opc-deepbrain 0.4.1

Local-first self-learning knowledge base. Turn your documents into searchable, evolving knowledge.

🧠 OPC DeepBrain

Self-Evolving Knowledge Engine for AI Agents — 6-Layer Memory That Grows With You

AI Agent 自进化知识引擎 — 6 层记忆，越用越聪明

Website · Quick Start · API Reference · vs Mem0

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💡 Why DeepBrain?

Memory solutions like Mem0 store facts. DeepBrain evolves knowledge.

Problem	Mem0 / Others	DeepBrain
Memory model	Flat key-value	6-layer evolving hierarchy
Quality control	None	4-Gate validation system
Knowledge growth	Manual CRUD	Auto-promotion through layers
Dependencies	Redis, Qdrant, OpenAI…	Zero (stdlib only)
Storage	Cloud vectors	SQLite (100% local)
Self-awareness	❌	✅ Meta-knowledge layer

DeepBrain is a standalone, embeddable knowledge engine that gives any AI agent long-term, self-evolving memory — in 3 lines of code, with zero dependencies.

✨ Key Features

• 🏗️ 6-Layer Memory Architecture — From flash memory to meta-knowledge, just like the human brain
• 🚪 4-Gate Quality Control — Every piece of knowledge passes Relevance → Novelty → Consistency → Utility gates
• 📦 Zero Dependencies — Pure Python stdlib. No numpy, no torch, no API keys
• 💾 100% Local — SQLite storage. Your knowledge never leaves your machine
• 🔌 Embeddable — Drop into any Python agent framework in 3 lines
• 🔄 Auto-Evolution — Knowledge automatically promotes, consolidates, and archives

🚀 Quick Start

pip install opc-deepbrain

from opc_deepbrain import DeepBrain

# Initialize
brain = DeepBrain("./my_brain.db")

# Learn
brain.learn("User prefers concise, technical answers", source="conversation")

# Recall
results = brain.recall("What communication style does the user prefer?")
print(results[0].content)  # → "User prefers concise, technical answers"

That's it. No API keys. No config. No cloud. 3 lines to persistent, evolving memory.

🏗️ 6-Layer Memory Architecture

┌─────────────────────────────────────────────────┐
│  Layer 5: 🔮 Meta-Knowledge                     │
│  "I know that I know X well, but Y is uncertain"│
├─────────────────────────────────────────────────┤
│  Layer 4: 🗄️ Archived                           │
│  Historical reference, low-access but preserved  │
├─────────────────────────────────────────────────┤
│  Layer 3: 🏗️ Consolidated                       │
│  Cross-session patterns, validated over time     │
├─────────────────────────────────────────────────┤
│  Layer 2: 📚 Long-Term                          │
│  Validated knowledge, frequently accessed        │
├─────────────────────────────────────────────────┤
│  Layer 1: 📝 Short-Term                         │
│  Recent interactions, hours to days              │
├─────────────────────────────────────────────────┤
│  Layer 0: ⚡ Flash Memory                        │
│  Current session buffer, minutes                 │
└─────────────────────────────────────────────────┘
         ↑ Auto-promotion based on relevance,
           frequency, and validation scores

How Knowledge Evolves

1. Ingestion → New knowledge enters Layer 0 (Flash)
2. 4-Gate Check → Relevance, Novelty, Consistency, Utility scoring
3. Promotion → High-quality knowledge moves up layers over time
4. Consolidation → Related facts merge into coherent understanding
5. Meta-Learning → The system learns its own knowledge strengths/gaps

🚪 4-Gate Quality Control

Every piece of knowledge must pass through 4 gates:

Gate	Purpose	Question Asked
🎯 Relevance	Is this useful?	Does this relate to active contexts?
🆕 Novelty	Is this new?	Do we already know this?
✅ Consistency	Does this fit?	Does it contradict existing knowledge?
🔧 Utility	Is this actionable?	Can this improve future responses?

📖 API Reference

Core API

from opc_deepbrain import DeepBrain

brain = DeepBrain(db_path="./brain.db")

# Learn — store knowledge
brain.learn(
    content="FastAPI is preferred over Flask for new projects",
    source="architecture-review",
    category="tech-decisions",
    tags=["python", "web", "architecture"]
)

# Recall — retrieve relevant knowledge
results = brain.recall(
    query="Which web framework should we use?",
    top_k=5,
    min_score=0.3
)

# Search — keyword search
results = brain.search("FastAPI", category="tech-decisions")

# Stats — memory statistics
stats = brain.stats()
print(f"Total entries: {stats['total']}")
print(f"By layer: {stats['by_layer']}")

# Evolve — trigger manual evolution cycle
brain.evolve()

# Export / Import
brain.export("backup.json")
brain.load("backup.json")

Embedding in Your Agent

# Works with any agent framework
class MyAgent:
    def __init__(self):
        self.brain = DeepBrain("./agent_brain.db")

    def chat(self, user_message):
        # Recall relevant context
        context = self.brain.recall(user_message, top_k=3)

        # Generate response (your LLM call here)
        response = self.llm.generate(user_message, context=context)

        # Learn from the interaction
        self.brain.learn(
            f"User asked about: {user_message}",
            source="conversation"
        )

        return response

⚖️ Comparison / 对比

Feature	OPC DeepBrain	Mem0	ChromaDB	Pinecone
Memory Model	6-layer evolving	Flat store	Vector store	Vector store
Quality Control	4-Gate system	❌	❌	❌
Auto-Evolution	✅	❌	❌	❌
Meta-Knowledge	✅	❌	❌	❌
Dependencies	0	5+	3+	2+
Storage	SQLite (local)	Redis + Qdrant	Local/Cloud	Cloud only
Cloud Required	❌	⚠️ Optional	⚠️ Optional	✅ Yes
Pricing	Free	Free/Paid	Free/Paid	Paid
Self-Evolving	✅	❌	❌	❌
Python stdlib only	✅	❌	❌	❌

🔌 Integrations

DeepBrain powers memory in:

• OPC Agent — Local AI agent
• Leaper Agent — Global agent framework
• Leaper Agent CN — China-optimized agent
• Your project — pip install opc-deepbrain and go

📄 License

BSL-1.1 — see LICENSE for details.

🤝 Contributing

We welcome contributions! See CONTRIBUTING.md.

📧 Contact: [e-mail]

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Built with ❤️ by Deepleaper Technology / 跃盟科技

Give your AI agent a brain that evolves.