Insights on AI memory, routing, and intelligent systems
The MCP server collection ships a knowledge graph memory server: one JSONL file on your machine, nine tools, and a system prompt you write. MemoryRouter runs the same job from a hosted vault that every connected client can reach.
Two ways to give DeepSeek Harness a memory: dsh-memento's approval-gated local SQLite store, or MemoryRouter's hosted vault that every connected tool reads. The decision comes down to where the record lives and who signs for it.
Mem0 now ships an official OpenClaw plugin that extracts facts into a store you can rent or run yourself. MemoryRouter relays direct exchanges into one vault. How the slot decision really splits.
ByteRover curates OpenClaw memory into Markdown files inside your workspace. MemoryRouter stores one vault that every tool you connect can read. Where each design earns its keep.
OpenClaw's docs describe an official memory plugin that runs locally in LanceDB. MemoryRouter keeps a vault your other tools read. Honest split, with sources.
The export email can take up to a week, the download link dies a day after it arrives, and neither is where most attempts fail. Here is the fix at each stage.
A shared vault is empty and the context that would fill it lives in someone's personal vault. Two operations move it: transfer copies the whole core vault, graft copies reviewed semantic matches. Test both on throwaway vaults before anything real moves.
A folder of reviewed notes and a terminal are the shortest path to a vault your AI can read. The path has two halves: the upload fills the vault, and the recall happens in a client you connect separately. Here is the whole sequence, plus the one-file rehearsal that catches an upload that never landed.
A NemoClaw sandbox denies network egress by default, so the memory plugin installs cleanly and the call behind it gets dropped at the policy layer. This is the one endpoint that has to be allowed, the diff an operator reviews, and the fresh-session test that proves recall before real conversations go through it.
Open WebUI has memory features of its own, and they stay inside the instance. The question worth answering is whether a decision can show up in your next tool too. Here is the provider route and the boundaries that keep a shared instance honest.
Retrieval and conversational memory both look like search from inside a LangChain app, so teams ship one and assume they got the other. Two separate Python processes and one synthetic fact will show which one you actually built.
Claude's built-in import is the right answer for a one-time move. If you keep both assistants in use, the decision changes, because imported memory lives inside Claude while a vault belongs to no single tool.
Model comparisons quietly measure two things at once: the model, and the context each run happened to start with. Hold memory fixed, change one model ID, and the difference finally belongs to the model.
Adding a memory connector to Claude is a permissions decision before it is a feature. What each scope grants, what a read-only connection actually blocks, and what a workspace can lock down.
Point your SDK at a memory proxy and every request carries that user's context: retrieval before the provider call, storage after, one vault per end user. The request path, the two-request proof, and where this shape is wrong.
Run the model on localhost and the next session still opens empty. Local inference and local memory are separate decisions, and the boundary is yours to place.
A connector captures from the moment you install it. The months before that are still sitting in local session files, and the import path brings the reviewed parts back: the agent extracts, the connector validates, and nothing uploads without approval.
A memory layer is the component that turns what your agent did into context it can use next time. Here is what it is, what it is not, and how to add one.
Your chatbot forgets between sessions because a new conversation is a blank slate. Here is the production shape that fixes it, from identity to deletion.
ChatGPT's memory is useful inside ChatGPT. If you want context that survives switching tools, the replacement is not a better button inside one app. It is a vault you own.
CLAUDE.md holds the rules you remembered to write down. It cannot hold the decisions, dead ends, and task state from yesterday's session. Here is what fills that gap, and where the two layers split.
Claude remembers more than people assume and less than they need. Here is what actually survives between sessions on each Claude surface, and how to make the next session start where the last one stopped.
Rules files tell Cursor how to behave. They cannot tell it what happened. Teams keep confusing the two, then wonder why every new chat starts cold.
Conversation context that survives the session is infrastructure, not a feature. Here is what a long-term memory API must provide, and a fifteen minute test to run.
An agent that starts cold every session is a demo, not a teammate. Here is what persistent memory actually requires, where implementations break, and how to add it.
The project brief lives in whichever tool you typed it into last. One vault, connected per tool, fixes that. Here is the setup for ChatGPT, Claude, Cursor, and Codex, and the test that proves it works.
Both use embeddings, so people assume one system covers both. RAG answers what your documents say. Agent memory remembers what you decided and why.
Zed starts every agent thread from zero, and one entry in settings.json changes that. Wire in a remote context server, watch the tool calls land, and prove recall in a thread that never saw the original decision.
Settle a decision in the dsh Web UI, then open tomorrow's Telegram session and find no trace of it. Session logs replay the past; recall serves sessions that never saw it, and one plugin hooks the harness core so every channel shares one vault.
A checkpointer resumes a thread. It does not carry a user's preferences into the next one. Two fresh processes on one Memory Key is the test that separates the two systems, and recall is only half of the job.
Aider has no plugin system and no MCP support, so the usual memory integrations have nowhere to attach. Point the OpenAI-compatible endpoint at MemoryRouter and every session through that route is stored and recalled, with no tool call and nothing to install.
ChatGPT's built-in memory is useful, and it stays inside ChatGPT. The decisions you save on purpose can live in a vault you own, and that context is waiting in Claude when you switch. Here is the setup and the two-assistant test.
OpenClaw's built-in files hold what someone took the time to write down. The reasoning behind the decisions lives in the conversations, and the mr-memory plugin captures them automatically, recalling the relevant parts in later sessions while inference stays inside your gateway.
Windsurf is transitioning to Devin Desktop, and legacy Cascade and Devin Local do not remember the same things. What the native path covers today, where it stops, and the one entry that keeps your decisions available across tools.
The Memory Bank holds the project documentation somebody reviewed and kept current, which is why it works. The reasoning from conversations that never made it into those files has nowhere to live. One config entry and a browser sign-in give it a home.
No. Cursor removed its Memories feature in version 2.1, so a new chat starts from Rules and AGENTS.md only. Here is what still persists, what to use instead, and the one mcp.json entry that gives Cursor memory across conversations and tools.
Past a few hundred captured turns, a vault that only stores raw history starts answering questions with near-duplicates. The consolidation layer is what turns captured sessions into tiered long-term memory with receipts, and in Claude Code it runs automatically.
MCP gives a client memory tools. It does not force a save, and a green connection never proved a recall worked. One synthetic fact and a second client can verify the whole path in about two minutes.
Yes, but only locally and only if you turn it on. Codex has AGENTS.md for instructions and opt-in Memories that summarize idle chats on one machine. Here is exactly what Codex remembers, what it does not, and how to give it persistent context across sessions, machines and tools.
The native MemoryRouter plugin for Hermes Agent is live. Three install commands, clean conversational capture, and cross-session recall tested before and after publishing.
Use the official ChatGPT export with a deterministic local preview, exact approval, safe resume, cancellation, and an immutable MemoryRouter receipt.
Create a structured ChatGPT memory profile, review facts and inferences, and save only the version you approve.
One command gives Claude Code portable semantic memory that follows you across machines and survives compaction. Here is how the lifecycle hooks work.
Connect MemoryRouter to Claude Cowork for explicit, model-directed recall and user-approved saving across conversations without pretending every turn is captured automatically.
Drop one node into your LangGraph agent and every user gets a private, persistent memory layer. No transcript stuffing, no manual recall, no forgetting between sessions. This is the integration LangChain builders have been waiting for.
MemoryRouter just got a full-company redesign: a new Glacier visual system, refreshed logo, sharper palette, and a product experience that finally matches the infrastructure underneath.
Persistent memory for Open WebUI - one-click history upload, automatic storage going forward, and a new key suffix trick that changes everything.
We threw an OpenClaw meetup in NYC. 100+ developers showed up. Here's what went down and why we're doing it again.
I hit the same wall every OpenClaw user hits. Memory. So I stopped waiting and built the fix in a no-sleep weekend. What happened next surprised even me.
