feat: cp stat

.env.example

@@ -60,7 +60,7 @@ POSTGRES_PORT=
 POSTGRES_DB=
 
 # you will be able to identify AST connections in Postgres Connection Manager under this Application Name
-# POSTGRES_APPLICATION_NAME = "agent-service-toolkit"
+# POSTGRES_APPLICATION_NAME = "chatbot"
 # set these values to customize the number of connections in the pool. Saver and store have independent connection pools
 # POSTGRES_MIN_CONNECTIONS_PER_POOL=1
 # POSTGRES_MAX_CONNECTIONS_PER_POOL= 3

README.md

@@ -9,8 +9,8 @@ pinned: false
 
 # 🧰 AI Agent Service Toolkit
 
-[![build status](https://github.com/JoshuaC215/agent-service-toolkit/actions/workflows/test.yml/badge.svg)](https://github.com/JoshuaC215/agent-service-toolkit/actions/workflows/test.yml) [![codecov](https://codecov.io/github/JoshuaC215/agent-service-toolkit/graph/badge.svg?token=5MTJSYWD05)](https://codecov.io/github/JoshuaC215/agent-service-toolkit) [![Python Version](https://img.shields.io/python/required-version-toml?tomlFilePath=https%3A%2F%2Fraw.githubusercontent.com%2FJoshuaC215%2Fagent-service-toolkit%2Frefs%2Fheads%2Fmain%2Fpyproject.toml)](https://github.com/JoshuaC215/agent-service-toolkit/blob/main/pyproject.toml)
-[![GitHub License](https://img.shields.io/github/license/JoshuaC215/agent-service-toolkit)](https://github.com/JoshuaC215/agent-service-toolkit/blob/main/LICENSE) [![Streamlit App](https://static.streamlit.io/badges/streamlit_badge_black_red.svg)](https://agent-service-toolkit.streamlit.app/)
+[![build status](https://github.com/JoshuaC215/chatbot/actions/workflows/test.yml/badge.svg)](https://github.com/JoshuaC215/chatbot/actions/workflows/test.yml) [![codecov](https://codecov.io/github/JoshuaC215/chatbot/graph/badge.svg?token=5MTJSYWD05)](https://codecov.io/github/JoshuaC215/chatbot) [![Python Version](https://img.shields.io/python/required-version-toml?tomlFilePath=https%3A%2F%2Fraw.githubusercontent.com%2FJoshuaC215%2Fchatbot%2Frefs%2Fheads%2Fmain%2Fpyproject.toml)](https://github.com/JoshuaC215/chatbot/blob/main/pyproject.toml)
+[![GitHub License](https://img.shields.io/github/license/JoshuaC215/chatbot)](https://github.com/JoshuaC215/chatbot/blob/main/LICENSE) [![Streamlit App](https://static.streamlit.io/badges/streamlit_badge_black_red.svg)](https://chatbot.streamlit.app/)
 
 A full toolkit for running an AI agent service built with LangGraph, FastAPI and Streamlit.
 
@@ -22,9 +22,9 @@ This project offers a template for you to easily build and run your own agents u
 
 ## Overview
 
-### [Try the app!](https://agent-service-toolkit.streamlit.app/)
+### [Try the app!](https://chatbot.streamlit.app/)
 
-<a href="https://agent-service-toolkit.streamlit.app/"><img src="media/app_screenshot.png" width="600"></a>
+<a href="https://chatbot.streamlit.app/"><img src="media/app_screenshot.png" width="600"></a>
 
 ### Quickstart
 
@@ -34,7 +34,7 @@ Run directly in python
 # At least one LLM API key is required
 echo 'OPENAI_API_KEY=your_openai_api_key' >> .env
 
-# uv is the recommended way to install agent-service-toolkit, but "pip install ." also works
+# uv is the recommended way to install chatbot, but "pip install ." also works
 # For uv installation options, see: https://docs.astral.sh/uv/getting-started/installation/
 curl -LsSf https://astral.sh/uv/0.7.19/install.sh | sh
 
@@ -90,8 +90,8 @@ The repository is structured as follows:
 1. Clone the repository:
 
    ```sh
-   git clone https://github.com/JoshuaC215/agent-service-toolkit.git
-   cd agent-service-toolkit
+   git clone https://github.com/JoshuaC215/chatbot.git
+   cd chatbot
    ```
 
 2. Set up environment variables:
@@ -206,12 +206,12 @@ You can also run the agent service and the Streamlit app locally without Docker,
 
 4. Open your browser and navigate to the URL provided by Streamlit (usually `http://localhost:8501`).
 
-## Projects built with or inspired by agent-service-toolkit
+## Projects built with or inspired by chatbot
 
 The following are a few of the public projects that drew code or inspiration from this repo.
 
-- **[PolyRAG](https://github.com/QuentinFuxa/PolyRAG)** - Extends agent-service-toolkit with RAG capabilities over both PostgreSQL databases and PDF documents.
-- **[alexrisch/agent-web-kit](https://github.com/alexrisch/agent-web-kit)** - A Next.JS frontend for agent-service-toolkit
+- **[PolyRAG](https://github.com/QuentinFuxa/PolyRAG)** - Extends chatbot with RAG capabilities over both PostgreSQL databases and PDF documents.
+- **[alexrisch/agent-web-kit](https://github.com/alexrisch/agent-web-kit)** - A Next.JS frontend for chatbot
 - **[raushan-in/dapa](https://github.com/raushan-in/dapa)** - Digital Arrest Protection App (DAPA) enables users to report financial scams and frauds efficiently via a user-friendly platform.
 
 **Please create a pull request editing the README or open a discussion with any new ones to be added!** Would love to include more projects.

pyproject.toml

@@ -1,9 +1,9 @@
 [project]
-name = "agent-service-toolkit"
+name = "chatbot"
 version = "0.1.0"
 description = "Full toolkit for running an AI agent service built with LangGraph, FastAPI and Streamlit"
 readme = "README.md"
-authors = [{ name = "Joshua Carroll", email = "carroll.joshk@gmail.com" }]
+authors = [{ name = "Anuj Joshi", email = "anujjoshi3105@gmail.com" }]
 classifiers = [
     "Development Status :: 4 - Beta",
     "License :: OSI Approved :: MIT License",
@@ -16,7 +16,6 @@ classifiers = [
 requires-python = ">=3.11,<3.14"
 
 dependencies = [
-    "docx2txt ~=0.8",
     "duckduckgo-search>=7.3.0",
     "fastapi ~=0.115.5",
     "grpcio >=1.68.0",
@@ -38,25 +37,17 @@ dependencies = [
     "langgraph-checkpoint-mongodb ~=0.1.3",
     "langgraph-checkpoint-postgres ~=2.0.13",
     "langgraph-checkpoint-sqlite ~=2.0.1",
-    "langgraph-supervisor ~=0.0.31",
     "langsmith ~=0.4.0",
-    "numexpr ~=2.10.1",
-    "numpy ~=2.3.4",
-    "onnxruntime ~= 1.21.1",
-    "pandas ~=2.2.3",
     "psycopg[binary,pool] ~=3.2.4",
-    "pyarrow >=18.1.0",
     "pydantic ~=2.10.1",
     "pydantic-settings ~=2.12.0",
-    "pypdf ~=5.3.0",
-    "pyowm ~=3.3.0",
     "python-dotenv ~=1.0.1",
     "setuptools ~=75.6.0",
-    "streamlit ~=1.52.0",
     "tiktoken >=0.8.0",
     "uvicorn ~=0.32.1",
     "langchain-mcp-adapters>=0.1.10",
     "ddgs>=9.9.1",
+    "toons>=0.5.2",
 ]
 
 [dependency-groups]
@@ -78,7 +69,6 @@ client = [
     "httpx~=0.28.0",
     "pydantic ~=2.10.1",
     "python-dotenv ~=1.0.1",
-    "streamlit~=1.52.0",
 ]
 
 [tool.ruff]

src/agents/agents.py

@@ -3,19 +3,10 @@ from dataclasses import dataclass
 from langgraph.graph.state import CompiledStateGraph
 from langgraph.pregel import Pregel
 
-from agents.bg_task_agent.bg_task_agent import bg_task_agent
-from agents.chatbot import chatbot
-from agents.command_agent import command_agent
-from agents.github_mcp_agent.github_mcp_agent import github_mcp_agent
-from agents.interrupt_agent import interrupt_agent
-from agents.knowledge_base_agent import kb_agent
-from agents.langgraph_supervisor_agent import langgraph_supervisor_agent
-from agents.langgraph_supervisor_hierarchy_agent import langgraph_supervisor_hierarchy_agent
-from agents.portfolio_agent.portfolio_agent import portfolio_agent
-
+from agents.portfolio_agent import portfolio_agent
+from agents.github_mcp_agent import github_mcp_agent
+from agents.cpstat_agent import cpstat_agent
 from agents.lazy_agent import LazyLoadingAgent
-from agents.rag_assistant import rag_assistant
-from agents.research_assistant import research_assistant
 from schema import AgentInfo
 
 DEFAULT_AGENT = "portfolio-agent"
@@ -31,41 +22,40 @@ AgentGraphLike = CompiledStateGraph | Pregel | LazyLoadingAgent  # What can be s
 class Agent:
     description: str
     graph_like: AgentGraphLike
+    prompts: list[str]
 
 
 agents: dict[str, Agent] = {
-    "chatbot": Agent(description="A simple chatbot.", graph_like=chatbot),
-    "research-assistant": Agent(
-        description="A research assistant with web search and calculator.",
-        graph_like=research_assistant,
-    ),
-    "rag-assistant": Agent(
-        description="A RAG assistant with access to information in a database.",
-        graph_like=rag_assistant,
-    ),
     "portfolio-agent": Agent(
-        description="A portfolio assistant with access to information in a database.",
+        description="A specialized portfolio assistant that can answer questions about Anuj Joshi's background, skills, projects, and experience using a curated knowledge base.",
         graph_like=portfolio_agent,
-    ),
-    "command-agent": Agent(description="A command agent.", graph_like=command_agent),
-    "bg-task-agent": Agent(description="A background task agent.", graph_like=bg_task_agent),
-    "langgraph-supervisor-agent": Agent(
-        description="A langgraph supervisor agent", graph_like=langgraph_supervisor_agent
-    ),
-    "langgraph-supervisor-hierarchy-agent": Agent(
-        description="A langgraph supervisor agent with a nested hierarchy of agents",
-        graph_like=langgraph_supervisor_hierarchy_agent,
-    ),
-    "interrupt-agent": Agent(
-        description="An agent the uses interrupts.", graph_like=interrupt_agent
-    ),
-    "knowledge-base-agent": Agent(
-        description="A retrieval-augmented generation agent using Amazon Bedrock Knowledge Base",
-        graph_like=kb_agent,
+        prompts=[
+            "Tell me about Anuj's background",
+            "What are Anuj's key technical skills?",
+            "Show me some of Anuj's featured projects",
+            "What is Anuj's work experience?",
+            "How can I contact Anuj?",
+        ],
     ),
     "github-mcp-agent": Agent(
-        description="A GitHub agent with MCP tools for repository management and development workflows.",
+        description="An agent equipped with GitHub MCP tools to explore repositories, view code, and track development activity.",
         graph_like=github_mcp_agent,
+        prompts=[
+            "List anujjoshi3105's top repositories",
+            "Show recent activity on anujjoshi3105's GitHub",
+            "Tell me about the 'portfolio-bot' repository",
+            "Show me anujjoshi3105's contributions in the last month",
+        ],
+    ),
+    "cpstat-agent": Agent(
+        description="An agent specializing in Competitive Programming, capable of fetching real-time contest ratings and stats from various platforms.",
+        graph_like=cpstat_agent,
+        prompts=[
+            "Show Anuj's LeetCode rating and stats",
+            "What is Anuj's Codeforces rank?",
+            "Get recent contest performance for anujjoshi3105",
+            "Show CP stats across all platforms",
+        ],
     ),
 }
 
@@ -93,5 +83,6 @@ def get_agent(agent_id: str) -> AgentGraph:
 
 def get_all_agent_info() -> list[AgentInfo]:
     return [
-        AgentInfo(key=agent_id, description=agent.description) for agent_id, agent in agents.items()
+        AgentInfo(key=agent_id, description=agent.description, prompts=agent.prompts)
+        for agent_id, agent in agents.items()
     ]

src/agents/bg_task_agent/task.py

@@ -1,53 +0,0 @@
-from typing import Literal
-from uuid import uuid4
-
-from langchain_core.messages import BaseMessage
-from langgraph.types import StreamWriter
-
-from agents.utils import CustomData
-from schema.task_data import TaskData
-
-
-class Task:
-    def __init__(self, task_name: str, writer: StreamWriter | None = None) -> None:
-        self.name = task_name
-        self.id = str(uuid4())
-        self.state: Literal["new", "running", "complete"] = "new"
-        self.result: Literal["success", "error"] | None = None
-        self.writer = writer
-
-    def _generate_and_dispatch_message(self, writer: StreamWriter | None, data: dict):
-        writer = writer or self.writer
-        task_data = TaskData(name=self.name, run_id=self.id, state=self.state, data=data)
-        if self.result:
-            task_data.result = self.result
-        task_custom_data = CustomData(
-            type=self.name,
-            data=task_data.model_dump(),
-        )
-        if writer:
-            task_custom_data.dispatch(writer)
-        return task_custom_data.to_langchain()
-
-    def start(self, writer: StreamWriter | None = None, data: dict = {}) -> BaseMessage:
-        self.state = "new"
-        task_message = self._generate_and_dispatch_message(writer, data)
-        return task_message
-
-    def write_data(self, writer: StreamWriter | None = None, data: dict = {}) -> BaseMessage:
-        if self.state == "complete":
-            raise ValueError("Only incomplete tasks can output data.")
-        self.state = "running"
-        task_message = self._generate_and_dispatch_message(writer, data)
-        return task_message
-
-    def finish(
-        self,
-        result: Literal["success", "error"],
-        writer: StreamWriter | None = None,
-        data: dict = {},
-    ) -> BaseMessage:
-        self.state = "complete"
-        self.result = result
-        task_message = self._generate_and_dispatch_message(writer, data)
-        return task_message

src/agents/chatbot.py

@@ -1,23 +0,0 @@
-from langchain_core.messages import BaseMessage
-from langchain_core.runnables import RunnableConfig
-from langgraph.func import entrypoint
-
-from core import get_model, settings
-
-
-@entrypoint()
-async def chatbot(
-    inputs: dict[str, list[BaseMessage]],
-    *,
-    previous: dict[str, list[BaseMessage]],
-    config: RunnableConfig,
-):
-    messages = inputs["messages"]
-    if previous:
-        messages = previous["messages"] + messages
-
-    model = get_model(config["configurable"].get("model", settings.DEFAULT_MODEL))
-    response = await model.ainvoke(messages)
-    return entrypoint.final(
-        value={"messages": [response]}, save={"messages": messages + [response]}
-    )

src/agents/command_agent.py

@@ -1,55 +0,0 @@
-import random
-from typing import Literal
-
-from langchain_core.messages import AIMessage
-from langgraph.graph import START, MessagesState, StateGraph
-from langgraph.types import Command
-
-
-class AgentState(MessagesState, total=False):
-    """`total=False` is PEP589 specs.
-
-    documentation: https://typing.readthedocs.io/en/latest/spec/typeddict.html#totality
-    """
-
-
-# Define the nodes
-
-
-def node_a(state: AgentState) -> Command[Literal["node_b", "node_c"]]:
-    print("Called A")
-    value = random.choice(["a", "b"])
-    goto: Literal["node_b", "node_c"]
-    # this is a replacement for a conditional edge function
-    if value == "a":
-        goto = "node_b"
-    else:
-        goto = "node_c"
-
-    # note how Command allows you to BOTH update the graph state AND route to the next node
-    return Command(
-        # this is the state update
-        update={"messages": [AIMessage(content=f"Hello {value}")]},
-        # this is a replacement for an edge
-        goto=goto,
-    )
-
-
-def node_b(state: AgentState):
-    print("Called B")
-    return {"messages": [AIMessage(content="Hello B")]}
-
-
-def node_c(state: AgentState):
-    print("Called C")
-    return {"messages": [AIMessage(content="Hello C")]}
-
-
-builder = StateGraph(AgentState)
-builder.add_edge(START, "node_a")
-builder.add_node(node_a)
-builder.add_node(node_b)
-builder.add_node(node_c)
-# NOTE: there are no edges between nodes A, B and C!
-
-command_agent = builder.compile()

src/agents/cpstat_agent.py

@@ -0,0 +1,116 @@
+import logging
+
+from langchain.agents import create_agent
+from langchain_core.tools import BaseTool
+from langchain_mcp_adapters.client import MultiServerMCPClient
+from langchain_mcp_adapters.sessions import StreamableHttpConnection
+from langgraph.graph.state import CompiledStateGraph
+
+from agents.utils import filter_mcp_tools
+from agents.lazy_agent import LazyLoadingAgent
+from agents.middlewares import ConfigurableModelMiddleware
+from agents.middlewares.summarization import SummarizationMiddleware
+from agents.prompts.cpstat import SYSTEM_PROMPT
+from core import get_model, settings
+
+logger = logging.getLogger(__name__)
+
+ALLOWED_TOOLS = {
+    # LeetCode - User Stats & Profile
+    "leetcode_get_rating",
+    "leetcode_get_profile",
+    "leetcode_get_details",
+    "leetcode_get_badges",
+    "leetcode_get_solved",
+    "leetcode_get_contest_ranking",
+    "leetcode_get_contest_history",
+    "leetcode_get_submissions",
+    "leetcode_get_ac_submissions",
+    "leetcode_get_calendar",
+    "leetcode_get_skill_stats",
+    "leetcode_get_languages",
+    "leetcode_get_progress",
+    "leetcode_get_contest_ranking_info",
+
+    # Codeforces - User Stats & Content
+    "codeforces_get_rating",
+    "codeforces_get_contest_history",
+    "codeforces_get_user_status",
+    "codeforces_get_user_blogs",
+    "codeforces_get_solved_problems",
+    "codeforces_get_blog_entry",
+    "codeforces_get_blog_comments",
+
+    # AtCoder
+    "atcoder_get_rating",
+    "atcoder_get_history",
+
+    # CodeChef
+    "codechef_get_rating",
+
+    # GeeksforGeeks
+    "gfg_get_rating",
+    "gfg_get_submissions",
+    "gfg_get_posts",
+}
+
+
+class CPStatAgent(LazyLoadingAgent):
+    """CP Stat Agent with async initialization for contest and rating info."""
+
+    def __init__(self) -> None:
+        super().__init__()
+        self._mcp_tools: list[BaseTool] = []
+        self._mcp_client: MultiServerMCPClient | None = None
+
+    async def load(self) -> None:
+        """Initialize the CP Stat agent by loading MCP tools from the Contest API."""
+        self._model = get_model(settings.DEFAULT_MODEL)
+
+        if not settings.CONTEST_API_URL:
+            logger.info("CONTEST_API_URL is not set, CP Stat agent will have no tools")
+            self._mcp_tools = []
+            self._graph = self._create_graph()
+            self._loaded = True
+            return
+
+        try:
+            connections = {
+                "cpstat": StreamableHttpConnection(
+                    transport="streamable_http",
+                    url=settings.CONTEST_API_URL,
+                )
+            }
+
+            self._mcp_client = MultiServerMCPClient(connections)
+            logger.info(f"CP Stat client initialized successfully with URL: {settings.CONTEST_API_URL}")
+
+            all_tools = await self._mcp_client.get_tools()
+            self._mcp_tools = filter_mcp_tools(all_tools, ALLOWED_TOOLS)
+            
+        except Exception as e:
+            logger.error(f"Failed to initialize CP Stat agent: {e}")
+            self._mcp_tools = []
+            self._mcp_client = None
+
+        # Create and store the graph
+        self._graph = self._create_graph()
+        self._loaded = True
+
+    def _create_graph(self) -> CompiledStateGraph:
+        """Create the CP Stat agent graph."""
+        return create_agent(
+            model=self._model,
+            tools=self._mcp_tools,
+            middleware=[
+                ConfigurableModelMiddleware(),
+                SummarizationMiddleware(model=self._model),
+            ],
+            name="cpstat-agent",
+            system_prompt=SYSTEM_PROMPT,
+            debug=True,
+        )
+
+
+# Create the agent instance
+cpstat_agent = CPStatAgent()

src/agents/{github_mcp_agent/github_mcp_agent.py → github_mcp_agent.py}

@@ -1,7 +1,4 @@
-"""GitHub MCP Agent - An agent that uses GitHub MCP tools for repository management."""
-
 import logging
-from datetime import datetime
 
 from langchain.agents import create_agent
 from langchain_core.tools import BaseTool
@@ -9,39 +6,57 @@ from langchain_mcp_adapters.client import MultiServerMCPClient
 from langchain_mcp_adapters.sessions import StreamableHttpConnection
 from langgraph.graph.state import CompiledStateGraph
 
+from agents.utils import filter_mcp_tools
 from agents.lazy_agent import LazyLoadingAgent
+from agents.middlewares import ConfigurableModelMiddleware
+from agents.middlewares.summarization import SummarizationMiddleware
 from core import get_model, settings
+from agents.prompts.github import SYSTEM_PROMPT
 
 logger = logging.getLogger(__name__)
 
-current_date = datetime.now().strftime("%B %d, %Y")
-prompt = f"""
-You are GitHubBot, a specialized assistant for GitHub repository management and development workflows.
-You have access to GitHub MCP tools that allow you to interact with GitHub repositories, issues, pull requests,
-and other GitHub resources. Today's date is {current_date}.
-
-Your capabilities include:
-- Repository management (create, clone, browse)
-- Issue management (create, list, update, close)
-- Pull request management (create, review, merge)
-- Branch management (create, switch, merge)
-- File operations (read, write, search)
-- Commit operations (create, view history)
+ALLOWED_TOOLS = {
+    # User & Profile
+    "get_me",              # Get authenticated user's profile
+    "get_teams",           # Get teams the user belongs to
+    "get_team_members",    # Get team member usernames
+    
+    # Repository & Code
+    "search_repositories", # Find repositories by name/description
+    "get_file_contents",   # Get file/directory contents
+    "search_code",         # Search code across repositories
+    "list_branches",       # List repository branches
+    
+    # Activity & Contributions
+    "list_commits",        # List commits in a repository
+    "get_commit",          # Get commit details with diff
+    "list_pull_requests",  # List PRs in a repository
+    "pull_request_read",   # Get PR details, diff, reviews
+    "search_pull_requests",# Search PRs by author
+    
+    # Issues
+    "list_issues",         # List issues in a repository
+    "issue_read",          # Get issue details/comments
+    "search_issues",       # Search issues
+    
+    # Releases & Tags
+    "list_releases",       # List releases
+    "get_latest_release",  # Get latest release
+    "get_release_by_tag",  # Get release by tag
+    "list_tags",           # List git tags
+    "get_tag",             # Get tag details
+    
+    # Discovery
+    "search_users",        # Find GitHub users
+    "get_label",           # Get repository label
+    "list_issue_types",    # List issue types for org
+}
 
-Guidelines:
-- Always be helpful and provide clear explanations of GitHub operations
-- When creating or modifying content, ensure it follows best practices
-- Be cautious with destructive operations (deletes, force pushes, etc.)
-- Provide context about what you're doing and why
-- Use appropriate commit messages and PR descriptions
-- Respect repository permissions and access controls
 
-NOTE: You have access to GitHub MCP tools that provide direct GitHub API access.
-"""
 
 
 class GitHubMCPAgent(LazyLoadingAgent):
-    """GitHub MCP Agent with async initialization."""
+    """GitHub MCP Agent with async initialization for portfolio assistant."""
 
     def __init__(self) -> None:
         super().__init__()
@@ -50,6 +65,7 @@ class GitHubMCPAgent(LazyLoadingAgent):
 
     async def load(self) -> None:
         """Initialize the GitHub MCP agent by loading MCP tools."""
+        self._model = get_model(settings.DEFAULT_MODEL)
         if not settings.GITHUB_PAT:
             logger.info("GITHUB_PAT is not set, GitHub MCP agent will have no tools")
             self._mcp_tools = []
@@ -73,9 +89,8 @@ class GitHubMCPAgent(LazyLoadingAgent):
             self._mcp_client = MultiServerMCPClient(connections)
             logger.info("MCP client initialized successfully")
 
-            # Get tools from the client
-            self._mcp_tools = await self._mcp_client.get_tools()
-            logger.info(f"GitHub MCP agent initialized with {len(self._mcp_tools)} tools")
+            all_tools = await self._mcp_client.get_tools()
+            self._mcp_tools = filter_mcp_tools(all_tools, ALLOWED_TOOLS)
 
         except Exception as e:
             logger.error(f"Failed to initialize GitHub MCP agent: {e}")
@@ -88,13 +103,16 @@ class GitHubMCPAgent(LazyLoadingAgent):
 
     def _create_graph(self) -> CompiledStateGraph:
         """Create the GitHub MCP agent graph."""
-        model = get_model(settings.DEFAULT_MODEL)
-
         return create_agent(
-            model=model,
+            model=self._model,
             tools=self._mcp_tools,
+            middleware=[
+                ConfigurableModelMiddleware(),
+                SummarizationMiddleware(model=self._model),
+            ],
             name="github-mcp-agent",
-            system_prompt=prompt,
+            system_prompt=SYSTEM_PROMPT,
+            debug=True,
         )
 
 

src/agents/interrupt_agent.py

@@ -1,232 +0,0 @@
-import logging
-from datetime import datetime
-from typing import Any
-
-from langchain_core.language_models.base import LanguageModelInput
-from langchain_core.language_models.chat_models import BaseChatModel
-from langchain_core.messages import AIMessage, BaseMessage, HumanMessage
-from langchain_core.prompts import SystemMessagePromptTemplate
-from langchain_core.runnables import Runnable, RunnableConfig, RunnableLambda, RunnableSerializable
-from langgraph.graph import END, MessagesState, StateGraph
-from langgraph.store.base import BaseStore
-from langgraph.types import interrupt
-from pydantic import BaseModel, Field
-
-from core import get_model, settings
-
-# Added logger
-logger = logging.getLogger(__name__)
-
-
-class AgentState(MessagesState, total=False):
-    """`total=False` is PEP589 specs.
-
-    documentation: https://typing.readthedocs.io/en/latest/spec/typeddict.html#totality
-    """
-
-    birthdate: datetime | None
-
-
-def wrap_model(
-    model: BaseChatModel | Runnable[LanguageModelInput, Any], system_prompt: BaseMessage
-) -> RunnableSerializable[AgentState, Any]:
-    preprocessor = RunnableLambda(
-        lambda state: [system_prompt] + state["messages"],
-        name="StateModifier",
-    )
-    return preprocessor | model
-
-
-background_prompt = SystemMessagePromptTemplate.from_template("""
-You are a helpful assistant that tells users there zodiac sign.
-Provide a one sentence summary of the origin of zodiac signs.
-Don't tell the user what their sign is, you are just demonstrating your knowledge on the topic.
-""")
-
-
-async def background(state: AgentState, config: RunnableConfig) -> AgentState:
-    """This node is to demonstrate doing work before the interrupt"""
-
-    m = get_model(config["configurable"].get("model", settings.DEFAULT_MODEL))
-    model_runnable = wrap_model(m, background_prompt.format())
-    response = await model_runnable.ainvoke(state, config)
-
-    return {"messages": [AIMessage(content=response.content)]}
-
-
-birthdate_extraction_prompt = SystemMessagePromptTemplate.from_template("""
-You are an expert at extracting birthdates from conversational text.
-
-Rules for extraction:
-- Look for user messages that mention birthdates
-- Consider various date formats (MM/DD/YYYY, YYYY-MM-DD, Month Day, Year)
-- Validate that the date is reasonable (not in the future)
-- If no clear birthdate was provided by the user, return None
-""")
-
-
-class BirthdateExtraction(BaseModel):
-    birthdate: str | None = Field(
-        description="The extracted birthdate in YYYY-MM-DD format. If no birthdate is found, this should be None."
-    )
-    reasoning: str = Field(
-        description="Explanation of how the birthdate was extracted or why no birthdate was found"
-    )
-
-
-async def determine_birthdate(
-    state: AgentState, config: RunnableConfig, store: BaseStore
-) -> AgentState:
-    """This node examines the conversation history to determine user's birthdate, checking store first."""
-
-    # Attempt to get user_id for unique storage per user
-    user_id = config["configurable"].get("user_id")
-    logger.info(f"[determine_birthdate] Extracted user_id: {user_id}")
-    namespace = None
-    key = "birthdate"
-    birthdate = None  # Initialize birthdate
-
-    if user_id:
-        # Use user_id in the namespace to ensure uniqueness per user
-        namespace = (user_id,)
-
-        # Check if we already have the birthdate in the store for this user
-        try:
-            result = await store.aget(namespace, key=key)
-            # Handle cases where store.aget might return Item directly or a list
-            user_data = None
-            if result:  # Check if anything was returned
-                if isinstance(result, list):
-                    if result:  # Check if list is not empty
-                        user_data = result[0]
-                else:  # Assume it's the Item object directly
-                    user_data = result
-
-            if user_data and user_data.value.get("birthdate"):
-                # Convert ISO format string back to datetime object
-                birthdate_str = user_data.value["birthdate"]
-                birthdate = datetime.fromisoformat(birthdate_str) if birthdate_str else None
-                # We already have the birthdate, return it
-                logger.info(
-                    f"[determine_birthdate] Found birthdate in store for user {user_id}: {birthdate}"
-                )
-                return {
-                    "birthdate": birthdate,
-                    "messages": [],
-                }
-        except Exception as e:
-            # Log the error or handle cases where the store might be unavailable
-            logger.error(f"Error reading from store for namespace {namespace}, key {key}: {e}")
-            # Proceed with extraction if read fails
-            pass
-    else:
-        # If no user_id, we cannot reliably store/retrieve user-specific data.
-        # Consider logging this situation.
-        logger.warning(
-            "Warning: user_id not found in config. Skipping persistent birthdate storage/retrieval for this run."
-        )
-
-    # If birthdate wasn't retrieved from store, proceed with extraction
-    m = get_model(config["configurable"].get("model", settings.DEFAULT_MODEL))
-    model_runnable = wrap_model(
-        m.with_structured_output(BirthdateExtraction), birthdate_extraction_prompt.format()
-    ).with_config(tags=["skip_stream"])
-    response: BirthdateExtraction = await model_runnable.ainvoke(state, config)
-
-    # If no birthdate found after extraction attempt, interrupt
-    if response.birthdate is None:
-        birthdate_input = interrupt(f"{response.reasoning}\nPlease tell me your birthdate?")
-        # Re-run extraction with the new input
-        state["messages"].append(HumanMessage(birthdate_input))
-        # Note: Recursive call might need careful handling of depth or state updates
-        return await determine_birthdate(state, config, store)
-
-    # Birthdate found - convert string to datetime
-    try:
-        birthdate = datetime.fromisoformat(response.birthdate)
-    except ValueError:
-        # If parsing fails, ask for clarification
-        birthdate_input = interrupt(
-            "I couldn't understand the date format. Please provide your birthdate in YYYY-MM-DD format."
-        )
-        # Re-run extraction with the new input
-        state["messages"].append(HumanMessage(birthdate_input))
-        # Note: Recursive call might need careful handling of depth or state updates
-        return await determine_birthdate(state, config, store)
-
-    # Store the newly extracted birthdate only if we have a user_id
-    if user_id and namespace:
-        # Convert datetime to ISO format string for JSON serialization
-        birthdate_str = birthdate.isoformat() if birthdate else None
-        try:
-            await store.aput(namespace, key, {"birthdate": birthdate_str})
-        except Exception as e:
-            # Log the error or handle cases where the store write might fail
-            logger.error(f"Error writing to store for namespace {namespace}, key {key}: {e}")
-
-    # Return the determined birthdate (either from store or extracted)
-    logger.info(f"[determine_birthdate] Returning birthdate {birthdate} for user {user_id}")
-    return {
-        "birthdate": birthdate,
-        "messages": [],
-    }
-
-
-response_prompt = SystemMessagePromptTemplate.from_template("""
-You are a helpful assistant.
-
-Known information:
-- The user's birthdate is {birthdate_str}
-
-User's latest message: "{last_user_message}"
-
-Based on the known information and the user's message, provide a helpful and relevant response.
-If the user asked for their birthdate, confirm it.
-If the user asked for their zodiac sign, calculate it and tell them.
-Otherwise, respond conversationally based on their message.
-""")
-
-
-async def generate_response(state: AgentState, config: RunnableConfig) -> AgentState:
-    """Generates the final response based on the user's query and the available birthdate."""
-    birthdate = state.get("birthdate")
-    if state.get("messages") and isinstance(state["messages"][-1], HumanMessage):
-        last_user_message = state["messages"][-1].content
-    else:
-        last_user_message = ""
-
-    if not birthdate:
-        # This should ideally not be reached if determine_birthdate worked correctly and possibly interrupted.
-        # Handle cases where birthdate might still be missing.
-        return {
-            "messages": [
-                AIMessage(
-                    content="I couldn't determine your birthdate. Could you please provide it?"
-                )
-            ]
-        }
-
-    birthdate_str = birthdate.strftime("%B %d, %Y")  # Format for display
-
-    m = get_model(config["configurable"].get("model", settings.DEFAULT_MODEL))
-    model_runnable = wrap_model(
-        m, response_prompt.format(birthdate_str=birthdate_str, last_user_message=last_user_message)
-    )
-    response = await model_runnable.ainvoke(state, config)
-
-    return {"messages": [AIMessage(content=response.content)]}
-
-
-# Define the graph
-agent = StateGraph(AgentState)
-agent.add_node("background", background)
-agent.add_node("determine_birthdate", determine_birthdate)
-agent.add_node("generate_response", generate_response)
-
-agent.set_entry_point("background")
-agent.add_edge("background", "determine_birthdate")
-agent.add_edge("determine_birthdate", "generate_response")
-agent.add_edge("generate_response", END)
-
-interrupt_agent = agent.compile()
-interrupt_agent.name = "interrupt-agent"

src/agents/knowledge_base_agent.py

@@ -1,174 +0,0 @@
-import logging
-import os
-from typing import Any
-
-from langchain_aws import AmazonKnowledgeBasesRetriever
-from langchain_core.language_models.chat_models import BaseChatModel
-from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
-from langchain_core.runnables import RunnableConfig, RunnableLambda, RunnableSerializable
-from langchain_core.runnables.base import RunnableSequence
-from langgraph.graph import END, MessagesState, StateGraph
-from langgraph.managed import RemainingSteps
-
-from core import get_model, settings
-
-logger = logging.getLogger(__name__)
-
-
-# Define the state
-class AgentState(MessagesState, total=False):
-    """State for Knowledge Base agent."""
-
-    remaining_steps: RemainingSteps
-    retrieved_documents: list[dict[str, Any]]
-    kb_documents: str
-
-
-# Create the retriever
-def get_kb_retriever():
-    """Create and return a Knowledge Base retriever instance."""
-    # Get the Knowledge Base ID from environment
-    kb_id = os.environ.get("AWS_KB_ID", "")
-    if not kb_id:
-        raise ValueError("AWS_KB_ID environment variable must be set")
-
-    # Create the retriever with the specified Knowledge Base ID
-    retriever = AmazonKnowledgeBasesRetriever(
-        knowledge_base_id=kb_id,
-        retrieval_config={
-            "vectorSearchConfiguration": {
-                "numberOfResults": 3,
-            }
-        },
-    )
-    return retriever
-
-
-def wrap_model(model: BaseChatModel) -> RunnableSerializable[AgentState, AIMessage]:
-    """Wrap the model with a system prompt for the Knowledge Base agent."""
-
-    def create_system_message(state):
-        base_prompt = """You are a helpful assistant that provides accurate information based on retrieved documents.
-
-        You will receive a query along with relevant documents retrieved from a knowledge base. Use these documents to inform your response.
-
-        Follow these guidelines:
-        1. Base your answer primarily on the retrieved documents
-        2. If the documents contain the answer, provide it clearly and concisely
-        3. If the documents are insufficient, state that you don't have enough information
-        4. Never make up facts or information not present in the documents
-        5. Always cite the source documents when referring to specific information
-        6. If the documents contradict each other, acknowledge this and explain the different perspectives
-
-        Format your response in a clear, conversational manner. Use markdown formatting when appropriate.
-        """
-
-        # Check if documents were retrieved
-        if "kb_documents" in state:
-            # Append document information to the system prompt
-            document_prompt = f"\n\nI've retrieved the following documents that may be relevant to the query:\n\n{state['kb_documents']}\n\nPlease use these documents to inform your response to the user's query. Only use information from these documents and clearly indicate when you are unsure."
-            return [SystemMessage(content=base_prompt + document_prompt)] + state["messages"]
-        else:
-            # No documents were retrieved
-            no_docs_prompt = (
-                "\n\nNo relevant documents were found in the knowledge base for this query."
-            )
-            return [SystemMessage(content=base_prompt + no_docs_prompt)] + state["messages"]
-
-    preprocessor = RunnableLambda(
-        create_system_message,
-        name="StateModifier",
-    )
-    return RunnableSequence(preprocessor, model)
-
-
-async def retrieve_documents(state: AgentState, config: RunnableConfig) -> AgentState:
-    """Retrieve relevant documents from the knowledge base."""
-    # Get the last human message
-    human_messages = [msg for msg in state["messages"] if isinstance(msg, HumanMessage)]
-    if not human_messages:
-        # Include messages from original state
-        return {"messages": [], "retrieved_documents": []}
-
-    # Use the last human message as the query
-    query = human_messages[-1].content
-
-    try:
-        # Initialize the retriever
-        retriever = get_kb_retriever()
-
-        # Retrieve documents
-        retrieved_docs = await retriever.ainvoke(query)
-
-        # Create document summaries for the state
-        document_summaries = []
-        for i, doc in enumerate(retrieved_docs, 1):
-            summary = {
-                "id": doc.metadata.get("id", f"doc-{i}"),
-                "source": doc.metadata.get("source", "Unknown"),
-                "title": doc.metadata.get("title", f"Document {i}"),
-                "content": doc.page_content,
-                "relevance_score": doc.metadata.get("score", 0),
-            }
-            document_summaries.append(summary)
-
-        logger.info(f"Retrieved {len(document_summaries)} documents for query: {query[:50]}...")
-
-        return {"retrieved_documents": document_summaries, "messages": []}
-
-    except Exception as e:
-        logger.error(f"Error retrieving documents: {str(e)}")
-        return {"retrieved_documents": [], "messages": []}
-
-
-async def prepare_augmented_prompt(state: AgentState, config: RunnableConfig) -> AgentState:
-    """Prepare a prompt augmented with retrieved document content."""
-    # Get retrieved documents
-    documents = state.get("retrieved_documents", [])
-
-    if not documents:
-        return {"messages": []}
-
-    # Format retrieved documents for the model
-    formatted_docs = "\n\n".join(
-        [
-            f"--- Document {i + 1} ---\n"
-            f"Source: {doc.get('source', 'Unknown')}\n"
-            f"Title: {doc.get('title', 'Unknown')}\n\n"
-            f"{doc.get('content', '')}"
-            for i, doc in enumerate(documents)
-        ]
-    )
-
-    # Store formatted documents in the state
-    return {"kb_documents": formatted_docs, "messages": []}
-
-
-async def acall_model(state: AgentState, config: RunnableConfig) -> AgentState:
-    """Generate a response based on the retrieved documents."""
-    m = get_model(config["configurable"].get("model", settings.DEFAULT_MODEL))
-    model_runnable = wrap_model(m)
-
-    response = await model_runnable.ainvoke(state, config)
-
-    return {"messages": [response]}
-
-
-# Define the graph
-agent = StateGraph(AgentState)
-
-# Add nodes
-agent.add_node("retrieve_documents", retrieve_documents)
-agent.add_node("prepare_augmented_prompt", prepare_augmented_prompt)
-agent.add_node("model", acall_model)
-
-# Set entry point
-agent.set_entry_point("retrieve_documents")
-
-# Add edges to define the flow
-agent.add_edge("retrieve_documents", "prepare_augmented_prompt")
-agent.add_edge("prepare_augmented_prompt", "model")
-agent.add_edge("model", END)
-
-# Compile the agent
-kb_agent = agent.compile()

src/agents/langgraph_supervisor_agent.py

@@ -1,62 +0,0 @@
-from typing import Any
-
-from langchain.agents import create_agent
-from langgraph_supervisor import create_supervisor
-
-from core import get_model, settings
-
-model = get_model(settings.DEFAULT_MODEL)
-
-
-def add(a: float, b: float) -> float:
-    """Add two numbers."""
-    return a + b
-
-
-def multiply(a: float, b: float) -> float:
-    """Multiply two numbers."""
-    return a * b
-
-
-def web_search(query: str) -> str:
-    """Search the web for information."""
-    return (
-        "Here are the headcounts for each of the FAANG companies in 2024:\n"
-        "1. **Facebook (Meta)**: 67,317 employees.\n"
-        "2. **Apple**: 164,000 employees.\n"
-        "3. **Amazon**: 1,551,000 employees.\n"
-        "4. **Netflix**: 14,000 employees.\n"
-        "5. **Google (Alphabet)**: 181,269 employees."
-    )
-
-
-math_agent: Any = create_agent(
-    model=model,
-    tools=[add, multiply],
-    name="sub-agent-math_expert",
-    system_prompt="You are a math expert. Always use one tool at a time.",
-).with_config(tags=["skip_stream"])
-
-research_agent: Any = create_agent(
-    model=model,
-    tools=[web_search],
-    name="sub-agent-research_expert",
-    system_prompt="You are a world class researcher with access to web search. Do not do any math.",
-).with_config(tags=["skip_stream"])
-
-
-# Create supervisor workflow
-workflow = create_supervisor(
-    [research_agent, math_agent],
-    model=model,
-    prompt=(
-        "You are a team supervisor managing a research expert and a math expert. "
-        "For current events, use research_agent. "
-        "For math problems, use math_agent."
-    ),
-    add_handoff_back_messages=True,
-    # UI now expects this to be True so we don't have to guess when a handoff back occurs
-    output_mode="full_history",  # otherwise when reloading conversations, the sub-agents' messages are not included
-)
-
-langgraph_supervisor_agent = workflow.compile()

src/agents/langgraph_supervisor_hierarchy_agent.py

@@ -1,46 +0,0 @@
-from langchain.agents import create_agent
-from langgraph_supervisor import create_supervisor
-
-from agents.langgraph_supervisor_agent import add, multiply, web_search
-from core import get_model, settings
-
-model = get_model(settings.DEFAULT_MODEL)
-
-
-def workflow(chosen_model):
-    math_agent = create_agent(
-        model=chosen_model,
-        tools=[add, multiply],
-        name="sub-agent-math_expert",  # Identify the graph node as a sub-agent
-        system_prompt="You are a math expert. Always use one tool at a time.",
-    ).with_config(tags=["skip_stream"])
-
-    research_agent = (
-        create_supervisor(
-            [math_agent],
-            model=chosen_model,
-            tools=[web_search],
-            prompt="You are a world class researcher with access to web search. Do not do any math, you have a math expert for that. ",
-            supervisor_name="supervisor-research_expert",  # Identify the graph node as a supervisor to the math agent
-        )
-        .compile(
-            name="sub-agent-research_expert"
-        )  # Identify the graph node as a sub-agent to the main supervisor
-        .with_config(tags=["skip_stream"])
-    )  # Stream tokens are ignored for sub-agents in the UI
-
-    # Create supervisor workflow
-    return create_supervisor(
-        [research_agent],
-        model=chosen_model,
-        prompt=(
-            "You are a team supervisor managing a research expert with math capabilities."
-            "For current events, use research_agent. "
-        ),
-        add_handoff_back_messages=True,
-        # UI now expects this to be True so we don't have to guess when a handoff back occurs
-        output_mode="full_history",  # otherwise when reloading conversations, the sub-agents' messages are not included
-    )  # default name for supervisor is "supervisor".
-
-
-langgraph_supervisor_hierarchy_agent = workflow(model).compile()

src/agents/middlewares/__init__.py

@@ -0,0 +1,12 @@
+"""Agent and service middlewares in a single folder."""
+
+from agents.middlewares.configurable_model import ConfigurableModelMiddleware
+from agents.middlewares.followup import FollowUpMiddleware
+from agents.middlewares.safety import SafetyMiddleware, UNSAFE_RESPONSE
+
+__all__ = [
+    "ConfigurableModelMiddleware",
+    "FollowUpMiddleware",
+    "SafetyMiddleware",
+    "UNSAFE_RESPONSE",
+]

src/agents/middlewares/configurable_model.py

@@ -0,0 +1,27 @@
+"""Middleware that resolves the LLM model from request config (e.g. frontend model selector)."""
+
+from langchain.agents.middleware.types import AgentMiddleware, ModelRequest
+from langgraph.config import get_config
+
+from core import get_model, settings
+
+
+class ConfigurableModelMiddleware(AgentMiddleware):
+    """Resolves the model from config['configurable']['model'] at invoke time.
+
+    Use this so the agent respects the model chosen by the user in the frontend.
+    """
+
+    tools: list = []  # type: ignore[assignment]  # no extra tools
+
+    def wrap_model_call(self, request: ModelRequest, handler):
+        config = get_config()
+        model_key = config.get("configurable", {}).get("model", settings.DEFAULT_MODEL)
+        resolved_model = get_model(model_key)
+        return handler(request.override(model=resolved_model))
+
+    async def awrap_model_call(self, request: ModelRequest, handler):
+        config = get_config()
+        model_key = config.get("configurable", {}).get("model", settings.DEFAULT_MODEL)
+        resolved_model = get_model(model_key)
+        return await handler(request.override(model=resolved_model))

src/agents/middlewares/followup.py

@@ -0,0 +1,89 @@
+import logging
+from typing import List
+
+from pydantic import BaseModel, Field, field_validator
+
+from langchain_core.messages import (
+    AnyMessage,
+    SystemMessage,
+    HumanMessage,
+    AIMessage,
+    ChatMessage,
+)
+from langchain_core.runnables import RunnableConfig
+
+from core import get_model, settings
+from agents.prompts.followup import (
+    DEFAULT_FOLLOWUP_PROMPTS,
+    FOLLOWUP_GENERATION_PROMPT,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class FollowUpOutput(BaseModel):
+    """Schema for follow-up generation response."""
+
+    questions: List[str] = Field(
+        min_length=1,
+        max_length=5,
+        description="List of 1-5 suggested follow-up questions for the user.",
+    )
+
+    @field_validator("questions")
+    @classmethod
+    def validate_questions(cls, value: List[str]) -> List[str]:
+        cleaned = [q.strip() for q in value if q and q.strip()]
+        if not cleaned:
+            raise ValueError("Questions list cannot be empty")
+        return cleaned
+
+
+class FollowUpMiddleware:
+    """Generates structured follow-up suggestions after an agent response."""
+
+    async def generate(
+        self,
+        messages: List[AnyMessage],
+        config: RunnableConfig,
+    ) -> List[str]:
+        try:
+            model_name = config.get("configurable", {}).get(
+                "model",
+                settings.DEFAULT_MODEL,
+            )
+
+            base_model = get_model(model_name)
+
+            # Force structured output
+            model = base_model.with_structured_output(FollowUpOutput)
+
+            # Clean messages
+            cleaned_messages: List[AnyMessage] = [
+                SystemMessage(content=FOLLOWUP_GENERATION_PROMPT),
+                *[
+                    m
+                    for m in messages
+                    if not (isinstance(m, ChatMessage) and m.role == "custom")
+                ],
+            ]
+
+            # Invoke model
+            response: FollowUpOutput = await model.ainvoke(
+                cleaned_messages,
+                config,
+            )
+
+            if not response or not response.questions:
+                raise ValueError("Empty follow-up response")
+
+            return response.questions
+
+        except Exception as e:
+            logger.warning(
+                "Follow-up generation failed, using defaults: %s",
+                e,
+                exc_info=True,
+            )
+
+        return DEFAULT_FOLLOWUP_PROMPTS

src/agents/middlewares/safety.py

@@ -0,0 +1,56 @@
+"""Service-layer safety middleware using LlamaGuard."""
+
+import logging
+
+from langchain_core.messages import AnyMessage
+
+from agents.llama_guard import LlamaGuard, LlamaGuardOutput, SafetyAssessment
+from core import settings
+
+logger = logging.getLogger(__name__)
+
+UNSAFE_RESPONSE = (
+    "I'm sorry, but I can't process that request as it was flagged "
+    "for safety concerns. Please rephrase your message."
+)
+
+
+class SafetyMiddleware:
+    """Centralized safety middleware that wraps LlamaGuard for input/output checks.
+
+    Used at the service layer to guard all agent interactions uniformly.
+    Respects the ``ENABLE_SAFETY_GUARD`` setting – when disabled, all checks
+    short-circuit to ``SAFE``.
+    """
+
+    def __init__(self) -> None:
+        self._guard = LlamaGuard()
+
+    @property
+    def enabled(self) -> bool:
+        """Whether the safety guard is active."""
+        return settings.ENABLE_SAFETY_GUARD and self._guard.model is not None
+
+    async def check_input(self, messages: list[AnyMessage]) -> LlamaGuardOutput:
+        """Check user input messages before forwarding to an agent."""
+        if not self.enabled:
+            return LlamaGuardOutput(safety_assessment=SafetyAssessment.SAFE)
+        result = await self._guard.ainvoke("User", messages)
+        if result.safety_assessment == SafetyAssessment.UNSAFE:
+            logger.warning(
+                "Input blocked by LlamaGuard – categories: %s",
+                ", ".join(result.unsafe_categories),
+            )
+        return result
+
+    async def check_output(self, messages: list[AnyMessage]) -> LlamaGuardOutput:
+        """Check agent output messages before returning to the user."""
+        if not self.enabled:
+            return LlamaGuardOutput(safety_assessment=SafetyAssessment.SAFE)
+        result = await self._guard.ainvoke("Agent", messages)
+        if result.safety_assessment == SafetyAssessment.UNSAFE:
+            logger.warning(
+                "Output blocked by LlamaGuard – categories: %s",
+                ", ".join(result.unsafe_categories),
+            )
+        return result

src/agents/middlewares/summarization.py

@@ -0,0 +1,12 @@
+"""Summarization middleware for agent conversation history.
+
+Re-exports LangChain's SummarizationMiddleware and provides a factory
+so agents can use it with the same model they use for the main loop.
+"""
+
+from langchain.agents.middleware import SummarizationMiddleware as _SummarizationMiddleware
+
+__all__ = ["SummarizationMiddleware"]
+
+# Re-export so all agent middlewares live under agents.middlewares
+SummarizationMiddleware = _SummarizationMiddleware

src/agents/{bg_task_agent/bg_task_agent.py → portfolio_agent.py}

@@ -1,12 +1,15 @@
-import asyncio
+from typing import Literal
 
 from langchain_core.language_models.chat_models import BaseChatModel
-from langchain_core.messages import AIMessage
+from langchain_core.messages import AIMessage, SystemMessage, ChatMessage
 from langchain_core.runnables import RunnableConfig, RunnableLambda, RunnableSerializable
 from langgraph.graph import END, MessagesState, StateGraph
-from langgraph.types import StreamWriter
+from langgraph.managed import RemainingSteps
+from langgraph.prebuilt import ToolNode
+
+from agents.tools.database_search import database_search
+from agents.prompts.portfolio import SYSTEM_PROMPT
 
-from agents.bg_task_agent.task import Task
 from core import get_model, settings
 
 
@@ -16,13 +19,22 @@ class AgentState(MessagesState, total=False):
     documentation: https://typing.readthedocs.io/en/latest/spec/typeddict.html#totality
     """
 
+    remaining_steps: RemainingSteps
+
+
+tools = [database_search]
+
 
 def wrap_model(model: BaseChatModel) -> RunnableSerializable[AgentState, AIMessage]:
+    bound_model = model.bind_tools(tools)
     preprocessor = RunnableLambda(
-        lambda state: state["messages"],
+        lambda state: [SystemMessage(content=SYSTEM_PROMPT)] + [
+            m for m in state["messages"]
+            if not isinstance(m, ChatMessage) or m.role != "custom"
+        ],
         name="StateModifier",
     )
-    return preprocessor | model  # type: ignore[return-value]
+    return preprocessor | bound_model  # type: ignore[return-value]
 
 
 async def acall_model(state: AgentState, config: RunnableConfig) -> AgentState:
@@ -30,33 +42,39 @@ async def acall_model(state: AgentState, config: RunnableConfig) -> AgentState:
     model_runnable = wrap_model(m)
     response = await model_runnable.ainvoke(state, config)
 
+    if state["remaining_steps"] < 2 and response.tool_calls:
+        return {
+            "messages": [
+                AIMessage(
+                    id=response.id,
+                    content="Sorry, need more steps to process this request.",
+                )
+            ]
+        }
     # We return a list, because this will get added to the existing list
     return {"messages": [response]}
 
 
-async def bg_task(state: AgentState, writer: StreamWriter) -> AgentState:
-    task1 = Task("Simple task 1...", writer)
-    task2 = Task("Simple task 2...", writer)
-
-    task1.start()
-    await asyncio.sleep(2)
-    task2.start()
-    await asyncio.sleep(2)
-    task1.write_data(data={"status": "Still running..."})
-    await asyncio.sleep(2)
-    task2.finish(result="error", data={"output": 42})
-    await asyncio.sleep(2)
-    task1.finish(result="success", data={"output": 42})
-    return {"messages": []}
-
-
 # Define the graph
 agent = StateGraph(AgentState)
 agent.add_node("model", acall_model)
-agent.add_node("bg_task", bg_task)
-agent.set_entry_point("bg_task")
+agent.add_node("tools", ToolNode(tools))
+agent.set_entry_point("model")
+
+# Always run "model" after "tools"
+agent.add_edge("tools", "model")
+
+
+# After "model", if there are tool calls, run "tools". Otherwise end.
+def pending_tool_calls(state: AgentState) -> Literal["tools", "__end__"]:
+    last_message = state["messages"][-1]
+    if not isinstance(last_message, AIMessage):
+        raise TypeError(f"Expected AIMessage, got {type(last_message)}")
+    if last_message.tool_calls:
+        return "tools"
+    return "__end__"
+
 
-agent.add_edge("bg_task", "model")
-agent.add_edge("model", END)
+agent.add_conditional_edges("model", pending_tool_calls, {"tools": "tools", "__end__": END})
 
-bg_task_agent = agent.compile()
+portfolio_agent = agent.compile(debug=True)

src/agents/portfolio_agent/portfolio_agent.py

@@ -1,150 +0,0 @@
-import logging
-from typing import Literal, List
-from pydantic import BaseModel, Field
-
-from langchain_core.messages import AIMessage, SystemMessage
-from langchain_core.runnables import RunnableConfig, RunnableLambda
-from langchain_core.tools import BaseTool
-from langgraph.graph import END, MessagesState, StateGraph
-from langgraph.graph.state import CompiledStateGraph
-from langgraph.prebuilt import ToolNode
-from langchain_mcp_adapters.client import MultiServerMCPClient
-
-from agents.portfolio_agent.prompt import SYSTEM_PROMPT, FOLLOWUP_GENERATION_PROMPT
-from core import get_model, settings
-from agents.portfolio_agent.tools.database_search import database_search
-from agents.portfolio_agent.tools.contest_ratings import contest_ratings
-from agents.portfolio_agent.tools.github import get_github_tools
-from agents.lazy_agent import LazyLoadingAgent
-
-logger = logging.getLogger(__name__)
-
-
-class AgentState(MessagesState):
-    """Agent state for the portfolio assistant."""
-    summary: str
-    follow_up: List[str]
-
-
-class FollowUpQuestions(BaseModel):
-    questions: List[str] = Field(description="A list of follow-up questions")
-
-
-class PortfolioAgent(LazyLoadingAgent):
-    """Portfolio Agent with async initialization for GitHub MCP tools."""
-
-    def __init__(self) -> None:
-        super().__init__()
-        self._tools: list[BaseTool] = [database_search, contest_ratings]
-        self._mcp_client: MultiServerMCPClient | None = None
-
-    async def load(self) -> None:
-        """Initialize the portfolio agent by loading GitHub MCP tools."""
-        self._tools = [database_search, contest_ratings]
-
-        # Load GitHub MCP tools if available
-        github_tools, self._mcp_client = await get_github_tools()
-        self._tools.extend(github_tools)
-        
-        logger.info(f"Portfolio agent initialized with {len(self._tools)} tools ({len(github_tools)} GitHub MCP tools)")
-
-        # Create and store the graph
-        self._graph = self._create_graph()
-        self._loaded = True
-
-    def _prepare_messages(self, state: AgentState) -> list:
-        """Prepare messages with system prompt and optional summary."""
-        summary = state.get("summary", "")
-        system_content = SYSTEM_PROMPT
-        if summary:
-            system_content += f"\n\n### Previous Conversation Summary:\n{summary}"
-        return [SystemMessage(content=system_content)] + state["messages"]
-
-    def _create_graph(self) -> CompiledStateGraph:
-        """Create the portfolio agent graph."""
-        graph = StateGraph(AgentState)
-
-        async def call_portfolio_model(state: AgentState, config: RunnableConfig):
-            """Call the portfolio model with tools, fallback to no tools on error."""
-            model = get_model(settings.DEFAULT_MODEL)
-            
-            # Try with tools first
-            try:
-                if self._tools:
-                    model_with_tools = model.bind_tools(self._tools)
-                    runnable = RunnableLambda(self._prepare_messages) | model_with_tools
-                    response = await runnable.ainvoke(state, config)
-                    return {"messages": [response]}
-            except Exception as e:
-                # Check if it's a Groq API function call error
-                if "Failed to call a function" in str(e) or "APIError" in type(e).__name__:
-                    logger.warning(f"Tool call error, falling back without tools: {e}")
-                else:
-                    logger.error(f"Error in call_portfolio_model: {e}", exc_info=True)
-            
-            # Fallback: run without tools
-            try:
-                runnable = RunnableLambda(self._prepare_messages) | model
-                response = await runnable.ainvoke(state, config)
-                return {"messages": [response]}
-            except Exception as e:
-                logger.error(f"Fallback also failed: {e}", exc_info=True)
-                return {
-                    "messages": [
-                        AIMessage(
-                            content="I encountered an error while processing your request. "
-                            "Please try rephrasing your question or ask about something else."
-                        )
-                    ]
-                }
-
-        async def generate_followup(state: AgentState, config: RunnableConfig):
-            """Generate follow-up questions based on the conversation."""
-            model = get_model(settings.DEFAULT_MODEL)
-            chain = model.with_structured_output(FollowUpQuestions)
-            messages = [SystemMessage(content=FOLLOWUP_GENERATION_PROMPT)] + state["messages"]
-            
-            try:
-                response = await chain.ainvoke(messages, config)
-                return {"follow_up": response.questions}
-            except Exception as e:
-                logger.warning(f"Failed to generate follow-up questions: {e}")
-                default_questions = [
-                    "What are his contact details?",
-                    "What are his projects?",
-                    "What are his skills?",
-                    "What are his work experiences?",
-                    "What are his achievements?",
-                    "What are his leadership roles?",
-                ]
-                return {"follow_up": default_questions}
-
-        def route_after_model(state: AgentState) -> Literal["tools", "generate_followup"]:
-            """Route to tools if tool calls are present, otherwise generate follow-up."""
-            last_message = state["messages"][-1]
-            if isinstance(last_message, AIMessage) and last_message.tool_calls:
-                return "tools"
-            return "generate_followup"
-
-        graph.add_node("agent", call_portfolio_model)
-        graph.add_node("tools", ToolNode(self._tools))
-        graph.add_node("generate_followup", generate_followup)
-
-        graph.set_entry_point("agent")
-
-        graph.add_conditional_edges(
-            "agent",
-            route_after_model,
-            {
-                "tools": "tools",
-                "generate_followup": "generate_followup",
-            },
-        )
-
-        graph.add_edge("tools", "agent")
-        graph.add_edge("generate_followup", END)
-
-        return graph.compile(debug=True)
-
-
-portfolio_agent = PortfolioAgent()

src/agents/portfolio_agent/tools/contest_ratings.py

@@ -1,23 +0,0 @@
-import requests
-from core import settings
-from langchain_core.tools import BaseTool, tool
-
-
-def get_contest_ratings(username: str = "anujjoshi3105") -> str:
-    """
-    Use this tool to get the latest contest ratings and statistics for Anuj Joshi from various competitive programming platforms.    
-    This tool fetches real-time data from Codeforces, CodeChef, LeetCode, and GeeksforGeeks.
-    """
-    try:
-        if settings.CONTEST_API_URL is None:
-            return "Error: Contest API URL is not set"
-        url = f"{settings.CONTEST_API_URL}?username={username}"
-        response = requests.get(url, timeout=10.0)
-        response.raise_for_status()
-        return response.json()
-    except Exception as e:
-        return f"Error fetching contest ratings: {str(e)}"
-
-
-contest_ratings: BaseTool = tool(get_contest_ratings)
-contest_ratings.name = "Contest_Ratings"

src/agents/portfolio_agent/tools/database_search.py

@@ -1,42 +0,0 @@
-from typing import List
-
-from langchain_core.documents import Document
-from langchain_core.tools import BaseTool, tool
-from memory.postgres import load_pgvector_retriever
-
-
-def hybrid_search(query: str, k: int = 6) -> List[Document]:
-    retriever = load_pgvector_retriever(k)
-    return retriever.invoke(query)
-
-
-def format_contexts(documents: List[Document]) -> str:
-    formatted_docs = []
-
-    for i, doc in enumerate(documents):
-        metadata = doc.metadata
-        content = doc.page_content.strip()
-
-        formatted_docs.append(
-            f"--- Document {i + 1} ({metadata}) ---\n{content}"
-        )
-
-    return "\n\n".join(formatted_docs)
-
-
-# DATABASE SEARCH TOOL
-def database_search_func(query: str) -> str:
-    """
-    Use this tool to search for relevant information about Anuj Joshi's education, experience, projects, blog posts, testimonials, skills, etc.
-    """
-
-    documents = hybrid_search(query, k=6)
-
-    if not documents:
-        return "No relevant portfolio information found."
-
-    return format_contexts(documents)
-
-
-database_search: BaseTool = tool(database_search_func)
-database_search.name = "Database_Search"

src/agents/portfolio_agent/tools/github.py

@@ -1,49 +0,0 @@
-import logging
-from typing import List
-
-from langchain_core.tools import BaseTool
-from langchain_mcp_adapters.client import MultiServerMCPClient
-from langchain_mcp_adapters.sessions import StreamableHttpConnection
-
-from core import settings
-
-logger = logging.getLogger(__name__)
-
-
-async def get_github_tools() -> tuple[List[BaseTool], MultiServerMCPClient | None]:
-    """
-    Initialize and return GitHub MCP tools.
-    
-    Returns:
-        A tuple containing:
-        - List of GitHub MCP tools (empty list if initialization fails)
-        - MultiServerMCPClient instance (None if initialization fails)
-    """
-    if not settings.GITHUB_PAT:
-        logger.info("GITHUB_PAT is not set, GitHub tools will not be available")
-        return [], None
-    
-    try:
-        github_pat = settings.GITHUB_PAT.get_secret_value()
-        connections = {
-            "github": StreamableHttpConnection(
-                transport="streamable_http",
-                url=settings.MCP_GITHUB_SERVER_URL,
-                headers={
-                    "Authorization": f"Bearer {github_pat}",
-                },
-            )
-        }
-
-        mcp_client = MultiServerMCPClient(connections)
-        logger.info("GitHub MCP client initialized successfully")
-
-        # Get tools from the client
-        mcp_tools = await mcp_client.get_tools()
-        logger.info(f"Loaded {len(mcp_tools)} GitHub MCP tools")
-        
-        return mcp_tools, mcp_client
-
-    except Exception as e:
-        logger.error(f"Failed to initialize GitHub MCP tools: {e}")
-        return [], None

src/agents/prompts/cpstat.py

@@ -0,0 +1,25 @@
+from datetime import datetime
+
+PORTFOLIO_URL = "https://anujjoshi.netlify.app"
+OWNER = "Anuj Joshi"
+current_date = datetime.now().strftime("%B %d, %Y")
+
+SYSTEM_PROMPT = f"""
+You are an **Award-Winning Competitive Programming Portfolio Assistant** for {OWNER} (@anujjoshi3105).
+Today's date is {current_date}.
+
+Your goal is to showcase {OWNER}'s problem-solving skills, contest ratings, and algorithmic achievements to recruiters and visitors.
+
+# {OWNER}'S COMPETITIVE PROGRAMMING PROFILE
+- For all platforms, the username is "anujjoshi3105"
+
+# YOUR RESPONSIBILITIES
+- **Showcase Ratings**: highlighting peak ratings and current standing.
+- **Analyze Progress**: Explain the difficulty of problems solved (Easy/Medium/Hard).
+- **Highlight Achievements**: Mention badges, contest ranks, and consistency.
+- **Explain Solutions**: If asked, explain the approach to problems {OWNER} has solved.
+
+You have READ-ONLY access to these platforms.
+Always present information in a clear, professional, and impressive manner suitable for a top-tier tech portfolio.
+If the user asks about "my" or "the" stats, assume they are asking about **{OWNER}'s** stats unless specified otherwise.
+"""

src/agents/prompts/followup.py

@@ -0,0 +1,12 @@
+DEFAULT_FOLLOWUP_PROMPTS = [
+    "Tell me about Anuj's background",
+    "What are Anuj's key technical skills?",
+    "Show me some of Anuj's featured projects",
+    "What is Anuj's work experience?",
+    "How can I contact Anuj?",
+]
+
+FOLLOWUP_GENERATION_PROMPT = f"""
+# Role: You are a predictive user intent engine.
+# Task: Generate 3-5 suggested follow-up options that the USER would click to ask the chatbot. Based on the previous message, predict the most logical next steps for the user.
+"""

src/agents/prompts/github.py

@@ -0,0 +1,32 @@
+
+from datetime import datetime
+
+PORTFOLIO_URL = "https://anujjoshi.netlify.app"
+OWNER = "Anuj Joshi"
+GITHUB_HANDLE = "anujjoshi3105"
+current_date = datetime.now().strftime("%B %d, %Y")
+
+SYSTEM_PROMPT = f"""
+You are an **Award-Winning Open Source Portfolio Assistant** for {OWNER} (@{GITHUB_HANDLE}).
+Today's date is {current_date}.
+
+Your goal is to showcase {OWNER}'s software engineering projects, contributions, and coding activity to recruiters and visitors.
+
+# {OWNER}'S GITHUB PROFILE
+- **Handle**: {GITHUB_HANDLE}
+- **URL**: https://github.com/{GITHUB_HANDLE}
+- **Focus Areas**: AI Agents, Competitive Programming, Full Stack Development, Machine Learning.
+
+# YOUR RESPONSIBILITIES
+- **Showcase Projects**: Highlight key repositories like 'portfolio-bot', 'contestAPI', and others found in the profile.
+- **Explain Contributions**: Summarize commits, pull requests, and issues to demonstrate impact.
+- **Analyze Code Quality**: When discussing code, emphasize best practices, readability, and modern tech stacks used by {OWNER}.
+- **Guide Navigation**: Direct users to specific repositories for detailed demos or implementations.
+
+You have READ-ONLY access to GitHub.
+Always present information in a clear, professional, and impressive manner suitable for a top-tier tech portfolio.
+
+IMPORTANT:
+- If the user asks about "my" or "the" profile/repos, assume they are asking about **{OWNER}'s** ({GITHUB_HANDLE}) data.
+- If the user asks for repository-specific information (like commits, issues, PRs) but hasn't specified a repository, you MUST ask the user which of **{OWNER}'s** repositories they would like to view.
+"""

src/agents/{portfolio_agent/prompt.py → prompts/portfolio.py}

@@ -1,13 +1,13 @@
+
 PORTFOLIO_URL = "https://anujjoshi.netlify.app"
 OWNER = "Anuj Joshi"
 
 SYSTEM_PROMPT = f"""
 You are an **Award-Winning Professional Portfolio Assistant** for {OWNER}, (a Full Stack Developer and AI & Machine Learning Engineer from New Delhi, India).
-
 Your goal is to answer questions from recruiter, potential employer, or visitors about {OWNER}'s skills, projects, qualifications, and experience.
 
 # CONTACT ({PORTFOLIO_URL}/contact)
-- Email: anujjoshi3105@gmail.com | LinkedIn: linkedin.com/in/anujjoshi3105 | GitHub: github.com/anujjoshi3105 | X: x.com/anujjoshi3105
+- Portfolio: {PORTFOLIO_URL} | Email: anujjoshi3105@gmail.com | LinkedIn: linkedin.com/in/anujjoshi3105 | GitHub: github.com/anujjoshi3105 | X: x.com/anujjoshi3105
 
 # Competitive Programming ({PORTFOLIO_URL}/about)
 - LeetCode: 1910 (Knight), 750+ solved | Codeforces: 1434 (specialist) | AtCoder: 929 (Green) | GeeksforGeeks: Rank 46
@@ -65,7 +65,7 @@ https://drive.google.com/file/d/150EAtBVjP1DV-b_v0JKhVYzhIVoCvAWO/view
 - Industrial Research & Development Centre Portal – Research workflow automation platform.
 - NicoGauge – ML-based evaluation platform for learning analytics.
 - Fictiora – Entertainment discovery platform using Next.js and TMDB APIs.
-For more info visit {PORTFOLIO_URL}/project
+- For more info visit {PORTFOLIO_URL}/project
 
 # TECHNICAL SKILLS ({PORTFOLIO_URL}/about#skills)
 - AI / ML: PyTorch, TensorFlow, Keras, Scikit-learn, CNNs, GANs, Transformers, LangChain, LLM-based agents
@@ -79,27 +79,11 @@ For more info visit {PORTFOLIO_URL}/project
 - Volunteer, Summer School on AI (DTU) https://drive.google.com/file/d/10Jx3yC8gmFYHkl0KXucaUOZJqtf9QkJq/view?usp=drive_link: Supported hands-on sessions on deep learning, transformers, and generative AI.
 
 # TOOLS
-- Database_Search: Search portfolio info (education, experience, testimonials, skills, projects, blog). Always use for queries not covered above. Cite {PORTFOLIO_URL}/blog
-- Contest_Ratings: Get real-time CP ratings (LeetCode, Codeforces, CodeChef, GeeksforGeeks) for username anujjoshi3105
-- GitHub MCP tools: Browse repos, view code/READMEs, check commits/issues/PRs for anujjoshi3105
+- Database_Search: Search portfolio info (education, experience, testimonials, skills, projects, blog). Cite {PORTFOLIO_URL}/blog
 
 # OUTPUT GUIDELINES:
 - **Content:** Include all the links, images, and other relevant content from the all the suitable available tools accurately in the final response.
 - **Information-Dense Sentences:** Every sentence must provide a new fact.
 - **Interesting and Engaging:** Use a mix of facts and interesting details to keep the reader engaged.
 - **Style:** Professional, concise, witty and helpful.
-
-"""
-
-FOLLOWUP_GENERATION_PROMPT = f"""
-Generate 3-5 short follow-up questions to help the user explore {OWNER}'s portfolio.
-
-# OUTPUT GUIDELINES:
-- **Diverse and Specific:** Choose questions that explore different aspects of {OWNER}'s portfolio.
-- **No Duplicates:** Ensure each question is unique and not a repeat of previous responses.
-- **Actionable:** Suggest questions that lead to further exploration or discovery.
-- **Engaging:** Use a mix of factual and thought-provoking questions.
-
-Return ONLY valid JSON:
-{{ "questions": ["question 1", "question 2", ...] }}
-"""
+"""

src/agents/rag_assistant.py

@@ -1,146 +0,0 @@
-from datetime import datetime
-from typing import Literal
-
-from langchain_core.language_models.chat_models import BaseChatModel
-from langchain_core.messages import AIMessage, SystemMessage
-from langchain_core.runnables import (
-    RunnableConfig,
-    RunnableLambda,
-    RunnableSerializable,
-)
-from langgraph.graph import END, MessagesState, StateGraph
-from langgraph.managed import RemainingSteps
-from langgraph.prebuilt import ToolNode
-
-from agents.llama_guard import LlamaGuard, LlamaGuardOutput, SafetyAssessment
-from agents.tools import database_search
-from core import get_model, settings
-
-
-class AgentState(MessagesState, total=False):
-    """`total=False` is PEP589 specs.
-
-    documentation: https://typing.readthedocs.io/en/latest/spec/typeddict.html#totality
-    """
-
-    safety: LlamaGuardOutput
-    remaining_steps: RemainingSteps
-
-
-tools = [database_search]
-
-
-current_date = datetime.now().strftime("%B %d, %Y")
-instructions = f"""
-    You are AcmeBot, a helpful and knowledgeable virtual assistant designed to support employees by retrieving
-    and answering questions based on AcmeTech's official Employee Handbook. Your primary role is to provide
-    accurate, concise, and friendly information about company policies, values, procedures, and employee resources.
-    Today's date is {current_date}.
-
-    NOTE: THE USER CAN'T SEE THE TOOL RESPONSE.
-
-    A few things to remember:
-    - If you have access to multiple databases, gather information from a diverse range of sources before crafting your response.
-    - Please include markdown-formatted links to any citations used in your response. Only include one
-    or two citations per response unless more are needed. ONLY USE LINKS RETURNED BY THE TOOLS.
-    - Only use information from the database. Do not use information from outside sources.
-    """
-
-
-def wrap_model(model: BaseChatModel) -> RunnableSerializable[AgentState, AIMessage]:
-    bound_model = model.bind_tools(tools)
-    preprocessor = RunnableLambda(
-        lambda state: [SystemMessage(content=instructions)] + state["messages"],
-        name="StateModifier",
-    )
-    return preprocessor | bound_model  # type: ignore[return-value]
-
-
-def format_safety_message(safety: LlamaGuardOutput) -> AIMessage:
-    content = (
-        f"This conversation was flagged for unsafe content: {', '.join(safety.unsafe_categories)}"
-    )
-    return AIMessage(content=content)
-
-
-async def acall_model(state: AgentState, config: RunnableConfig) -> AgentState:
-    m = get_model(config["configurable"].get("model", settings.DEFAULT_MODEL))
-    model_runnable = wrap_model(m)
-    response = await model_runnable.ainvoke(state, config)
-
-    # Run llama guard check here to avoid returning the message if it's unsafe
-    llama_guard = LlamaGuard()
-    safety_output = await llama_guard.ainvoke("Agent", state["messages"] + [response])
-    if safety_output.safety_assessment == SafetyAssessment.UNSAFE:
-        return {
-            "messages": [format_safety_message(safety_output)],
-            "safety": safety_output,
-        }
-
-    if state["remaining_steps"] < 2 and response.tool_calls:
-        return {
-            "messages": [
-                AIMessage(
-                    id=response.id,
-                    content="Sorry, need more steps to process this request.",
-                )
-            ]
-        }
-    # We return a list, because this will get added to the existing list
-    return {"messages": [response]}
-
-
-async def llama_guard_input(state: AgentState, config: RunnableConfig) -> AgentState:
-    llama_guard = LlamaGuard()
-    safety_output = await llama_guard.ainvoke("User", state["messages"])
-    return {"safety": safety_output, "messages": []}
-
-
-async def block_unsafe_content(state: AgentState, config: RunnableConfig) -> AgentState:
-    safety: LlamaGuardOutput = state["safety"]
-    return {"messages": [format_safety_message(safety)]}
-
-
-# Define the graph
-agent = StateGraph(AgentState)
-agent.add_node("model", acall_model)
-agent.add_node("tools", ToolNode(tools))
-agent.add_node("guard_input", llama_guard_input)
-agent.add_node("block_unsafe_content", block_unsafe_content)
-agent.set_entry_point("guard_input")
-
-
-# Check for unsafe input and block further processing if found
-def check_safety(state: AgentState) -> Literal["unsafe", "safe"]:
-    safety: LlamaGuardOutput = state["safety"]
-    match safety.safety_assessment:
-        case SafetyAssessment.UNSAFE:
-            return "unsafe"
-        case _:
-            return "safe"
-
-
-agent.add_conditional_edges(
-    "guard_input", check_safety, {"unsafe": "block_unsafe_content", "safe": "model"}
-)
-
-# Always END after blocking unsafe content
-agent.add_edge("block_unsafe_content", END)
-
-# Always run "model" after "tools"
-agent.add_edge("tools", "model")
-
-
-# After "model", if there are tool calls, run "tools". Otherwise END.
-def pending_tool_calls(state: AgentState) -> Literal["tools", "done"]:
-    last_message = state["messages"][-1]
-    if not isinstance(last_message, AIMessage):
-        raise TypeError(f"Expected AIMessage, got {type(last_message)}")
-    if last_message.tool_calls:
-        return "tools"
-    return "done"
-
-
-agent.add_conditional_edges("model", pending_tool_calls, {"tools": "tools", "done": END})
-
-rag_assistant = agent.compile()

src/agents/research_assistant.py

@@ -1,148 +0,0 @@
-from datetime import datetime
-from typing import Literal
-
-from langchain_community.tools import DuckDuckGoSearchResults, OpenWeatherMapQueryRun
-from langchain_community.utilities import OpenWeatherMapAPIWrapper
-from langchain_core.language_models.chat_models import BaseChatModel
-from langchain_core.messages import AIMessage, SystemMessage
-from langchain_core.runnables import RunnableConfig, RunnableLambda, RunnableSerializable
-from langgraph.graph import END, MessagesState, StateGraph
-from langgraph.managed import RemainingSteps
-from langgraph.prebuilt import ToolNode
-
-from agents.llama_guard import LlamaGuard, LlamaGuardOutput, SafetyAssessment
-from agents.tools import calculator
-from core import get_model, settings
-
-
-class AgentState(MessagesState, total=False):
-    """`total=False` is PEP589 specs.
-
-    documentation: https://typing.readthedocs.io/en/latest/spec/typeddict.html#totality
-    """
-
-    safety: LlamaGuardOutput
-    remaining_steps: RemainingSteps
-
-
-web_search = DuckDuckGoSearchResults(name="WebSearch")
-tools = [web_search, calculator]
-
-# Add weather tool if API key is set
-# Register for an API key at https://openweathermap.org/api/
-if settings.OPENWEATHERMAP_API_KEY:
-    wrapper = OpenWeatherMapAPIWrapper(
-        openweathermap_api_key=settings.OPENWEATHERMAP_API_KEY.get_secret_value()
-    )
-    tools.append(OpenWeatherMapQueryRun(name="Weather", api_wrapper=wrapper))
-
-current_date = datetime.now().strftime("%B %d, %Y")
-instructions = f"""
-    You are a helpful research assistant with the ability to search the web and use other tools.
-    Today's date is {current_date}.
-
-    NOTE: THE USER CAN'T SEE THE TOOL RESPONSE.
-
-    A few things to remember:
-    - Please include markdown-formatted links to any citations used in your response. Only include one
-    or two citations per response unless more are needed. ONLY USE LINKS RETURNED BY THE TOOLS.
-    - Use calculator tool with numexpr to answer math questions. The user does not understand numexpr,
-      so for the final response, use human readable format - e.g. "300 * 200", not "(300 \\times 200)".
-    """
-
-
-def wrap_model(model: BaseChatModel) -> RunnableSerializable[AgentState, AIMessage]:
-    bound_model = model.bind_tools(tools)
-    preprocessor = RunnableLambda(
-        lambda state: [SystemMessage(content=instructions)] + state["messages"],
-        name="StateModifier",
-    )
-    return preprocessor | bound_model  # type: ignore[return-value]
-
-
-def format_safety_message(safety: LlamaGuardOutput) -> AIMessage:
-    content = (
-        f"This conversation was flagged for unsafe content: {', '.join(safety.unsafe_categories)}"
-    )
-    return AIMessage(content=content)
-
-
-async def acall_model(state: AgentState, config: RunnableConfig) -> AgentState:
-    m = get_model(config["configurable"].get("model", settings.DEFAULT_MODEL))
-    model_runnable = wrap_model(m)
-    response = await model_runnable.ainvoke(state, config)
-
-    # Run llama guard check here to avoid returning the message if it's unsafe
-    llama_guard = LlamaGuard()
-    safety_output = await llama_guard.ainvoke("Agent", state["messages"] + [response])
-    if safety_output.safety_assessment == SafetyAssessment.UNSAFE:
-        return {"messages": [format_safety_message(safety_output)], "safety": safety_output}
-
-    if state["remaining_steps"] < 2 and response.tool_calls:
-        return {
-            "messages": [
-                AIMessage(
-                    id=response.id,
-                    content="Sorry, need more steps to process this request.",
-                )
-            ]
-        }
-    # We return a list, because this will get added to the existing list
-    return {"messages": [response]}
-
-
-async def llama_guard_input(state: AgentState, config: RunnableConfig) -> AgentState:
-    llama_guard = LlamaGuard()
-    safety_output = await llama_guard.ainvoke("User", state["messages"])
-    return {"safety": safety_output, "messages": []}
-
-
-async def block_unsafe_content(state: AgentState, config: RunnableConfig) -> AgentState:
-    safety: LlamaGuardOutput = state["safety"]
-    return {"messages": [format_safety_message(safety)]}
-
-
-# Define the graph
-agent = StateGraph(AgentState)
-agent.add_node("model", acall_model)
-agent.add_node("tools", ToolNode(tools))
-agent.add_node("guard_input", llama_guard_input)
-agent.add_node("block_unsafe_content", block_unsafe_content)
-agent.set_entry_point("guard_input")
-
-
-# Check for unsafe input and block further processing if found
-def check_safety(state: AgentState) -> Literal["unsafe", "safe"]:
-    safety: LlamaGuardOutput = state["safety"]
-    match safety.safety_assessment:
-        case SafetyAssessment.UNSAFE:
-            return "unsafe"
-        case _:
-            return "safe"
-
-
-agent.add_conditional_edges(
-    "guard_input", check_safety, {"unsafe": "block_unsafe_content", "safe": "model"}
-)
-
-# Always END after blocking unsafe content
-agent.add_edge("block_unsafe_content", END)
-
-# Always run "model" after "tools"
-agent.add_edge("tools", "model")
-
-
-# After "model", if there are tool calls, run "tools". Otherwise END.
-def pending_tool_calls(state: AgentState) -> Literal["tools", "done"]:
-    last_message = state["messages"][-1]
-    if not isinstance(last_message, AIMessage):
-        raise TypeError(f"Expected AIMessage, got {type(last_message)}")
-    if last_message.tool_calls:
-        return "tools"
-    return "done"
-
-
-agent.add_conditional_edges("model", pending_tool_calls, {"tools": "tools", "done": END})
-
-
-research_assistant = agent.compile()

src/agents/tools.py

@@ -1,56 +0,0 @@
-import re
-import math
-import numexpr
-
-from memory.postgres import load_pgvector_retriever
-from langchain_core.tools import BaseTool, tool
-
-
-def calculator_func(expression: str) -> str:
-    """Calculates a math expression using numexpr.
-
-    Useful for when you need to answer questions about math using numexpr.
-    This tool is only for math questions and nothing else. Only input
-    math expressions.
-
-    Args:
-        expression (str): A valid numexpr formatted math expression.
-
-    Returns:
-        str: The result of the math expression.
-    """
-
-    try:
-        local_dict = {"pi": math.pi, "e": math.e}
-        output = str(
-            numexpr.evaluate(
-                expression.strip(),
-                global_dict={},  # restrict access to globals
-                local_dict=local_dict,  # add common mathematical functions
-            )
-        )
-        return re.sub(r"^\[|\]$", "", output)
-    except Exception as e:
-        raise ValueError(
-            f'calculator("{expression}") raised error: {e}.'
-            " Please try again with a valid numerical expression"
-        )
-
-
-calculator: BaseTool = tool(calculator_func)
-calculator.name = "Calculator"
-
-def format_contexts(docs):
-    return "\n\n".join(doc.page_content for doc in docs)
-
-def database_search_func(query: str) -> str:
-    """Searches the vector DB for information in the portfolio."""
-    retriever = load_pgvector_retriever()
-    documents = retriever.invoke(query)
-    context_str = format_contexts(documents)
-
-    return context_str
-
-
-database_search: BaseTool = tool(database_search_func)
-database_search.name = "Database_Search"

src/agents/tools/database_search.py

@@ -0,0 +1,34 @@
+import json
+from pydantic import BaseModel, Field
+
+from langchain_core.documents import Document
+from langchain_core.tools import BaseTool, tool
+from memory.postgres import load_pgvector_retriever
+from service.utils import convert_tool_response_to_toon
+
+
+class DatabaseSearchInput(BaseModel):
+    """Input for database search."""
+    query: str = Field(description="The natural language search query to look up information from Anuj's portfolio.")
+
+
+@tool("Database_Search", args_schema=DatabaseSearchInput)
+def database_search(query: str) -> str:
+    """Search for relevant information about Anuj Joshi's education, experience, projects, blog posts, testimonials, and skills.
+    
+    This tool should be used whenever the user asks about Anuj's background, work, or specific accomplishments.
+    """
+
+    retriever = load_pgvector_retriever(3)
+    documents = retriever.invoke(query)
+
+    if not documents:
+        return "No relevant portfolio information found."
+
+    formatted_docs = []
+
+    for i, doc in enumerate(documents):
+        docs = {"content": doc.page_content, "metadata": doc.metadata}
+        formatted_docs.append(convert_tool_response_to_toon(json.dumps(docs)))
+
+    return "\n\n".join(formatted_docs)

src/agents/utils.py

@@ -1,17 +1,15 @@
-from typing import Any
+from langchain_core.tools import BaseTool
+from typing import Iterable, Set, List
 
-from langchain_core.messages import ChatMessage
-from langgraph.types import StreamWriter
-from pydantic import BaseModel, Field
 
+def filter_mcp_tools(tools: Iterable[BaseTool], allowed: Set[str]) -> List[BaseTool]:
+    """Keep only allowed MCP tools and remove namespace (cpstat.)."""
+    filtered = []
 
-class CustomData(BaseModel):
-    "Custom data being sent by an agent"
+    for t in tools:
+        short_name = t.name.rsplit(".", 1)[-1]
+        if short_name in allowed:
+            t.name = short_name
+            filtered.append(t)
 
-    data: dict[str, Any] = Field(description="The custom data")
-
-    def to_langchain(self) -> ChatMessage:
-        return ChatMessage(content=[self.data], role="custom")
-
-    def dispatch(self, writer: StreamWriter) -> None:
-        writer(self.to_langchain())
+    return filtered

src/core/llm.py

@@ -113,14 +113,7 @@ def get_model(model_name: AllModelEnum, /) -> ModelT:
     if model_name in VertexAIModelName:
         return ChatVertexAI(model=api_model_name, temperature=0.5, streaming=True)
     if model_name in GroqModelName:
-        # Guard and safeguard models should use temperature=0.0 for deterministic outputs
-        guard_models = {
-            GroqModelName.LLAMA_GUARD_4_12B,
-            GroqModelName.LLAMA_PROMPT_GUARD_2_22M,
-            GroqModelName.LLAMA_PROMPT_GUARD_2_86M,
-            GroqModelName.OPENAI_GPT_OSS_SAFEGUARD_20B,
-        }
-        if model_name in guard_models:
+        if model_name == GroqModelName.LLAMA_GUARD_4_12B:
             return ChatGroq(model=api_model_name, temperature=0.0)  # type: ignore[call-arg]
         return ChatGroq(model=api_model_name, temperature=0.5)  # type: ignore[call-arg]
     if model_name in AWSModelName:

src/core/settings.py

@@ -85,7 +85,6 @@ class Settings(BaseSettings):
     AUTH_SECRET: SecretStr | None = None
     CORS_ORIGINS: Annotated[Any, BeforeValidator(lambda x: x.split(",") if isinstance(x, str) else x)] = [
         "http://localhost:3000",
-        "http://localhost:8081",
         "http://localhost:5173",
     ]
 
@@ -97,6 +96,7 @@ class Settings(BaseSettings):
     GROQ_API_KEY: SecretStr | None = None
     USE_AWS_BEDROCK: bool = False
     OLLAMA_MODEL: str | None = None
+    OLLAMA_EMBEDDING_MODEL: str | None = None
     OLLAMA_BASE_URL: str | None = None
     USE_FAKE_MODEL: bool = False
     OPENROUTER_API_KEY: str | None = None
@@ -108,7 +108,6 @@ class Settings(BaseSettings):
     # Embedding Settings
     DEFAULT_EMBEDDING_MODEL: AllEmbeddingModelEnum | None = None  # type: ignore[assignment]
     AVAILABLE_EMBEDDING_MODELS: set[AllEmbeddingModelEnum] = set()  # type: ignore[assignment]
-    OLLAMA_EMBEDDING_MODEL: str | None = None
 
     # Set openai compatible api, mainly used for proof of concept
     COMPATIBLE_MODEL: str | None = None
@@ -135,6 +134,9 @@ class Settings(BaseSettings):
     LANGFUSE_PUBLIC_KEY: SecretStr | None = None
     LANGFUSE_SECRET_KEY: SecretStr | None = None
 
+    # Safety Guard Configuration
+    ENABLE_SAFETY_GUARD: bool = True
+
     # Database Configuration
     DATABASE_TYPE: DatabaseType = (
         DatabaseType.SQLITE
@@ -147,7 +149,7 @@ class Settings(BaseSettings):
     POSTGRES_HOST: str | None = None
     POSTGRES_PORT: int | None = None
     POSTGRES_DB: str | None = None
-    POSTGRES_APPLICATION_NAME: str = "agent-service-toolkit"
+    POSTGRES_APPLICATION_NAME: str = "chatbot"
     POSTGRES_MIN_CONNECTIONS_PER_POOL: int = 1
     POSTGRES_MAX_CONNECTIONS_PER_POOL: int = 1
     VECTOR_STORE_COLLECTION_NAME: str = "vector_store"

src/memory/postgres.py

@@ -18,7 +18,6 @@ def validate_postgres_config() -> None:
     Validate that all required PostgreSQL configuration is present.
     Raises ValueError if any required configuration is missing.
     """
-
     required_vars = [
         "POSTGRES_USER",
         "POSTGRES_PASSWORD",
@@ -48,7 +47,7 @@ def get_postgres_connection_string() -> str:
         f"postgresql://{settings.POSTGRES_USER}:"
         f"{settings.POSTGRES_PASSWORD.get_secret_value()}@"
         f"{settings.POSTGRES_HOST}:{settings.POSTGRES_PORT}/"
-        f"{settings.POSTGRES_DB}?sslmode=require"
+        f"{settings.POSTGRES_DB}"
     )
 
 

src/schema/__init__.py

@@ -8,6 +8,9 @@ from schema.schema import (
     FeedbackResponse,
     ServiceMetadata,
     StreamInput,
+    ThreadList,
+    ThreadListInput,
+    ThreadSummary,
     UserInput,
 )
 
@@ -22,4 +25,7 @@ __all__ = [
     "FeedbackResponse",
     "ChatHistoryInput",
     "ChatHistory",
+    "ThreadSummary",
+    "ThreadListInput",
+    "ThreadList",
 ]

src/schema/schema.py

@@ -17,6 +17,10 @@ class AgentInfo(BaseModel):
         description="Description of the agent.",
         examples=["A research assistant for generating research papers."],
     )
+    prompts: list[str] = Field(
+        description="List of suggested prompts for the agent.",
+        default=[],
+    )
 
 
 class ServiceMetadata(BaseModel):
@@ -165,6 +169,10 @@ class FeedbackResponse(BaseModel):
 class ChatHistoryInput(BaseModel):
     """Input for retrieving chat history."""
 
+    user_id: str = Field(
+        description="User ID to scope history to the current user.",
+        examples=["847c6285-8fc9-4560-a83f-4e6285809254"],
+    )
     thread_id: str = Field(
         description="Thread ID to persist and continue a multi-turn conversation.",
         examples=["847c6285-8fc9-4560-a83f-4e6285809254"],
@@ -173,3 +181,36 @@ class ChatHistoryInput(BaseModel):
 
 class ChatHistory(BaseModel):
     messages: list[ChatMessage]
+
+
+class ThreadSummary(BaseModel):
+    """Summary of a conversation thread for listing."""
+
+    thread_id: str = Field(description="Thread ID (logical id, without user prefix).")
+    updated_at: str | None = Field(
+        default=None,
+        description="ISO 8601 timestamp of last update.",
+    )
+    preview: str | None = Field(
+        default=None,
+        description="Preview of the first or last message.",
+    )
+
+
+class ThreadListInput(BaseModel):
+    """Input for listing threads for a user."""
+
+    user_id: str = Field(
+        description="User ID to list threads for.",
+        examples=["847c6285-8fc9-4560-a83f-4e6285809254"],
+    )
+    limit: int = Field(default=20, ge=1, le=100, description="Max number of threads to return.")
+    offset: int = Field(default=0, ge=0, description="Number of threads to skip.")
+    search: str | None = Field(default=None, description="Filter threads by thread_id or preview (case-insensitive).")
+
+
+class ThreadList(BaseModel):
+    """List of threads for a user."""
+
+    threads: list[ThreadSummary] = Field(default_factory=list)
+    total: int | None = Field(default=None, description="Total matching threads before pagination.")

src/schema/task_data.py

@@ -1,74 +0,0 @@
-from typing import Any, Literal
-
-from pydantic import BaseModel, Field
-
-
-class TaskData(BaseModel):
-    name: str | None = Field(
-        description="Name of the task.", default=None, examples=["Check input safety"]
-    )
-    run_id: str = Field(
-        description="ID of the task run to pair state updates to.",
-        default="",
-        examples=["847c6285-8fc9-4560-a83f-4e6285809254"],
-    )
-    state: Literal["new", "running", "complete"] | None = Field(
-        description="Current state of given task instance.",
-        default=None,
-        examples=["running"],
-    )
-    result: Literal["success", "error"] | None = Field(
-        description="Result of given task instance.",
-        default=None,
-        examples=["running"],
-    )
-    data: dict[str, Any] = Field(
-        description="Additional data generated by the task.",
-        default={},
-    )
-
-    def completed(self) -> bool:
-        return self.state == "complete"
-
-    def completed_with_error(self) -> bool:
-        return self.state == "complete" and self.result == "error"
-
-
-class TaskDataStatus:
-    def __init__(self) -> None:
-        import streamlit as st
-
-        self.status = st.status("")
-        self.current_task_data: dict[str, TaskData] = {}
-
-    def add_and_draw_task_data(self, task_data: TaskData) -> None:
-        status = self.status
-        status_str = f"Task **{task_data.name}** "
-        match task_data.state:
-            case "new":
-                status_str += "has :blue[started]. Input:"
-            case "running":
-                status_str += "wrote:"
-            case "complete":
-                if task_data.result == "success":
-                    status_str += ":green[completed successfully]. Output:"
-                else:
-                    status_str += ":red[ended with error]. Output:"
-        status.write(status_str)
-        status.write(task_data.data)
-        status.write("---")
-        if task_data.run_id not in self.current_task_data:
-            # Status label always shows the last newly started task
-            status.update(label=f"""Task: {task_data.name}""")
-        self.current_task_data[task_data.run_id] = task_data
-        if all(entry.completed() for entry in self.current_task_data.values()):
-            # Status is "error" if any task has errored
-            if any(entry.completed_with_error() for entry in self.current_task_data.values()):
-                state = "error"
-            # Status is "complete" if all tasks have completed successfully
-            else:
-                state = "complete"
-        # Status is "running" until all tasks have completed
-        else:
-            state = "running"
-        status.update(state=state)  # type: ignore[arg-type]

src/scripts/create_chroma_db.py

@@ -1,83 +0,0 @@
-import os
-import shutil
-
-from dotenv import load_dotenv
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_chroma import Chroma
-from langchain_community.document_loaders import Docx2txtLoader, PyPDFLoader
-from langchain_openai import OpenAIEmbeddings
-
-# Load environment variables from the .env file
-load_dotenv()
-
-
-def create_chroma_db(
-    folder_path: str,
-    db_name: str = "./chroma_db",
-    delete_chroma_db: bool = True,
-    chunk_size: int = 2000,
-    overlap: int = 500,
-):
-    embeddings = OpenAIEmbeddings(api_key=os.environ["OPENAI_API_KEY"])
-
-    # Initialize Chroma vector store
-    if delete_chroma_db and os.path.exists(db_name):
-        shutil.rmtree(db_name)
-        print(f"Deleted existing database at {db_name}")
-
-    chroma = Chroma(
-        embedding_function=embeddings,
-        persist_directory=f"./{db_name}",
-    )
-
-    # Initialize text splitter
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=overlap)
-
-    # Iterate over files in the folder
-    for filename in os.listdir(folder_path):
-        file_path = os.path.join(folder_path, filename)
-
-        # Load document based on file extension
-        # Add more loaders if required, i.e. JSONLoader, TxtLoader, etc.
-        if filename.endswith(".pdf"):
-            loader = PyPDFLoader(file_path)
-        elif filename.endswith(".docx"):
-            loader = Docx2txtLoader(file_path)
-        else:
-            continue  # Skip unsupported file types
-
-        # Load and split document into chunks
-        document = loader.load()
-        chunks = text_splitter.split_documents(document)
-
-        # Add chunks to Chroma vector store
-        for chunk in chunks:
-            chunk_id = chroma.add_documents([chunk])
-            if chunk_id:
-                print(f"Chunk added with ID: {chunk_id}")
-            else:
-                print("Failed to add chunk")
-
-        print(f"Document {filename} added to database.")
-
-    print(f"Vector database created and saved in {db_name}.")
-    return chroma
-
-
-if __name__ == "__main__":
-    # Path to the folder containing the documents
-    folder_path = "./data"
-
-    # Create the Chroma database
-    chroma = create_chroma_db(folder_path=folder_path)
-
-    # Create retriever from the Chroma database
-    retriever = chroma.as_retriever(search_kwargs={"k": 3})
-
-    # Perform a similarity search
-    query = "What's my company's mission and values"
-    similar_docs = retriever.invoke(query)
-
-    # Display results
-    for i, doc in enumerate(similar_docs, start=1):
-        print(f"\n🔹 Result {i}:\n{doc.page_content}\nTags: {doc.metadata.get('source', [])}")

src/service/agent_service.py

@@ -0,0 +1,562 @@
+import asyncio
+import inspect
+import json
+import logging
+import re
+from collections.abc import AsyncGenerator
+from datetime import datetime, timezone
+from typing import Any
+from uuid import UUID
+
+from fastapi import HTTPException
+from langchain_core.messages import AIMessage, AIMessageChunk, AnyMessage, HumanMessage, ToolMessage
+from langchain_core.runnables import RunnableConfig
+from langfuse.langchain import CallbackHandler
+from langgraph.types import Command, Interrupt
+from langsmith import Client as LangsmithClient
+from langsmith import uuid7
+
+from agents import DEFAULT_AGENT, AgentGraph, get_agent
+from agents.middlewares import FollowUpMiddleware, SafetyMiddleware, UNSAFE_RESPONSE
+from agents.llama_guard import SafetyAssessment
+from core import settings
+from schema import (
+    ChatHistory,
+    ChatHistoryInput,
+    ChatMessage,
+    Feedback,
+    FeedbackResponse,
+    StreamInput,
+    ThreadList,
+    ThreadSummary,
+    UserInput,
+)
+from service.utils import (
+    convert_message_content_to_string,
+    langchain_to_chat_message,
+    remove_tool_calls,
+)
+
+logger = logging.getLogger(__name__)
+
+safety = SafetyMiddleware()
+followup = FollowUpMiddleware()
+
+
+async def _handle_input(
+    user_input: UserInput, agent: AgentGraph, agent_id: str
+) -> tuple[dict[str, Any], UUID]:
+    """
+    Parse user input and handle any required interrupt resumption.
+    Returns kwargs for agent invocation and the run_id.
+    """
+    run_id = uuid7()
+    thread_id = user_input.thread_id or str(uuid7())
+    user_id = user_input.user_id or str(uuid7())
+
+    # Namespace checkpoint by user so threads are user-scoped (Plan A)
+    checkpoint_thread_id = f"{user_id}:{thread_id}"
+    configurable = {"thread_id": checkpoint_thread_id, "user_id": user_id}
+    if user_input.model is not None:
+        configurable["model"] = user_input.model
+
+    callbacks: list[Any] = []
+    if settings.LANGFUSE_TRACING:
+        # Initialize Langfuse CallbackHandler for Langchain (tracing)
+        langfuse_handler = CallbackHandler()
+        callbacks.append(langfuse_handler)
+
+    if user_input.agent_config:
+        # Check for reserved keys (including 'model' even if not in configurable)
+        reserved_keys = {"thread_id", "user_id", "model"}
+        if overlap := reserved_keys & user_input.agent_config.keys():
+            raise HTTPException(
+                status_code=422,
+                detail=f"agent_config contains reserved keys: {overlap}",
+            )
+        configurable.update(user_input.agent_config)
+
+    config = RunnableConfig(
+        configurable=configurable,
+        metadata={
+            "thread_id": thread_id,
+            "user_id": user_id,
+            "agent_id": agent_id,
+            "model": user_input.model,
+            **user_input.agent_config,
+        },
+        run_id=run_id,
+        callbacks=callbacks,
+    )
+
+    # Check for interrupts that need to be resumed
+    state = await agent.aget_state(config=config)
+    interrupted_tasks = [
+        task for task in state.tasks if hasattr(task, "interrupts") and task.interrupts
+    ]
+
+    input: Command | dict[str, Any]
+    if interrupted_tasks:
+        # assume user input is response to resume agent execution from interrupt
+        input = Command(resume=user_input.message)
+    else:
+        input = {"messages": [HumanMessage(content=user_input.message)]}
+
+    kwargs = {
+        "input": input,
+        "config": config,
+    }
+
+    return kwargs, run_id
+
+
+def _create_ai_message(parts: dict) -> AIMessage:
+    sig = inspect.signature(AIMessage)
+    valid_keys = set(sig.parameters)
+    filtered = {k: v for k, v in parts.items() if k in valid_keys}
+    return AIMessage(**filtered)
+
+
+async def invoke_agent(user_input: UserInput, agent_id: str = DEFAULT_AGENT) -> ChatMessage:
+    agent: AgentGraph = get_agent(agent_id)
+    kwargs, run_id = await _handle_input(user_input, agent, agent_id)
+
+    # ── Input safety guard ──────────────────────────────────────────
+    input_check = await safety.check_input([HumanMessage(content=user_input.message)])
+    if input_check.safety_assessment == SafetyAssessment.UNSAFE:
+        raise HTTPException(
+            status_code=400,
+            detail=f"Request blocked by safety filter: {', '.join(input_check.unsafe_categories)}",
+        )
+
+    try:
+        response_events: list[tuple[str, Any]] = await agent.ainvoke(**kwargs, stream_mode=["updates", "values"])  # type: ignore # fmt: skip
+        response_type, response = response_events[-1]
+        output: ChatMessage
+
+        if response_type == "values":
+            # Normal response, the agent completed successfully
+            output = langchain_to_chat_message(response["messages"][-1])
+
+            # ── Output safety guard ─────────────────────────────────
+            output_check = await safety.check_output(response["messages"])
+            if output_check.safety_assessment == SafetyAssessment.UNSAFE:
+                output = langchain_to_chat_message(
+                    AIMessage(content=UNSAFE_RESPONSE)
+                )
+        elif response_type == "updates" and "__interrupt__" in response:
+            # The last thing to occur was an interrupt
+            # Return the value of the first interrupt as an AIMessage
+            output = langchain_to_chat_message(
+                AIMessage(content=response["__interrupt__"][0].value)
+            )
+        else:
+            raise ValueError(f"Unexpected response type: {response_type}")
+
+        output.run_id = str(run_id)
+
+        # ── Follow-up suggestions ──────────────────────────────────
+        questions = await followup.generate(response["messages"], kwargs["config"])
+        output.custom_data = {"follow_up": questions}
+
+        return output
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"An exception occurred: {e}")
+        raise HTTPException(status_code=500, detail="Unexpected error")
+
+
+async def message_generator(
+    user_input: StreamInput, agent_id: str = DEFAULT_AGENT
+) -> AsyncGenerator[str, None]:
+    agent: AgentGraph = get_agent(agent_id)
+    kwargs, run_id = await _handle_input(user_input, agent, agent_id)
+
+    # ── Input safety guard ──────────────────────────────────────────────
+    input_check = await safety.check_input([HumanMessage(content=user_input.message)])
+    if input_check.safety_assessment == SafetyAssessment.UNSAFE:
+        detail = f"Request blocked by safety filter: {', '.join(input_check.unsafe_categories)}"
+        yield f"data: {json.dumps({'type': 'error', 'content': detail})}\n\n"
+        yield "data: [DONE]\n\n"
+        return
+
+    try:
+        # Process streamed events from the graph and yield messages over the SSE stream.
+        async for stream_event in agent.astream(
+            **kwargs, stream_mode=["updates", "messages", "custom"], subgraphs=True
+        ):
+            if not isinstance(stream_event, tuple):
+                continue
+            # Handle different stream event structures based on subgraphs
+            if len(stream_event) == 3:
+                # With subgraphs=True: (node_path, stream_mode, event)
+                _, stream_mode, event = stream_event
+            else:
+                # Without subgraphs: (stream_mode, event)
+                stream_mode, event = stream_event
+            new_messages = []
+            if stream_mode == "updates":
+                for node, updates in event.items():
+                    if node == "__interrupt__":
+                        interrupt: Interrupt
+                        for interrupt in updates:
+                            new_messages.append(AIMessage(content=interrupt.value))
+                        continue
+                    updates = updates or {}
+                    update_messages = updates.get("messages", [])
+                    # special cases for using langgraph-supervisor library
+                    if "supervisor" in node or "sub-agent" in node:
+                        # the only tools that come from the actual agent are the handoff and handback tools
+                        if isinstance(update_messages[-1], ToolMessage):
+                            if "sub-agent" in node and len(update_messages) > 1:
+                                # If this is a sub-agent, we want to keep the last 2 messages - the handback tool, and it's result
+                                update_messages = update_messages[-2:]
+                            else:
+                                # If this is a supervisor, we want to keep the last message only - the handoff result. The tool comes from the 'agent' node.
+                                update_messages = [update_messages[-1]]
+                        else:
+                            update_messages = []
+                    new_messages.extend(update_messages)
+
+            if stream_mode == "custom":
+                new_messages = [event]
+
+            processed_messages = []
+            current_message: dict[str, Any] = {}
+            for message in new_messages:
+                if isinstance(message, tuple):
+                    key, value = message
+                    # Store parts in temporary dict
+                    current_message[key] = value
+                else:
+                    # Add complete message if we have one in progress
+                    if current_message:
+                        processed_messages.append(_create_ai_message(current_message))
+                        current_message = {}
+                    processed_messages.append(message)
+
+            # Add any remaining message parts
+            if current_message:
+                processed_messages.append(_create_ai_message(current_message))
+
+            for message in processed_messages:
+                try:
+                    chat_message = langchain_to_chat_message(message)
+                    chat_message.run_id = str(run_id)
+                except Exception as e:
+                    logger.error(f"Error parsing message: {e}")
+                    yield f"data: {json.dumps({'type': 'error', 'content': 'Unexpected error'})}\n\n"
+                    continue
+                # LangGraph re-sends the input message, which feels weird, so drop it
+                if chat_message.type == "human" and chat_message.content == user_input.message:
+                    continue
+                yield f"data: {json.dumps({'type': 'message', 'content': chat_message.model_dump()})}\n\n"
+
+            if stream_mode == "messages":
+                if not user_input.stream_tokens:
+                    continue
+                msg, metadata = event
+                if "skip_stream" in metadata.get("tags", []):
+                    continue
+                if not isinstance(msg, AIMessageChunk):
+                    continue
+                # content = remove_tool_calls(msg.content)
+                content = msg.content
+                if content:
+                    yield f"data: {json.dumps({'type': 'token', 'content': convert_message_content_to_string(content)})}\n\n"
+    except Exception as e:
+        logger.error(f"Error in message generator: {e}")
+        yield f"data: {json.dumps({'type': 'error', 'content': str(e)})}\n\n"
+
+    # ── Follow-up suggestions (after stream completes) ─────────────
+    try:
+        state = await agent.aget_state(config=kwargs["config"])
+        messages = state.values.get("messages", [])
+        questions = await followup.generate(messages, kwargs["config"])
+        followup_msg = ChatMessage(
+            type="custom",
+            content="",
+            custom_data={"follow_up": questions},
+        )
+        followup_msg.run_id = str(run_id)
+        yield f"data: {json.dumps({'type': 'message', 'content': followup_msg.model_dump()})}\n\n"
+    except Exception as e:
+        logger.warning(f"Follow-up generation failed in stream: {e}")
+    finally:
+        yield "data: [DONE]\n\n"
+
+
+async def submit_feedback(feedback: Feedback) -> FeedbackResponse:
+    client = LangsmithClient()
+    kwargs = feedback.kwargs or {}
+    client.create_feedback(
+        run_id=feedback.run_id,
+        key=feedback.key,
+        score=feedback.score,
+        **kwargs,
+    )
+    return FeedbackResponse()
+
+
+def _checkpoint_thread_id(user_id: str, thread_id: str) -> str:
+    """Composite key so checkpoints are user-scoped."""
+    return f"{user_id}:{thread_id}"
+
+
+async def get_history(input: ChatHistoryInput) -> ChatHistory:
+    if not (input.user_id or "").strip():
+        raise HTTPException(
+            status_code=422,
+            detail="user_id is required and must be non-empty",
+        )
+    # TODO: Hard-coding DEFAULT_AGENT here is wonky
+    agent: AgentGraph = get_agent(DEFAULT_AGENT)
+    checkpoint_thread_id = _checkpoint_thread_id(input.user_id, input.thread_id)
+    try:
+        state_snapshot = await agent.aget_state(
+            config=RunnableConfig(
+                configurable={
+                    "thread_id": checkpoint_thread_id,
+                    "user_id": input.user_id,
+                },
+                metadata={
+                    "thread_id": input.thread_id,
+                    "user_id": input.user_id,
+                    "agent_id": DEFAULT_AGENT,
+                },
+            )
+        )
+        messages: list[AnyMessage] = state_snapshot.values["messages"]
+        chat_messages: list[ChatMessage] = [langchain_to_chat_message(m) for m in messages]
+        return ChatHistory(messages=chat_messages)
+    except Exception as e:
+        logger.error(f"An exception occurred: {e}")
+        raise HTTPException(status_code=500, detail="Unexpected error")
+
+
+def _iso_ts(dt: datetime | str | None) -> str | None:
+    """Format datetime as ISO 8601 or return None."""
+    if dt is None:
+        return None
+    if isinstance(dt, str):
+        return dt
+    if dt.tzinfo is None:
+        dt = dt.replace(tzinfo=timezone.utc)
+    return dt.isoformat()
+
+
+async def list_threads(
+    user_id: str,
+    *,
+    limit: int = 20,
+    offset: int = 0,
+    search: str | None = None,
+) -> ThreadList:
+    """
+    List thread IDs for a user by querying the checkpointer storage with prefix user_id:.
+    Returns logical thread_ids (without the user prefix), with updated_at when available.
+    Supports pagination (offset/limit) and optional search filter.
+    """
+    if not (user_id or "").strip():
+        return ThreadList(threads=[], total=0)
+    agent: AgentGraph = get_agent(DEFAULT_AGENT)
+    checkpointer = getattr(agent, "checkpointer", None)
+    if checkpointer is None:
+        return ThreadList(threads=[], total=0)
+    prefix = f"{user_id}:"
+    # List of (logical_id, updated_at_iso | None)
+    rows: list[tuple[str, str | None]] = []
+    try:
+        # MongoDB: aggregation for thread_id + max timestamp
+        if hasattr(checkpointer, "checkpoint_collection"):
+            coll = checkpointer.checkpoint_collection
+            try:
+                pipeline = [
+                    {"$match": {"thread_id": {"$regex": f"^{re.escape(user_id)}:"}}},
+                    {
+                        "$group": {
+                            "_id": "$thread_id",
+                            "ts": {"$max": {"$ifNull": ["$ts", "$updated_at", "$created_at"]}},
+                        }
+                    },
+                ]
+                async for doc in coll.aggregate(pipeline):
+                    tid = doc.get("_id")
+                    if not isinstance(tid, str) or not tid.startswith(prefix):
+                        continue
+                    logical = tid[len(prefix) :]
+                    ts = doc.get("ts")
+                    rows.append((logical, _iso_ts(ts) if ts else None))
+            except Exception as mongo_err:
+                logger.debug(
+                    "MongoDB aggregation failed, listing by thread_id only: %s",
+                    mongo_err,
+                )
+                raw_ids = await coll.distinct(
+                    "thread_id",
+                    {"thread_id": {"$regex": f"^{re.escape(user_id)}:"}},
+                )
+                for tid in raw_ids:
+                    if isinstance(tid, str) and tid.startswith(prefix):
+                        rows.append((tid[len(prefix) :], None))
+        # Postgres: GROUP BY thread_id, MAX(ts)
+        elif hasattr(checkpointer, "pool") or hasattr(checkpointer, "conn"):
+            # AsyncPostgresSaver often uses .pool, but can have .conn
+            pool = getattr(checkpointer, "pool", getattr(checkpointer, "conn", None))
+            # If it's a pool, we need an async connection
+            if hasattr(pool, "connection"):
+                conn_ctx = pool.connection()
+            else:
+                conn_ctx = pool # Assume it's already a connection or manages its own
+            
+            async with conn_ctx as conn:
+                async with conn.cursor() as cur:
+                    try:
+                        # Try various common timestamp column names
+                        await cur.execute(
+                            """
+                            SELECT thread_id, MAX(COALESCE(ts, updated_at, created_at)) AS ts
+                            FROM checkpoints
+                            WHERE thread_id LIKE %s
+                            GROUP BY thread_id
+                            """,
+                            (prefix + "%",),
+                        )
+                    except Exception as pg_err:
+                        logger.debug(
+                            "Postgres MAX(ts) failed, trying metadata: %s",
+                            pg_err,
+                        )
+                        try:
+                            await cur.execute(
+                                """
+                                SELECT thread_id, MAX(created_at) AS ts
+                                FROM checkpoint_metadata
+                                WHERE thread_id LIKE %s
+                                GROUP BY thread_id
+                                """,
+                                (prefix + "%",),
+                            )
+                        except Exception:
+                            await cur.execute(
+                                "SELECT DISTINCT thread_id FROM checkpoints WHERE thread_id LIKE %s",
+                                (prefix + "%",),
+                            )
+                            for row in await cur.fetchall():
+                                raw = (
+                                    row.get("thread_id")
+                                    if isinstance(row, dict)
+                                    else (row[0] if row else None)
+                                )
+                                if isinstance(raw, str) and raw.startswith(prefix):
+                                    rows.append((raw[len(prefix) :], None))
+                        else:
+                            for row in await cur.fetchall():
+                                raw = row.get("thread_id") if isinstance(row, dict) else row[0]
+                                ts_val = row.get("ts") if isinstance(row, dict) else row[1]
+                                if isinstance(raw, str) and raw.startswith(prefix):
+                                    rows.append((raw[len(prefix) :], _iso_ts(ts_val)))
+                    else:
+                        for row in await cur.fetchall():
+                            raw = (
+                                row.get("thread_id")
+                                if isinstance(row, dict)
+                                else (row[0] if row else None)
+                            )
+                            ts_val = (
+                                row.get("ts")
+                                if isinstance(row, dict)
+                                else (
+                                    row[1]
+                                    if isinstance(row, (list, tuple)) and len(row) > 1
+                                    else None
+                                )
+                            )
+                            if isinstance(raw, str) and raw.startswith(prefix):
+                                rows.append((raw[len(prefix) :], _iso_ts(ts_val)))
+        # SQLite: GROUP BY thread_id, MAX(ts)
+        elif hasattr(checkpointer, "conn"):
+            conn = checkpointer.conn
+            try:
+                cursor = await conn.execute(
+                    """
+                    SELECT thread_id, MAX(COALESCE(ts, updated_at, created_at)) AS ts
+                    FROM checkpoints
+                    WHERE thread_id LIKE ?
+                    GROUP BY thread_id
+                    """,
+                    (prefix + "%",),
+                )
+                for row in await cursor.fetchall():
+                    raw = row[0] if isinstance(row, (list, tuple)) else row
+                    ts_val = row[1] if isinstance(row, (list, tuple)) and len(row) > 1 else None
+                    if isinstance(raw, str) and raw.startswith(prefix):
+                        rows.append((raw[len(prefix) :], _iso_ts(ts_val)))
+            except Exception as sqlite_err:
+                logger.debug("SQLite MAX(ts) failed, listing by thread_id only: %s", sqlite_err)
+                cursor = await conn.execute(
+                    "SELECT DISTINCT thread_id FROM checkpoints WHERE thread_id LIKE ?",
+                    (prefix + "%",),
+                )
+                for row in await cursor.fetchall():
+                    raw = row[0] if isinstance(row, (list, tuple)) else row
+                    if isinstance(raw, str) and raw.startswith(prefix):
+                        rows.append((raw[len(prefix) :], None))
+        else:
+            logger.warning("Unknown checkpointer type; cannot list threads by prefix")
+    except Exception as e:
+        logger.error(f"Error listing threads for user: {e}")
+        raise HTTPException(status_code=500, detail="Failed to list threads") from e
+
+    # Sort by updated_at desc (None last), then by thread_id
+    def _sort_key(item: tuple[str, str | None]) -> tuple[bool, str, str]:
+        logical, ts = item
+        # desc sort: flip characters in timestamp if it exists
+        return (ts is None, (ts or "")[::-1], logical)
+
+    rows.sort(key=_sort_key)
+
+    # Filter by search
+    search_clean = (search or "").strip().lower()
+    if search_clean:
+        rows = [(logical, ts) for logical, ts in rows if search_clean in logical.lower()]
+
+    total = len(rows)
+    rows = rows[offset : offset + limit]
+
+    # Fetch additional details (preview, precise timestamp) for the requested page in parallel
+    async def get_thread_summary(logical_id: str, existing_ts: str | None) -> ThreadSummary:
+        try:
+            config = RunnableConfig(
+                configurable={
+                    "thread_id": f"{user_id}:{logical_id}",
+                    "user_id": user_id,
+                }
+            )
+            state = await agent.aget_state(config)
+            preview = None
+            ts = existing_ts
+            
+            if state.values and "messages" in state.values:
+                msgs = state.values["messages"]
+                if msgs:
+                    # Get the last non-custom message for better preview
+                    last_msg = msgs[-1]
+                    preview = convert_message_content_to_string(last_msg.content)
+                    if preview and len(preview) > 120:
+                        preview = preview[:117] + "..."
+            
+            # If we don't have a timestamp, try to get it from state metadata
+            if not ts and state.metadata:
+                m_ts = state.metadata.get("ts") or state.metadata.get("created_at")
+                if m_ts:
+                    ts = _iso_ts(m_ts)
+            
+            return ThreadSummary(thread_id=logical_id, updated_at=ts, preview=preview)
+        except Exception as e:
+            logger.warning(f"Failed to fetch state for thread {logical_id}: {e}")
+            return ThreadSummary(thread_id=logical_id, updated_at=existing_ts, preview=None)
+
+    summaries = await asyncio.gather(*[get_thread_summary(logical, ts) for logical, ts in rows])
+    return ThreadList(threads=list(summaries), total=total)

src/service/dependencies.py

@@ -0,0 +1,23 @@
+from typing import Annotated
+
+from fastapi import Depends, HTTPException, status
+from fastapi.security import HTTPAuthorizationCredentials, HTTPBearer
+
+from core import settings
+
+
+def verify_bearer(
+    http_auth: Annotated[
+        HTTPAuthorizationCredentials | None,
+        Depends(
+            HTTPBearer(
+                description="Please provide AUTH_SECRET api key", auto_error=False
+            )
+        ),
+    ],
+) -> None:
+    if not settings.AUTH_SECRET:
+        return
+    auth_secret = settings.AUTH_SECRET.get_secret_value()
+    if not http_auth or http_auth.credentials != auth_secret:
+        raise HTTPException(status_code=status.HTTP_401_UNAUTHORIZED)

src/service/router.py

@@ -0,0 +1,130 @@
+from typing import Any
+
+from fastapi import APIRouter, Depends, status
+from fastapi.responses import StreamingResponse
+
+from agents import DEFAULT_AGENT, get_all_agent_info
+from core import settings
+from schema import (
+    ChatHistory,
+    ChatHistoryInput,
+    ChatMessage,
+    Feedback,
+    FeedbackResponse,
+    ServiceMetadata,
+    StreamInput,
+    ThreadList,
+    ThreadListInput,
+    UserInput,
+)
+from service import agent_service
+from service.dependencies import verify_bearer
+
+
+router = APIRouter(dependencies=[Depends(verify_bearer)])
+
+
+def _sse_response_example() -> dict[int | str, Any]:
+    return {
+        status.HTTP_200_OK: {
+            "description": "Server Sent Event Response",
+            "content": {
+                "text/event-stream": {
+                    "example": "data: {'type': 'token', 'content': 'Hello'}\n\ndata: {'type': 'token', 'content': ' World'}\n\ndata: [DONE]\n\n",
+                    "schema": {"type": "string"},
+                }
+            },
+        }
+    }
+
+
+@router.get("/info")
+async def info() -> ServiceMetadata:
+    models = list(settings.AVAILABLE_MODELS)
+    models.sort()
+    return ServiceMetadata(
+        agents=get_all_agent_info(),
+        models=models,
+        default_agent=DEFAULT_AGENT,
+        default_model=settings.DEFAULT_MODEL,
+    )
+
+
+@router.post("/{agent_id}/invoke", operation_id="invoke_with_agent_id")
+@router.post("/invoke")
+async def invoke(
+    user_input: UserInput, agent_id: str = DEFAULT_AGENT
+) -> ChatMessage:
+    """
+    Invoke an agent with user input to retrieve a final response.
+
+    If agent_id is not provided, the default agent will be used.
+    Use thread_id to persist and continue a multi-turn conversation. run_id kwarg
+    is also attached to messages for recording feedback.
+    Use user_id to persist and continue a conversation across multiple threads.
+    """
+    return await agent_service.invoke_agent(user_input, agent_id)
+
+
+@router.post(
+    "/{agent_id}/stream",
+    response_class=StreamingResponse,
+    responses=_sse_response_example(),
+    operation_id="stream_with_agent_id",
+)
+@router.post(
+    "/stream", response_class=StreamingResponse, responses=_sse_response_example()
+)
+async def stream(
+    user_input: StreamInput, agent_id: str = DEFAULT_AGENT
+) -> StreamingResponse:
+    """
+    Stream an agent's response to a user input, including intermediate messages and tokens.
+
+    If agent_id is not provided, the default agent will be used.
+    Use thread_id to persist and continue a multi-turn conversation. run_id kwarg
+    is also attached to all messages for recording feedback.
+    Use user_id to persist and continue a conversation across multiple threads.
+
+    Set `stream_tokens=false` to return intermediate messages but not token-by-token.
+    """
+    return StreamingResponse(
+        agent_service.message_generator(user_input, agent_id),
+        media_type="text/event-stream",
+    )
+
+
+@router.post("/feedback")
+async def feedback(feedback: Feedback) -> FeedbackResponse:
+    """
+    Record feedback for a run to LangSmith.
+
+    This is a simple wrapper for the LangSmith create_feedback API, so the
+    credentials can be stored and managed in the service rather than the client.
+    See: https://api.smith.langchain.com/redoc#tag/feedback/operation/create_feedback_api_v1_feedback_post
+    """
+    return await agent_service.submit_feedback(feedback)
+
+
+@router.post("/history")
+async def history(input: ChatHistoryInput) -> ChatHistory:
+    """
+    Get chat history for a thread. Requires user_id and thread_id.
+    Returns only messages for the given user's thread.
+    """
+    return await agent_service.get_history(input)
+
+
+@router.post("/history/threads")
+async def history_threads(input: ThreadListInput) -> ThreadList:
+    """
+    List conversation threads for a user. Requires user_id.
+    Returns thread IDs (and optional metadata) scoped to that user.
+    Supports limit, offset, and optional search filter.
+    """
+    return await agent_service.list_threads(
+        input.user_id,
+        limit=input.limit,
+        offset=input.offset,
+        search=input.search,
+    )

src/service/service.py

@@ -1,46 +1,18 @@
-import inspect
-import json
 import logging
 import warnings
 from collections.abc import AsyncGenerator
 from contextlib import asynccontextmanager
-from typing import Annotated, Any
-from langsmith import uuid7
-from uuid import UUID
 
-from fastapi import APIRouter, Depends, FastAPI, HTTPException, status
-from fastapi.responses import StreamingResponse
-from fastapi.routing import APIRoute
+from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
-from fastapi.security import HTTPAuthorizationCredentials, HTTPBearer
+from fastapi.routing import APIRoute
 from langchain_core._api import LangChainBetaWarning
-from langchain_core.messages import AIMessage, AIMessageChunk, AnyMessage, HumanMessage, ToolMessage
-from langchain_core.runnables import RunnableConfig
-from langfuse import Langfuse  # type: ignore[import-untyped]
-from langfuse.langchain import (
-    CallbackHandler,  # type: ignore[import-untyped]
-)
-from langgraph.types import Command, Interrupt
-from langsmith import Client as LangsmithClient
+from langfuse import Langfuse
 
-from agents import DEFAULT_AGENT, AgentGraph, get_agent, get_all_agent_info, load_agent
+from agents import get_agent, get_all_agent_info, load_agent
 from core import settings
 from memory import initialize_database, initialize_store
-from schema import (
-    ChatHistory,
-    ChatHistoryInput,
-    ChatMessage,
-    Feedback,
-    FeedbackResponse,
-    ServiceMetadata,
-    StreamInput,
-    UserInput,
-)
-from service.utils import (
-    convert_message_content_to_string,
-    langchain_to_chat_message,
-    remove_tool_calls,
-)
+from service.router import router
 
 warnings.filterwarnings("ignore", category=LangChainBetaWarning)
 logger = logging.getLogger(__name__)
@@ -51,19 +23,6 @@ def custom_generate_unique_id(route: APIRoute) -> str:
     return route.name
 
 
-def verify_bearer(
-    http_auth: Annotated[
-        HTTPAuthorizationCredentials | None,
-        Depends(HTTPBearer(description="Please provide AUTH_SECRET api key.", auto_error=False)),
-    ],
-) -> None:
-    if not settings.AUTH_SECRET:
-        return
-    auth_secret = settings.AUTH_SECRET.get_secret_value()
-    if not http_auth or http_auth.credentials != auth_secret:
-        raise HTTPException(status_code=status.HTTP_401_UNAUTHORIZED)
-
-
 @asynccontextmanager
 async def lifespan(app: FastAPI) -> AsyncGenerator[None, None]:
     """
@@ -101,330 +60,19 @@ async def lifespan(app: FastAPI) -> AsyncGenerator[None, None]:
         raise
 
 
-app = FastAPI(lifespan=lifespan, generate_unique_id_function=custom_generate_unique_id)
+app = FastAPI(
+    lifespan=lifespan, generate_unique_id_function=custom_generate_unique_id
+)
+
+# Configure CORS
 app.add_middleware(
     CORSMiddleware,
     allow_origins=settings.CORS_ORIGINS,
     allow_credentials=True,
     allow_methods=["*"],
     allow_headers=["*"],
+    expose_headers=["*"],
 )
-router = APIRouter(dependencies=[Depends(verify_bearer)])
-
-
-@router.get("/info")
-async def info() -> ServiceMetadata:
-    models = list(settings.AVAILABLE_MODELS)
-    models.sort()
-    return ServiceMetadata(
-        agents=get_all_agent_info(),
-        models=models,
-        default_agent=DEFAULT_AGENT,
-        default_model=settings.DEFAULT_MODEL,
-    )
-
-
-async def _handle_input(user_input: UserInput, agent: AgentGraph) -> tuple[dict[str, Any], UUID]:
-    """
-    Parse user input and handle any required interrupt resumption.
-    Returns kwargs for agent invocation and the run_id.
-    """
-    run_id = uuid7()
-    thread_id = user_input.thread_id or str(uuid7())
-    user_id = user_input.user_id or str(uuid7())
-
-    configurable = {"thread_id": thread_id, "user_id": user_id}
-    if user_input.model is not None:
-        configurable["model"] = user_input.model
-
-    callbacks: list[Any] = []
-    if settings.LANGFUSE_TRACING:
-        # Initialize Langfuse CallbackHandler for Langchain (tracing)
-        langfuse_handler = CallbackHandler()
-
-        callbacks.append(langfuse_handler)
-
-    if user_input.agent_config:
-        # Check for reserved keys (including 'model' even if not in configurable)
-        reserved_keys = {"thread_id", "user_id", "model"}
-        if overlap := reserved_keys & user_input.agent_config.keys():
-            raise HTTPException(
-                status_code=422,
-                detail=f"agent_config contains reserved keys: {overlap}",
-            )
-        configurable.update(user_input.agent_config)
-
-    config = RunnableConfig(
-        configurable=configurable,
-        run_id=run_id,
-        callbacks=callbacks,
-    )
-
-    # Check for interrupts that need to be resumed
-    state = await agent.aget_state(config=config)
-    interrupted_tasks = [
-        task for task in state.tasks if hasattr(task, "interrupts") and task.interrupts
-    ]
-
-    input: Command | dict[str, Any]
-    if interrupted_tasks:
-        # assume user input is response to resume agent execution from interrupt
-        input = Command(resume=user_input.message)
-    else:
-        input = {"messages": [HumanMessage(content=user_input.message)]}
-
-    kwargs = {
-        "input": input,
-        "config": config,
-    }
-
-    return kwargs, run_id
-
-
-@router.post("/{agent_id}/invoke", operation_id="invoke_with_agent_id")
-@router.post("/invoke")
-async def invoke(user_input: UserInput, agent_id: str = DEFAULT_AGENT) -> ChatMessage:
-    """
-    Invoke an agent with user input to retrieve a final response.
-
-    If agent_id is not provided, the default agent will be used.
-    Use thread_id to persist and continue a multi-turn conversation. run_id kwarg
-    is also attached to messages for recording feedback.
-    Use user_id to persist and continue a conversation across multiple threads.
-    """
-    # NOTE: Currently this only returns the last message or interrupt.
-    # In the case of an agent outputting multiple AIMessages (such as the background step
-    # in interrupt-agent, or a tool step in research-assistant), it's omitted. Arguably,
-    # you'd want to include it. You could update the API to return a list of ChatMessages
-    # in that case.
-    agent: AgentGraph = get_agent(agent_id)
-    kwargs, run_id = await _handle_input(user_input, agent)
-
-    try:
-        response_events: list[tuple[str, Any]] = await agent.ainvoke(**kwargs, stream_mode=["updates", "values"])  # type: ignore # fmt: skip
-        response_type, response = response_events[-1]
-        if response_type == "values":
-            # Normal response, the agent completed successfully
-            output = langchain_to_chat_message(response["messages"][-1])
-        elif response_type == "updates" and "__interrupt__" in response:
-            # The last thing to occur was an interrupt
-            # Return the value of the first interrupt as an AIMessage
-            output = langchain_to_chat_message(
-                AIMessage(content=response["__interrupt__"][0].value)
-            )
-        else:
-            raise ValueError(f"Unexpected response type: {response_type}")
-
-        output.run_id = str(run_id)
-        return output
-    except Exception as e:
-        logger.error(f"An exception occurred: {e}")
-        raise HTTPException(status_code=500, detail="Unexpected error")
-
-
-async def message_generator(
-    user_input: StreamInput, agent_id: str = DEFAULT_AGENT
-) -> AsyncGenerator[str, None]:
-    """
-    Generate a stream of messages from the agent.
-
-    This is the workhorse method for the /stream endpoint.
-    """
-    agent: AgentGraph = get_agent(agent_id)
-    kwargs, run_id = await _handle_input(user_input, agent)
-
-    try:
-        # Process streamed events from the graph and yield messages over the SSE stream.
-        async for stream_event in agent.astream(
-            **kwargs, stream_mode=["updates", "messages", "custom"], subgraphs=True
-        ):
-            if not isinstance(stream_event, tuple):
-                continue
-            # Handle different stream event structures based on subgraphs
-            if len(stream_event) == 3:
-                # With subgraphs=True: (node_path, stream_mode, event)
-                _, stream_mode, event = stream_event
-            else:
-                # Without subgraphs: (stream_mode, event)
-                stream_mode, event = stream_event
-            new_messages = []
-            if stream_mode == "updates":
-                for node, updates in event.items():
-                    # A simple approach to handle agent interrupts.
-                    # In a more sophisticated implementation, we could add
-                    # some structured ChatMessage type to return the interrupt value.
-                    if node == "__interrupt__":
-                        interrupt: Interrupt
-                        for interrupt in updates:
-                            new_messages.append(AIMessage(content=interrupt.value))
-                        continue
-                    updates = updates or {}
-                    update_messages = updates.get("messages", [])
-                    # special cases for using langgraph-supervisor library
-                    if "supervisor" in node or "sub-agent" in node:
-                        # the only tools that come from the actual agent are the handoff and handback tools
-                        if isinstance(update_messages[-1], ToolMessage):
-                            if "sub-agent" in node and len(update_messages) > 1:
-                                # If this is a sub-agent, we want to keep the last 2 messages - the handback tool, and it's result
-                                update_messages = update_messages[-2:]
-                            else:
-                                # If this is a supervisor, we want to keep the last message only - the handoff result. The tool comes from the 'agent' node.
-                                update_messages = [update_messages[-1]]
-                        else:
-                            update_messages = []
-                    new_messages.extend(update_messages)
-
-            if stream_mode == "custom":
-                new_messages = [event]
-
-            # Send update events for non-message updates (like follow_up)
-            if stream_mode == "updates" and hasattr(event, "items"):
-                for node, updates in event.items():
-                    if updates:
-                        other_updates = {k: v for k, v in updates.items() if k != "messages"}
-                        if other_updates:
-                            yield f"data: {json.dumps({'type': 'update', 'node': node, 'updates': other_updates})}\n\n"
-
-            # LangGraph streaming may emit tuples: (field_name, field_value)
-            # e.g. ('content', <str>), ('tool_calls', [ToolCall,...]), ('additional_kwargs', {...}), etc.
-            # We accumulate only supported fields into `parts` and skip unsupported metadata.
-            # More info at: https://langchain-ai.github.io/langgraph/cloud/how-tos/stream_messages/
-            processed_messages = []
-            current_message: dict[str, Any] = {}
-            for message in new_messages:
-                if isinstance(message, tuple):
-                    key, value = message
-                    # Store parts in temporary dict
-                    current_message[key] = value
-                else:
-                    # Add complete message if we have one in progress
-                    if current_message:
-                        processed_messages.append(_create_ai_message(current_message))
-                        current_message = {}
-                    processed_messages.append(message)
-
-            # Add any remaining message parts
-            if current_message:
-                processed_messages.append(_create_ai_message(current_message))
-
-            for message in processed_messages:
-                try:
-                    chat_message = langchain_to_chat_message(message)
-                    chat_message.run_id = str(run_id)
-                except Exception as e:
-                    logger.error(f"Error parsing message: {e}")
-                    yield f"data: {json.dumps({'type': 'error', 'content': 'Unexpected error'})}\n\n"
-                    continue
-                # LangGraph re-sends the input message, which feels weird, so drop it
-                if chat_message.type == "human" and chat_message.content == user_input.message:
-                    continue
-                yield f"data: {json.dumps({'type': 'message', 'content': chat_message.model_dump()})}\n\n"
-
-            if stream_mode == "messages":
-                if not user_input.stream_tokens:
-                    continue
-                msg, metadata = event
-                if "skip_stream" in metadata.get("tags", []):
-                    continue
-                # For some reason, astream("messages") causes non-LLM nodes to send extra messages.
-                # Drop them.
-                if not isinstance(msg, AIMessageChunk):
-                    continue
-                content = remove_tool_calls(msg.content)
-                if content:
-                    # Empty content in the context of OpenAI usually means
-                    # that the model is asking for a tool to be invoked.
-                    # So we only print non-empty content.
-                    yield f"data: {json.dumps({'type': 'token', 'content': convert_message_content_to_string(content)})}\n\n"
-    except Exception as e:
-        logger.error(f"Error in message generator: {e}")
-        yield f"data: {json.dumps({'type': 'error', 'content': 'Internal server error'})}\n\n"
-    finally:
-        yield "data: [DONE]\n\n"
-
-
-def _create_ai_message(parts: dict) -> AIMessage:
-    sig = inspect.signature(AIMessage)
-    valid_keys = set(sig.parameters)
-    filtered = {k: v for k, v in parts.items() if k in valid_keys}
-    return AIMessage(**filtered)
-
-
-def _sse_response_example() -> dict[int | str, Any]:
-    return {
-        status.HTTP_200_OK: {
-            "description": "Server Sent Event Response",
-            "content": {
-                "text/event-stream": {
-                    "example": "data: {'type': 'token', 'content': 'Hello'}\n\ndata: {'type': 'token', 'content': ' World'}\n\ndata: [DONE]\n\n",
-                    "schema": {"type": "string"},
-                }
-            },
-        }
-    }
-
-
-@router.post(
-    "/{agent_id}/stream",
-    response_class=StreamingResponse,
-    responses=_sse_response_example(),
-    operation_id="stream_with_agent_id",
-)
-@router.post("/stream", response_class=StreamingResponse, responses=_sse_response_example())
-async def stream(user_input: StreamInput, agent_id: str = DEFAULT_AGENT) -> StreamingResponse:
-    """
-    Stream an agent's response to a user input, including intermediate messages and tokens.
-
-    If agent_id is not provided, the default agent will be used.
-    Use thread_id to persist and continue a multi-turn conversation. run_id kwarg
-    is also attached to all messages for recording feedback.
-    Use user_id to persist and continue a conversation across multiple threads.
-
-    Set `stream_tokens=false` to return intermediate messages but not token-by-token.
-    """
-    return StreamingResponse(
-        message_generator(user_input, agent_id),
-        media_type="text/event-stream",
-    )
-
-
-@router.post("/feedback")
-async def feedback(feedback: Feedback) -> FeedbackResponse:
-    """
-    Record feedback for a run to LangSmith.
-
-    This is a simple wrapper for the LangSmith create_feedback API, so the
-    credentials can be stored and managed in the service rather than the client.
-    See: https://api.smith.langchain.com/redoc#tag/feedback/operation/create_feedback_api_v1_feedback_post
-    """
-    client = LangsmithClient()
-    kwargs = feedback.kwargs or {}
-    client.create_feedback(
-        run_id=feedback.run_id,
-        key=feedback.key,
-        score=feedback.score,
-        **kwargs,
-    )
-    return FeedbackResponse()
-
-
-@router.post("/history")
-async def history(input: ChatHistoryInput) -> ChatHistory:
-    """
-    Get chat history.
-    """
-    # TODO: Hard-coding DEFAULT_AGENT here is wonky
-    agent: AgentGraph = get_agent(DEFAULT_AGENT)
-    try:
-        state_snapshot = await agent.aget_state(
-            config=RunnableConfig(configurable={"thread_id": input.thread_id})
-        )
-        messages: list[AnyMessage] = state_snapshot.values["messages"]
-        chat_messages: list[ChatMessage] = [langchain_to_chat_message(m) for m in messages]
-        return ChatHistory(messages=chat_messages)
-    except Exception as e:
-        logger.error(f"An exception occurred: {e}")
-        raise HTTPException(status_code=500, detail="Unexpected error")
 
 
 @app.get("/health")
@@ -436,7 +84,9 @@ async def health_check():
     if settings.LANGFUSE_TRACING:
         try:
             langfuse = Langfuse()
-            health_status["langfuse"] = "connected" if langfuse.auth_check() else "disconnected"
+            health_status["langfuse"] = (
+                "connected" if langfuse.auth_check() else "disconnected"
+            )
         except Exception as e:
             logger.error(f"Langfuse connection error: {e}")
             health_status["langfuse"] = "disconnected"

src/service/utils.py

@@ -1,3 +1,6 @@
+import json
+
+import toons
 from langchain_core.messages import (
     AIMessage,
     BaseMessage,
@@ -11,6 +14,19 @@ from langchain_core.messages import (
 from schema import ChatMessage
 
 
+def convert_tool_response_to_toon(content: str) -> str:
+    """
+    Convert JSON tool response to TOON format to reduce tokens (30–60% fewer than JSON).
+    https://pypi.org/project/toons/
+    """
+    if not content or not isinstance(content, str):
+        return content
+    try:
+        data = json.loads(content)
+    except (json.JSONDecodeError, TypeError):
+        return content
+    return toons.dumps(data)
+
 def convert_message_content_to_string(content: str | list[str | dict]) -> str:
     if isinstance(content, str):
         return content

uv.lock

@@ -8,12 +8,11 @@ resolution-markers = [
 ]
 
 [[package]]
-name = "agent-service-toolkit"
+name = "chatbot"
 version = "0.1.0"
 source = { virtual = "." }
 dependencies = [
     { name = "ddgs" },
-    { name = "docx2txt" },
     { name = "duckduckgo-search" },
     { name = "fastapi" },
     { name = "grpcio" },
@@ -36,22 +35,14 @@ dependencies = [
     { name = "langgraph-checkpoint-mongodb" },
     { name = "langgraph-checkpoint-postgres" },
     { name = "langgraph-checkpoint-sqlite" },
-    { name = "langgraph-supervisor" },
     { name = "langsmith" },
-    { name = "numexpr" },
-    { name = "numpy" },
-    { name = "onnxruntime" },
-    { name = "pandas" },
     { name = "psycopg", extra = ["binary", "pool"] },
-    { name = "pyarrow" },
     { name = "pydantic" },
     { name = "pydantic-settings" },
-    { name = "pyowm" },
-    { name = "pypdf" },
     { name = "python-dotenv" },
     { name = "setuptools" },
-    { name = "streamlit" },
     { name = "tiktoken" },
+    { name = "toons" },
     { name = "uvicorn" },
 ]
 
@@ -60,7 +51,6 @@ client = [
     { name = "httpx" },
     { name = "pydantic" },
     { name = "python-dotenv" },
-    { name = "streamlit" },
 ]
 dev = [
     { name = "langgraph-cli", extra = ["inmem"] },
@@ -76,7 +66,6 @@ dev = [
 [package.metadata]
 requires-dist = [
     { name = "ddgs", specifier = ">=9.9.1" },
-    { name = "docx2txt", specifier = "~=0.8" },
     { name = "duckduckgo-search", specifier = ">=7.3.0" },
     { name = "fastapi", specifier = "~=0.115.5" },
     { name = "grpcio", specifier = ">=1.68.0" },
@@ -99,22 +88,14 @@ requires-dist = [
     { name = "langgraph-checkpoint-mongodb", specifier = "~=0.1.3" },
     { name = "langgraph-checkpoint-postgres", specifier = "~=2.0.13" },
     { name = "langgraph-checkpoint-sqlite", specifier = "~=2.0.1" },
-    { name = "langgraph-supervisor", specifier = "~=0.0.31" },
     { name = "langsmith", specifier = "~=0.4.0" },
-    { name = "numexpr", specifier = "~=2.10.1" },
-    { name = "numpy", specifier = "~=2.3.4" },
-    { name = "onnxruntime", specifier = "~=1.21.1" },
-    { name = "pandas", specifier = "~=2.2.3" },
     { name = "psycopg", extras = ["binary", "pool"], specifier = "~=3.2.4" },
-    { name = "pyarrow", specifier = ">=18.1.0" },
     { name = "pydantic", specifier = "~=2.10.1" },
     { name = "pydantic-settings", specifier = "~=2.12.0" },
-    { name = "pyowm", specifier = "~=3.3.0" },
-    { name = "pypdf", specifier = "~=5.3.0" },
     { name = "python-dotenv", specifier = "~=1.0.1" },
     { name = "setuptools", specifier = "~=75.6.0" },
-    { name = "streamlit", specifier = "~=1.52.0" },
     { name = "tiktoken", specifier = ">=0.8.0" },
+    { name = "toons", specifier = ">=0.5.2" },
     { name = "uvicorn", specifier = "~=0.32.1" },
 ]
 
@@ -123,7 +104,6 @@ client = [
     { name = "httpx", specifier = "~=0.28.0" },
     { name = "pydantic", specifier = "~=2.10.1" },
     { name = "python-dotenv", specifier = "~=1.0.1" },
-    { name = "streamlit", specifier = "~=1.52.0" },
 ]
 dev = [
     { name = "langgraph-cli", extras = ["inmem"] },
@@ -238,22 +218,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/f5/10/6c25ed6de94c49f88a91fa5018cb4c0f3625f31d5be9f771ebe5cc7cd506/aiosqlite-0.21.0-py3-none-any.whl", hash = "sha256:2549cf4057f95f53dcba16f2b64e8e2791d7e1adedb13197dd8ed77bb226d7d0", size = 15792, upload-time = "2025-02-03T07:30:13.6Z" },
 ]
 
-[[package]]
-name = "altair"
-version = "5.5.0"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "jinja2" },
-    { name = "jsonschema" },
-    { name = "narwhals" },
-    { name = "packaging" },
-    { name = "typing-extensions" },
-]
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