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Until Now.
Never start from zero again.
Save in Claude. Resume in ChatGPT. Never re-explain.
Your AI is the engine. MrAgentˣ is the live map of your work: where everything stands, what changed, what is next.
Join the waitlist →The Cost Of Forgetting
The client, the budget, the decision from last Tuesday, pasted in one more time. The bill is not the thinking. The bill is the re-explaining, and it grows with every month of history. Yesterday's session knew all of it. This one starts from zero.
How It Works
Three steps, start to finish. No new habit, no separate app.
Under the Hood
Load any of the three tracks as your context, based on what you need.
RE: CHAPS transfer
Funds will be credited within 2 hours, no later than 20 June. Mapped as a [COMMITMENT] (open) and a [RISK] (deadline 20 Jun, open), dated records, not a wall of forwarded text.
Real Test · Same Data, Same Questions
Not a retrieval quiz. The story makes memory do the hard things: track the same people over time (entity continuity), version facts as they change (temporal versioning), close promises when evidence arrives (commitments and payments), and hold a dispute without quietly picking a side (conflict resolution).
None of this is retrieval. Retrieval finds what was said. State knows what is true.
The models were never the problem. Paste the full record into Claude or ChatGPT and both score perfect. The failures live in the memory layer underneath, so that is what we tested.
Run it yourself. Takes 5 minutes, before you find out the hard way your memory layer was wrong.
What This Makes Possible
Six moments you already recognize, and what changes once MrAgentˣ is in the loop.
Who It Is For
The Context Layer
Your work is the territory: what you received and what you worked out with your AI. Your Context Map is the live view: where everything stands, what changed, and what is next. Queryable by you or your agent.
No account connections. Nothing enters your Context Map without your action. The trust model is architectural, not a policy.
The emails and documents that arrived sit on one track. The decisions, risks, and next steps from your AI sessions sit on another. What MrAgentˣ inferred across both sits on a third. Each atom is stamped by the Context Engine the moment it is written, and the stamp survives every supersession. A year later you can still tell what the emails prove from what an AI concluded.
Not stored as raw text. Every capture is structured into your Context Map. Relationships preserved, contradictions flagged.
In the background, MrAgentˣ triangulates: when independent facts converge, a pattern is fixed on your Context Map and linked to the atoms that prove it. One source is a signal. Agreement is knowledge. Every insight traces back to its evidence.
Every fact carries its lifespan: when it became true, when it stopped, and when it was last checked. Superseded facts close their window and the newer state takes over, history kept. Stale research gets flagged, not served as current. Your AI never reasons from expired truth.
Not a prompt injection. A native tool call. Your agent gets structured data with full provenance, not a text summary.
Your AI bill drops because context loads at atom granularity, not file dumps. Answers come from the context layer first, so most requests are marginal retrieval instead of full reasoning.
Search runs on extracted signals and meaning, never on decrypted content. One result decrypts only when you open it. Keys hashed and shown once. Sessions expire.
Retrieval · What Asking Looks Like
SEE THE DISAGREEMENT
When the record disagrees with itself, you see it. Nothing is silently deleted.
ASK AND GET PROOF
Ask in plain English. The answer arrives with a breadcrumb, a date, and a source.
KNOW WHERE THINGS STAND
See where everything stands without asking a single question.
Where MrAgentˣ Fits
Obsidian / Notion
You write it, you link it, you search it. The system decays the week you get busy.
MRAGENTˣ ADDS
Captures from what already happens. Your AI reads it directly.
ChatGPT / Claude built-in memory
A best-effort summary inside one vendor's model. It fills up, resets, or gets overwritten.
MRAGENTˣ ADDS
Typed records outside any model. Loads into all of them.
Your email inbox
The record exists but it is buried in threads. Every time you need it, your AI re-reads the prose and you pay the tokens again.
MRAGENTˣ ADDS
Understood once at ingest. After that, commitments, decisions, and risks load into any AI as compact atoms, never a re-read.
Spreadsheets and trackers
Maintained by hand. They stop being true the moment you stop updating them.
MRAGENTˣ ADDS
Updates itself from what you forward. Never depends on discipline.
Note apps
Notes are prose. Your AI cannot query prose for what is open.
MRAGENTˣ ADDS
Ask for open commitments and get exactly those. Answers come from the record itself, no re-reading, no tokens burned. See the Story Test.
Memory APIs
Store chunks for developers to build on.
MRAGENTˣ ADDS
Typed state you can resolve, with a receipt, and a finished product on top: files, topics, history, one load.
Pricing
Metered on storage only. Never on tokens, never on loads, never on AI usage.
FREE
PRO
FAQ
MrAgentˣ is the context layer for humans and their agents, the layer that has been missing across every AI tool you already use.
It follows you. Save in Claude, resume in ChatGPT or your own agent, and the new session picks up mid-thought. Your Context Map lives outside every model, so switching is one load, not a re-explanation. Connecting a new tool takes one real OAuth login, once, not a manual API key workaround. After that, every switch is just a load, not a new connection.
Bring any of them. MrAgentˣ connects to any client that speaks the MCP standard, with a real one-click OAuth connection, not a workaround. Claude connects on its own dedicated path. Everything else, ChatGPT, Hermes, a self-hosted agent like OpenClaw, or a client that doesn't exist yet, authenticates through CIMD (Client ID Metadata Documents), the current MCP spec's preferred connection method, with RFC 7591 Dynamic Client Registration (DCR) as a fallback for clients that haven't adopted CIMD yet. Both are open standards, not something we built one-off per client. That means a brand-new AI client can connect the day it ships, with zero integration work on our side. Most tools in this space still hardcode a short list of "supported" clients and leave you waiting for the next one to be added.
Yes, and this is where provenance earns its keep. What you ingested (emails, documents) is stamped verified. What an AI contributed in a session is stamped by which AI said it: Claude's take and ChatGPT's take live side by side on the same topic, each labeled. Shop a topic around every AI you use, then isolate what advice came from where and decide with the full picture. Your facts never blur into anyone's opinion, including the AI's.
Yes, and it is kept apart from the email on purpose. Load a topic into Claude or ChatGPT, discuss it, save. The discussion is compiled into the same typed atoms as the email (decisions, risks, assumptions, next steps), stamped as an AI session, and kept separate from what the emails prove. No transcripts, no flat files. Everything lands in the same MrAgentˣ file, a container of atoms, not a document, on the same context map, and you choose what to load: the original emails, what you concluded in the AI, or the latest picture across both. Which model you used is recorded on every atom, so you can filter by it later.
Example: a deal email arrives with a long list of conditions. Most are routine, three need thought. You load the email thread into Claude, read the sender's tone, and work out two approaches. Save. Over the next week more emails come in. Load your last Claude discussion to see which approach you chose and why, then load the latest emails to see what the other side actually did. Plan on one side, reality on the other, and the context map always knows which one is the plan and which one is the fact.
Built-in AI memory is a best-effort summary stored inside the model. It forms without you asking, fills up, resets, or gets silently overwritten, and you cannot see or edit the full set. MrAgentˣ memory is the opposite in every mechanical detail. It is made of the same atoms as the rest of your Context Map, so changes supersede with dates instead of overwriting. It updates when you say so or at key events, never behind your back. And it has a real UI. Read every entry, delete anything, pin what should load in every session. Chat memory remembers you on its terms. Your memory remembers what you decide, on yours.
Yes. That is the entire point. Every session loads your Context Map as structured tool calls. Your agent knows what was agreed, what is open, who said what, and what changed. Not because it was told. Because the record exists and it can read it.
Compare the way you do it today: you search Obsidian or your notes, copy prose in, and pay the model to re-read and re-understand it, every single session. With MrAgentˣ the understanding is paid once, when the content arrives. After that, remembering is one click or one search term, returning a compact set of typed atoms scoped to a file or topic, at a fraction of the tokens, every session after.
Memory is not one thing. It is three. Corpus knowledge answers "what does this material say." Document compilers and repo wikis live there. User memory answers "what happened." Preference stores, chat memories, and the agent platforms that carry transcripts across model swaps all live there: portable, but still a pile of what was said. MrAgentˣ is built on the third axis, verified state. It answers "what is currently true." Every fact carries its source and date. Changes supersede with a trail instead of overwriting. Guesses are stamped separately from facts, and forecasts expire instead of aging into truth. Try this test on any memory system. Ask it what is true today, and how it knows. MrAgentˣ answers with the fact, its source, and its date. That is the product.
Quietly outdated facts are the most dangerous thing in any memory system: a confidently recalled old answer is worse than no answer. Every fact on your Context Map has a validity window and a last-verified date. When something is superseded, its window closes and the newer state takes over, with the old record kept. When research has not been re-checked in a while, it is flagged as stale instead of being served as current. You always know not just what the record says, but whether it is still alive.
This is where most memory tools quietly fail. Some pile both versions into prose and let you guess which is current. At least one leading tool silently deletes both sides. MrAgentˣ does neither. Every item on your Context Map is a dated record with a state. When a commitment is fulfilled, the record moves to done and a link connects the resolution back to the original. When two positions genuinely conflict, both stay visible and the conflict itself is flagged, because the disagreement is information. Nothing is ever deleted. You always see what is currently true, with the full history of how it got there.
Yes, it is a knowledge base. The difference is who it is built for. Confluence and Notion are built for people to read, so nobody does. MrAgentˣ is built for your AI to read. It captures from what already happens, compiles it into dated atoms, and when you open Claude or ChatGPT it loads only the slice the conversation needs. That is how a context window stops being a limit: the relevant part of your whole history arrives in one load, and nothing gets re-explained. You never open it to search. Your AI reads it for you.
Completely. Nothing enters your Context Map without your action. Delete any atom and the Context Map updates instantly. Add as much as you want without watching the size: a memory of 100 atoms or 100,000 costs the same to search, because only the relevant handful ever loads into a session. Load selectively for a light session start, then request specific context on demand. You decide what is captured, what is removed, and what your agent sees. No background sync. No surprises.
Obsidian and Notion store things you manually write. You build the structure, you create the links, you do the search. MrAgentˣ is built for one purpose: giving your AI the right context before every session. It captures from your actual work, the emails and threads you forward and the sessions you save, structures it into your Context Map, and loads it directly into any AI. No file-opening. No manual linking. Your AI reads the Context Map. Obsidian cannot do that.
The mechanism is a skill, not a plugin. Say MrAgent plus what you're looking for in Claude or ChatGPT, and the context arrives as atoms. Work. Say MrAgent, update my file, and the session is compiled back onto the same map, stamped as an AI session. The same skill works in every AI client that speaks MCP, so the map follows you and the AI never needs to be told where you left off.
Yes, and this is the core discipline. MrAgentˣ never guesses: it surfaces what is stored, every atom with its source and date attached. A session starts small: the topic's goal and its most recent atoms, a working spine, not a file dump. Then it loads dynamically: each question pulls in the next group of atoms most relevant to progressing the discussion, and nothing else. Most tools load file after file and make the AI dig. MrAgentˣ loads meaning at atom granularity, which is why it is both more accurate and cheaper. The record stays honest underneath: emails are never edited, and documents update by re-ingesting. Your context window carries only what the conversation earned.
No model, no middleman. Most memory tools route every request through their own servers and an extra AI call before you get an answer. MrAgentˣ retrieval runs on a dedicated context engine: a meaning pass runs first, falling back to a keyword pass only when nothing clears the bar, with sources and dates attached to every result. Nothing is decrypted on the way, nothing is re-reasoned, nothing leaves except the answer. Intelligence enters only when you want reasoning, and that is your own Claude or ChatGPT, not a proxy. Fast by architecture, not by benchmark.
No. MrAgentˣ never meters or resells AI usage. The reasoning runs on intelligence you already control: your Claude or ChatGPT plan, or your own API key. MrAgentˣ itself is a flat monthly fee. And it lowers your AI bill rather than adding to it. Starting from zero is what makes AI expensive: every session re-reads and re-reasons over the same history. MrAgentˣ answers from the context layer first, so most requests become marginal retrieval instead of full reasoning. Every new discussion enriches your Context Map, and because loading is selective, cost stays flat while answers get sharper. Never starting from zero is a cost model.
Nothing, and that is the point. Deduplication runs at three levels. Every document is fingerprinted at arrival, so a re-forwarded email or re-uploaded file is skipped before any processing runs. Every extracted fact is fingerprinted too, so an exact repeat is never written twice. And when new content merely restates or updates something the map already knows, it is matched by meaning: the existing fact is re-confirmed or superseded, never copied. Your map stays clean at every layer, and you are never charged for knowledge you already own. The same content never costs twice.
This is the hidden cost of tools like Obsidian long term. At 10,000 to 30,000 documents, every AI search means loading more context, and your token bill grows with your library. MrAgentˣ inverts that. We built a multi-layer retrieval model where each layer narrows context before the next one runs. By the time your agent gets an answer, it has loaded a fraction of your library and none of the noise. At 100,000 documents, token cost per query is no higher than at 10,000.
Most agent memory setups are a file. Frameworks like OpenClaw or Hermes read and write memory as a document your agent loads whole. A file has a size, and size has a cost. Load all of it and you spend tokens re-reading old memory to find one fact. Trim it yourself and the old problem returns: manual maintenance, deciding what to keep and what to lose. MrAgentˣ memory has never been a file. It is atoms on your context map, retrieved by search. Save one thing or ten thousand things. A load or search returns only the relevant handful, at the same cost either way. Nothing to trim, no size tax for using it. Save everything. Search finds what matters. And memory is not a bolt-on here. It is part of the same context map, built on the same principle, growing from the same doors: AI sessions, emails, documents. One map, one set of rules, whatever the source.
Context Windows Close. AI Forgets Everything. Your Work Should Never Start From Zero.