Memory and Context

Structured knowledge management, empathy modulation, and memory operations for attune-ai.

attune-ai's memory system sits ABOVE Anthropic's native memory features. It provides security-classified storage, cross-agent pattern sharing, empathy-level modulation, and a structured pattern lifecycle that native memory does not offer. Use the decision table below to determine which system to use for a given task.

When to Use This Skill

Layer Positioning

Use Case	Use Anthropic Native	Use attune-ai
"Remember my code style"	CLAUDE.md	--
"What did we do last session?"	Session Memory	--
"Store this pattern for reuse across agents"	--	memory_store (shared library)
"Classify this finding as SENSITIVE"	--	Security pipeline
"Coordinate memory across 3 parallel agents"	--	Redis pub/sub
"Promote this pattern after 5 successful uses"	--	Pattern lifecycle
"Adjust response depth based on user context"	--	Empathy modulation
"Track what this project uses"	Auto Memory	--

The rule is simple: if Anthropic's native memory handles it, use that. If you need classification, cross-agent sharing, empathy modulation, or pattern lifecycle management, use attune-ai.

Memory Operations

MCP tools for memory management.

memory_store

Store structured knowledge in attune-ai memory.

Parameters:

•key (required): Unique identifier for the stored data.
•value (required): Content to store. Can be a string, dict, or structured object.
•classification (optional): Security classification. One of PUBLIC (default), INTERNAL, or SENSITIVE. See Security Classification below.
•pattern_type (optional): Category string for pattern matching and lifecycle management.

When to use: Cross-agent patterns, security-classified data, structured knowledge that must persist beyond a single session or be shared across agents.

When NOT to use: Simple preferences belong in CLAUDE.md. Conversation history is handled by native Session Memory.

Example:

python

memory_store(
    key="python-import-ordering",
    value="stdlib first, then third-party, then local. Enforce with isort.",
    classification="PUBLIC",
    pattern_type="coding-convention"
)

memory_retrieve

Retrieve data by key or pattern ID.

Parameters:

•key (required): The key or pattern_id to retrieve.

Returns: The stored value, or null if not found.

memory_search

Search memory for patterns matching a query.

Parameters:

•query (required): Search string to match against stored keys and values.
•pattern_type (optional): Filter results to a specific pattern type category.

Returns: A list of matching memory entries, ranked by relevance.

memory_forget

Remove data from memory.

Parameters:

•key (required): The key or pattern_id to remove.
•
scope (optional): Which storage layer to remove from. One of:
- •"session" -- Remove from short-term session storage only.
- •"persistent" -- Remove from long-term persistent storage only.
- •"all" -- Remove from both layers. This is the default.

Empathy Levels

attune-ai uses 5 empathy levels that modulate response depth and proactivity. Each level maps to a named class in the framework.

Level	Class Name	Behavior
1	Level1Reactive	Respond when asked. Minimal proactive guidance. Answer the question, nothing more.
2	Level2Guided	Collaborative exploration with clarifying questions. Offer options but let the user choose.
3	Level3Proactive	Act before being asked. Suggest improvements, flag issues, and offer next steps.
4	Level4Anticipatory	Predict future needs. Prepare for likely next steps. Surface relevant context before it is requested.
5	Level5Systems	Build structures that help at scale. Design for growth. Create reusable patterns, automation, and frameworks.

Level 2-3 is appropriate for most development tasks. Reserve Level 4-5 for architecture work, release planning, or explicit user request.

empathy_get_level

Returns the current empathy level (integer 1-5) for this session.

Parameters: None.

empathy_set_level

Set the empathy level for this session.

Parameters:

•level (required): Integer from 1 to 5.

Example:

python

empathy_set_level(level=3)  # Switch to proactive mode

Context Operations

Session-scoped key-value store for transient state. Context values are discarded when the session ends. Use these for temporary coordination data that does not need to persist.

context_get

Get a session context value.

Parameters:

•key (required): The context key to retrieve.

Returns: The stored value, or null if not set.

context_set

Set a session context value.

Parameters:

•key (required): The context key to set.
•value (required): The value to store.

Example:

python

context_set(key="current_review_file", value="src/attune/workflows/base.py")
file = context_get(key="current_review_file")

Security Classification

When storing data with memory_store, choose the appropriate classification level.

•PUBLIC: Safe to share across agents and sessions. No special handling. This is the default.
•INTERNAL: Limited to current project or team scope. Not shared externally.
•SENSITIVE: PII is scrubbed before storage. Data is encrypted with AES-256-GCM. All access is audit logged.

Choose the minimum classification that meets your needs. Over-classifying creates unnecessary overhead.

Example -- storing a security finding:

python

memory_store(
    key="vuln-2026-02-cve-1234",
    value={"severity": "HIGH", "file": "src/auth.py", "line": 42, "description": "Hardcoded credential"},
    classification="SENSITIVE",
    pattern_type="security-finding"
)

Pattern Lifecycle

Patterns progress through three stages:

•Staged: Initial storage, awaiting validation. Created by any memory_store call with a pattern_type.
•Validated: Confirmed useful through repeated access (automatic) or explicit promotion. The system tracks access frequency and confidence scores.
•Promoted: Available in the shared library for cross-agent access. Other agents and sessions can discover and use promoted patterns.

The system handles lifecycle transitions automatically based on confidence thresholds and usage frequency. You do not need to manually promote patterns in most cases.

Creating a staged pattern:

python

memory_store(
    key="error-handling-api-calls",
    value="Always wrap external API calls in try/except with specific exceptions. Log before re-raising.",
    pattern_type="coding-convention"
)

This pattern starts as Staged. After it is retrieved or matched 5+ times with positive outcomes, the system promotes it to Validated, then eventually to Promoted.

Redis Upgrade Path

By default, attune-ai uses in-memory storage. All features described in this skill work without Redis.

What Redis adds:

•Multi-agent coordination via pub/sub channels.
•Sub-millisecond lookups for large memory stores.
•Shared state across sessions and agents running in parallel.
•Pattern persistence with configurable TTL.

Upgrade:

bash

pip install attune-ai[redis]

Zero configuration needed -- connects to localhost:6379 by default. For custom configuration, set the ATTUNE_REDIS_URL environment variable:

bash

export ATTUNE_REDIS_URL="redis://custom-host:6380/0"

If Redis disconnects mid-session, the system falls back to in-memory storage gracefully. No data loss occurs for the current session; cross-agent coordination pauses until reconnection.

Anti-Patterns

•DO NOT use memory_store for simple preferences -- use CLAUDE.md instead.
•DO NOT push Redis on developers who do not need multi-agent coordination.
•DO NOT store conversation history -- native Session Memory handles this.
•DO NOT set empathy level higher than needed -- Level 2-3 is appropriate for most development tasks.
•DO NOT over-classify data as SENSITIVE -- use PUBLIC unless the data genuinely contains PII or credentials.
•DO NOT bypass the pattern lifecycle by manually marking patterns as Promoted -- let the confidence system validate them.