In almost any workflow with AI assistants for coding, the same pattern repeats: open a session, explain the context to the model, continue with code, fix bugs… and when the session ends or the context window fills up, all that knowledge disappears. In the next session, start over: “Do you remember…?”. No, it doesn’t remember.
Claude-Mem is precisely designed to address this structural problem. It’s a plugin for Claude Code that functions as a persistent and compressed memory system: it captures what happens during your development sessions, distills important ideas, and makes them available so the assistant can reuse them later, even days afterward, without you having to re-explain everything.
It’s not just about “saving chat history” but about building a true working memory and archive system around the assistant.
The bottleneck: context doesn’t scale with the project
Language models operate within a limited context window. Every time you run tools, open files, generate code, or ask for explanations, tokens accumulate. Eventually,:
- The model starts to forget decisions made 30 or 40 interactions ago,
- Repeats resolved questions (“What was the final authentication layer?”),
- And the developer ends up clearing the context to “start fresh,” losing the entire session history.
The fundamental issue isn’t that the model is “dumb,” but that it’s forced to keep everything in RAM, as if it could never offload anything to disk. Claude-Mem introduces exactly that layer of “disk”: a structured memory that lives outside the context, which the assistant can access when needed.
What is Claude-Mem and how it integrates with Claude Code
Claude-Mem installs directly from the Claude Code plugin marketplace. Once added, it starts working automatically, without needing to run special commands each session.
Under the hood, its architecture relies on several components:
- Lifecycle hooks: scripts that run at key moments during the session (at start, when sending a prompt, after using tools, on stop, and at finish).
- Worker service: a local HTTP service managed with PM2 that processes data, generates summaries, and displays a real-time web UI at
localhost:37777. - SQLite database: stores sessions, observations, and summaries, with full-text search indices (FTS5).
- Vector DB (Chroma): enables semantic search combined with keyword search.
The typical flow is:
- Session start: Claude-Mem injects relevant observations from previous sessions about the same project into the context.
- During work: logs tool usage, touched files, changes, and decisions.
- Background processing: a worker calls Claude, generates semantic summaries of what happened, and classifies them (decision, bugfix, feature, refactor, discovery, etc.).
- Session end: a high-level summary is saved, ready to be reused the next time you open the project.
From the developer’s perspective, the change is subtle but significant: Claude no longer starts each day “from scratch”.
mem-search: ask the project history like a teammate
The most visible feature for the user is the search skill called mem-search. Instead of being just another command, it’s integrated naturally into conversations.
Examples of questions triggered by mem-search:
- “How did we implement authentication in this project?”
- “What bugs did we fix in the last session?”
- “What changes were made in
worker-service.ts?” - “Show me recent work on this repo.”
When it detects a query of this style, Claude-Mem:
- Searches in observations (granular events: changes, decisions, bugs).
- Queries session summaries (macro view).
- Traverses the original user prompts if needed.
- Applies filters by type (decision, bugfix, feature, refactor, discovery…) or by file.
- Returns to the model a set of relevant compressed fragments, ready to be included in the context.
The philosophy behind this is progressive disclosure:
- Start with a lightweight index of what exists (what was decided, when, and which file it affected);
- Only if needed, fetch more detail (full narrative, code snippets, etc.).
This approach prevents consuming thousands of tokens to bring all past information for each response.
Endless Mode: nearly infinite sessions thanks to real-time compression
Beyond the standard mode, Claude-Mem features a beta channel with a particularly interesting function for enthusiasts: Endless Mode.
The initial challenge: in intensive coding sessions, each tool invocation adds between 1,000 and 10,000 tokens to the context. If each new response re-includes everything, re-synthesized, the cost grows almost quadratically. After 40–50 tool uses, the context is at its limit.
Endless Mode proposes an alternative approach:
- Each tool output is compressed into an observation of ~500 tokens,
- The transcript gets “re-written” in real-time, replacing long blocks with distilled versions,
- And it clearly separates between Working Memory (active observations in context) and Archive Memory (full outputs stored on disk for quick recall).
Roughly, this translates to:
- Up to 95% reduction in tokens in context,
- Approximately 20 times more tool uses before hitting the window limit,
- And a complexity that shifts from something approaching O(N²) to a much more linear scale.
The trade-off is that Endless Mode introduces more latency (about 60–90 seconds per tool while generating compressed observations) and is considered experimental at this stage. It’s designed for long sessions where users want to explore how far the context can be extended without losing traceability.
Web UI: streaming memory visualization
Another interesting detail for tech-savvy users: Claude-Mem isn’t just backend and hooks; it offers a real-time web UI.
From http://localhost:37777, you can:
- View the memory flow as it’s generated,
- Filter by project, event type, or timeline,
- Query individual observations with their token costs,
- Switch between stable and beta channels (e.g., activate Endless Mode) without touching Git or the command line.
It’s essentially a live viewer of “what Claude is learning about your project.”
Privacy and data handling: all local, but with responsibility
In a context where teams are concerned about what leaves their repositories, Claude-Mem’s data approach is notable:
- Everything is stored locally (by default in
~/.claude-mem/), using SQLite and auxiliary files. - It does not add external destinations; it relies on the same APIs Claude Code uses to interact with the model.
- Offers a private tag system (
<private/>) to mark content you don’t want persisted in memory. - And adds internal tags to prevent the memory context from recursively storing itself.
However, this system stores:
- prompts,
- code observations,
- decisions made,
- and session summaries.
Therefore, security and retention policies are the team’s responsibility: who has access, how backups are handled, when purging occurs, etc.
Licensing and team adoption: AGPL and auditable code
Claude-Mem is published under the AGPL-3.0 license, a deliberate choice:
- It permits free use, modification, and deployment of the software,
- but requires releasing the source code of any modified version offered as a network service,
- and mandates that derivative works carry the same license.
This has two implications for companies and tech teams:
- On one hand, it provides guarantees: the code is auditable, you can review what’s stored and how it’s processed, and adapt it to your workflows;
- On the other hand, it imposes conditions if you wish to embed Claude-Mem into internal platforms exposed as a service to third parties.
In environments already working with open-source AGPL tools (e.g., internal collaboration or development tools), integration is more straightforward. Conservative teams will need legal review before adoption.
Clear use cases in a tech team’s daily workflow
Beyond theory, the situations where Claude-Mem adds tangible value include:
- Long refactorings: working over days on complex refactors, persistent memory helps the assistant recall design decisions made early on, avoiding rehashing debates.
- Team rotation: when members alternate on the same project, Claude can serve as a “history keeper,” reducing reliance on perfectly documented logs.
- Legacy maintenance: in legacy code with quirks, remembering the rationale behind decisions can save hours of exploration each time.
- Consultants and agencies: switching between projects and clients, having a semantic history per repo drastically lowers the mental cost of reorienting.
Practically, the value isn’t just “AI remembering more,” but that the human team spends less time re-teaching context.
Points for media and tech teams to watch
Like any powerful tool, it’s wise to maintain a critical perspective:
- Claude-Mem isn’t perfect at documentation: summaries are generated by a model, with its biases and omissions.
- The memory database is another asset to protect and manage: it may contain sensitive project information.
- Endless Mode is still beta: best to experiment in controlled settings before making it central to your workflow.
Still, the direction is clear: as AI assistants become deeply integrated into software development, they must stop being amnesiacs. This requires memory systems like Claude-Mem or equivalents, which treat context as a scarce and valuable resource.
For now, Claude-Mem is one of the first serious implementations of this concept within the Claude Code ecosystem: an “extended brain” that lives alongside the assistant, finally remembering what you did together two weeks ago.
Source: Noticias inteligencia artificial