Hey everyone, Riley Fox here, back at agntkit.net with another dive into the tools that make our digital lives a little easier (or, let’s be honest, sometimes a lot more complicated before they get easier). Today, I want to talk about something that’s been on my mind a lot lately, especially as I’ve been trying to streamline my own workflows for a new project. We’re going to talk about “starter kits.” Not just any starter kits, though – I’m specifically looking at the idea of a “Minimal Viable Agent Starter Kit”. Think of it as the bare bones, no-frills setup you need to get an intelligent agent project off the ground without drowning in dependencies or analysis paralysis.
I know, I know. “Starter kit” can sound a bit generic. But hear me out. For those of us building anything from a simple chatbot to a complex autonomous system, the initial setup can be a real time sink. You’re looking at frameworks, deciding on libraries for specific tasks (NLP, vision, database interaction), figuring out deployment, and before you know it, you’ve spent three days just getting a “Hello World” to run in a container. My goal with this article is to share my recent struggles and solutions in trying to build something functional, fast, and extendable, using only the absolute essentials. It’s about getting to that first “aha!” moment quicker, so you can iterate and innovate, rather than just configure.
The Problem with “Everything and the Kitchen Sink” Starter Kits
My last big project involved building a semi-autonomous research assistant. The idea was to have an agent that could scour academic papers, summarize findings, and even generate follow-up questions. Naturally, I started by looking for existing “AI Agent Starter Kits.” What I found was a mixed bag. Some were fantastic, but many felt like they were trying to be everything to everyone. They’d include a full-blown web framework, multiple database options, half a dozen NLP libraries, and a deployment pipeline for every cloud provider under the sun. While comprehensive, this often meant:
- Bloat: My simple agent ended up with a massive dependency tree.
- Complexity: More moving parts meant more things to understand, more configurations, and more potential points of failure.
- Decision Fatigue: Instead of focusing on my agent’s core logic, I was busy deciding which ORM to use or which messaging queue was “best” for a project that didn’t even have users yet.
I distinctly remember spending an entire afternoon trying to debug a conflict between two seemingly unrelated Python libraries that both wanted a specific version of a C++ compiler. It was infuriating. That’s when I had my epiphany: I needed less, not more. I needed a minimal viable agent starter kit.
Defining “Minimal Viable Agent Starter Kit” (MVASK)
For me, an MVASK isn’t about cutting corners on functionality; it’s about focusing on the absolute core components needed to demonstrate an agent’s basic loop: perceiving, thinking, acting. Anything beyond that initial loop can (and should) be added iteratively as the project evolves.
Here’s what I believe constitutes the absolute essentials for a Python-based intelligent agent, aimed at getting a proof-of-concept running quickly:
- Core Orchestration: A simple way to manage the agent’s state and decision-making flow.
- Language Model Interaction: A standard, straightforward interface for talking to an LLM.
- Basic Tooling/Function Calling: A mechanism for the LLM to interact with external functions (e.g., search, calculator).
- Ephemeral State Management: For short-term memory or conversational context.
- Containerization: A simple way to package and run the agent consistently.
Notice what’s missing? No specific web framework (unless your agent is a web service), no complex database, no fancy UI, no extensive observability suite. These are all things you can layer on top once your core agent logic is sound.
My Go-To MVASK Components (Python Edition)
After much trial and error, here’s the stack I’ve landed on for my MVASK. It’s heavily Python-centric because that’s where most of the agentic development is happening right now, and it’s what I’m most comfortable with.
1. Orchestration: Simple Python Classes & Asyncio
Forget complex frameworks for the initial proof-of-concept. I start with a basic Python class for my agent, managing its internal state and calling methods for perception, thought, and action. For handling concurrent operations (like multiple tool calls or waiting for LLM responses), Python’s asyncio is a lifesaver and built right in.
Here’s a simplified snippet of how I might structure a basic agent class:
import asyncio
class SimpleAgent:
def __init__(self, name="AgentX"):
self.name = name
self.memory = [] # Simple list for ephemeral memory
print(f"{self.name} initialized.")
async def perceive(self, observation):
"""Processes incoming information."""
print(f"{self.name} perceived: {observation}")
self.memory.append(observation)
return observation
async def think(self):
"""Decides on the next action based on memory."""
# This is where LLM interaction and decision logic would go
prompt = "Based on my memory: " + " ".join(self.memory[-5:]) + " What should I do next?"
print(f"{self.name} thinking with prompt: '{prompt}'")
# Simulate LLM call
await asyncio.sleep(1)
decision = "Call a tool to search for more info."
print(f"{self.name} decided: {decision}")
return decision
async def act(self, action):
"""Executes the decided action."""
print(f"{self.name} acting: {action}")
if "search" in action:
result = await self._call_search_tool("some query")
print(f"Search tool returned: {result}")
self.memory.append(f"Tool result: {result}")
# More action handling here
return "Action completed."
async def _call_search_tool(self, query):
"""Placeholder for an external tool call."""
print(f" Calling search tool for: '{query}'")
await asyncio.sleep(0.5) # Simulate API call
return f"Found results for '{query}'"
async def run(self, initial_input):
await self.perceive(initial_input)
for _ in range(3): # Simple loop for demonstration
decision = await self.think()
await self.act(decision)
await asyncio.sleep(0.1)
if __name__ == "__main__":
agent = SimpleAgent("ResearchBot")
asyncio.run(agent.run("Find me recent papers on quantum computing optimization."))
This might look overly simple, but that’s the point! It gives you a clear structure to build on without being locked into a framework’s specific patterns from day one.
2. LLM Interaction: OpenAI Python Client (or similar)
For talking to Large Language Models, the official client libraries are usually the best bet. They handle authentication, retries, and formatting. I almost always start with the OpenAI Python client because of the widespread availability and good documentation, even if I plan to switch to another provider later. Its function-calling capabilities are now mature and incredibly useful.
Adding an LLM call to our agent’s think method might look like this:
# ... inside SimpleAgent.think() method ...
from openai import OpenAI
client = OpenAI() # Assumes OPENAI_API_KEY is set in environment
async def think(self):
prompt_messages = [
{"role": "system", "content": "You are a helpful research assistant."},
{"role": "user", "content": "Based on my memory: " + " ".join(self.memory[-5:]) + " What should I do next? Suggest a tool call if appropriate."}
]
# Define a simple tool
tools = [
{
"type": "function",
"function": {
"name": "search_web",
"description": "Searches the web for a given query.",
"parameters": {
"type": "object",
"properties": {
"query": {"type": "string", "description": "The search query"},
},
"required": ["query"],
},
},
}
]
try:
response = await client.chat.completions.create(
model="gpt-4-turbo-preview",
messages=prompt_messages,
tools=tools,
tool_choice="auto", # Let the model decide if it needs a tool
temperature=0.7,
max_tokens=200,
)
choice = response.choices[0].message
if choice.tool_calls:
tool_call = choice.tool_calls[0]
function_name = tool_call.function.name
function_args = json.loads(tool_call.function.arguments)
print(f"{self.name} decided to call tool: {function_name} with args: {function_args}")
# This would then be passed to the act method
return {"action_type": "tool_call", "name": function_name, "args": function_args}
else:
print(f"{self.name} decided: {choice.content}")
return {"action_type": "respond", "content": choice.content}
except Exception as e:
print(f"Error during LLM call: {e}")
return {"action_type": "respond", "content": "Error during thought process."}
# ... (You'd need to adapt the 'act' method to handle the new return format)
This immediately gives your agent the power to “think” and decide on actions, including calling external tools, which is fundamental to any interesting agent.
3. Tooling: Simple Python Functions
For tools, I keep it incredibly basic at first: just plain Python functions. Each function takes arguments and returns a result. No fancy tool registries or schemas beyond what the LLM client library expects. If a tool needs external dependencies (like a web scraper or a database client), those are added just for that specific tool, not globally for the whole agent. This keeps the dependency footprint small and manageable.
Our _call_search_tool from the first example is a perfect candidate. Later, if you need a more structured approach, you can always abstract these into a tool manager.
4. Containerization: Docker
Finally, for packaging and deployment, Docker is my go-to. A simple Dockerfile ensures that my agent runs the same way on my machine as it does on a server. It isolates dependencies and makes it easy to share the agent with others. For an MVASK, the Dockerfile can be incredibly minimal.
# Dockerfile
FROM python:3.11-slim-bookworm
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY . .
ENV OPENAI_API_KEY="your_api_key_here" # For development, use --build-arg or secrets in production
CMD ["python", "your_agent_script.py"]
And a corresponding requirements.txt:
# requirements.txt
openai~=1.16.0
This setup is lightweight, reproducible, and gets you from code to runnable agent incredibly fast. It addresses the common “it works on my machine” problem before it even starts.
Actionable Takeaways for Your Next Agent Project
If you’re embarking on a new agent project, especially one where you’re just exploring an idea, I urge you to consider the MVASK approach. Here’s what I’ve learned and what you can apply:
- Start with Core Logic, Not Frameworks: Before you even think about LangChain, LlamaIndex, or any other agent framework, write down the absolute minimum your agent needs to do. Can you implement that in plain Python? If so, start there. Add frameworks later if their benefits clearly outweigh the added complexity for your specific needs.
- Isolate Dependencies: Only add libraries when you absolutely need them for a specific piece of functionality. Don’t pull in a full web scraping library if all you need is a single HTTP request. Use separate virtual environments or Docker builds to keep things clean.
- Embrace Ephemeral State Initially: For your first few iterations, don’t worry about complex database schemas for memory. A simple list or dictionary in your agent’s class is often enough to prove out the core conversational or decision-making loop.
- Standardize LLM Interaction: Pick one robust LLM client library and stick with it. Learn its tool-calling patterns well. This is your agent’s voice and brain interface; keep it consistent.
- Containerize Early: Even if it’s just a simple
Dockerfile, getting your agent into a container early saves headaches down the line when you want to share it or deploy it.
The goal isn’t to avoid powerful tools entirely. It’s to use them judiciously. By focusing on a Minimal Viable Agent Starter Kit, you can rapidly prototype, test your agent’s core hypothesis, and then, with confidence, begin to scale and add the richer features that your project truly needs. It’s about building a solid foundation, one essential brick at a time, rather than trying to lift a skyscraper all at once.
Let me know in the comments if you’ve tried a similar approach or if you have different essentials for your own agent starter kits! Until next time, keep building those smarter agents!
🕒 Published: