:\n{plan_step.description}"
+                    content=f"The {current_step.agent} agent should perform the following action:\n{current_step.description}"
                 )
-            ],
-            "current_step": plan_step,
+            ]
+        logger.info(f"The Executor is executing step: {current_step}")
+        return {
+            "plan_step": plan_step_index + 1,
+            "messages": messages,
+            "current_step": current_step,
         }
 
     async def acall_executor_agent(self, state: PlanningState):
@@ -36,10 +41,12 @@ class ExecutorAgent:
         messages = []
         if len(state.plan.steps) > plan_step_index:
             current_step = state.plan.steps[plan_step_index]
-            messages = [HumanMessage(
+            messages = [
+                HumanMessage(
                     content=f"The {current_step.agent} agent should perform the following action:\n{current_step.description}"
-                )]
-                
+                )
+            ]
+        logger.info(f"The Executor is executing step: {current_step}")
         return {
             "plan_step": plan_step_index + 1,
             "messages": messages,

pmcp/agents/github_agent.py

@@ -2,10 +2,10 @@ from typing import List, Optional
 
 from pmcp.agents.agent_base import AgentBlueprint
 from langchain_core.tools import BaseTool
-from langchain_core.messages import SystemMessage
 from langchain_openai import ChatOpenAI
 
 from pmcp.models.state import PlanningState
+from loguru import logger
 
 SYSTEM_PROMPT = """
 You are an assistant that can manage Trello boards and projects.
@@ -24,13 +24,11 @@ class GithubAgent:
         )
 
     def call_github_agent(self, state: PlanningState):
-        response = self.agent.call_agent(
-            [SystemMessage(content=self.agent.system_prompt)] + state.messages
-        )
+        logger.info("Calling Github agent...")
+        response = self.agent.call_agent(state.messages)
         return {"messages": [response]}
 
     async def acall_github_agent(self, state: PlanningState):
-        response = await self.agent.acall_agent(
-            [SystemMessage(content=self.agent.system_prompt)] + state.messages
-        )
+        logger.info("Calling Github agent...")
+        response = await self.agent.acall_agent(state.messages)
         return {"messages": [response]}

pmcp/agents/planner.py

@@ -2,11 +2,11 @@ from typing import List, Optional
 
 from pmcp.agents.agent_base import AgentBlueprint
 from langchain_core.tools import BaseTool
-from langchain_core.messages import SystemMessage
 from langchain_openai import ChatOpenAI
 
 from pmcp.models.plan import Plan
 from pmcp.models.state import PlanningState
+from loguru import logger
 
 SYSTEM_PROMPT = """
 You are a Planner Agent responsible for breaking down high-level project goals into clear, actionable steps. You do not execute tasks yourself — instead, you delegate them to two specialized agents:
@@ -38,15 +38,19 @@ class PlannerAgent:
         )
 
     def call_planner_agent(self, state: PlanningState):
+        logger.info("Calling Planner agent...")
         response = self.agent.call_agent_structured(
-            messages=[SystemMessage(content=self.agent.system_prompt)] + state.messages,
+            messages=state.messages,
             clazz=Plan,
         )
+        logger.info(f"Building plan: {response}")
         return {"plan": response, "plan_step": 0, "current_step": None}
 
     async def acall_planner_agent(self, state: PlanningState):
+        logger.info("Calling Planner agent...")
         response = await self.agent.acall_agent_structured(
-            messages=[SystemMessage(content=self.agent.system_prompt)] + state.messages,
+            messages=state.messages,
             clazz=Plan,
         )
+        logger.info(f"Building plan: {response}")
         return {"plan": response, "plan_step": 0, "current_step": None}

pmcp/agents/trello_agent.py

@@ -2,10 +2,11 @@ from typing import List
 
 from pmcp.agents.agent_base import AgentBlueprint
 from langchain_core.tools import BaseTool
-from langchain_core.messages import SystemMessage
 from langchain_openai import ChatOpenAI
 
 from pmcp.models.state import PlanningState
+from loguru import logger
+
 
 SYSTEM_PROMPT = """
 You are an assistant that can manage Trello boards and projects.
@@ -24,13 +25,11 @@ class TrelloAgent:
         )
 
     def call_trello_agent(self, state: PlanningState):
-        response = self.agent.call_agent(
-            [SystemMessage(content=self.agent.system_prompt)] + state.messages
-        )
+        logger.info("Calling Trello Agent...")
+        response = self.agent.call_agent(state.messages)
         return {"messages": [response]}
 
     async def acall_trello_agent(self, state: PlanningState):
-        response = await self.agent.acall_agent(
-            [SystemMessage(content=self.agent.system_prompt)] + state.messages
-        )
+        logger.info("Calling Trello Agent...")
+        response = await self.agent.acall_agent(state.messages)
         return {"messages": [response]}

pmcp/nodes/human_interrupt_node.py

@@ -2,9 +2,10 @@ from typing import List, Optional
 
 from pmcp.agents.agent_base import AgentBlueprint
 from langchain_core.tools import BaseTool
-from langchain_core.messages import SystemMessage, AIMessage
+from langchain_core.messages import AIMessage
 from langchain_openai import ChatOpenAI
 from langgraph.types import Command, interrupt
+from langchain_core.messages.utils import filter_messages
 
 from pmcp.models.state import PlanningState
 
@@ -27,28 +28,31 @@ class HumanInterruptNode:
         )
 
     def call_human_interrupt_agent(self, state: PlanningState):
-        last_message = state.messages[-1]
-    
-        #TODO: chiedere a Gerlax lo strumento
+        last_message = filter_messages(state.messages, include_types=[AIMessage])[-1]
+
+        # TODO: chiedere a Gerlax lo strumento
         try:
             tool_call = last_message.tool_calls[-1]
         except Exception:
-            last_message = state.messages[-2]
+            last_message = filter_messages(state.messages, include_types=[AIMessage])[
+                -2
+            ]
             tool_call = last_message.tool_calls[-1]
 
-
-
         if tool_call.get("name", "").startswith("get_"):
-            return Command(goto="tool")            
-        
+            return Command(goto="tool")
+
         response = self.agent.call_agent(
-            messages=[SystemMessage(content=self.agent.system_prompt), AIMessage(content= f"Tool Calling details: {str(tool_call)}")] + state.messages,
+            messages=[
+                AIMessage(content=f"Tool Calling details: {str(tool_call)}"),
+            ]
+            + state.messages,
         )
         human_review = interrupt(response.content)
 
         confirm_action = human_review.confirm_action
         changes_description = human_review.changes_description
-        
+
         if confirm_action:
             return Command(goto="tool")
 

pmcp/nodes/human_resume_node.py

@@ -2,11 +2,12 @@ from typing import List, Optional
 
 from pmcp.agents.agent_base import AgentBlueprint
 from langchain_core.tools import BaseTool
-from langchain_core.messages import SystemMessage, HumanMessage
+from langchain_core.messages import HumanMessage
 from langchain_openai import ChatOpenAI
 from langgraph.types import Command
 
 from pmcp.models.resume_trigger import ResumeTrigger
+from loguru import logger
 
 
 SYSTEM_PROMPT = """
@@ -29,8 +30,11 @@ class HumanResumeNode:
         )
 
     def call_human_interrupt_agent(self, user_message: str):
+        logger.info("Human resumer agent...")
         response = self.agent.call_agent_structured(
-            [SystemMessage(content=self.agent.system_prompt), HumanMessage(content= user_message)],
+            [
+                HumanMessage(content=user_message),
+            ],
             clazz=ResumeTrigger,
         )
 

pyproject.toml

@@ -16,6 +16,7 @@ dependencies = [
     "requests>=2.32.3",
     "grandalf>=0.8",
     "fastmcp>=2.5.2",
+    "loguru>=0.7.3",
 ]
 
 [dependency-groups]

requirements.txt

@@ -49,6 +49,7 @@ click==8.2.1
 colorama==0.4.6 ; sys_platform == 'win32'
     # via
     #   click
+    #   loguru
     #   tqdm
 distlib==0.3.9
     # via virtualenv
@@ -167,6 +168,8 @@ langsmith==0.3.43
     # via langchain-core
 litellm==1.72.0
     # via smolagents
+loguru==0.7.3
+    # via pmcp
 lxml==5.4.0
     # via duckduckgo-search
 markdown-it-py==3.0.0
@@ -391,6 +394,8 @@ websockets==15.0.1
     # via
     #   fastmcp
     #   gradio-client
+win32-setctime==1.2.0 ; sys_platform == 'win32'
+    # via loguru
 xxhash==3.5.0
     # via langgraph
 yarl==1.20.0

uv.lock

@@ -922,6 +922,19 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/c2/98/bec08f5a3e504013db6f52b5fd68375bd92b463c91eb454d5a6460e957af/litellm-1.72.0-py3-none-any.whl", hash = "sha256:88360a7ae9aa9c96278ae1bb0a459226f909e711c5d350781296d0640386a824", size = 7979630, upload-time = "2025-06-01T02:12:50.458Z" },
 ]
 
+[[package]]
+name = "loguru"
+version = "0.7.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "colorama", marker = "sys_platform == 'win32'" },
+    { name = "win32-setctime", marker = "sys_platform == 'win32'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/3a/05/a1dae3dffd1116099471c643b8924f5aa6524411dc6c63fdae648c4f1aca/loguru-0.7.3.tar.gz", hash = "sha256:19480589e77d47b8d85b2c827ad95d49bf31b0dcde16593892eb51dd18706eb6", size = 63559, upload-time = "2024-12-06T11:20:56.608Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/0c/29/0348de65b8cc732daa3e33e67806420b2ae89bdce2b04af740289c5c6c8c/loguru-0.7.3-py3-none-any.whl", hash = "sha256:31a33c10c8e1e10422bfd431aeb5d351c7cf7fa671e3c4df004162264b28220c", size = 61595, upload-time = "2024-12-06T11:20:54.538Z" },
+]
+
 [[package]]
 name = "lxml"
 version = "5.4.0"
@@ -1383,6 +1396,7 @@ dependencies = [
     { name = "langchain-openai" },
     { name = "langgraph" },
     { name = "langgraph-checkpoint-sqlite" },
+    { name = "loguru" },
     { name = "mcp", extra = ["cli"] },
     { name = "requests" },
     { name = "smolagents", extra = ["litellm", "mcp", "toolkit"] },
@@ -1404,6 +1418,7 @@ requires-dist = [
     { name = "langchain-openai", specifier = ">=0.3.18" },
     { name = "langgraph", specifier = ">=0.4.7" },
     { name = "langgraph-checkpoint-sqlite", specifier = ">=2.0.10" },
+    { name = "loguru", specifier = ">=0.7.3" },
     { name = "mcp", extras = ["cli"], specifier = ">=1.9.0" },
     { name = "requests", specifier = ">=2.32.3" },
     { name = "smolagents", extras = ["litellm", "mcp", "toolkit"], specifier = ">=1.17.0" },
@@ -2156,6 +2171,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/fa/a8/5b41e0da817d64113292ab1f8247140aac61cbf6cfd085d6a0fa77f4984f/websockets-15.0.1-py3-none-any.whl", hash = "sha256:f7a866fbc1e97b5c617ee4116daaa09b722101d4a3c170c787450ba409f9736f", size = 169743, upload-time = "2025-03-05T20:03:39.41Z" },
 ]
 
+[[package]]
+name = "win32-setctime"
+version = "1.2.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/b3/8f/705086c9d734d3b663af0e9bb3d4de6578d08f46b1b101c2442fd9aecaa2/win32_setctime-1.2.0.tar.gz", hash = "sha256:ae1fdf948f5640aae05c511ade119313fb6a30d7eabe25fef9764dca5873c4c0", size = 4867, upload-time = "2024-12-07T15:28:28.314Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/e1/07/c6fe3ad3e685340704d314d765b7912993bcb8dc198f0e7a89382d37974b/win32_setctime-1.2.0-py3-none-any.whl", hash = "sha256:95d644c4e708aba81dc3704a116d8cbc974d70b3bdb8be1d150e36be6e9d1390", size = 4083, upload-time = "2024-12-07T15:28:26.465Z" },
+]
+
 [[package]]
 name = "xxhash"
 version = "3.5.0"