feishu-group-memory（飞书组记忆）

Feishu Group Memory

Architecture

Scripts handle data only — no LLM calls inside scripts:

• onboarding.py — read/write industry knowledge pack config
• listener.py — fetch raw messages from Feishu; save analyzed records
• query.py — keyword search over stored records
• billing.py — SkillPay charge/balance/payment-link

All AI analysis is done by you (the OpenClaw model): deciding what to record, extracting structured fields, generating advice, writing summaries.

Quick Reference

First Use: Onboarding

At the start of every session, check whether an industry pack is configured:

python3 {baseDir}/scripts/onboarding.py check --workspace ~/.openclaw/workspace

• {"configured": true, "context": "..."} → load the context field and proceed
• {"configured": false} → run onboarding before anything else

Onboarding conversation

Ask the user:

"Before we start, I'd like to understand what your group is mainly used for so I can record and analyze the right things. Choose one, or describe it in your own words: 1. 📈 Sales tracking (leads, quotes, deals) 2. 🎧 Customer support (tickets, issues, complaints) 3. ⚖️ Legal matters (contracts, risks, cases) 4. 📋 Project management (tasks, milestones, blockers) 5. ✍️ Describe my own use case"

Saving the config

Built-in industry (options 1–4):

python3 {baseDir}/scripts/onboarding.py setup \
  --industry sales \
  --workspace ~/.openclaw/workspace

Valid slugs: sales / customer-service / legal / project

Custom description (option 5):

Using the user's description and the template at {baseDir}/templates/context-template.md, generate the knowledge pack yourself, then save it:

python3 {baseDir}/scripts/onboarding.py save \
  --content "YOUR GENERATED CONTENT" \
  --workspace ~/.openclaw/workspace

Confirm with the user: "Got it! I'll use this context going forward. Which group would you like me to start recording?"

Feature: Record Group Messages

Trigger: "record X group", "fetch messages from X", "capture what's been discussed in X"

Step 1 — Find the group

python3 {baseDir}/scripts/listener.py find_chat --name "KEYWORD"

If multiple results, show them and ask the user to pick one.

Step 2 — Fetch raw messages

python3 {baseDir}/scripts/listener.py fetch_raw \
  --chat_id CHAT_ID \
  --limit 100 \
  --workspace ~/.openclaw/workspace

Returns an array of {msg_id, time, sender, text} objects.

Step 3 — You analyze

Using the loaded industry knowledge pack (from onboarding check), go through each message and decide:

• Is it worth recording?
• What category does it belong to?
• Who or what is the key entity (person, company, project)?
• What structured fields can be extracted?
• What is the urgency (high / medium / low)?

Step 4 — Save the records

python3 {baseDir}/scripts/listener.py save_records \
  --chat_id CHAT_ID \
  --workspace ~/.openclaw/workspace \
  --records '[{"msg_id":"...","time":"...","sender":"...","raw_text":"...","category":"...","key_entity":"...","summary":"...","fields":{...},"urgency":"high"}]'

Step 5 — Report to user

Summarize what was found, e.g.:

"Analyzed 100 messages. Saved 12 items: - 3 customer intent signals (Li, Wang, Chen) - 5 follow-up actions - 4 pricing discussions 2 high-urgency items — want me to walk through them?"

Billing

python3 {baseDir}/scripts/billing.py charge \
  --user_id USER_ID --amount 0.005 --label "message analysis"

If payment_required is returned, show the top-up link and stop.

Feature: Query Records

Trigger: "how is Wang doing", "what happened with Acme last week", "show me recent follow-ups"

python3 {baseDir}/scripts/query.py search \
  --query "KEYWORD" \
  --workspace ~/.openclaw/workspace

Returns matching records as raw JSON. You turn them into a natural-language answer, e.g.:

"Here's what I have on Wang (Wang Zong): - Jan 15: Said he can sign next week (high priority) - Jan 12: Asked about discount options, still considering Last contact was 3 days ago — worth reaching out today."

No charge for queries.

Feature: AI Advice

Trigger: "how should I follow up with X", "give me some advice", "help me think through this"

First, search for relevant records:

python3 {baseDir}/scripts/query.py search \
  --query "KEYWORD" \
  --workspace ~/.openclaw/workspace

Then reload the industry pack if needed:

python3 {baseDir}/scripts/onboarding.py check --workspace ~/.openclaw/workspace

Using the "Advice Templates" section of the knowledge pack and the retrieved records, give the user concrete, actionable advice directly.

python3 {baseDir}/scripts/billing.py charge \
  --user_id USER_ID --amount 0.003 --label "AI advice"

Feature: Summary Report

Trigger: "summarize today", "weekly report", "what happened this week"

python3 {baseDir}/scripts/query.py list_records \
  --period today|week|all \
  --workspace ~/.openclaw/workspace

You write the summary. Example structure:

Weekly Summary (Jan 13–19) 28 items recorded across 7 customers. Action required (3) - Li Zong: ready to sign — prepare draft contract - Wang Zong: price sticking point — request special approval By category - Customer intent: 12 | Follow-ups: 8 | Pricing: 5 | Other: 3 Suggestion: 2 customers haven't been contacted in 5+ days.

python3 {baseDir}/scripts/billing.py charge \
  --user_id USER_ID --amount 0.005 --label "summary report"

Error Handling