Conversational AI has moved beyond rigid decision trees. Modern chatbots leverage large language models to maintain context, interpret intent, and generate human-like responses in real time. For developers building these systems, the infrastructure choices around API compatibility, context window management, and pricing directly impact both user experience and operational cost.
Why LLMs change the chatbot landscape
Traditional chatbots rely on intent classifiers and scripted flows. LLMs replace this fragility with probabilistic reasoning, allowing users to express goals in natural language without matching predefined utterances. A single model can handle open-domain questions, slot filling, and tone adaptation, provided the developer manages context and grounding carefully.
Architecture of a modern conversational agent
A production chatbot typically requires four components working in sequence:
- Message history: A rolling buffer of user and assistant turns that preserves conversational context.
- System prompt: A static instruction that defines persona, constraints, and safety boundaries.
- Retrieval layer (optional): External knowledge injected via RAG to reduce hallucination.
- Tool executor: Functions that let the agent act on the user's behalf, such as querying a database or updating a ticket.
Oxlo.ai supports all of these patterns through standard chat completions, function calling, and JSON mode, without requiring custom SDKs.
Getting started with the OpenAI SDK and Oxlo.ai
Because Oxlo.ai is fully OpenAI SDK compatible, you can prototype with existing Python or Node.js code by changing the base URL. Below is a minimal example using Python:
from openai import OpenAI
client = OpenAI(
base_url="https://api.oxlo.ai/v1",
api_key="your-oxlo.ai-api-key"
)
response = client.chat.completions.create(
model="llama-3.3-70b",
messages=[
{"role": "system", "content": "You are a helpful customer support assistant."},
{"role": "user", "content": "How do I reset my password?"}
]
)
print(response.choices[0].message.content)
This same pattern works for multi-turn conversations, streaming, and tool use.
Managing multi-turn context and state
Conversational quality depends on how well the system remembers prior turns. In practice, developers append each exchange to a messages list and truncate or summarize when approaching the model's context limit. The challenge is that longer histories increase token count, which on token-based platforms inflates cost per request.
Oxlo.ai uses request-based pricing, so a single API call costs the same flat amount regardless of how many tokens are in the prompt. For chatbots with long transcripts or detailed system instructions, this removes the penalty typically associated with deep context windows. You can send full conversation histories without watching metered token costs scale with each turn.
messages = [
{"role": "system", "content": "You are a technical support agent for a SaaS platform."},
{"role": "user", "content": "My database connection is timing out."},
{"role": "assistant", "content": "Let's check your connection string. Are you using SSL?"},
{"role": "user", "content": "Yes, but the certificate might be expired."}
]
response = client.chat.completions.create(
model="qwen-3-32b",
messages=messages
)
Extending chatbots with tool use
Function calling turns a chatbot from a passive responder into an active agent. You define schemas for external tools, and the model decides when to invoke them based on user intent. Oxlo.ai supports function calling on compatible models, including Llama 3.3 70B, Qwen 3 32B, and Kimi K2.6.
tools = [
{
"type": "function",
"function": {
"name": "get_order_status",
"description": "Retrieve the status of a customer order",
"parameters": {
"type": "object",
"properties": {
"order_id": {"type": "string"}
},
"required": ["order_id"]
}
}
}
]
response = client.chat.completions.create(
model="kimi-k2-6",
messages=[{"role": "user", "content": "Where is order 8842?"}],
tools=tools
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