Fix no tools logic, initial team leader name and delegate bug. Rename…

… summariser to FinalAnswer.

backend/app/api/routes/teams.py

@@ -141,7 +141,7 @@ def create_team(
     # Create team leader
     member = Member(
         **{
-            "name": "Team Leader",
+            "name": "TeamLeader",
             "type": "root",
             "role": "Gather inputs from your team and answer the question.",
             "owner_of": team.id,

backend/app/core/graph/build.py

@@ -178,7 +178,7 @@ def create_graph(teams: dict[str, Team], leader_name: str):
         ),
     )
     build.add_node(
-        "summariser",
+        "FinalAnswer",
         RunnableLambda(
             SummariserNode(
                 teams[leader_name].provider,
@@ -210,11 +210,11 @@ def create_graph(teams: dict[str, Team], leader_name: str):
         build.add_edge(name, leader_name)
 
     conditional_mapping = {v: v for v in members}
-    conditional_mapping["FINISH"] = "summariser"
+    conditional_mapping["FINISH"] = "FinalAnswer"
     build.add_conditional_edges(leader_name, router, conditional_mapping)
 
     build.set_entry_point(leader_name)
-    build.set_finish_point("summariser")
+    build.set_finish_point("FinalAnswer")
     graph = build.compile()
     return graph
 

backend/app/core/graph/members.py

@@ -5,7 +5,7 @@
     AgentExecutor,
     create_tool_calling_agent,
 )
-from langchain_core.language_models import BaseLanguageModel
+from langchain_core.language_models.chat_models import BaseChatModel
 from langchain_core.messages import AIMessage, BaseMessage, HumanMessage
 from langchain_core.output_parsers.openai_tools import JsonOutputKeyToolsParser
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
@@ -80,24 +80,32 @@ class TeamState(TypedDict):
 class BaseNode:
     def __init__(self, provider: str, model: str, temperature: float):
         self.model = all_models[provider](model=model, temperature=temperature)
+        self.final_answer_model = all_models[provider](model=model, temperature=0)
 
     def tag_with_name(self, ai_message: AIMessage, name: str):
         """Tag a name to the AI message"""
         ai_message.name = name
         return ai_message
 
+    def get_team_members_name(self, team_members: dict[str, Person]):
+        """Get the names of all team members as a string"""
+        return ",".join(list(team_members))
+
 
 class WorkerNode(BaseNode):
     worker_prompt = ChatPromptTemplate.from_messages(
         [
             (
                 "system",
-                """You are a team member of {team_name} and you are one of the following team members: {team_members}.
-        Your team members (and other teams) will collaborate with you with their own set of skills.
-        You are chosen by one of your team member to perform this task. Try your best to perform it using your skills.
-        Stay true to your perspective:
-        {persona}""",
+                (
+                    "You are a team member of {team_name} and you are one of the following team members: {team_members_name}.\n"
+                    "Your team members (and other teams) will collaborate with you with their own set of skills. "
+                    "You are chosen by one of your team member to perform this task. Try your best to perform it using your skills. "
+                    "Stay true to your perspective:\n"
+                    "{persona}"
+                ),
             ),
+            MessagesPlaceholder(variable_name="messages"),
             MessagesPlaceholder(variable_name="task"),
             MessagesPlaceholder(variable_name="agent_scratchpad"),
         ]
@@ -109,30 +117,30 @@ def convert_output_to_ai_message(self, state: TeamState):
         return AIMessage(content=output)
 
     def create_agent(
-        self, llm: BaseLanguageModel, prompt: ChatPromptTemplate, tools: list[str]
+        self, llm: BaseChatModel, prompt: ChatPromptTemplate, tools: list[str]
     ):
-        """Create the agent executor"""
+        """Create the agent executor. Tools must non-empty."""
         tools = [all_skills[tool].tool for tool in tools]
-        # Tools cannot be empty, add a placeholder
-        if len(tools) < 1:
-            tools = [all_skills["nothing"].tool]
-
         agent = create_tool_calling_agent(llm, tools, prompt)
-        # agent = create_openai_functions_agent(llm, tools, prompt)
         executor = AgentExecutor(agent=agent, tools=tools)
         return executor
 
     async def work(self, state: TeamState):
         name = state["next"]
         member = state["team_members"][name]
         tools = member.tools
-        prompt = self.worker_prompt.partial(persona=member.persona)
-        agent = self.create_agent(self.model, prompt, tools)
-        work_chain = (
-            agent
-            | RunnableLambda(self.convert_output_to_ai_message)
-            | RunnableLambda(self.tag_with_name).bind(name=member.name)
+        team_members_name = self.get_team_members_name(state["team_members"])
+        prompt = self.worker_prompt.partial(
+            team_members_name=team_members_name,
+            persona=member.persona,
         )
+        # If member has no tools, then use a regular model instead of an agent
+        if len(tools) >= 1:
+            agent = self.create_agent(self.model, prompt, tools)
+            chain = agent | RunnableLambda(self.convert_output_to_ai_message)
+        else:
+            chain = prompt.partial(agent_scratchpad=[]) | self.model
+        work_chain = chain | RunnableLambda(self.tag_with_name).bind(name=member.name)
         result = await work_chain.ainvoke(state)
         return {"messages": [result]}
 
@@ -142,10 +150,12 @@ class LeaderNode(BaseNode):
         [
             (
                 "system",
-                """You are the team leader of {team_name} and you have the following team members: {team_members}.
-      Your team is given a task and you have to delegate the work among your team members based on their skills.
-      Team member info:
-      {team_members_info}""",
+                (
+                    "You are the team leader of {team_name} and you have the following team members: {team_members_name}.\n"
+                    "Your team is given a task and you have to delegate the work among your team members based on their skills.\n"
+                    "Team member info:\n\n"
+                    "{team_members_info}"
+                ),
             ),
             MessagesPlaceholder(variable_name="messages"),
             (
@@ -190,7 +200,7 @@ def get_tool_definition(self, options: list[str]):
         }
 
     async def delegate(self, state: TeamState):
-        team_members = ", ".join(state["team_members"])
+        team_members_name = self.get_team_members_name(state["team_members"])
         team_name = state["team_name"]
         team_members_info = self.get_team_members_info(state["team_members"])
         options = list(state["team_members"]) + ["FINISH"]
@@ -199,34 +209,47 @@ async def delegate(self, state: TeamState):
         delegate_chain = (
             self.leader_prompt.partial(
                 team_name=team_name,
-                team_members=team_members,
+                team_members_name=team_members_name,
                 team_members_info=team_members_info,
                 options=str(options),
             )
             | self.model.bind_tools(tools=tools)
             | JsonOutputKeyToolsParser(key_name="route", first_tool_only=True)
         )
         result = await delegate_chain.ainvoke(state)
-        # Convert task from string to list[HumanMessage] because Worker's MessagesPlaceholder only accepts list of messages.
-        result["task"] = [
-            HumanMessage(content=result.get("task", "None"), name=team_name)
-        ]
-        return result
+        if not result:
+            return {
+                "task": [HumanMessage(content="No further tasks.", name=team_name)],
+                "next": "FINISH",
+            }
+        else:
+            # Convert task from string to list[HumanMessage] because Worker's MessagesPlaceholder only accepts list of messages.
+            result["task"] = [
+                HumanMessage(
+                    content=result.get("task", "No further tasks."), name=team_name
+                )
+            ]
+            return result
 
 
 class SummariserNode(BaseNode):
     summariser_prompt = ChatPromptTemplate.from_messages(
         [
             (
                 "system",
-                """You are a team member of {team_name} and you have the following team members: {team_members}.
-          Your team was given a task and your team members have performed their roles and returned their responses to the team leader.
-          Your role as a Summariser is to summarise the responses by your team members and give the final answer.
-          Here is the team's task:
-          {team_task}
-
-          These are the responses from your team members:
-          {team_responses}""",
+                (
+                    "You are a team member of {team_name} and you have the following team members: {team_members_name}. "
+                    "Your team was given a task and your team members have performed their roles and returned their responses to the team leader.\n\n"
+                    "Here is the team's task:\n"
+                    "'''\n"
+                    "{team_task}\n"
+                    "'''\n\n"
+                    "These are the responses from your team members:\n"
+                    "'''\n"
+                    "{team_responses}\n"
+                    "'''\n\n"
+                    "Your role is to interpret all the responses and give the final answer to the team's task.\n"
+                ),
             )
         ]
     )
@@ -239,20 +262,20 @@ def get_team_responses(self, messages: list[BaseMessage]):
         return result
 
     async def summarise(self, state: TeamState):
-        team_members = ", ".join(state["team_members"])
+        team_members_name = self.get_team_members_name(state["team_members"])
         team_name = state["team_name"]
         team_responses = self.get_team_responses(state["messages"])
         team_task = state["messages"][0].content
 
         summarise_chain = (
             self.summariser_prompt.partial(
                 team_name=team_name,
-                team_members=team_members,
+                team_members_name=team_members_name,
                 team_task=team_task,
                 team_responses=team_responses,
             )
-            | self.model
-            | RunnableLambda(self.tag_with_name).bind(name="summariser")
+            | self.final_answer_model
+            | RunnableLambda(self.tag_with_name).bind(name="FinalAnswer")
         )
         result = await summarise_chain.ainvoke(state)
         return {"messages": [result]}

backend/app/core/graph/models.py

@@ -1,10 +1,11 @@
 from langchain_anthropic import ChatAnthropic
 from langchain_cohere import ChatCohere
+from langchain_core.language_models.chat_models import BaseChatModel
 from langchain_google_genai import ChatGoogleGenerativeAI
 from langchain_openai import ChatOpenAI
 
 # Define a dictionary to store all models
-all_models = {
+all_models: dict[str, type[BaseChatModel]] = {
     "ChatOpenAI": ChatOpenAI,
     "ChatAnthropic": ChatAnthropic,
     "ChatCohere": ChatCohere,

backend/app/core/graph/skills.py

@@ -11,14 +11,7 @@ class SkillInfo(BaseModel):
     tool: Callable
 
 
-@tool
-def nothing(query: str) -> str:
-    """Placeholder Tool. Does nothing"""
-    return ""
-
-
 all_skills: dict[str, SkillInfo] = {
-    "nothing": SkillInfo(description="Does nothing", tool=nothing),
     "search": SkillInfo(
         description="Searches the web using Duck Duck Go", tool=DuckDuckGoSearchRun()
     ),