synapse-pkg 0.1.1

Knowledge graph extraction library powered by LLMs

🧠 Synapse

Transform natural language into a persistent knowledge graph.

Synapse is a Python library that converts unstructured text into a structured knowledge graph by extracting concepts and relationships from natural language.

Instead of storing information as raw text, Synapse organizes information into a graph structure that can be queried, traversed, and extended for building intelligent systems and memory systems.

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✨ Features

• Natural language → structured relationships
• Persistent graph storage
• Duplicate prevention
• Case-insensitive querying
• Multi-hop relationship traversal
• Local-first architecture
• Provider-agnostic design
• Extensible memory engine

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📦 Installation

pip install synapse-ai

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⚡ Quick Start

from synapse import Synapse

brain = Synapse()

brain.add(
    "CEO of Apple met Microsoft executives in Seattle."
)

brain.save()

print(
    brain.get_relations("Apple")
)

print(
    brain.get_indirect_relations("CEO of Apple")
)

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Example

Input

Python is used for AI and TensorFlow is used in AI.

Extracted Graph

{
    "python": [
        ("used_for", "ai")
    ],
    "tensorflow": [
        ("used_in", "ai")
    ]
}

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Project Structure

synapse/
├── __init__.py
├── synapse.py
├── graph.py
├── query.py
├── processor.py
├── extractor.py
├── persistence.py
└── utils.py

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⚙️ Design Principles

• Simplicity over complexity
• Local-first architecture
• Incremental development
• Extensibility
• No overengineering

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📌 Current Status

✅ V1 Complete

Implemented features:

• Text ingestion
• Relationship extraction
• Graph construction
• Graph persistence
• Query system
• Multi-hop traversal
• Python package support
• Provider-agnostic extractor architecture

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🗺️ Roadmap

V2 — Conversational Memory Layer

Build a conversational interface on top of the graph.

Planned capabilities:

• Memory-backed conversations
• Graph-aware retrieval
• Contextual question answering
• Better reasoning over stored knowledge
• Persistent long-term memory

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V3 — Agentic Knowledge Graph

Integrate coding agents and sandboxed environments.

Planned capabilities:

• Claude Code integration
• Codex integration
• Live graph construction during conversations
• Automatic graph updates
• Multi-session memory
• Knowledge graph visualization

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V4 — Portable Intelligence

Turn Synapse into a transferable memory engine.

Planned capabilities:

• Import knowledge graphs
• Export knowledge graphs
• Transfer memory between LLMs
• Local execution
• Offline mode
• Personal AI memory system

Example:

Claude
    ↓
Knowledge Graph
    ↓
Export
    ↓
GPT
    ↓
Continue with the same memory

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🚀 Vision

Modern AI systems own the intelligence, but they also own the memory.

Synapse aims to separate the two.

The long-term goal is to make memory portable, persistent, and independent of any single model provider.

Memory should belong to the user, not the model.

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🧑‍💻 Author

Piyush Garg

AI/ML enthusiast interested in:

• Knowledge Graphs
• LLM Systems
• Intelligent Agents
• Machine Learning Infrastructure

Building systems at the intersection of memory and intelligence.

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📄 License

MIT License