diff --git a/packages/openassistants/openassistants/contrib/advisor_function.py b/packages/openassistants/openassistants/contrib/advisor_function.py
new file mode 100644
index 0000000..684b53c
--- /dev/null
+++ b/packages/openassistants/openassistants/contrib/advisor_function.py
@@ -0,0 +1,161 @@
+import os
+from typing import Literal, Sequence
+
+import pandas as pd
+import requests
+from openassistants.data_models.function_output import (
+    DataFrameOutput,
+    FunctionOutput,
+    SerializedDataFrame,
+    TextOutput,
+)
+from openassistants.data_models.json_schema import JSONSchema
+from openassistants.functions.base import (
+    BaseFunction,
+    BaseFunctionParameters,
+    FunctionExecutionDependency,
+)
+from openassistants.functions.utils import AsyncStreamVersion
+from pydantic import TypeAdapter
+
+
+async def advisor_query(
+    user_query: str,
+) -> AsyncStreamVersion[Sequence[FunctionOutput]]:
+    outputs = ""
+
+    # Set your Bearer token as an environment variable for security
+    BEARER_TOKEN = os.getenv("ADVISOR_BEARER_TOKEN")
+    ADVISOR_API_BASE = os.getenv("ADVISOR_API_BASE")
+
+    # Headers for the requests
+    HEADERS = {
+        "accept": "application/json",
+        "Authorization": f"Bearer {BEARER_TOKEN}",
+        "Content-Type": "application/json",
+    }
+
+    # Step 1: Find relevant dataset
+    recommend_payload = {"prompt": user_query}
+
+    outputs += "Searching for the relevant dataset...  \n"
+    yield [TextOutput(text=outputs)]
+
+    response = requests.post(
+        ADVISOR_API_BASE + "/v0/datasets/recommend",
+        headers=HEADERS,
+        json=recommend_payload,
+    )
+    if response.status_code != 200:
+        print("Error in recommend step")
+        return
+
+    datasets = response.json().get("datasets")
+    if not datasets:
+        # Override yield
+        yield [TextOutput(text="No relevant datasets found.  \n")]
+        return
+
+    outputs += f"Found relevant dataset: `{datasets[0]['id']}`.  \n"
+    yield [TextOutput(text=outputs)]
+
+    # Step 2: Generate SQL based on the dataset and user query
+    prompt_payload = {"prompt": user_query, "dataset": datasets[0], "timeout": 60}
+
+    outputs += "Generating SQL query...  \n"
+    yield [TextOutput(text=outputs)]
+
+    response = requests.post(
+        ADVISOR_API_BASE + "/v0/datasets/prompt", headers=HEADERS, json=prompt_payload
+    )
+    if response.status_code != 200:
+        print("Error in prompt step")
+        return
+
+    sql_query = response.json().get("response", {}).get("sql")
+    if not sql_query:
+        print("No SQL query found in prompt response")
+        return
+
+    # Step 3: Execute SQL
+    execute_payload = {
+        "query": {"type": "sql", "sql": sql_query},
+        "dataset": datasets[0],
+        "timeout": 60,
+        "mode": "nonblocking",
+    }
+
+    outputs += "Running SQL query...  \n"
+    yield [TextOutput(text=outputs)]
+
+    response = requests.post(
+        ADVISOR_API_BASE + "/v0/datasets/execute", headers=HEADERS, json=execute_payload
+    )
+    if response.status_code != 200 or "error" in response.json():
+        print("Error in execute step")
+        return
+
+    # Parsing the result into a DataFrame
+    result = response.json().get("result", {}).get("dataframe", {})
+    error = response.json().get("error")
+
+    if error:
+        yield [TextOutput(text=f"Failed to execute SQL: {error['message']}")]
+    elif result:
+        columns = result.get("columns")
+        data = result.get("data")
+
+        if not columns or not data:
+            print("No data returned in execute step")
+            return
+
+        # Override yield
+        yield [
+            DataFrameOutput(
+                dataframe=SerializedDataFrame.from_pd(
+                    pd.DataFrame(data, columns=columns)
+                )
+            ),
+            TextOutput(
+                text=(
+                    f"```sql\n{sql_query}\n```\n\n"
+                    "*⚠️ This query was dynamically generated."
+                    " Please verify the results.*"
+                )
+            ),
+        ]
+
+
+class AdvisorFunction(BaseFunction):
+    type: Literal["AdvisorFunction"] = "AdvisorFunction"
+
+    description: str = (
+        "Take a natural language user query and run it against the Advisor"
+        " Text to SQL service. It takes a user question and tries to generate SQL"
+        " and return a dataframe containing the requested information. Only use this"
+        "function if using advisor is explicitly mentioned by the user."
+    )
+    display_name: str = "Advisor Text to SQL"
+
+    parameters: BaseFunctionParameters = BaseFunctionParameters(
+        json_schema=TypeAdapter(JSONSchema).validate_python(
+            {
+                "type": "object",
+                "properties": {
+                    "user_query": {
+                        "type": "string",
+                        "description": "The query to send to the Advisor",  # noqa: E501
+                    }
+                },
+                "required": ["user_query"],
+            }
+        )
+    )
+
+    async def execute(
+        self,
+        deps: FunctionExecutionDependency,
+    ) -> AsyncStreamVersion[Sequence[FunctionOutput]]:
+        user_query = deps.arguments["user_query"]
+        async for output in advisor_query(user_query):
+            yield output
diff --git a/packages/openassistants/openassistants/functions/crud.py b/packages/openassistants/openassistants/functions/crud.py
index e779e75..434c11b 100644
--- a/packages/openassistants/openassistants/functions/crud.py
+++ b/packages/openassistants/openassistants/functions/crud.py
@@ -3,6 +3,7 @@
 from pathlib import Path
 from typing import Annotated, List, Optional
 
+from openassistants.contrib.advisor_function import AdvisorFunction
 from openassistants.contrib.duckdb_query import DuckDBQueryFunction
 from openassistants.contrib.langchain_ddg_tool import DuckDuckGoToolFunction
 from openassistants.contrib.python_eval import PythonEvalFunction
@@ -14,7 +15,12 @@
 from starlette.concurrency import run_in_threadpool
 
 AllFunctionTypes = Annotated[
-    QueryFunction | DuckDBQueryFunction | PythonEvalFunction | DuckDuckGoToolFunction | TextResponseFunction,
+    QueryFunction
+    | DuckDBQueryFunction
+    | PythonEvalFunction
+    | DuckDuckGoToolFunction
+    | TextResponseFunction
+    | AdvisorFunction,
     Field(json_schema_extra={"discriminator": "type"}),
 ]