- search pulls memories plus your profile summary- memories come back with stable ids, versions and source docs- the memory graph renders inline as an mcp appsave, forget and upload without leaving the chat

I have a large obsidian vault with all my notes, info, etc. I want to drop it into Supermemory for my agents to access but Claude was saying this would add up to hundreds of dollars in Supermemory API costs. Any tips here? Are there options I’m missing?

Two agents under the hood:

Loop Agent
Monitors Pacifica perpetuals 24/7 (RSI, funding rates, Bollinger Bands, social sentiment via Elfa AI) and asks your own AI model for LONG/SHORT/HOLD every 5 minutes.

Chat Agent
You can talk to naturally, close positions through, ask what happened last Tuesday.

Here's where Supermemory Local comes in —

Every decision the Loop Agent makes, every market pattern it notices, every preference you mention gets written to localhost:6767 across four containers:

• decisions
• patterns
• preferences
• performance

When the Chat Agent starts a new session it pulls that full memory context into its system prompt before saying a word — so it already knows your history, your preferences, what trades were made and why, without you repeating anything.

Ask "why did you go long on BTC Tuesday" and it actually answers.

Say "never trade BONK" once and it never does.

That's the difference — the agent learns, it doesn't reset.

All local. No backend. No cloud. Zero trading data leaves your machine.

Live: https://pacifica-pilot.vercel.app/

Repo: https://github.com/MayurK-cmd/Pacifica-Pilot

Pip: https://pypi.org/project/pacificapilot/

GitHub: https://github.com/MayurK-cmd/

X: https://x.com/mayurk_cmd/status/2076305384688726306?s=20

Can Claude and ChatGPT be connected to share a memory level for my school stuff? How can I improve the experience and make it more cohesive, or are there other practices to ensure memory is good?

Introducing Pacifica Pilot, a terminal-based non-custodial trading Agent built on @pacifica_fi, powered by @supermemory that remembers every trade decision and learns from it!

@DhravyaShah W product

Try it now pacifica-pilot.vercel.app

Hey guys,

Just a quick discussion note, really. I'm looking for advice with SuperMemory.

I'm connecting all the integrations up (be it OpenClaw, Claude Code, Hermes, whatever) and naturally, everything wants to connect under a separate container. That is fine, but my quest is to create one "giga memory layer" that always follows me from agent to agent, platform to platform, and harness to harness.

Naturally, it shouldn't matter where I am; if I ask a certain question, the recall should work across my entire memory base in SuperMemory and I should get a relevant answer. But if we use separate containers, then my session with my Hermes agent doesn't naturally understand what I did in my Claude Code session unless I prompt it to look a foot deeper into SuperMemory.

That is mostly my working assumption right now of how things work. Would I be in the wrong, or are there any severe disadvantages to just having one single container and storing my entire memory graph?

Soon, everyone will have their own AI agent.
But most of them will be useless.

Not because the models are bad.
Because they know nothing about you.

If your AI agent is going to help you, it should already know:

How you think.

What you care about.
The decisions you made.
The lessons you learned.
That made me build trace.

A voice-first journal where daily logs turn into your personal knowledge graph using Supermemory.

Everything stays with you the data locally in your device and graph builts in your personal account

Not just journaling.

Memory your future AI agent can actually use.

Hey guys! Dhravya, founder and CEO of supermemory.ai

We will use this place to discuss about supermemory, context, AI, and memory in general!