Percept Summarize

Generates AI summaries of conversations after silence, extracting entities, action items, and relationships for searchable meeting notes and context retrieval.

Automatic conversation summaries with entity extraction and relationship mapping.

What it does

When a conversation ends (60 seconds of silence), Percept generates an AI-powered summary with extracted entities (people, companies, topics), action items, and relationship connections. Summaries are stored locally and searchable.

When to use

• User asks "what did we talk about?" or "summarize that meeting"
• User wants meeting notes or action items from a conversation
• Agent needs context from a recent conversation

Requirements

• percept-listen skill installed and running
• OpenClaw agent accessible via CLI (used for LLM summarization)

How it works

1. Conversation ends (60s silence timeout)
2. Percept builds a speaker-tagged transcript
3. Sends transcript to OpenClaw for AI summarization
4. Extracts entities (people, orgs, topics) and relationships
5. Stores summary + entities in SQLite
6. Entities linked via relationship graph (works_on, client_of, mentioned_with)

Entity resolution

5-tier cascade for identifying entities:

1. Exact match (confidence 1.0)
2. Fuzzy match (0.8) — handles typos, nicknames
3. Contextual/graph (0.7) — uses relationship connections
4. Recency (0.6) — recently mentioned entities ranked higher
5. Semantic search (0.5) — vector similarity via LanceDB

Querying summaries

Summaries are searchable via the Percept dashboard (port 8960) or SQLite directly:

SELECT * FROM conversations WHERE summary LIKE '%action items%' ORDER BY end_time DESC;

Full-text search via FTS5:

SELECT * FROM utterances_fts WHERE utterances_fts MATCH 'project deadline';

Data retention

• Utterances: 30 days
• Summaries: 90 days
• Relationships: 180 days
• Speaker profiles: never expire

Links

• GitHub: https://github.com/GetPercept/percept