Conversational AI systems require more than a simple prompt wrapper. Production assistants need memory management, tool use, multimodal understanding, and cost controls that scale with complexity rather than token count. Building these systems means choosing an inference backend that supports long context windows, function calling, and streaming without introducing latency penalties or unpredictable billing.
Architecture Overview
A production conversational stack typically separates concerns into four layers: the inference backend, a context manager, a tool router, and a persistent memory store. The inference backend handles token generation and must support multi-turn dialogue, system prompts, and structured outputs. The context manager truncates or compresses history to fit within model limits. The tool router translates natural language into API calls, and the memory store retains facts across sessions.
Oxlo.ai provides the inference layer through a fully OpenAI-compatible API. With https://api.oxlo.ai/v1 as the base URL, you can route existing Python or Node.js clients to 45+ models across chat, reasoning, vision, code, and audio without modifying application logic. Request-based pricing means the cost per turn stays flat even as conversation history grows, which directly benefits the context manager design.
Managing Context and Memory
Long conversations inflate prompt size quickly. A customer support assistant that retains 20 turns of dialogue plus retrieved documentation can push token counts into the tens of thousands. On token-based platforms, this linearly increases cost per request and often forces aggressive truncation strategies that degrade user experience.
Oxlo.ai uses request-based pricing: one flat cost per API call regardless of input length. This removes the penalty for sending full conversation history or large retrieved contexts, so engineers can prioritize accuracy over token economy. For workloads that naturally run long, such as agentic research or coding companions, this pricing structure can yield significant savings compared to token-based providers like Together AI, Fireworks AI, OpenRouter, Replicate, or Anyscale.
Model selection also matters. For extended context, Oxlo.ai offers DeepSeek V4 Flash with a 1 million token context window, Kimi K2.6 with 131K context and advanced reasoning, and Llama 3.3 70B as a general-purpose flagship. When conversations exceed even these limits, implement a sliding window or summary-based memory strategy, but do so for quality reasons rather than cost avoidance.
Tool Use and Function Calling
Modern assistants do not just generate text. They query databases, call calculators, and trigger actions. Function calling lets the model emit structured JSON that your application validates and executes.
Oxlo.ai supports function calling across its chat models, including Qwen 3 32B for multilingual agent workflows, GLM 5 for long-horizon agentic tasks, and Minimax M2.5 for coding and tool use. Because the platform is fully OpenAI SDK compatible, you define tools exactly as you would for any standard client.
import openai
client = openai.OpenAI(
base_url="https://api.oxlo.ai/v1",
api_key="YOUR_OXLO_API_KEY"
)
tools = [
{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get current weather for a location",
"parameters": {
"type": "object",
"properties": {
"location": {"type": "string"},
"unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}
},
"required": ["location"]
}
}
}
]
response = client.chat.completions.create(
model="qwen-3-32b",
messages=[
{"role": "system", "content": "You are a helpful assistant with tool access."},
{"role": "user", "content": "What is the weather in Tokyo?"}
],
tools=tools,
tool_choice="auto"
)
print(response.choices[0].message)
The model returns a tool call object that your router executes. After obtaining the result, append it to the message list and send a follow-up request to generate the final user-facing response. Because Oxlo.ai charges per request, plan your turn budget around the number of round trips rather than the length of each JSON payload.
Multimodal Inputs
Conversational interfaces increasingly accept voice and images. A user might upload a screenshot of an error message or send a voice memo instead of typing. Handling these flows inside a single conversation thread requires endpoints beyond standard text chat.
Oxlo.ai exposes unified endpoints for these modalities. You can process images with vision-capable models such as Gemma 3 27B or Kimi VL A3B, transcribe audio with Whisper Large v3, and synthesize responses with Kokoro 82M text-to-speech. All endpoints share the same base URL and authentication, so you can build a multimodal pipeline without juggling separate providers.
Optimizing for Production
Latency and reliability shape user perception. Oxlo.ai streams responses via Server-Sent Events, letting you render tokens as they arrive rather than blocking on full generation. JSON mode constrains output to valid schemas, which is useful when the assistant must emit structured configuration or database queries. There are no cold starts on popular models, so p50 latency remains stable even after idle periods.
Cost planning is straightforward. The Free tier includes 60 requests per day across 16+ models, which is sufficient for prototyping. The Pro tier offers 1,000 requests per day, and Premium provides 5,000 requests per day with priority queue access. For high-volume deployments, Enterprise plans include dedicated GPUs and a guaranteed 30% reduction versus your current provider. See https://oxlo.ai/pricing for current plan details.
Implementation Example
Below is a minimal but complete conversational agent in Python. It maintains a message history, delegates weather queries to a function, and routes to Oxlo.ai. Because pricing is request-based, you can prepend a long system prompt or few-shot examples without increasing the per-turn cost.
import openai
import json
client = openai.OpenAI(
base_url="https://api.oxlo.ai/v1",
api_key="YOUR_OXLO_API_KEY"
)
def get_weather(location, unit="celsius"):
# Stub for external API call
return {"location": location, "temperature": 22, "unit": unit}
def run_conversation():
messages = [
{"role": "system", "content": "You are a concise travel assistant. Use the get_weather tool when asked about weather."}
]
tools = [{
"type": "function",
"function": {
"name": "get_weather",
"description": "Retrieve weather for a given city",
"parameters": {
"type": "object",
"properties": {
"location": {"type": "string"},
"unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}
},
"required": ["location"]
}
}
}]
messages.append({"role": "user", "content": "Is it warm in Barcelona right now?"})
# Initial request
resp = client.chat.completions.create(
model="llama-3.3-70b",
messages=messages,
tools=tools,
tool_choice="auto",
stream=False
)
msg = resp.choices[0].message
messages.append(msg)
if msg.tool_calls:
for tc in msg.tool_calls:
if tc.function.name == "get_weather":
args = json.loads(tc.function.arguments)
result = get_weather(**args)
messages.append({
"role": "tool",
"tool_call_id": tc.id,
"content": json.dumps(result)
})
# Follow-up request with tool results
final = client.chat.completions.create(
model="llama-3.3-70b",
messages=messages,
tools=tools
)
print(final.choices[0].message.content)
if __name__ == "__main__":
run_conversation()
This pattern extends to multi-step agent workflows. You can add retrieval-augmented generation by including embedding queries via Oxlo.ai's embeddings endpoint, or you can branch into vision analysis by switching to a model like Kimi K2.6 when image inputs are present.
Conclusion
Building conversational AI requires an inference layer that handles complexity without compounding costs. Oxlo.ai offers a developer-first platform with request-based pricing, full OpenAI SDK compatibility, and broad model coverage for chat, reasoning, vision, code, and audio. By decoupling cost from context length, you can ship assistants that retain richer memory, execute more tools, and sustain longer sessions. Start with the Free tier to validate your architecture, then scale through Pro, Premium, or Enterprise as user volume grows.
