diff --git a/README.md b/README.md
index c47cc74..71bcac3 100644
--- a/README.md
+++ b/README.md
@@ -29,6 +29,7 @@ Build your AI agents in three lines of code!
 * Extensible Tooling
 * Automatic Tool Workflows
 * Autonomous Operation
+* Structured Outputs
 * Knowledge Base
 * MCP Support
 * Guardrails
@@ -60,6 +61,7 @@ Build your AI agents in three lines of code!
 * Input and output guardrails for content filtering, safety, and data sanitization
 * Generate custom images based on text prompts with storage on S3 compatible services
 * Automatic sequential tool workflows allowing agents to chain multiple tools
+* Deterministically return structured outputs
 * Combine with event-driven systems to create autonomous agents
 
 ## Stack
@@ -306,6 +308,38 @@ async for response in solana_agent.process("user123", "What is in this image? De
     print(response, end="")
 ```
 
+### Structured Outputs
+
+```python
+from solana_agent import SolanaAgent
+
+config = {
+    "openai": {
+        "api_key": "your-openai-api-key",
+    },
+    "agents": [
+        {
+            "name": "researcher",
+            "instructions": "You are a research expert.",
+            "specialization": "Researcher",
+        }
+    ],
+}
+
+solana_agent = SolanaAgent(config=config)
+
+class ResearchProposal(BaseModel):
+    title: str
+    abstract: str
+    key_points: list[str]
+
+full_response = None
+async for response in solana_agent.process("user123", "Research the life of Ben Franklin - the founding Father.", output_model=ResearchProposal):
+    full_response = response
+
+print(full_response.model_dump())
+```
+
 ### Command Line Interface (CLI)
 
 Solana Agent includes a command-line interface (CLI) for text-based chat using a configuration file.
@@ -540,6 +574,8 @@ async for response in solana_agent.process("user123", "Summarize the annual repo
 
 Guardrails allow you to process and potentially modify user input before it reaches the agent (Input Guardrails) and agent output before it's sent back to the user (Output Guardrails). This is useful for implementing safety checks, content moderation, data sanitization, or custom transformations.
 
+Guardrails don't work with structured outputs.
+
 Solana Agent provides a built-in PII scrubber based on [scrubadub](https://github.com/LeapBeyond/scrubadub).
 
 ```python
@@ -580,6 +616,8 @@ config = {
 
 #### Example Custom Guardrails
 
+Guardrails don't work with structured outputs.
+
 ```python
 from solana_agent import InputGuardrail, OutputGuardrail
 import logging
diff --git a/docs/index.rst b/docs/index.rst
index be14603..bb8d22f 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -234,6 +234,40 @@ Image/Text Streaming
       print(response, end="")
 
 
+Structured Outputs
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: python
+
+   from solana_agent import SolanaAgent
+
+   config = {
+      "openai": {
+         "api_key": "your-openai-api-key",
+      },
+      "agents": [
+         {
+               "name": "researcher",
+               "instructions": "You are a research expert.",
+               "specialization": "Researcher",
+         }
+      ],
+   }
+
+   solana_agent = SolanaAgent(config=config)
+
+   class ResearchProposal(BaseModel):
+      title: str
+      abstract: str
+      key_points: list[str]
+
+   full_response = None
+   async for response in solana_agent.process("user123", "Research the life of Ben Franklin - the founding Father.", output_model=ResearchProposal):
+      full_response = response
+
+   print(full_response.model_dump())
+
+
 Command Line Interface (CLI)
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -474,6 +508,8 @@ Guardrails - Optional
 
 Guardrails allow you to process and potentially modify user input before it reaches the agent (Input Guardrails) and agent output before it's sent back to the user (Output Guardrails). This is useful for implementing safety checks, content moderation, data sanitization, or custom transformations.
 
+Guardrails don't apply to structured outputs.
+
 Solana Agent provides a built-in PII scrubber based on scrubadub.
 
 .. code-block:: python
@@ -515,6 +551,8 @@ Solana Agent provides a built-in PII scrubber based on scrubadub.
 Example Custom Guardrails - Optional
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
+Guardrails don't apply to structured outputs.
+
 .. code-block:: python
 
    from solana_agent import InputGuardrail, OutputGuardrail
diff --git a/poetry.lock b/poetry.lock
index c25767e..880a637 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -14,98 +14,98 @@ files = [
 
 [[package]]
 name = "aiohttp"
-version = "3.12.2"
+version = "3.12.6"
 description = "Async http client/server framework (asyncio)"
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
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+    {file = "aiohttp-3.12.6-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:2cd7c7018cee1638fc64cbdceb47c870985ce5650161c7e3c5b578850f74b113"},
+    {file = "aiohttp-3.12.6-cp39-cp39-win32.whl", hash = "sha256:79ab680ff7dd0b6c36073738b5f6336e2f018fc07ef0486dd7dd68b2e888ce46"},
+    {file = "aiohttp-3.12.6-cp39-cp39-win_amd64.whl", hash = "sha256:a68cb45d2b01f1599e762d382ddac7c6bd62c95210db339827e973a7ba61673c"},
+    {file = "aiohttp-3.12.6.tar.gz", hash = "sha256:37b1c6034a1e14764adad1829cd710543b1699d7985e1d336f0aa52a2dd76ba9"},
 ]
 
 [package.dependencies]
@@ -1195,14 +1195,14 @@ files = [
 
 [[package]]
 name = "llama-index-core"
-version = "0.12.37"
+version = "0.12.39"
 description = "Interface between LLMs and your data"
 optional = false
 python-versions = "<4.0,>=3.9"
 groups = ["main"]
 files = [
-    {file = "llama_index_core-0.12.37-py3-none-any.whl", hash = "sha256:1a841db0dcdace161d2fcecfee0ab94d28e50196973974c5142f0d18d3f7663f"},
-    {file = "llama_index_core-0.12.37.tar.gz", hash = "sha256:c2c13c36229bc9cfffc7bf1daca888e382d5ae484af96273c53f001ed84dc253"},
+    {file = "llama_index_core-0.12.39-py3-none-any.whl", hash = "sha256:c255ed87aa85e43893f2bb05870b61ce7701d7a6a931d174ba925def5856b4c2"},
+    {file = "llama_index_core-0.12.39.tar.gz", hash = "sha256:0cca9de59953542a3c2f1db61327c5204e0b1e997f31f1200e49392b2879593a"},
 ]
 
 [package.dependencies]
@@ -1626,23 +1626,24 @@ files = [
 
 [[package]]
 name = "networkx"
-version = "3.4.2"
+version = "3.5"
 description = "Python package for creating and manipulating graphs and networks"
 optional = false
-python-versions = ">=3.10"
+python-versions = ">=3.11"
 groups = ["main"]
 files = [
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+    {file = "networkx-3.5-py3-none-any.whl", hash = "sha256:0030d386a9a06dee3565298b4a734b68589749a544acbb6c412dc9e2489ec6ec"},
+    {file = "networkx-3.5.tar.gz", hash = "sha256:d4c6f9cf81f52d69230866796b82afbccdec3db7ae4fbd1b65ea750feed50037"},
 ]
 
 [package.extras]
-default = ["matplotlib (>=3.7)", "numpy (>=1.24)", "pandas (>=2.0)", "scipy (>=1.10,!=1.11.0,!=1.11.1)"]
-developer = ["changelist (==0.5)", "mypy (>=1.1)", "pre-commit (>=3.2)", "rtoml"]
-doc = ["intersphinx-registry", "myst-nb (>=1.1)", "numpydoc (>=1.8.0)", "pillow (>=9.4)", "pydata-sphinx-theme (>=0.15)", "sphinx (>=7.3)", "sphinx-gallery (>=0.16)", "texext (>=0.6.7)"]
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+default = ["matplotlib (>=3.8)", "numpy (>=1.25)", "pandas (>=2.0)", "scipy (>=1.11.2)"]
+developer = ["mypy (>=1.15)", "pre-commit (>=4.1)"]
+doc = ["intersphinx-registry", "myst-nb (>=1.1)", "numpydoc (>=1.8.0)", "pillow (>=10)", "pydata-sphinx-theme (>=0.16)", "sphinx (>=8.0)", "sphinx-gallery (>=0.18)", "texext (>=0.6.7)"]
+example = ["cairocffi (>=1.7)", "contextily (>=1.6)", "igraph (>=0.11)", "momepy (>=0.7.2)", "osmnx (>=2.0.0)", "scikit-learn (>=1.5)", "seaborn (>=0.13)"]
 extra = ["lxml (>=4.6)", "pydot (>=3.0.1)", "pygraphviz (>=1.14)", "sympy (>=1.10)"]
-test = ["pytest (>=7.2)", "pytest-cov (>=4.0)"]
+test = ["pytest (>=7.2)", "pytest-cov (>=4.0)", "pytest-xdist (>=3.0)"]
+test-extras = ["pytest-mpl", "pytest-randomly"]
 
 [[package]]
 name = "nltk"
@@ -1737,14 +1738,14 @@ files = [
 
 [[package]]
 name = "openai"
-version = "1.82.0"
+version = "1.82.1"
 description = "The official Python library for the openai API"
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
 files = [
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+    {file = "openai-1.82.1-py3-none-any.whl", hash = "sha256:334eb5006edf59aa464c9e932b9d137468d810b2659e5daea9b3a8c39d052395"},
+    {file = "openai-1.82.1.tar.gz", hash = "sha256:ffc529680018e0417acac85f926f92aa0bbcbc26e82e2621087303c66bc7f95d"},
 ]
 
 [package.dependencies]
@@ -1882,14 +1883,14 @@ opentelemetry-api = "1.33.1"
 
 [[package]]
 name = "packaging"
-version = "25.0"
+version = "24.2"
 description = "Core utilities for Python packages"
 optional = false
 python-versions = ">=3.8"
 groups = ["main", "dev"]
 files = [
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 ]
 
 [[package]]
@@ -2006,18 +2007,19 @@ xmp = ["defusedxml"]
 
 [[package]]
 name = "pinecone"
-version = "7.0.1"
+version = "7.0.2"
 description = "Pinecone client and SDK"
 optional = false
 python-versions = "<4.0,>=3.9"
 groups = ["main"]
 files = [
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+    {file = "pinecone-7.0.2-py3-none-any.whl", hash = "sha256:c112c6be314178da13701e92df77d9e32db4f92b5e8a4d9b3e0b071abc49fe39"},
+    {file = "pinecone-7.0.2.tar.gz", hash = "sha256:ff17f6ac798979adf0df0817e8f1d356a3363768f42e341b2a47f2bf895eb46a"},
 ]
 
 [package.dependencies]
 certifi = ">=2019.11.17"
+pinecone-plugin-assistant = ">=1.6.0,<2.0.0"
 pinecone-plugin-interface = ">=0.0.7,<0.0.8"
 python-dateutil = ">=2.5.3"
 typing-extensions = ">=3.7.4"
@@ -2027,6 +2029,22 @@ urllib3 = {version = ">=1.26.5", markers = "python_version >= \"3.12\" and pytho
 asyncio = ["aiohttp (>=3.9.0)", "aiohttp-retry (>=2.9.1,<3.0.0)"]
 grpc = ["googleapis-common-protos (>=1.66.0)", "grpcio (>=1.44.0) ; python_version >= \"3.8\" and python_version < \"3.11\"", "grpcio (>=1.59.0) ; python_version >= \"3.11\" and python_version < \"4.0\"", "grpcio (>=1.68.0) ; python_version >= \"3.13\" and python_version < \"4.0\"", "lz4 (>=3.1.3)", "protobuf (>=5.29,<6.0)", "protoc-gen-openapiv2 (>=0.0.1,<0.0.2)"]
 
+[[package]]
+name = "pinecone-plugin-assistant"
+version = "1.6.1"
+description = "Assistant plugin for Pinecone SDK"
+optional = false
+python-versions = "<4.0,>=3.9"
+groups = ["main"]
+files = [
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+    {file = "pinecone_plugin_assistant-1.6.1.tar.gz", hash = "sha256:d05826327bd8b715451b3b2c84ce335c41cb23fd7d9fb9f92f3e9965d5bab06b"},
+]
+
+[package.dependencies]
+packaging = ">=24.2,<25.0"
+requests = ">=2.32.3,<3.0.0"
+
 [[package]]
 name = "pinecone-plugin-interface"
 version = "0.0.7"
@@ -2182,23 +2200,23 @@ files = [
 
 [[package]]
 name = "protobuf"
-version = "5.29.4"
+version = "5.29.5"
 description = ""
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
 files = [
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 ]
 
 [[package]]
@@ -2810,30 +2828,30 @@ test = ["pytest (>=8)"]
 
 [[package]]
 name = "ruff"
-version = "0.11.11"
+version = "0.11.12"
 description = "An extremely fast Python linter and code formatter, written in Rust."
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 python-versions = ">=3.7"
 groups = ["dev"]
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+    {file = "ruff-0.11.12-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:08033320e979df3b20dba567c62f69c45e01df708b0f9c83912d7abd3e0801cd"},
+    {file = "ruff-0.11.12-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:929b7706584f5bfd61d67d5070f399057d07c70585fa8c4491d78ada452d3bef"},
+    {file = "ruff-0.11.12-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:7de4a73205dc5756b8e09ee3ed67c38312dce1aa28972b93150f5751199981b5"},
+    {file = "ruff-0.11.12-py3-none-musllinux_1_2_i686.whl", hash = "sha256:2635c2a90ac1b8ca9e93b70af59dfd1dd2026a40e2d6eebaa3efb0465dd9cf02"},
+    {file = "ruff-0.11.12-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:d05d6a78a89166f03f03a198ecc9d18779076ad0eec476819467acb401028c0c"},
+    {file = "ruff-0.11.12-py3-none-win32.whl", hash = "sha256:f5a07f49767c4be4772d161bfc049c1f242db0cfe1bd976e0f0886732a4765d6"},
+    {file = "ruff-0.11.12-py3-none-win_amd64.whl", hash = "sha256:5a4d9f8030d8c3a45df201d7fb3ed38d0219bccd7955268e863ee4a115fa0832"},
+    {file = "ruff-0.11.12-py3-none-win_arm64.whl", hash = "sha256:65194e37853158d368e333ba282217941029a28ea90913c67e558c611d04daa5"},
+    {file = "ruff-0.11.12.tar.gz", hash = "sha256:43cf7f69c7d7c7d7513b9d59c5d8cafd704e05944f978614aa9faff6ac202603"},
 ]
 
 [[package]]
@@ -3328,14 +3346,14 @@ sqlcipher = ["sqlcipher3_binary"]
 
 [[package]]
 name = "starlette"
-version = "0.46.2"
+version = "0.47.0"
 description = "The little ASGI library that shines."
 optional = false
 python-versions = ">=3.9"
 groups = ["dev"]
 files = [
-    {file = "starlette-0.46.2-py3-none-any.whl", hash = "sha256:595633ce89f8ffa71a015caed34a5b2dc1c0cdb3f0f1fbd1e69339cf2abeec35"},
-    {file = "starlette-0.46.2.tar.gz", hash = "sha256:7f7361f34eed179294600af672f565727419830b54b7b084efe44bb82d2fccd5"},
+    {file = "starlette-0.47.0-py3-none-any.whl", hash = "sha256:9d052d4933683af40ffd47c7465433570b4949dc937e20ad1d73b34e72f10c37"},
+    {file = "starlette-0.47.0.tar.gz", hash = "sha256:1f64887e94a447fed5f23309fb6890ef23349b7e478faa7b24a851cd4eb844af"},
 ]
 
 [package.dependencies]
@@ -3997,4 +4015,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.12,<4.0"
-content-hash = "aad771f7971f8d8754c5f86088ecb9de0385fd320324ce415c0d6ae42bc14c46"
+content-hash = "90e9ffe443824c3b2a5bc8a01eef9f2c9a6469249e04162fcceab80f7c96b17e"
diff --git a/pyproject.toml b/pyproject.toml
index ffcc499..341975b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "solana-agent"
-version = "29.2.3"
+version = "29.3.0"
 description = "AI Agents for Solana"
 authors = ["Bevan Hunt <bevan@bevanhunt.com>"]
 license = "MIT"
@@ -24,13 +24,13 @@ python_paths = [".", "tests"]
 
 [tool.poetry.dependencies]
 python = ">=3.12,<4.0"
-openai = "1.82.0"
+openai = "1.82.1"
 pydantic = ">=2"
 pymongo = "4.13.0"
 zep-cloud = "2.12.3"
 instructor = "1.8.3"
-pinecone = "7.0.1"
-llama-index-core = "0.12.37"
+pinecone = "7.0.2"
+llama-index-core = "0.12.39"
 llama-index-embeddings-openai = "0.3.1"
 pypdf = "5.5.0"
 scrubadub = "2.0.1"
diff --git a/solana_agent/adapters/openai_adapter.py b/solana_agent/adapters/openai_adapter.py
index dd7611b..5579789 100644
--- a/solana_agent/adapters/openai_adapter.py
+++ b/solana_agent/adapters/openai_adapter.py
@@ -410,6 +410,8 @@ async def parse_structured_output(
         api_key: Optional[str] = None,
         base_url: Optional[str] = None,
         model: Optional[str] = None,
+        functions: Optional[List[Dict[str, Any]]] = None,
+        function_call: Optional[Union[str, Dict[str, Any]]] = None,
     ) -> T:  # pragma: no cover
         """Generate structured output using Pydantic model parsing with Instructor."""
 
@@ -431,13 +433,18 @@ async def parse_structured_output(
 
             patched_client = instructor.from_openai(client, mode=Mode.TOOLS_STRICT)
 
-            # Use instructor's structured generation with function calling
-            response = await patched_client.chat.completions.create(
-                model=current_parse_model,  # Use the determined model
-                messages=messages,
-                response_model=model_class,
-                max_retries=2,  # Automatically retry on validation errors
-            )
+            create_args = {
+                "model": current_parse_model,
+                "messages": messages,
+                "response_model": model_class,
+                "max_retries": 2,  # Automatically retry on validation errors
+            }
+            if functions:
+                create_args["tools"] = functions
+            if function_call:
+                create_args["function_call"] = function_call
+
+            response = await patched_client.chat.completions.create(**create_args)
             return response
         except Exception as e:
             logger.warning(
diff --git a/solana_agent/client/solana_agent.py b/solana_agent/client/solana_agent.py
index f99fe09..38e550b 100644
--- a/solana_agent/client/solana_agent.py
+++ b/solana_agent/client/solana_agent.py
@@ -7,7 +7,9 @@
 
 import json
 import importlib.util
-from typing import AsyncGenerator, Dict, Any, List, Literal, Optional, Union
+from typing import AsyncGenerator, Dict, Any, List, Literal, Optional, Type, Union
+
+from pydantic import BaseModel
 
 from solana_agent.factories.agent_factory import SolanaAgentFactory
 from solana_agent.interfaces.client.client import SolanaAgent as SolanaAgentInterface
@@ -69,7 +71,8 @@ async def process(
         ] = "mp4",
         router: Optional[RoutingInterface] = None,
         images: Optional[List[Union[str, bytes]]] = None,
-    ) -> AsyncGenerator[Union[str, bytes], None]:  # pragma: no cover
+        output_model: Optional[Type[BaseModel]] = None,
+    ) -> AsyncGenerator[Union[str, bytes, BaseModel], None]:  # pragma: no cover
         """Process a user message (text or audio) and optional images, returning the response stream.
 
         Args:
@@ -83,6 +86,7 @@ async def process(
             audio_input_format: Audio input format
             router: Optional routing service for processing
             images: Optional list of image URLs (str) or image bytes.
+            output_model: Optional Pydantic model for structured output
 
         Returns:
             Async generator yielding response chunks (text strings or audio bytes)
@@ -98,6 +102,7 @@ async def process(
             audio_input_format=audio_input_format,
             prompt=prompt,
             router=router,
+            output_model=output_model,
         ):
             yield chunk
 
diff --git a/solana_agent/interfaces/client/client.py b/solana_agent/interfaces/client/client.py
index e298cc2..22e3786 100644
--- a/solana_agent/interfaces/client/client.py
+++ b/solana_agent/interfaces/client/client.py
@@ -1,5 +1,7 @@
 from abc import ABC, abstractmethod
-from typing import AsyncGenerator, Dict, Any, List, Literal, Optional, Union
+from typing import AsyncGenerator, Dict, Any, List, Literal, Optional, Type, Union
+
+from pydantic import BaseModel
 from solana_agent.interfaces.plugins.plugins import Tool
 from solana_agent.interfaces.services.routing import RoutingService as RoutingInterface
 
@@ -35,7 +37,8 @@ async def process(
         ] = "mp4",
         router: Optional[RoutingInterface] = None,
         images: Optional[List[Union[str, bytes]]] = None,
-    ) -> AsyncGenerator[Union[str, bytes], None]:
+        output_model: Optional[Type[BaseModel]] = None,
+    ) -> AsyncGenerator[Union[str, bytes, BaseModel], None]:
         """Process a user message and return the response stream."""
         pass
 
diff --git a/solana_agent/interfaces/providers/llm.py b/solana_agent/interfaces/providers/llm.py
index b25f878..1e38e46 100644
--- a/solana_agent/interfaces/providers/llm.py
+++ b/solana_agent/interfaces/providers/llm.py
@@ -43,6 +43,8 @@ async def parse_structured_output(
         api_key: Optional[str] = None,
         base_url: Optional[str] = None,
         model: Optional[str] = None,
+        functions: Optional[List[Dict[str, Any]]] = None,
+        function_call: Optional[Union[str, Dict[str, Any]]] = None,
     ) -> T:
         """Generate structured output using a specific model class."""
         pass
diff --git a/solana_agent/interfaces/services/agent.py b/solana_agent/interfaces/services/agent.py
index 0dc3f44..9410ddd 100644
--- a/solana_agent/interfaces/services/agent.py
+++ b/solana_agent/interfaces/services/agent.py
@@ -1,5 +1,7 @@
 from abc import ABC, abstractmethod
-from typing import Any, AsyncGenerator, Dict, List, Literal, Optional, Union
+from typing import Any, AsyncGenerator, Dict, List, Literal, Optional, Type, Union
+
+from pydantic import BaseModel
 
 from solana_agent.domains.agent import AIAgent
 
@@ -45,7 +47,8 @@ async def generate_response(
         ] = "aac",
         prompt: Optional[str] = None,
         images: Optional[List[Union[str, bytes]]] = None,
-    ) -> AsyncGenerator[Union[str, bytes], None]:
+        output_model: Optional[Type[BaseModel]] = None,
+    ) -> AsyncGenerator[Union[str, bytes, BaseModel], None]:
         """Generate a response from an agent."""
         pass
 
diff --git a/solana_agent/interfaces/services/query.py b/solana_agent/interfaces/services/query.py
index 9be4fcd..51cdcb3 100644
--- a/solana_agent/interfaces/services/query.py
+++ b/solana_agent/interfaces/services/query.py
@@ -1,5 +1,7 @@
 from abc import ABC, abstractmethod
-from typing import Any, AsyncGenerator, Dict, List, Literal, Optional, Union
+from typing import Any, AsyncGenerator, Dict, List, Literal, Optional, Type, Union
+
+from pydantic import BaseModel
 
 from solana_agent.interfaces.services.routing import RoutingService as RoutingInterface
 
@@ -35,7 +37,8 @@ async def process(
         prompt: Optional[str] = None,
         router: Optional[RoutingInterface] = None,
         images: Optional[List[Union[str, bytes]]] = None,
-    ) -> AsyncGenerator[Union[str, bytes], None]:
+        output_model: Optional[Type[BaseModel]] = None,
+    ) -> AsyncGenerator[Union[str, bytes, BaseModel], None]:
         """Process the user request and generate a response."""
         pass
 
diff --git a/solana_agent/services/agent.py b/solana_agent/services/agent.py
index e3d0dc3..bd0a293 100644
--- a/solana_agent/services/agent.py
+++ b/solana_agent/services/agent.py
@@ -10,7 +10,9 @@
 import json
 import logging  # Add logging
 import re
-from typing import AsyncGenerator, Dict, List, Literal, Optional, Any, Union
+from typing import AsyncGenerator, Dict, List, Literal, Optional, Any, Type, Union
+
+from pydantic import BaseModel
 
 from solana_agent.interfaces.services.agent import AgentService as AgentServiceInterface
 from solana_agent.interfaces.providers.llm import LLMProvider
@@ -229,8 +231,9 @@ async def generate_response(
             "mp3", "opus", "aac", "flac", "wav", "pcm"
         ] = "aac",
         prompt: Optional[str] = None,
-    ) -> AsyncGenerator[Union[str, bytes], None]:  # pragma: no cover
-        """Generate a response using OpenAI function calling (tools API) via generate_text."""
+        output_model: Optional[Type[BaseModel]] = None,
+    ) -> AsyncGenerator[Union[str, bytes, BaseModel], None]:  # pragma: no cover
+        """Generate a response using OpenAI function calling (tools API) or structured output."""
 
         agent = next((a for a in self.agents if a.name == agent_name), None)
         if not agent:
@@ -252,9 +255,7 @@ async def generate_response(
         system_prompt = self.get_agent_system_prompt(agent_name)
         user_content = str(query)
         if images:
-            user_content += (
-                "\n\n[Images attached]"  # Optionally, handle images as needed
-            )
+            user_content += "\n\n[Images attached]"
 
         # Compose the prompt for generate_text
         full_prompt = ""
@@ -266,18 +267,33 @@ async def generate_response(
         full_prompt += f"USER IDENTIFIER: {user_id}"
 
         # Get OpenAI function schemas for this agent's tools
-        functions = []
-        for tool in self.get_agent_tools(agent_name):
-            functions.append(
-                {
-                    "name": tool["name"],
-                    "description": tool.get("description", ""),
-                    "parameters": tool.get("parameters", {}),
-                }
-            )
+        functions = [
+            {
+                "name": tool["name"],
+                "description": tool.get("description", ""),
+                "parameters": tool.get("parameters", {}),
+            }
+            for tool in self.get_agent_tools(agent_name)
+        ]
 
-        response_text = ""
         try:
+            if output_model is not None:
+                # --- Structured output with tool support ---
+                model_instance = await self.llm_provider.parse_structured_output(
+                    prompt=full_prompt,
+                    system_prompt=system_prompt,
+                    model_class=output_model,
+                    api_key=self.api_key,
+                    base_url=self.base_url,
+                    model=self.model,
+                    functions=functions if functions else None,
+                    function_call="auto" if functions else None,
+                )
+                yield model_instance
+                return
+
+            # --- Streaming text/audio with tool support (as before) ---
+            response_text = ""
             while True:
                 response = await self.llm_provider.generate_text(
                     prompt=full_prompt,
@@ -298,9 +314,7 @@ async def generate_response(
                     break
 
                 choice = response.choices[0]
-                message = getattr(
-                    choice, "message", choice
-                )  # Support both OpenAI and instructor
+                message = getattr(choice, "message", choice)
 
                 # If the model wants to call a function/tool
                 if hasattr(message, "function_call") and message.function_call:
@@ -316,7 +330,6 @@ async def generate_response(
                     )
 
                     # Add the tool result to the prompt for the next round
-                    # (You may want to format this differently for your use case)
                     full_prompt += (
                         f"\n\nTool '{function_name}' was called with arguments {arguments}.\n"
                         f"Result: {tool_result}\n"
@@ -381,6 +394,7 @@ def _clean_for_audio(self, text: str) -> str:
 
         if not text:
             return ""
+        text = text.replace("’", "'").replace("‘", "'")
         text = re.sub(r"\[([^\]]+)\]\([^\)]+\)", r"\1", text)
         text = re.sub(r"`([^`]+)`", r"\1", text)
         text = re.sub(r"(\*\*|__)(.*?)\1", r"\2", text)
@@ -411,9 +425,7 @@ def _clean_for_audio(self, text: str) -> str:
             flags=re.UNICODE,
         )
         text = emoji_pattern.sub(r" ", text)
-        text = re.sub(
-            r"[^\w\s\.\,\;\:\?\!\'\"\-\(\)]", " ", text
-        )  # Keep basic punctuation
+        text = re.sub(r"[^\w\s\.\,\;\:\?\!\'\"\-\(\)]", " ", text)
         text = re.sub(r"\s+", " ", text)
         return text.strip()
 
diff --git a/solana_agent/services/query.py b/solana_agent/services/query.py
index b48a387..1ccb764 100644
--- a/solana_agent/services/query.py
+++ b/solana_agent/services/query.py
@@ -7,7 +7,9 @@
 """
 
 import logging
-from typing import Any, AsyncGenerator, Dict, List, Literal, Optional, Union
+from typing import Any, AsyncGenerator, Dict, List, Literal, Optional, Type, Union
+
+from pydantic import BaseModel
 
 # Interface imports
 from solana_agent.interfaces.services.query import QueryService as QueryServiceInterface
@@ -86,7 +88,8 @@ async def process(
         ] = "mp4",
         prompt: Optional[str] = None,
         router: Optional[RoutingServiceInterface] = None,
-    ) -> AsyncGenerator[Union[str, bytes], None]:  # pragma: no cover
+        output_model: Optional[Type[BaseModel]] = None,
+    ) -> AsyncGenerator[Union[str, bytes, BaseModel], None]:  # pragma: no cover
         """Process the user request with appropriate agent and apply input guardrails.
 
         Args:
@@ -100,6 +103,7 @@ async def process(
             audio_input_format: Audio input format
             prompt: Optional prompt for the agent
             router: Optional routing service for processing
+            output_model: Optional Pydantic model for structured output
 
         Yields:
             Response chunks (text strings or audio bytes)
@@ -267,9 +271,11 @@ async def process(
                     memory_context=combined_context,
                     output_format="text",
                     prompt=prompt,
+                    output_model=output_model,
                 ):
                     yield chunk
-                    full_text_response += chunk
+                    if output_model is None:
+                        full_text_response += chunk
 
                 # Store conversation using processed user_text
                 # Note: Storing images in history is not directly supported by current memory provider interface