OpenAI Agents SDK Memory:
How to Add Persistent State

The OpenAI Agents SDK is designed to be stateless — clean, composable, and easy to reason about. But stateless means your agent forgets everything the moment a session ends. Here's how to wire in persistent memory without fighting the framework.

Why the OpenAI Agents SDK is stateless by design

The OpenAI Agents SDK (released in early 2025) is built around a straightforward execution model: each Runner.run() call starts from scratch. You pass in a list of messages, the agent processes them, and it returns a response. There is no built-in mechanism to persist state between calls.

This is a deliberate design choice. Stateless agents are easier to test, easier to scale horizontally, and easier to reason about in a distributed environment. When OpenAI introduced the Agents SDK alongside the Responses API, they separated the concern of what the agent knows right now (the input messages) from what it has learned over time (which they left to the developer).

What you lose without persistence

Without a memory layer, every session is a blank slate. The practical consequences compound quickly:

• Context window bloat — to approximate memory, developers stuff previous conversations into the system prompt. At ~$15/M tokens for GPT-4o, this adds up fast.
• Poor user experience — the agent asks the same onboarding questions on every session, forgets user preferences, and cannot reference past decisions.
• No learning curve — the agent cannot get better at serving a specific user over time, making it feel generic regardless of how many interactions have occurred.
• Broken workflows — multi-step processes that span days (e.g., a procurement agent managing an RFP) fall apart when state is lost between sessions.

Integration architecture

The pattern is straightforward: intercept the agent's inputs and outputs to read and write to Kronvex. The SDK's hook system makes this clean.

# ┌─────────────────────────────────────────────────────────────────┐
# │                      User request                               │
# └────────────────────────────┬────────────────────────────────────┘
#                              │
#                    ┌─────────▼──────────┐
#                    │  recall() — fetch  │  ← Kronvex (pgvector)
#                    │  top-k memories    │
#                    └─────────┬──────────┘
#                              │ inject as system context
#                    ┌─────────▼──────────┐
#                    │  OpenAI Agent SDK  │
#                    │  Runner.run()      │
#                    └─────────┬──────────┘
#                              │
#                    ┌─────────▼──────────┐
#                    │  remember() — save │  ← Kronvex (pgvector)
#                    │  exchange + facts  │
#                    └─────────┬──────────┘
#                              │
#                    ┌─────────▼──────────┐
#                    │  Response to user  │
#                    └────────────────────┘

Full code example

The OpenAI Agents SDK exposes lifecycle hooks via RunHooks. We implement on_agent_start to inject context and on_agent_end to persist the exchange.

pip install "kronvex[openai-agents]" openai-agents

from agents import Agent, Runner
from kronvex.integrations.openai_agents import KronvexHooks
import os

agent = Agent(
    name="SupportAgent",
    instructions="You are a helpful customer support agent.",
    model="gpt-4o",
)

hooks = KronvexHooks(
    api_key=os.environ["KRONVEX_API_KEY"],
    agent_id=os.environ["KRONVEX_AGENT_ID"],
    session_id="user-42",  # scope per user
)

# Session 1
result = await Runner.run(
    agent,
    messages=[{"role": "user", "content": "I'm on the Pro plan, invoice #4821."}],
    hooks=hooks,
)

# Session 2 — new process, same user
result2 = await Runner.run(
    agent,
    messages=[{"role": "user", "content": "What plan am I on?"}],
    hooks=hooks,
)
# → "You're on the Pro plan. I can also see your invoice #4821."

Before / after comparison

The difference is visible at the first return session:

Production considerations

EU hosting and GDPR

Kronvex stores all memory vectors in an EU-hosted PostgreSQL instance (Supabase Frankfurt region). For B2B agents handling personal data, this matters: Article 46 transfer mechanisms, DPA availability, and data residency SLAs are all covered. No extra configuration needed.

Rate limits and quotas

The Kronvex API enforces per-plan limits on remember calls (writes) and recall calls (reads) separately. For high-throughput agents, batch your remember calls or use the async client to avoid blocking the response path.

Memory hygiene

• Use memory_type="episodic" for conversation turns with ttl_days=30 to auto-expire stale exchanges
• Use memory_type="semantic" for durable user facts (plan, preferences, company) with no TTL
• Use memory_type="procedural" for agent-learned workflows and decision patterns
• Set top_k=6 as a starting point — more memories means larger prompts and higher latency

Quick start

Three steps to add memory to any OpenAI agent:

# Sign up at https://kronvex.io — free plan, no credit card
# Create an agent in the dashboard → copy the agent ID

pip install "kronvex[openai-agents]" openai-agents

export KRONVEX_API_KEY="kv-your-key"
export KRONVEX_AGENT_ID="your-agent-id"

from agents import Agent, Runner
from kronvex.integrations.openai_agents import KronvexHooks
import os

agent = Agent(name="MyAgent", instructions="You are a helpful assistant.", model="gpt-4o")
hooks = KronvexHooks(
    api_key=os.environ["KRONVEX_API_KEY"],
    agent_id=os.environ["KRONVEX_AGENT_ID"],
    session_id="user-42",
)
result = await Runner.run(agent, messages=[...], hooks=hooks)