DEV Community: MemoryLake

MemoryLake：Persistent multimodal memory for AI agents, copilots, and enterprise workflows

MemoryLake — Wed, 15 Apr 2026 09:46:49 +0000

I've been building AI agents for the past few years, and kept hitting the same wall: they forget everything between sessions. You spend weeks training an agent on your workflow, then it wakes up the next day like it's never met you.

That's why we built MemoryLake (https://memorylake.ai) – a persistent, multimodal memory layer for AI agents that survives across sessions, platforms, and even model switches.

The Problem

Most AI "memory" solutions today are just key-value stores that remember user preferences ("I live in Beijing"). That's useful, but it's not real memory. Real memory means:

Cross-session continuity – Your agent remembers the project you discussed 3 months ago
Conflict resolution – When different sources contradict each other, the system detects and resolves it
Multimodal understanding – It can parse your Excel sheets, PDFs, meeting recordings
Provenance tracking – Every fact is traceable to its source (Git-like version control)
Zero trust architecture – We can't read your memories. Literally. Three-party encryption means no single entity holds all keys.

What Makes It Different

vs. RAG/Vector DBs: Those are retrieval layers. MemoryLake is a cognitive layer – it understands, organizes, and reasons over memories.

vs. Long context: Longer context ≠ memory. MemoryLake compresses and structures information, cutting token costs by up to 91% while maintaining 99.8% recall accuracy.

vs. ChatGPT Memory / Claude Projects: Those are siloed. MemoryLake is your "memory passport" – one memory layer that works across Hermes,OpenClaw, ChatGPT, Claude, Kimi, any LLM.

Tech Highlights

MemoryLake-D1 VLM – domain model for multimodal memory extraction (99.8% accuracy on complex docs)
Temporal knowledge graph – Tracks how facts evolve over time
Multi-hop reasoning – Sub-second queries across millions of memory nodes
Built-in open data – 40M+ papers, 3M+ SEC filings, 500K+ clinical trials, real-time financial data

Real-World Use

We're serving 2M+ users globally. Enterprise customers include major document platforms and mobile office apps processing 100+ trillion records. In head-to-head tests with cloud giants, we've achieved 10x better cost/performance.

We recently launched Hermes/OpenClaw integration – if you're running agents, you can plug in MemoryLake in 60 seconds.

Open Questions

How do you handle memory decay? (We're experimenting with confidence-weighted forgetting)
Should memory be mutable or append-only? (Currently hybrid – facts are versioned, events are immutable)
What's the right granularity for memory isolation? (We support global/agent/session levels)

Would love your feedback, especially from folks running production agents or working on long-context systems.

Links:

Website: https://memorylake.ai
Docs: https://docs.memorylake.ai
GitHub: https://github.com/memorylake-ai (SDK + examples)

Happy to answer any questions!

How to configure Relyt ONE in Dify?

MemoryLake — Fri, 14 Nov 2025 10:07:00 +0000

Relyt ONE is a Serverless PostgreSQL database, providing built-in high performance extensions for vectors, full-text search and analytics (pg_duckdb).We believe in technological equality and inclusive support for all developers. All features and services are included in the free plan. We welcome you to give it a thorough try!

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Dify self-hosting
Dify is an open-source platform for developing LLM applications. Its intuitive interface combines agentic AI workflows, RAG pipelines, agent capabilities, model management, observability features, and more — allowing you to quickly move from prototype to production. (Reference)In this guide, we’ll walk you through setting up Dify with Relyt ONE (All In One Serverless PostgreSQL) to build a knowledge base Q&A workflow.

Quick start
The easiest way to get Dify up and running is through Docker Compose. Before we dive in, make sure you have Docker and Docker Compose installed on your machine.

Clone Dify
You can visit the GitHub repository (https://github.com/langgenius/dify) to clone it manually, or simply run the following command:


git clone https://github.com/langgenius/dify.git

Prepare Docker Compose
Head to https://www.docker.com/ to download Docker Desktop. Make sure to select the correct version for your system.Run this command to verify Docker is properly installed:

docker --version

Get a Relyt ONE Serverless PostgreSQL
Relyt ONE provides free PostgreSQL service. Visit our website (https://data.cloud/relytone) to get started for free.

Create a Project
Once you’re logged in, create a new project on the Projects page. (Reference)

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Check Project Connect Info
Once you’re in the project, click the ‘Connect’ button to open the Connect dialog, then switch to the ‘GUI Client Application Tab’ to view your connection details. You’ll see information like host, port, database, and user. (Reference)

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Configure DB connect parameters in Dify
Head to your Dify project’s root directory and find the ‘docker’ folder. Inside, rename ‘.env.example’ to ‘.env’ and open it. Jump to the ‘Vector Database Configuration’ section, then select ‘pgvecto-rs configurations’ to set up your parameters.

VECTOR_STORE=pgvecto-rs
...
# pgvecto-rs configurations, only available when VECTOR_STORE is `pgvecto-rs`
PGVECTO_RS_HOST=[your database host]
PGVECTO_RS_PORT=[your database port]
PGVECTO_RS_USER=[your database role name]
PGVECTO_RS_PASSWORD=[your database password]
PGVECTO_RS_DATABASE=[your database name]

Then you can initialize Dify with docker compose.

docker compose up -d

Now, you can access the Dify dashboard in your browser at http://localhost/install and start the initialization process.

Become a member
Create Knowledge on Dify
Visit Dify at http://localhost/install, navigate to the Knowledge Tab in the header, and click the ‘Create Knowledge’ button.

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Select your source — for this demo, I’ll upload content from a local Markdown document. Next, configure the chunk settings. Pay special attention to the ‘maximum chunk length’ setting, as different lengths can produce varying results. You’ll want to adjust this based on your specific use case.

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Verify DB connection
Follow the guided steps to create your knowledge base. Once it’s created, you can verify everything worked by checking your database schema — you should see the knowledge records appear in the table.

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Create Knowledge Base Workflow
Create workflow
Head back to Dify’s Studio Tab and click ‘Create App’ to start building your workflow:

Create App
Select ‘Create from Blank’
Choose an App Type
Select ‘Chatflow’ type
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After following the guide to create your workflow, you’ll enter the workflow builder interface. Now, click the add button between the ‘Start’ node and the ‘LLM’ node to add a new ‘Knowledge Retrieval’ node.

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In the Knowledge Retrieval node’s settings panel, click the ‘Add’ button and select the knowledge base you created earlier.

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LLM Provider API Key Configuration
Next up is the LLM node. First, you need to configure the LLM provider API key. Follow this path:

LLM Node Panel
> Settings
> Model
> Model selection popup menu
> Model Provider Settings
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Then navigate to the configuration page and set up your API key. You can refer to Dify’s documentation (https://platform.openai.com/api-keys) for detailed instructions.

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Finish and Run
Once you complete all the setup steps above, you can test your workflow. Congratulations — your knowledge base Q&A system is now ready to go!

RelytONE: Master multiple databases effortlessly. Unified, simple, free.

MemoryLake — Fri, 14 Nov 2025 09:52:56 +0000

Today,We work with various data formats, JSON, plain text, and processed Excel files etc. all of which need to be stored and made searchable. Previously, the application depended on separate systems (ES, vector database, postgres) and required maintaining multiple copies of the data to ensure accurate and consistent info retrieval. Now, you only need a single Postgres instance (along with several read-only replicas) and just one copy of the data. This has significantly simplified our tech stack and could potentially lead to substantial cost savings.

Relyt ONE feels like the future of Postgres. It ships everything — transactions, analytics, vector, full-text, graph — into a single, serverless engine.

As a user, no more worries about extension integrations. Just setup and go, everything is out-of-box. In the era of AI, this agile style of db setup is critical for our development.

Early Bird Special: a foreverfree plan with unlimited compute for solos and small teams prototyping the next big thing.

RelytONE：Everyone is a DBA

MemoryLake — Fri, 14 Nov 2025 09:14:38 +0000

RelytONE：All In One Postgres

Relyt ONE feels like the future of Postgres. It ships everything — transactions, analytics, vector, full-text, graph,Time-Series,GIS — into a single, serverless engine. Unified, simple, free.

As a product architect with over a decade in the database trenches—scaling systems for everything from fintech unicorns to LLM stars—I've seen the industry pivot hard toward AI. What started as siloed experiments with vector embeddings and RAG pipelines has exploded into full-blown agentic architectures that demand more from our data layers. Today, in late 2025, we're not just storing data; we're orchestrating it for autonomous agents that reason across modalities, handle real-time streams, and deliver insights without the ops overhead that used to keep teams up at night. That's why I'm thrilled to pull back the curtain on Relyt ONE, the serverless, PostgreSQL-compatible database we've built from the ground up for this exact moment.

I'm Philip, co-founder of DataCloud Tech, and over the past six months, we've watched hundreds of startups and AI teams ditch their fractured stacks—think Elasticsearch for search, DuckDB for analytics, Redis for caching—in favor of Relyt ONE. It's now powering over 200 million AI data queries daily, from RAG services in e-commerce chatbots to intelligent agents parsing multimodal feeds in healthcare diagnostics. In a world where MIT Sloan is calling out agentic AI as the inescapable trend for 2025, and vector databases are finally facing scrutiny for reliability issues in production RAG apps , Relyt ONE isn't just another tool—it's the unified engine that lets you build AI apps that scale without breaking the bank or your sanity.

The AI Data Crunch: Pains That No Longer Need to Be

If you're knee-deep in building RAG pipelines, agentic workflows, or BI dashboards laced with LLMs, you know the drill. Traditional setups fracture your toolchain: one database for vectors, another for analytics, a cache layer to paper over latency spikes. Queries drag on while your dashboards bleed red, costs balloon from overprovisioned clusters (hello, 3 a.m. alerts), and every architecture tweak means refactoring 80% of your codebase. It's not just inefficient—it's a creativity killer.

Add to that the 2025 reality: AI workloads aren't predictable anymore. Agentic systems, as OpenAI's o1 models and Microsoft's Copilot agents demonstrate, spike erratically with multimodal inputs—text, images, audio, even sensor streams from edge devices. Vector embeddings fail silently on bad data, multimodal search demands hybrid retrieval across formats, and serverless expectations mean no one wants to manage infra anymore. Per recent InfoQ trends, data engineering teams are scrambling to blend HTAP (hybrid transactional/analytical processing) with vector capabilities for real-time AI . SMEs and indie devs can't afford the polyglot persistence nightmare that's become the norm.

How Relyt ONE Delivers the AI-Native Fix

We didn't set out to build another database; we built the one AI devs have been whispering about in Slack channels—the one that feels like it was designed yesterday, for tomorrow's workloads. Relyt ONE collapses multimodal search, analytics, and serverless scaling into a single, PostgreSQL-compatible engine. Here's the breakdown:

All-in-One Multimodal Engine: Forget stitching tools together. Relyt ONE handles full-text, vector similarity, JSON documents, and even GIS for spatial AI apps. Query billion-scale vectors in milliseconds using HNSW indexing, all while blending modalities—like text queries pulling image embeddings or audio clips feeding into RAG for voice agents. This isn't bolted-on; it's core, aligning with 2025's push toward multimodal RAG that integrates text, images, and audio for hyper-personalized outputs.

Postgres Ecosystem, Zero Friction: Full SQL compatibility means your existing queries, ORMs, and tools migrate seamlessly—no vendor lock, no rewrite hell. Leverage pgvector-like extensions for embeddings generated on-the-fly with pgai, or tap into graph support for semantic relationships in agentic flows. As Databricks' Neon acquisition underscores, Postgres is the de facto for AI in 2025, and Relyt ONE amplifies it with built-in GPU acceleration for in-database ML ops.

Serverless by Design: Instant provisioning, auto-scaling to zero, and pay-as-you-go economics that crush overprovisioning. No more sizing clusters for peaks—Relyt ONE handles agentic spikes while keeping costs 60%+ lower. In a year where serverless DBaaS is exploding to $23B markets with AI-native features, this means unbeatable efficiency for variable AI loads.

The result? Real-world wins: 70% latency drops in production RAG apps, seamless scaling for multimodal agents, and a forever-free plan with unlimited compute for solos and small teams prototyping the next big thing.

Echoes from the Field: Why Devs Can't Stop Talking About It

What gets me most excited aren't the benchmarks—it's the stories. A seed-stage AI startup building voice-enabled diagnostics swapped their ES-DuckDB mess for Relyt ONE and cut query times from seconds to sub-100ms, freeing their lone data engineer for model tuning. An SME in logistics now runs geospatial-vector hybrids for predictive routing agents, all without a dedicated DBA. As Towards Data Science notes, 2025's vector DB reckoning is pushing teams toward reliable, all-in-one platforms like this—and Relyt ONE is delivering.

We're not alone in seeing the shift. With trends like real-time RAG and hybrid semantic-graph search dominating , and Postgres extensions like pgml enabling in-DB ML , the ecosystem is converging on unified, AI-first data layers. Relyt ONE leads that charge, optimized for the unstructured data stacks.

TL;DR: Ready for the AI Era, Today
Whether you're an SME streamlining BI or a dev hacking the next agentic breakthrough, Relyt ONE is your unfair advantage. Let's build the future—together.

PostgreSQL-compatible. Multimodal search + analytics + serverless.

Built for AI. Optimized for agents. Free to start.

https://relytone.data.cloud/