diff --git a/.github/workflows/test_examples.yml b/.github/workflows/test_examples.yml
new file mode 100644
index 00000000..dada60dd
--- /dev/null
+++ b/.github/workflows/test_examples.yml
@@ -0,0 +1,69 @@
+name: Examples (documentation)
+
+on:
+  push:
+    branches: [ main ]
+  pull_request:
+    branches: [ main ]
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    timeout-minutes: 15
+    steps:
+    - name: Checkout
+      uses: actions/checkout@v4
+    - name: Python
+      uses: actions/setup-python@v5
+      with:
+        python-version: '3.12'
+
+    - name: Set permissions for log directory
+      run: |
+        mkdir -p /home/runner/.letta/logs
+        sudo chown -R $USER:$USER /home/runner/.letta/logs
+        chmod -R 755 /home/runner/.letta/logs
+
+    - name: Build and run docker dev server
+      env:
+        LETTA_PG_DB:   letta
+        LETTA_PG_USER: letta
+        LETTA_PG_PASSWORD: letta
+        LETTA_PG_PORT: 8888
+        OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+
+      run: docker compose -f dev-compose.yaml up --build -d
+    #- name: "Setup Python, Poetry and Dependencies"
+    #  uses: packetcoders/action-setup-cache-python-poetry@v1.2.0
+    #  with:
+    #    python-version: "3.12"
+    #    poetry-version: "1.8.2"
+    #    install-args: "--all-extras"
+
+    - name: Wait for service
+      run: bash scripts/wait_for_service.sh http://localhost:8283 -- echo "Service is ready"
+
+    - name: Run tests with pytest
+      env:
+        LETTA_PG_DB:   letta
+        LETTA_PG_USER: letta
+        LETTA_PG_PASSWORD: letta
+        LETTA_PG_PORT: 8888
+        LETTA_SERVER_PASS: test_server_token
+        LETTA_SERVER_URL: http://localhost:8283
+        OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+        PYTHONPATH: ${{ github.workspace }}:${{ env.PYTHONPATH }}
+      run: |
+        pipx install poetry==1.8.2
+        poetry install -E dev -E postgres -E external-tools
+        poetry run python examples/docs/agent_advanced.py
+        poetry run python examples/docs/agent_basic.py
+        poetry run python examples/docs/memory.py
+        poetry run python examples/docs/rest_client.py
+        poetry run python examples/docs/tools.py
+
+    - name: Print docker logs if tests fail
+      if: failure()
+      run: |
+        echo "Printing Docker Logs..."
+        docker compose -f dev-compose.yaml logs
diff --git a/examples/crewai_tool_usage.py b/examples/crewai_tool_usage.py
index ddd2715e..dfd8f361 100644
--- a/examples/crewai_tool_usage.py
+++ b/examples/crewai_tool_usage.py
@@ -11,6 +11,10 @@ This example show how you can add CrewAI tools .
 
 First, make sure you have CrewAI and some of the extras downloaded.
 ```
+# from pypi
+pip install 'letta[external-tools]'
+
+# from source
 poetry install --extras "external-tools"
 ```
 then setup letta with `letta configure`.
diff --git a/examples/docs/agent_advanced.py b/examples/docs/agent_advanced.py
new file mode 100644
index 00000000..a42f5722
--- /dev/null
+++ b/examples/docs/agent_advanced.py
@@ -0,0 +1,40 @@
+from letta import ChatMemory, EmbeddingConfig, LLMConfig, create_client
+from letta.prompts import gpt_system
+
+client = create_client()
+
+# create a new agent
+agent_state = client.create_agent(
+    name="agent_name",
+    memory=ChatMemory(human="Name: Sarah", persona="You are a helpful assistant that loves emojis"),
+    llm_config=LLMConfig(
+        model="gpt-4",
+        model_endpoint_type="openai",
+        model_endpoint="https://api.openai.com/v1",
+        context_window=8000,
+    ),
+    embedding_config=EmbeddingConfig(
+        embedding_endpoint_type="openai",
+        embedding_endpoint="https://api.openai.com/v1",
+        embedding_model="text-embedding-ada-002",
+        embedding_dim=1536,
+        embedding_chunk_size=300,
+    ),
+    system=gpt_system.get_system_text("memgpt_chat"),
+    tools=[],
+    include_base_tools=True,
+)
+print(f"Created agent with name {agent_state.name} and unique ID {agent_state.id}")
+
+# message an agent as a user
+response = client.send_message(agent_id=agent_state.id, role="user", message="hello")
+print("Usage", response.usage)
+print("Agent messages", response.messages)
+
+# message a system message (non-user)
+response = client.send_message(agent_id=agent_state.id, role="system", message="[system] user has logged in. send a friendly message.")
+print("Usage", response.usage)
+print("Agent messages", response.messages)
+
+# delete the agent
+client.delete_agent(agent_id=agent_state.id)
diff --git a/examples/docs/agent_basic.py b/examples/docs/agent_basic.py
new file mode 100644
index 00000000..6f2195e7
--- /dev/null
+++ b/examples/docs/agent_basic.py
@@ -0,0 +1,40 @@
+from letta import EmbeddingConfig, LLMConfig, create_client
+
+client = create_client()
+
+# set automatic defaults for LLM/embedding config
+client.set_default_llm_config(
+    LLMConfig(model="gpt-4o-mini", model_endpoint_type="openai", model_endpoint="https://api.openai.com/v1", context_window=128000)
+)
+client.set_default_embedding_config(
+    EmbeddingConfig(
+        embedding_endpoint_type="openai",
+        embedding_endpoint="https://api.openai.com/v1",
+        embedding_model="text-embedding-ada-002",
+        embedding_dim=1536,
+        embedding_chunk_size=300,
+    )
+)
+
+
+# create a new agent
+agent_state = client.create_agent()
+print(f"Created agent with name {agent_state.name} and unique ID {agent_state.id}")
+
+# Message an agent
+response = client.send_message(agent_id=agent_state.id, role="user", message="hello")
+print("Usage", response.usage)
+print("Agent messages", response.messages)
+
+# list all agents
+agents = client.list_agents()
+
+# get the agent by ID
+agent_state = client.get_agent(agent_id=agent_state.id)
+
+# get the agent by name
+agent_id = client.get_agent_id(agent_name=agent_state.name)
+agent_state = client.get_agent(agent_id=agent_id)
+
+# delete an agent
+client.delete_agent(agent_id=agent_state.id)
diff --git a/examples/docs/memory.py b/examples/docs/memory.py
new file mode 100644
index 00000000..e69de29b
diff --git a/examples/memgpt_rest_client.py b/examples/docs/rest_client.py
similarity index 52%
rename from examples/memgpt_rest_client.py
rename to examples/docs/rest_client.py
index e0f810f4..d0db8c4d 100644
--- a/examples/memgpt_rest_client.py
+++ b/examples/docs/rest_client.py
@@ -1,5 +1,3 @@
-import json
-
 from letta import create_client
 from letta.schemas.memory import ChatMemory
 
@@ -15,14 +13,25 @@ def main():
     # Connect to the server as a user
     client = create_client(base_url="http://localhost:8283")
 
+    # list available configs on the server
+    llm_configs = client.list_llm_configs()
+    print(f"Available LLM configs: {llm_configs}")
+    embedding_configs = client.list_embedding_configs()
+    print(f"Available embedding configs: {embedding_configs}")
+
     # Create an agent
-    agent_state = client.create_agent(name="my_agent", memory=ChatMemory(human="My name is Sarah.", persona="I am a friendly AI."))
+    agent_state = client.create_agent(
+        name="my_agent",
+        memory=ChatMemory(human="My name is Sarah.", persona="I am a friendly AI."),
+        embedding_config=embedding_configs[0],
+        llm_config=llm_configs[0],
+    )
     print(f"Created agent: {agent_state.name} with ID {str(agent_state.id)}")
 
     # Send a message to the agent
     print(f"Created agent: {agent_state.name} with ID {str(agent_state.id)}")
-    send_message_response = client.user_message(agent_id=agent_state.id, message="Whats my name?")
-    print(f"Recieved response: \n{json.dumps(send_message_response.messages, indent=4)}")
+    response = client.user_message(agent_id=agent_state.id, message="Whats my name?")
+    print(f"Recieved response:", response.messages)
 
     # Delete agent
     client.delete_agent(agent_id=agent_state.id)
diff --git a/examples/docs/tools.py b/examples/docs/tools.py
new file mode 100644
index 00000000..14f3a0e0
--- /dev/null
+++ b/examples/docs/tools.py
@@ -0,0 +1,68 @@
+from letta import EmbeddingConfig, LLMConfig, create_client
+
+client = create_client()
+# set automatic defaults for LLM/embedding config
+client.set_default_llm_config(
+    LLMConfig(model="gpt-4", model_endpoint_type="openai", model_endpoint="https://api.openai.com/v1", context_window=8000)
+)
+client.set_default_embedding_config(
+    EmbeddingConfig(
+        embedding_endpoint_type="openai",
+        embedding_endpoint="https://api.openai.com/v1",
+        embedding_model="text-embedding-ada-002",
+        embedding_dim=1536,
+        embedding_chunk_size=300,
+    )
+)
+
+
+# define a function with a docstring
+def roll_d20() -> str:
+    """
+    Simulate the roll of a 20-sided die (d20).
+
+    This function generates a random integer between 1 and 20, inclusive,
+    which represents the outcome of a single roll of a d20.
+
+    Returns:
+        int: A random integer between 1 and 20, representing the die roll.
+
+    Example:
+        >>> roll_d20()
+        15  # This is an example output and may vary each time the function is called.
+    """
+    import random
+
+    dice_role_outcome = random.randint(1, 20)
+    output_string = f"You rolled a {dice_role_outcome}"
+    return output_string
+
+
+tool = client.create_tool(roll_d20, name="roll_dice")
+
+# create a new agent
+agent_state = client.create_agent(tools=[tool.name])
+print(f"Created agent with name {agent_state.name} with tools {agent_state.tools}")
+
+# Message an agent
+response = client.send_message(agent_id=agent_state.id, role="user", message="roll a dice")
+print("Usage", response.usage)
+print("Agent messages", response.messages)
+
+# remove a tool from the agent
+client.remove_tool_from_agent(agent_id=agent_state.id, tool_id=tool.id)
+
+# add a tool to the agent
+client.add_tool_to_agent(agent_id=agent_state.id, tool_id=tool.id)
+
+client.delete_agent(agent_id=agent_state.id)
+
+# create an agent with only a subset of default tools
+agent_state = client.create_agent(include_base_tools=False, tools=[tool.name, "send_message"])
+
+# message the agent to search archival memory (will be unable to do so)
+response = client.send_message(agent_id=agent_state.id, role="user", message="search your archival memory")
+print("Usage", response.usage)
+print("Agent messages", response.messages)
+
+client.delete_agent(agent_id=agent_state.id)
diff --git a/examples/memgpt_local_client.py b/examples/memgpt_local_client.py
deleted file mode 100644
index 593ec852..00000000
--- a/examples/memgpt_local_client.py
+++ /dev/null
@@ -1,27 +0,0 @@
-import json
-
-from letta import create_client
-from letta.memory import ChatMemory
-
-
-def main():
-
-    # Create a `LocalClient`
-    client = create_client()
-
-    # Create an agent
-    agent_state = client.create_agent(name="my_agent", memory=ChatMemory(human="My name is Sarah.", persona="I am a friendly AI."))
-    print(f"Created agent: {agent_state.name} with ID {str(agent_state.id)}")
-
-    # Send a message to the agent
-    print(f"Created agent: {agent_state.name} with ID {str(agent_state.id)}")
-    send_message_response = client.user_message(agent_id=agent_state.id, message="Whats my name?")
-    print(f"Recieved response: \n{json.dumps(send_message_response.messages, indent=4)}")
-
-    # Delete agent
-    client.delete_agent(agent_id=agent_state.id)
-    print(f"Deleted agent: {agent_state.name} with ID {str(agent_state.id)}")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/examples/openai_client_assistants.py b/examples/openai_client_assistants.py
deleted file mode 100644
index 907185d8..00000000
--- a/examples/openai_client_assistants.py
+++ /dev/null
@@ -1,48 +0,0 @@
-from openai import OpenAI
-
-"""
-This script provides an example of how you can use OpenAI's python client with a Letta server.
-
-Before running this example, make sure you start the OpenAI-compatible REST server with `letta server`.
-"""
-
-
-def main():
-    client = OpenAI(base_url="http://localhost:8283/v1")
-
-    # create assistant (creates a letta preset)
-    assistant = client.beta.assistants.create(
-        name="Math Tutor",
-        instructions="You are a personal math tutor. Write and run code to answer math questions.",
-        model="gpt-4-turbo-preview",
-    )
-
-    # create thread (creates a letta agent)
-    thread = client.beta.threads.create()
-
-    # create a message (appends a message to the letta agent)
-    message = client.beta.threads.messages.create(
-        thread_id=thread.id, role="user", content="I need to solve the equation `3x + 11 = 14`. Can you help me?"
-    )
-
-    # create a run (executes the agent on the messages)
-    # NOTE: Letta does not support polling yet, so run status is always "completed"
-    run = client.beta.threads.runs.create(
-        thread_id=thread.id, assistant_id=assistant.id, instructions="Please address the user as Jane Doe. The user has a premium account."
-    )
-
-    # Store the run ID
-    run.id
-
-    # Retrieve all messages from the thread
-    messages = client.beta.threads.messages.list(thread_id=thread.id)
-
-    # Print all messages from the thread
-    for msg in messages.messages:
-        role = msg["role"]
-        content = msg["content"][0]
-        print(f"{role.capitalize()}: {content}")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/letta/schemas/llm_config.py b/letta/schemas/llm_config.py
index 867eaa9a..6695cecf 100644
--- a/letta/schemas/llm_config.py
+++ b/letta/schemas/llm_config.py
@@ -13,7 +13,7 @@ class LLMConfig(BaseModel):
         model_endpoint (str): The endpoint for the model.
         model_wrapper (str): The wrapper for the model. This is used to wrap additional text around the input/output of the model. This is useful for text-to-text completions, such as the Completions API in OpenAI.
         context_window (int): The context window size for the model.
-        put_inner_thoughts_in_kwargs (bool): Puts 'inner_thoughts' as a kwarg in the function call if this is set to True. This helps with function calling performance and also the generation of inner thoughts.
+        put_inner_thoughts_in_kwargs (bool): Puts `inner_thoughts` as a kwarg in the function call if this is set to True. This helps with function calling performance and also the generation of inner thoughts.
     """
 
     # TODO: 🤮 don't default to a vendor! bug city!
@@ -67,6 +67,12 @@ class LLMConfig(BaseModel):
 
     @classmethod
     def default_config(cls, model_name: str):
+        """
+        Convinience function to generate a default `LLMConfig` from a model name. Only some models are supported in this function.
+
+        Args:
+            model_name (str): The name of the model (gpt-4, gpt-4o-mini, letta).
+        """
         if model_name == "gpt-4":
             return cls(
                 model="gpt-4",
diff --git a/letta/schemas/tool_rule.py b/letta/schemas/tool_rule.py
index 9fa20dd8..d1540a61 100644
--- a/letta/schemas/tool_rule.py
+++ b/letta/schemas/tool_rule.py
@@ -11,15 +11,25 @@ class BaseToolRule(LettaBase):
 
 
 class ToolRule(BaseToolRule):
+    """
+    A ToolRule represents a tool that can be invoked by the agent.
+    """
+
     type: str = Field("ToolRule")
     children: List[str] = Field(..., description="The children tools that can be invoked.")
 
 
 class InitToolRule(BaseToolRule):
+    """
+    Represents the initial tool rule configuration.
+    """
+
     type: str = Field("InitToolRule")
-    """Represents the initial tool rule configuration."""
 
 
 class TerminalToolRule(BaseToolRule):
+    """
+    Represents a terminal tool rule configuration where if this tool gets called, it must end the agent loop.
+    """
+
     type: str = Field("TerminalToolRule")
-    """Represents a terminal tool rule configuration where if this tool gets called, it must end the agent loop."""