diff --git a/mypy.ini b/mypy.ini
index 7a254d85..13261f67 100644
--- a/mypy.ini
+++ b/mypy.ini
@@ -19,7 +19,7 @@ ignore_missing_imports = True
 # We don't want to ignore all missing imports as we want to catch those in our own code
 # But for certain libraries they don't have a stub file, so we only enforce import checking for our own libraries.
 # Another alternative would be to list out every single dependency that does not have a stub.
-[mypy-prediction_market_agent.*]
+[mypy-prediction_market_agent_tooling.*]
 ignore_missing_imports = False
 [mypy-scripts.*]
 ignore_missing_imports = False
diff --git a/poetry.lock b/poetry.lock
index ce1732de..1f7942a8 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
 
 [[package]]
 name = "aiohappyeyeballs"
@@ -195,17 +195,17 @@ trio = ["trio (>=0.23)"]
 
 [[package]]
 name = "ape-foundry"
-version = "0.8.3"
+version = "0.8.4"
 description = "ape-foundry: Ape network provider for Foundry"
 optional = false
 python-versions = "<4,>=3.9"
 files = [
-    {file = "ape-foundry-0.8.3.tar.gz", hash = "sha256:be8b1daac986f9e402a9a72554ae7ef3b121cc9acf251c05d18227433660577e"},
-    {file = "ape_foundry-0.8.3-py3-none-any.whl", hash = "sha256:6f2d60bbc31439144214194ed3c3fd3b1559829db3296d4793f89e5ff3bcb12f"},
+    {file = "ape-foundry-0.8.4.tar.gz", hash = "sha256:ba8e838cec06115ecfa67db6c5e953ded7ba677305ace834576ffaed45bae998"},
+    {file = "ape_foundry-0.8.4-py3-none-any.whl", hash = "sha256:de7b3fde5fb0b887a541bbd3d9e3166b837df7ad3e5c39b47396e29287956333"},
 ]
 
 [package.dependencies]
-eth-ape = ">=0.8.11,<0.9"
+eth-ape = ">=0.8.12,<0.9"
 eth-pydantic-types = "*"
 ethpm-types = "*"
 evm-trace = "*"
@@ -214,11 +214,11 @@ web3 = "*"
 yarl = "*"
 
 [package.extras]
-dev = ["IPython", "Sphinx (>=6.1.3,<7)", "ape-alchemy", "ape-optimism", "ape-polygon", "black (>=24.8.0,<25)", "commitizen", "flake8 (>=7.1.1,<8)", "flake8-breakpoint (>=1.1.0,<2)", "flake8-print (>=5.0.0,<6)", "flake8-pydantic", "hypothesis (>=6.2.0,<7.0)", "ipdb", "isort (>=5.13.2,<6)", "mdformat (>=0.7.17)", "mdformat-frontmatter (>=0.4.1)", "mdformat-gfm (>=0.3.5)", "mdformat-pyproject (>=0.0.1)", "mypy (>=1.11.1,<2)", "pre-commit", "pytest (>=6.0)", "pytest-cov", "pytest-mock", "pytest-xdist", "setuptools", "setuptools-scm", "sphinx-rtd-theme (>=1.2.0,<2)", "towncrier (>=19.2.0,<20)", "twine", "types-PyYAML", "types-requests", "types-setuptools", "wheel"]
+dev = ["IPython", "Sphinx (>=6.1.3,<7)", "ape-alchemy", "ape-optimism", "ape-polygon", "black (>=24.8.0,<25)", "commitizen", "flake8 (>=7.1.1,<8)", "flake8-breakpoint (>=1.1.0,<2)", "flake8-print (>=5.0.0,<6)", "flake8-pydantic", "hypothesis (>=6.2.0,<7.0)", "ipdb", "isort (>=5.13.2,<6)", "mdformat (>=0.7.17)", "mdformat-frontmatter (>=0.4.1)", "mdformat-gfm (>=0.3.5)", "mdformat-pyproject (>=0.0.1)", "mypy (>=1.11.1,<2)", "pre-commit", "pytest (>=6.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-xdist", "setuptools", "setuptools-scm", "sphinx-rtd-theme (>=1.2.0,<2)", "towncrier (>=19.2.0,<20)", "twine", "types-PyYAML", "types-requests", "types-setuptools", "wheel"]
 doc = ["Sphinx (>=6.1.3,<7)", "sphinx-rtd-theme (>=1.2.0,<2)", "towncrier (>=19.2.0,<20)"]
 lint = ["black (>=24.8.0,<25)", "flake8 (>=7.1.1,<8)", "flake8-breakpoint (>=1.1.0,<2)", "flake8-print (>=5.0.0,<6)", "flake8-pydantic", "isort (>=5.13.2,<6)", "mdformat (>=0.7.17)", "mdformat-frontmatter (>=0.4.1)", "mdformat-gfm (>=0.3.5)", "mdformat-pyproject (>=0.0.1)", "mypy (>=1.11.1,<2)", "types-PyYAML", "types-requests", "types-setuptools"]
 release = ["setuptools", "setuptools-scm", "twine", "wheel"]
-test = ["ape-alchemy", "ape-optimism", "ape-polygon", "hypothesis (>=6.2.0,<7.0)", "pytest (>=6.0)", "pytest-cov", "pytest-mock", "pytest-xdist"]
+test = ["ape-alchemy", "ape-optimism", "ape-polygon", "hypothesis (>=6.2.0,<7.0)", "pytest (>=6.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-xdist"]
 
 [[package]]
 name = "art"
@@ -642,96 +642,105 @@ files = [
 
 [[package]]
 name = "ckzg"
-version = "1.0.2"
+version = "2.0.0"
 description = "Python bindings for C-KZG-4844"
 optional = false
 python-versions = "*"
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 [[package]]
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 ]
 
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+description = "DNS toolkit"
+optional = false
+python-versions = ">=3.8"
+files = [
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+idna = ["idna (>=3.6)"]
+trio = ["trio (>=0.23)"]
+wmi = ["wmi (>=1.5.1)"]
+
 [[package]]
 name = "eip712"
 version = "0.2.7"
@@ -2492,13 +2521,13 @@ langchain-core = ">=0.2.10,<0.3.0"
 
 [[package]]
 name = "langfuse"
-version = "2.43.3"
+version = "2.44.0"
 description = "A client library for accessing langfuse"
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
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 [package.dependencies]
@@ -2517,16 +2546,17 @@ openai = ["openai (>=0.27.8)"]
 
 [[package]]
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+version = "0.1.100"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
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 python-versions = "<4.0,>=3.8.1"
 files = [
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 ]
 
 [package.dependencies]
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 orjson = ">=3.9.14,<4.0.0"
 pydantic = {version = ">=1,<3", markers = "python_full_version < \"3.12.4\""}
 requests = ">=2,<3"
@@ -2748,13 +2778,13 @@ files = [
 
 [[package]]
 name = "marshmallow"
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+version = "3.22.0"
 description = "A lightweight library for converting complex datatypes to and from native Python datatypes."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -2762,7 +2792,7 @@ packaging = ">=17.0"
 
 [package.extras]
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 tests = ["pytest", "pytz", "simplejson"]
 
 [[package]]
@@ -3011,17 +3041,19 @@ files = [
 
 [[package]]
 name = "narwhals"
-version = "1.4.2"
+version = "1.5.0"
 description = "Extremely lightweight compatibility layer between dataframe libraries"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
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 dask = ["dask[dataframe] (>=2024.7)"]
+modin = ["modin"]
 pandas = ["pandas (>=0.25.3)"]
 polars = ["polars (>=0.20.3)"]
 pyarrow = ["pyarrow (>=11.0.0)"]
@@ -3073,13 +3105,13 @@ files = [
 
 [[package]]
 name = "openai"
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+version = "1.42.0"
 description = "The official Python library for the openai API"
 optional = true
 python-versions = ">=3.7.1"
 files = [
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 ]
 
 [package.dependencies]
@@ -4037,6 +4069,78 @@ files = [
 [package.extras]
 windows-terminal = ["colorama (>=0.4.6)"]
 
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 version = "3.1.2"
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 [package.dependencies]
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 [[package]]
 name = "semantic-version"
@@ -5338,83 +5450,97 @@ tester = ["eth-tester[py-evm] (>=0.11.0b1,<0.12.0b1)", "eth-tester[py-evm] (>=0.
 
 [[package]]
 name = "websockets"
-version = "12.0"
+version = "13.0"
 description = "An implementation of the WebSocket Protocol (RFC 6455 & 7692)"
 optional = false
 python-versions = ">=3.8"
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 ]
 
 [[package]]
@@ -5638,4 +5764,4 @@ openai = ["openai"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<3.12"
-content-hash = "e3fe50a3bc4b39040645963fc9152d63dd74f3315895d89cdf583f9b65c59960"
+content-hash = "a64f518927f4584112a602c1df5c77a1fc9c1bf745d0b48b311248cea6c60291"
diff --git a/prediction_market_agent_tooling/config.py b/prediction_market_agent_tooling/config.py
index a64ac378..312acf22 100644
--- a/prediction_market_agent_tooling/config.py
+++ b/prediction_market_agent_tooling/config.py
@@ -40,8 +40,9 @@ class APIKeys(BaseSettings):
     GOOGLE_SEARCH_ENGINE_ID: t.Optional[SecretStr] = None
 
     LANGFUSE_SECRET_KEY: t.Optional[SecretStr] = None
-    LANGFUSE_PUBLIC_KEY: t.Optional[SecretStr] = None
+    LANGFUSE_PUBLIC_KEY: t.Optional[str] = None
     LANGFUSE_HOST: t.Optional[str] = None
+    LANGFUSE_DEPLOYMENT_VERSION: t.Optional[str] = None
 
     TAVILY_API_KEY: t.Optional[SecretStr] = None
 
@@ -127,7 +128,7 @@ def langfuse_secret_key(self) -> SecretStr:
         )
 
     @property
-    def langfuse_public_key(self) -> SecretStr:
+    def langfuse_public_key(self) -> str:
         return check_not_none(
             self.LANGFUSE_PUBLIC_KEY, "LANGFUSE_PUBLIC_KEY missing in the environment."
         )
@@ -138,6 +139,14 @@ def langfuse_host(self) -> str:
             self.LANGFUSE_HOST, "LANGFUSE_HOST missing in the environment."
         )
 
+    @property
+    def default_enable_langfuse(self) -> bool:
+        return (
+            self.LANGFUSE_SECRET_KEY is not None
+            and self.LANGFUSE_PUBLIC_KEY is not None
+            and self.LANGFUSE_HOST is not None
+        )
+
     @property
     def tavily_api_key(self) -> SecretStr:
         return check_not_none(
diff --git a/prediction_market_agent_tooling/deploy/agent.py b/prediction_market_agent_tooling/deploy/agent.py
index 1076786f..31bb00fb 100644
--- a/prediction_market_agent_tooling/deploy/agent.py
+++ b/prediction_market_agent_tooling/deploy/agent.py
@@ -1,11 +1,14 @@
+import getpass
 import inspect
 import os
 import tempfile
 import time
 import typing as t
 from datetime import datetime, timedelta
+from enum import Enum
+from functools import cached_property
 
-from pydantic import BaseModel, BeforeValidator
+from pydantic import BaseModel, BeforeValidator, computed_field
 from typing_extensions import Annotated
 
 from prediction_market_agent_tooling.config import APIKeys
@@ -39,16 +42,15 @@
     redeem_from_all_user_positions,
     withdraw_wxdai_to_xdai_to_keep_balance,
 )
-from prediction_market_agent_tooling.monitor.langfuse.langfuse_wrapper import (
-    LangfuseWrapper,
-)
 from prediction_market_agent_tooling.monitor.monitor_app import (
     MARKET_TYPE_TO_DEPLOYED_AGENT,
 )
 from prediction_market_agent_tooling.tools.is_predictable import is_predictable_binary
+from prediction_market_agent_tooling.tools.langfuse_ import langfuse_context, observe
 from prediction_market_agent_tooling.tools.utils import DatetimeWithTimezone, utcnow
 
 MAX_AVAILABLE_MARKETS = 20
+TRADER_TAG = "trader"
 
 
 def to_boolean_outcome(value: str | bool) -> bool:
@@ -71,6 +73,21 @@ def to_boolean_outcome(value: str | bool) -> bool:
         raise ValueError(f"Expected a boolean or a string, but got {value}")
 
 
+def initialize_langfuse(enable_langfuse: bool) -> None:
+    # Configure Langfuse singleton with our APIKeys.
+    # If langfuse is disabled, it will just ignore all the calls, so no need to do if-else around the code.
+    keys = APIKeys()
+    if enable_langfuse:
+        langfuse_context.configure(
+            public_key=keys.langfuse_public_key,
+            secret_key=keys.langfuse_secret_key.get_secret_value(),
+            host=keys.langfuse_host,
+            enabled=enable_langfuse,
+        )
+    else:
+        langfuse_context.configure(enabled=enable_langfuse)
+
+
 Decision = Annotated[bool, BeforeValidator(to_boolean_outcome)]
 
 
@@ -89,11 +106,66 @@ def p_no(self) -> Probability:
         return Probability(1 - self.p_yes)
 
 
+class ProcessedMarket(BaseModel):
+    answer: Answer
+    amount: BetAmount
+
+
+class AnsweredEnum(str, Enum):
+    ANSWERED = "answered"
+    NOT_ANSWERED = "not_answered"
+
+
 class DeployableAgent:
-    def __init__(self) -> None:
-        self.langfuse_wrapper = LangfuseWrapper(agent_name=self.__class__.__name__)
+    def __init__(
+        self,
+        enable_langfuse: bool = APIKeys().default_enable_langfuse,
+    ) -> None:
+        self.start_time = utcnow()
+        self.enable_langfuse = enable_langfuse
+        self.initialize_langfuse()
         self.load()
 
+    def initialize_langfuse(self) -> None:
+        initialize_langfuse(self.enable_langfuse)
+
+    def langfuse_update_current_trace(
+        self,
+        name: str | None = None,
+        input: t.Any | None = None,
+        output: t.Any | None = None,
+        user_id: str | None = None,
+        session_id: str | None = None,
+        version: str | None = None,
+        release: str | None = None,
+        metadata: t.Any | None = None,
+        tags: list[str] | None = None,
+        public: bool | None = None,
+    ) -> None:
+        """
+        Provide some useful default arguments when updating the current trace in our agents.
+        """
+        langfuse_context.update_current_trace(
+            name=name,
+            input=input,
+            output=output,
+            user_id=user_id or getpass.getuser(),
+            session_id=session_id
+            or self.session_id,  # All traces within a single run execution will be grouped under a single session.
+            version=version
+            or APIKeys().LANGFUSE_DEPLOYMENT_VERSION,  # Optionally, mark the current deployment with version (e.g. add git commit hash during docker building).
+            release=release,
+            metadata=metadata,
+            tags=tags,
+            public=public,
+        )
+
+    @computed_field  # type: ignore[prop-decorator] # Mypy issue: https://github.com/python/mypy/issues/14461
+    @cached_property
+    def session_id(self) -> str:
+        # Each agent should be an unique class.
+        return f"{self.__class__.__name__} - {self.start_time.strftime('%Y-%m-%d %H:%M:%S')}"
+
     def __init_subclass__(cls, **kwargs: t.Any) -> None:
         if "DeployableAgent" not in str(
             cls.__init__
@@ -209,12 +281,50 @@ class DeployableTraderAgent(DeployableAgent):
     min_required_balance_to_operate: xDai | None = xdai_type(1)
     min_balance_to_keep_in_native_currency: xDai | None = xdai_type(0.1)
 
-    def __init__(self, place_bet: bool = True) -> None:
-        super().__init__()
+    def __init__(
+        self,
+        enable_langfuse: bool = APIKeys().default_enable_langfuse,
+        place_bet: bool = True,
+    ) -> None:
+        super().__init__(enable_langfuse=enable_langfuse)
         self.place_bet = place_bet
 
-    def have_bet_on_market_since(self, market: AgentMarket, since: timedelta) -> bool:
-        return have_bet_on_market_since(keys=APIKeys(), market=market, since=since)
+    def initialize_langfuse(self) -> None:
+        super().initialize_langfuse()
+        # Auto-observe all the methods where it makes sense, so that subclassses don't need to do it manually.
+        self.have_bet_on_market_since = observe()(self.have_bet_on_market_since)  # type: ignore[method-assign]
+        self.verify_market = observe()(self.verify_market)  # type: ignore[method-assign]
+        self.answer_binary_market = observe()(self.answer_binary_market)  # type: ignore[method-assign]
+        self.calculate_bet_amount = observe()(self.calculate_bet_amount)  # type: ignore[method-assign]
+        self.process_market = observe()(self.process_market)  # type: ignore[method-assign]
+
+    def update_langfuse_trace_by_market(
+        self, market_type: MarketType, market: AgentMarket
+    ) -> None:
+        self.langfuse_update_current_trace(
+            # UI allows to do filtering by these.
+            metadata={
+                "agent_class": self.__class__.__name__,
+                "market_id": market.id,
+                "market_question": market.question,
+                "market_outcomes": market.outcomes,
+            },
+        )
+
+    def update_langfuse_trace_by_processed_market(
+        self, market_type: MarketType, processed_market: ProcessedMarket | None
+    ) -> None:
+        self.langfuse_update_current_trace(
+            tags=[
+                TRADER_TAG,
+                (
+                    AnsweredEnum.ANSWERED
+                    if processed_market is not None
+                    else AnsweredEnum.NOT_ANSWERED
+                ),
+                market_type.value,
+            ]
+        )
 
     def check_min_required_balance_to_operate(self, market_type: MarketType) -> None:
         api_keys = APIKeys()
@@ -229,35 +339,26 @@ def check_min_required_balance_to_operate(self, market_type: MarketType) -> None
                 f"for agent with address {api_keys.public_key} is not met."
             )
 
-    def pick_markets(
-        self, market_type: MarketType, markets: t.Sequence[AgentMarket]
-    ) -> t.Sequence[AgentMarket]:
+    def have_bet_on_market_since(self, market: AgentMarket, since: timedelta) -> bool:
+        return have_bet_on_market_since(keys=APIKeys(), market=market, since=since)
+
+    def verify_market(self, market_type: MarketType, market: AgentMarket) -> bool:
         """
         Subclasses can implement their own logic instead of this one, or on top of this one.
-        By default, it picks only the first {n_markets_per_run} markets where user didn't bet recently and it's a reasonable question.
+        By default, it allows only markets where user didn't bet recently and it's a reasonable question.
         """
-        picked: list[AgentMarket] = []
-
-        for market in markets:
-            if len(picked) >= self.bet_on_n_markets_per_run:
-                break
-
-            if self.have_bet_on_market_since(market, since=timedelta(hours=24)):
-                continue
-
-            # Do as a last check, as it uses paid OpenAI API.
-            if not is_predictable_binary(market.question):
-                continue
+        if self.have_bet_on_market_since(market, since=timedelta(hours=24)):
+            return False
 
-            # Manifold allows to bet only on markets with probability between 1 and 99.
-            if market_type == MarketType.MANIFOLD and not (
-                1 < market.current_p_yes < 99
-            ):
-                continue
+        # Manifold allows to bet only on markets with probability between 1 and 99.
+        if market_type == MarketType.MANIFOLD and not (1 < market.current_p_yes < 99):
+            return False
 
-            picked.append(market)
+        # Do as a last check, as it uses paid OpenAI API.
+        if not is_predictable_binary(market.question):
+            return False
 
-        return picked
+        return True
 
     def answer_binary_market(self, market: AgentMarket) -> Answer | None:
         """
@@ -285,7 +386,55 @@ def get_markets(
         )
         return available_markets
 
-    def before(self, market_type: MarketType) -> None:
+    def before_process_market(
+        self, market_type: MarketType, market: AgentMarket
+    ) -> None:
+        self.update_langfuse_trace_by_market(market_type, market)
+
+    def process_market(
+        self, market_type: MarketType, market: AgentMarket, verify_market: bool = True
+    ) -> ProcessedMarket | None:
+        self.before_process_market(market_type, market)
+
+        if verify_market and not self.verify_market(market_type, market):
+            logger.info(f"Market '{market.question}' doesn't meet the criteria.")
+            self.update_langfuse_trace_by_processed_market(market_type, None)
+            return None
+
+        answer = self.answer_binary_market(market)
+
+        if answer is None:
+            logger.info(f"No answer for market '{market.question}'.")
+            self.update_langfuse_trace_by_processed_market(market_type, None)
+            return None
+
+        amount = self.calculate_bet_amount(answer, market)
+
+        if self.place_bet:
+            logger.info(
+                f"Placing bet on {market} with result {answer} and amount {amount}"
+            )
+            market.place_bet(
+                amount=amount,
+                outcome=answer.decision,
+            )
+
+        self.after_process_market(market_type, market)
+
+        processed_market = ProcessedMarket(
+            answer=answer,
+            amount=amount,
+        )
+        self.update_langfuse_trace_by_processed_market(market_type, processed_market)
+
+        return processed_market
+
+    def after_process_market(
+        self, market_type: MarketType, market: AgentMarket
+    ) -> None:
+        pass
+
+    def before_process_markets(self, market_type: MarketType) -> None:
         """
         Executes actions that occur before bets are placed.
         """
@@ -302,33 +451,29 @@ def before(self, market_type: MarketType) -> None:
                     withdraw_multiplier=2,
                 )
 
-    def process_bets(self, market_type: MarketType) -> None:
+    def process_markets(self, market_type: MarketType) -> None:
         """
         Processes bets placed by agents on a given market.
         """
         available_markets = self.get_markets(market_type)
-        markets = self.pick_markets(market_type, available_markets)
-        for market in markets:
+        processed = 0
+
+        for market in available_markets:
             # We need to check it again before each market bet, as the balance might have changed.
             self.check_min_required_balance_to_operate(market_type)
-            result = self.answer_binary_market(market)
-            if result is None:
-                logger.info(f"Skipping market {market} as no answer was provided")
-                continue
-            if self.place_bet:
-                amount = self.calculate_bet_amount(result, market)
-                logger.info(
-                    f"Placing bet on {market} with result {result} and amount {amount}"
-                )
-                market.place_bet(
-                    amount=amount,
-                    outcome=result.decision,
-                )
 
-    def after(self, market_type: MarketType) -> None:
+            processed_market = self.process_market(market_type, market)
+
+            if processed_market is not None:
+                processed += 1
+
+            if processed == self.bet_on_n_markets_per_run:
+                break
+
+    def after_process_markets(self, market_type: MarketType) -> None:
         pass
 
     def run(self, market_type: MarketType) -> None:
-        self.before(market_type)
-        self.process_bets(market_type)
-        self.after(market_type)
+        self.before_process_markets(market_type)
+        self.process_markets(market_type)
+        self.after_process_markets(market_type)
diff --git a/prediction_market_agent_tooling/deploy/agent_example.py b/prediction_market_agent_tooling/deploy/agent_example.py
index 9c5e12e6..aaf5da81 100644
--- a/prediction_market_agent_tooling/deploy/agent_example.py
+++ b/prediction_market_agent_tooling/deploy/agent_example.py
@@ -1,5 +1,4 @@
 import random
-import typing as t
 
 from prediction_market_agent_tooling.deploy.agent import (
     Answer,
@@ -11,10 +10,8 @@
 
 
 class DeployableCoinFlipAgent(DeployableTraderAgent):
-    def pick_markets(
-        self, market_type: MarketType, markets: t.Sequence[AgentMarket]
-    ) -> t.Sequence[AgentMarket]:
-        return random.sample(markets, 1)
+    def verify_market(self, market_type: MarketType, market: AgentMarket) -> bool:
+        return True
 
     def answer_binary_market(self, market: AgentMarket) -> Answer | None:
         decision = random.choice([True, False])
diff --git a/prediction_market_agent_tooling/markets/categorize.py b/prediction_market_agent_tooling/markets/categorize.py
index e7e306dd..87669886 100644
--- a/prediction_market_agent_tooling/markets/categorize.py
+++ b/prediction_market_agent_tooling/markets/categorize.py
@@ -1,13 +1,24 @@
-from langchain.prompts import ChatPromptTemplate
-from langchain.schema.output_parser import StrOutputParser
-from langchain_openai import ChatOpenAI
-
 from prediction_market_agent_tooling.config import APIKeys
+from prediction_market_agent_tooling.tools.langfuse_ import (
+    get_langfuse_langchain_config,
+    observe,
+)
 
 
+@observe()
 def infer_category(
-    question: str, categories: set[str], model: str = "gpt-3.5-turbo-0125"
+    question: str,
+    categories: set[str],
+    model: str = "gpt-3.5-turbo-0125",
 ) -> str:
+    try:
+        from langchain.prompts import ChatPromptTemplate
+        from langchain.schema.output_parser import StrOutputParser
+        from langchain_openai import ChatOpenAI
+    except ImportError:
+        raise ImportError(
+            "openai not installed, please install extras `langchain` to use this function."
+        )
     prompt = ChatPromptTemplate.from_template(
         template="""Assign the following question: {question}
 
@@ -27,7 +38,8 @@ def infer_category(
     )
 
     response: str = research_evaluation_chain.invoke(
-        {"question": question, "categories": sorted(categories)}
+        {"question": question, "categories": sorted(categories)},
+        config=get_langfuse_langchain_config(),
     )
 
     formatted = response.strip().strip("'").strip('"').strip()
diff --git a/prediction_market_agent_tooling/monitor/langfuse/langfuse_wrapper.py b/prediction_market_agent_tooling/monitor/langfuse/langfuse_wrapper.py
deleted file mode 100644
index 84997da8..00000000
--- a/prediction_market_agent_tooling/monitor/langfuse/langfuse_wrapper.py
+++ /dev/null
@@ -1,26 +0,0 @@
-from functools import cached_property
-
-from langfuse.callback import CallbackHandler
-from pydantic import BaseModel, computed_field
-
-from prediction_market_agent_tooling.config import APIKeys
-from prediction_market_agent_tooling.tools.utils import utcnow
-
-
-class LangfuseWrapper(BaseModel):
-    agent_name: str
-
-    @computed_field  # type: ignore[prop-decorator] # Mypy issue: https://github.com/python/mypy/issues/14461
-    @cached_property
-    def session_id(self) -> str:
-        return f"{self.agent_name} - {utcnow()}"
-
-    def get_langfuse_handler(self) -> CallbackHandler:
-        keys = APIKeys()
-        langfuse_handler = CallbackHandler(
-            secret_key=keys.langfuse_secret_key.get_secret_value(),
-            public_key=keys.langfuse_public_key.get_secret_value(),
-            host=keys.langfuse_host,
-            session_id=self.session_id,
-        )
-        return langfuse_handler
diff --git a/prediction_market_agent_tooling/monitor/monitor.py b/prediction_market_agent_tooling/monitor/monitor.py
index 59774270..44a9438d 100644
--- a/prediction_market_agent_tooling/monitor/monitor.py
+++ b/prediction_market_agent_tooling/monitor/monitor.py
@@ -229,8 +229,7 @@ def monitor_agent(agent: DeployedAgent) -> None:
         )
         .interactive()
     )
-
-    st.altair_chart(  # type: ignore
+    st.altair_chart(  # type: ignore # Doesn't expect `LayerChart`, but `Chart`, yet it works.
         per_day_accuracy_chart.mark_line()
         + per_day_accuracy_chart.transform_loess("x-axis-day", "Is Correct").mark_line(
             color="red", strokeDash=[5, 5]
diff --git a/prediction_market_agent_tooling/tools/image_gen/market_thumbnail_gen.py b/prediction_market_agent_tooling/tools/image_gen/market_thumbnail_gen.py
index 8bf3aa26..60b2aa5f 100644
--- a/prediction_market_agent_tooling/tools/image_gen/market_thumbnail_gen.py
+++ b/prediction_market_agent_tooling/tools/image_gen/market_thumbnail_gen.py
@@ -2,8 +2,13 @@
 
 from prediction_market_agent_tooling.config import APIKeys
 from prediction_market_agent_tooling.tools.image_gen.image_gen import generate_image
+from prediction_market_agent_tooling.tools.langfuse_ import (
+    get_langfuse_langchain_config,
+    observe,
+)
 
 
+@observe()
 def rewrite_question_into_image_generation_prompt(question: str) -> str:
     try:
         from langchain_openai import ChatOpenAI
@@ -19,7 +24,8 @@ def rewrite_question_into_image_generation_prompt(question: str) -> str:
     rewritten = str(
         llm.invoke(
             f"Rewrite this prediction market question '{question}' into a form that will generate nice thumbnail with DALL-E-3."
-            "The thumbnail should be catchy and visually appealing. With a large object in the center of the image."
+            "The thumbnail should be catchy and visually appealing. With a large object in the center of the image.",
+            config=get_langfuse_langchain_config(),
         ).content
     )
     return rewritten
diff --git a/prediction_market_agent_tooling/tools/is_predictable.py b/prediction_market_agent_tooling/tools/is_predictable.py
index 607273ec..57c6d132 100644
--- a/prediction_market_agent_tooling/tools/is_predictable.py
+++ b/prediction_market_agent_tooling/tools/is_predictable.py
@@ -2,6 +2,10 @@
 
 from prediction_market_agent_tooling.config import APIKeys
 from prediction_market_agent_tooling.tools.cache import persistent_inmemory_cache
+from prediction_market_agent_tooling.tools.langfuse_ import (
+    get_langfuse_langchain_config,
+    observe,
+)
 from prediction_market_agent_tooling.tools.utils import LLM_SUPER_LOW_TEMPERATURE
 
 # I tried to make it return a JSON, but it didn't work well in combo with asking it to do chain of thought.
@@ -72,6 +76,7 @@
 
 
 @persistent_inmemory_cache
+@observe()
 def is_predictable_binary(
     question: str,
     engine: str = "gpt-4-1106-preview",
@@ -96,12 +101,17 @@ def is_predictable_binary(
 
     prompt = ChatPromptTemplate.from_template(template=prompt_template)
     messages = prompt.format_messages(question=question)
-    completion = str(llm(messages, max_tokens=max_tokens).content)
+    completion = str(
+        llm.invoke(
+            messages, max_tokens=max_tokens, config=get_langfuse_langchain_config()
+        ).content
+    )
 
     return parse_decision_yes_no_completion(question, completion)
 
 
 @persistent_inmemory_cache
+@observe()
 def is_predictable_without_description(
     question: str,
     description: str,
@@ -132,7 +142,11 @@ def is_predictable_without_description(
         question=question,
         description=description,
     )
-    completion = str(llm(messages, max_tokens=max_tokens).content)
+    completion = str(
+        llm(
+            messages, max_tokens=max_tokens, config=get_langfuse_langchain_config()
+        ).content
+    )
 
     return parse_decision_yes_no_completion(question, completion)
 
diff --git a/prediction_market_agent_tooling/tools/langfuse_.py b/prediction_market_agent_tooling/tools/langfuse_.py
new file mode 100644
index 00000000..aaa5f813
--- /dev/null
+++ b/prediction_market_agent_tooling/tools/langfuse_.py
@@ -0,0 +1,34 @@
+from typing import Any, Callable, Iterable, Literal, Optional, ParamSpec, TypeVar
+
+from langchain_core.runnables.config import RunnableConfig
+from langfuse.decorators.langfuse_decorator import (  # noqa: F401  # Import for the sake of easy importing with others from here.
+    langfuse_context,
+)
+from langfuse.decorators.langfuse_decorator import observe as original_observe
+
+P = ParamSpec("P")
+R = TypeVar("R")
+
+
+def observe(
+    name: Optional[str] = None,
+    as_type: Optional[Literal["generation"]] = None,
+    capture_input: bool = True,
+    capture_output: bool = True,
+    transform_to_string: Optional[Callable[[Iterable[Any]], str]] = None,
+) -> Callable[[Callable[P, R]], Callable[P, R]]:
+    casted: Callable[[Callable[P, R]], Callable[P, R]] = original_observe(
+        name=name,
+        as_type=as_type,
+        capture_input=capture_input,
+        capture_output=capture_output,
+        transform_to_string=transform_to_string,
+    )
+    return casted
+
+
+def get_langfuse_langchain_config() -> RunnableConfig:
+    config: RunnableConfig = {
+        "callbacks": [langfuse_context.get_current_langchain_handler()]
+    }
+    return config
diff --git a/pyproject.toml b/pyproject.toml
index 4bf43c01..37c915f3 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "prediction-market-agent-tooling"
-version = "0.48.2"
+version = "0.48.3"
 description = "Tools to benchmark, deploy and monitor prediction market agents."
 authors = ["Gnosis"]
 readme = "README.md"
@@ -40,8 +40,9 @@ safe-eth-py = "^6.0.0b14"
 eth-account = ">=0.8.0,<0.12.0"
 prompt-toolkit = "^3.0.43"
 safe-cli = "^1.0.0"
-langfuse = "^2.27.1"
+langfuse = "^2.42.0"
 openai = { version = "^1.0.0", optional = true}
+pymongo = "^4.8.0"
 tavily-python = "^0.3.9"
 sqlmodel = "^0.0.21"
 psycopg2-binary = "^2.9.9"