Feishu Group Memory

---

name: feishu-group-memory description: "Extract and store structured information from Feishu group messages, then query it and get AI-generated insights. Use when the user wants to: record what's been discussed in a group, look up a customer or project status, get a summary of recent activity, or ask for advice based on chat history. Supports built-in industry knowledge packs (sales, customer service, legal, project management) and custom packs generated from a plain-language description. Read operations are free; analysis and advice are billed per call via SkillPay." homepage: https://github.com/your-github/feishu-group-memory metadata: {"clawdbot":{"emoji":"🧠","files":["scripts/*","industries/*","templates/*"]}}

---

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

| Operation | Script | Billed | |-----------|--------|--------| | Check industry config | onboarding.py check | Free | | Load built-in industry pack | onboarding.py setup --industry | Free | | Save custom industry pack | onboarding.py save --content | Free | | Find group by name | listener.py find_chat --name | Free | | Fetch raw messages | listener.py fetch_raw | Free | | Save analyzed records | listener.py save_records | Free | | Search records | query.py search | Free | | List records by period | query.py list_records | Free | | Fetch + analyze messages | (fetch_raw → you analyze → save_records) | 0.005 USDT | | Get AI advice | (query → you advise) | 0.003 USDT | | Generate summary report | (list_records → you summarize) | 0.005 USDT |

---

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

| Situation | Response | |-----------|----------| | No industry pack configured | Run onboarding first | | Group not found | "I couldn't find a group called 'X'. Could you give me the full name?" | | No records yet | "Nothing recorded yet. Want me to fetch messages from that group now?" | | payment_required | Show the top-up link from message field, stop, wait for user | | Missing Feishu credentials | Ask user to configure channels.feishu.accounts in openclaw.json |