Never Lose a Session
Native save paths, connector live capture, local backfill, 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. Native integrations save real session transcripts where the host supports it. Local import backfills older coding-agent sessions from disk. 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
Native live capture (Claude Code, Codex, Gemini CLI, OpenCode): Dedicated connectors save the real transcript from the machine where the agent runs. For OpenCode, the plugin captures after OpenCode reports idle and flushes before compaction.
Local backfill (Claude Code, Cursor, Codex, OpenCode):
Nowledge Mem can scan local coding sessions from disk when you want older history in Threads. Use the import view or run nmem t sync --from <tool> on the machine that stores those sessions.
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 connector save or local auto-sync | Full session with code context |
| Gemini CLI | Native extension save-thread | Real recorded Gemini session |
| Droid | Native connector save-handoff | Resumable handoff summaries inside Droid, with honest boundaries around full session import |
| Codex | Native connector Stop hook 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 | Native plugin idle-event capture; CLI/import backfill for older sessions | New conversations as they happen, plus searchable history when imported |
| 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
- Connectors -> Setup guides for each tool