Claude Mem
Claude Mem provides persistent context across sessions for every agent — captures everything your agent does during sessions, compresses it with AI, and injects relevant context back into future sessions. Works with Claude Code, OpenClaw, Codex, Gemini, Hermes, Copilot, OpenCode and more.
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What is Claude Mem?
Claude Mem (Claude‑Mem) is a persistent memory and observability layer for Claude Code that automatically records, compresses, and indexes what an AI does during coding sessions so it can recall past work in future interactions. Instead of you having to re‑explain context and bug fixes each time you restart, Claude‑Mem runs a lightweight observer AI that watches tool usage, logs decisions, bugfixes, features, and discoveries, then surfaces relevant summaries on demand. The tool structures this memory as a searchable, time‑lined archive tied to file paths and semantic concepts, letting you query things like “decisions about token refresh” or “bugfixes in auth.ts”.
Key features include real‑time observation of Claude Code sessions, auto‑categorization of events into types such as bugfix, decision, feature, and discovery, and file‑ or concept‑scoped search so you can target specific parts of a codebase. The system uses progressive disclosure: each session starts with a compact index of observation titles and metadata, then only loads full logs when the LLM needs deeper context, which keeps token usage low. You also get before‑and‑after context for each observation, so the AI can reason about why a certain change led to a later bug or refactoring, rather than just seeing isolated snapshots.
Claude‑Mem is aimed primarily at developers using Claude Code for non‑trivial projects, especially those that span multiple sessions or involve complex architectures such as authentication flows, state management, or distributed systems. It suits solo engineers who work across days as well as small teams who want shared, searchable records of how design choices evolved over time. The tool is particularly valuable when you frequently switch between projects, take breaks, or hand off work, because it preserves decision history and implementation details that would otherwise be lost when a session ends.
Claude Mem pricing
Pricing model: Freemium
Claude‑Mem is distributed as a free, open‑source plugin that does not require a separate paid subscription; it is installed via the Claude Code plugin marketplace and runs locally without per‑user or per‑session fees. The core memory‑capture and search functionality is available at no cost, and there are no locked‑behind‑paywall tiers mentioned on the site; the readme and documentation emphasize that the project is open source and community‑driven rather than a commercial product with paid plans.
Claude Mem pros
- Automatically captures all Claude Code sessions without manual logging
- Maintains persistent memory across restarts and new sessions
- Reduces time spent re‑explaining context to Claude
- Compresses observations to minimize token overhead
- Searchable archive by time, file path, and semantic concept
- Auto‑categorizes entries as bugfix, decision, feature, discovery
- Provides before‑and‑after context for each observation
- Shows causal links between decisions and later bugs or changes
- Progressive disclosure keeps index light but allows full detail on demand
- Integrates directly into Claude Code via plugin marketplace
- Runs as a local observer alongside your main agent
- Supports query patterns like “decisions about X” or “bugfixes in Y”
- Helps track architectural decisions over long‑lived projects
- Makes it easier to onboarding new contributors onto existing projects
- Preserves reasoning and code‑diff context for future reference
Claude Mem cons
- Tightly coupled to Claude Code and Claude ecosystem
- Limited unless the user already works heavily with Claude Code
- Observations are only as good as the underlying tool usage it watches
- Can become noisy if the AI runs many small or low‑signal steps
- Adds local process overhead from running a second observer agent
- Learning curve around query syntax and conceptual search
- Prime focus is code and decisions, not general‑purpose knowledge management
- Some advanced features are still in development or upcoming
Frequently asked questions about Claude Mem
What problem does Claude‑Mem solve?
Claude‑Mem solves the problem that AI coding assistants forget everything when a session ends, so you constantly have to re‑explain context, bug fixes, and design choices. It gives Claude Code persistent memory by automatically recording what the AI does, compressing those observations, and letting future sessions search and retrieve them, so you can pick up where you left off without manually reconstructing history.
How does Claude‑Mem watch my sessions?
Claude‑Mem runs a dedicated observer AI alongside Claude Code that passively watches tool usage, file edits, and reasoning steps in real time. This observer generates structured, searchable observations without interrupting your workflow, then stores them in a local index that can be queried later by the same or a new session.
Is my data stored in the cloud or on my machine?
The architecture is designed so that observations and logs remain on your local machine, not transmitted to external servers, because the observer runs locally as part of your Claude Code environment. This means project‑specific memory and code‑related context stay local unless you explicitly sync or export it yourself.
How does memory compression work?
Claude‑Mem uses semantic summarization and indexing to compress detailed logs into compact titles and metadata, which are then stored as lightweight tokens. When the LLM needs more detail, it fetches the full observation on demand, which reduces upfront token usage while still preserving the full reasoning and diffs when necessary.
Can I search by file or by concept?
Yes, you can search using both file‑path scoping such as “decisions for src/auth/index.ts” and conceptual queries such as “decisions about token refresh” or “bugfixes about race condition”. The system combines file‑based filters with semantic tags so you can locate relevant decisions or bugs either by code location or by topic.
What kinds of observations does it capture?
Claude‑Mem captures events such as bugfixes, architectural decisions, new features, refactors, and discoveries, each automatically categorized with labels like bugfix, decision, feature, or discovery. It also records the associated code changes and reasoning, along with timestamps and file paths, so you can see what changed and why.
Does it help teams collaborate?
Claude‑Mem can help small teams by preserving a shared history of what the AI did per project, including design choices and bug resolutions, which can be queried by any team member using the same setup. However, the tool itself is currently focused on per‑project, local archives rather than a centralized multi‑user collaboration platform, so effective team use depends on how you share or back up the local index.
How much extra overhead does it add to my workflow?
The overhead is minimal in terms of active effort because logging and categorization happen automatically in the background; you do not need to annotate or tag events yourself. Resource‑wise, it runs a second lightweight observer agent locally, so the main impact is modest additional CPU and memory usage during sessions, but it avoids heavy upfront token costs by using compressed indices and on‑demand fetching.
Can I use Claude‑Mem with other AI tools besides Claude Code?
At present, Claude‑Mem is built specifically for Claude Code and the Claude ecosystem, and the documentation and examples are centered on that integration. While the project mentions an open standard for agent memory in the future, current usage is not advertised as a generic layer for arbitrary AI tools.
Is Claude‑Mem open source and can I modify it?
Yes, Claude‑Mem is open source and distributed under an open‑source license, with the source hosted on GitHub. You can inspect, modify, and extend the plugin and its observer logic yourself, which is why the installation instructions point both to the plugin marketplace and to the underlying repository for power users who want to customize behavior.