Building conversational AI that maintains coherence across dozens of turns requires more than a capable large language model. You need a system that manages context windows, handles tool use, streams responses with low latency, and controls costs as conversation history grows. This guide walks through the architectural decisions, implementation patterns, and infrastructure choices that separate prototype chatbots from production conversational agents.
Core Architecture
A production conversational system typically splits responsibilities across four layers: the interface, the orchestration engine, the model inference layer, and memory storage. The interface handles user input and rendering. The orchestration engine manages conversation state, decides when to call tools, and formats prompts. The inference layer generates completions, and memory storage persists conversation history beyond the active context window.
State management is usually the first bottleneck. A naive implementation sends the entire conversation history on every turn. For short interactions, this works. For long-running support agents or personal assistants, token costs and latency scale linearly with history length. A better approach uses a sliding window for recent messages and a separate summarization step for older turns.
Model Selection
Your choice of model determines reasoning quality, multilingual support, and tool-use reliability. For general-purpose conversational agents, Llama 3.3 70B offers strong instruction following and broad knowledge. If your users expect deep reasoning or complex coding assistance, DeepSeek R1 671B MoE or Kimi K2.6 provide advanced chain-of-thought capabilities and a 131K context window. For multilingual agent workflows, Qwen 3 32B is purpose-built for cross-lingual reasoning.
You do not need to self-host or negotiate separate contracts for each of these. Oxlo.ai hosts 45+ open-source and proprietary models across seven categories, all exposed through a single OpenAI-compatible endpoint. Because Oxlo.ai uses request-based pricing with one flat cost per API call regardless of prompt length, long conversations do not trigger the cost spikes you see with token-based providers. For agentic workloads that accumulate large contexts, this pricing model can be 10-100x cheaper than token-based billing on platforms like Together AI, Fireworks AI, OpenRouter, Replicate, or Anyscale.
Context Management and Memory
Context windows are expanding, but they are not infinite. Even models with 128K or 1M token limits benefit from selective memory. Implement a retrieval layer that fetches relevant historical messages or external documents, then injects them into the system prompt. Vector databases work well here, but for pure conversation history, simple recency-based retrieval with summary compression often suffices.
Because context length directly impacts cost on token-based platforms, architectures that preload extensive documentation or maintain long multi-turn histories can become expensive. Oxlo.ai removes this variable. You pay per request, so you can pass full conversation threads, retrieved documents, and system instructions without watching metered token costs accumulate. This flat structure encourages richer context, which typically improves response quality.
Tool Use and Function Calling
Conversational agents stop being interesting when they only generate text. Function calling lets the model invoke external APIs, query databases, or execute code. The pattern is straightforward: define JSON schemas for available tools, include them in the chat completion request, and let the model decide when to call them.
Oxlo.ai supports function calling and tool use across its chat completions endpoint, and the API is fully compatible with the OpenAI SDK. Switching from another provider is a one-line change to the base URL.
from openai import OpenAI
client = 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"}
},
"required": ["location"]
}
}
}
]
response = client.chat.completions.create(
model="llama-3.3-70b",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "What is the weather in Berlin?"}
],
tools=tools
)
print(response.choices[0].message.tool_calls)
After receiving a tool call, your orchestration layer executes the function, appends the result to the message list, and sends a second request to the model so it can generate a natural language response.
Streaming Responses
Perceived latency matters more than total latency. Streaming tokens as they are generated keeps users engaged, especially for long answers. Most modern inference platforms support server-sent events for chat completions. Enabling streaming is typically a single boolean flag.
On Oxlo.ai, streaming is available with no cold starts on popular models, so the first chunk arrives quickly even if traffic patterns are sporadic.
stream = client.chat.completions.create(
model="qwen3-32b",
messages=[{"role": "user", "content": "Explain recursion with examples."}],
stream=True
)
for chunk in stream:
print(chunk.choices[0].delta.content or "", end="")
Multimodal Inputs
Text-only chat is no longer the default. Users expect to share screenshots, diagrams, or photos. Vision-capable models like Kimi K2.6 and Gemma 3 27B can accept image inputs alongside text, enabling use cases such as troubleshooting user interfaces or interpreting charts.
Oxlo.ai exposes vision models through the same chat completions endpoint. You pass image URLs or base64-encoded payloads in the message content array, exactly as you would with the OpenAI SDK. This unified interface means adding vision to an existing text-based agent requires no architectural rewrite.
Evaluation and Safety
Before shipping, establish benchmarks for helpfulness, hallucination rate, and tool-use accuracy. Use held-out conversation datasets to test edge cases: ambiguous user intent, tool failures, and off-topic requests. JSON mode can constrain the model to structured outputs for evaluation pipelines, making it easier to grade responses programmatically.
Safety filtering should happen at both the input and output layers. Input moderation blocks jailbreak attempts and policy violations. Output filtering catches unintended disclosures or harmful completions. These layers are application-specific, but the inference provider must support low-latency filtering without degrading the user experience.
Deployment Considerations
When moving from prototype to production, traffic patterns become unpredictable. You need an inference backend that scales without queuing delays and pricing that stays predictable under load.
Oxlo.ai offers tiered plans ranging from a free tier with 60 requests per day and 16+ models to enterprise deployments with dedicated GPUs and unlimited volume. The request-based model means your bill is tied to interaction volume, not the length of each conversation. For long-context and agentic workloads, this predictability is a structural advantage over token-based billing.
Because Oxlo.ai is fully OpenAI SDK compatible, integration into existing Python, Node.js, or cURL workflows takes minutes. You can prototype with one provider and migrate to Oxlo.ai by changing the base URL to https://api.oxlo.ai/v1, without rewriting prompts or parsing custom response formats.
Check the Oxlo.ai pricing page to compare plans and calculate expected costs for your conversational workload.
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