🤔 The Problem

Every agent framework gives you tool-calling and multi-model support. None of them give you real memory.

• CrewAI? Flat key-value store, resets between runs.
• AutoGen? Conversation history only, no knowledge evolution.
• LangGraph? Checkpoints, not understanding.
• Hermes Agent? Skill files on disk — smart, but static.

Your agent forgets everything the moment the session ends.

💡 The Solution

Leaper Agent = Hermes Agent base + DeepBrain memory engine.

We took the proven Hermes Agent architecture — its agent loop, tool runtime, and multi-provider support — and integrated DeepBrain's 6-layer self-evolving memory engine. The result: an agent that gets smarter every time you talk to it.

Session 1: "I'm building a SaaS product with FastAPI and React."
Session 2: "What stack should I use for the admin panel?"
→ Agent recalls your tech choices, suggests FastAPI Admin + React
→ No manual context injection. It just knows.

🧠 Why Memory Matters

6-Layer Memory Architecture

┌─────────────────────────────────────────┐
│  Layer 5: Meta-Knowledge               │
│  "I know your tech preferences well,   │
│   but I'm uncertain about your budget"  │
├─────────────────────────────────────────┤
│  Layer 4: Archived — Historical ref     │
├─────────────────────────────────────────┤
│  Layer 3: Consolidated — Cross-session  │
├─────────────────────────────────────────┤
│  Layer 2: Long-Term — Validated facts   │
├─────────────────────────────────────────┤
│  Layer 1: Short-Term — Recent context   │
├─────────────────────────────────────────┤
│  Layer 0: Flash — Current session       │
└─────────────────────────────────────────┘
    ↑ Auto-promotion via 4-Gate QC ↑
    (Relevance · Novelty · Consistency · Utility)

This isn't RAG. This isn't vector search. This is knowledge that evolves — facts get validated, promoted, consolidated, and the agent develops meta-awareness of what it knows well and what it doesn't.

🚀 Quick Start

# Install
pip install leaper-agent

# Set your API key
export OPENAI_API_KEY=sk-xxx
# or: export ANTHROPIC_API_KEY=sk-ant-xxx
# or: export GEMINI_API_KEY=xxx

# Create an agent with the CEO Coach template
leaper create my-agent --template ceo-coach

# Start
leaper start my-agent

Deploy as Telegram Bot

leaper create my-bot --template ceo-coach --bot-token YOUR_TELEGRAM_TOKEN
leaper start my-bot

🏗️ Architecture

┌─────────────────────────────────────────┐
│         Leaper Agent Runtime            │
│    (Hermes-compatible agent loop)       │
├─────────┬──────────┬──────────┬────────┤
│ OpenAI  │ Claude   │ Gemini   │ Ollama │
├─────────┴──────────┴──────────┴────────┤
│         Agent Orchestration             │
│    Role templates · Tool calling        │
├─────────────────────────────────────────┤
│      🧠 DeepBrain Memory Engine         │
│   6-Layer · 4-Gate · Zero Dependencies  │
│   SQLite-only · 100% Local · Evolving   │
└─────────────────────────────────────────┘

Key difference from Hermes: Hermes stores skills as static Markdown files. Leaper Agent's DeepBrain automatically extracts, validates, and evolves knowledge from every conversation — no manual curation needed.

⚡ Hermes Compatibility

Leaper Agent is built on the Hermes Agent foundation. If you're familiar with Hermes, you'll feel at home:

• Same agent loop architecture
• Same multi-provider model support
• Same tool runtime
• Plus: 6-layer evolving memory that Hermes doesn't have

🔌 Supported Providers

📊 vs Other Frameworks

🇨🇳 China Version / 中国版

For Chinese LLM providers (DeepSeek, 通义千问, 智谱, Moonshot) + 150 role templates:

pip install leaper-agent-cn

→ Leaper Agent CN

🔗 Ecosystem

📄 License

BSL-1.1 — Free for non-competitive use. Converts to Apache-2.0 after 4 years.

🤝 Contributing

We welcome contributions! See CONTRIBUTING.md.

📧 Contact: tech@deepleaper.com