v1.0 to v1.0

5 added, 5 removed. Audit A to A.

---

name: knowledge-base

description: Ingest URLs, documents, and transcripts into a searchable knowledge base. Query past research and curated documentation using full-text search. Trigger words: ingest, knowledge base, look up, search knowledge, what do we know about, research, index this, add to knowledge base.

license: MIT

metadata:

author: sagemindai

version: "1.0"

requires: instar

homepage: https://instar.sh

user_invocable: "true"

---

# knowledge-base -- Searchable Knowledge Base for Instar Agents

Build a searchable knowledge base from external sources -- URLs, documents, transcripts, PDFs. Uses the existing MemoryIndex (FTS5) for search, so no new dependencies.

---

## How It Works

The knowledge base is a set of markdown files in `.instar/knowledge/` that MemoryIndex indexes alongside your other memory files. Each file has YAML frontmatter for metadata and is tracked in a catalog for browsing.

```

.instar/knowledge/

catalog.json # Registry of all ingested sources

articles/ # Ingested web articles

transcripts/ # Video/audio transcripts

docs/ # Curated reference documentation

```

---

## Ingesting Content

### Via CLI

```bash

# Ingest text content directly

instar knowledge ingest "Article content here..." --title "My Article" --tags "AI,agents"

# Ingest from a URL (fetch first, then ingest)

# Step 1: Fetch the content

python3 .claude/scripts/smart-fetch.py "https://example.com/article" --auto > /tmp/fetched.md

# Step 2: Ingest it

instar knowledge ingest "$(cat /tmp/fetched.md)" --title "Article Title" --url "https://example.com/article" --tags "topic1,topic2"

```

### Via API

```bash

- curl -X POST http://localhost:4040/knowledge/ingest \

+ curl -X POST http://localhost:${INSTAR_PORT:-4040}/knowledge/ingest \

-H "Content-Type: application/json" \

-d '{

"content": "The article content...",

"title": "Article Title",

"url": "https://example.com/article",

"type": "article",

"tags": ["AI", "infrastructure"],

"summary": "Brief description"

```

### Via Agent Workflow

When the agent wants to ingest content during a session:

1. Fetch the content (WebFetch, smart-fetch, transcript tools, or Read for local files)

2. Clean it (strip navigation, ads, boilerplate)

3. Call the ingest API or write the file manually:

```bash

# Write the markdown file with frontmatter

cat > .instar/knowledge/articles/2026-02-25-my-article.md << 'EOF'

---

title: "My Article"

source: "https://example.com/article"

ingested: "2026-02-25"

tags: ["AI", "infrastructure"]

---

# My Article

[Cleaned article content here]

EOF

# Sync the index to pick up the new file

instar memory sync

```

---

## Searching Knowledge

### CLI

```bash

# Search within knowledge base only

instar knowledge search "notification batching"

# Search all memory (including knowledge)

instar memory search "notification batching"

```

### API

```bash

# Knowledge-scoped search

- curl "http://localhost:4040/memory/search?q=notification+batching&source=knowledge/&limit=5"

+ curl "http://localhost:${INSTAR_PORT:-4040}/memory/search?q=notification+batching&source=knowledge/&limit=5"

# Browse the catalog

- curl "http://localhost:4040/knowledge/catalog"

- curl "http://localhost:4040/knowledge/catalog?tag=AI"

+ curl "http://localhost:${INSTAR_PORT:-4040}/knowledge/catalog"

+ curl "http://localhost:${INSTAR_PORT:-4040}/knowledge/catalog?tag=AI"

```

---

## Managing Sources

### List all sources

```bash

instar knowledge list

instar knowledge list --tag AI

```

### Remove a source

```bash

# Find the source ID from the list

instar knowledge list

# Remove it

instar knowledge remove kb_20260225123456_abc123

# Re-sync the index

instar memory sync

```

### Via API

```bash

# Remove

- curl -X DELETE "http://localhost:4040/knowledge/kb_20260225123456_abc123"

+ curl -X DELETE "http://localhost:${INSTAR_PORT:-4040}/knowledge/kb_20260225123456_abc123"

```

---

## MemoryIndex Configuration

To enable knowledge base indexing, add these sources to your `.instar/config.json` memory section:

```json

{

"memory": {

"enabled": true,

"sources": [

{ "path": "AGENT.md", "type": "markdown", "evergreen": true },

{ "path": "USER.md", "type": "markdown", "evergreen": true },

{ "path": "knowledge/articles/", "type": "markdown", "evergreen": false },

{ "path": "knowledge/transcripts/", "type": "markdown", "evergreen": false },

{ "path": "knowledge/docs/", "type": "markdown", "evergreen": true }

]

}

```

**Source behavior:**

- `articles/` and `transcripts/` use `evergreen: false` -- recent content ranks higher (30-day temporal decay)

- `docs/` uses `evergreen: true` -- reference documentation doesn't decay

---

## Content Types

|------|-----------|----------------|----------|

---

## Tips

- **Always sync after ingesting**: `instar memory sync` updates the FTS5 index

- **Use tags consistently**: Tags enable filtered browsing via `instar knowledge list --tag X`

- **Include source URLs**: Helps trace back to original content

- **Clean before ingesting**: Strip navigation, ads, cookie banners for better search results

- **Use smart-fetch for URLs**: `python3 .claude/scripts/smart-fetch.py URL --auto` gets clean markdown