# ProllyTree Python Bindings
[](https://prollytree.readthedocs.io/en/latest/)
[](https://pypi.org/project/prollytree/)
Python bindings for ProllyTree - a probabilistic tree data structure that combines B-trees and Merkle trees for efficient, verifiable data storage.
## 🚀 Quick Start
### Installation
```bash
pip install prollytree
```
### Basic Usage
```python
from prollytree import ProllyTree
# Create a tree and insert data
tree = ProllyTree()
tree.insert(b"hello", b"world")
value = tree.find(b"hello") # Returns b"world"
```
## 📚 Documentation
**👉 [Complete Documentation](https://prollytree.readthedocs.io/en/latest/)**
The full documentation includes:
- [Quickstart Guide](https://prollytree.readthedocs.io/en/latest/quickstart.html)
- [API Reference](https://prollytree.readthedocs.io/en/latest/api.html)
- [Examples](https://prollytree.readthedocs.io/en/latest/examples.html)
- [Advanced Usage](https://prollytree.readthedocs.io/en/latest/advanced.html)
## ✨ Features
- **🌳 Probabilistic Trees** - High-performance data storage with automatic balancing
- **🤖 AI Agent Memory** - Multi-layered memory systems for intelligent agents
- **📚 Versioned Storage** - Git-like version control for key-value data
- **🔐 Cryptographic Verification** - Merkle proofs for data integrity across trees and versioned storage
- **⚡ SQL Queries** - Query your data using SQL syntax
## 🔥 Key Use Cases
### Probabilistic Trees
```python
from prollytree import ProllyTree
tree = ProllyTree()
tree.insert(b"user:123", b"Alice")
tree.insert(b"user:456", b"Bob")
# Cryptographic verification
proof = tree.generate_proof(b"user:123")
is_valid = tree.verify_proof(proof, b"user:123", b"Alice")
```
### AI Agent Memory
```python
from prollytree import AgentMemorySystem
memory = AgentMemorySystem("./agent_memory", "agent_001")
# Store conversation
memory.store_conversation_turn("chat_123", "user", "Hello!")
memory.store_conversation_turn("chat_123", "assistant", "Hi there!")
# Store facts
memory.store_fact("person", "john", '{"name": "John", "age": 30}',
confidence=0.95, source="profile")
```
### Versioned Storage
```python
from prollytree import VersionedKvStore
store = VersionedKvStore("./data")
store.insert(b"config", b"production_settings")
commit_id = store.commit("Add production config")
# Branch and experiment
store.create_branch("experiment")
store.insert(b"feature", b"experimental_data")
store.commit("Add experimental feature")
# Cryptographic verification on versioned data
proof = store.generate_proof(b"config")
is_valid = store.verify_proof(proof, b"config", b"production_settings")
```
### SQL Queries
```python
from prollytree import ProllySQLStore
sql_store = ProllySQLStore("./database")
sql_store.execute("CREATE TABLE users (id INT, name TEXT)")
sql_store.execute("INSERT INTO users VALUES (1, 'Alice')")
results = sql_store.execute("SELECT * FROM users WHERE name = 'Alice'")
```
## 🛠️ Development
### Building from Source
```bash
git clone https://github.com/zhangfengcdt/prollytree
cd prollytree
./python/build_python.sh --all-features --install
```
### Running Tests
```bash
cd python/tests
python test_prollytree.py
```
## 📄 License
Licensed under the Apache License, Version 2.0
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"description": "# ProllyTree Python Bindings\n\n[](https://prollytree.readthedocs.io/en/latest/)\n[](https://pypi.org/project/prollytree/)\n\nPython bindings for ProllyTree - a probabilistic tree data structure that combines B-trees and Merkle trees for efficient, verifiable data storage.\n\n## \ud83d\ude80 Quick Start\n\n### Installation\n\n```bash\npip install prollytree\n```\n\n### Basic Usage\n\n```python\nfrom prollytree import ProllyTree\n\n# Create a tree and insert data\ntree = ProllyTree()\ntree.insert(b\"hello\", b\"world\")\nvalue = tree.find(b\"hello\") # Returns b\"world\"\n```\n\n## \ud83d\udcda Documentation\n\n**\ud83d\udc49 [Complete Documentation](https://prollytree.readthedocs.io/en/latest/)**\n\nThe full documentation includes:\n- [Quickstart Guide](https://prollytree.readthedocs.io/en/latest/quickstart.html)\n- [API Reference](https://prollytree.readthedocs.io/en/latest/api.html)\n- [Examples](https://prollytree.readthedocs.io/en/latest/examples.html)\n- [Advanced Usage](https://prollytree.readthedocs.io/en/latest/advanced.html)\n\n## \u2728 Features\n\n- **\ud83c\udf33 Probabilistic Trees** - High-performance data storage with automatic balancing\n- **\ud83e\udd16 AI Agent Memory** - Multi-layered memory systems for intelligent agents\n- **\ud83d\udcda Versioned Storage** - Git-like version control for key-value data\n- **\ud83d\udd10 Cryptographic Verification** - Merkle proofs for data integrity across trees and versioned storage\n- **\u26a1 SQL Queries** - Query your data using SQL syntax\n\n## \ud83d\udd25 Key Use Cases\n\n### Probabilistic Trees\n```python\nfrom prollytree import ProllyTree\n\ntree = ProllyTree()\ntree.insert(b\"user:123\", b\"Alice\")\ntree.insert(b\"user:456\", b\"Bob\")\n\n# Cryptographic verification\nproof = tree.generate_proof(b\"user:123\")\nis_valid = tree.verify_proof(proof, b\"user:123\", b\"Alice\")\n```\n\n### AI Agent Memory\n```python\nfrom prollytree import AgentMemorySystem\n\nmemory = AgentMemorySystem(\"./agent_memory\", \"agent_001\")\n\n# Store conversation\nmemory.store_conversation_turn(\"chat_123\", \"user\", \"Hello!\")\nmemory.store_conversation_turn(\"chat_123\", \"assistant\", \"Hi there!\")\n\n# Store facts\nmemory.store_fact(\"person\", \"john\", '{\"name\": \"John\", \"age\": 30}',\n confidence=0.95, source=\"profile\")\n```\n\n### Versioned Storage\n```python\nfrom prollytree import VersionedKvStore\n\nstore = VersionedKvStore(\"./data\")\nstore.insert(b\"config\", b\"production_settings\")\ncommit_id = store.commit(\"Add production config\")\n\n# Branch and experiment\nstore.create_branch(\"experiment\")\nstore.insert(b\"feature\", b\"experimental_data\")\nstore.commit(\"Add experimental feature\")\n\n# Cryptographic verification on versioned data\nproof = store.generate_proof(b\"config\")\nis_valid = store.verify_proof(proof, b\"config\", b\"production_settings\")\n```\n\n### SQL Queries\n```python\nfrom prollytree import ProllySQLStore\n\nsql_store = ProllySQLStore(\"./database\")\nsql_store.execute(\"CREATE TABLE users (id INT, name TEXT)\")\nsql_store.execute(\"INSERT INTO users VALUES (1, 'Alice')\")\nresults = sql_store.execute(\"SELECT * FROM users WHERE name = 'Alice'\")\n```\n\n## \ud83d\udee0\ufe0f Development\n\n### Building from Source\n```bash\ngit clone https://github.com/zhangfengcdt/prollytree\ncd prollytree\n./python/build_python.sh --all-features --install\n```\n\n### Running Tests\n```bash\ncd python/tests\npython test_prollytree.py\n```\n\n## \ud83d\udcc4 License\n\nLicensed under the Apache License, Version 2.0\n\n",
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