Save

Save conversations to memory index — FTS5 rebuild, log cross-ref, saved convos dispatch

Save — Conversation Memory Index

Three-tier memory system for OpenClaw agents. Zero external dependencies, zero API keys, zero cloud services. Runs entirely on local files and Python stdlib.

Save session context as a standalone document, cross-reference it in a human-readable log, and index it for fast FTS5 full-text search — all with one command.

The Architecture: Three-Tier Memory

Tier 1 — Conversation Log (always in context, ~7KB)

File: ~/.openclaw/workspace/saved/conversation-log.md

A date-sorted cross-reference index. Every saved conversation has a one-line entry: date, filename, topic summary. The agent reads this at session start and instantly knows what's stored.

text

- **2026-07-19** 2026-07-19_gif-library-and-picker-skill.md: GIF library + picker built
- **2026-07-18** 2026-07-11_movie-rec-animal-and-all-we-imagine-as-light.md: Discussion about movies

Tier 2 — FTS5 Search Index (on-demand, ~5ms)

File: /dev/shm/memory-index.db Built by: memory-index.py

Full-text search over all workspace markdown files and saved conversations. BM25 ranking. Porter stemmer. Synonym awareness.

text

python3 ~/.openclaw/workspace/saved/memory-index.py search "Playwright in production"
→ [0.12] memory/2026-07-18.md: Playwright testcases — One-Shot Test Generation

Tier 3 — Full File Read (on-demand, ~2ms)

Directory: ~/.openclaw/workspace/saved/

The full saved conversation file. Read when the agent needs complete context.

How /save Works

When the user says "save this conversation":

1. Synthesize a summary from session context — a standalone document with context, decisions, findings, action items. No tool call noise, no system messages, no chat-log verbatim.

2. Write the file to ~/.openclaw/workspace/saved/YYYY-MM-DD_topic-slug.md

3. Update the conversation log — prepend an entry with date, filename, and one-line description. Update the header count.

4. Rebuild the FTS5 index — runs memory-index.py build which scans all tracked files, builds a Porter-stemmed FTS5 table on tmpfs, generates a topic map JSON, and generates a stub index markdown for Tier 1 initial context.

Files

memory-index.py (386 lines)

• FTS5 index builder and query engine
• Porter tokenizer + unicode61 for Unicode support
• Synonym-aware query expansion (15 groups: "error" → "error OR bug OR fail OR issue OR problem")
• Topic map JSON generation (topic → file paths + snippets)
• Stub index markdown generation for inline context
• fallback grep search when FTS5 returns no results
• No pip packages — Python 3.8+ stdlib only

conversation-log.md

• Date-sorted cross-reference
• Maintained in strict sync with files on disk
• Validated by reconcile tools (no orphan entries, no missing files)

Commands

The agent handles /save automatically. For manual operations:

bash

# Build/rebuild the FTS5 index
python3 ~/.openclaw/workspace/saved/memory-index.py build

# Search the index
python3 ~/.openclaw/workspace/saved/memory-index.py search "your query here"

# List all detected topic tags
python3 ~/.openclaw/workspace/saved/memory-index.py tags

Environment Variables (optional overrides)

Variable	Default	Purpose
OPENCLAW_WORKSPACE	~/.openclaw/workspace	Root workspace directory
SAVED_CONVERSATIONS_DIR	~/.openclaw/workspace/saved	Where saved session files live

First-Time Setup

bash

# 0. Prerequisites
#    - Python 3.8+ (stdlib only — no pip packages needed)
#    - An OpenClaw agent with write/edit/exec/read tools

# 1. Create the saved conversations directory (inside the workspace)
mkdir -p ~/.openclaw/workspace/saved

# 2. Initialize the conversation log
echo -e '# Conversation Log\n_0 conversations_\n' > ~/.openclaw/workspace/saved/conversation-log.md

# 3. Copy memory-index.py to an accessible location
#    (it's at {baseDir}/memory-index.py)

# 4. Build the initial index
python3 ~/.openclaw/workspace/saved/memory-index.py build

# 5. The /save flow: write file → update log → rebuild index

In-Session Usage

markdown

When the user says "save this", "save this conversation", or invokes /save:
1. Synthesize a clean summary from your session context
2. Write to ~/.openclaw/workspace/saved/YYYY-MM-DD_topic-slug.md
3. Prepend entry to conversation-log.md with date, filename, one-line description
4. Rebuild FTS5 index via memory-index.py build

Edge Cases

• "Don't index it" or "off the record" — write the file but skip the log update and index rebuild
• Duplicate filename — append a counter: 2026-07-09_topic-slug-2.md
• Long conversation (>50KB) — write a summary/executive brief instead of full content
• "Save this as [custom name]" — use the custom name as filename

Performance

• 616 files indexed in ~360ms on a Raspberry Pi 4 (ARM Cortex-A72)
• FTS5 DB: ~6.9MB for 616 files
• Topic map: ~845KB JSON with 1,400+ topics
• Stub index: ~75KB — fits in any agent's context

Why This Exists

Agents don't have persistent memory. They can't remember what happened last session — unless they write it down. The save skill is a structured writing system that turns ephemeral conversations into durable, queryable knowledge. It's the difference between a chatbot and an assistant that learns over time.