Chat Memory Archiver

Extract decisions, todos, knowledge, preferences, and risks from AI chat sessions into structured memory

Session Archiver

Parse AI chat session logs and extract structured knowledge: decisions made, pending todos, learned facts, user preferences, and flagged risks. Merge across sessions and export to Markdown, JSON, Obsidian, or Notion.

Workflow

1. Parse conversation — Read session log, extract question-answer pairs, tool calls, and decision points.
2. Segment by phase — Label each segment as problem / exploration / decision / action.
3. Extract 5 categories:
   • 📌 Decisions — Choices made, with rationale and alternatives considered.
   • ✅ Todos — Action items, owners, and deadlines.
   • 📚 Knowledge — Facts, code snippets, links, and explanations.
   • ⭐ Preferences — User style, terminology, tools, conventions.
   • ⚠️ Risks — Security concerns, known issues, caveats.
4. De-duplicate & merge — Fuse repeated information across multiple sessions, keep latest version.
5. Topic tagging — Auto-tag each session with relevant domain labels (e.g. #python, #api-design, #deployment).
6. Cross-session graph — Build lightweight association graph showing which sessions share topics or reference each other.
7. Format export — Generate output in Markdown, JSON, Obsidian-flavored wiki links, or Notion JSON.
8. Summary — Produce a concise 5-sentence summary of each session for quick scanning.

Sample Prompts

• session-archiver extract --sessions session-2026-06-01.log session-2026-06-02.log
• session-archiver extract --sessions . --format obsidian --outdir ./vault
• session-archiver merge --sessions . --dedup --out summary.json
• session-archiver report --sessions . --graph > session-graph.dot

Safety

• Session logs are parsed locally; never sent to external services.
• Sensitive content (passwords, keys) in logs is flagged during extraction; user must explicitly confirm before inclusion in output.