Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Developer Tools
AgentOS SDK
AgentOS SDK provides APIs and CLI tools for persistent AI agent memory, project and task management, activity logging, inter-agent communication, and self-ev...
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AgentOS SDK provides APIs and CLI tools for persistent AI agent memory, project and task management, activity logging, inter-agent communication, and self-ev...
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- AGENT-OPS.md, DOCS.md, SELF-EVOLUTION.md, SKILL.md, agentos.sh, examples/clawdbot-integration.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 3.7.0
Provenance
- Publisher
- AgentOSsoftware
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
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 <id> --status todo --priority high aos kanban move <task-id> done # Brainstorms aos brainstorm add "Use WebSocket" --project <id> --type idea # Activity logging aos activity log "Completed API refactor" --project <id> # Mesh communication aos mesh send <agent> "Topic" "Message body" Run aos help or aos <command> 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: CategoryColorUse ForPath PrefixPrimary TagIdentity🔴 RedWho you are, user profiles, team structureidentity/["identity", ...]Knowledge🟠 OrangeFacts, research, documentationknowledge/["knowledge", ...]Memory🟣 PurpleLong-term memories, learnings, decisionsmemory/["memory", ...]Preferences🔵 BlueUser preferences, settings, stylepreferences/["preferences", ...]Projects🟢 GreenActive work, tasks, code contextprojects/["project", "<name>"]Operations🟤 BrownDaily logs, status, heartbeat stateoperations/["operations", ...]Secrets⚪ GrayAccess info, server locations (NOT actual keys!)secrets/["secrets", ...]Protocols🔵 CyanSOPs, checklists, proceduresprotocols/["protocols", ...]
Path Structure
<category>/<subcategory>/<item> 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: Primary category tag — one of the 8 categories Subcategory tag — more specific classification 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
VariableRequiredDescriptionAGENTOS_API_KEYYesYour API key from agentos.software dashboardAGENTOS_BASE_URLYesAPI endpoint (default: http://178.156.216.106:3100)AGENTOS_AGENT_IDYesUnique identifier for this agent instance
aos_put - Store Memory
aos_put <path> <value_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 <path> # 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 <query> [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 <path> # Creates a tombstone version (soft delete, keeps history) aos_delete "/cache/old-data"
aos_list - List Children
aos_list <prefix> # Returns direct children under a path aos_list "/learnings" # → {"items": [{"path": "/learnings/2026-02-04", "type": "file"}, ...]}
aos_glob - Pattern Match
aos_glob <pattern> # 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 <path> [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
FunctionPurposeaos_before_actionCheck mistakes/solutions before actingaos_mistakeDocument a failure + lessonaos_problem_solvedRegister a solved problemaos_check_solvedSearch for similar solved problemsaos_save_progressLog completed task (anti-compaction)aos_checkpointSave working state (every 15-20 min)aos_session_startRestore context at session startaos_verify_loggedLog verification evidenceaos_daily_summaryReview today's workaos_evolve_checkShow evolution checklist
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/ <person-id>.json # Context about people I work with /projects/ <project-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
EventPayloadDescriptionmemory:created{agentId, path, versionId, value, tags, createdAt}New memory storedmemory:deleted{agentId, path, versionId, deletedAt}Memory deleted
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
OperationDefault LimitRead ops (get, list, glob, history)60/minWrite ops (put, delete)60/minSearch ops20/minWebSocket connections5 per tenant
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/$(date +%Y-%m-%d).md)" This ensures your context is backed up both locally AND to the AgentOS cloud.
1. Use Meaningful Paths
# Good - hierarchical, descriptive aos_put "/projects/raptor/decisions/2026-02-04-architecture" '...' # Bad - flat, ambiguous aos_put "/data123" '...'
2. Tag Everything Important
AOS_TAGS='["decision","architecture","raptor"]' \ AOS_SEARCHABLE=true \ aos_put "/projects/raptor/decisions/..." '...'
3. Use TTL for Ephemeral Data
# Cache that expires in 1 hour AOS_TTL=3600 aos_put "/cache/api-response" '...'
4. Search Before Asking
# Before asking user for info, check memory result=$(aos_search "user preferences for $TOPIC" 3)
5. Version Important Changes
# Check history before overwriting aos_history "/config/critical-setting" 5 # Then update aos_put "/config/critical-setting" '...'
"Unauthorized" errors
Check AGENTOS_API_KEY is set correctly Verify key has required scopes (memory:read, memory:write, search:read)
Empty search results
Ensure memories were stored with searchable=true Check if the embedding was generated (may take a few seconds)
Rate limit errors
Implement exponential backoff Batch operations where possible Check X-PreAuth-RateLimit-Remaining header
Mesh Communication (Agent-to-Agent)
AgentOS Mesh enables real-time communication between AI agents.
Mesh Functions
# Send a message to another agent aos_mesh_send <to_agent> <topic> <body> # Get inbox messages (sent to you) aos_mesh_inbox [limit] # Get outbox messages (sent by you) aos_mesh_outbox [limit] # Check for locally queued messages (from daemon) aos_mesh_pending # Process queued messages (returns JSON, clears queue) aos_mesh_process # List all agents on the mesh aos_mesh_agents # Create a task for another agent aos_mesh_task <assigned_to> <title> [description] # List tasks assigned to you aos_mesh_tasks [status] # Get mesh overview stats aos_mesh_stats # Get recent activity feed aos_mesh_activity [limit] # Check mesh connection status aos_mesh_status
Example: Sending Messages
# Send a message to another agent aos_mesh_send "kai" "Project Update" "Finished the API integration, ready for review" # Send with context aos_mesh_send "icarus" "Research Request" "Please analyze the latest DeFi trends on Solana"
Example: Processing Incoming Messages
# Check if there are pending messages aos_mesh_pending # Process and respond to messages messages=$(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)
EventPayloadDescriptionmesh:message{fromAgent, toAgent, topic, body, messageId}New message receivedmesh:task_update{taskId, assignedTo, title, status}Task status changed
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 <to> "<topic>" "<body>" ~/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
Category context
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
5 Docs1 Scripts
- SKILL.md
- AGENT-OPS.md
- DOCS.md
- examples/clawdbot-integration.md
- SELF-EVOLUTION.md
- agentos.sh