OpenClaw Personal Knowledge Base

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Personal Knowledge Base

Build a searchable, semantic knowledge base from everything you read — articles, tweets, YouTube transcripts, PDFs — all ingested and retrievable via natural language chat with OpenClaw Ultra.

Core System Overview

This system turns your agent’s memory into a personal research library. Drop any URL or file into chat, and it’s automatically ingested, chunked, and indexed for semantic search. Later, ask questions and get ranked results with source attribution — no more lost bookmarks.

System Layer	Core Function	Output Result
Ingestion Layer	URL fetching, content extraction, format normalization	Clean, structured text with metadata
Processing Layer	Chunking, embedding generation, vector indexing	Semantically searchable knowledge store
Retrieval Layer	Hybrid search (semantic + keyword), relevance ranking	Ranked results with source context
Memory Layer	Cross-session persistence, auto-tagging, deduplication	Growing, non-duplicating knowledge base
Integration Layer	Feed into other workflows (SEO, social, meeting prep)	Reusable research across all agent tasks

Prerequisites

Item	Requirement
OpenClaw Ultra	Installed and running
Knowledge Base Skill	Install from ClawdHub — search “knowledge-base”
Ingestion Channel	Telegram topic or Slack channel (recommended for auto-ingest)

Step 0 — Initialize Knowledge Base System

Set up OpenClaw Ultra as your personal knowledge management engine.

Operation Steps

Open OpenClaw Ultra new chat session
Install the knowledge-base skill
Create a dedicated Telegram topic called “knowledge-base” (or use a Slack channel)
Paste initialization prompt

Ready-to-Use Prompt

Act as my personal knowledge base system.

I want to save everything I find valuable — articles, tweets, YouTube videos, PDFs, code snippets — and be able to search them conversationally.

Build a system that:
- ingests content from URLs I drop in chat
- extracts and indexes the full content
- supports natural language queries over saved knowledge
- deduplicates and tags content automatically
- connects to other workflows when they need research context

Step 1 — Set Up Auto-Ingestion Pipeline

Configure the agent to automatically process any URL or file you send.

1.1 Configure Ingestion Channel

Prompt

Set up the "knowledge-base" topic for automatic content ingestion.

When I drop a URL in this topic:
1. Fetch the full content (article, tweet thread, YouTube transcript, PDF)
2. Extract clean text with metadata: title, URL, date, content type
3. Chunk into semantic segments with embeddings
4. Index with tags: source type, topic, key entities
5. Reply with: what was ingested, chunk count, suggested tags

Supported sources:
- Web articles (any URL)
- YouTube videos (auto-fetch transcript)
- Tweets and X threads
- PDF documents (via file upload)
- GitHub READMEs and docs

1.2 Batch Import Existing Bookmarks

Prompt

I have a collection of saved links I want to import:

[list URLs or export file]

Process each one through the ingestion pipeline.
Report progress: [X/N] ingested, any failed URLs with error reasons.

ℹ️ Info

Your knowledge base grows automatically from this point forward — every interesting link you encounter, just drop it in the topic.

Step 2 — Semantic Search & Retrieval

Query your knowledge base conversationally.

2.1 Basic Query

Prompt

Search my knowledge base for: [your question or topic]

Return:
- top 5 most relevant results
- for each: title, source URL, key excerpt, relevance score
- if no good matches, tell me explicitly

2.2 Cross-Reference Query

Prompt

I'm working on [current project/task].
Search my knowledge base for anything related to:
[list relevant topics or keywords]

Summarize what I already know, what sources I have, and what gaps exist.

Step 2 Output

Instant access to everything you’ve saved, organized by relevance.

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