The Agent Stack: Paperclip + Honcho + OpenClaw + Hermes

# 1. Paperclip — the organizational layer
npx paperclipai onboard --yes
# Now running at http://localhost:3100
 
# 2. Honcho — the memory layer
docker run -d --name honcho \
  -p 8000:8000 \
  -e DATABASE_URL=postgres://honcho:honcho@localhost:5432/honcho \
  ghcr.io/plastic-labs/honcho:latest
# Now running at http://localhost:8000
 
# 3. Hermes — the learning agent engine
curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash
hermes model set anthropic/claude-sonnet-4
 
# 4. OpenClaw — the messaging interface
npm install -g openclaw@latest
openclaw onboard --install-daemon

curl -X POST http://localhost:3100/api/companies \
  -H "Content-Type: application/json" \
  -d '{
    "name": "Harbor View Mixed-Use",
    "mission": "Deliver Harbor View on schedule ($8.2M GMP) with zero lost-time incidents."
  }'

import honcho
 
# One workspace per project — keeps memories separate
workspace = honcho.Workspace.create(
    name="harbor-view-mixed-use",
    metadata={
        "project_value": 8200000,
        "start_date": "2026-04-01",
        "completion_date": "2027-06-30"
    }
)
 
# Register everyone the agents will interact with
# Humans:
workspace.create_peer(
    name="John_Superintendent",
    type="human",
    metadata={"role": "superintendent", "phone": "+1-555-0147"}
)
workspace.create_peer(
    name="Sarah_Henderson_Design",
    type="human",
    metadata={"role": "architect", "avg_rfi_response_days": 8}
)
 
# AI Agents:
workspace.create_peer(
    name="PMAgent",
    type="ai",
    metadata={"role": "project_manager", "adapter": "hermes"}
)
workspace.create_peer(
    name="FieldBot",
    type="ai",
    metadata={"role": "field_communications", "adapter": "openclaw"}
)

# Estimating Agent — uses Hermes (learns cost patterns over time)
npx paperclipai agent create \
  --company-id <company-id> \
  --name "EstimatingAgent" \
  --adapter hermes_local \
  --title "Chief Estimator" \
  --role manager \
  --adapter-config '{
    "model": "anthropic/claude-sonnet-4",
    "persistSession": true,
    "toolsets": ["terminal", "file-ops", "web"],
    "checkpoints": true
  }'
 
# Field Agent — uses OpenClaw (accessible via WhatsApp)
npx paperclipai agent create \
  --company-id <company-id> \
  --name "FieldBot" \
  --adapter openclaw_gateway \
  --title "Field Communications" \
  --role ic \
  --adapter-config '{
    "gatewayUrl": "ws://localhost:18789",
    "channels": ["whatsapp", "telegram"]
  }'
 
# PM Agent — uses Hermes (learns scheduling patterns)
npx paperclipai agent create \
  --company-id <company-id> \
  --name "PMAgent" \
  --adapter hermes_local \
  --title "Project Manager" \
  --role manager \
  --adapter-config '{
    "model": "anthropic/claude-sonnet-4",
    "persistSession": true,
    "toolsets": ["terminal", "file-ops", "web", "browser"]
  }'

# This runs at the start of every agent heartbeat
import honcho
import os
 
# Connect to the project's memory workspace
workspace = honcho.Workspace.get(name="harbor-view-mixed-use")
agent_peer = workspace.get_peer(name=os.environ["PAPERCLIP_AGENT_ID"])
 
# Ask Honcho: "What do I need to know about this task?"
task_id = os.environ["PAPERCLIP_TASK_ID"]
context = workspace.chat(
    peer=agent_peer,
    query=f"What do I need to know about task {task_id}? "
          f"Include relevant stakeholder preferences and project history."
)
 
# Now the agent starts work with FULL project context —
# it remembers everything from previous sessions

# Monthly budgets per agent (controls AI API costs)
npx paperclipai budget set --agent EstimatingAgent --monthly 500
npx paperclipai budget set --agent FieldBot --monthly 150
npx paperclipai budget set --agent PMAgent --monthly 400
 
# Require human approval for sensitive actions:
# - Sending any email/message to external parties
# - Any financial commitment (change orders, POs)
# - Any change to safety procedures