AgentOS SDK Skill

Overview

AgentOS is a complete accountability infrastructure for AI agents. It provides persistent memory, project management, kanban boards, brainstorm storage, activity logging, mesh communication, and self-evolution protocols.

Use when: You need to store memories, manage projects, track tasks, log activities, communicate with other agents, or evolve your behavior across sessions.

🆕 Agent Operations Guide

Read AGENT-OPS.md for a complete guide on how to operate as an agent on AgentOS. It covers:

• Memory organization (paths, tags, importance)
• Project management (create, update, track)
• Kanban workflow (tasks, statuses, priorities)
• Brainstorm storage (ideas, decisions, learnings)
• Daily operations (session start/end checklists)
• Self-evolution protocols

🆕 aos CLI - Full Dashboard Control

The aos CLI gives you complete control over the AgentOS dashboard:

# Memory
aos memory put "/learnings/today" '{"lesson": "verify first"}'
aos memory search "how to handle errors"

# Projects
aos project list
aos project create "New Feature" --status active

# Kanban
aos kanban add "Fix bug" --project \x3Cid> --status todo --priority high
aos kanban move \x3Ctask-id> done

# Brainstorms
aos brainstorm add "Use WebSocket" --project \x3Cid> --type idea

# Activity logging
aos activity log "Completed API refactor" --project \x3Cid>

# Mesh communication
aos mesh send \x3Cagent> "Topic" "Message body"

Run aos help or aos \x3Ccommand> for detailed usage.

Golden Sync (Recommended)

For a bulletproof dashboard (Memory + Projects cards), run:

~/clawd/bin/agentos-golden-sync.sh

This syncs memory AND upserts per-project markdown cards: TASKS.md, IDEAS.md, CHANGELOG.md, CHALLENGES.md → DB → Brain Dashboard.

🏷️ Memory Categorization (REQUIRED)

Every memory MUST be properly categorized. Use these 8 standard categories:

Path Structure

\x3Ccategory>/\x3Csubcategory>/\x3Citem>

Examples:
identity/user/ben-profile
knowledge/research/ai-agents-market
memory/learnings/2026-02-mistakes
preferences/user/communication-style
projects/agentos/tasks
operations/daily/2026-02-13
secrets/access/hetzner-server
protocols/deploy/agentos-checklist

Tagging Rules

Every memory MUST have:

1. Primary category tag — one of the 8 categories
2. Subcategory tag — more specific classification
3. Optional project tag — if project-related

# Example: Store a learning with proper tags
AOS_TAGS='["memory", "learnings"]' AOS_SEARCHABLE=true \
  aos_put "/memory/learnings/2026-02-13" '{"lesson": "Always categorize memories"}'

# Example: Store user preference
AOS_TAGS='["preferences", "user"]' \
  aos_put "/preferences/user/communication" '{"style": "direct, no fluff"}'

Quick Start

# Set environment variables
export AGENTOS_API_KEY="your-api-key"
export AGENTOS_BASE_URL="http://178.156.216.106:3100"  # or https://api.agentos.software
export AGENTOS_AGENT_ID="your-agent-id"

# Source the SDK
source /path/to/agentos.sh

# Store a memory
aos_put "/memories/today" '{"learned": "something important"}'

# Retrieve it
aos_get "/memories/today"

# Search semantically
aos_search "what did I learn today"

Configuration

Core API Functions

aos_put - Store Memory

aos_put \x3Cpath> \x3Cvalue_json> [options]

# Options (as env vars before call):
#   AOS_TTL=3600          # Expire after N seconds
#   AOS_TAGS='["tag1"]'   # JSON array of tags
#   AOS_IMPORTANCE=0.8    # 0-1 importance score
#   AOS_SEARCHABLE=true   # Enable semantic search

# Examples:
aos_put "/learnings/2026-02-04" '{"lesson": "Always verify before claiming done"}'
AOS_SEARCHABLE=true aos_put "/facts/solana" '{"info": "Solana uses proof of history"}'
AOS_TTL=86400 aos_put "/cache/price" '{"sol": 120.50}'

aos_get - Retrieve Memory

aos_get \x3Cpath>

# Returns JSON: {"found": true, "path": "...", "value": {...}, "version_id": "...", "created_at": "..."}
# Or: {"found": false}

aos_get "/learnings/2026-02-04"

aos_search - Semantic Search

aos_search \x3Cquery> [limit] [path_prefix]

# Returns ranked results by semantic similarity
# Only searches memories marked as searchable=true

aos_search "what mistakes have I made" 10
aos_search "solana facts" 5 "/facts"

aos_delete - Remove Memory

aos_delete \x3Cpath>

# Creates a tombstone version (soft delete, keeps history)
aos_delete "/cache/old-data"

aos_list - List Children

aos_list \x3Cprefix>

# Returns direct children under a path
aos_list "/learnings"
# → {"items": [{"path": "/learnings/2026-02-04", "type": "file"}, ...]}

aos_glob - Pattern Match

aos_glob \x3Cpattern>

# Supports * and ** wildcards
aos_glob "/learnings/*"           # Direct children
aos_glob "/memories/**"           # All descendants
aos_glob "/projects/*/config"     # Wildcard segments

aos_history - Version History

aos_history \x3Cpath> [limit]

# Returns all versions of a memory (for time travel)
aos_history "/config/settings" 20

aos_agents - List All Agents

aos_agents

# Returns all agent IDs in your tenant with memory counts
# Useful for discovering other agent instances

aos_dump - Bulk Export

aos_dump [agent_id] [limit]

# Export all memories for an agent (default: current agent)
aos_dump "" 500

Self-Evolution Framework

For the complete self-evolution guide, see SELF-EVOLUTION.md.

AgentOS enables agents to get smarter every day through:

• Mistake tracking — Never repeat the same error
• Problem registry — Solutions indexed for future reference
• Pre-task checks — Search learnings before acting
• Progress checkpoints — Anti-compaction memory saves
• Verification logging — Prove tasks are actually done

Quick Start: Self-Evolution

# Before any task: check past learnings
aos_before_action "deployment"

# After a mistake: document it
aos_mistake "What happened" "Root cause" "Lesson learned" "severity"

# After solving a problem: register it
aos_problem_solved "OAuth 401 Error" "JWT format mismatch" "Added JWT branch to auth" "auth,oauth"

# After completing work: save progress
aos_save_progress "Deployed API v2" "success" "JWT auth now working"

# Every 15-20 min: checkpoint context
aos_checkpoint "Building payment flow" "Stripe webhook incomplete" "Test mode works"

# At session start: restore context
aos_session_start

# Run the evolution checklist
aos_evolve_check

Core Functions

Recommended Memory Structure

/self/
  identity.json       # Who am I? Core traits, values
  capabilities.json   # What can I do? Skills, tools
  preferences.json    # How do I prefer to work?
  
/learnings/
  YYYY-MM-DD.json     # Daily learnings
  mistakes/           # Documented failures
  successes/          # What worked well
  
/patterns/
  communication/      # How to talk to specific people
  problem-solving/    # Approaches that work
  tools/              # Tool-specific knowledge
  
/relationships/
  \x3Cperson-id>.json    # Context about people I work with
  
/projects/
  \x3Cproject-name>/     # Project-specific context
    context.json
    decisions.json
    todos.json

/reflections/
  weekly/             # Weekly self-assessments
  monthly/            # Monthly reviews

Self-Reflection Protocol

After completing significant tasks, store reflections:

# After a mistake
aos_put "/learnings/mistakes/$(date +%Y-%m-%d)-$(uuidgen | cut -c1-8)" '{
  "type": "mistake",
  "what_happened": "I claimed a task was done without verifying",
  "root_cause": "Rushed to respond, skipped verification step",
  "lesson": "Always verify state before claiming completion",
  "prevention": "Add verification checklist to task completion flow",
  "severity": "high",
  "timestamp": "'$(date -Iseconds)'"
}' 

# Mark as searchable so you can find it later
AOS_SEARCHABLE=true AOS_TAGS='["mistake","verification","lesson"]' \
aos_put "/learnings/mistakes/..." '...'

Self-Improvement Loop

# 1. Before starting work, recall relevant learnings
aos_search "mistakes I've made with $TASK_TYPE" 5

# 2. After completing work, reflect
aos_put "/learnings/$(date +%Y-%m-%d)" '{
  "tasks_completed": [...],
  "challenges_faced": [...],
  "lessons_learned": [...],
  "improvements_identified": [...]
}'

# 3. Periodically consolidate learnings
aos_search "lessons from the past week" 20
# Then synthesize and store in /reflections/weekly/

Real-Time Sync (WebSocket)

Connect to receive live updates when memories change:

const ws = new WebSocket('ws://178.156.216.106:3100');

ws.onopen = () => {
  // Authenticate
  ws.send(JSON.stringify({
    type: 'auth',
    token: process.env.AGENTOS_API_KEY
  }));
  
  // Subscribe to updates for your agent
  ws.send(JSON.stringify({
    type: 'subscribe',
    agent_id: 'your-agent-id'
  }));
};

ws.onmessage = (event) => {
  const msg = JSON.parse(event.data);
  
  if (msg.type === 'memory:created') {
    console.log('New memory:', msg.path, msg.value);
  }
  
  if (msg.type === 'memory:deleted') {
    console.log('Memory deleted:', msg.path);
  }
};

WebSocket Events

Webhook Integration

Register webhooks to receive HTTP callbacks when memories change:

# Register a webhook (via dashboard or API)
curl -X POST "$AGENTOS_BASE_URL/v1/webhooks" \
  -H "Authorization: Bearer $AGENTOS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "url": "https://your-server.com/agentos-webhook",
    "events": ["memory:created", "memory:deleted"],
    "agent_id": "your-agent-id",
    "path_prefix": "/learnings"
  }'

Webhook Payload

{
  "event": "memory:created",
  "timestamp": "2026-02-04T09:50:00Z",
  "data": {
    "tenant_id": "...",
    "agent_id": "your-agent-id",
    "path": "/learnings/2026-02-04",
    "version_id": "...",
    "value": {"lesson": "..."},
    "tags": ["learning"],
    "created_at": "..."
  },
  "signature": "sha256=..."
}

Rate Limits & Quotas

Heartbeat Context Backup Protocol (CRITICAL)

Every agent using AgentOS MUST implement mandatory context backup on every heartbeat.

Why This Exists

• AI agents lose context during session compaction
• "Remember to back up after each task" doesn't work — agents forget
• Heartbeat-driven backup ensures context is NEVER lost

Clawdbot Configuration

Set heartbeat to 10 minutes in your clawdbot.json:

{
  "agents": {
    "defaults": {
      "heartbeat": {
        "every": "10m",
        "model": "anthropic/claude-3-5-haiku-latest"
      }
    }
  }
}

HEARTBEAT.md Template

Add this to your workspace's HEARTBEAT.md:

## 🔴 MANDATORY: Context Backup (DO THIS FIRST)

**On EVERY heartbeat, before anything else:**

1. **Read:** CONTEXT.md + today's daily notes + yesterday's daily notes
2. **Update CONTEXT.md** with:
   - Current timestamp
   - What's happening in the session
   - Recent accomplishments
   - Active tasks
   - Important conversation notes
3. **Update daily notes** (`memory/daily/YYYY-MM-DD.md`) with significant events
4. **Only then** proceed with other heartbeat checks

This is a HARD RULE. Never skip this step.

AGENTS.md Hard Rule

Add this to your AGENTS.md:

## HARD RULE: Context Backup on EVERY Heartbeat

**Every single heartbeat MUST include a context backup.** No exceptions.

### Protocol (MANDATORY on every heartbeat)

1. **Read current state:**
   - CONTEXT.md
   - Today's daily notes (`memory/daily/YYYY-MM-DD.md`)
   - Yesterday's daily notes (for continuity)

2. **Update CONTEXT.md with:**
   - Current session focus
   - Recent accomplishments (what just happened)
   - Active tasks/threads
   - Important notes from conversation
   - Timestamp of update

3. **Update daily notes with:**
   - Significant events
   - Decisions made
   - Tasks completed
   - Context that might be needed later

4. **Only THEN proceed with other heartbeat tasks**

### Heartbeat Frequency
Heartbeats should run every **10 minutes** to ensure context is preserved frequently.

### The Golden Rule
**If you wouldn't remember it after a restart, write it down NOW.**

AgentOS Integration

Sync your CONTEXT.md to AgentOS on every heartbeat:

# In your heartbeat routine, after updating local files:
aos_put "/context/current" "$(cat CONTEXT.md)"
aos_put "/daily/$(date +%Y-%m-%d)" "$(cat memory/daily/$(date +%Y-%m-%d).md)"

This ensures your context is backed up both locally AND to the AgentOS cloud.

Best Practices

1. Use Meaningful Paths

# Good - hierarchical, descriptive
aos_put "/projects/raptor/decisions/2026-02-04-architecture" '...'

# Bad - flat, ambiguous
aos_put "/data123" '...'

2. Tag Everything Important

AOS_TAGS='["decision","architecture","raptor"]' \
AOS_SEARCHABLE=true \
aos_put "/projects/raptor/decisions/..." '...'

3. Use TTL for Ephemeral Data

# Cache that expires in 1 hour
AOS_TTL=3600 aos_put "/cache/api-response" '...'

4. Search Before Asking

# Before asking user for info, check memory
result=$(aos_search "user preferences for $TOPIC" 3)

5. Version Important Changes

# Check history before overwriting
aos_history "/config/critical-setting" 5
# Then update
aos_put "/config/critical-setting" '...'

Troubleshooting

"Unauthorized" errors

• Check AGENTOS_API_KEY is set correctly
• Verify key has required scopes (memory:read, memory:write, search:read)

Empty search results

• Ensure memories were stored with searchable=true
• Check if the embedding was generated (may take a few seconds)

Rate limit errors

• Implement exponential backoff
• Batch operations where possible
• Check X-PreAuth-RateLimit-Remaining header

Mesh Communication (Agent-to-Agent)

AgentOS Mesh enables real-time communication between AI agents.

Mesh Functions

# Send a message to another agent
aos_mesh_send \x3Cto_agent> \x3Ctopic> \x3Cbody>

# Get inbox messages (sent to you)
aos_mesh_inbox [limit]

# Get outbox messages (sent by you)
aos_mesh_outbox [limit]

# Check for locally queued messages (from daemon)
aos_mesh_pending

# Process queued messages (returns JSON, clears queue)
aos_mesh_process

# List all agents on the mesh
aos_mesh_agents

# Create a task for another agent
aos_mesh_task \x3Cassigned_to> \x3Ctitle> [description]

# List tasks assigned to you
aos_mesh_tasks [status]

# Get mesh overview stats
aos_mesh_stats

# Get recent activity feed
aos_mesh_activity [limit]

# Check mesh connection status
aos_mesh_status

Example: Sending Messages

# Send a message to another agent
aos_mesh_send "kai" "Project Update" "Finished the API integration, ready for review"

# Send with context
aos_mesh_send "icarus" "Research Request" "Please analyze the latest DeFi trends on Solana"

Example: Processing Incoming Messages

# Check if there are pending messages
aos_mesh_pending

# Process and respond to messages
messages=$(aos_mesh_process)
echo "$messages" | jq -r '.[] | "From: \(.from) - \(.topic)"'

# Respond to each message
aos_mesh_send "kai" "Re: Project Update" "Thanks for the update, looks good!"

Real-Time Mesh Daemon

For real-time message reception, run the mesh daemon:

node ~/clawd/bin/mesh-daemon.mjs

The daemon connects via WebSocket and queues incoming messages for processing.

Mesh Events (WebSocket)

CLI Shortcut

A standalone CLI is also available:

~/clawd/bin/mesh status    # Connection status
~/clawd/bin/mesh pending   # List pending messages
~/clawd/bin/mesh send \x3Cto> "\x3Ctopic>" "\x3Cbody>"
~/clawd/bin/mesh agents    # List agents

API Reference

Full OpenAPI spec available at: $AGENTOS_BASE_URL/docs

AgentOS - Persistent memory and mesh communication for evolving AI agents