> ## Documentation Index
> Fetch the complete documentation index at: https://docs.runtimelabs.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Memory Tagging

Memory tagging in Chronologue allows users and agents to organize, retrieve, and share meaningful subsets of memory traces. Tags act as **lightweight semantic labels**—designed to give users **control**, agents **precision**, and teams a **shared language for context**.

This document walks through the design motivations for tagging, the tension between structured control and intelligent retrieval, and the practical advantages of using tags for planning, reflection, and agent collaboration.

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## 1. Why Tagging Matters

While language models can infer patterns from raw memory traces, Chronologue supports tagging for two core reasons:

### a. **Control Interface**

Tags let users shape the context used in agent tasks. For example:

* Summarize my goals from `#q2_review`
* Schedule around `#deep_work` blocks
* Reflect only on `#travel` memories this weekend

This is more than filtering—it’s a **way to think with memory**, not just about it.

### b. **Communication Interface**

Tags create a common vocabulary between users, agents, and collaborators. This enables:

* Sharing a memory cluster (`#team_sync`) with a new agent
* Broadcasting `#customer_feedback` traces to a product team
* Subscribing an agent to `#urgent_tasks` for autonomous triage

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## 2. Schema Overview

Every memory trace in Chronologue can include an array of tags:

tags: \["deep\_work", "2025Q2", "team\_sync", "urgent"]

These tags are:

* Plain strings
* User- or agent-defined
* Stored alongside trace metadata

***

## 3. Use Cases

### a. Personal Context Scoping

User prompt:

"Can you generate a personal retrospective based on my #weekly\_reflection and #fitness logs from April?"

System behavior:

* Fetches all memory traces with `tags` containing both `weekly_reflection` and `fitness`
* Limits by `timestamp` from `2025-04-01` to `2025-04-30`
* Passes to LLM as structured context block

### b. Multi-Agent Collaboration

* A user tags a batch of customer support transcripts with `#feature_request`
* Shares this tag cluster with a product roadmap agent and an investor report agent
* Each agent retrieves the same core memory, but interprets it for different planning tasks

### c. Cross-User Shared Memory

In a team workspace:

* A lead tags a set of traces with `#onboarding_guides`
* New team members can access this tag’s contents for reference
* An onboarding agent uses only `#onboarding_guides` to generate customized start plans

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## 4. Prompt-Aware Tag Filters

Chronologue supports `#hashtags` in chat prompts:

Prompt:

"Summarize everything in #deep\_work and #2025Q1 related to travel planning."

System:

* Parses `#deep_work` and `#2025Q1` as tag filters
* Retrieves intersected trace cluster
* Conditionally scopes memory for planning or reflection agents

This pattern allows users to **dynamically reframe memory context** with a single prompt.

***

## 5. Design Tension: Intelligence vs. Explicit Control

### Argument Against Tagging

* Modern LLMs can infer themes without tags
* Embeddings can cluster memories without manual labeling
* Tags require upkeep and structure

### Why Chronologue Still Embraces Tagging

* Tags allow **repeatable, editable, and inspectable** context control
* They enable **human-to-agent communication of memory intent**
* Tags support **parallel views** of the same memory (e.g., `#urgent`, `#revisit`, `#weekly`)

Tagging is not about redundancy—it’s about empowering users to **decide what matters and when**.

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## 6. Best Practices

* Use simple, memorable tags: `#weekly`, `#team_sync`, `#urgent`
* Combine temporal and semantic tags: `#may`, `#q2_review`, `#travel`
* Let agents suggest tags during or after execution (e.g., “Would you like to tag this #deep\_work?”)
* Regularly audit and summarize high-frequency tag clusters

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## 7. Future Directions

* Tag versioning and namespaces for shared workspaces
* Auto-generated tags based on NLP themes and calendar context
* Agent-driven tag propagation and conditional triggers (e.g., if tagged `#urgent`, notify)
* Visual tag-based context composer for multi-agent systems

***