My Journey Building Conversational AI with SDKs

Hey everyone, Nina here, back from my digital lab (which, let’s be honest, is mostly just my kitchen table with a lot of lukewarm coffee). Today, I want to talk about something that’s been buzzing in my Slack channels and haunting my late-night coding sessions: the sometimes-frustrating, often-brilliant world of AI SDKs, specifically when you’re trying to build a conversational agent that actually feels like a conversation, not just a glorified FAQ bot.

My particular obsession lately has been with integrating OpenAI’s Assistants API. Now, I know what you’re thinking, “Nina, Assistants API? That’s old news!” And yeah, you’re not wrong. It’s been out for a bit now. But hear me out: I’m not here to give you a basic “how to call client.beta.assistants.create()” tutorial. We’ve all seen those. My focus today is on a specific, thorny problem I ran into and how I wrestled with the SDK to get it to do what I wanted: managing stateful, multi-turn conversations with custom tools without losing my mind (or my user’s context). Think beyond simple Q&A. Think complex workflows, follow-up questions, and dynamic tool calls based on evolving user intent.

The State of My Sanity: Why Assistants API (Still) Matters

Before the Assistants API, building anything stateful with OpenAI’s models often felt like being a juggler with too many balls in the air. You were responsible for managing message history, prompt engineering for context, and orchestrating tool calls all by yourself. It was… exhausting. The Assistants API promised to take a lot of that burden off our shoulders by managing threads, messages, and even tool orchestration. And for simple cases, it delivers beautifully.

My current project involves building a “smart” personal finance assistant. Not just one that tells you your balance, but one that can help you plan budgets, suggest investment strategies based on your risk tolerance, and even simulate future financial scenarios. This requires a lot of back-and-forth, remembering previous statements (“I want to save for a house,” “My income is X,” “What if I invest in Y?”), and crucially, calling specific functions (like fetching real-time stock data or running a budget projection algorithm) at the right time.

My first attempt was a glorious mess. I was passing the entire message history back and forth, trying to jam complex instructions into the system prompt, and manually checking if a tool call was needed. It worked, but it was brittle, slow, and a nightmare to debug. That’s when I decided to really lean into the Assistants API and its SDK.

The SDK’s Embrace: Threads, Messages, and the Elusive `run`

The core of the Assistants API is the concept of `Threads` and `Messages`. You create a `Thread` for a conversation, add `Messages` to it, and then “run” the `Assistant` on that `Thread`. The `Assistant` then processes the messages, decides if it needs to call a tool, and generates a response. Simple enough, right?

Here’s where it gets interesting. My finance assistant needs to do things like:

• Track goals: “I want to save $50,000 for a down payment.”
• Gather information: “What’s your current monthly income?”
• Execute calculations: “Based on that, how long will it take if I save $1,000 per month?”
• Provide advice: “Consider diversifying your portfolio with low-cost index funds.”

Each of these often requires a custom tool call. For example, `calculate_savings_timeline(goal_amount, monthly_income, monthly_savings)`. The challenge isn’t just defining the tool; it’s getting the Assistant to:

1. Recognize it needs the tool.
2. Identify all the necessary parameters (even if they’re spread across multiple user messages).
3. Prompt the user for missing parameters gracefully.
4. Execute the tool and incorporate the results.

The Parameter Problem: When the Assistant Asks for More

Let’s say a user says, “How long will it take me to save for a house?” My `calculate_savings_timeline` tool needs `goal_amount`, `monthly_income`, and `monthly_savings`. The Assistant, being smart, will often realize it’s missing information. The SDK, through the `run` object, will tell you it’s `requires_action`.

My initial approach was to just wait for the `requires_action` status and then present a generic “What information do you need?” to the user. This was clunky. The user often didn’t know what parameters the tool needed. A better approach, which I eventually landed on, involved inspecting the `tool_calls` within the `requires_action` status.

def submit_tool_outputs(client, thread_id, run_id, tool_outputs):
 return client.beta.threads.runs.submit_tool_outputs(
 thread_id=thread_id,
 run_id=run_id,
 tool_outputs=tool_outputs
 )

# ... inside your conversation loop ...

if run.status == 'requires_action':
 tool_outputs = []
 missing_params = {}
 for tool_call in run.required_action.submit_tool_outputs.tool_calls:
 function_name = tool_call.function.name
 arguments = json.loads(tool_call.function.arguments)

 # Here's the trick: check if a required parameter is missing
 # This assumes your tools are defined with a schema
 # For simplicity, let's say we know 'monthly_savings' is always needed
 if function_name == "calculate_savings_timeline" and "monthly_savings" not in arguments:
 missing_params['monthly_savings'] = 'How much do you plan to save each month?'
 
 # ... more sophisticated parameter checking here ...

 if missing_params:
 # Inform the user what's missing
 user_prompt = "I need a bit more information to help you with that. "
 for param, question in missing_params.items():
 user_prompt += f"{question} "
 return {"status": "waiting_for_user_input", "prompt": user_prompt}
 else:
 # All parameters are present, execute the tool
 output = execute_tool_function(function_name, arguments) # Your function to run the actual tool logic
 tool_outputs.append({
 "tool_call_id": tool_call.id,
 "output": json.dumps(output)
 })
 
 if tool_outputs:
 run = submit_tool_outputs(client, thread.id, run.id, tool_outputs)
 # Continue polling the run until it's completed or requires new action
 else:
 # This case handles if we detected missing params and prompted the user
 pass

This snippet is a simplified version, but the core idea is to intercept `requires_action`, look at what the Assistant wants to do, and if it’s missing arguments, prompt the user specifically for them. This makes the conversation feel much more natural, like a human asking clarifying questions, instead of a generic “error.”

The Elusive State: Keeping Track Across Turns

One of the biggest headaches was ensuring that if a user provided a piece of information (“My income is $5,000 a month”), the Assistant would remember it for subsequent tool calls within the same conversation, even if it wasn’t immediately used. The Assistants API handles this pretty well within a `Thread` by keeping the message history. However, sometimes you want to explicitly guide it or give it background that isn’t part of a direct user message.

I found myself using `metadata` on the `Thread` and `Message` objects more than I initially thought. For instance, if the user explicitly states their risk tolerance, I might store that in the thread’s metadata. While the Assistant doesn’t directly “read” this metadata in its reasoning process, it’s invaluable for my application logic when I need to pre-populate arguments for tool calls or make decisions about which tools are even relevant.

Another pattern I adopted was injecting “system-like” messages into the thread if I needed to explicitly remind the Assistant of certain facts that were derived from a tool call or an internal process. For instance, after `calculate_savings_timeline` returns that it will take 10 years, I might add a message like: `{“role”: “user”, “content”: “The user’s savings plan indicates a 10-year timeline to reach their goal.”}`. This isn’t from the user directly, but it helps reinforce context for the Assistant’s subsequent responses.

Handling Tool Output and Subsequent Actions

When a tool executes, you get an output. The Assistant then takes this output and generates a response. But what if the tool output itself prompts a new set of questions or a new tool call? For example, the `calculate_savings_timeline` tool might return that the goal is unattainable with current savings. The Assistant should then ideally suggest, “Perhaps we should look at ways to increase your monthly savings or reduce your goal amount.” This isn’t just about outputting text; it’s about chaining logical steps.

The Assistants API SDK handles this beautifully by keeping the `run` object alive. After you `submit_tool_outputs`, the `run` often transitions back to `in_progress` and then might generate a new `requires_action` (for another tool call) or `completed`. My main loop for handling conversation flow looks something like this:

def get_assistant_response(client, thread_id, assistant_id, user_message):
 client.beta.threads.messages.create(
 thread_id=thread_id,
 role="user",
 content=user_message
 )

 run = client.beta.threads.runs.create(
 thread_id=thread_id,
 assistant_id=assistant_id
 )

 while run.status in ['queued', 'in_progress', 'cancelling']:
 time.sleep(1) # Be a good citizen, don't hammer the API
 run = client.beta.threads.runs.retrieve(thread_id=thread_id, run_id=run.id)

 if run.status == 'completed':
 messages = client.beta.threads.messages.list(thread_id=thread_id, order="desc")
 for msg in messages.data:
 if msg.role == "assistant":
 # Find the latest assistant message, often the first one
 return msg.content[0].text.value # Assuming text content
 return "No response from assistant."

 elif run.status == 'requires_action':
 tool_outputs = []
 for tool_call in run.required_action.submit_tool_outputs.tool_calls:
 function_name = tool_call.function.name
 arguments = json.loads(tool_call.function.arguments)
 
 # This is where your custom logic for handling missing params or executing tools goes
 # For this simplified example, let's assume all params are present and we execute
 print(f"Assistant wants to call {function_name} with args: {arguments}")
 
 output = execute_tool_function(function_name, arguments) # Your tool execution logic
 tool_outputs.append({
 "tool_call_id": tool_call.id,
 "output": json.dumps(output)
 })
 
 # Submit tool outputs and continue the run
 run = client.beta.threads.runs.submit_tool_outputs(
 thread_id=thread_id,
 run_id=run.id,
 tool_outputs=tool_outputs
 )
 # Recursively call this function to process the updated run
 # Careful with recursion depth in production, might need a loop
 return get_assistant_response(client, thread_id, assistant_id, "") # Pass empty message
 
 elif run.status == 'failed':
 return f"Assistant run failed: {run.last_error.message}"
 
 else:
 return f"Unexpected run status: {run.status}"

This `get_assistant_response` function, while simplified, shows the core loop. The key is that `requires_action` isn’t a dead end. It’s an opportunity for your application to step in, execute the requested tool, and then pass the results back to the Assistant to continue its reasoning. This closed-loop feedback is what makes truly stateful, dynamic conversations possible.

Actionable Takeaways for Your Next AI Assistant Project

1. Embrace the `run.status` lifecycle: Don’t just check for `completed`. `requires_action` is your friend, not an error. Build solid handlers for each relevant status.
2. Inspect `tool_calls` for missing parameters: Instead of generic prompts, dig into `run.required_action.submit_tool_outputs.tool_calls` to understand *exactly* what the Assistant needs. This elevates your UX.
3. Strategic use of System-like Messages: If your internal application logic derives new facts or needs to emphasize certain context, consider injecting “user” messages that represent these facts. The Assistant will process them as part of the thread history.
4. Metadata for Application State: While the Assistant might not directly “read” thread or message metadata for its reasoning, it’s a powerful place for your application to store and retrieve state relevant to the conversation. Think user preferences, current session variables, etc.
5. Test Edge Cases for Tool Orchestration: What happens if a tool fails? What if the user changes their mind mid-tool invocation? Design your `execute_tool_function` and error handling within the `requires_action` loop carefully.

Building truly intelligent, conversational AI agents isn’t just about picking the latest model. It’s about skillfully wielding the SDKs they provide to manage the complexities of human interaction. The OpenAI Assistants API, with a bit of thoughtful engineering around its state management and tool orchestration, can really elevate your conversational experience beyond simple turn-taking. It’s still a journey, and I’m still learning new tricks, but I hope these insights save you a few late nights and cold coffees!

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🕒 Last updated: March 26, 2026  ·  Originally published: March 25, 2026