xmem

xmem

xMem is a hybrid memory orchestrator for LLMs that combines persistent long-term knowledge and session (real-time) context to improve relevance and accuracy of LLM responses, providing an API and dashboard for integration with vector DBs and LLM providers.

xmem is chatbots & assistants software teams evaluate for personal & entertainment. Use this page to review pricing, integration signals, and the best alternatives before you commit.

Free API
One of 161 tools in Chatbots & Assistants
Added 1 month ago
Data reviewed Sep 3, 2026

Profile facts come from the vendor source. AiMatch labels unknown pricing or API details instead of estimating them.

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Quick Overview

Best for: Personal & Entertainment

What it does

Chatbots & Assistants software for decision-makers comparing workflow fit and alternatives.

Best fit

Personal & Entertainment

Pricing snapshot

Free

Next step

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xMem is a hybrid memory orchestrator designed for LLM applications that combines long-term persistent memory with real-time session memory to keep AI assistants relevant and accurate. It orchestrates persistent and session memory for every LLM call, enabling applications to avoid losing context between sessions and to recall past conversations, notes, and documents. The product targets developers and teams building LLM apps and offers an API and dashboard with compatibility across open-source LLMs and common vector databases.

xmem centralizes company knowledge, streamlines sharing, and integrates with APIs & LLMs.

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Key Features

Long-Term Memory

Store and retrieve knowledge, notes, and documents with vector search to maintain persistent user and application knowledge.

Session Memory

Track recent chats, instructions, and context in-memory or via session stores for recency and personalization.

RAG Orchestration

Automatically assemble the best context for every LLM call for retrieval-augmented generation without manual tuning.

Knowledge Graph

Visualize connections between concepts, facts, and user context in real time to help LLMs reason and recall linked information.

Memory Orchestration / Real-time Context Assembly

Orchestrates both persistent and session memory for each LLM call to ensure responses are relevant and up-to-date.

Open-Source First & LLM Compatibility

Works with open-source LLMs such as Llama and Mistral and supports multiple LLM providers.

Easy API & Dashboard

Provides an API, SDK snippet, and dashboard for integration, monitoring, and management of memory and context.

Pricing

Free Tier Available

Offers a 'Get Started Free' option (no further pricing details provided on the page).

Use Cases

Persistent user context and personalization

Keep user-specific knowledge and conversation history available across sessions so agents remember users and prior discussions.

RAG, agents, and copilots

Serve as the memory and retrieval layer for retrieval-augmented generation systems, agents, and copilots to assemble relevant context on each call.

Preventing lost context in multi-session workflows

Avoid repeated explanations and lost project or team context by maintaining persistent memories and session recollection.

Integrations

Qdrant

Supported vector database option for storing and retrieving long-term memory vectors.

ChromaDB

Supported vector database option used in examples and for vector storage.

Pinecone

Listed as a compatible vector DB for memory storage and retrieval.

Llama.cpp

Listed as a compatible LLM provider for use with xMem.

Ollama

Listed as a compatible LLM provider for use with xMem.

OpenAI

Listed as a compatible LLM provider for use with xMem.

Mistral

Referenced in an SDK example as an LLM provider (example integration in memory-orchestration.ts).

MongoDB / in-memory session stores

Session store examples include in-memory and MongoDB for session memory handling.

Benefits

Never lose user knowledge or context across sessions through persistent memory.
Improve LLM accuracy and relevance by orchestrating the best mix of long-term and session context.
Works with open-source LLMs and common vector DBs, supporting flexible deployment choices.
Easy integration and observability via API and dashboard for faster developer onboarding.
Real-time knowledge graph linking enables better reasoning and connected responses.

Limitations

No verified limitations are available.

Frequently Asked Questions

No verified FAQs are available.

Getting Started

  1. 1 Visit the xMem site and choose 'Get Started Free' or go to the dashboard.
  2. 2 Consult the APIDocs and dashboard to configure memory stores and vector DB connections.
  3. 3 Integrate the SDK/API (example shown in memory-orchestration.ts) and call orchestrator.query to include memory in LLM prompts.

Support

docs

APIDocs referenced on the site for integration and usage guidance.

dashboard

Web dashboard available for monitoring and configuration (link: Go to dashboard).

contact

Contact entry listed in the site's footer for inquiries.

API

Available: Yes
Documentation:

APIDocs referenced on site (no direct documentation URL provided on the page).

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