On Monday a coding agent works out why the release script pins an older compiler. On Tuesday a new session opens in the same repository and starts without that reason, unless someone wrote it where the agent looks. Wordcell keeps that kind of record as Markdown notes in a folder you control. This launch adds three things: a Model Context Protocol (MCP) server so agents read and write those notes through their own tools, an importer for Supermemory exports, and a skill workflow that saves what a session decided as a note.
Latest release: v0.22.5. The MCP server, the Supermemory importer, and the skill’s session-memory workflow are available from source until the next release. The migration page starts with the source install, and bunx skills add hraness/wordcell --skill wordcell adds the skill from the main branch.
What the launch adds
wordcell mcp --root <vault> serves a vault over standard input and output to local MCP clients such as Claude Code, Claude Desktop, Cursor, and Codex. Agents search, list, and read notes, follow links and backlinks, create notes, replace a note body at the revision they read, and add relations between notes. With --repo, the server also returns context for a repository. Every write goes through the same checks as the command line, and --read-only leaves the write tools out. The MCP server reference lists each tool and shows how to connect a client.
wordcell import supermemory <export.json> turns documents and memory entries saved from the Supermemory API into notes. Each version of a memory becomes its own note, linked newest to oldest by supersedes relations. Running the import again updates notes you have not edited, skips unchanged ones, and reports notes you changed yourself as conflicts without touching them. The importer reads export files only and makes no network calls. Import from Supermemory lists the fields and where each kind of item lands.
The wordcell skill includes a session-memory workflow. When you ask, the agent saves what the conversation decided as a dated session note, links it to the notes it changed, and keeps a profile note with a Stable section and a Recent section. Wordcell extracts nothing on its own: the agent writes the note, and you can read it before anything depends on it. The steps are in the session-memory reference.
Two guides cover the rest of a move. Migrate from Supermemory exports your data, imports it, and lists what does not transfer. Sync a vault with Git keeps one vault current on several machines through a private repository.
What Wordcell has measured
Wordcell’s own measurements are narrow. Across four queries on a seven-note public vault, packed snippets used 79.98% fewer UTF-8 bytes than the same notes in full: 12,126 bytes against 60,584 bytes, measured with Wordcell 0.21.3. That is payload size only. It does not measure tokens, answer quality, speed, or an advantage over another search tool.
On 300 BEIR SciFact queries over scientific abstracts, exact search put a relevant abstract first for 33.7% of queries. Optional Jev reranking, which sends each query and candidate snippets to a paid provider, raised that to 53.7% in a study run on September 19, 2026. The study does not establish answer quality. The benchmarks page has both studies with raw results.
What Oh measured on its own
Wordcell builds its graph with Oh, memory for agents that stores each fact with its sources and history. Oh also has its own memory-retrieval API and publishes conversation-memory results for it. LoCoMo is a benchmark of questions about long conversations held over many sessions. In Oh’s LoCoMo run, published September 10, 2026, Oh’s semantic retrieval had 84.4% of 1,540 answers judged correct with GPT-5 mini as the reader and 81.0% with GPT-5 nano. BM25, a keyword-search baseline over the same conversations, scored 81.6% and 78.1% with the same two readers. A GPT-4o mini judge graded every answer. Oh’s conversation-memory benchmarks evaluate its own memory-retrieval API, reader models, and evaluation protocols. Those scores do not transfer to a Wordcell vault merely because it uses the same library.
The run covered 10 conversations once, with no confidence interval, and every question had been seen before: 1,226 in earlier evaluations and 314 during development. The result file records no run date, so the date above is when Oh published it. Oh’s result file says the figures “do not establish fresh confirmation, statistical superiority or benchmark saturation”.
Oh also ran a small pilot of its API against Supermemory, dated September 24, 2026. It used 60 questions from LongMemEval-S, a benchmark of questions about long chat histories, and Oh had seen those questions during development. Supermemory answered 75.00% of the 60 questions correctly, Oh 71.67%, and BM25 68.33%. Oh and BM25 each did not finish three of the questions, and those count as misses. GPT-4o wrote and judged the answers, called through a gateway name that is not pinned to one model version. Oh minus Supermemory came to −3.33 percentage points, with a 95% interval from −13.33 to +6.67. The interval includes zero, and Oh’s pilot report says “the paired primary comparison does not separate Oh from Supermemory”. Supermemory ran with one fixed profile and was indexed per session, while Oh and BM25 were indexed per turn, so the pilot says nothing about Supermemory’s defaults or best configuration. The benchmarks page sets out both studies, and its comparison section lists figures other memory systems publish.
Why the agent writes the memory
Supermemory builds user profiles automatically through ingestion: a model reads your content for facts about you and adds, updates, or removes them. Its graph memory goes further and “infers a fact you never stated in one place, from patterns across memories” (graph memory and user profiles, checked September 26, 2026). That suits an application that wants memory built for it. It also means a stored fact can come from a step you never saw.
In Wordcell the agent writes the memory as Markdown, and the file is the memory. You can read a note, diff it, and revert it with Git. update_note_body applies an edit only at the revision the agent read, so an edit based on an older copy is refused instead of overwriting a newer one. A supersedes relation keeps the older claim readable beside the newer one. Supermemory marks the latest fact for retrieval, and its documentation says the history “can remain for audit” (graph memory, checked September 26, 2026). Graph answers carry a proof that names each source note and a digest of its content, so an edited note no longer matches the proof (Query the derived graph). Search by meaning uses an embedding model that runs on your machine. When a memory is wrong, it is a line in a file you can find and fix. Markdown memory for coding agents covers the approach.
Limits
Wordcell itself has not been measured against Supermemory; the pilot above tested Oh’s API. Wordcell is a command-line tool over a folder, not a hosted memory service for your product’s users. Choose Supermemory when you want extraction and connectors run for you: its documentation points to the hosted platform for “connectors, MCP, and the best-tuned extraction pipeline” (self-hosting overview, checked September 26, 2026). When Supermemory fits better lists more cases. The payload measurement used a seven-note vault, and the SciFact study searched 5,183 public abstracts with exact search, without the graph or Git history. Nothing here measures a large vault of notes your agents wrote and linked, or the graph, search by meaning, or the MCP server at that size.
Free, open source, and yours
Memory for your agents should be free, open source, and superb. That is a position, not a measurement. Free and open source are facts about Wordcell: it is MIT licensed, and the commands above run on your machine without an account. Superb is the goal, and the figures above do not establish it. The same self-hosting overview says Supermemory’s self-hosted edition is free and open source, so price does not separate the two.
To move an existing Supermemory account, start with Migrate from Supermemory.