diff --git a/poetry.lock b/poetry.lock
index 2b1c4aed..156ab007 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -6860,13 +6860,13 @@ test = ["coverage", "django", "flake8", "freezegun (==0.3.15)", "mock (>=2.0.0)"
 
 [[package]]
 name = "prediction-market-agent-tooling"
-version = "0.56.3"
+version = "0.57.0"
 description = "Tools to benchmark, deploy and monitor prediction market agents."
 optional = false
 python-versions = "<3.12,>=3.10"
 files = [
-    {file = "prediction_market_agent_tooling-0.56.3-py3-none-any.whl", hash = "sha256:dd8edc7c12bbc7228a7d4e60599104b1c6a625dbed509eefccf14077b860669c"},
-    {file = "prediction_market_agent_tooling-0.56.3.tar.gz", hash = "sha256:ddd119aa72779cf2356ee573c847a0b8b7ed635191e8ae9caad873ea6baef3b5"},
+    {file = "prediction_market_agent_tooling-0.57.0-py3-none-any.whl", hash = "sha256:6d9c3129f9e658bc703805c60168efecef8b7b747e928f9e81d87f744f782643"},
+    {file = "prediction_market_agent_tooling-0.57.0.tar.gz", hash = "sha256:be9b9cbfbb4c646e60fe1be61ce7a2220d6e50f6e51a3cdde7d53c752615e767"},
 ]
 
 [package.dependencies]
@@ -11106,4 +11106,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.0"
 python-versions = "~3.10.0"
-content-hash = "4505c4c85ac911308f6e3886e07e224c01ba94ed0bb4beb08d3af34dac06f1e0"
+content-hash = "4619718806c0ef63b97367858b245184e85e420f3ab76aa830dcb4fc0ecbf628"
diff --git a/prediction_market_agent/agents/social_media_agent/deploy.py b/prediction_market_agent/agents/social_media_agent/deploy.py
index 61b86679..d8c6f682 100644
--- a/prediction_market_agent/agents/social_media_agent/deploy.py
+++ b/prediction_market_agent/agents/social_media_agent/deploy.py
@@ -88,4 +88,4 @@ def post(self, tweet: str | None, reasoning_reply_tweet: str | None) -> None:
 
 if __name__ == "__main__":
     agent = DeployableSocialMediaAgent()
-    agent.deploy_local(market_type=MarketType.OMEN, sleep_time=540, timeout=180)
+    agent.deploy_local(market_type=MarketType.OMEN, sleep_time=540, run_time=180)
diff --git a/prediction_market_agent/agents/think_thoroughly_agent/deploy.py b/prediction_market_agent/agents/think_thoroughly_agent/deploy.py
index b24d4bcc..85fb514c 100644
--- a/prediction_market_agent/agents/think_thoroughly_agent/deploy.py
+++ b/prediction_market_agent/agents/think_thoroughly_agent/deploy.py
@@ -59,10 +59,10 @@ def get_betting_strategy(self, market: AgentMarket) -> BettingStrategy:
 
 if __name__ == "__main__":
     agent = DeployableThinkThoroughlyAgent(
-        place_trades=False, store_prediction=False, store_trades=False
+        place_trades=False, store_predictions=False, store_trades=False
     )
     agent.deploy_local(
         market_type=MarketType.OMEN,
         sleep_time=540,
-        timeout=180,
+        run_time=180,
     )
diff --git a/prediction_market_agent/ai_models/abstract_ai_models.py b/prediction_market_agent/ai_models/abstract_ai_models.py
deleted file mode 100644
index 82132e54..00000000
--- a/prediction_market_agent/ai_models/abstract_ai_models.py
+++ /dev/null
@@ -1,16 +0,0 @@
-from typing import Optional
-
-from pydantic import BaseModel
-
-
-class Message(BaseModel):
-    role: str
-    content: str
-
-
-class AbstractAiChatModel:
-    def complete(self, messages: list[Message]) -> Optional[str]:
-        """
-        Execute the model, and return the completion result as a string.
-        """
-        raise NotImplementedError
diff --git a/prediction_market_agent/ai_models/llama_ai_models.py b/prediction_market_agent/ai_models/llama_ai_models.py
deleted file mode 100644
index 6f62871e..00000000
--- a/prediction_market_agent/ai_models/llama_ai_models.py
+++ /dev/null
@@ -1,68 +0,0 @@
-from enum import Enum
-from typing import Literal, Optional
-
-import replicate
-from prediction_market_agent_tooling.tools.utils import should_not_happen
-
-from prediction_market_agent.ai_models.abstract_ai_models import (
-    AbstractAiChatModel,
-    Message,
-)
-
-
-class LlamaRole(Enum):
-    user = "user"
-    assistant = "assistant"
-
-
-class ChatReplicateLLamaModel(AbstractAiChatModel):
-    def __init__(
-        self,
-        model: Literal["meta/llama-2-70b-chat",] = "meta/llama-2-70b-chat",
-        system_prompt: Optional[str] = None,
-        max_tokens: int = 256,
-        temperature: float = 0.01,
-        top_p: float = 1.0,
-        min_new_tokens: int = -1,
-    ):
-        self.model = model
-        self.system_prompt = system_prompt
-        self.max_tokens = max_tokens
-        self.temperature = temperature
-        self.top_p = top_p
-        self.min_new_tokens = min_new_tokens
-
-    def complete(self, messages: list[Message]) -> Optional[str]:
-        prompt = construct_llama_prompt(messages)
-        completion = "".join(
-            replicate.run(
-                self.model,
-                input={
-                    "prompt": prompt,
-                    "system_prompt": self.system_prompt or "",
-                    "top_p": self.top_p,
-                    "temperature": self.temperature,
-                    "max_new_tokens": self.max_tokens,
-                    "min_new_tokens": self.min_new_tokens,
-                },
-            )
-        )
-        return completion
-
-
-def construct_llama_prompt(messages: list[Message]) -> str:
-    """
-    Based on https://replicate.com/blog/how-to-prompt-llama.
-    """
-    return "\n".join(
-        (
-            message.content
-            if message.role == LlamaRole.assistant.value
-            else (
-                f"[INST] {message.content} [/INST]"
-                if message.role == LlamaRole.user.value
-                else should_not_happen(f"Invalid role in the message: {message}")
-            )
-        )
-        for message in messages
-    )
diff --git a/prediction_market_agent/ai_models/openai_ai_models.py b/prediction_market_agent/ai_models/openai_ai_models.py
deleted file mode 100644
index 5b824185..00000000
--- a/prediction_market_agent/ai_models/openai_ai_models.py
+++ /dev/null
@@ -1,95 +0,0 @@
-from enum import Enum
-from typing import Literal, Optional
-
-from openai import OpenAI
-from openai.types.chat.chat_completion import ChatCompletion
-from openai.types.chat.chat_completion_message_param import (
-    ChatCompletionMessageParam,
-    ChatCompletionSystemMessageParam,
-    ChatCompletionUserMessageParam,
-)
-
-from prediction_market_agent.ai_models.abstract_ai_models import (
-    AbstractAiChatModel,
-    Message,
-)
-
-ROLE_KEY = "role"
-CONTENT_KEY = "content"
-
-
-class OpenAiRole(str, Enum):
-    user = "user"
-    assistant = "assistant"
-    system = "system"
-
-
-class ChatOpenAIModel(AbstractAiChatModel):
-    def __init__(
-        self,
-        model: Literal[
-            "gpt-4o-2024-08-06",
-            "gpt-4-1106-preview",
-            "gpt-4-vision-preview",
-            "gpt-4",
-            "gpt-4-0314",
-            "gpt-4-0613",
-            "gpt-4-32k",
-            "gpt-4-32k-0314",
-            "gpt-4-32k-0613",
-            "gpt-3.5-turbo",
-            "gpt-3.5-turbo-16k",
-            "gpt-3.5-turbo-0301",
-            "gpt-3.5-turbo-0613",
-            "gpt-3.5-turbo-1106",
-            "gpt-3.5-turbo-16k-0613",
-        ] = "gpt-4o-2024-08-06",
-        system_prompt: Optional[str] = None,
-        max_tokens: int = 256,
-        temperature: float = 0.0,
-        top_p: float = 1.0,
-        presence_penalty: float = 0.0,
-        frequency_penalty: float = 0.0,
-        api_key: Optional[str] = None,
-    ):
-        self.model = model
-        self.system_prompt = system_prompt
-        self.max_tokens = max_tokens
-        self.temperature = temperature
-        self.top_p = top_p
-        self.presence_penalty = presence_penalty
-        self.frequency_penalty = frequency_penalty
-        self.client = OpenAI(api_key=api_key)
-
-    def complete(self, messages: list[Message]) -> Optional[str]:
-        messages_formatted: list[ChatCompletionMessageParam] = []
-        if self.system_prompt is not None:
-            messages_formatted.append(
-                ChatCompletionSystemMessageParam(
-                    role=OpenAiRole.system.value, content=self.system_prompt
-                )
-            )
-        for message in messages:
-            if message.role == OpenAiRole.user.value:
-                messages_formatted.append(
-                    ChatCompletionUserMessageParam(
-                        role=message.role, content=message.content  # type: ignore # This is OK due to the if check.
-                    )
-                )
-            else:
-                # TODO: Check `ChatCompletionMessageParam` to support all roles, not just hardcoded system and user.
-                raise ValueError(
-                    f"Only `user` role is supported at the moment, but got `{message.role}`."
-                )
-        response: ChatCompletion = self.client.chat.completions.create(
-            model=self.model,
-            messages=messages_formatted,
-            n=1,
-            max_tokens=self.max_tokens,
-            temperature=self.temperature,
-            top_p=self.top_p,
-            presence_penalty=self.presence_penalty,
-            frequency_penalty=self.frequency_penalty,
-        )
-        completion = response.choices[0].message.content
-        return completion
diff --git a/prediction_market_agent/tools/web_scrape/markdown.py b/prediction_market_agent/tools/web_scrape/markdown.py
index e4f5e294..f5ff857e 100644
--- a/prediction_market_agent/tools/web_scrape/markdown.py
+++ b/prediction_market_agent/tools/web_scrape/markdown.py
@@ -22,7 +22,7 @@ def fetch_html(url: str, timeout: int) -> Response:
 
 
 @observe()
-@db_cache(max_age=timedelta(days=1))
+@db_cache(max_age=timedelta(days=1), ignore_args=["timeout"])
 def web_scrape(url: str, timeout: int = 10) -> str:
     """
     Taken from agentcoinorg/predictionprophet
diff --git a/pyproject.toml b/pyproject.toml
index b213fcf9..c96e7d12 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -34,7 +34,7 @@ poetry = "^1.7.1"
 poetry-plugin-export = "^1.6.0"
 functions-framework = "^3.5.0"
 cron-validator = "^1.0.8"
-prediction-market-agent-tooling = { version = "0.56.3", extras = ["langchain", "google"] }
+prediction-market-agent-tooling = { version = "^0.57.0", extras = ["langchain", "google"] }
 pydantic-settings = "^2.1.0"
 autoflake = "^2.2.1"
 isort = "^5.13.2"
diff --git a/scripts/agent_app.py b/scripts/agent_app.py
index 9d609ddf..1a070bd0 100644
--- a/scripts/agent_app.py
+++ b/scripts/agent_app.py
@@ -79,7 +79,7 @@ def predict(
 ) -> None:
     agent = AgentClass(
         place_trades=False,
-        store_prediction=False,
+        store_predictions=False,
         store_trades=False,
         enable_langfuse=enable_langfuse,
     )
diff --git a/tests/ai_models/test_llama_ai_models.py b/tests/ai_models/test_llama_ai_models.py
deleted file mode 100644
index 78252279..00000000
--- a/tests/ai_models/test_llama_ai_models.py
+++ /dev/null
@@ -1,27 +0,0 @@
-from prediction_market_agent.ai_models.llama_ai_models import (
-    LlamaRole,
-    Message,
-    construct_llama_prompt,
-)
-
-
-def test_construct_llama_prompt_0() -> None:
-    prompt = construct_llama_prompt([])
-    assert prompt == ""
-
-
-def test_construct_llama_prompt_1() -> None:
-    prompt = construct_llama_prompt(
-        [Message(role=LlamaRole.user.value, content="Hello!")]
-    )
-    assert prompt == "[INST] Hello! [/INST]"
-
-
-def test_construct_llama_prompt_2() -> None:
-    prompt = construct_llama_prompt(
-        [
-            Message(role=LlamaRole.user.value, content="Hello!"),
-            Message(role=LlamaRole.assistant.value, content="Bonjour!"),
-        ]
-    )
-    assert prompt == "[INST] Hello! [/INST]\nBonjour!"