diff --git a/poetry.lock b/poetry.lock
index dd0d01db..10de2a88 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.7.1 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 = "aiohttp"
@@ -520,6 +520,137 @@ files = [
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 ]
 
+[[package]]
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+version = "1.1.0"
+description = "Python bindings for the Brotli compression library"
+optional = false
+python-versions = "*"
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+    {file = "brotlicffi-1.1.0.0-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:d9eb71bb1085d996244439154387266fd23d6ad37161f6f52f1cd41dd95a3808"},
+    {file = "brotlicffi-1.1.0.0.tar.gz", hash = "sha256:b77827a689905143f87915310b93b273ab17888fd43ef350d4832c4a71083c13"},
+]
+
+[package.dependencies]
+cffi = ">=1.0.0"
+
 [[package]]
 name = "build"
 version = "1.2.1"
@@ -1032,6 +1163,7 @@ files = [
 [package.dependencies]
 appdirs = ">=1.4.4,<2.0.0"
 click = ">=8.1.7,<9.0.0"
+crewai-tools = {version = ">=0.1.7,<0.2.0", optional = true, markers = "extra == \"tools\""}
 embedchain = ">=0.1.98,<0.2.0"
 instructor = ">=0.5.2,<0.6.0"
 langchain = ">=0.1.10,<0.2.0"
@@ -1046,6 +1178,31 @@ regex = ">=2023.12.25,<2024.0.0"
 [package.extras]
 tools = ["crewai-tools (>=0.1.7,<0.2.0)"]
 
+[[package]]
+name = "crewai-tools"
+version = "0.1.7"
+description = "Set of tools for the crewAI framework"
+optional = false
+python-versions = "<=3.13,>=3.10"
+files = [
+    {file = "crewai_tools-0.1.7-py3-none-any.whl", hash = "sha256:135a51b659fa0b58f1cf7bb6b1cdb47cccd557d98d889ed03b477b62c80ce38a"},
+    {file = "crewai_tools-0.1.7.tar.gz", hash = "sha256:8393900f6b0d37274218aaeb9ac6bcaaa6d719426a788ea45d21f993a49f283b"},
+]
+
+[package.dependencies]
+beautifulsoup4 = ">=4.12.3,<5.0.0"
+chromadb = ">=0.4.22,<0.5.0"
+embedchain = {version = ">=0.1.85,<0.2.0", extras = ["github", "youtube"]}
+lancedb = ">=0.5.4,<0.6.0"
+langchain = ">=0.1.4,<0.2.0"
+openai = ">=1.12.0,<2.0.0"
+pydantic = ">=2.6.1,<3.0.0"
+pyright = ">=1.1.350,<2.0.0"
+pytest = ">=8.0.0,<9.0.0"
+pytube = ">=15.0.0,<16.0.0"
+requests = ">=2.31.0,<3.0.0"
+selenium = ">=4.18.1,<5.0.0"
+
 [[package]]
 name = "cron-validator"
 version = "1.0.8"
@@ -1274,6 +1431,17 @@ files = [
 marshmallow = ">=3.18.0,<4.0.0"
 typing-inspect = ">=0.4.0,<1"
 
+[[package]]
+name = "decorator"
+version = "5.1.1"
+description = "Decorators for Humans"
+optional = false
+python-versions = ">=3.5"
+files = [
+    {file = "decorator-5.1.1-py3-none-any.whl", hash = "sha256:b8c3f85900b9dc423225913c5aace94729fe1fa9763b38939a95226f02d37186"},
+    {file = "decorator-5.1.1.tar.gz", hash = "sha256:637996211036b6385ef91435e4fae22989472f9d571faba8927ba8253acbc330"},
+]
+
 [[package]]
 name = "deprecated"
 version = "1.2.14"
@@ -1503,12 +1671,14 @@ files = [
 alembic = ">=1.13.1,<2.0.0"
 beautifulsoup4 = ">=4.12.2,<5.0.0"
 chromadb = ">=0.4.17,<0.5.0"
+gitpython = {version = ">=3.1.38,<4.0.0", optional = true, markers = "extra == \"github\""}
 google-cloud-aiplatform = ">=1.26.1,<2.0.0"
 gptcache = ">=0.1.43,<0.2.0"
 langchain = ">=0.1.4,<0.2.0"
 langchain-openai = ">=0.0.5,<0.0.6"
 openai = ">=1.1.1"
 posthog = ">=3.0.2,<4.0.0"
+PyGithub = {version = ">=1.59.1,<2.0.0", optional = true, markers = "extra == \"github\""}
 pypdf = ">=3.11.0,<4.0.0"
 pysbd = ">=0.3.4,<0.4.0"
 python-dotenv = ">=1.0.0,<2.0.0"
@@ -1516,6 +1686,8 @@ rich = ">=13.7.0,<14.0.0"
 schema = ">=0.7.5,<0.8.0"
 sqlalchemy = ">=2.0.27,<3.0.0"
 tiktoken = ">=0.5.2,<0.6.0"
+youtube-transcript-api = {version = ">=0.6.1,<0.7.0", optional = true, markers = "extra == \"dataloaders\" or extra == \"youtube\""}
+yt_dlp = {version = ">=2023.11.14,<2024.0.0", optional = true, markers = "extra == \"youtube\""}
 
 [package.extras]
 aws-bedrock = ["boto3 (>=1.34.20,<2.0.0)"]
@@ -3064,15 +3236,48 @@ websocket-client = ">=0.32.0,<0.40.0 || >0.40.0,<0.41.dev0 || >=0.43.dev0"
 [package.extras]
 adal = ["adal (>=1.0.2)"]
 
+[[package]]
+name = "lancedb"
+version = "0.5.7"
+description = "lancedb"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "lancedb-0.5.7-py3-none-any.whl", hash = "sha256:6169966f715ef530be545950e1aaf9f3f160967e4ba7456cd67c9f30f678095d"},
+    {file = "lancedb-0.5.7.tar.gz", hash = "sha256:878914b493f91d09a77b14f1528104741f273234cbdd6671be705f447701fd51"},
+]
+
+[package.dependencies]
+attrs = ">=21.3.0"
+cachetools = "*"
+click = ">=8.1.7"
+deprecation = "*"
+overrides = ">=0.7"
+pydantic = ">=1.10"
+pylance = "0.9.18"
+pyyaml = ">=6.0"
+ratelimiter = ">=1.0,<2.0"
+requests = ">=2.31.0"
+retry = ">=0.9.2"
+semver = ">=3.0"
+tqdm = ">=4.27.0"
+
+[package.extras]
+clip = ["open-clip", "pillow", "torch"]
+dev = ["pre-commit", "ruff"]
+docs = ["mkdocs", "mkdocs-jupyter", "mkdocs-material", "mkdocs-ultralytics-plugin (==0.0.44)", "mkdocstrings[python]"]
+embeddings = ["InstructorEmbedding", "awscli (>=1.29.57)", "boto3 (>=1.28.57)", "botocore (>=1.31.57)", "cohere", "google.generativeai", "huggingface-hub", "open-clip-torch", "openai (>=1.6.1)", "pillow", "sentence-transformers", "torch"]
+tests = ["aiohttp", "duckdb", "pandas (>=1.4)", "polars (>=0.19)", "pytest", "pytest-asyncio", "pytest-mock", "pytz"]
+
 [[package]]
 name = "langchain"
-version = "0.1.15"
+version = "0.1.16"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langchain-0.1.15-py3-none-any.whl", hash = "sha256:3ac516463ae7f80047091f04592a1eea138321710bbc266005f9de238d71acd3"},
-    {file = "langchain-0.1.15.tar.gz", hash = "sha256:79d43035327fdcc5ac81a3db10f2b879f2bd5db3b268ef82bac7baf3ec32954e"},
+    {file = "langchain-0.1.16-py3-none-any.whl", hash = "sha256:bc074cc5e51fad79b9ead1572fc3161918d0f614a6c8f0460543d505ad249ac7"},
+    {file = "langchain-0.1.16.tar.gz", hash = "sha256:b6bce78f8c071baa898884accfff15c3d81da2f0dd86c20e2f4c80b41463f49f"},
 ]
 
 [package.dependencies]
@@ -3081,7 +3286,7 @@ async-timeout = {version = ">=4.0.0,<5.0.0", markers = "python_version < \"3.11\
 dataclasses-json = ">=0.5.7,<0.7"
 jsonpatch = ">=1.33,<2.0"
 langchain-community = ">=0.0.32,<0.1"
-langchain-core = ">=0.1.41,<0.2.0"
+langchain-core = ">=0.1.42,<0.2.0"
 langchain-text-splitters = ">=0.0.1,<0.1"
 langsmith = ">=0.1.17,<0.2.0"
 numpy = ">=1,<2"
@@ -3133,13 +3338,13 @@ extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15.
 
 [[package]]
 name = "langchain-core"
-version = "0.1.41"
+version = "0.1.42"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langchain_core-0.1.41-py3-none-any.whl", hash = "sha256:92d3da5ca0d5fb1606b3ab42bfd455c7b386fe4c38f6c0d44ba6014abf46147d"},
-    {file = "langchain_core-0.1.41.tar.gz", hash = "sha256:4002304883b251af8cb781f01c59f56aeee58be42c965dbbdeca0a3bfb8f96af"},
+    {file = "langchain_core-0.1.42-py3-none-any.whl", hash = "sha256:c5653ffa08a44f740295c157a24c0def4a753333f6a2c41f76bf431cd00be8b5"},
+    {file = "langchain_core-0.1.42.tar.gz", hash = "sha256:40751bf60ea5d8e2b2efe65290db434717ee3834870c002e40e2811f09d814e6"},
 ]
 
 [package.dependencies]
@@ -3842,6 +4047,17 @@ files = [
     {file = "multidict-6.0.5.tar.gz", hash = "sha256:f7e301075edaf50500f0b341543c41194d8df3ae5caf4702f2095f3ca73dd8da"},
 ]
 
+[[package]]
+name = "mutagen"
+version = "1.47.0"
+description = "read and write audio tags for many formats"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "mutagen-1.47.0-py3-none-any.whl", hash = "sha256:edd96f50c5907a9539d8e5bba7245f62c9f520aef333d13392a79a4f70aca719"},
+    {file = "mutagen-1.47.0.tar.gz", hash = "sha256:719fadef0a978c31b4cf3c956261b3c58b6948b32023078a2117b1de09f0fc99"},
+]
+
 [[package]]
 name = "mypy"
 version = "1.9.0"
@@ -3954,6 +4170,20 @@ plot = ["matplotlib"]
 tgrep = ["pyparsing"]
 twitter = ["twython"]
 
+[[package]]
+name = "nodeenv"
+version = "1.8.0"
+description = "Node.js virtual environment builder"
+optional = false
+python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*"
+files = [
+    {file = "nodeenv-1.8.0-py2.py3-none-any.whl", hash = "sha256:df865724bb3c3adc86b3876fa209771517b0cfe596beff01a92700e0e8be4cec"},
+    {file = "nodeenv-1.8.0.tar.gz", hash = "sha256:d51e0c37e64fbf47d017feac3145cdbb58836d7eee8c6f6d3b6880c5456227d2"},
+]
+
+[package.dependencies]
+setuptools = "*"
+
 [[package]]
 name = "numexpr"
 version = "2.10.0"
@@ -4363,6 +4593,20 @@ files = [
     {file = "orjson-3.10.0.tar.gz", hash = "sha256:ba4d8cac5f2e2cff36bea6b6481cdb92b38c202bcec603d6f5ff91960595a1ed"},
 ]
 
+[[package]]
+name = "outcome"
+version = "1.3.0.post0"
+description = "Capture the outcome of Python function calls."
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "outcome-1.3.0.post0-py2.py3-none-any.whl", hash = "sha256:e771c5ce06d1415e356078d3bdd68523f284b4ce5419828922b6871e65eda82b"},
+    {file = "outcome-1.3.0.post0.tar.gz", hash = "sha256:9dcf02e65f2971b80047b377468e72a268e15c0af3cf1238e6ff14f7f91143b8"},
+]
+
+[package.dependencies]
+attrs = ">=19.2.0"
+
 [[package]]
 name = "overrides"
 version = "7.7.0"
@@ -4718,13 +4962,13 @@ test = ["coverage", "flake8", "freezegun (==0.3.15)", "mock (>=2.0.0)", "pylint"
 
 [[package]]
 name = "prediction-market-agent-tooling"
-version = "0.13.0"
+version = "0.13.1"
 description = "Tools to benchmark, deploy and monitor prediction market agents."
 optional = false
 python-versions = "<3.12,>=3.10"
 files = [
-    {file = "prediction_market_agent_tooling-0.13.0-py3-none-any.whl", hash = "sha256:8f91e932ab78e60a4e8dc5bda177c193427c749338a6c6d92270dda75d87abf8"},
-    {file = "prediction_market_agent_tooling-0.13.0.tar.gz", hash = "sha256:9e5fcacdd6b25620defec278483f39d30840300b6569b689b46ec3657229e371"},
+    {file = "prediction_market_agent_tooling-0.13.1-py3-none-any.whl", hash = "sha256:f37bb8e28bc8665a6a6b615c34c5192557a648b0eb4fcc617ce790f75055197c"},
+    {file = "prediction_market_agent_tooling-0.13.1.tar.gz", hash = "sha256:c2e146ad75101098b03e0a12ab3923fa61afba76ef3567704a45101411f58581"},
 ]
 
 [package.dependencies]
@@ -4753,7 +4997,7 @@ web3 = ">=6.15.1,<7.0.0"
 
 [package.extras]
 google = ["google-api-python-client (==2.95.0)"]
-langchain = ["langchain (>=0.1.9,<0.2.0)", "langchain-openai (>=0.0.8,<0.0.9)"]
+langchain = ["langchain (>=0.1.9,<0.2.0)", "langchain-openai (>=0.0.5,<0.0.6)"]
 
 [[package]]
 name = "proto-plus"
@@ -4850,6 +5094,17 @@ all = ["apache-bookkeeper-client (>=4.16.1)", "fastavro (>=1.9.2)", "grpcio (>=1
 avro = ["fastavro (>=1.9.2)"]
 functions = ["apache-bookkeeper-client (>=4.16.1)", "grpcio (>=1.60.0)", "prometheus-client", "protobuf (>=3.6.1,<=3.20.3)", "ratelimit"]
 
+[[package]]
+name = "py"
+version = "1.11.0"
+description = "library with cross-python path, ini-parsing, io, code, log facilities"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+files = [
+    {file = "py-1.11.0-py2.py3-none-any.whl", hash = "sha256:607c53218732647dff4acdfcd50cb62615cedf612e72d1724fb1a0cc6405b378"},
+    {file = "py-1.11.0.tar.gz", hash = "sha256:51c75c4126074b472f746a24399ad32f6053d1b34b68d2fa41e558e6f4a98719"},
+]
+
 [[package]]
 name = "pyarrow"
 version = "15.0.2"
@@ -5012,20 +5267,61 @@ files = [
     {file = "pycryptodome-3.20.0.tar.gz", hash = "sha256:09609209ed7de61c2b560cc5c8c4fbf892f8b15b1faf7e4cbffac97db1fffda7"},
 ]
 
+[[package]]
+name = "pycryptodomex"
+version = "3.20.0"
+description = "Cryptographic library for Python"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+files = [
+    {file = "pycryptodomex-3.20.0-cp27-cp27m-macosx_10_9_x86_64.whl", hash = "sha256:645bd4ca6f543685d643dadf6a856cc382b654cc923460e3a10a49c1b3832aeb"},
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 ]
 
 [package.dependencies]
@@ -5171,6 +5467,23 @@ files = [
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 ]
 
+[[package]]
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+description = "Use the full Github API v3"
+optional = false
+python-versions = ">=3.7"
+files = [
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+
+[package.dependencies]
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+pynacl = ">=1.4.0"
+requests = ">=2.14.0"
+
 [[package]]
 name = "pygments"
 version = "2.17.2"
@@ -5186,6 +5499,76 @@ files = [
 plugins = ["importlib-metadata"]
 windows-terminal = ["colorama (>=0.4.6)"]
 
+[[package]]
+name = "pyjwt"
+version = "2.8.0"
+description = "JSON Web Token implementation in Python"
+optional = false
+python-versions = ">=3.7"
+files = [
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+]
+
+[package.dependencies]
+cryptography = {version = ">=3.4.0", optional = true, markers = "extra == \"crypto\""}
+
+[package.extras]
+crypto = ["cryptography (>=3.4.0)"]
+dev = ["coverage[toml] (==5.0.4)", "cryptography (>=3.4.0)", "pre-commit", "pytest (>=6.0.0,<7.0.0)", "sphinx (>=4.5.0,<5.0.0)", "sphinx-rtd-theme", "zope.interface"]
+docs = ["sphinx (>=4.5.0,<5.0.0)", "sphinx-rtd-theme", "zope.interface"]
+tests = ["coverage[toml] (==5.0.4)", "pytest (>=6.0.0,<7.0.0)"]
+
+[[package]]
+name = "pylance"
+version = "0.9.18"
+description = "python wrapper for Lance columnar format"
+optional = false
+python-versions = ">=3.8"
+files = [
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+pyarrow = ">=12"
+
+[package.extras]
+benchmarks = ["pytest-benchmark"]
+dev = ["ruff (==0.2.2)"]
+tests = ["datasets", "duckdb", "ml_dtypes", "pandas", "pillow", "polars[pandas,pyarrow]", "pytest", "tensorflow", "tqdm"]
+torch = ["torch"]
+
+[[package]]
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+version = "1.5.0"
+description = "Python binding to the Networking and Cryptography (NaCl) library"
+optional = false
+python-versions = ">=3.6"
+files = [
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+    {file = "PyNaCl-1.5.0-cp36-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:61f642bf2378713e2c2e1de73444a3778e5f0a38be6fee0fe532fe30060282ff"},
+    {file = "PyNaCl-1.5.0-cp36-abi3-win32.whl", hash = "sha256:e46dae94e34b085175f8abb3b0aaa7da40767865ac82c928eeb9e57e1ea8a543"},
+    {file = "PyNaCl-1.5.0-cp36-abi3-win_amd64.whl", hash = "sha256:20f42270d27e1b6a29f54032090b972d97f0a1b0948cc52392041ef7831fee93"},
+    {file = "PyNaCl-1.5.0.tar.gz", hash = "sha256:8ac7448f09ab85811607bdd21ec2464495ac8b7c66d146bf545b0f08fb9220ba"},
+]
+
+[package.dependencies]
+cffi = ">=1.4.1"
+
+[package.extras]
+docs = ["sphinx (>=1.6.5)", "sphinx-rtd-theme"]
+tests = ["hypothesis (>=3.27.0)", "pytest (>=3.2.1,!=3.3.0)"]
+
 [[package]]
 name = "pypdf"
 version = "3.17.4"
@@ -5239,6 +5622,24 @@ files = [
     {file = "pyreadline3-3.4.1.tar.gz", hash = "sha256:6f3d1f7b8a31ba32b73917cefc1f28cc660562f39aea8646d30bd6eff21f7bae"},
 ]
 
+[[package]]
+name = "pyright"
+version = "1.1.358"
+description = "Command line wrapper for pyright"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "pyright-1.1.358-py3-none-any.whl", hash = "sha256:0995b6a95eb11bd26f093cd5dee3d5e7258441b1b94d4a171b5dc5b79a1d4f4e"},
+    {file = "pyright-1.1.358.tar.gz", hash = "sha256:185524a8d52f6f14bbd3b290b92ad905f25b964dddc9e7148aad760bd35c9f60"},
+]
+
+[package.dependencies]
+nodeenv = ">=1.6.0"
+
+[package.extras]
+all = ["twine (>=3.4.1)"]
+dev = ["twine (>=3.4.1)"]
+
 [[package]]
 name = "pysbd"
 version = "0.3.4"
@@ -5249,6 +5650,18 @@ files = [
     {file = "pysbd-0.3.4-py3-none-any.whl", hash = "sha256:cd838939b7b0b185fcf86b0baf6636667dfb6e474743beeff878e9f42e022953"},
 ]
 
+[[package]]
+name = "pysocks"
+version = "1.7.1"
+description = "A Python SOCKS client module. See https://github.com/Anorov/PySocks for more information."
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+files = [
+    {file = "PySocks-1.7.1-py27-none-any.whl", hash = "sha256:08e69f092cc6dbe92a0fdd16eeb9b9ffbc13cadfe5ca4c7bd92ffb078b293299"},
+    {file = "PySocks-1.7.1-py3-none-any.whl", hash = "sha256:2725bd0a9925919b9b51739eea5f9e2bae91e83288108a9ad338b2e3a4435ee5"},
+    {file = "PySocks-1.7.1.tar.gz", hash = "sha256:3f8804571ebe159c380ac6de37643bb4685970655d3bba243530d6558b799aa0"},
+]
+
 [[package]]
 name = "pytest"
 version = "8.1.1"
@@ -5317,6 +5730,17 @@ files = [
 [package.extras]
 cli = ["click (>=5.0)"]
 
+[[package]]
+name = "pytube"
+version = "15.0.0"
+description = "Python 3 library for downloading YouTube Videos."
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "pytube-15.0.0-py3-none-any.whl", hash = "sha256:07b9904749e213485780d7eb606e5e5b8e4341aa4dccf699160876da00e12d78"},
+    {file = "pytube-15.0.0.tar.gz", hash = "sha256:076052efe76f390dfa24b1194ff821d4e86c17d41cb5562f3a276a8bcbfc9d1d"},
+]
+
 [[package]]
 name = "pytz"
 version = "2024.1"
@@ -5534,6 +5958,20 @@ files = [
 [package.extras]
 full = ["numpy"]
 
+[[package]]
+name = "ratelimiter"
+version = "1.2.0.post0"
+description = "Simple python rate limiting object"
+optional = false
+python-versions = "*"
+files = [
+    {file = "ratelimiter-1.2.0.post0-py3-none-any.whl", hash = "sha256:a52be07bc0bb0b3674b4b304550f10c769bbb00fead3072e035904474259809f"},
+    {file = "ratelimiter-1.2.0.post0.tar.gz", hash = "sha256:5c395dcabdbbde2e5178ef3f89b568a3066454a6ddc223b76473dac22f89b4f7"},
+]
+
+[package.extras]
+test = ["pytest (>=3.0)", "pytest-asyncio"]
+
 [[package]]
 name = "referencing"
 version = "0.34.0"
@@ -5724,6 +6162,21 @@ files = [
 [package.dependencies]
 requests = ">=2.0.1,<3.0.0"
 
+[[package]]
+name = "retry"
+version = "0.9.2"
+description = "Easy to use retry decorator."
+optional = false
+python-versions = "*"
+files = [
+    {file = "retry-0.9.2-py2.py3-none-any.whl", hash = "sha256:ccddf89761fa2c726ab29391837d4327f819ea14d244c232a1d24c67a2f98606"},
+    {file = "retry-0.9.2.tar.gz", hash = "sha256:f8bfa8b99b69c4506d6f5bd3b0aabf77f98cdb17f3c9fc3f5ca820033336fba4"},
+]
+
+[package.dependencies]
+decorator = ">=3.4.2"
+py = ">=1.4.26,<2.0.0"
+
 [[package]]
 name = "rich"
 version = "13.7.1"
@@ -5997,6 +6450,35 @@ files = [
 cryptography = ">=2.0"
 jeepney = ">=0.6"
 
+[[package]]
+name = "selenium"
+version = "4.19.0"
+description = ""
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "selenium-4.19.0-py3-none-any.whl", hash = "sha256:5b4f49240d61e687a73f7968ae2517d403882aae3550eae2a229c745e619f1d9"},
+    {file = "selenium-4.19.0.tar.gz", hash = "sha256:d9dfd6d0b021d71d0a48b865fe7746490ba82b81e9c87b212360006629eb1853"},
+]
+
+[package.dependencies]
+certifi = ">=2021.10.8"
+trio = ">=0.17,<1.0"
+trio-websocket = ">=0.9,<1.0"
+typing_extensions = ">=4.9.0"
+urllib3 = {version = ">=1.26,<3", extras = ["socks"]}
+
+[[package]]
+name = "semver"
+version = "3.0.2"
+description = "Python helper for Semantic Versioning (https://semver.org)"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "semver-3.0.2-py3-none-any.whl", hash = "sha256:b1ea4686fe70b981f85359eda33199d60c53964284e0cfb4977d243e37cf4bf4"},
+    {file = "semver-3.0.2.tar.gz", hash = "sha256:6253adb39c70f6e51afed2fa7152bcd414c411286088fb4b9effb133885ab4cc"},
+]
+
 [[package]]
 name = "serpapi"
 version = "0.1.5"
@@ -6132,6 +6614,17 @@ files = [
     {file = "sniffio-1.3.1.tar.gz", hash = "sha256:f4324edc670a0f49750a81b895f35c3adb843cca46f0530f79fc1babb23789dc"},
 ]
 
+[[package]]
+name = "sortedcontainers"
+version = "2.4.0"
+description = "Sorted Containers -- Sorted List, Sorted Dict, Sorted Set"
+optional = false
+python-versions = "*"
+files = [
+    {file = "sortedcontainers-2.4.0-py2.py3-none-any.whl", hash = "sha256:a163dcaede0f1c021485e957a39245190e74249897e2ae4b2aa38595db237ee0"},
+    {file = "sortedcontainers-2.4.0.tar.gz", hash = "sha256:25caa5a06cc30b6b83d11423433f65d1f9d76c4c6a0c90e3379eaa43b9bfdb88"},
+]
+
 [[package]]
 name = "soupsieve"
 version = "2.5"
@@ -6650,6 +7143,42 @@ notebook = ["ipywidgets (>=6)"]
 slack = ["slack-sdk"]
 telegram = ["requests"]
 
+[[package]]
+name = "trio"
+version = "0.25.0"
+description = "A friendly Python library for async concurrency and I/O"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "trio-0.25.0-py3-none-any.whl", hash = "sha256:e6458efe29cc543e557a91e614e2b51710eba2961669329ce9c862d50c6e8e81"},
+    {file = "trio-0.25.0.tar.gz", hash = "sha256:9b41f5993ad2c0e5f62d0acca320ec657fdb6b2a2c22b8c7aed6caf154475c4e"},
+]
+
+[package.dependencies]
+attrs = ">=23.2.0"
+cffi = {version = ">=1.14", markers = "os_name == \"nt\" and implementation_name != \"pypy\""}
+exceptiongroup = {version = "*", markers = "python_version < \"3.11\""}
+idna = "*"
+outcome = "*"
+sniffio = ">=1.3.0"
+sortedcontainers = "*"
+
+[[package]]
+name = "trio-websocket"
+version = "0.11.1"
+description = "WebSocket library for Trio"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "trio-websocket-0.11.1.tar.gz", hash = "sha256:18c11793647703c158b1f6e62de638acada927344d534e3c7628eedcb746839f"},
+    {file = "trio_websocket-0.11.1-py3-none-any.whl", hash = "sha256:520d046b0d030cf970b8b2b2e00c4c2245b3807853ecd44214acd33d74581638"},
+]
+
+[package.dependencies]
+exceptiongroup = {version = "*", markers = "python_version < \"3.11\""}
+trio = ">=0.11"
+wsproto = ">=0.14"
+
 [[package]]
 name = "trove-classifiers"
 version = "2024.4.10"
@@ -6755,6 +7284,9 @@ files = [
     {file = "urllib3-2.2.1.tar.gz", hash = "sha256:d0570876c61ab9e520d776c38acbbb5b05a776d3f9ff98a5c8fd5162a444cf19"},
 ]
 
+[package.dependencies]
+pysocks = {version = ">=1.5.6,<1.5.7 || >1.5.7,<2.0", optional = true, markers = "extra == \"socks\""}
+
 [package.extras]
 brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"]
 h2 = ["h2 (>=4,<5)"]
@@ -7221,6 +7753,20 @@ files = [
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 ]
 
+[[package]]
+name = "wsproto"
+version = "1.2.0"
+description = "WebSockets state-machine based protocol implementation"
+optional = false
+python-versions = ">=3.7.0"
+files = [
+    {file = "wsproto-1.2.0-py3-none-any.whl", hash = "sha256:b9acddd652b585d75b20477888c56642fdade28bdfd3579aa24a4d2c037dd736"},
+    {file = "wsproto-1.2.0.tar.gz", hash = "sha256:ad565f26ecb92588a3e43bc3d96164de84cd9902482b130d0ddbaa9664a85065"},
+]
+
+[package.dependencies]
+h11 = ">=0.9.0,<1"
+
 [[package]]
 name = "xattr"
 version = "1.1.0"
@@ -7397,6 +7943,41 @@ files = [
 idna = ">=2.0"
 multidict = ">=4.0"
 
+[[package]]
+name = "youtube-transcript-api"
+version = "0.6.2"
+description = "This is an python API which allows you to get the transcripts/subtitles for a given YouTube video. It also works for automatically generated subtitles, supports translating subtitles and it does not require a headless browser, like other selenium based solutions do!"
+optional = false
+python-versions = "*"
+files = [
+    {file = "youtube_transcript_api-0.6.2-py3-none-any.whl", hash = "sha256:019dbf265c6a68a0591c513fff25ed5a116ce6525832aefdfb34d4df5567121c"},
+    {file = "youtube_transcript_api-0.6.2.tar.gz", hash = "sha256:cad223d7620633cec44f657646bffc8bbc5598bd8e70b1ad2fa8277dec305eb7"},
+]
+
+[package.dependencies]
+requests = "*"
+
+[[package]]
+name = "yt-dlp"
+version = "2023.12.30"
+description = "A youtube-dl fork with additional features and patches"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "yt-dlp-2023.12.30.tar.gz", hash = "sha256:a11862e57721b0a0f0883dfeb5a4d79ba213a2d4c45e1880e9fd70f8e6570c38"},
+    {file = "yt_dlp-2023.12.30-py2.py3-none-any.whl", hash = "sha256:c00d9a71d64472ad441bcaa1ec0c3797d6e60c9f934f270096a96fe51657e7b3"},
+]
+
+[package.dependencies]
+brotli = {version = "*", markers = "implementation_name == \"cpython\""}
+brotlicffi = {version = "*", markers = "implementation_name != \"cpython\""}
+certifi = "*"
+mutagen = "*"
+pycryptodomex = "*"
+requests = ">=2.31.0,<3"
+urllib3 = ">=1.26.17,<3"
+websockets = ">=12.0"
+
 [[package]]
 name = "zipp"
 version = "3.18.1"
@@ -7415,4 +7996,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 [metadata]
 lock-version = "2.0"
 python-versions = "~3.10.0"
-content-hash = "5087cace496f1b381c7c1b082e6c5f4f2133fcb49a243e35ba57639a757768dd"
+content-hash = "ebb1051adda542e4aab11151d71a0a7d21e55d5571dc86544db00eedb69eae95"
diff --git a/prediction_market_agent/agents/crewai_subsequential_agent/benchmark.py b/prediction_market_agent/agents/crewai_subsequential_agent/benchmark.py
new file mode 100644
index 00000000..1137b0aa
--- /dev/null
+++ b/prediction_market_agent/agents/crewai_subsequential_agent/benchmark.py
@@ -0,0 +1,118 @@
+import typing as t
+from datetime import datetime, timedelta
+
+import typer
+from loguru import logger
+from prediction_market_agent_tooling.benchmark.agents import (
+    AbstractBenchmarkedAgent,
+    FixedAgent,
+    RandomAgent,
+)
+from prediction_market_agent_tooling.benchmark.benchmark import Benchmarker
+from prediction_market_agent_tooling.benchmark.utils import (
+    OutcomePrediction,
+    Prediction,
+)
+from prediction_market_agent_tooling.gtypes import Probability
+from prediction_market_agent_tooling.markets.agent_market import (
+    AgentMarket,
+    FilterBy,
+    SortBy,
+)
+from prediction_market_agent_tooling.markets.markets import (
+    MarketType,
+    get_binary_markets,
+)
+from prediction_market_agent_tooling.tools.utils import utcnow
+
+from prediction_market_agent.agents.crewai_subsequential_agent.crewai_agent_subquestions import (
+    CrewAIAgentSubquestions,
+)
+
+
+def build_binary_agent_market_from_question(question: str) -> AgentMarket:
+    return AgentMarket(
+        id=question,
+        url=question,
+        close_time=utcnow() + timedelta(days=1),
+        volume=None,
+        question=question,
+        p_yes=Probability(0.5),
+        created_time=datetime(2024, 1, 1),
+        resolution=None,
+        outcomes=["YES", "NO"],
+    )
+
+
+class CrewAIAgentSubquestionsBenchmark(AbstractBenchmarkedAgent):
+    def __init__(
+        self,
+        max_workers: int,
+        agent_name: str,
+    ) -> None:
+        self.agent = CrewAIAgentSubquestions()
+        super().__init__(agent_name=agent_name, max_workers=max_workers)
+
+    def predict(self, market_question: str) -> Prediction:
+        result = self.agent.answer_binary_market(market_question)
+        return Prediction(
+            outcome_prediction=OutcomePrediction(
+                p_yes=result.p_yes, confidence=result.confidence, info_utility=None
+            )
+        )
+
+
+def main(
+    n: int = 50,
+    output: str = "./benchmark_report_50markets.md",
+    reference: MarketType = MarketType.MANIFOLD,
+    filter: FilterBy = FilterBy.OPEN,
+    sort: SortBy = SortBy.NONE,
+    max_workers: int = 1,
+    cache_path: t.Optional[str] = "predictions_cache.json",
+    only_cached: bool = False,
+) -> None:
+    """
+    Polymarket usually contains higher quality questions,
+    but on Manifold, additionally to filtering by MarketFilter.resolved, you can sort by MarketSort.newest.
+    """
+    markets = get_binary_markets(n, reference, filter_by=filter, sort_by=sort)
+    markets_deduplicated = list(({m.question: m for m in markets}.values()))
+    if len(markets) != len(markets_deduplicated):
+        logger.debug(
+            f"Warning: Deduplicated markets from {len(markets)} to {len(markets_deduplicated)}."
+        )
+
+    logger.debug(f"Found {len(markets_deduplicated)} markets.")
+
+    benchmarker = Benchmarker(
+        markets=markets_deduplicated,
+        agents=[
+            CrewAIAgentSubquestionsBenchmark(
+                agent_name="subsequential-questions-crewai",
+                max_workers=max_workers,
+            ),
+            RandomAgent(agent_name="random", max_workers=max_workers),
+            FixedAgent(
+                fixed_answer=False, agent_name="fixed-no", max_workers=max_workers
+            ),
+            FixedAgent(
+                fixed_answer=True, agent_name="fixed-yes", max_workers=max_workers
+            ),
+        ],
+        cache_path=cache_path,
+        only_cached=only_cached,
+    )
+
+    benchmarker.run_agents(
+        enable_timing=False
+    )  # Caching of search etc. can distort timings
+    md = benchmarker.generate_markdown_report()
+
+    with open(output, "w") as f:
+        logger.info(f"Writing benchmark report to: {output}")
+        f.write(md)
+
+
+if __name__ == "__main__":
+    typer.run(main)
diff --git a/prediction_market_agent/agents/crewai_subsequential_agent/crewai_agent_subquestions.py b/prediction_market_agent/agents/crewai_subsequential_agent/crewai_agent_subquestions.py
new file mode 100644
index 00000000..25831e0d
--- /dev/null
+++ b/prediction_market_agent/agents/crewai_subsequential_agent/crewai_agent_subquestions.py
@@ -0,0 +1,189 @@
+import typing as t
+
+from crewai import Agent, Crew, Process, Task
+from langchain_core.language_models import BaseChatModel
+from langchain_openai import ChatOpenAI
+from loguru import logger
+from pydantic import BaseModel
+
+from prediction_market_agent.agents.crewai_subsequential_agent.prompts import (
+    CREATE_OUTCOMES_FROM_SCENARIO_OUTPUT,
+    CREATE_OUTCOMES_FROM_SCENARIO_PROMPT,
+    FINAL_DECISION_PROMPT,
+    PROBABILITY_CLASS_OUTPUT,
+    PROBABILITY_FOR_ONE_OUTCOME_PROMPT,
+    RESEARCH_OUTCOME_OUTPUT,
+    RESEARCH_OUTCOME_PROMPT,
+)
+from prediction_market_agent.tools.crewai_tools import TavilyDevTool
+from prediction_market_agent.utils import APIKeys
+
+tavily_search = TavilyDevTool()
+
+
+class Outcomes(BaseModel):
+    outcomes: list[str]
+
+
+class ProbabilityOutput(BaseModel):
+    decision: str
+    p_yes: float
+    p_no: float
+    confidence: float
+
+
+class CrewAIAgentSubquestions:
+    def __init__(self) -> None:
+        llm = self._build_llm()
+        self.researcher = Agent(
+            role="Research Analyst",
+            goal="Research and report on some future event, giving high quality and nuanced analysis",
+            backstory="You are a senior research analyst who is adept at researching and reporting on future events.",
+            verbose=True,
+            allow_delegation=False,
+            tools=[tavily_search],
+            llm=llm,
+        )
+
+        self.predictor = Agent(
+            role="Professional Gambler",
+            goal="Predict, based on some research you are presented with, whether or not a given event will occur",
+            backstory="You are a professional gambler who is adept at predicting and betting on the outcomes of future events.",
+            verbose=True,
+            allow_delegation=False,
+            llm=llm,
+        )
+
+    def _build_llm(self) -> BaseChatModel:
+        keys = APIKeys()
+        llm = ChatOpenAI(
+            model="gpt-3.5-turbo-0125",
+            api_key=keys.openai_api_key.get_secret_value(),
+        )
+        return llm
+
+    def split_research_into_outcomes(self, question: str) -> Outcomes:
+        create_outcomes_task = Task(
+            description=CREATE_OUTCOMES_FROM_SCENARIO_PROMPT,
+            expected_output=CREATE_OUTCOMES_FROM_SCENARIO_OUTPUT,
+            output_json=Outcomes,
+            agent=self.researcher,
+        )
+
+        report_crew = Crew(
+            agents=[self.researcher],
+            tasks=[create_outcomes_task],
+        )
+        result = report_crew.kickoff(inputs={"scenario": question})
+        return Outcomes.model_validate_json(result)
+
+    def build_tasks_for_outcome(self, input_dict: dict[str, t.Any] = {}) -> list[Task]:
+        task_research_one_outcome = Task(
+            description=RESEARCH_OUTCOME_PROMPT.format(**input_dict),
+            agent=self.researcher,
+            expected_output=RESEARCH_OUTCOME_OUTPUT,
+            async_execution=True,
+        )
+        task_create_probability_for_one_outcome = Task(
+            description=PROBABILITY_FOR_ONE_OUTCOME_PROMPT,
+            expected_output=PROBABILITY_CLASS_OUTPUT,
+            agent=self.predictor,
+            output_json=ProbabilityOutput,
+            async_execution=False,
+            context=[task_research_one_outcome],
+        )
+
+        return [task_research_one_outcome, task_create_probability_for_one_outcome]
+
+    def generate_prediction_for_one_outcome(self, sentence: str) -> ProbabilityOutput:
+        task_research_one_outcome = Task(
+            description=RESEARCH_OUTCOME_PROMPT,
+            agent=self.researcher,
+            expected_output=RESEARCH_OUTCOME_OUTPUT,
+        )
+        task_create_probability_for_one_outcome = Task(
+            description=PROBABILITY_FOR_ONE_OUTCOME_PROMPT,
+            expected_output=PROBABILITY_CLASS_OUTPUT,
+            agent=self.predictor,
+            output_json=ProbabilityOutput,
+            context=[task_research_one_outcome],
+        )
+        crew = Crew(
+            agents=[self.researcher, self.predictor],
+            tasks=[task_research_one_outcome, task_create_probability_for_one_outcome],
+            verbose=2,
+            process=Process.sequential,
+        )
+
+        result = crew.kickoff(inputs={"sentence": sentence})
+        return ProbabilityOutput.model_validate_json(result)
+
+    def generate_final_decision(
+        self, outcomes_with_probabilities: list[t.Tuple[str, ProbabilityOutput]]
+    ) -> ProbabilityOutput:
+        task_final_decision = Task(
+            description=FINAL_DECISION_PROMPT,
+            agent=self.predictor,
+            expected_output=PROBABILITY_CLASS_OUTPUT,
+            output_json=ProbabilityOutput,
+        )
+
+        crew = Crew(
+            agents=[self.predictor],
+            tasks=[task_final_decision],
+            verbose=2,
+        )
+
+        crew.kickoff(
+            inputs={
+                "outcomes_with_probabilities": [
+                    (i[0], i[1].dict()) for i in outcomes_with_probabilities
+                ],
+                "number_of_outcomes": len(outcomes_with_probabilities),
+                "outcome_to_assess": outcomes_with_probabilities[0][0],
+            }
+        )
+        return ProbabilityOutput.model_validate_json(
+            task_final_decision.output.raw_output
+        )
+
+    def answer_binary_market(self, question: str) -> ProbabilityOutput:
+        outcomes = self.split_research_into_outcomes(question)
+        logger.debug("outcomes ", outcomes)
+
+        outcomes_with_probs = []
+        task_map = {}
+        for outcome in outcomes.outcomes:
+            tasks_for_outcome = self.build_tasks_for_outcome(
+                input_dict={"sentence": outcome}
+            )
+            task_map[outcome] = tasks_for_outcome
+
+        # flatten nested list
+        all_tasks = sum(task_map.values(), [])
+        crew = Crew(
+            agents=[self.researcher, self.predictor],
+            tasks=all_tasks,
+            verbose=2,
+            process=Process.sequential,
+        )
+
+        # crew.kickoff doesn't finish all async tasks when done.
+        crew.kickoff()
+
+        # We parse individual task results to build outcomes_with_probs
+        for outcome, tasks in task_map.items():
+            try:
+                prediction_result = ProbabilityOutput.model_validate_json(
+                    tasks[1].output.raw_output
+                )
+            except Exception as e:
+                logger.error("Could not parse result as ProbabilityOutput ", e)
+                prediction_result = ProbabilityOutput(
+                    p_yes=0.5, p_no=0.5, confidence=0, decision=""
+                )
+
+            outcomes_with_probs.append((outcome, prediction_result))
+
+        final_answer = self.generate_final_decision(outcomes_with_probs)
+        return final_answer
diff --git a/prediction_market_agent/agents/crewai_subsequential_agent/deploy.py b/prediction_market_agent/agents/crewai_subsequential_agent/deploy.py
new file mode 100644
index 00000000..5cf8f82e
--- /dev/null
+++ b/prediction_market_agent/agents/crewai_subsequential_agent/deploy.py
@@ -0,0 +1,53 @@
+import random
+import typing as t
+
+from prediction_market_agent_tooling.deploy.agent import DeployableAgent
+from prediction_market_agent_tooling.markets.agent_market import AgentMarket
+from prediction_market_agent_tooling.markets.markets import MarketType
+from prediction_market_agent_tooling.tools.utils import should_not_happen
+
+from prediction_market_agent.agents.crewai_subsequential_agent.crewai_agent_subquestions import (
+    CrewAIAgentSubquestions,
+)
+from prediction_market_agent.agents.utils import market_is_saturated
+
+
+class DeployableThinkThoroughlyAgent(DeployableAgent):
+    # For cheaper credits at this experimental stage
+    def __init__(self) -> None:
+        super().__init__()
+
+    def pick_markets(self, markets: t.Sequence[AgentMarket]) -> t.Sequence[AgentMarket]:
+        # We simply pick 5 random markets to bet on
+        picked_markets: list[AgentMarket] = []
+        markets = list(markets)
+        random.shuffle(markets)
+        for market in markets:
+            # Assume very high probability markets are already known, and have
+            # been correctly bet on, and therefore the value of betting on them
+            # is low.
+            if not market_is_saturated(market=market):
+                picked_markets.append(market)
+                if len(picked_markets) == 5:
+                    break
+
+        return picked_markets
+
+    def answer_binary_market(self, market: AgentMarket) -> bool:
+        # The answer has already been determined in `pick_markets` so we just
+        # return it here.
+        result = CrewAIAgentSubquestions().answer_binary_market(market.question)
+        return (
+            True
+            if result.decision == "y"
+            else False
+            if result.decision == "n"
+            else should_not_happen()
+        )
+
+
+if __name__ == "__main__":
+    agent = DeployableThinkThoroughlyAgent()
+    agent.deploy_local(
+        market_type=MarketType.OMEN, sleep_time=540, timeout=180, place_bet=False
+    )
diff --git a/prediction_market_agent/agents/crewai_subsequential_agent/prompts.py b/prediction_market_agent/agents/crewai_subsequential_agent/prompts.py
new file mode 100644
index 00000000..a543f396
--- /dev/null
+++ b/prediction_market_agent/agents/crewai_subsequential_agent/prompts.py
@@ -0,0 +1,100 @@
+CREATE_OUTCOMES_FROM_SCENARIO_PROMPT = """
+        You are asked about a SCENARIO, which can have multiple, mutually-exclusive outcomes.
+        You should break down the SCENARIO into a list of possible outcomes.
+        
+        Example 1:
+        - SCENARIO: Will Gnosis Pay reach 100,000 users by 2025?
+        - Answer: '''
+            - Gnosis Pay reaches less than 25,000 users by 2025
+            - Gnosis Pay reaches more than 25,000 users but less than 50,000 users by 2025
+            - Gnosis Pay reaches more than 50,000 users but less than 75,000 users by 2025
+            - Gnosis Pay reaches 100,000 users or more by 2025
+        '''
+
+        Example 2:
+        - SCENARIO: Will the price of Bitcoin go up again in the next 2 months?
+        - Answer: '''
+            - The price of crypto will go up in the next 2 months
+            - The price of crypto will not go up in the next 2 months
+        '''
+        
+        [SCENARIO]
+        {scenario}
+        """
+
+CREATE_OUTCOMES_FROM_SCENARIO_OUTPUT = """
+  A list containing multiple bullet points. Each bullet point should start with '-'.
+  Each bullet point should contain a possible outcome resulting from the
+  provided SCENARIO. The produced outcomes should be mutually exclusive, i.e. only one of them should be true whereas
+  the remaining ones should be false.
+  """
+
+PROBABILITY_FOR_ONE_OUTCOME_PROMPT = """
+                            Your task is to determine the probability of a prediction market affirmation being answered 'Yes' or 'No'.
+        Use the sentence provided in 'SENTENCE' and follow these guidelines:
+        * Focus on the affirmation inside double quotes in 'SENTENCE'.
+        * The question must have only 'Yes' or 'No' outcomes. If not, respond with "Error".
+        * Use the tools provided to aid your estimation.
+        * Evaluate recent information more heavily than older information.
+        * Your response should include:
+            - "decision": The decision you made. Either `y` (for `Yes`) or `n` (for `No`).
+            - "p_yes": Probability that the sentence outcome will be `Yes`. Ranging from 0 (lowest probability) to 1 (maximum probability).
+            - "p_no": Probability that the sentence outcome will be `No`. Ranging from 0 (lowest probability) to 1 (maximum probability).
+            - "confidence": Indicating the confidence in the estimated probabilities you provided ranging from 0 (lowest confidence) to 1 (maximum confidence). 
+            Confidence can be calculated based on the quality and quantity of data used for the estimation.
+            
+            Ensure p_yes + p_no equals 1.
+        
+            SENTENCE: {sentence}
+"""
+
+RESEARCH_OUTCOME_PROMPT = """
+                Research and report on the following sentence:
+                {sentence}
+                Search and scrape the web for information that will help you give a high quality, nuanced answer to the question.
+                """
+RESEARCH_OUTCOME_OUTPUT = """
+            Return your answer in raw JSON format, with no special formatting such as newlines, as follows:
+                {{"report": <REPORT>}}
+                where <REPORT> is a free text field that contains a well though out justification 
+                for the predicition based on the summary of your findings.
+                The report must not be longer than 7000 characters.
+            """
+
+FINAL_DECISION_PROMPT = """
+Your task is to determine the probability of a given outcome being true.
+Below you can find the , a list of tuples where the first element
+is an outcome
+
+[Outcomes with probabilities]
+"""
+
+FINAL_DECISION_PROMPT = """
+Your task is to determine the probability of a binary outcome being answered 'Yes' or 'No'.
+OUTCOMES_WITH_PROBABILITIES is provided, which contains a set of {number_of_outcomes} outcomes.
+The object OUTCOMES_WITH_PROBABILITIES contains a list of tuple, where each tuple contains, as its
+first element, an outcome, and as its second entry, a probability dictionary, having properties such
+ as p_yes (probability that the outcome is true), p_no (probability that the outcome is false),
+confidence (confidence level in the values of p_yes and p_no).
+Observe that the outcomes contained inside OUTCOMES_WITH_PROBABILITIES are mutually exclusive, i.e.
+only one of them can be true. 
+You should determine the probability of the outcome OUTCOME_TO_ASSESS being true, 
+considering the probabilities of the other related outcomes.
+
+OUTCOME_TO_ASSESS: {outcome_to_assess}
+OUTCOMES_WITH_PROBABILITIES: {outcomes_with_probabilities}
+"""
+
+PROBABILITY_CLASS_OUTPUT = """
+    Your response should be a JSON string containing the following keys:
+    - "decision": The decision you made. Either `y` (for `Yes`) or `n` (for `No`).
+    - "p_yes": Probability that the sentence outcome will be `Yes`. Ranging from 0 (lowest probability) to 1 (maximum probability).
+    - "p_no": Probability that the sentence outcome will be `No`. Ranging from 0 (lowest probability) to 1 (maximum probability).
+    - "confidence": Indicating the confidence in the estimated probabilities you provided ranging from 0 (lowest confidence) to 
+    1 (maximum confidence). Confidence can be calculated based on the quality and quantity of data used for the estimation.
+
+    Do not surround the output object with escape quotes, line breaks nor '''. 
+    Do not output any reasoning, only the JSON object.
+
+  Ensure p_yes + p_no equals 1.
+"""
diff --git a/prediction_market_agent/agents/known_outcome_agent/benchmark.py b/prediction_market_agent/agents/known_outcome_agent/benchmark.py
index adb3ac2e..f7296530 100644
--- a/prediction_market_agent/agents/known_outcome_agent/benchmark.py
+++ b/prediction_market_agent/agents/known_outcome_agent/benchmark.py
@@ -1,3 +1,4 @@
+# type: ignore
 import time
 import typing as t
 from datetime import timedelta
diff --git a/prediction_market_agent/agents/known_outcome_agent/deploy.py b/prediction_market_agent/agents/known_outcome_agent/deploy.py
index 2c3f9f91..855fcd8c 100644
--- a/prediction_market_agent/agents/known_outcome_agent/deploy.py
+++ b/prediction_market_agent/agents/known_outcome_agent/deploy.py
@@ -22,10 +22,7 @@
     Result,
     get_known_outcome,
 )
-
-
-def market_is_saturated(market: AgentMarket) -> bool:
-    return market.p_yes > 0.95 or market.p_no > 0.95
+from prediction_market_agent.agents.utils import market_is_saturated
 
 
 class DeployableKnownOutcomeAgent(DeployableAgent):
diff --git a/prediction_market_agent/agents/microchain_agent/functions.py b/prediction_market_agent/agents/microchain_agent/functions.py
index a6745ce6..c7eb52ea 100644
--- a/prediction_market_agent/agents/microchain_agent/functions.py
+++ b/prediction_market_agent/agents/microchain_agent/functions.py
@@ -22,10 +22,6 @@
 )
 from prediction_market_agent.utils import APIKeys
 
-outcomeTokens = {}
-outcomeTokens["Will Joe Biden get reelected in 2024?"] = {"yes": 0, "no": 0}
-outcomeTokens["Will Bitcoin hit 100k in 2024?"] = {"yes": 0, "no": 0}
-
 
 class Sum(Function):
     @property
diff --git a/prediction_market_agent/agents/utils.py b/prediction_market_agent/agents/utils.py
new file mode 100644
index 00000000..d6e5ef5a
--- /dev/null
+++ b/prediction_market_agent/agents/utils.py
@@ -0,0 +1,5 @@
+from prediction_market_agent_tooling.markets.agent_market import AgentMarket
+
+
+def market_is_saturated(market: AgentMarket) -> bool:
+    return market.p_yes > 0.95 or market.p_no > 0.95
diff --git a/prediction_market_agent/tools/crewai_tools.py b/prediction_market_agent/tools/crewai_tools.py
new file mode 100644
index 00000000..42893290
--- /dev/null
+++ b/prediction_market_agent/tools/crewai_tools.py
@@ -0,0 +1,38 @@
+from typing import Any, Type
+
+from crewai_tools.tools.base_tool import BaseTool
+from langchain_community.utilities.tavily_search import TavilySearchAPIWrapper
+from pydantic.v1 import BaseModel, Field
+from pydantic.v1.types import SecretStr
+
+from prediction_market_agent.utils import APIKeys
+
+
+class TavilyDevToolSchema(BaseModel):
+    """Input for TXTSearchTool."""
+
+    search_query: str = Field(
+        ..., description="Mandatory search query you want to use to search the internet"
+    )
+
+
+class TavilyDevTool(BaseTool):
+    name: str = "Search the internet"
+    # From Langchain's Tavily integration
+    description: str = """"A search engine optimized for comprehensive, accurate, \
+and trusted results. Useful for when you need to answer questions \
+about current events or about recent information. \
+Input should be a search query. \
+If the user is asking about something that you don't know about, \
+you should probably use this tool to see if that can provide any information."""
+    args_schema: Type[BaseModel] = TavilyDevToolSchema
+
+    def _run(
+        self,
+        search_query: str,
+        **kwargs: Any,
+    ) -> Any:
+        keys = APIKeys()
+        return TavilySearchAPIWrapper(
+            tavily_api_key=SecretStr(keys.tavily_api_key.get_secret_value())
+        ).results(query=search_query)
diff --git a/prediction_market_agent/utils.py b/prediction_market_agent/utils.py
index 4ed88513..10a9834e 100644
--- a/prediction_market_agent/utils.py
+++ b/prediction_market_agent/utils.py
@@ -11,6 +11,7 @@
 class APIKeys(APIKeysBase):
     SERP_API_KEY: t.Optional[SecretStr] = None
     OPENAI_API_KEY: t.Optional[SecretStr] = None
+    TAVILY_API_KEY: t.Optional[SecretStr] = None
 
     @property
     def serp_api_key(self) -> SecretStr:
@@ -24,6 +25,12 @@ def openai_api_key(self) -> SecretStr:
             self.OPENAI_API_KEY, "OPENAI_API_KEY missing in the environment."
         )
 
+    @property
+    def tavily_api_key(self) -> SecretStr:
+        return check_not_none(
+            self.TAVILY_API_KEY, "OPENAI_API_KEY missing in the environment."
+        )
+
 
 def get_market_prompt(question: str) -> str:
     prompt = (
diff --git a/pyproject.toml b/pyproject.toml
index 0a2caac1..52abd92a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -21,7 +21,7 @@ google-search-results = "*"
 pytest = "*"
 llama-index = "~0.9.0"
 duckduckgo-search = "*"
-crewai = "*"
+crewai = {extras = ["tools"], version = ">=0.22.5"}
 # metagpt = "*" # Commented out because requires super old version of langchain, and conflicts with crewai.
 replicate = "*"
 typer = "^0.9.0"