Building a conversational AI application requires more than wrapping a chat endpoint in a UI. Production systems need persistent state management, context window optimization, tool integration, and an inference backend that stays responsive as conversation history grows. Oxlo.ai is a developer-first inference platform built for this exact workload, offering flat per-request pricing, full OpenAI SDK compatibility, and over 45 models with no cold starts.
Core Architecture for Conversational LLM Apps
A typical conversational stack has three layers: a client interface, an orchestration service that manages history and business logic, and an inference backend. The orchestration layer maintains message history, handles user identity, and decides when to inject system prompts or retrieve external context. The inference backend executes the model and returns completions.
For the inference layer, API compatibility and cost predictability matter as much as raw model quality. Oxlo.ai provides a fully OpenAI-compatible API with request-based pricing, so you can swap the base URL without rewriting client logic. Unlike token-based providers such as Together AI, Fireworks AI, OpenRouter, Replicate, or Anyscale, Oxlo.ai charges one flat cost per request regardless of how many tokens are in the prompt. This changes how you design context retention, especially for agentic or long-context conversational flows.
Managing Context and Conversation State
Every turn in a conversation appends new messages to the history array. Over time, this increases the input size for each subsequent request. Under token-based billing, longer histories mean higher costs per turn, which forces developers to aggressively truncate or summarize context. With Oxlo.ai, the cost stays flat per request, so you can maintain richer multi-turn context without linear cost growth.
When selecting a model, consider the context ceiling. Oxlo.ai offers several options for long-context conversational workloads:
- DeepSeek V4 Flash: efficient MoE architecture with a 1 million token context window for near state-of-the-art open-source reasoning.
- Kimi K2.6: advanced reasoning with a 131K context window, agentic coding, and vision support.
- DeepSeek R1 671B MoE: deep reasoning and complex coding across extended contexts.
SDK Setup and Drop-in Compatibility
Oxlo.ai is built as a drop-in replacement for the OpenAI SDK. Change the base URL and API key, and existing conversational code runs unchanged. This includes support for streaming responses, function calling, JSON mode, and vision inputs.
from openai import OpenAI
client = OpenAI(
base_url="https://api.oxlo.ai/v1",
api_key="YOUR_OXLO_API_KEY"
)
response = client.chat.completions.create(
model="llama-3.3-70b",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Explain how conversational state works."}
],
stream=True
)
for chunk in response:
if chunk.choices[0].delta.content:
print(chunk.choices[0].delta.content, end="")
This compatibility extends across Python, Node.js, and cURL. You can prototype against Oxlo.ai's free tier and promote the same code to production without SDK refactoring.
Implementing the Conversation Loop
A minimal conversational backend stores history in memory, appends each new user message, and streams the model response back to the client. Below is a pattern that works with Oxlo.ai's chat/completions endpoint.
import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.oxlo.ai/v1",
api_key=os.getenv("OXLO_API_KEY")
)
class ConversationEngine:
def __init__(self, model: str = "qwen3-32b"):
self.model = model
self.history = [
{"role": "system", "content": "You are a concise technical assistant."}
]
def chat(self, user_input: str) -> str:
self.history.append({"role": "user", "content": user_input})
completion = client.chat.completions.create(
model=self.model,
messages=self.history,
stream=True
)
reply_chunks = []
for chunk in completion:
content = chunk.choices[0].delta.content
if content:
reply_chunks.append(content)
print(content, end="", flush=True)
reply = "".join(reply_chunks)
self.history.append({"role": "assistant", "content": reply})
return reply
if __name__ == "__main__":
engine = ConversationEngine(model="llama-3.3-70b")
while True:
user_msg = input("\nUser: ")
if user_msg.lower() in {"exit", "quit"}:
break
print("Assistant: ", end="")
engine.chat(user_msg)
Because Oxlo.ai does not impose cold starts on popular models, the first response after idle time is immediate. That consistency is critical for chat interfaces where latency is perceived as unresponsiveness.
Tool Use and Function Calling
Conversational apps often need to query APIs, search knowledge bases, or trigger actions. Oxlo.ai supports function calling across its chat and reasoning models, enabling agentic loops where the model decides which tool to invoke.
Models such as Qwen 3 32B, GLM 5, Minimax M2.5, and Kimi K2.6 are particularly strong at agentic tool use. A typical loop looks like this:
tools = [
{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get current weather for a city",
"parameters": {
"type": "object",
"properties": {
"city": {"type": "string"}
},
"required": ["city"]
}
}
}
]
response = client.chat.completions.create(
model="kimi-k2.6",
messages=engine.history,
tools=tools,
tool_choice="auto"
)
message = response.choices[0].message
if message.tool_calls:
# Execute tool logic, append results, and send follow-up request
pass
The request-based pricing model is especially advantageous here. Agentic workflows can involve large tool schemas and lengthy reasoning chains. With token-based providers, those inputs inflate costs quickly. On Oxlo.ai, the flat per-request price keeps agentic iteration predictable.
Model Selection by Use Case
Oxlo.ai hosts over 45 models across seven categories. For conversational AI, these are the most relevant:
- General-purpose chat: Llama 3.3 70B and GPT-Oss 120B serve as reliable flagships for open-ended dialogue.
- Advanced reasoning and agents: DeepSeek R1 671B MoE, DeepSeek V4 Flash, Kimi K2.5, Kimi K2 Thinking, and GLM 5 handle multi-step reasoning and long-horizon agentic tasks.
- Coding assistants: Qwen 3 Coder 30B, DeepSeek Coder, Minimax M2.5, and DeepSeek V3.2 excel at generating and explaining code inside a conversation.
- Vision-enabled chat: Gemma 3 27B and Kimi VL A3B support image inputs for multimodal conversations.
All of these are accessible through the same /v1/chat/completions endpoint with identical request formatting.
Production Costs and Scaling
Cost structure directly influences conversational UX. Under token-based pricing, every additional line of context increases the bill, so developers compress history or offload memory to RAG systems earlier than necessary. Oxlo.ai's request-based pricing removes that constraint, making it 10 to 100x cheaper for long-context workloads depending on conversation length.
For early development, the Oxlo.ai free tier includes 60 requests per day across 16-plus models with a 7-day full-access trial. Production tiers scale as follows:
- Pro: $80 per month, 1,000 requests per day, all models included.
- Premium: $350 per month, 5,000 requests per day, all models, plus priority queue access.
- Enterprise: Custom contracts with unlimited requests, dedicated GPUs, and a guaranteed 30% savings against your current provider.
Exact per-request rates are listed on the Oxlo.ai pricing page.
Deployment Tips
In production, store conversation history in a fast key-value store such as Redis rather than holding it in application memory. Keep system prompts static and versioned, and instrument latency per model to detect regressions. Because Oxlo.ai offers no cold starts on popular models, you can autoscale your orchestration layer without worrying about warmup penalties on the inference side.
If you need speech-to-text or text-to-speech as part of the conversational pipeline, Oxlo.ai also exposes audio endpoints for Whisper Large v3, Whisper Turbo, Whisper Medium, and Kokoro 82M TTS, all under the same request-based scheme and API structure.
Conclusion
Conversational AI apps succeed when context is retained accurately, responses arrive quickly, and costs stay predictable as usage grows. Oxlo.ai addresses all three: OpenAI SDK compatibility minimizes integration work, a broad model catalog covers chat, reasoning, coding, and vision, and request-based pricing ensures that long conversations and agentic loops do not trigger runaway token bills. Start with the free tier, point your existing client at https://api.oxlo.ai/v1, and scale without rewriting your stack.
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