Stop rebuilding context every session. Store user preferences, decisions, and conversation history. Build AI agents that actually remember.
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Without memory, every conversation starts from zero. Your users repeat themselves, agents make the same mistakes, and context is lost between sessions.
Remember how each user likes to be addressed, their communication style, and specific requirements across all interactions.
Agents pick up where they left off. No need to re-explain context when returning to a conversation after hours or days.
Agents get smarter by remembering what worked, what didn't, and building on past interactions.
Use memory.add() to store facts, preferences, decisions, and context during agent execution. RetainDB handles async writes so your agents never block on storage.
await client.memory.add({
project: "my-agent",
user_id: "user_123",
content: "Prefers detailed technical answers",
memory_type: "preference"
})Use memory.search() to fetch relevant memories before each LLM call. Hybrid search finds the most relevant context for the current query.
const memories = await client.memory.search({
project: "my-agent",
user_id: "user_123",
query: "How should I answer this?",
top_k: 5
})These pages support the commercial memory cluster around persistent memory, benchmarks, and buyer comparisons.
Compare RetainDB and Mem0 as memory layers for teams shipping now.
See how the product story differs for startup and enterprise buyers.
A comparison for teams evaluating framework-first vs productized memory.
Compare another benchmark-heavy memory and context platform.
Useful for teams comparing general memory layers against identity-rich agent memory.
Review the benchmark proof behind the memory claims.
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