You don't use one AI anymore — Claude to code, ChatGPT to think, Cursor in your editor, Gemini to search. Each one quietly learns how you work, then keeps it to itself, and AI providers have no incentive to share. OneLamp is the self-curating, neutral layer across all of them: a per-user context layer every agent writes to and reads from through a single MCP endpoint. Reach for the next tool and it already knows your stack, your conventions, the decision you made an hour ago in another tab.
- Cross-provider. One endpoint that Claude, ChatGPT, Cursor, Codex, and any other MCP client share.
- Self-curating. It keeps itself current as you work — surfacing what you rely on, letting go of what's gone stale — so it's never a file you hand-maintain.
- Hooks into the agent loop, not just an endpoint. Beyond MCP, our plugins for Claude Code, Codex, and Cursor install secure agent lifecycle hooks — auto-loading your context when a session starts and capturing learnings before it ends — so context adherence is enforced in the loop, not left to the agent to "remember".
- Teams, by promotion not pooling. Opt in to share a learning with your team without exposing your personal context. Use preset/custom policies to control context sharing.
- Yours. Export or delete everything at any time.
- Create an account at app.onelamp.ai — sign in with email (one-time code), Google, or GitHub.
- Connect an agent — copy your MCP endpoint and add it to your AI client (below). The client runs the sign-in flow itself; there's no API key to paste.
- Use it. Your agents now auto-load context at the start of a session and save durable learnings as you work.
Your endpoint:
https://api.onelamp.ai/mcp
It's a remote, streamable-HTTP MCP server secured with OAuth 2.1 — the first connection opens a browser to sign in to OneLamp, then the client caches the token.
- Cross-agent handoff (the "aha"). Tell Claude a decision about your architecture; open Cursor an hour later and it already knows — no re-explaining.
- Your preferences travel with you. Your stack, coding conventions, and writing voice follow you into every agent, so each one sounds like you from the first message.
- Pick up where you left off. Move to the next tool mid-task and your current focus and open work come along — no cold start in the new agent.
- Onboard a new agent or tool instantly. Point a fresh agent at OneLamp and it starts from everything the others learned, not from zero.
- Bring your own knowledge. Connect Notion, Drive, or any MCP server and pull your specs and docs in on demand — indexed into the same context every agent draws on.
- Stop re-explaining across tools. A ranked context pack beats re-pasting the same background into every new chat (fewer tokens is the side effect, not the point).
- Share what works with your team. Promote a hard-won fix or convention to a team; teammates' agents build on it — shared learnings are corroborated before they're trusted, without pooling anyone's private context.
- Own and move your context. Export your entire context as JSON from your account — anytime, no lock-in.
Add OneLamp as a remote HTTP MCP server:
{
"mcpServers": {
"onelamp": {
"type": "http",
"url": "https://api.onelamp.ai/mcp"
}
}
}In the Claude app, add it as a custom connector; in ChatGPT, enable developer mode and add the remote MCP server.
Install the plugin (bundles the MCP server, auto get/save session hooks, and the
/onelamp command, aliased /ol):
# add the OneLamp marketplace, then install the plugin
/plugin marketplace add https://app.onelamp.ai/marketplace.json
/plugin install onelamp@onelampRun these in Claude Code itself (terminal or IDE extension) — /plugin isn't
available in the Claude desktop app's chat.
curl -fsSL https://app.onelamp.ai/codex/install.sh | bashRegisters OneLamp as a native remote MCP server and wires SessionStart/Stop hooks (auto load/save, fail-open).
curl -fsSL https://app.onelamp.ai/cursor/install.sh | bashOr one-click — Add to Cursor:
cursor://anysphere.cursor-deeplink/mcp/install?name=onelamp&config=eyJ1cmwiOiJodHRwczovL2FwaS5vbmVsYW1wLmFpL21jcCJ9
No installer — ChatGPT adds OneLamp as a custom connector in Developer Mode:
Settings → Connectors → Developer Mode → add a custom connector with the URL
https://api.onelamp.ai/mcp, then authorize. ChatGPT has no lifecycle hooks, so
paste integrations/chatgpt/custom-instructions.md
into Settings → Personalization → Custom instructions as a capture backstop.
A first-class ChatGPT app with inline UI (via the OpenAI Apps SDK) is in
progress.
Point the MCP Inspector at
https://api.onelamp.ai/mcp to confirm the OAuth flow and list the tools.
An MCP endpoint exposes the tools — but an endpoint alone can't guarantee an agent actually uses them at the right moment. OneLamp's plugins go a layer deeper. For Claude Code, Codex, and Cursor, installing the plugin securely wires session lifecycle hooks directly into the agent's loop:
- On session start, the hook loads your ranked context pack before the agent takes its first step — so it begins every task already grounded in your preferences, decisions, and conventions.
- On session end, the hook captures durable learnings back to your context — so nothing has to be remembered manually.
Because loading and capture run as part of the agent's own lifecycle rather than as optional tool calls, context adherence is enforced in the loop, not left to chance. The hooks are fail-open (a OneLamp hiccup never blocks your agent) and scoped to the lifecycle events they need — nothing more.
A small set of memory tools is the whole surface most agents use:
| Tool | What it does |
|---|---|
save_context |
Save a durable learning — a preference, decision, or fact — to your context. |
get_context |
Retrieve a relevance-ranked context pack for the current task. |
save_session |
Capture a session's takeaways as a handoff for the next tool. |
resume_session |
Pick up cross-tool work where you left off — list recent sessions, or load one in full. |
list_context / forget_context |
Browse what's saved, or delete an entry you no longer want. |
The Claude Code and Codex plugins call get_context at session start and
save_context / save_session before a session ends automatically, so capture
and recall happen without you thinking about it.
get_context also takes an optional scope — personal, team, team:<id>,
or all (the default) — so a query can draw on your personal context, a team's
shared context, or both at once. See Teams below. Export and account
management live in the app, not as chat tools.
Beyond what your agents capture directly, you can connect the tools you already work in — Notion, Google Drive, Slack, or any remote MCP server. When you pull content in, OneLamp indexes it on demand and folds it into the same self-curating context layer your agents draw on — it enriches your context rather than relaying a source straight through. Connect once, then pull in fresh material whenever you want.
On-demand, not crawled. Connected sources are indexed only when you explicitly pull them in — there's no background crawl. Capture is capture-on-write (agents call
save_context); freshness for connected sources is an explicit action you take.
Connect a source, pull content in, and browse the compiled Library — a searchable catalog your agents draw on — all from the Data sources and Library pages in the app. These are managed in the app rather than as chat tools, so they never crowd an agent's tool list.
A team shares one context layer — but by promotion, not pooling. Your personal context is never merged into a team's. Instead, you explicitly promote a selected learning into a separate, ACL'd team store, where every teammate's agents can draw on it. Joining a team never exposes your private entries.
- Opt-in and explicit.
save_contextalways writes to your personal store. A learning reaches the team only when you choose to promote it — the Promote action in the app. - Secret-redacted at the boundary. Promotions are scrubbed of secrets before they cross into the shared store, and tagged with you as the contributor.
- Corroborated before it's trusted. A shared learning is held as needing corroboration until another teammate independently confirms it; a conflicting learning is flagged for an admin to resolve — so one wrong fix can't silently spread.
- Read with
scope.get_contextdraws on your personal pack plus in-scope team packs in one ranked result; each entry is tagged with its scope and contributor.
Promote learnings, and manage members and roles, from the Teams area in the app.
Flat, no metering, no usage billing — OneLamp is a retrieval/indexing layer, so there's no per-request generation cost to pass through. See Pricing for current details.
| Plan | Price | For |
|---|---|---|
| Free | $0 | 2 agents, 5 data sources, share with 1 team, export anytime — no card. |
| Pro | $3/mo (or $2/mo billed annually) | Unlimited agents, data sources, and teams. |
| Enterprise | Contact sales | Everything in Pro org-wide, plus SSO/SAML, audit logs, and data residency. |
OneLamp is privacy-first by design: your context is per-user, never mixed across users, and fully exportable and deletable. Use of the hosted service is governed by the Terms of Service and Privacy Policy.
Licensed under the Apache License 2.0. "OneLamp", "Saho Labs", and the
OneLamp logo are trademarks of Saho Labs, Inc. — see NOTICE. This license
covers the docs and examples in this repository; the OneLamp product source is
proprietary and owned by Saho Labs, Inc.