Unlocking Conversational AI with LLMs

Conversational AI has moved beyond simple intent classification. Modern applications rely on large language models to maintain context across dozens of turns, invoke external tools, and generate structured outputs that drive user interfaces. As dialogue length grows, however, infrastructure costs become a primary constraint. Token-based billing scales linearly with conversation history, making long-context assistants and agentic workflows disproportionately expensive. Oxlo.ai addresses this with request-based pricing: one flat cost per API call regardless of how many tokens are in the prompt. For teams building stateful chat systems, this cost structure removes the penalty for retaining full conversation context.

Managing Multi-Turn Context

A production conversational agent must remember what the user said three turns ago without hallucinating or drifting. This requires passing the full message history into each new request. Under token-based pricing, every additional message in the history incurs a cost on every subsequent turn. Over a hundred-turn session, this compounds quickly.

With Oxlo.ai, the price remains flat per request. You can pass the complete conversation history, system prompts, and retrieval-augmented context without watching token counters climb. The platform exposes this through a standard /v1/chat/completions endpoint that accepts the familiar OpenAI messages array.

import openai

client = openai.OpenAI(
    base_url="https://api.oxlo.ai/v1",
    api_key="YOUR_OXLO_API_KEY"
)

messages = [
    {"role": "system", "content": "You are a technical support assistant. Be concise."},
    {"role": "user", "content": "My database connection is timing out."},
    {"role": "assistant", "content": "Check your connection pool settings. What is the current max_connections value?"},
    {"role": "user", "content": "It is set to 10."}
]

response = client.chat.completions.create(
    model="llama-3.3-70b",
    messages=messages,
    stream=True
)

for chunk in response:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

This pattern works identically for any Oxlo.ai chat model, including general-purpose options like Llama 3.3 70B, multilingual agents like Qwen 3 32B, and reasoning-heavy systems like DeepSeek R1 671B MoE.

Function Calling for Active Agents

Conversational AI becomes useful when it can act, not just reply. Function calling lets an LLM decide when to query an API, retrieve records, or execute code. Oxlo.ai supports tool use across its LLM catalog, so you can define JSON schemas that the model invokes as needed.

tools = [
    {
        "type": "function",
        "function": {
            "name": "check_ticket_status",
            "description": "Look up the status of a support ticket",
            "parameters": {
                "type": "object",
                "properties": {
                    "ticket_id": {"type": "string"}
                },
                "required": ["ticket_id"]
            }
        }
    }
]

response = client.chat.completions.create(
    model="qwen3-32b",
    messages=[{"role": "user", "content": "What is the status of ticket 48291?"}],
    tools=tools,
    tool_choice="auto"
)

if response.choices[0].message.tool_calls:
    # Execute the function and send the result back in a follow-up request
    pass

For agentic coding and long-horizon tasks, models like Kimi K2.6, GLM 5, and Minimax M2.5 handle extended tool chains reliably. Because Oxlo.ai charges per request rather than per token, multi-step agent loops that carry large tool definitions and history buffers do not trigger runaway costs.

Structured Output and JSON Mode

Chat interfaces often need structured data to populate forms, trigger workflows, or render custom UI components. JSON mode constrains the model to valid JSON, eliminating fragile regex parsing. Oxlo.ai supports this on compatible models through the standard response_format={"type": "json_object"} parameter.

This is particularly valuable when combining vision and language. A user might upload a screenshot and ask for a structured damage report. Models like Kimi VL A3B and Gemma 3 27B process the image and return JSON that feeds directly into your application layer, all within a single flat-priced request.

Model Selection for Dialogue Quality

Not every conversation requires the same reasoning depth. Oxlo.ai offers more than 45 models across seven categories, letting you route queries intelligently.

• General dialogue: Llama 3.3 70B provides fast, balanced responses for customer support and open-ended chat.
• Deep reasoning: DeepSeek R1 671B MoE and Kimi K2 Thinking excel at step-by-step problem solving inside a conversation.
• Extended context: DeepSeek V4 Flash supports 1M tokens of context, and Kimi K2.6 offers 131K context for document-heavy interactions.
• Efficiency: DeepSeek V3.2 handles coding and reasoning queries, and is available on the free tier for experimentation.

Switching between these models is a single parameter change in the OpenAI SDK. There are no cold starts on popular models, so latency stays consistent even when you scale traffic.

Infrastructure Costs and Pricing Models

The dominant pricing model in AI infrastructure bills by the token. For conversational workloads, this creates a misalignment: longer, more helpful sessions cost more to serve. Providers like Together AI, Fireworks AI, OpenRouter, Replicate, and Anyscale all use token-based scales. As users paste logs, documents, or prior conversation history into the chat, token counts rise and margins compress.

Oxlo.ai uses request-based pricing. Each API call costs one flat amount, whether you send a one-line greeting or a 100,000-token context window. For long-context and agentic workloads, this architecture can be 10-100x cheaper than token-based alternatives. You can see the exact structure at https://oxlo.ai/pricing.

Getting Started

Because Oxlo.ai is fully OpenAI SDK compatible, migrating an existing conversational AI project requires only a base URL change. You keep your retry logic, streaming parsers, and conversation state managers exactly as they are.

# Existing OpenAI client
client = openai.OpenAI(base_url="https://api.oxlo.ai/v1", api_key=os.environ["OXLO_API_KEY"])

# Same streaming, same tool schemas, same JSON mode
response = client.chat.completions.create(
    model="deepseek-v4-flash",
    messages=history,
    stream=True
)

Conversational AI is ultimately a loop of memory, reasoning, and action. The infrastructure behind it should reward richer context, not penalize it. Oxlo.ai’s flat per-request pricing, broad model catalog, and drop-in compatibility make it a natural backbone for chat applications that are designed to scale in depth as well as volume.