add human in the loop

.env.example

@@ -0,0 +1,4 @@
+MCP_TRELLO_PATH=C:/Users/Matteo/Desktop/dev/trello-mcp-server/main.py
+MCP_GITHUB_PATH=C:/Users/Matteo/Desktop/dev/github-mcp-server/main.py
+
+NEBIUS_API_KEY=

main.py

@@ -1,6 +1,8 @@
 import os
 import pprint
+from typing import Literal
 import uuid
+from dotenv import load_dotenv
 
 from langchain_mcp_adapters.client import MultiServerMCPClient
 from langchain_openai import ChatOpenAI
@@ -8,6 +10,8 @@ from langgraph.prebuilt import ToolNode
 from langgraph.graph import MessagesState, END, StateGraph
 from langchain_core.messages import HumanMessage
 from langgraph.checkpoint.memory import MemorySaver
+from langgraph.types import Command, interrupt
+
 
 from pmcp.agents.executor import ExecutorAgent
 from pmcp.agents.trello_agent import TrelloAgent
@@ -16,27 +20,60 @@ from pmcp.agents.planner import PlannerAgent
 
 from pmcp.models.state import PlanningState
 
+load_dotenv()
+
 
 async def call_llm(llm_with_tools: ChatOpenAI, state: MessagesState):
     response = llm_with_tools.invoke(state["messages"])
     return {"messages": [response]}
 
 
+def human_review_node(state) -> Command[Literal["PLANNER_AGENT", "tool"]]:
+    last_message = state["messages"][-1]
+    tool_call = last_message.tool_calls[-1]
+    if tool_call.get("name", "").startswith("get_"):
+        return Command(goto="tool")
+
+    human_review = interrupt(
+        {
+            "question": "Is this correct?",
+            # Surface tool calls for review
+            "tool_call": tool_call,
+        }
+    )
+
+    review_action = human_review["action"]
+    review_data = human_review.get("data")
+
+    if review_action == "continue":
+        return Command(goto="tool")
+
+    else:
+        tool_message = {
+            "role": "tool",
+            "content": review_data,
+            "name": tool_call["name"],
+            "tool_call_id": tool_call["id"],
+        }
+        return Command(goto="PLANNER_AGENT", update={"messages": [tool_message]})
+
+
 async def main():
     mcp_client_trello = MultiServerMCPClient(
         {
             "trello": {
-                "url": "http://localhost:8000/sse",
-                "transport": "sse",
+                "command": "python",
+                "args": [os.getenv("MCP_TRELLO_PATH")],
+                "transport": "stdio",
             }
         }
     )
-
     mcp_client_github = MultiServerMCPClient(
         {
             "github": {
-                "url": "http://localhost:8002/sse",
-                "transport": "sse",
+                "command": "python",
+                "args": [os.getenv("MCP_GITHUB_PATH")],
+                "transport": "stdio",
             }
         }
     )
@@ -45,6 +82,7 @@ async def main():
 
     trello_tools = await mcp_client_trello.get_tools()
     github_tools = await mcp_client_github.get_tools()
+
     tool_node = ToolNode(github_tools + trello_tools)
 
     llm = ChatOpenAI(
@@ -72,17 +110,19 @@ async def main():
     graph.add_node(github_agent.agent.agent_name, github_agent.acall_github_agent)
     graph.add_node(executor_agent.agent.agent_name, executor_agent.acall_executor_agent)
     graph.add_node("tool", tool_node)
+    graph.add_node("human_review", human_review_node)
     graph.set_entry_point(planner_agent.agent.agent_name)
 
     def should_continue(state: PlanningState):
         last_message = state.messages[-1]
         if last_message.tool_calls:
-            return "tool"
+            return "human_review"
         return executor_agent.agent.agent_name
 
     def execute_agent(state: PlanningState):
         if state.current_step:
             return state.current_step.agent
+        
         return END
 
     graph.add_conditional_edges(trello_agent.agent.agent_name, should_continue)
@@ -94,10 +134,14 @@ async def main():
     graph.add_edge(planner_agent.agent.agent_name, executor_agent.agent.agent_name)
 
     app = graph.compile(checkpointer=memory)
-    app.get_graph(xray=True).draw_mermaid_png()
+    app.get_graph(xray=True).draw_mermaid()
+
 
     user_input = input("user >")
-    config = {"configurable": {"thread_id": f"{str(uuid.uuid4())}"}}
+    config = {
+        "configurable": {"thread_id": f"{str(uuid.uuid4())}"},
+        "recursion_limit": 100,
+    }
 
     while user_input.lower() != "q":
         async for res in app.astream(

pmcp/agents/executor.py

@@ -31,18 +31,17 @@ class ExecutorAgent:
         }
 
     async def acall_executor_agent(self, state: PlanningState):
-        current_step = state.plan_step
-
-        plan_step = None
-        if len(state.plan.steps) > current_step:
-            plan_step = state.plan.steps[current_step]
-
+        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 {current_step.agent} agent should perform the following action:\n{current_step.description}"
+                )]
+                
         return {
-            "plan_step": current_step + 1,
-            "messages": [
-                HumanMessage(
-                    content=f"The {plan_step.agent} agent should perform the following action:\n{plan_step.description}"
-                )
-            ],
-            "current_step": plan_step,
+            "plan_step": plan_step_index + 1,
+            "messages": messages,
+            "current_step": current_step,
         }

pmcp/agents/planner.py

@@ -10,8 +10,8 @@ from pmcp.models.state import PlanningState
 
 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:
-- Trello Agent – Handles all operations related to Trello (boards (only read), lists, cards, assignments, due dates, etc.).
-- GitHub Agent – Handles all operations related to GitHub (issues, can see in textual form the repository).
+- TRELLO_AGENT – Handles all operations related to Trello (boards (only read), lists, cards, assignments, due dates, etc.).
+- GITHUB_AGENT – Handles all operations related to GitHub (issues, can see in textual form the repository).
 Your job is to:
 - Analyze the user’s request or project goal.
 - Decompose it into a step-by-step plan with granular, unambiguous tasks.
@@ -42,11 +42,11 @@ class PlannerAgent:
             messages=[SystemMessage(content=self.agent.system_prompt)] + state.messages,
             clazz=Plan,
         )
-        return {"plan": response}
+        return {"plan": response, "plan_step": 0, "current_step": None}
 
     async def acall_planner_agent(self, state: PlanningState):
         response = await self.agent.acall_agent_structured(
             messages=[SystemMessage(content=self.agent.system_prompt)] + state.messages,
             clazz=Plan,
         )
-        return {"plan": response}
+        return {"plan": response, "plan_step": 0, "current_step": None}

pyproject.toml

@@ -10,8 +10,11 @@ dependencies = [
     "langchain-openai>=0.3.18",
     "langgraph>=0.4.7",
     "langgraph-checkpoint-sqlite>=2.0.10",
-    "mcp>=1.9.0",
+    "mcp[cli]>=1.9.0",
     "smolagents[litellm,mcp,toolkit]>=1.17.0",
+    "httpx>=0.28.1",
+    "requests>=2.32.3",
+    "grandalf>=0.8",
 ]
 
 [dependency-groups]

uv.lock

@@ -488,6 +488,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/9b/1b/b372308c263379ae3ebc440512432979458330113bdee26cef86c89bf48e/gradio_client-1.10.2-py3-none-any.whl", hash = "sha256:6de67b6224123d264c7887caa0586b2a9e2c369ec32ca38927cf8a841694edcd", size = 323311, upload-time = "2025-05-30T13:59:54.555Z" },
 ]
 
+[[package]]
+name = "grandalf"
+version = "0.8"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "pyparsing" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/95/0e/4ac934b416857969f9135dec17ac80660634327e003a870835dd1f382659/grandalf-0.8.tar.gz", hash = "sha256:2813f7aab87f0d20f334a3162ccfbcbf085977134a17a5b516940a93a77ea974", size = 38128, upload-time = "2023-01-26T07:37:06.668Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/61/30/44c7eb0a952478dbb5f2f67df806686d6a7e4b19f6204e091c4f49dc7c69/grandalf-0.8-py3-none-any.whl", hash = "sha256:793ca254442f4a79252ea9ff1ab998e852c1e071b863593e5383afee906b4185", size = 41802, upload-time = "2023-01-10T15:16:19.753Z" },
+]
+
 [[package]]
 name = "groovy"
 version = "0.1.2"
@@ -1004,6 +1016,12 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/a5/d5/22e36c95c83c80eb47c83f231095419cf57cf5cca5416f1c960032074c78/mcp-1.9.0-py3-none-any.whl", hash = "sha256:9dfb89c8c56f742da10a5910a1f64b0d2ac2c3ed2bd572ddb1cfab7f35957178", size = 125082, upload-time = "2025-05-15T18:51:04.916Z" },
 ]
 
+[package.optional-dependencies]
+cli = [
+    { name = "python-dotenv" },
+    { name = "typer" },
+]
+
 [[package]]
 name = "mcpadapt"
 version = "0.1.9"
@@ -1315,11 +1333,14 @@ version = "0.1.0"
 source = { virtual = "." }
 dependencies = [
     { name = "gradio", extra = ["mcp"] },
+    { name = "grandalf" },
+    { name = "httpx" },
     { name = "langchain-mcp-adapters" },
     { name = "langchain-openai" },
     { name = "langgraph" },
     { name = "langgraph-checkpoint-sqlite" },
-    { name = "mcp" },
+    { name = "mcp", extra = ["cli"] },
+    { name = "requests" },
     { name = "smolagents", extra = ["litellm", "mcp", "toolkit"] },
 ]
 
@@ -1332,11 +1353,14 @@ dev = [
 [package.metadata]
 requires-dist = [
     { name = "gradio", extras = ["mcp"], specifier = ">=5.32.0" },
+    { name = "grandalf", specifier = ">=0.8" },
+    { name = "httpx", specifier = ">=0.28.1" },
     { name = "langchain-mcp-adapters", specifier = ">=0.1.1" },
     { name = "langchain-openai", specifier = ">=0.3.18" },
     { name = "langgraph", specifier = ">=0.4.7" },
     { name = "langgraph-checkpoint-sqlite", specifier = ">=2.0.10" },
-    { name = "mcp", specifier = ">=1.9.0" },
+    { name = "mcp", extras = ["cli"], specifier = ">=1.9.0" },
+    { name = "requests", specifier = ">=2.32.3" },
     { name = "smolagents", extras = ["litellm", "mcp", "toolkit"], specifier = ">=1.17.0" },
 ]
 
@@ -1533,6 +1557,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/8a/0b/9fcc47d19c48b59121088dd6da2488a49d5f72dacf8262e2790a1d2c7d15/pygments-2.19.1-py3-none-any.whl", hash = "sha256:9ea1544ad55cecf4b8242fab6dd35a93bbce657034b0611ee383099054ab6d8c", size = 1225293, upload-time = "2025-01-06T17:26:25.553Z" },
 ]
 
+[[package]]
+name = "pyparsing"
+version = "3.2.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/bb/22/f1129e69d94ffff626bdb5c835506b3a5b4f3d070f17ea295e12c2c6f60f/pyparsing-3.2.3.tar.gz", hash = "sha256:b9c13f1ab8b3b542f72e28f634bad4de758ab3ce4546e4301970ad6fa77c38be", size = 1088608, upload-time = "2025-03-25T05:01:28.114Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/05/e7/df2285f3d08fee213f2d041540fa4fc9ca6c2d44cf36d3a035bf2a8d2bcc/pyparsing-3.2.3-py3-none-any.whl", hash = "sha256:a749938e02d6fd0b59b356ca504a24982314bb090c383e3cf201c95ef7e2bfcf", size = 111120, upload-time = "2025-03-25T05:01:24.908Z" },
+]
+
 [[package]]
 name = "python-dateutil"
 version = "2.9.0.post0"