airsync

name	airsync
description	Collaborative memory system for AI agents and teams. Three-layer architecture (INBOX → PUBLISHED → ARCHIVED) ensures only high-quality knowledge reaches the shared team memory.

Airsync Memory Workflow

Purpose

Airsync is a managed memory system where AI agents and human developers collaboratively build a shared knowledge base. Unlike traditional knowledge bases where everything is searchable immediately, Airsync uses a gated lifecycle to prevent low-quality content from contaminating team-wide search.

The Three Layers

Layer	Visibility	TTL	Purpose
INBOX	Explicit only	14 days	Raw captures awaiting review
PUBLISHED	Default search	Permanent	Vetted, high-quality knowledge
ARCHIVED	Explicit only	Permanent	Retired or obsolete content

Key Principle: Writing to the knowledge base ≠ Making it searchable.

---

Required Workflow

1. Retrieve Context First

Before making architectural decisions or implementing changes:

memory_search(
    query="your search terms",
    memory_kind="best_practice",  # optional filter
    tags_any=["relevant", "tags"]   # optional filter
)

• Default search only returns PUBLISHED memories
• Use layers=["PUBLISHED", "ARCHIVED"] to include archived content
• Use layers=["INBOX"] only when explicitly reviewing pending memories

2. Produce Output With Traceability

• Cite memory IDs for major decisions: "Based on mem_01hz3kp8x2a5mv7b..."
• If memories conflict, prefer higher quality scores and newer timestamps
• Note when you're overriding documented best practices

3. Capture Learnings (INBOX)

When you discover something worth remembering:

memory_propose(
    entry={
        "memory_kind": "best_practice",  # or "learning", "observation", "note", "model"
        "scope": "team",                 # or "project", "agent"
        "author_agent_id": "your-agent-id",
        "title": "Clear outcome-focused title",
        "content": "Detailed explanation with context...",
        "tags": ["architecture", "patterns"],
    }
)

Important: All proposals go to INBOX, not directly to PUBLISHED. They won't appear in default searches until promoted.

4. Quality Self-Check Before Proposing

The quality score (0.0-1.0) is computed automatically:

Criterion	Points
Title > 10 chars	+0.20
Content > 50 chars	+0.20
Has memory_kind	+0.15
Scope = project/team	+0.15
≥2 tags	+0.15
Content > 200 chars	+0.15

Minimum 0.5 required for promotion. Improve your entry if quality is low.

---

Memory Kinds

Kind	Use For	Example
best_practice	Validated approaches that work	"Keep framework modules aligned with the selected architecture boundaries"
learning	Insights from experience	"Discovered that batching reduces API calls by 80%"
observation	Noted patterns without full validation	"Seems like Qdrant queries are faster with pre-filtering"
note	General documentation	"API rate limits are 1000 req/min"
model	Mental models or frameworks	"The CAP tradeoff in distributed systems"

---

Tool Reference

Write Operations

memory_propose(entry, team_id?) → MemoryRecord
    Creates a new memory in INBOX. Performs exact deduplication check.
    Returns error if exact duplicate exists in team.

memory_promote(memory_id, team_id?) → PromoteResult
    Moves INBOX memory to PUBLISHED. Requirements:
    - Quality >= 0.5
    - No semantic duplicates (>0.95 similarity) in PUBLISHED
    Returns duplicate_of if similar memory exists.

memory_archive(memory_id, reason, team_id?) → MemoryRecord
    Archives a memory (from any layer). Reasons:
    - "superseded" - replaced by newer content
    - "obsolete" - no longer relevant
    - "incorrect" - contains errors
    - "consolidated" - merged into another memory
    - "expired" - TTL expired (usually automatic)

Read Operations

memory_search(query, top_k?, team_id?, layers?, memory_kind?, scope?, tags_any?) → [SearchResult]
    Semantic search with layer-aware filtering.
    Default: only searches PUBLISHED.
    Automatically excludes expired INBOX entries.

memory_get(memory_id, team_id?) → MemoryRecord
    Retrieves any memory by ID (any layer).

memory_list_inbox(team_id?) → [MemoryRecord]
    Lists pending INBOX memories for review.
    Sorted by created_at desc (newest first).

memory_find_duplicates(memory_id, team_id?, threshold?) → DuplicateCheckResult
    Finds exact and semantic duplicates.
    Useful before promotion.

---

Content Quality Guidelines

DO Capture

• Root cause + fix patterns applicable beyond this task
• Architectural decisions with clear rationale
• Validated runbooks and operational commands
• Repeated pitfalls and how to avoid them
• Team-wide conventions and standards
• Mental models that explain complex systems

DO NOT Capture

• Project-specific implementation details other teams can't reuse
• Debugging sessions without transferable lessons
• Scratch notes or in-progress findings
• Content already in codebase, docs, or prior memory
• Transient observations without context

The Collaboration Test

Before proposing, ask: "Would a developer on a different project benefit from this in 3 months?"

If no → don't propose.

---

Deduplication

The system prevents duplication at two levels:

1. Exact Duplication (propose phase)

• SHA-256 hash of normalised content
• Rejects identical content within same team
• Returns existing memory ID on conflict

2. Semantic Duplication (promote phase)

• Vector similarity > 0.95 threshold
• Checked against PUBLISHED layer only
• Suggests merge instead of promotion

Best Practice: Search before proposing. If similar content exists, enhance it rather than creating a duplicate.

---

Example Workflows

Adding a New Best Practice

# 1. Check if it already exists
results = memory_search(
    query="selected architecture framework module boundaries",
    memory_kind="best_practice"
)

# 2. If not found, propose to INBOX
memory = memory_propose(entry={
    "memory_kind": "best_practice",
    "scope": "team",
    "author_agent_id": "claude-opus-4",
    "title": "Keep framework modules aligned with selected architecture boundaries",
    "content": "All framework modules must respect the project's selected architecture boundaries...",
    "tags": ["architecture", "boundaries", "framework"],
})

# 3. When confident, promote to PUBLISHED
memory_promote(memory_id=memory.id)

Reviewing Pending Memories

# List INBOX for review
inbox = memory_list_inbox(team_id="my-team")

for memory in inbox:
    if memory.quality >= 0.5:
        # Check for duplicates first
        dups = memory_find_duplicates(memory.id)
        if not dups.semantic_duplicates:
            memory_promote(memory.id)
        else:
            print(f"Merge candidate: similar to {dups.semantic_duplicates[0].memory.id}")
    else:
        print(f"Low quality ({memory.quality}), needs improvement")

---

Configuration

Environment variables:

Variable	Default	Description
AIRSYNC_TEAM_ID	"default"	Default team for all operations
AIRSYNC_INBOX_TTL_DAYS	14	Days before INBOX expires
AIRSYNC_DEDUP_THRESHOLD	0.95	Semantic similarity threshold
AIRSYNC_MIN_QUALITY_FOR_PUBLISH	0.5	Minimum quality for promotion
AIRSYNC_DEFAULT_SEARCH_LAYERS	"PUBLISHED"	Default layers for search

---

Safety Rules

1. Never store secrets - No credentials, tokens, or personal data
2. Never fabricate - Don't invent prior knowledge when search returns nothing
3. Cite your sources - Use source_refs for traceability
4. Prefer promotion over direct publish - All entries should pass through INBOX
5. Archive obsolete content - Don't let outdated knowledge pollute search