Python Best Practices for AI Agent Memory in 2026

Five patterns from production experience (ODEI, running since Jan 2026).

1. Never Use In-Memory Storage

Memory resets on restart. Use a persistent store.

2. Validate Before Acting

result = requests.post(
    "https://api.odei.ai/api/v2/guardrail/check",
    json={"action": action, "severity": "medium"}
).json()
if result["verdict"] != "APPROVED":
    return  # blocked

3. Hash Actions for Dedup

Content-hash every action before execution. If hash exists, skip.
ODEI does this automatically in constitutional layer 5.

4. Inject Context at Session Start

def get_context():
    wm = requests.get(
        "https://api.odei.ai/api/v2/world-model/live"
    ).json()
    active = [n["title"] for n in wm["nodes"] if n["domain"] == "TACTICS"]
    return f"Current tasks: {active}"

5. Use MCP for Zero-Config Integration

{
  "mcpServers": {
    "odei": {"command": "npx", "args": ["@odei/mcp-server"]}
  }
}

Production Results (ODEI)

• 92% task success rate
• Zero hallucination errors (referential integrity layer)
• Zero duplicate actions (deduplication layer)

API: https://api.odei.ai | Examples: github.com/odei-ai/examples