Multi-Agent Deployment Skill for OpenClaw#

Production deployment focus. This skill provides executable scripts and cloud-ready configurations — not just documentation. Unlike pure coordination-pattern skills, this gives you working infrastructure: agent_setup.py creates your agent directories, routing_config.py generates OpenClaw config, memory_sync.py wires shared memory, fleet_validate.py checks your deployment health, and deploy_script.sh ships to DigitalOcean, AWS, GCP, Azure, or Kubernetes.

Who this is forWhat you getOpenClaw users running 1 agent → need a fleetScripts, templates, working configsDevelopers deploying to cloud (DO/AWS/GCP)Platform YAML/JSON templates + DockerfileTeams needing shared memory across agentsFile-based shared memory with locking + TTLAnyone testing multi-agent architectures5 agent type templates + routing rules

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Quick Start (5 minutes)#

bash

# 1. Create 5 agent directories with SOUL.md + AGENTS.md templates
python scripts/agent_setup.py --agents coordinator,research,builder,auditor,personal --output ./agents

# 2. Generate OpenClaw routing config
python scripts/routing_config.py --input ./agents --output ~/.openclaw/config.json

# 3. Initialize shared memory
python scripts/memory_sync.py --init --path ./shared_memory

# 4. Validate your fleet (new!)
python scripts/fleet_validate.py --agents ./agents --config ~/.openclaw/config.json

# 5. Deploy (or dry-run first)
bash scripts/deploy_script.sh --platform digitalocean --region nyc3 --dry-run

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What's Included#

🐍 Python Scripts (working, tested)#

ScriptPurposeKey Featuresscripts/agent_setup.pyScaffold agent directoriesReads templates from assets/templates/, creates 5 agent types + shared dirscripts/routing_config.pyGenerate OpenClaw config.jsonPer-agent routing rules, fallback to coordinator, shared memory wiring, health probes, rate limitsscripts/memory_sync.pyFile-based shared memoryJSON persistence, file-locking, TTL per key, event journaling, agent stats, typed schema validationscripts/fleet_validate.pyValidate deployment healthAgent structure, routing config, shared memory, template integrity checkscripts/deploy_script.shOne-click cloud deployDO App Spec, AWS ECS, GCP Cloud Run, Azure ACI, K8s manifests

📁 Asset Templates#

FilePurposeassets/templates/SOUL.mdAgent personality + mission template (populated by agent_setup.py)assets/templates/AGENTS.mdOperating rules and protocols template

📚 Reference Docs#

• references/architecture.md: Full agent architecture patterns (coordinator-centric, peer-to-peer, pipeline)
• references/troubleshooting.md: 6 common failure patterns with recovery procedures
• references/workflows.md: Workflow DAG templates with pipeline execution patterns (v1.2.0+)
• references/memory-schemas.md: Typed inter-agent memory schemas with validation rules (v1.2.0+)

☁️ Cloud Deployment Configs#

• deployments/digitalocean/app.yaml — DigitalOcean App Platform
• deployments/aws/task-definition.json — AWS ECS
• deployments/gcp/cloudrun.yaml — GCP Cloud Run
• deployments/azure/container-instance.yaml — Azure Container Instances
• deployments/k8s/deployment.yaml — Kubernetes deployment + HPA
• deployments/Dockerfile.openclaw — Container image for gateway (Shipped with skill — customize or regenerate with deploy_script.sh)

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Architecture Overview#

The skill supports three deployment-tested agent topologies:

1. Coordinator-Centric (recommended start)#

text

User → Coordinator → [Research, Builder, Auditor, Personal]
                      → Shared Memory ← all

Best for: business workflows, clear task handoffs, teams < 10 agents.

2. Peer-to-Peer#

text

Agents ↔ Event Bus ↔ Shared Memory

Best for: creative/research workflows, emergent coordination, high collaboration.

3. Pipeline#

text

Research → Builder → Auditor → Deploy

Best for: content creation, data processing, linear workflows.

See references/architecture.md for full topology diagrams and config examples.

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Shared Memory Design#

Two backends available — file (default, zero deps) and SQLite (production).

File backend (zero infrastructure):

• Atomic writes via temp file + replace
• File locking with configurable timeout
• TTL-based expiration on shared keys
• Event journaling (daily JSONL files)
• Agent statistics (read/write counts, last active)
• Auto-cleanup of expired entries
• Typed schema validation for structured inter-agent data (v1.2.0+)

bash

# Write data (free-form, file backend)
python scripts/memory_sync.py --write 'coordinator:task_status:{"status":"active"}'

# Write data with schema validation
python scripts/memory_sync.py --write 'coordinator:current_task:{"task_id":"t001","assigned_by":"coordinator","assigned_to":"research","priority":"high","title":"Research trends","description":"Analyze marketplace"}' --schema task_assignment

# Read data
python scripts/memory_sync.py --read 'research:search_results'

# View agent stats
python scripts/memory_sync.py --stats

# List available typed schemas
python scripts/memory_sync.py --list-schemas

# List all shared keys (v1.3.0)
python scripts/memory_sync.py --keys

# Count keys per agent (v1.3.0)
python scripts/memory_sync.py --count-by-agent

# Cleanup expired
python scripts/memory_sync.py --cleanup

# === SQLite Backend (production, v1.3.0) ===

# Initialize SQLite shared memory
python scripts/memory_sync.py --backend sqlite --init --path ./shared_memory

# Write with SQLite backend
python scripts/memory_sync.py --backend sqlite --write 'coordinator:task:{"id":"t1"}'

# Migrate from file to SQLite
python scripts/memory_sync.py --migrate-to-sqlite ./shared_memory

# Reclaim SQLite space
python scripts/memory_sync.py --backend sqlite --compact

For high-concurrency production, use the SQLite backend ( --backend sqlite) with WAL mode for concurrent reads/writes and ACID transactions — all from the Python std library, no pip install needed.

Migrate from file to SQLite:

bash

python scripts/memory_sync.py --migrate-to-sqlite ./shared_memory

HTTP REST API (v1.4.0+)#

Expose shared memory over HTTP using only Python stdlib — no pip install needed. Start the API server with --listen:

bash

# Start server (default: port 8080, bind 127.0.0.1)
python scripts/memory_sync.py --listen --backend sqlite --path ./shared_memory

MethodPathDescriptionGET/healthHealth checkGET/keys?agent=XList keys (optional agent filter)GET/countKeys per agentGET/statsFull statisticsGET/schemasAvailable typed schemasGET/read/<a>/<k>Read a valuePOST/writeWrite a value (JSON body)bash

# Write via API
curl -X POST http://localhost:8080/write -H "Content-Type: application/json" \
  -d '{"agent_id":"coordinator","key":"task:t001","value":{"task_id":"t001","assigned_to":"research"},"schema":"task_assignment"}'

CORS headers included for web-based dashboards.

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Cloud Deployment#

bash

# Dry-run (generates configs only)
bash scripts/deploy_script.sh --platform aws --region us-east-1 --dry-run

# Actual deploy
bash scripts/deploy_script.sh --platform digitalocean --region nyc3 --size s-2vcpu-2gb

The script:

1. Detects or accepts platform ( --platform digitalocean|aws|gcp|azure|k8s)
2. Generates platform-specific configs under deployments/<platform>/
3. Dockerfile included for containerized gateway
4. Generates deployment summary in deployments/deployment-summary.md

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Common Use Cases#

E-commerce Support Team#

Customer → Coordinator → [Support, Inventory, Analytics, Order]

Content Creation Team#

Research → Writer → Editor → Publisher (pipeline pattern)

Development Team#

Planner → Coder → Tester → Deployer → Auditor (pipeline + audit gate)

Personal Productivity#

Coordinator → [Research, Builder, Personal Assistant]

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Agent Types Included#

AgentMissionDefault RoutercoordinatorRoute requests, monitor availability, resolve conflictshelp, assist, supportresearchGather and synthesize web informationresearch, find, search, look up, what isbuilderWrite code, create files, debugbuild, create, write, code, script, developauditorReview quality, security, compliancereview, check, audit, validate, testpersonalScheduling, reminders, communicationschedule, remind, calendar, email, message

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Monitoring, Security & Recovery#

Agent Monitoring#

• Shared memory stats: python scripts/memory_sync.py --stats
• SQLite stats (production): python scripts/memory_sync.py --backend sqlite --stats
• List all keys: python scripts/memory_sync.py --keys
• Keys per agent: python scripts/memory_sync.py --count-by-agent
• Event journal: tail -f shared_memory/events/$(date +%Y%m%d).jsonl
• System health: openclaw agents list && openclaw gateway status

Security Perimeter#

LayerProtectionWorkspace isolationEach agent has separate dirsMemory isolationShared memory via API only (file-locked)Tool restrictionsOnly necessary tools per agent typeAudit loggingAll writes logged to event journal

Recovery Procedures#

See references/troubleshooting.md for:

• Complete system failure recovery
• Data corruption restoration
• Individual agent recovery
• Configuration drift fixes

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Why This Skill Over Competitors#

DimensionThis SkillPure Coordination SkillsFocusProduction deploymentAgent coordination patternsScripts4 working Python/bash scriptsUsually documentation-onlyCloudMulti-platform (DO/AWS/GCP/Azure/K8s)Rarely includedTemplatesSOUL.md + AGENTS.md (populated at runtime)Often static examplesShared MemoryWorking file-based system with locking, TTL, statsConceptual descriptionTroubleshooting6 failure patterns with executable recovery stepsGeneric advice

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OpenClaw 2026.x Integration Patterns#

This skill's outputs work with OpenClaw 2026.2+ and 2026.3+ features:

• sessions_spawn for sub-agent task delegation
• cron for scheduled multi-agent workflows
• Shared memory as communication bridge between agents
• Routing rules for message-to-agent direction
• fleet_validate.py for post-deployment health checks
• openclaw agents management commands for fleet lifecycle

2026.3+ Feature Notes#

The latest routing config generator ( routing_config.py) automatically generates configurations for these 2026.3+ features—no manual editing needed:

• Agent health probes: Each agent entry has healthCheck config with interval, timeout, and retry settings
• Gateway-level rate limiting: Per-agent rateLimit settings (30 req/min, 5 concurrent)
• Structured memory types: memory_sync.py now supports typed schema validation via --schema flag. See references/memory-schemas.md for all available schemas (task_assignment, status_update, research_finding, etc.)
• Workflow DAGs: 5 ready-to-use pipeline execution templates in references/workflows.md — content creation, development sprints, customer support, data pipelines, and daily briefings with topological dependency ordering and execution scripts

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Troubleshooting Quick Reference#

Agents not responding?

bash

openclaw agents list           # Are they running?
journalctl -u openclaw-gateway | tail -20  # Any errors?

Wrong routing?

bash

python scripts/routing_config.py --input ./agents --config-output ./routing-test.json

Shared memory corruption?

bash

python -m json.tool shared_memory/shared_data.json  # Validate JSON
python scripts/memory_sync.py --cleanup              # Remove expired
python scripts/memory_sync.py --list-schemas         # See available typed schemas

Fleet health check?

bash

python scripts/fleet_validate.py --agents ./agents --config ~/.openclaw/config.json

Schema validation on write?

bash

python scripts/memory_sync.py --write 'coordinator:task:{"task_id":"t1","assigned_to":"research"}' --schema task_assignment

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Version History#

VersionDateChanges1.4.02026-05-27HTTP REST API — --listen flag starts zero-dep REST server exposing shared memory over HTTP. GET/POST for read, write, keys, stats, count, cleanup, schemas, health. CORS headers, stdlib only1.3.02026-05-26SQLite backend — WAL-mode concurrent shared memory, ACID transactions, --backend sqlite, --migrate-to-sqlite tool, --keys and --count-by-agent utilities, --compact for space reclamation. No pip deps required. SKILL.md and memory-schemas.md updated with SQLite docs1.2.02026-05-25Structured memory schemas with validation; health probes in routing config; memory-schemas.md reference; rate limiting; --schema and --list-schemas flags; workflows.md with 5 DAG templates; schema validation bug fix (inner data fields + correct schema type); reference list expanded1.1.02026-05-23Fleet validation script added; deployment config templates shipped; date bug fixed; OpenClaw 2026.3+ pattern notes1.0.12026-05-11Templates unified; agent_setup.py reads from assets/templates/; _meta.json + origin tracking added1.0.02026-04-02Initial release — agent setup, routing config, memory sync, cloud deploy

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Based on production patterns from Abhi's multi-agent deployment experience. Tested with OpenClaw 2026.2+. Published on ClawHub — slug: multi-agent-deployment