diff --git a/example/cli/main.py b/example/cli/main.py
index f1cd9e2..a44f0a6 100644
--- a/example/cli/main.py
+++ b/example/cli/main.py
@@ -2,14 +2,18 @@
 from uuid import uuid4
 
 from langchain.prompts import ChatPromptTemplate
+
 from langchain.schema import AIMessage, HumanMessage, SystemMessage
 from langchain_community.chat_models.fake import FakeListChatModel
 
 from honcho import Honcho
+from honcho.ext.langchain import langchain_message_converter
 
 app_name = str(uuid4())
 
-# honcho = Honcho(app_id=app_id, base_url="http://localhost:8000") # uncomment to use local
+# honcho = Honcho(
+#     app_name=app_name, base_url="http://localhost:8000"
+# )  # uncomment to use local
 honcho = Honcho(app_name=app_name)  # uses demo server at https://demo.honcho.dev
 honcho.initialize()
 
@@ -24,16 +28,6 @@
 session = user.create_session()
 
 
-def langchain_message_converter(messages: List):
-    new_messages = []
-    for message in messages:
-        if message.is_user:
-            new_messages.append(HumanMessage(content=message.content))
-        else:
-            new_messages.append(AIMessage(content=message.content))
-    return new_messages
-
-
 def chat():
     while True:
         user_input = input("User: ")
diff --git a/example/cli/poetry.lock b/example/cli/poetry.lock
index 0632c39..546d3c0 100644
--- a/example/cli/poetry.lock
+++ b/example/cli/poetry.lock
@@ -125,14 +125,14 @@ files = [
 
 [[package]]
 name = "anyio"
-version = "4.2.0"
+version = "4.3.0"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 category = "main"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "anyio-4.2.0-py3-none-any.whl", hash = "sha256:745843b39e829e108e518c489b31dc757de7d2131d53fac32bd8df268227bfee"},
-    {file = "anyio-4.2.0.tar.gz", hash = "sha256:e1875bb4b4e2de1669f4bc7869b6d3f54231cdced71605e6e64c9be77e3be50f"},
+    {file = "anyio-4.3.0-py3-none-any.whl", hash = "sha256:048e05d0f6caeed70d731f3db756d35dcc1f35747c8c403364a8332c630441b8"},
+    {file = "anyio-4.3.0.tar.gz", hash = "sha256:f75253795a87df48568485fd18cdd2a3fa5c4f7c5be8e5e36637733fce06fed6"},
 ]
 
 [package.dependencies]
@@ -166,14 +166,14 @@ tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "p
 
 [[package]]
 name = "certifi"
-version = "2023.11.17"
+version = "2024.2.2"
 description = "Python package for providing Mozilla's CA Bundle."
 category = "main"
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "certifi-2023.11.17-py3-none-any.whl", hash = "sha256:e036ab49d5b79556f99cfc2d9320b34cfbe5be05c5871b51de9329f0603b0474"},
-    {file = "certifi-2023.11.17.tar.gz", hash = "sha256:9b469f3a900bf28dc19b8cfbf8019bf47f7fdd1a65a1d4ffb98fc14166beb4d1"},
+    {file = "certifi-2024.2.2-py3-none-any.whl", hash = "sha256:dc383c07b76109f368f6106eee2b593b04a011ea4d55f652c6ca24a754d1cdd1"},
+    {file = "certifi-2024.2.2.tar.gz", hash = "sha256:0569859f95fc761b18b45ef421b1290a0f65f147e92a1e5eb3e635f9a5e4e66f"},
 ]
 
 [[package]]
@@ -278,14 +278,14 @@ files = [
 
 [[package]]
 name = "dataclasses-json"
-version = "0.6.3"
+version = "0.6.4"
 description = "Easily serialize dataclasses to and from JSON."
 category = "dev"
 optional = false
 python-versions = ">=3.7,<4.0"
 files = [
-    {file = "dataclasses_json-0.6.3-py3-none-any.whl", hash = "sha256:4aeb343357997396f6bca1acae64e486c3a723d8f5c76301888abeccf0c45176"},
-    {file = "dataclasses_json-0.6.3.tar.gz", hash = "sha256:35cb40aae824736fdf959801356641836365219cfe14caeb115c39136f775d2a"},
+    {file = "dataclasses_json-0.6.4-py3-none-any.whl", hash = "sha256:f90578b8a3177f7552f4e1a6e535e84293cd5da421fcce0642d49c0d7bdf8df2"},
+    {file = "dataclasses_json-0.6.4.tar.gz", hash = "sha256:73696ebf24936560cca79a2430cbc4f3dd23ac7bf46ed17f38e5e5e7657a6377"},
 ]
 
 [package.dependencies]
@@ -568,23 +568,24 @@ files = [
 
 [[package]]
 name = "langchain"
-version = "0.1.4"
+version = "0.1.11"
 description = "Building applications with LLMs through composability"
 category = "dev"
 optional = false
 python-versions = ">=3.8.1,<4.0"
 files = [
-    {file = "langchain-0.1.4-py3-none-any.whl", hash = "sha256:6befdd6221f5f326092e31a3c19efdc7ce3d7d1f2e2cab065141071451730ed7"},
-    {file = "langchain-0.1.4.tar.gz", hash = "sha256:8767a9461e2b717ce9a35b1fa20659de89ea86ba9c2a4ff516e05d47ab2d195d"},
+    {file = "langchain-0.1.11-py3-none-any.whl", hash = "sha256:b5e678ac50d85370b9bc28f2c97ad5f029aac1c0cca79cac9354adf72741bc6e"},
+    {file = "langchain-0.1.11.tar.gz", hash = "sha256:03f08cae7cd3f341c54f1042b3fe24d88f39eba7b7eda942735d8ced13fe6da9"},
 ]
 
 [package.dependencies]
 aiohttp = ">=3.8.3,<4.0.0"
 dataclasses-json = ">=0.5.7,<0.7"
 jsonpatch = ">=1.33,<2.0"
-langchain-community = ">=0.0.14,<0.1"
-langchain-core = ">=0.1.16,<0.2"
-langsmith = ">=0.0.83,<0.1"
+langchain-community = ">=0.0.25,<0.1"
+langchain-core = ">=0.1.29,<0.2"
+langchain-text-splitters = ">=0.0.1,<0.1"
+langsmith = ">=0.1.17,<0.2.0"
 numpy = ">=1,<2"
 pydantic = ">=1,<3"
 PyYAML = ">=5.3"
@@ -593,13 +594,13 @@ SQLAlchemy = ">=1.4,<3"
 tenacity = ">=8.1.0,<9.0.0"
 
 [package.extras]
-azure = ["azure-ai-formrecognizer (>=3.2.1,<4.0.0)", "azure-ai-textanalytics (>=5.3.0,<6.0.0)", "azure-ai-vision (>=0.11.1b1,<0.12.0)", "azure-cognitiveservices-speech (>=1.28.0,<2.0.0)", "azure-core (>=1.26.4,<2.0.0)", "azure-cosmos (>=4.4.0b1,<5.0.0)", "azure-identity (>=1.12.0,<2.0.0)", "azure-search-documents (==11.4.0b8)", "openai (<2)"]
+azure = ["azure-ai-formrecognizer (>=3.2.1,<4.0.0)", "azure-ai-textanalytics (>=5.3.0,<6.0.0)", "azure-cognitiveservices-speech (>=1.28.0,<2.0.0)", "azure-core (>=1.26.4,<2.0.0)", "azure-cosmos (>=4.4.0b1,<5.0.0)", "azure-identity (>=1.12.0,<2.0.0)", "azure-search-documents (==11.4.0b8)", "openai (<2)"]
 clarifai = ["clarifai (>=9.1.0)"]
 cli = ["typer (>=0.9.0,<0.10.0)"]
 cohere = ["cohere (>=4,<5)"]
 docarray = ["docarray[hnswlib] (>=0.32.0,<0.33.0)"]
 embeddings = ["sentence-transformers (>=2,<3)"]
-extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", "anthropic (>=0.3.11,<0.4.0)", "arxiv (>=1.4,<2.0)", "assemblyai (>=0.17.0,<0.18.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "beautifulsoup4 (>=4,<5)", "bibtexparser (>=1.4.0,<2.0.0)", "cassio (>=0.1.0,<0.2.0)", "chardet (>=5.1.0,<6.0.0)", "cohere (>=4,<5)", "couchbase (>=4.1.9,<5.0.0)", "dashvector (>=1.0.1,<2.0.0)", "databricks-vectorsearch (>=0.21,<0.22)", "datasets (>=2.15.0,<3.0.0)", "dgml-utils (>=0.3.0,<0.4.0)", "esprima (>=4.0.1,<5.0.0)", "faiss-cpu (>=1,<2)", "feedparser (>=6.0.10,<7.0.0)", "fireworks-ai (>=0.9.0,<0.10.0)", "geopandas (>=0.13.1,<0.14.0)", "gitpython (>=3.1.32,<4.0.0)", "google-cloud-documentai (>=2.20.1,<3.0.0)", "gql (>=3.4.1,<4.0.0)", "hologres-vector (>=0.0.6,<0.0.7)", "html2text (>=2020.1.16,<2021.0.0)", "javelin-sdk (>=0.1.8,<0.2.0)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "jsonschema (>1)", "langchain-openai (>=0.0.2,<0.1)", "lxml (>=4.9.2,<5.0.0)", "markdownify (>=0.11.6,<0.12.0)", "motor (>=3.3.1,<4.0.0)", "msal (>=1.25.0,<2.0.0)", "mwparserfromhell (>=0.6.4,<0.7.0)", "mwxml (>=0.3.3,<0.4.0)", "newspaper3k (>=0.2.8,<0.3.0)", "numexpr (>=2.8.6,<3.0.0)", "openai (<2)", "openapi-pydantic (>=0.3.2,<0.4.0)", "pandas (>=2.0.1,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pgvector (>=0.1.6,<0.2.0)", "praw (>=7.7.1,<8.0.0)", "psychicapi (>=0.8.0,<0.9.0)", "py-trello (>=0.19.0,<0.20.0)", "pymupdf (>=1.22.3,<2.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pypdfium2 (>=4.10.0,<5.0.0)", "pyspark (>=3.4.0,<4.0.0)", "rank-bm25 (>=0.2.2,<0.3.0)", "rapidfuzz (>=3.1.1,<4.0.0)", "rapidocr-onnxruntime (>=1.3.2,<2.0.0)", "requests-toolbelt (>=1.0.0,<2.0.0)", "rspace_client (>=2.5.0,<3.0.0)", "scikit-learn (>=1.2.2,<2.0.0)", "sqlite-vss (>=0.1.2,<0.2.0)", "streamlit (>=1.18.0,<2.0.0)", "sympy (>=1.12,<2.0)", "telethon (>=1.28.5,<2.0.0)", "timescale-vector (>=0.0.1,<0.0.2)", "tqdm (>=4.48.0)", "upstash-redis (>=0.15.0,<0.16.0)", "xata (>=1.0.0a7,<2.0.0)", "xmltodict (>=0.13.0,<0.14.0)"]
+extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", "anthropic (>=0.3.11,<0.4.0)", "arxiv (>=1.4,<2.0)", "assemblyai (>=0.17.0,<0.18.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "beautifulsoup4 (>=4,<5)", "bibtexparser (>=1.4.0,<2.0.0)", "cassio (>=0.1.0,<0.2.0)", "chardet (>=5.1.0,<6.0.0)", "cohere (>=4,<5)", "couchbase (>=4.1.9,<5.0.0)", "dashvector (>=1.0.1,<2.0.0)", "databricks-vectorsearch (>=0.21,<0.22)", "datasets (>=2.15.0,<3.0.0)", "dgml-utils (>=0.3.0,<0.4.0)", "esprima (>=4.0.1,<5.0.0)", "faiss-cpu (>=1,<2)", "feedparser (>=6.0.10,<7.0.0)", "fireworks-ai (>=0.9.0,<0.10.0)", "geopandas (>=0.13.1,<0.14.0)", "gitpython (>=3.1.32,<4.0.0)", "google-cloud-documentai (>=2.20.1,<3.0.0)", "gql (>=3.4.1,<4.0.0)", "hologres-vector (>=0.0.6,<0.0.7)", "html2text (>=2020.1.16,<2021.0.0)", "javelin-sdk (>=0.1.8,<0.2.0)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "jsonschema (>1)", "langchain-openai (>=0.0.2,<0.1)", "lxml (>=4.9.2,<5.0.0)", "markdownify (>=0.11.6,<0.12.0)", "motor (>=3.3.1,<4.0.0)", "msal (>=1.25.0,<2.0.0)", "mwparserfromhell (>=0.6.4,<0.7.0)", "mwxml (>=0.3.3,<0.4.0)", "newspaper3k (>=0.2.8,<0.3.0)", "numexpr (>=2.8.6,<3.0.0)", "openai (<2)", "openapi-pydantic (>=0.3.2,<0.4.0)", "pandas (>=2.0.1,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pgvector (>=0.1.6,<0.2.0)", "praw (>=7.7.1,<8.0.0)", "psychicapi (>=0.8.0,<0.9.0)", "py-trello (>=0.19.0,<0.20.0)", "pymupdf (>=1.22.3,<2.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pypdfium2 (>=4.10.0,<5.0.0)", "pyspark (>=3.4.0,<4.0.0)", "rank-bm25 (>=0.2.2,<0.3.0)", "rapidfuzz (>=3.1.1,<4.0.0)", "rapidocr-onnxruntime (>=1.3.2,<2.0.0)", "rdflib (==7.0.0)", "requests-toolbelt (>=1.0.0,<2.0.0)", "rspace_client (>=2.5.0,<3.0.0)", "scikit-learn (>=1.2.2,<2.0.0)", "sqlite-vss (>=0.1.2,<0.2.0)", "streamlit (>=1.18.0,<2.0.0)", "sympy (>=1.12,<2.0)", "telethon (>=1.28.5,<2.0.0)", "timescale-vector (>=0.0.1,<0.0.2)", "tqdm (>=4.48.0)", "upstash-redis (>=0.15.0,<0.16.0)", "xata (>=1.0.0a7,<2.0.0)", "xmltodict (>=0.13.0,<0.14.0)"]
 javascript = ["esprima (>=4.0.1,<5.0.0)"]
 llms = ["clarifai (>=9.1.0)", "cohere (>=4,<5)", "huggingface_hub (>=0,<1)", "manifest-ml (>=0.0.1,<0.0.2)", "nlpcloud (>=1,<2)", "openai (<2)", "openlm (>=0.0.5,<0.0.6)", "torch (>=1,<3)", "transformers (>=4,<5)"]
 openai = ["openai (<2)", "tiktoken (>=0.3.2,<0.6.0)"]
@@ -608,21 +609,21 @@ text-helpers = ["chardet (>=5.1.0,<6.0.0)"]
 
 [[package]]
 name = "langchain-community"
-version = "0.0.16"
+version = "0.0.28"
 description = "Community contributed LangChain integrations."
 category = "dev"
 optional = false
 python-versions = ">=3.8.1,<4.0"
 files = [
-    {file = "langchain_community-0.0.16-py3-none-any.whl", hash = "sha256:0f1dfc1a6205ce8d39931d3515974a208a9f69c16157c649f83490a7cc830b73"},
-    {file = "langchain_community-0.0.16.tar.gz", hash = "sha256:c06512a93013a06fba7679cd5a1254ff8b927cddd2d1fbe0cc444bf7bbdf0b8c"},
+    {file = "langchain_community-0.0.28-py3-none-any.whl", hash = "sha256:bdb015ac455ae68432ea104628717583dce041e1abdfcefe86e39f034f5e90b8"},
+    {file = "langchain_community-0.0.28.tar.gz", hash = "sha256:8664d243a90550fc5ddc137b712034e02c8d43afc8d4cc832ba5842b44c864ce"},
 ]
 
 [package.dependencies]
 aiohttp = ">=3.8.3,<4.0.0"
 dataclasses-json = ">=0.5.7,<0.7"
-langchain-core = ">=0.1.16,<0.2"
-langsmith = ">=0.0.83,<0.1"
+langchain-core = ">=0.1.31,<0.2.0"
+langsmith = ">=0.1.0,<0.2.0"
 numpy = ">=1,<2"
 PyYAML = ">=5.3"
 requests = ">=2,<3"
@@ -631,24 +632,24 @@ tenacity = ">=8.1.0,<9.0.0"
 
 [package.extras]
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 extended-testing = ["jinja2 (>=3,<4)"]
 
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+    {file = "SQLAlchemy-2.0.28.tar.gz", hash = "sha256:dd53b6c4e6d960600fd6532b79ee28e2da489322fcf6648738134587faf767b6"},
 ]
 
 [package.dependencies]
@@ -1150,14 +1245,14 @@ doc = ["reno", "sphinx", "tornado (>=4.5)"]
 
 [[package]]
 name = "typing-extensions"
-version = "4.9.0"
+version = "4.10.0"
 description = "Backported and Experimental Type Hints for Python 3.8+"
 category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "typing_extensions-4.9.0-py3-none-any.whl", hash = "sha256:af72aea155e91adfc61c3ae9e0e342dbc0cba726d6cba4b6c72c1f34e47291cd"},
-    {file = "typing_extensions-4.9.0.tar.gz", hash = "sha256:23478f88c37f27d76ac8aee6c905017a143b0b1b886c3c9f66bc2fd94f9f5783"},
+    {file = "typing_extensions-4.10.0-py3-none-any.whl", hash = "sha256:69b1a937c3a517342112fb4c6df7e72fc39a38e7891a5730ed4985b5214b5475"},
+    {file = "typing_extensions-4.10.0.tar.gz", hash = "sha256:b0abd7c89e8fb96f98db18d86106ff1d90ab692004eb746cf6eda2682f91b3cb"},
 ]
 
 [[package]]
@@ -1178,14 +1273,14 @@ typing-extensions = ">=3.7.4"
 
 [[package]]
 name = "urllib3"
-version = "2.2.0"
+version = "2.2.1"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "urllib3-2.2.0-py3-none-any.whl", hash = "sha256:ce3711610ddce217e6d113a2732fafad960a03fd0318c91faa79481e35c11224"},
-    {file = "urllib3-2.2.0.tar.gz", hash = "sha256:051d961ad0c62a94e50ecf1af379c3aba230c66c710493493560c0c223c49f20"},
+    {file = "urllib3-2.2.1-py3-none-any.whl", hash = "sha256:450b20ec296a467077128bff42b73080516e71b56ff59a60a02bef2232c4fa9d"},
+    {file = "urllib3-2.2.1.tar.gz", hash = "sha256:d0570876c61ab9e520d776c38acbbb5b05a776d3f9ff98a5c8fd5162a444cf19"},
 ]
 
 [package.extras]
diff --git a/example/discord/honcho-dspy-personas/bot.py b/example/discord/honcho-dspy-personas/bot.py
index 77a7abf..ea91ec7 100644
--- a/example/discord/honcho-dspy-personas/bot.py
+++ b/example/discord/honcho-dspy-personas/bot.py
@@ -2,9 +2,9 @@
 from uuid import uuid1
 import discord
 from honcho import Honcho
+from honcho.ext.langchain import langchain_message_converter
 from graph import chat
 from dspy import Example
-from chain import langchain_message_converter
 
 intents = discord.Intents.default()
 intents.messages = True
@@ -51,7 +51,9 @@ async def on_message(message):
     user = honcho.get_or_create_user(user_id)
     location_id = str(message.channel.id)
 
-    sessions = list(user.get_sessions_generator(location_id, is_active=True, reverse=True))
+    sessions = list(
+        user.get_sessions_generator(location_id, is_active=True, reverse=True)
+    )
 
     if len(sessions) > 0:
         session = sessions[0]
@@ -86,7 +88,9 @@ async def on_reaction_add(reaction, user):
     honcho_user = honcho.get_or_create_user(user_id)
     location_id = str(reaction.message.channel.id)
 
-    sessions = list(honcho_user.get_sessions_generator(location_id, is_active=True, reverse=True))
+    sessions = list(
+        honcho_user.get_sessions_generator(location_id, is_active=True, reverse=True)
+    )
     if len(sessions) > 0:
         session = sessions[0]
     else:
@@ -100,25 +104,29 @@ async def on_reaction_add(reaction, user):
     user_response = user_responses[0]
 
     user_state_storage = dict(honcho_user.metadata)
-    user_state = list(session.get_metamessages_generator(metamessage_type="user_state", message=user_response, reverse=True))[0].content
+    user_state = list(
+        session.get_metamessages_generator(
+            metamessage_type="user_state", message=user_response, reverse=True
+        )
+    )[0].content
     examples = user_state_storage[user_state]["examples"]
 
     # Check if the reaction is a thumbs up
     if str(reaction.emoji) == "👍":
         example = Example(
-            chat_input=user_response.content,  
+            chat_input=user_response.content,
             response=ai_response,
             assessment_dimension=user_state,
-            label='yes'
+            label="yes",
         ).with_inputs("chat_input", "response", "assessment_dimension")
         examples.append(example.toDict())
     # Check if the reaction is a thumbs down
     elif str(reaction.emoji) == "👎":
         example = Example(
-            chat_input=user_response.content,  
+            chat_input=user_response.content,
             response=ai_response,
             assessment_dimension=user_state,
-            label='no'
+            label="no",
         ).with_inputs("chat_input", "response", "assessment_dimension")
         examples.append(example.toDict())
 
diff --git a/example/discord/honcho-dspy-personas/chain.py b/example/discord/honcho-dspy-personas/chain.py
index eede40e..be92c21 100644
--- a/example/discord/honcho-dspy-personas/chain.py
+++ b/example/discord/honcho-dspy-personas/chain.py
@@ -2,7 +2,11 @@
 from typing import List, Union
 from dotenv import load_dotenv
 from langchain_openai import ChatOpenAI
-from langchain_core.prompts import ChatPromptTemplate, SystemMessagePromptTemplate, load_prompt
+from langchain_core.prompts import (
+    ChatPromptTemplate,
+    SystemMessagePromptTemplate,
+    load_prompt,
+)
 from langchain_core.messages import AIMessage, HumanMessage
 
 from honcho import Message
@@ -10,84 +14,103 @@
 load_dotenv()
 
 # langchain prompts
-SYSTEM_STATE_COMMENTARY = load_prompt(os.path.join(os.path.dirname(__file__), 'langchain_prompts/state_commentary.yaml'))
-SYSTEM_STATE_LABELING = load_prompt(os.path.join(os.path.dirname(__file__), 'langchain_prompts/state_labeling.yaml'))
-SYSTEM_STATE_CHECK = load_prompt(os.path.join(os.path.dirname(__file__), 'langchain_prompts/state_check.yaml'))
-
-# quick utility function to convert messages from honcho to langchain
-def langchain_message_converter(messages: List[Message]) -> List[Union[AIMessage, HumanMessage]]:
-    new_messages = []
-    for message in messages:
-        if message.is_user:
-            new_messages.append(HumanMessage(content=message.content))
-        else:
-            new_messages.append(AIMessage(content=message.content))
-    return new_messages
+SYSTEM_STATE_COMMENTARY = load_prompt(
+    os.path.join(os.path.dirname(__file__), "langchain_prompts/state_commentary.yaml")
+)
+SYSTEM_STATE_LABELING = load_prompt(
+    os.path.join(os.path.dirname(__file__), "langchain_prompts/state_labeling.yaml")
+)
+SYSTEM_STATE_CHECK = load_prompt(
+    os.path.join(os.path.dirname(__file__), "langchain_prompts/state_check.yaml")
+)
 
 
 # convert chat history and user input into a string
 def format_chat_history(chat_history: List[Message], user_input=None):
-    messages = [("user: " + message.content if isinstance(message, HumanMessage) else "ai: " + message.content) for message in chat_history]
+    messages = [
+        (
+            "user: " + message.content
+            if isinstance(message, HumanMessage)
+            else "ai: " + message.content
+        )
+        for message in chat_history
+    ]
     if user_input:
         messages.append(f"user: {user_input}")
 
     return "\n".join(messages)
 
 
-
 class StateExtractor:
     """Wrapper class for all the DSPy and LangChain code for user state labeling and pipeline optimization"""
-    lc_gpt_4: ChatOpenAI = ChatOpenAI(model_name = "gpt-4")
-    lc_gpt_turbo: ChatOpenAI = ChatOpenAI(model_name = "gpt-3.5-turbo")
-    system_state_commentary: SystemMessagePromptTemplate = SystemMessagePromptTemplate(prompt=SYSTEM_STATE_COMMENTARY)
-    system_state_labeling: SystemMessagePromptTemplate = SystemMessagePromptTemplate(prompt=SYSTEM_STATE_LABELING)
-    system_state_check: SystemMessagePromptTemplate = SystemMessagePromptTemplate(prompt=SYSTEM_STATE_CHECK)
+
+    lc_gpt_4: ChatOpenAI = ChatOpenAI(model_name="gpt-4")
+    lc_gpt_turbo: ChatOpenAI = ChatOpenAI(model_name="gpt-3.5-turbo")
+    system_state_commentary: SystemMessagePromptTemplate = SystemMessagePromptTemplate(
+        prompt=SYSTEM_STATE_COMMENTARY
+    )
+    system_state_labeling: SystemMessagePromptTemplate = SystemMessagePromptTemplate(
+        prompt=SYSTEM_STATE_LABELING
+    )
+    system_state_check: SystemMessagePromptTemplate = SystemMessagePromptTemplate(
+        prompt=SYSTEM_STATE_CHECK
+    )
 
     def __init__(self) -> None:
         pass
 
     @classmethod
-    async def generate_state_commentary(cls, existing_states: List[str], chat_history: List[Message], input: str) -> str:
+    async def generate_state_commentary(
+        cls, existing_states: List[str], chat_history: List[Message], input: str
+    ) -> str:
         """Generate a commentary on the current state of the user"""
         # format existing states
         existing_states = "\n".join(existing_states)
         # format prompt
-        state_commentary = ChatPromptTemplate.from_messages([
-            cls.system_state_commentary
-        ])
+        state_commentary = ChatPromptTemplate.from_messages(
+            [cls.system_state_commentary]
+        )
         # LCEL
         chain = state_commentary | cls.lc_gpt_4
         # inference
-        response = await chain.ainvoke({
-            "chat_history": chat_history,
-            "user_input": input,
-            "existing_states": existing_states,
-        })
+        response = await chain.ainvoke(
+            {
+                "chat_history": chat_history,
+                "user_input": input,
+                "existing_states": existing_states,
+            }
+        )
         # return output
         return response.content
-    
+
     @classmethod
-    async def generate_state_label(cls, existing_states: List[str],  state_commentary: str) -> str:
+    async def generate_state_label(
+        cls, existing_states: List[str], state_commentary: str
+    ) -> str:
         """Generate a state label from a commetary on the user's state"""
         # format existing states
         existing_states = "\n".join(existing_states)
         # format prompt
-        state_labeling = ChatPromptTemplate.from_messages([
-            cls.system_state_labeling,
-        ])
+        state_labeling = ChatPromptTemplate.from_messages(
+            [
+                cls.system_state_labeling,
+            ]
+        )
         # LCEL
         chain = state_labeling | cls.lc_gpt_4
         # inference
-        response = await chain.ainvoke({
-            "state_commentary": state_commentary,
-            "existing_states": existing_states,
-        })
+        response = await chain.ainvoke(
+            {
+                "state_commentary": state_commentary,
+                "existing_states": existing_states,
+            }
+        )
 
         # strip anything that's not letters
-        clean_response =  ''.join(c for c in response.content if c.isalpha())
+        clean_response = "".join(c for c in response.content if c.isalpha())
         # return output
         return clean_response
-    
+
     @classmethod
     async def check_state_exists(cls, existing_states: List[str], state: str):
         """Check if a user state is new or already is stored"""
@@ -96,32 +119,36 @@ async def check_state_exists(cls, existing_states: List[str], state: str):
         existing_states = "\n".join(existing_states)
 
         # format prompt
-        state_check = ChatPromptTemplate.from_messages([
-            cls.system_state_check
-        ])
+        state_check = ChatPromptTemplate.from_messages([cls.system_state_check])
         # LCEL
         chain = state_check | cls.lc_gpt_turbo
         # inference
-        response = await chain.ainvoke({
-            "existing_states": existing_states,
-            "state": state,
-        })
+        response = await chain.ainvoke(
+            {
+                "existing_states": existing_states,
+                "state": state,
+            }
+        )
         # return output
         return response.content
 
     @classmethod
-    async def generate_state(cls, existing_states: List[str], chat_history: List[Message], input: str):
-        """"Determine the user's state from the current conversation state"""
+    async def generate_state(
+        cls, existing_states: List[str], chat_history: List[Message], input: str
+    ):
+        """ "Determine the user's state from the current conversation state"""
 
         # Generate label
-        state_commentary = await cls.generate_state_commentary(existing_states, chat_history, input)
+        state_commentary = await cls.generate_state_commentary(
+            existing_states, chat_history, input
+        )
         state_label = await cls.generate_state_label(existing_states, state_commentary)
 
         # Determine if state is new
         # if True, it doesn't exist, state is new
         # if False, it does exist, state is not new, existing_state was returned
         existing_state = await cls.check_state_exists(existing_states, state_label)
-        is_state_new = existing_state == "None"  
+        is_state_new = existing_state == "None"
 
         # return existing state if we found one
         if is_state_new:
diff --git a/example/discord/honcho-fact-memory/bot.py b/example/discord/honcho-fact-memory/bot.py
index a72e54a..aca2650 100644
--- a/example/discord/honcho-fact-memory/bot.py
+++ b/example/discord/honcho-fact-memory/bot.py
@@ -2,7 +2,8 @@
 from uuid import uuid1
 import discord
 from honcho import Honcho
-from chain import langchain_message_converter, LMChain
+from honcho.ext.langchain import langchain_message_converter
+from chain import LMChain
 
 
 intents = discord.Intents.default()
diff --git a/example/discord/honcho-fact-memory/chain.py b/example/discord/honcho-fact-memory/chain.py
index a2b828b..0c528e1 100644
--- a/example/discord/honcho-fact-memory/chain.py
+++ b/example/discord/honcho-fact-memory/chain.py
@@ -2,7 +2,11 @@
 from typing import List
 from dotenv import load_dotenv
 from langchain_openai import ChatOpenAI
-from langchain_core.prompts import ChatPromptTemplate, SystemMessagePromptTemplate, load_prompt
+from langchain_core.prompts import (
+    ChatPromptTemplate,
+    SystemMessagePromptTemplate,
+    load_prompt,
+)
 from langchain_core.output_parsers import NumberedListOutputParser
 from langchain_core.messages import AIMessage, HumanMessage
 
@@ -10,30 +14,37 @@
 
 load_dotenv()
 
-SYSTEM_DERIVE_FACTS = load_prompt(os.path.join(os.path.dirname(__file__), 'prompts/core/derive_facts.yaml'))
-SYSTEM_INTROSPECTION = load_prompt(os.path.join(os.path.dirname(__file__), 'prompts/core/introspection.yaml'))
-SYSTEM_RESPONSE = load_prompt(os.path.join(os.path.dirname(__file__), 'prompts/core/response.yaml'))
-SYSTEM_CHECK_DUPS = load_prompt(os.path.join(os.path.dirname(__file__), 'prompts/utils/check_dup_facts.yaml'))
-
-
-def langchain_message_converter(messages: List):
-    new_messages = []
-    for message in messages:
-        if message.is_user:
-            new_messages.append(HumanMessage(content=message.content))
-        else:
-            new_messages.append(AIMessage(content=message.content))
-    return new_messages
+SYSTEM_DERIVE_FACTS = load_prompt(
+    os.path.join(os.path.dirname(__file__), "prompts/core/derive_facts.yaml")
+)
+SYSTEM_INTROSPECTION = load_prompt(
+    os.path.join(os.path.dirname(__file__), "prompts/core/introspection.yaml")
+)
+SYSTEM_RESPONSE = load_prompt(
+    os.path.join(os.path.dirname(__file__), "prompts/core/response.yaml")
+)
+SYSTEM_CHECK_DUPS = load_prompt(
+    os.path.join(os.path.dirname(__file__), "prompts/utils/check_dup_facts.yaml")
+)
 
 
 class LMChain:
     "Wrapper class for encapsulating the multiple different chains used"
+
     output_parser = NumberedListOutputParser()
-    llm: ChatOpenAI = ChatOpenAI(model_name = "gpt-3.5-turbo")
-    system_derive_facts: SystemMessagePromptTemplate = SystemMessagePromptTemplate(prompt=SYSTEM_DERIVE_FACTS)
-    system_introspection: SystemMessagePromptTemplate = SystemMessagePromptTemplate(prompt=SYSTEM_INTROSPECTION)
-    system_response: SystemMessagePromptTemplate = SystemMessagePromptTemplate(prompt=SYSTEM_RESPONSE)
-    system_check_dups: SystemMessagePromptTemplate = SystemMessagePromptTemplate(prompt=SYSTEM_CHECK_DUPS)
+    llm: ChatOpenAI = ChatOpenAI(model_name="gpt-3.5-turbo")
+    system_derive_facts: SystemMessagePromptTemplate = SystemMessagePromptTemplate(
+        prompt=SYSTEM_DERIVE_FACTS
+    )
+    system_introspection: SystemMessagePromptTemplate = SystemMessagePromptTemplate(
+        prompt=SYSTEM_INTROSPECTION
+    )
+    system_response: SystemMessagePromptTemplate = SystemMessagePromptTemplate(
+        prompt=SYSTEM_RESPONSE
+    )
+    system_check_dups: SystemMessagePromptTemplate = SystemMessagePromptTemplate(
+        prompt=SYSTEM_CHECK_DUPS
+    )
 
     def __init__(self) -> None:
         pass
@@ -43,18 +54,25 @@ async def derive_facts(cls, chat_history: List, input: str):
         """Derive facts from the user input"""
 
         # format prompt
-        fact_derivation = ChatPromptTemplate.from_messages([
-            cls.system_derive_facts
-        ])
+        fact_derivation = ChatPromptTemplate.from_messages([cls.system_derive_facts])
 
         # LCEL
         chain = fact_derivation | cls.llm
-        
+
         # inference
-        response = await chain.ainvoke({
-            "chat_history": [("user: " + message.content if isinstance(message, HumanMessage) else "ai: " + message.content) for message in chat_history],
-            "user_input": input
-        })
+        response = await chain.ainvoke(
+            {
+                "chat_history": [
+                    (
+                        "user: " + message.content
+                        if isinstance(message, HumanMessage)
+                        else "ai: " + message.content
+                    )
+                    for message in chat_history
+                ],
+                "user_input": input,
+            }
+        )
 
         # parse output
         facts = cls.output_parser.parse(response.content)
@@ -62,28 +80,31 @@ async def derive_facts(cls, chat_history: List, input: str):
         print(f"DERIVED FACTS: {facts}")
 
         return facts
-    
+
     @classmethod
-    async def check_dups(cls, user_message: Message, session: Session, collection: Collection, facts: List):
+    async def check_dups(
+        cls,
+        user_message: Message,
+        session: Session,
+        collection: Collection,
+        facts: List,
+    ):
         """Check that we're not storing duplicate facts"""
 
         # format prompt
-        check_duplication = ChatPromptTemplate.from_messages([
-            cls.system_check_dups
-        ])
+        check_duplication = ChatPromptTemplate.from_messages([cls.system_check_dups])
 
         query = " ".join(facts)
-        result = collection.query(query=query, top_k=10) 
+        result = collection.query(query=query, top_k=10)
         existing_facts = [document.content for document in result]
 
         # LCEL
         chain = check_duplication | cls.llm
 
         # inference
-        response = await chain.ainvoke({
-            "existing_facts": existing_facts,
-            "facts": facts
-        })
+        response = await chain.ainvoke(
+            {"existing_facts": existing_facts, "facts": facts}
+        )
 
         # parse output
         new_facts = cls.output_parser.parse(response.content)
@@ -96,28 +117,30 @@ async def check_dups(cls, user_message: Message, session: Session, collection: C
 
         # add facts as metamessages
         for fact in new_facts:
-            session.create_metamessage(message=user_message, metamessage_type="fact", content=fact)
+            session.create_metamessage(
+                message=user_message, metamessage_type="fact", content=fact
+            )
 
         return
-        
 
     @classmethod
-    async def introspect(cls, user_message: Message, session: Session, chat_history: List, input:str):
+    async def introspect(
+        cls, user_message: Message, session: Session, chat_history: List, input: str
+    ):
         """Generate questions about the user to use as retrieval over the fact store"""
 
         # format prompt
-        introspection_prompt = ChatPromptTemplate.from_messages([
-            cls.system_introspection
-        ])
+        introspection_prompt = ChatPromptTemplate.from_messages(
+            [cls.system_introspection]
+        )
 
         # LCEL
         chain = introspection_prompt | cls.llm
 
         # inference
-        response = await chain.ainvoke({
-            "chat_history": chat_history,
-            "user_input": input
-        })
+        response = await chain.ainvoke(
+            {"chat_history": chat_history, "user_input": input}
+        )
 
         # parse output
         questions = cls.output_parser.parse(response.content)
@@ -126,21 +149,22 @@ async def introspect(cls, user_message: Message, session: Session, chat_history:
 
         # write questions as metamessages
         for question in questions:
-            session.create_metamessage(message=user_message, metamessage_type="introspect", content=question)
+            session.create_metamessage(
+                message=user_message, metamessage_type="introspect", content=question
+            )
 
         return questions
 
-    
     @classmethod
-    async def respond(cls, collection: Collection, chat_history: List, questions: List, input: str):
+    async def respond(
+        cls, collection: Collection, chat_history: List, questions: List, input: str
+    ):
         """Take the facts and chat history and generate a personalized response"""
 
         # format prompt
-        response_prompt = ChatPromptTemplate.from_messages([
-            cls.system_response,
-            *chat_history,
-            HumanMessage(content=input)
-        ])
+        response_prompt = ChatPromptTemplate.from_messages(
+            [cls.system_response, *chat_history, HumanMessage(content=input)]
+        )
 
         retrieved_facts = collection.query(query=questions, top_k=10)
         retrieved_facts_content = [document.content for document in retrieved_facts]
@@ -149,18 +173,29 @@ async def respond(cls, collection: Collection, chat_history: List, questions: Li
         chain = response_prompt | cls.llm
 
         # inference
-        response = await chain.ainvoke({
-            "facts": retrieved_facts_content,
-        })
+        response = await chain.ainvoke(
+            {
+                "facts": retrieved_facts_content,
+            }
+        )
 
         return response.content
-    
+
     @classmethod
-    async def chat(cls, chat_history: List, user_message: Message, session: Session, collection: Collection, input: str):
+    async def chat(
+        cls,
+        chat_history: List,
+        user_message: Message,
+        session: Session,
+        collection: Collection,
+        input: str,
+    ):
         """Chat with the model"""
 
         facts = await cls.derive_facts(chat_history, input)
-        await cls.check_dups(user_message, session, collection, facts) if facts is not None else None
+        await cls.check_dups(
+            user_message, session, collection, facts
+        ) if facts is not None else None
 
         # introspect
         questions = await cls.introspect(user_message, session, chat_history, input)
@@ -169,7 +204,3 @@ async def chat(cls, chat_history: List, user_message: Message, session: Session,
         response = await cls.respond(collection, chat_history, questions, input)
 
         return response
-
-        
-        
-
diff --git a/example/discord/simple-roast-bot/main.py b/example/discord/simple-roast-bot/main.py
index 0f59931..2a7a4b2 100644
--- a/example/discord/simple-roast-bot/main.py
+++ b/example/discord/simple-roast-bot/main.py
@@ -11,6 +11,7 @@
 from langchain_core.messages import AIMessage, HumanMessage
 
 from honcho import Honcho
+from honcho.ext.langchain import langchain_message_converter
 
 load_dotenv()
 
@@ -43,16 +44,6 @@
 chain = prompt | model | output_parser
 
 
-def langchain_message_converter(messages: List):
-    new_messages = []
-    for message in messages:
-        if message.is_user:
-            new_messages.append(HumanMessage(content=message.content))
-        else:
-            new_messages.append(AIMessage(content=message.content))
-    return new_messages
-
-
 @bot.event
 async def on_ready():
     print(f"We have logged in as {bot.user}")
diff --git a/sdk/honcho/ext/__init__.py b/sdk/honcho/ext/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/sdk/honcho/ext/langchain.py b/sdk/honcho/ext/langchain.py
new file mode 100644
index 0000000..c84a0be
--- /dev/null
+++ b/sdk/honcho/ext/langchain.py
@@ -0,0 +1,29 @@
+import functools
+import importlib
+from typing import List
+
+from honcho.schemas import Message
+
+
+def requires_langchain(func):
+    @functools.wraps(func)
+    def wrapper(*args, **kwargs):
+        if importlib.util.find_spec("langchain") is None:
+            raise ImportError("Langchain must be installed to use this feature")
+            # raise RuntimeError("langchain is not installed")
+        return func(*args, **kwargs)
+
+    return wrapper
+
+
+@requires_langchain
+def langchain_message_converter(messages: List[Message]):
+    from langchain_core.messages import AIMessage, HumanMessage
+
+    new_messages = []
+    for message in messages:
+        if message.is_user:
+            new_messages.append(HumanMessage(content=message.content))
+        else:
+            new_messages.append(AIMessage(content=message.content))
+    return new_messages