AI Agent Development with OpenAI Conversations API
The Conversations API is how you build AI agents that remember context and take actions. Here's the complete guide.
Why Conversations API?
Before Conversations API:
- Each request was stateless
- You had to manage context yourself
- Token limits forced context truncation
After Conversations API:
- OpenAI manages conversation history
- Context persists indefinitely
- You focus on agent logic, not infrastructure
Getting Started
Basic Setup
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Sending Messages
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Complete Flow
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Adding Tools
Make agents that can take actions, not just talk.
Defining Tools
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Handling Tool Calls
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Real-World Agent: Customer Support
Here's a complete customer support agent with tools.
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Best Practices
1. Structured System Prompts
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2. Graceful Error Handling
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3. Conversation Limits
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4. Logging for Debugging
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Production Considerations
- Rate limiting: OpenAI has conversation limits
- Cost monitoring: Conversations accumulate tokens
- Cleanup: Delete old conversations to manage storage
- Fallbacks: Have backup models/responses for failures
Frequently Asked Questions
Q: What is the OpenAI Conversations API and how is it different from the Chat Completions API?
The Conversations API lets OpenAI manage conversation history server-side, so you no longer need to send the entire message history with each request. You create a conversation once, get a persistent ID, and all subsequent messages automatically have full context. This eliminates the need to manually manage context windows and token limits.
Q: How much does it cost to run an AI agent with the Conversations API?
Costs depend on the model and conversation length. Using GPT-5-mini at $0.15/$0.60 per million input/output tokens, a customer support agent handling 1,000 conversations per month with 10 messages each typically costs $30-80/month. Using GPT-5.2 for complex reasoning costs roughly 15x more, so most production agents use mini for routine tasks and escalate to larger models when needed.
Q: Can AI agents built with the Conversations API take real actions like processing payments or updating databases?
Yes, this is exactly what function calling enables. You define tools (functions) the agent can invoke, and the API returns structured requests to execute them. The agent can look up customer records, initiate refunds, cancel orders, or trigger any workflow your backend supports. The key is implementing proper validation and authorization on your side before executing any action.
Q: What are the rate limits and conversation limits for the Conversations API?
OpenAI enforces rate limits based on your account tier, typically measured in requests per minute (RPM) and tokens per minute (TPM). Conversations persist indefinitely when you set store: true, but you should implement conversation limits in your application (such as 50 messages max) and clean up old conversations to manage costs and storage.
Need an AI Agent?
We build production AI agents with the Conversations API.
AI 4U Labs builds production AI agents. 30+ applications shipped, including conversational payments and customer support systems.
