Claude-Mem: Building Episodic Memory for AI Coding Sessions
Hook
Your AI coding assistant forgets everything between sessions, forcing you to re-explain project context every time. Claude-Mem solves this by turning Claude Code into an AI with long-term memory—automatically capturing, compressing, and retrieving everything that happens across your development workflow.
Context
AI coding assistants have a fundamental problem: amnesia. Every new chat session starts from zero. You’ve probably experienced this—spending the first ten minutes of every Claude Code session explaining your project structure, your architectural decisions, the bug you’re chasing, or why you chose that particular library. The AI has no memory of yesterday’s conversation where you already covered all of this.
This isn’t just annoying; it’s a productivity killer. Long-running projects accumulate context that’s essential for making good decisions. Which API endpoints did you refactor last week? What was that edge case you discovered in the authentication flow? Why did you decide against using that popular library? Without memory, your AI pair programmer can’t learn from your shared history. Claude-Mem attacks this problem by building a memory layer that sits between you and Claude Code, automatically capturing tool usage observations, compressing them into semantic summaries using Claude’s own agent-sdk, and injecting relevant context back into future sessions through a hybrid search system.
Technical Insight
Claude-Mem’s architecture is built around interception and compression. At its core, it runs a worker service on port 37777 that hooks into Claude Code’s lifecycle through seven strategic points: pre/post hooks for tool calls and tool responses. Every time Claude reads a file, runs a command, or modifies code, these hooks capture the observation as structured data.
The capture mechanism is elegantly simple. Here’s what a tool observation looks like before compression:
{
"type": "tool_call",
"tool_name": "read_file",
"parameters": {
"path": "src/auth/middleware.ts"
},
"result": "[file contents...]",
"timestamp": "2024-01-15T10:23:45Z",
"session_id": "sess_abc123"
}Raw observations pile up fast—a typical coding session might generate hundreds of these, easily overwhelming the context window. This is where the compression layer comes in. Claude-Mem uses Claude’s agent-sdk to batch observations and ask Claude itself to summarize them semantically. Instead of storing “Claude read middleware.ts, then modified line 47, then ran tests,” you get compressed summaries like “Implemented rate limiting in auth middleware to prevent brute force attacks, using sliding window algorithm with Redis backend.”
The storage strategy is hybrid by design. Raw observations go into SQLite with FTS5 full-text search enabled, giving you fast keyword lookup. Simultaneously, compressed summaries are embedded using Claude’s API and stored in ChromaDB for vector similarity search. This dual-index approach means you can search both ways: “What did I do with Redis?” (keyword) or “Show me everything related to authentication security” (semantic).
The retrieval mechanism implements what the docs call ‘progressive disclosure.’ Instead of dumping all relevant context into the prompt upfront, Claude-Mem provides context in layers with explicit token costs:
interface ContextLayer {
priority: 'high' | 'medium' | 'low';
content: string;
tokenCost: number;
relevanceScore: number;
}
// Progressive disclosure in action
const context = await memoryEngine.retrieve(query, {
maxTokens: 4000,
layers: [
{ priority: 'high', maxTokens: 1500 }, // Most relevant summaries
{ priority: 'medium', maxTokens: 1500 }, // Related context
{ priority: 'low', maxTokens: 1000 } // Peripheral information
]
});This token-aware layering prevents context window bloat while ensuring the most relevant information always makes it through. If your current question is about database migrations, you’ll get high-priority context about schema changes and migration scripts, medium-priority context about related data models, and low-priority context about other database interactions.
The MCP (Model Context Protocol) integration is where this becomes practical. Claude-Mem exposes search tools that Claude Code can call natively:
// Claude can invoke this during a session
mcp.tools.search_memory({
query: "how did we handle API rate limiting",
max_results: 5,
time_range: "last_7_days"
});Privacy controls are baked into the capture layer. You can wrap sensitive code in <private> tags, and those sections won’t be stored:
// This won't be captured in memory
/* <private> */
const API_KEY = "sk_live_...";
const DATABASE_URL = "postgresql://...";
/* </private> */The system also includes a real-time web viewer on localhost:37777 that shows you exactly what’s being captured and stored, making the memory layer transparent rather than magic. You can see observations streaming in, watch compression happen, and manually search the accumulated context.
One clever architectural choice: the worker service runs independently of Claude Code. This means memory accumulation continues even if Claude crashes or you restart the editor. Your context survives across IDE sessions, not just chat sessions.
Gotcha
The tight coupling to Claude’s ecosystem is both Claude-Mem’s strength and its cage. This isn’t a general-purpose memory solution—it’s specifically built for Claude Code using Claude’s agent-sdk. If you’re using Cursor, GitHub Copilot, or any other AI coding assistant, this tool is completely useless to you. Even within Anthropic’s ecosystem, you’re locked into Claude Code; there’s no simple way to use this memory layer with raw Claude API calls or other Claude interfaces beyond the included Claude Desktop skill integration.
The worker service architecture introduces operational complexity. Running a persistent service on port 37777 means another process to manage, another potential point of failure, and possible port conflicts if you’re already running local development servers in that range. Docker environments and CI/CD pipelines become more complicated—you can’t just install a plugin and go. The AGPL 3.0 license is also a deal-breaker for commercial applications. If you’re building a proprietary tool or want to integrate this into a SaaS product, you’re legally required to open-source your entire application. For individual developers and open-source projects, this is fine. For startups and enterprises, it’s a non-starter without negotiating a separate license.
Token costs for compression are real and ongoing. Every time Claude-Mem compresses observations, that’s an API call to Claude, which means you’re paying for the memory system itself. Heavy coding sessions could rack up meaningful costs beyond your normal Claude usage. The documentation doesn’t provide clear guidance on typical token consumption, so budgeting is difficult.
Verdict
Use if: You’re a Claude Code power user working on long-running, complex projects where maintaining context across weeks or months is critical. The automatic capture eliminates the cognitive overhead of manually tracking what you’ve done, and the semantic search makes your entire project history queryable. If you’re comfortable with AGPL licensing (personal projects, open-source work, academic research), this tool genuinely extends Claude Code’s capabilities in a way that feels like a superpower. Skip if: You’re using any AI coding assistant other than Claude Code, you need a commercial-friendly license, or you work primarily on short-lived projects where session-to-session context doesn’t matter much. Also skip if you’re not willing to run and maintain a local worker service—the operational overhead isn’t trivial. For teams, the lack of shared memory across developers is a limitation; each developer builds their own memory store, so organizational knowledge doesn’t accumulate collectively.
