Python Best Practices for AI Agent Memory in 2026

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