> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/Gentleman-Programming/engram/llms.txt
> Use this file to discover all available pages before exploring further.
# mem_capture_passive
> Extract and save structured learnings from text output
Extract and save structured learnings from text output. This tool automatically detects and saves learning sections from your output.
## Overview
`mem_capture_passive` looks for sections like `## Key Learnings:` or `## Aprendizajes Clave:` in text output and extracts numbered or bulleted items. Each item is saved as a separate observation with type `passive`.
This is useful for:
* Automatically capturing knowledge at the end of tasks
* Extracting learnings from agent responses
* Building a knowledge base from subagent completions
* Saving discoveries without manual `mem_save` calls
## Parameters
The text output containing a learnings section. Must include a heading like `## Key Learnings:` followed by numbered or bulleted items.
Session ID to associate the learnings with. Defaults to `manual-save-{project}`.
Project name to scope the learnings. Defaults to the current directory name.
Source identifier for tracking where learnings came from (e.g., `subagent-stop`, `session-end`, `manual`).
## Response
Number of new learnings saved
Number of duplicate learnings skipped (already exist in memory)
Total number of learnings found in the input
## Expected Format
The tool recognizes two heading formats:
**English:**
```markdown theme={null}
## Key Learnings:
1. First learning item
2. Second learning item
```
**Spanish:**
```markdown theme={null}
## Aprendizajes Clave:
1. Primer aprendizaje
2. Segundo aprendizaje
```
You can also use bulleted lists:
```markdown theme={null}
## Key Learnings:
- Learning with bullet points
- Another learning
```
## Example Usage
### Basic Usage
```json theme={null}
{
"content": "## Key Learnings:\n\n1. bcrypt cost=12 is the right balance for our server performance\n2. JWT refresh tokens need atomic rotation to prevent race conditions",
"session_id": "session-abc123",
"project": "auth-service"
}
```
**Response:**
```json theme={null}
{
"saved": 2,
"skipped": 0,
"total": 2
}
```
Each learning is saved as a separate observation:
* Title: First 50 characters of the learning
* Type: `passive`
* Content: Full learning text
* Project: `auth-service`
* Session: `session-abc123`
### With Duplicates
```json theme={null}
{
"content": "## Key Learnings:\n\n1. bcrypt cost=12 is the right balance\n2. New learning about rate limiting",
"session_id": "session-xyz789",
"project": "auth-service"
}
```
**Response:**
```json theme={null}
{
"saved": 1,
"skipped": 1,
"total": 2
}
```
The first learning was already saved in a previous call, so it's skipped. Only the new learning about rate limiting is saved.
## Deduplication
The tool uses content-based deduplication:
* Each learning is hashed based on normalized content (lowercase, whitespace trimmed)
* If the hash matches an existing observation in the same project within a 15-minute window, it's skipped
* This prevents duplicate saves when the same learnings appear in multiple outputs
## Use Cases
### End-of-Task Knowledge Capture
When completing a task, include a Key Learnings section:
```markdown theme={null}
I've successfully implemented user authentication.
## Key Learnings:
1. Passport.js local strategy requires bcrypt version 5+ for async hash
2. JWT expiry should be configurable per environment (15m dev, 1h prod)
3. Refresh token rotation prevents token replay attacks
```
Call `mem_capture_passive` with this output to automatically save all three learnings.
### Subagent Completion Hooks
When a subagent finishes work, extract learnings automatically:
```json theme={null}
{
"content": "",
"source": "subagent-stop",
"project": "current-project"
}
```
### Session End Summary
Capture learnings from end-of-session summaries:
```json theme={null}
{
"content": "",
"source": "session-end",
"session_id": "session-final"
}
```
## Comparison with mem\_save
| Feature | mem\_capture\_passive | mem\_save |
| ----------------- | --------------------------------- | --------------------------------- |
| **Format** | Extracts from markdown headings | Structured What/Why/Where/Learned |
| **Granularity** | One observation per learning item | One observation per save call |
| **Use Case** | Automatic extraction from output | Explicit, proactive saves |
| **Control** | Less control (auto-parsed) | Full control over structure |
| **Deduplication** | Automatic within 15-min window | Manual topic key upserts |
Use `mem_save` for deliberate, structured memories. Use `mem_capture_passive` for automatic knowledge extraction from text.
## Profile
**Profile:** `agent` (deferred loading)
This tool is in the `agent` profile but has `DeferLoading` enabled, meaning it's available to agents but not loaded into context by default. Agents can discover it through tool search when needed.
**Why deferred?** Most agents use `mem_save` for explicit memory creation. Passive capture is specialized for automated workflows and may not be needed in every session.
## Related Tools
Save structured observations explicitly
Search saved learnings
Save comprehensive session summaries
HTTP API for passive capture