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**Memori Cloud Documentation**

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Agent-native memory infrastructure

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Open Source
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About **Memori Cloud Documentation**

Memori is a memory layer for LLM applications, agents, and copilots. It continuously captures interactions, extracts structured knowledge, and intelligently ranks, decays, and retrieves relevant memories so AI remembers the right things at the right time across every session. Memori uses Advanced Augmentation to turn raw conversations into structured, searchable memories, and Agent Trace Execution to capture tool calls, decisions, workflow steps, and outcomes from agent execution history. It runs asynchronously in the background with minimal impact on response paths. The platform supports OpenAI, Anthropic, Gemini, Grok (xAI), Bedrock (via LangChain), and OpenAI-compatible providers. It integrates natively with LangChain, Agno, and Pydantic AI, and works in sync, async, streamed, and unstreamed modes. The open-source version allows self-hosting with BYODB, and the Memori Cloud offers a free tier with 5,000 memories created and 15,000 recalled per month, with paid production plans starting at $60K/year.

Key Features

Advanced Augmentation – background AI extracts facts, preferences, and relationships from conversations
Agent Trace Execution – captures tool calls, decisions, workflow steps, and outcomes for structured memory
Zero Configuration – no database setup needed; get an API key and start building
Framework Integration – native support for LangChain, Agno, and Pydantic AI
Multi-LLM Support – OpenAI, Anthropic, Gemini, Grox, Bedrock, and OpenAI-compatible providers
Intelligent Recall – ranks memories by relevance and importance with decay to keep context clutter-free
Sync, Async, Streamed, and Unstreamed modes supported
Open-source self-hosted option with BYODB

Pros & Cons

Pros
  • Eliminates need for manual database configuration with cloud offering
  • Learns from both conversation content and agent execution traces
  • Supports multiple LLM providers and popular agent frameworks
  • Background processing minimizes latency impact on response
  • Intelligent memory decay avoids information overload
  • Open-source core allows full control over data and stack
Cons
  • Free cloud tier has memory limits (5,000 created, 15,000 recalled per month)
  • Production plans are expensive ($60K/year for single agent)
  • Relatively new tool; ecosystem and community may still be small
  • Cloud storage of memories may raise privacy concerns for some use cases
  • Requires API key and internet connection for cloud features

Best For

Building AI agents with persistent memory across sessionsTracking user preferences and historical context for copilots and assistantsDebugging agent execution by recalling tool calls and decision pathsEnhancing customer support bots with memory of past interactionsCreating personalized AI experiences that learn from user behavior over time

FAQ

What is Memori?
Memori is an agent-native memory infrastructure that provides a memory layer for LLM applications, agents, and copilots. It captures interactions, extracts structured knowledge, and intelligently ranks, decays, and retrieves relevant memories across sessions.
How do I get started with Memori Cloud?
Sign up at app.memorilabs.ai, get an API key, and connect your LLM client. No database setup is needed. Quick start examples are available in Python and TypeScript.
Which LLM providers are supported?
Memori supports OpenAI, Anthropic, Gemini, Grok (xAI), Bedrock via LangChain ChatBedrock, and any OpenAI-compatible provider using the base_url parameter.
Is Memori free?
Yes, there is a free Cloud tier with 5,000 memories created and 15,000 recalled per month. The open-source version is also free for self-hosting with your own database.
What is Agent Trace Execution?
Agent Trace Execution captures tool calls, decisions, workflow steps, and outcomes from agent execution history and turns them into structured memory primitives that agents can recall and reuse across sessions. This allows agents to remember not just what users said, but what actually happened.