First commit for microchain agent

prediction_market_agent/agents/general_agent/deploy.py

@@ -1,4 +1,5 @@
 from dotenv import load_dotenv
+
 load_dotenv()
 from prediction_market_agent_tooling.deploy.agent import DeployableAgent
 from prediction_market_agent_tooling.markets.agent_market import AgentMarket
@@ -16,16 +17,14 @@ class DeployableGeneralAgentAgent(DeployableAgent):
     agent = GeneralAgent()
 
     def run(self, market_type: MarketType, _place_bet: bool = True) -> None:
-        print (f"Agent {self.__class__} starting")
+        print(f"Agent {self.__class__} starting")
         self.agent.run()
-        print (f"Agent {self.__class__} finishing")
+        print(f"Agent {self.__class__} finishing")
+
 
 if __name__ == "__main__":
     load_dotenv()
     agent = DeployableGeneralAgentAgent()
     agent.deploy_local(
-        market_type=MarketType.OMEN,
-        sleep_time=60,
-        timeout=540,
-        place_bet=False
+        market_type=MarketType.OMEN, sleep_time=60, timeout=540, place_bet=False
     )

prediction_market_agent/agents/general_agent/general_agent.py

@@ -1,7 +1,10 @@
-from crewai import Agent, Task, Process, Crew
+from crewai import Agent, Crew, Process, Task
+from crewai_tools import BaseTool
 
 from prediction_market_agent.agents.abstract import AbstractAgent
-from prediction_market_agent.agents.general_agent.omen_functions import GetBinaryMarketsTool
+from prediction_market_agent.agents.general_agent.omen_functions import (
+    GetBinaryMarketsTool,
+)
 
 
 class GeneralAgent(AbstractAgent):
@@ -15,8 +18,7 @@ def __init__(self) -> None:
             tools=self.get_tools(),
         )
 
-
-    def run(self):
+    def run(self) -> None:
         """
         Method where agent should reason and execute actions on Omen.
         """
@@ -26,18 +28,17 @@ def run(self):
             tasks=[task],
             process=Process.sequential,  # Optional: Sequential task execution is default
         )
-        result = crew.kickoff(inputs={'topic': 'AI in healthcare'})
+        result = crew.kickoff(inputs={"topic": "AI in healthcare"})
         print(result)
 
-
-    def get_tools(self):
+    def get_tools(self) -> list[BaseTool]:
         return [GetBinaryMarketsTool()]
 
     def prepare_task(self) -> Task:
         return Task(
             description=(
                 "Find 1 or more markets on Omen that you could place bets on."
             ),
-            expected_output='A list of 1 or more markets, only displaying the market title.',
+            expected_output="A list of 1 or more markets, only displaying the market title.",
             agent=self.gambler,
-        )
+        )

prediction_market_agent/agents/general_agent/omen_functions.py

@@ -1,14 +1,19 @@
-import sys
+import typing as t
 
 from crewai_tools import BaseTool
 from prediction_market_agent_tooling.markets.agent_market import AgentMarket, FilterBy
-from prediction_market_agent_tooling.markets.markets import get_binary_markets, MarketType
+from prediction_market_agent_tooling.markets.markets import (
+    MarketType,
+    get_binary_markets,
+)
 
 
 # ToDo - Add filters as arguments to this function call, add it to description.
 class GetBinaryMarketsTool(BaseTool):
     name: str = "Tool for fetching binary markets"
-    description: str = "This tool returns all the markets on the prediction market platform Omen."
+    description: str = (
+        "This tool returns all the markets on the prediction market platform Omen."
+    )
 
     def _run(self) -> list[AgentMarket]:
         # Implementation goes here
@@ -19,6 +24,6 @@ def _run(self) -> list[AgentMarket]:
         )
         return markets
 
-    def cache_function(self, args, result) -> bool:
+    def cache_function(self, args: list[t.Any], result: t.Any) -> bool:
         # ToDo - implement caching if result was > 1 and depending on filter.
-        return False
+        return False