diff --git a/pyproject.toml b/pyproject.toml
index 4267f65319..5b66f8ba32 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -107,7 +107,6 @@ duckdb = []
 fabric = ["pyodbc>=5.0.0"]
 gcppostgres = ["cloud-sql-python-connector[pg8000]>=1.8.0"]
 github = ["PyGithub>=2.6.0"]
-llm = ["langchain", "openai"]
 motherduck = ["duckdb>=1.2.0"]
 mssql = ["pymssql"]
 mssql-odbc = ["pyodbc>=5.0.0"]
@@ -213,7 +212,6 @@ module = [
     "pymssql.*",
     "pyodbc.*",
     "psycopg2.*",
-    "langchain.*",
     "pytest_lazyfixture.*",
     "dbt.adapters.*",
     "slack_sdk.*",
diff --git a/sqlmesh/cli/main.py b/sqlmesh/cli/main.py
index 2d8673405f..2f18c0a4b7 100644
--- a/sqlmesh/cli/main.py
+++ b/sqlmesh/cli/main.py
@@ -1079,50 +1079,6 @@ def rewrite(obj: Context, sql: str, read: str = "", write: str = "") -> None:
     )
 
 
-@cli.command("prompt")
-@click.argument("prompt")
-@click.option(
-    "-e",
-    "--evaluate",
-    is_flag=True,
-    help="Evaluate the generated SQL query and display the results.",
-)
-@click.option(
-    "-t",
-    "--temperature",
-    type=float,
-    help="Sampling temperature. 0.0 - precise and predictable, 0.5 - balanced, 1.0 - creative. Default: 0.7",
-    default=0.7,
-)
-@opt.verbose
-@click.pass_context
-@error_handler
-@cli_analytics
-def prompt(
-    ctx: click.Context,
-    prompt: str,
-    evaluate: bool,
-    temperature: float,
-    verbose: int,
-) -> None:
-    """Uses LLM to generate a SQL query from a prompt."""
-    from sqlmesh.integrations.llm import LLMIntegration
-
-    context = ctx.obj
-
-    llm_integration = LLMIntegration(
-        context.models.values(),
-        context.engine_adapter.dialect,
-        temperature=temperature,
-        verbosity=Verbosity(verbose),
-    )
-    query = llm_integration.query(prompt)
-
-    context.console.log_status_update(query)
-    if evaluate:
-        context.console.log_success(context.fetchdf(query))
-
-
 @cli.command("clean")
 @click.pass_obj
 @error_handler
diff --git a/sqlmesh/integrations/llm.py b/sqlmesh/integrations/llm.py
deleted file mode 100644
index a44ec79997..0000000000
--- a/sqlmesh/integrations/llm.py
+++ /dev/null
@@ -1,56 +0,0 @@
-from __future__ import annotations
-
-import typing as t
-
-from langchain import LLMChain, PromptTemplate
-from langchain.chat_models import ChatOpenAI
-
-from sqlmesh.utils import Verbosity
-from sqlmesh.core.model import Model
-
-_QUERY_PROMPT_TEMPLATE = """Given an input request, create a syntactically correct {dialect} SQL query.
-Use full table names.
-Convert string operands to lowercase in the WHERE clause.
-Reply with a SQL query and nothing else.
-
-Use the following tables and columns:
-
-{table_info}
-
-Request: {input}"""
-
-
-class LLMIntegration:
-    def __init__(
-        self,
-        models: t.Iterable[Model],
-        dialect: str,
-        temperature: float = 0.7,
-        verbosity: Verbosity = Verbosity.DEFAULT,
-    ):
-        query_prompt_template = PromptTemplate.from_template(_QUERY_PROMPT_TEMPLATE).partial(
-            dialect=dialect, table_info=_to_table_info(models)
-        )
-        llm = ChatOpenAI(temperature=temperature)  # type: ignore
-        self._query_chain = LLMChain(
-            llm=llm, prompt=query_prompt_template, verbose=verbosity >= Verbosity.VERBOSE
-        )
-
-    def query(self, prompt: str) -> str:
-        result = self._query_chain.predict(input=prompt).strip()
-        select_pos = result.find("SELECT")
-        if select_pos >= 0:
-            return result[select_pos:]
-        return result
-
-
-def _to_table_info(models: t.Iterable[Model]) -> str:
-    infos = []
-    for model in models:
-        if not model.kind.is_materialized:
-            continue
-
-        columns_csv = ", ".join(model.columns_to_types_or_raise)
-        infos.append(f"Table: {model.name}\nColumns: {columns_csv}\n")
-
-    return "\n".join(infos)