PulseBot

Stream-native AI Agent powered by Timeplus

PulseBot is a lightweight, extensible AI agent framework that uses Timeplus streaming database as its backbone for real-time message routing, observability, and memory storage.

✨ Features

• Stream-Native Architecture - All communication flows through Timeplus streams
• Multi-Provider LLM Support - Anthropic Claude, OpenAI, OpenRouter, Ollama, and NVIDIA
• Multi-Agent Collaboration - Spawn coordinated agent teams for complex tasks via kanban-based message queues
• Vector Memory - Semantic search using embeddings stored in Timeplus
• SQL-Native Scheduling - Timeplus Tasks replace traditional cron jobs
• Interactive Workspaces - Build and publish dynamic artifacts and runnable web apps
• Extensible Skills - Plugin-based tool system with OpenClaw compatibility and ClawHub registry
• Tool Call Hooks - Intercept, approve, deny, or modify tool calls before/after execution
• Multi-Channel - Telegram, webchat, with easy extension to Slack/WhatsApp
• Real-Time Observability - All LLM calls and tool executions logged to streams
• Production Ready - Docker deployment, async architecture, structured logging

🏗️ Architecture

🚀 Quick Start

Prerequisites

• Python 3.11+
• Timeplus or Proton

Installation

# Clone repository
git clone https://github.com/timeplus-io/PulseBot.git
cd PulseBot

# Install package
pip install -e .

# Generate default config
pulsebot init

Configuration

Edit config.yaml with your API keys:

agent:
  name: "PulseBot"
  model: "claude-sonnet-4-20250514"  # or use ollama model

providers:
  anthropic:
    api_key: ${ANTHROPIC_API_KEY}
  
  # For local testing with Ollama
  ollama:
    enabled: true
    host: "http://localhost:11434"
    default_model: "llama3"

workspace:
  base_dir: "./workspaces"
  workspace_port: 8001
  internal_api_key: "${WORKSPACE_INTERNAL_KEY}"

Using Ollama (Local Testing)

# Install Ollama (macOS)
brew install ollama

# Pull a model
ollama pull llama3

# Start Ollama server
ollama serve

# Update config.yaml to use Ollama
# Set agent.provider: "ollama" and agent.model: "llama3"

Setup & Run