Never Lose a Session
Native save paths, local auto-sync, and browser capture keep your important AI conversations searchable.
The Problem
You just had an epic debugging session. Three hours with Claude Code. You found a race condition, traced it through 15 files, built a bulletproof fix with tests.
But AI conversations are ephemeral. Context gets compacted, token limits hit, and sessions expire. That 200-message thread? The early context is already gone.
"I solved this exact problem before. I just can't remember how. Or where. Or when."
The Solution
Your sessions can flow into Mem through the right path for each tool. Local coding sessions can auto-sync. Native integrations can save real session transcripts where the host supports it. Browser conversations from ChatGPT, Claude, and Gemini are captured by the extension. You only need exports when that is the source you already have.
When you're ready, distill a thread into permanent, searchable, graph-connected memories.
Your first proof
Pick one conversation you already care about, get it into Threads, then distill one useful memory from it. Once you can find both the original thread and the distilled takeaway later, this workflow is working.
How It Works
Sessions Reach Mem Through Different Paths
Local auto-sync (Claude Code, Cursor, Codex, OpenCode): Nowledge Mem can watch local coding sessions in real time. Open Threads to see them appear as you work.
Real transcript save through tool-specific paths (Claude Code, Gemini CLI, Codex CLI):
Some tools expose a real recorded session save path through their own integration. Claude Code and Gemini use native integrations. Codex uses its dedicated prompt-pack workflow and nmem t save --from codex.
Native handoff-first paths (Droid, Cursor): Some tools already have a dedicated native package, but intentionally stop at resumable handoff summaries until a real transcript importer exists. That still gives you cross-session continuity without pretending to store the full recorded conversation.
Browser capture (ChatGPT, Gemini, Claude Web): The Exchange v2 extension captures conversations from supported web AI chat platforms. Insights and thread backups flow into Mem as you chat.
Manual distill or handoff:
/sum -> Distill durable insights into memories
/save -> Create a resumable handoff or tool-specific save path, depending on the integrationDistill Into Permanent Knowledge
Open a saved thread and click Distill. The AI reads the entire conversation and extracts:
- Decisions: "Chose sliding window over token bucket because..."
- Insights: "Race conditions in async callbacks need mutex locks"
- Patterns: "Testing time-based bugs requires mock clocks"
- Facts: "Redis SETNX provides atomic lock acquisition"
Each becomes a standalone, searchable memory with proper labels.
Background Intelligence Connects It
Your new memories don't sit in isolation. Background Intelligence:
- Links them to previous work on the same codebase
- Detects if they update or contradict earlier decisions
- Connects them to related entities in the knowledge graph
- Surfaces them in your next morning's Working Memory briefing
Three months later, a colleague hits the same bug. Your briefing mentions it before they even ask.
Search Anytime
Three months later, similar bug appears:
Search: "payment race condition"
Nowledge Mem returns the full context: the problem, the debugging steps, the solution, the test approach.
No more re-solving solved problems.
What Gets Captured
| Source | How | What You Get |
|---|---|---|
| Claude Code | Native plugin save or local auto-sync | Full session with code context |
| Gemini CLI | Native extension save-thread | Real recorded Gemini session |
| Droid | Native plugin save-handoff | Resumable handoff summaries inside Droid, with honest boundaries around full session import |
| Codex | Tool-specific /save workflow or local auto-sync | Full session with code context |
| Cursor | Plugin save-handoff, local auto-sync, or manual import | Resumable handoff summaries in the plugin, plus local conversation import on your machine |
| OpenCode | Auto-sync (real-time watching) | Conversations as they happen |
| ChatGPT | Browser extension (auto-capture) | Insights and full thread backups from web chats |
| Claude Web | Browser extension (auto-capture) | Insights and full thread backups from web chats |
| Gemini | Browser extension (auto-capture) | Insights and full thread backups from web chats |
| More supported web AI chats | Browser extension | The same capture model on supported sites |
What Gets Extracted
When you distill a thread, the AI creates memories categorized by type:
| Type | Example | Labels |
|---|---|---|
| Decision | "Used Redis for distributed locking" | decision, architecture |
| Insight | "Async callbacks need careful ordering" | insight, debugging |
| Procedure | "Steps to reproduce race conditions" | procedure, testing |
| Fact | "SETNX returns 1 if key was set" | fact, redis |
| Experience | "Debugging session on payment service" | experience, project |
The Compound Effect
One thread saved is useful. Ten threads saved is a knowledge base. A hundred threads? That's institutional memory.
"Junior dev hit the same bug today. Sent them my memory. They fixed it in 20 minutes instead of 3 hours."
Your debugging sessions aren't just conversations. They're training data for your future self.
Pro Tips
Distill Selectively
You don't need to distill every thread. Save important sessions: the breakthroughs, the architectural decisions, the hard-won solutions.
Review Before Saving
For sensitive codebases, review what you're saving. Threads might contain proprietary code or credentials.
Next Steps
- Own Your Knowledge -> Use any tool without losing context
- Search Through Time -> Find memories from specific time periods
- Integrations -> Setup guides for each tool