chat-recall

chat-recall is a tool that turns the conversation history your team has built up with AI coding assistants into a single searchable archive. It reads what your assistants have already written down — the chats, the plans, the task lists and the notes — and consolidates them into one searchable history. As the product puts it, Ctrl+F doesn't work on your brain, but now it works on your chat history: your team has done months of work with AI assistants, and none of it is searchable until chat-recall is installed. It is aimed at developers and teams who use several AI coding tools at once and want the record of that work to be findable rather than scattered. The problem chat-recall addresses is fragmentation. Claude Code, Codex, Cursor and OpenCode each keep a full record of the work you do with them, in its own format, and none of them can read the others. Across five AI coding tools that means months of chats, plans, task lists and notes sit in separate silos that no single search can reach. The result is work that gets redone because nobody can find the original decision, plus a quieter risk: passwords and API keys that were pasted into old conversations and never noticed again. Without a way to search across tools, both the value and the risk hidden in that history stay invisible to the team that created it. You start with a single command: npx chat-recall init. One command reads what your assistants already wrote and turns it into one searchable history. The steps the product describes are straightforward. First, your conversations: everything your assistants have written down, the chats, the plans, the task lists and the notes, across five AI coding tools. Second, passwords are removed on your own computer. Third, everything lands in one searchable history, available the moment it arrives. There is nothing to restructure by hand and no export to perform, because chat-recall reads the records the assistants have already created and assembles them into a single index. Privacy is handled locally rather than in the cloud. Passwords and secrets are removed before anything leaves your computer, and the vendor states that it only ever sees the last few characters of a removed value; the interface shows a masked preview such as the start of a token, a run of asterisks, and a short tail. Steps one through three of the pipeline happen on your own machine. That local-first design matters because the content being indexed is often the most sensitive material a team produces: live credentials, internal plans and unfinished work that nobody wants uploaded to a third-party search index just to make it findable later. Beyond organising history, chat-recall actively looks for secrets that leaked into old chats. Its security view groups every leaked credential by rule, and each row shows a masked preview of the value, a live-or-dead verdict on whether the key still works, the detectors that matched it, and how many sessions it appeared in, so you can see whether a key is still live and how widely it was exposed on one screen. The detectors know the key formats that the big services publish. If your company has invented its own format, you can tell chat-recall the pattern and it gets checked too, which is how custom internal credentials end up covered as well. chat-recall also gives your assistant access to the history itself. Your assistant searches it directly, so it stops asking what you decided last month, and one shared memory means decisions, findings and tasks live in one place rather than in a single tool's silo. The product lists tasks with proof, and what to fix next, among its outputs, and bugs turn into tasks on their own: each one shows up with the fix already sketched out, and closes itself once the problem is actually gone. Rules can be set once per project — mark a project a prototype or a live product, and every assistant that opens it plays by the right rules. Because teams rarely use one assistant on one machine, chat-recall includes a toolkit coverage matrix: six skills down the left and a column for each AI tool on each of two machines. A filled cell means the skill is installed there; an empty ring means it is missing, and clicking it copies the skill across with a sync-to-all action. Each row carries a coverage count so you can see at a glance which of your assistants has which add-on. The payoff is portability. A new laptop already knows everything once you sign in, with nothing to copy over by hand, and every add-on you have built up follows you to whichever assistant you pick up next, so trying a new assistant does not mean starting over. Overall, chat-recall works as a local assembly line rather than a cloud service. A single command reads the records that five AI coding tools have already written, strips passwords on your own machine, and produces one searchable history that is ready immediately. The same history, the same tools and the same rules are then available everywhere you work, across assistants and across computers. Three of the steps happen on your computer, and the product describes the flow as an assembly of your conversations, the removal of passwords, and one searchable history — with leaked passwords, what to fix next, tasks with proof and one shared memory all downstream of that assembly. The benefits follow from that design. Work stops being redone because the original decision is searchable, and your assistant stops asking about things you already decided. A new laptop is useful the moment you sign in, and a new assistant arrives with your accumulated add-ons already in place. Secrets stop being invisible, because leaked keys are surfaced with a verdict on whether they still work and how many conversations they turned up in. Bugs become tasks with a sketch of the fix and disappear on their own once resolved. And rules can be applied consistently, so a prototype and a live product are treated differently wherever they happen to be opened. Concrete uses follow the same pattern. A developer wants to find a decision the team made weeks ago and searches the shared history instead of scrolling through one tool's transcripts. A security-minded team runs the leaked-key check to catch credentials pasted into old chats and to see which of them are still live. Someone setting up a new laptop signs in and finds the whole history already there. A developer trying a new assistant keeps the add-ons built up over time. A bug found in a chat becomes a task with a sketched fix that closes when the problem is gone. And a project marked as a prototype is opened under the right rules by every assistant. chat-recall is built for developers and teams who work across multiple AI coding assistants and want a searchable record of that work. The named integrations are Claude Code, Codex, Cursor and OpenCode, with the site referring to five AI coding tools in total, and to add-ons or skills that can be installed per tool and per machine. Installation runs through npx chat-recall init, which places it in the Node.js command-line ecosystem, and the product links to documentation on how it works and on what your assistant can ask. The takeaway is that chat-recall gives a team back the searchability of its AI work. One command reads what Claude Code, Codex, Cursor and OpenCode already wrote, removes passwords before anything leaves your computer, and produces one searchable history that your assistant can search itself — while also catching keys that leaked into old chats and checking which of them still work.