first langgraph workflow complete; integrations with zep and tools

api/event_handlers/__init__.py

@@ -0,0 +1 @@
+from .print_handler import PrintEventHandler

api/event_handlers/print_handler.py

@@ -0,0 +1,48 @@
+from colorama import Fore, Style
+from langchain_core.callbacks import AsyncCallbackHandler
+from langchain_core.outputs.llm_result import LLMResult
+
+
+class PrintEventHandler(AsyncCallbackHandler):
+    """
+    Example async event handler: prints streaming tokens and tool results.
+    Replace with websocket or other side effects as needed.
+    """
+
+    def __init__(self, *args, **kwargs):
+        print('[INIT]')
+        super().__init__(*args, **kwargs)
+
+    async def on_chat_model_start(self, *args, **kwargs):
+        print('[CHAT START]')
+
+    async def on_llm_new_token(self, token: str, **kwargs):
+        if token:
+            print(Fore.YELLOW + token + Style.RESET_ALL, end="", flush=True)
+
+    async def on_llm_end(self, result: LLMResult, *args, **kwargs):
+        if self.is_chat_stream_end(result):
+            print('\n[END]')
+
+    async def on_tool_end(self, output: any, **kwargs):
+        print(f"\n{Fore.CYAN}[TOOL RESULT] {output}{Style.RESET_ALL}")
+
+    async def on_tool_start(self, input: any, *args, **kwargs):
+        print(f"\n{Fore.CYAN}[TOOL START]{Style.RESET_ALL}")
+
+    @staticmethod
+    def is_chat_stream_end(result: LLMResult) -> bool:
+        try:
+            content = result.generations[0][0].message.content
+            return bool(content and content.strip())
+        except (IndexError, AttributeError):
+            return False
+
+    # def __getattribute__(self, name):
+    #     attr = super().__getattribute__(name)
+    #     if callable(attr) and name.startswith("on_"):
+    #         async def wrapper(*args, **kwargs):
+    #             print(f"[EVENT] {name} args={args} kwargs={kwargs}")
+    #             return await attr(*args, **kwargs)
+    #         return wrapper
+    #     return attr

api/poetry.lock

@@ -269,7 +269,6 @@ files = [
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 ]
-markers = {main = "platform_system == \"Windows\" or sys_platform == \"win32\"", dev = "sys_platform == \"win32\""}
 
 [[package]]
 name = "debugpy"
@@ -340,6 +339,69 @@ typing-extensions = ">=4.8.0"
 all = ["email-validator (>=2.0.0)", "fastapi-cli[standard] (>=0.0.5)", "httpx (>=0.23.0)", "itsdangerous (>=1.1.0)", "jinja2 (>=3.1.5)", "orjson (>=3.2.1)", "pydantic-extra-types (>=2.0.0)", "pydantic-settings (>=2.0.0)", "python-multipart (>=0.0.18)", "pyyaml (>=5.3.1)", "ujson (>=4.0.1,!=4.0.2,!=4.1.0,!=4.2.0,!=4.3.0,!=5.0.0,!=5.1.0)", "uvicorn[standard] (>=0.12.0)"]
 standard = ["email-validator (>=2.0.0)", "fastapi-cli[standard] (>=0.0.5)", "httpx (>=0.23.0)", "jinja2 (>=3.1.5)", "python-multipart (>=0.0.18)", "uvicorn[standard] (>=0.12.0)"]
 
+[[package]]
+name = "greenlet"
+version = "3.2.2"
+description = "Lightweight in-process concurrent programming"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+markers = "python_version < \"3.14\" and (platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\")"
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+]
+
+[package.extras]
+docs = ["Sphinx", "furo"]
+test = ["objgraph", "psutil"]
+
 [[package]]
 name = "h11"
 version = "0.14.0"
@@ -595,6 +657,46 @@ files = [
     {file = "jsonpointer-3.0.0.tar.gz", hash = "sha256:2b2d729f2091522d61c3b31f82e11870f60b68f43fbc705cb76bf4b832af59ef"},
 ]
 
+[[package]]
+name = "langchain"
+version = "0.3.25"
+description = "Building applications with LLMs through composability"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "langchain-0.3.25-py3-none-any.whl", hash = "sha256:931f7d2d1eaf182f9f41c5e3272859cfe7f94fc1f7cef6b3e5a46024b4884c21"},
+    {file = "langchain-0.3.25.tar.gz", hash = "sha256:a1d72aa39546a23db08492d7228464af35c9ee83379945535ceef877340d2a3a"},
+]
+
+[package.dependencies]
+langchain-core = ">=0.3.58,<1.0.0"
+langchain-text-splitters = ">=0.3.8,<1.0.0"
+langsmith = ">=0.1.17,<0.4"
+pydantic = ">=2.7.4,<3.0.0"
+PyYAML = ">=5.3"
+requests = ">=2,<3"
+SQLAlchemy = ">=1.4,<3"
+
+[package.extras]
+anthropic = ["langchain-anthropic"]
+aws = ["langchain-aws"]
+azure-ai = ["langchain-azure-ai"]
+cohere = ["langchain-cohere"]
+community = ["langchain-community"]
+deepseek = ["langchain-deepseek"]
+fireworks = ["langchain-fireworks"]
+google-genai = ["langchain-google-genai"]
+google-vertexai = ["langchain-google-vertexai"]
+groq = ["langchain-groq"]
+huggingface = ["langchain-huggingface"]
+mistralai = ["langchain-mistralai"]
+ollama = ["langchain-ollama"]
+openai = ["langchain-openai"]
+perplexity = ["langchain-perplexity"]
+together = ["langchain-together"]
+xai = ["langchain-xai"]
+
 [[package]]
 name = "langchain-core"
 version = "0.3.59"
@@ -636,6 +738,89 @@ langchain-core = ">=0.3.58,<1.0.0"
 openai = ">=1.68.2,<2.0.0"
 tiktoken = ">=0.7,<1"
 
+[[package]]
+name = "langchain-text-splitters"
+version = "0.3.8"
+description = "LangChain text splitting utilities"
+optional = false
+python-versions = "<4.0,>=3.9"
+groups = ["main"]
+files = [
+    {file = "langchain_text_splitters-0.3.8-py3-none-any.whl", hash = "sha256:e75cc0f4ae58dcf07d9f18776400cf8ade27fadd4ff6d264df6278bb302f6f02"},
+    {file = "langchain_text_splitters-0.3.8.tar.gz", hash = "sha256:116d4b9f2a22dda357d0b79e30acf005c5518177971c66a9f1ab0edfdb0f912e"},
+]
+
+[package.dependencies]
+langchain-core = ">=0.3.51,<1.0.0"
+
+[[package]]
+name = "langgraph"
+version = "0.4.3"
+description = "Building stateful, multi-actor applications with LLMs"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "langgraph-0.4.3-py3-none-any.whl", hash = "sha256:dec926e034f4d440b92a3c52139cb6e9763bc1791e79a6ea53a233309cec864f"},
+    {file = "langgraph-0.4.3.tar.gz", hash = "sha256:272d5d5903f2c2882dbeeba849846a0f2500bd83fb3734a3801ebe64c1a60bdd"},
+]
+
+[package.dependencies]
+langchain-core = {version = ">=0.1", markers = "python_version < \"4.0\""}
+langgraph-checkpoint = ">=2.0.10,<3.0.0"
+langgraph-prebuilt = {version = ">=0.1.8", markers = "python_version < \"4.0\""}
+langgraph-sdk = {version = ">=0.1.42", markers = "python_version < \"4.0\""}
+pydantic = ">=2.7.4"
+xxhash = ">=3.5.0,<4.0.0"
+
+[[package]]
+name = "langgraph-checkpoint"
+version = "2.0.25"
+description = "Library with base interfaces for LangGraph checkpoint savers."
+optional = false
+python-versions = "<4.0.0,>=3.9.0"
+groups = ["main"]
+files = [
+    {file = "langgraph_checkpoint-2.0.25-py3-none-any.whl", hash = "sha256:23416a0f5bc9dd712ac10918fc13e8c9c4530c419d2985a441df71a38fc81602"},
+    {file = "langgraph_checkpoint-2.0.25.tar.gz", hash = "sha256:77a63cab7b5f84dec1d49db561326ec28bdd48bcefb7fe4ac372069d2609287b"},
+]
+
+[package.dependencies]
+langchain-core = ">=0.2.38,<0.4"
+ormsgpack = ">=1.8.0,<2.0.0"
+
+[[package]]
+name = "langgraph-prebuilt"
+version = "0.1.8"
+description = "Library with high-level APIs for creating and executing LangGraph agents and tools."
+optional = false
+python-versions = "<4.0.0,>=3.9.0"
+groups = ["main"]
+files = [
+    {file = "langgraph_prebuilt-0.1.8-py3-none-any.whl", hash = "sha256:ae97b828ae00be2cefec503423aa782e1bff165e9b94592e224da132f2526968"},
+    {file = "langgraph_prebuilt-0.1.8.tar.gz", hash = "sha256:4de7659151829b2b955b6798df6800e580e617782c15c2c5b29b139697491831"},
+]
+
+[package.dependencies]
+langchain-core = ">=0.2.43,<0.3.0 || >0.3.0,<0.3.1 || >0.3.1,<0.3.2 || >0.3.2,<0.3.3 || >0.3.3,<0.3.4 || >0.3.4,<0.3.5 || >0.3.5,<0.3.6 || >0.3.6,<0.3.7 || >0.3.7,<0.3.8 || >0.3.8,<0.3.9 || >0.3.9,<0.3.10 || >0.3.10,<0.3.11 || >0.3.11,<0.3.12 || >0.3.12,<0.3.13 || >0.3.13,<0.3.14 || >0.3.14,<0.3.15 || >0.3.15,<0.3.16 || >0.3.16,<0.3.17 || >0.3.17,<0.3.18 || >0.3.18,<0.3.19 || >0.3.19,<0.3.20 || >0.3.20,<0.3.21 || >0.3.21,<0.3.22 || >0.3.22,<0.4.0"
+langgraph-checkpoint = ">=2.0.10,<3.0.0"
+
+[[package]]
+name = "langgraph-sdk"
+version = "0.1.66"
+description = "SDK for interacting with LangGraph API"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
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 python-versions = ">=3.9"
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 version = "24.2"
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+
 [[package]]
 name = "zep-cloud"
 version = "2.12.1"
@@ -1822,4 +2286,4 @@ cffi = ["cffi (>=1.11)"]
 [metadata]
 lock-version = "2.1"
 python-versions = "^3.12"
-content-hash = "c212f5e8502ec8ee9a6528940c67b96ce7117dcbed72522f0476680f68f8e01f"
+content-hash = "ecf9a8b4cd13afeac7a43e99a62dae386c72cc2656e8c7c2e9bced60e3983757"

api/prompts/__init__.py

@@ -0,0 +1,27 @@
+from langchain.prompts import (
+    ChatPromptTemplate,
+    HumanMessagePromptTemplate,
+    MessagesPlaceholder,
+    SystemMessagePromptTemplate,
+    MessagesPlaceholder,
+)
+from langchain_core.messages import (
+    BaseMessage,
+    FunctionMessage,
+    HumanMessage,
+    SystemMessage,
+    ToolMessage,
+    AIMessage,
+)
+
+from .system_prompts import casual_fan_prompt, super_fan_prompt
+
+casual_fan_prompt = ChatPromptTemplate.from_messages([
+    SystemMessagePromptTemplate.from_template(casual_fan_prompt),
+    MessagesPlaceholder("input")
+])
+
+super_fan_prompt = ChatPromptTemplate.from_messages([
+    SystemMessagePromptTemplate.from_template(super_fan_prompt),
+    MessagesPlaceholder("input")
+])

api/prompts/system_prompts.py

@@ -0,0 +1,63 @@
+
+casual_fan_prompt = """
+Current date: {now}
+PERSONA DIRECTIVE: CASUAL FAN MODE — SOCCER / HUGE LEAGUE
+
+You are speaking to a casual soccer fan of Huge League. You MUST:
+1. Keep explanations BRIEF (3-4 sentences max)
+2. Use EVERYDAY LANGUAGE, avoid technical soccer jargon
+3. EMPHASIZE exciting plays, goals, and star players
+4. FOCUS on "big moments" and "highlight-reel" goals
+5. AVOID deep tactics or technical analysis
+6. CREATE a sense of belonging using "we" and "our team"
+7. INCLUDE at least one exclamation point in longer responses for excitement!
+
+Casual soccer fans don't care about: formations, advanced tactics, or contract details.
+Casual soccer fans DO care about: goals, stars, rivalries, and feeling part of the action.
+
+ABOUT HUGE LEAGUE:
+- International soccer league, 23-player squads, 4-3-3 base formation.
+- Teams:
+  • Yucatán Force (Mérida, Mexico): Mayan pride, fortress stadium "El Templo del Sol".
+  • Tierra Alta FC (San José, Costa Rica): Highlanders, eco-friendly, smart play.
+  • Everglade FC (Miami, USA): Flashy, wild, South Florida flair.
+  • Fraser Valley United (Abbotsford, Canada): Vineyard roots, top youth academy.
+
+EXAMPLE RESPONSE FOR CASUAL FAN (about the draft):
+"Everglade FC did a great job picking up some exciting young players! There's a speedy winger who could score some highlight-reel goals for us next season. The team focused on adding instant impact, which is just what we needed!"
+
+<CONTEXT>{zep_context}</CONTEXT>
+
+"""
+
+
+super_fan_prompt = """
+Current date: {now}
+PERSONA DIRECTIVE: SUPER FAN MODE — SOCCER / HUGE LEAGUE
+
+You MUST speak to a die-hard Huge League soccer super fan with deep knowledge. You MUST:
+1. Provide DETAILED analysis beyond surface-level info
+2. Use SPECIFIC soccer terminology and tactical concepts
+3. REFERENCE role players and their contributions, not just stars
+4. ANALYZE strategic elements of matches, transfers, and team building
+5. COMPARE current scenarios to club and league history
+6. INCLUDE stats, metrics, or technical details in your analysis
+7. ACKNOWLEDGE the complexity of soccer decisions; avoid oversimplifying
+
+Super fans expect: tactical breakdowns, transfer/contract details, advanced stats, and historical context.
+Super fans value: strategic insights, player development, and recognition of unsung contributors.
+
+ABOUT HUGE LEAGUE:
+- International soccer league, 23-player squads, 4-3-3 base formation.
+- Teams:
+  • Yucatán Force (Mérida, Mexico): Mayan pride, fortress stadium "El Templo del Sol".
+  • Tierra Alta FC (San José, Costa Rica): Highlanders, eco-friendly, smart play.
+  • Everglade FC (Miami, USA): Flashy, wild, South Florida flair.
+  • Fraser Valley United (Abbotsford, Canada): Vineyard roots, top youth academy.
+
+EXAMPLE RESPONSE FOR SUPER FAN (about the draft):
+"Everglade FC's draft was a masterclass in squad optimization. By targeting a left-footed winger with high xG/90 from open play, they add width and unpredictability to their 4-3-3. Their emphasis on youth aligns with the club's recent pivot toward academy integration, mirroring Fraser Valley United's model. The only concern is squad depth at fullback, but the technical staff seems to be betting on positional flexibility and internal development."
+
+<CONTEXT>{zep_context}</CONTEXT>
+
+"""

api/pyproject.toml

@@ -20,6 +20,9 @@ langchain-core = "^0.3.59"
 python-slugify = "^8.0.4"
 numpy = "^2.2.5"
 zep-cloud = "^2.12.1"
+colorama = "^0.4.6"
+langchain = "^0.3.25"
+langgraph = "^0.4.3"
 
 [tool.poetry.group.dev.dependencies]
 pytest = "^7.4.0"

api/scripts/vectorstore_search.py

@@ -8,8 +8,13 @@ vector_store = InMemoryVectorStore.load("/code/data/vectorstore.json", embedding
 
 # query = "ryan brown"
 # query = "defensive midfielder"
-query = "goaly"
+query = "* FC Everglade"
 results = vector_store.similarity_search(query, k=3)
+results = vector_store.similarity_search(
+    query,
+    k=3,
+    filter=lambda doc: doc.metadata.get("type") == "player",
+)
 
 for result in results:
     pprint(result.page_content)

api/scripts/workflow_playground.py

@@ -0,0 +1,25 @@
+import asyncio
+from workflows.base import build_workflow_with_state
+from event_handlers import PrintEventHandler
+from langchain_core.messages import HumanMessage
+
+from prompts import (
+    casual_fan_prompt,
+    HumanMessage,
+    AIMessage,
+)
+
+
+workflow, state = build_workflow_with_state(
+    handler=PrintEventHandler(),
+    session_id='5aed14ff09fb415ba77439409f458909',
+    messages=[
+        HumanMessage(content="tell me about some players in everglade fc"),
+    ],
+)
+
+async def main():
+    await workflow.ainvoke(state)
+
+if __name__ == "__main__":
+    asyncio.run(main())

api/scripts/zep_playground.py

@@ -0,0 +1,163 @@
+import os
+import uuid
+import datetime
+from zep_cloud.client import Zep
+from zep_cloud.types import Message
+from langchain.chat_models import init_chat_model
+import colorama
+from colorama import Fore, Style
+from pprint import pprint, pformat
+
+from prompts import (
+    casual_fan_prompt,
+    HumanMessage,
+    AIMessage,
+)
+from tools import (
+    PlayerSearchTool,
+    GameSearchTool,
+)
+
+API_KEY = os.environ.get('ZEP_API_KEY')
+
+client = Zep(
+    api_key=API_KEY,
+)
+model = init_chat_model("gpt-4o-mini", model_provider="openai")
+available_tools = [
+    PlayerSearchTool(),
+    GameSearchTool(),
+]
+
+
+def create_user(email, first_name, last_name):
+    # Create a new user
+    new_user = client.user.add(
+        user_id=email,
+        email=email,
+        first_name=first_name,
+        last_name=last_name,
+    )
+    return new_user
+
+
+def create_session(user_id):
+# Generate a unique session ID
+    session_id = uuid.uuid4().hex
+    # Create a new session for the user
+    client.memory.add_session(
+        session_id=session_id,
+        user_id=user_id,
+    )
+    return session_id
+
+
+def add_fact(user_id, fact):
+    new_episode = client.graph.add(
+        user_id=user_id,
+        type="text",
+        data=fact,
+    )
+    return new_episode
+
+
+history = []
+def send_message(session_id, message):
+    global history
+    history.append(HumanMessage(content=message))
+    # always cap history at last 6 messages
+    history = history[-6:]
+
+    memory = client.memory.get(session_id=session_id)
+    print(Fore.LIGHTBLACK_EX + "\nMemory retrieved." + Style.RESET_ALL)
+    # print(Fore.YELLOW + pformat(memory.context, compact=False, width=120) + "\n" + Style.RESET_ALL)
+
+    prompt = casual_fan_prompt.format(
+        zep_context=memory.context,
+        input=history,
+        now=datetime.datetime.now(datetime.UTC).strftime('%Y-%m-%d'),
+    )
+
+    print(Fore.YELLOW + pformat(prompt, compact=False, width=120) + "\n" + Style.RESET_ALL)
+
+    chunks = []
+    for chunk in model.stream(prompt, tools=available_tools):
+        chunks.append(chunk)
+        print(Fore.GREEN + chunk.content + Style.RESET_ALL, end="", flush=True)
+
+    messages = [
+        Message(
+            role="user",
+            content=message,
+            role_type="user",
+        ),
+        Message(
+            role="assistant",
+            content="".join([chunk.content for chunk in chunks]),
+            role_type="assistant",
+        ),
+    ]
+    client.memory.add(session_id=session_id, messages=messages)
+    print(Fore.LIGHTBLACK_EX + "\nMemory updated." + Style.RESET_ALL)
+
+    history.append(
+        AIMessage(content="".join([chunk.content for chunk in chunks]))
+    )
+    
+    return chunks
+
+
+user_id = '[email protected]'
+# session_id = 'f0bfaa5b4b11486391e1c52bcebe0ad3' # blue sky
+# session_id = '9f0a03443fa44eb9a1e9967ad2ab137a'
+session_id = '5aed14ff09fb415ba77439409f458909'
+
+# user = client.user.get(user_id)
+# memory = client.memory.get(session_id=session_id)
+# print(memory)
+
+
+# if __name__ == "__main__":
+#     """
+#     Simple REPL loop for sending messages to the LLM.
+#     Prompts with '>', sends input to send_message, and prints response in green.
+#     Type 'exit' or press Ctrl+C to quit.
+#     """
+#     colorama.init(autoreset=True)
+
+#     print("\nType 'exit' or press Ctrl+C to quit.\n")
+#     try:
+#         while True:
+#             user_input = input("> ")
+#             if user_input.strip().lower() == "exit":
+#                 print("Exiting.")
+#                 break
+#             # Call send_message and print LLM response in green
+#             chunks = send_message(session_id, user_input)
+#             print("")  # Newline after response
+#     except KeyboardInterrupt:
+#         print("\nExiting.")
+
+
+# from prompts import (
+#     casual_fan_prompt,
+#     HumanMessage,
+#     AIMessage,
+# )
+
+
+
+memory = client.memory.get(session_id=session_id)
+
+history = [
+    HumanMessage(content="tell me about some players in everglade fc"),
+    # AIMessage(content="soccer is the best sport ever"),
+]
+
+prompt = casual_fan_prompt.format(
+    zep_context=memory.context,
+    input=history,
+    now=datetime.datetime.now(datetime.UTC).strftime('%Y-%m-%d'),
+)
+
+print(prompt)

api/tools/__init__.py

@@ -1,5 +1,7 @@
 from .player_search import PlayerSearchTool
+from .game_search import GameSearchTool
 
 __all__ = [
     "PlayerSearchTool",
+    "GameSearchTool",
 ]

api/tools/game_search.py

@@ -1,8 +1,11 @@
 from langchain_core.tools import BaseTool
 from pydantic import BaseModel, Field
 from langchain_core.documents import Document
-from typing import Type, List
-
+from typing import Type, List, Optional
+from langchain.callbacks.manager import (
+    AsyncCallbackManagerForToolRun,
+    CallbackManagerForToolRun,
+)
 from data.vectorstore_singleton import get_vector_store
 
 vector_store = get_vector_store()
@@ -23,9 +26,11 @@ class GameSearchTool(BaseTool):
     name: str = "game_search"
     description: str = "Search for games in the vector store"
     args_schema: Type[BaseModel] = GameSearchSchema 
-    description: str = ""
-    
-    def _run(self, query: str) -> List[Document]:
+
+    def _run(self,
+             query: str,
+             run_manager: Optional[CallbackManagerForToolRun] = None,
+             ) -> List[Document]:    
         result = vector_store.similarity_search(
             "",
             k=20,
@@ -33,7 +38,10 @@ class GameSearchTool(BaseTool):
         )
         return sorted(result, key=lambda doc: int(doc.id.split("_")[-1]))
     
-    async def _arun(self, query: str) -> List[Document]:
+    async def _arun(self,
+                    query: str,
+                    run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
+                    ) -> List[Document]:
         result = await vector_store.asimilarity_search(
             "",
             k=20,

api/tools/player_search.py

@@ -1,33 +1,55 @@
-from langchain_core.tools import BaseTool
 from pydantic import BaseModel, Field
+from langchain.tools import BaseTool, StructuredTool, tool
 from langchain_core.documents import Document
-from typing import Type, List
-
+from typing import Type, List, Optional
+from langchain.callbacks.manager import (
+    AsyncCallbackManagerForToolRun,
+    CallbackManagerForToolRun,
+)
 from data.vectorstore_singleton import get_vector_store
 
 vector_store = get_vector_store()
 
 
 class PlayerSearchSchema(BaseModel):
-    query: str = Field(description="The search query to identify a player. Examples: 'Deebo Samuel', '49ers QB', 'Number 19 on the 49ers'")
+    query: str = Field(description=(
+        "The search query to identify a soccer player in the fictional league. "
+        "You can search by player name, position, or use team and number (e.g., 'Number 10 on Everglade FC'). "
+        "To get random players from a team, send an asterisk '*' as the query (e.g., '* Everglade FC'). "
+        "Available teams:\n"
+        "  • Yucatán Force (Mérida, Mexico): Mayan pride, fortress stadium 'El Templo del Sol'.\n"
+        "  • Tierra Alta FC (San José, Costa Rica): Highlanders, eco-friendly, smart play.\n"
+        "  • Everglade FC (Miami, USA): Flashy, wild, South Florida flair.\n"
+        "  • Fraser Valley United (Abbotsford, Canada): Vineyard roots, top youth academy."
+    ))
 
 
 class PlayerSearchTool(BaseTool):
     name: str = "player_search"
-    description: str = "Search for players in the vector store"
     args_schema: Type[BaseModel] = PlayerSearchSchema 
-    description: str = "Search for players in the vector store based on their name, position, or other identifying information. Returns a list of documents with player details, stats, headshots, social media links, etc."
+    description: str = (
+        "Search for soccer players in the fictional league based on their name, position, or other identifying information. "
+        "Returns a list of documents with player details, stats, headshots, social media links, etc. "
+    )
     
-    def _run(self, query: str) -> List[Document]:
+    def _run(self,
+             query: str,
+             run_manager: Optional[CallbackManagerForToolRun] = None,
+             ) -> List[Document]:
+        k = 5 if query[0] == "*" else 3
         return vector_store.similarity_search(
             query,
-            k=3,
-            filter={"type": "player"},
+            k=k,
+            filter=lambda doc: doc.metadata.get("type") == "player",
         )
     
-    async def _arun(self, query: str) -> List[Document]:
-        return vector_store.asimilarity_search(
+    async def _arun(self,
+                    query: str,
+                    run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
+                    ) -> List[Document]:
+        k = 5 if query[0] == "*" else 3
+        return await vector_store.asimilarity_search(
             query,
-            k=3,
-            filter={"type": "player"},
+            k=k,
+            filter=lambda doc: doc.metadata.get("type") == "player",
         )

api/tools/team_search.py

@@ -0,0 +1 @@
+# TODO: returns news; in this case recaps of the semi-final games

api/utils/zep_helpers.py

@@ -0,0 +1,30 @@
+from functools import lru_cache
+from langchain_core.messages import BaseMessage
+from zep_cloud.client import AsyncZep
+from zep_cloud.types import Message
+
+
+@lru_cache(maxsize=1)
+def get_zep_client():
+    return AsyncZep()
+
+
+async def record_session(session_id: str, messages: list[BaseMessage]):
+    if len(messages) >= 2:
+        user_message = messages[0]
+        assistant_message = messages[-1]
+        messages = [
+            Message(
+                role="user",
+            content=user_message.content,
+            role_type="user",
+        ),
+        Message(
+            role="assistant",
+            content=assistant_message.content,
+            role_type="assistant",
+        ),
+    ]
+    zep_client = get_zep_client()
+    await zep_client.memory.add(session_id=session_id, messages=messages)
+    

api/workflows/base.py

@@ -0,0 +1,134 @@
+import asyncio
+import datetime
+import operator
+from functools import partial
+from typing import TypedDict, Annotated, Sequence
+from langchain_openai import ChatOpenAI
+from langchain_core.messages import (
+    BaseMessage,
+    AIMessage,
+    FunctionMessage,
+    HumanMessage,
+    SystemMessage,
+    ToolMessage,
+)
+from langgraph.graph import StateGraph, END
+from langchain_core.callbacks import AsyncCallbackHandler
+from langgraph.prebuilt import ToolNode
+
+
+from utils.zep_helpers import (
+    get_zep_client,
+    record_session,
+)
+from prompts import (
+    casual_fan_prompt,
+)
+from tools import (
+    PlayerSearchTool,
+    GameSearchTool,
+)
+
+
+available_tools = [
+    GameSearchTool(),
+    PlayerSearchTool(),
+]
+tool_node = ToolNode(available_tools)
+
+llm = ChatOpenAI(model="gpt-4o-mini")
+llm_with_tools = llm.bind_tools(tools=available_tools)
+
+zep_client = get_zep_client()
+
+
+class AgentState(TypedDict):
+    session_id: str
+    messages: Annotated[Sequence[BaseMessage], operator.add]
+
+
+async def call_model(state: AgentState, handler: AsyncCallbackHandler) -> dict:
+    session_id = state["session_id"]
+    memory = await zep_client.memory.get(session_id=session_id)
+    messages = state["messages"]
+    
+    prompt = casual_fan_prompt.format(
+        zep_context=memory.context,
+        input=messages,
+        now=datetime.datetime.now(datetime.UTC).strftime('%Y-%m-%d'),
+    )
+
+    # response = await llm_with_tools.ainvoke(prompt, stream=True,
+    #                                     config={"callbacks" :[handler]})
+    response = await llm_with_tools.with_config(callbacks=[handler]).ainvoke(prompt, stream=True)
+
+    return {'messages': [response]}
+
+
+async def call_tool(state: AgentState, handler: AsyncCallbackHandler) -> dict:
+    messages = state["messages"]
+    last_message = messages[-1]
+    tools_by_name = {tool.name: tool for tool in available_tools}
+
+    observations = []
+    for tool_call in last_message.tool_calls:
+        tool = tools_by_name[tool_call["name"]]
+        observations.append(
+            tool.with_config(callbacks=[handler]).ainvoke(tool_call["args"])
+        )
+
+    # await all observations
+    observations = await asyncio.gather(*observations)
+
+    results = []
+    for observation, tool_call in zip(observations, last_message.tool_calls):
+        results.append(ToolMessage(content=observation, tool_call_id=tool_call["id"]))
+    return {"messages": results}
+
+
+async def should_continue(state):
+    messages = state["messages"]
+    last_message = messages[-1]
+    if 'tool_calls' not in last_message.additional_kwargs:
+        # inform zep of final response
+        await record_session(state["session_id"], messages)
+        return 'end'
+    return 'continue'
+
+
+def build_workflow(handler: AsyncCallbackHandler):
+    workflow = StateGraph(AgentState)
+    workflow.add_node('agent', partial(call_model, handler=handler))
+    workflow.add_node('tools', partial(call_tool, handler=handler))
+    workflow.set_entry_point('agent')
+
+    workflow.add_conditional_edges(
+        'agent',
+        should_continue,
+        {
+            'continue': 'tools',
+            'end': END,
+        }   
+    )
+
+    workflow.add_edge('tools', 'agent')
+
+    return workflow.compile()
+
+
+def build_workflow_with_state(handler: AsyncCallbackHandler, session_id: str, messages=None):
+    """
+    Utility to build workflow and initial state in one step.
+    Args:
+        handler: AsyncCallbackHandler for this workflow
+        session_id: unique session identifier
+        messages: optional initial message list
+    Returns:
+        (workflow, state) tuple ready for execution
+    """
+    workflow = build_workflow(handler)
+    state = {
+        "session_id": session_id,
+        "messages": messages or [],
+    }
+    return workflow, state