Play 25
Conversation Memory
High✅ Ready
Persistent agent memory — short-term, long-term, and episodic stores.
Gives agents the ability to remember across sessions. Three-tier memory architecture: short-term (Redis, current conversation context), long-term (Cosmos DB, user preferences and facts), and episodic (AI Search, past interaction summaries). Agents learn from interactions, personalize responses, and build knowledge over time. Semantic recall finds relevant memories even when exact keywords differ. Memory decay prevents stale data from poisoning responses.
Architecture Pattern
Memory layer: 3-tier (short/long/episodic), semantic recall, decay policies
Azure Services
Azure OpenAI (gpt-4o)Cosmos DBAzure AI SearchAzure Redis Cache
DevKit (.github Agentic OS)
- agent.md — root orchestrator with builder→reviewer→tuner handoffs
- 3 agents — Memory Builder (gpt-4o), Reviewer (gpt-4o-mini), Tuner (gpt-4o-mini)
- 3 skills — deploy (102 lines), evaluate (103 lines), tune (101 lines)
- 4 prompts — /deploy, /test, /review, /evaluate with agent routing
- .vscode/mcp.json — FrootAI MCP with Cosmos DB + Redis inputs + envFile
TuneKit (AI Config)
- config/openai.json — embedding model for memory search
- config/memory.json — tier definitions, TTL per tier, max memories
- config/guardrails.json — PII filtering in memories, consent rules
- evaluation/eval.py — Recall accuracy >85%, Memory relevance >0.80
Tuning Parameters
Memory tier TTLs (short=1h, long=30d, episodic=90d)Retention policy (decay rate)Recall strategy (semantic vs recency)Embedding model configMax memories per userContradiction resolution rules
Estimated Cost
Dev/Test
$100–250/mo
Production
$1.5K–5K/mo