cogniz 1.0.1

Python SDK for Cogniz Memory Platform - Persistent memory for AI applications

Cogniz Python SDK

Official Python SDK for Cogniz Memory Platform. Build AI applications with persistent, searchable memory.

Features

• Persistent Memory - Store and recall information across sessions
• Semantic Search - Natural language memory retrieval
• Confidence Scoring - Filter memories by reliability
• Auto-Expiration - Automatic cleanup of stale data
• Agent Scoping - Separate AI personality from user data
• Async Support - Native asyncio for concurrent operations
• Prompt Optimizer - AI-powered prompt enhancement
• Playbooks - Automation workflows
• Self-Hosted - Run on your own infrastructure
• Multi-Client - Works with Browser Extension, VS Code, MCP

Installation

pip install cogniz

Quick Start

from cogniz import Client

# Initialize client
client = Client(api_key="mp_your_api_key_here")

# Store a memory
client.store(
    "User prefers Python for backend development",
    user_id="alice",
    category="preferences"
)

# Search memories
results = client.search("programming languages", user_id="alice")
for memory in results.get("results", []):
    print(f"- {memory['content']}")

# Get usage stats
stats = client.get_stats()
print(f"Plan: {stats['plan']}, Memories: {stats['total_memories']}")

Environment Variables

Set these in your .env file:

COGNIZ_API_KEY=mp_your_api_key_here
COGNIZ_PROJECT_ID=123  # Optional default project
COGNIZ_BASE_URL=https://cogniz.online  # Optional custom host

Then use without parameters:

from cogniz import Client

# Automatically reads from environment
client = Client()

Core Operations

Store Memories

# Simple storage
result = client.store(
    "User completed Python course",
    user_id="alice"
)

# With metadata and category
result = client.store(
    "User allergic to penicillin",
    user_id="patient_123",
    category="medical",
    metadata={"severity": "high", "verified": True},
    confidence=1.0
)

# With expiration
result = client.store(
    "User browsing electronics section",
    user_id="bob",
    category="session",
    auto_expire=True  # Expires based on category
)

Search Memories

# Basic search
results = client.search(
    "What programming languages does the user know?",
    user_id="alice"
)

# With filtering
results = client.search(
    "medical conditions",
    user_id="patient_123",
    threshold=0.8,  # High confidence only
    limit=5
)

# With agent context
results = client.search(
    "fitness goals",
    user_id="alice",
    agent_id="fitness_coach_v1"
)

Manage Memories

# Get all memories
memories = client.get_all(user_id="alice", limit=50)

# Update memory
client.update(
    memory_id="mem_123",
    content="Updated information",
    metadata={"verified": True}
)

# Delete memory
client.delete("mem_123")

# Delete all for user
client.delete_all(user_id="alice")

Advanced Features

Agent Memory Scoping

Store AI personality once, share across all users:

# Store agent personality (shared)
client.store(
    "You are a motivational fitness coach who celebrates wins",
    agent_id="fitness_coach_v1"
)

# Store user progress (private)
client.store(
    "User completed 5K run in 28 minutes",
    user_id="alice"
)

# Search combines both contexts
results = client.search(
    "my running progress",
    user_id="alice",
    agent_id="fitness_coach_v1"
)

Prompt Optimization

# Optimize your prompts with AI
result = client.optimize_prompt(
    "Write code to sort a list",
    preset="technical"  # or "comprehensive", "concise"
)

print(result["optimized"])
# Output: "Write production-ready, well-commented Python code..."

Automation Playbooks

# List available playbooks
playbooks = client.list_playbooks()

# Run a playbook
result = client.run_playbook(
    "web_scraper_v1",
    input_data={"url": "https://example.com"}
)

Knowledge Graph

# Extract entities from text
entities = client.extract_entities(
    "Emma works with David at Cogniz on the Python SDK"
)

# Get graph statistics
stats = client.get_graph_stats()
print(f"Entities: {stats['entity_count']}")

Async Usage

import asyncio
from cogniz import AsyncClient

async def main():
    async with AsyncClient(api_key="mp_...") as client:
        # Store memory
        await client.store(
            "User loves async Python",
            user_id="alice"
        )

        # Search memories
        results = await client.search(
            "async programming",
            user_id="alice"
        )

        print(results)

asyncio.run(main())

Project Management

# List all projects
projects = client.list_projects()
for project in projects:
    print(f"{project['id']}: {project['name']}")

# Use specific project
client = Client(api_key="mp_...", project_id=456)

Error Handling

from cogniz import (
    Client,
    AuthenticationError,
    NotFoundError,
    RateLimitError,
    ValidationError,
    QuotaExceededError
)

client = Client(api_key="mp_...")

try:
    result = client.store("User data", user_id="alice")
except AuthenticationError:
    print("Invalid API key")
except RateLimitError:
    print("Rate limit exceeded, please wait")
except QuotaExceededError:
    print("Storage quota exceeded, upgrade plan")
except ValidationError as e:
    print(f"Invalid input: {e}")

Context Manager

# Automatically closes HTTP connections
with Client(api_key="mp_...") as client:
    client.store("User data", user_id="alice")
    results = client.search("query", user_id="alice")
# Connection closed automatically

Compatibility

Works seamlessly with:

• Cogniz Browser Extension - Chrome/Firefox extension for web memory
• Cogniz VS Code Extension - IDE integration
• Cogniz MCP Server - Claude Desktop integration
• Self-Hosted Platform - Your own WordPress installation

All clients use the same API, so memories are synced across all surfaces.

API Reference

Client(api_key, base_url, project_id, config, client)

Main synchronous client.

Core Methods:

• store(content, **kwargs) - Store a memory
• search(query, **kwargs) - Search memories
• get_all(**kwargs) - Get all memories
• update(memory_id, **kwargs) - Update memory
• delete(memory_id) - Delete memory
• delete_all(**kwargs) - Bulk delete

Project Methods:

• list_projects() - List all projects

Cogniz Features:

• optimize_prompt(prompt, **kwargs) - Optimize prompts
• run_playbook(playbook_id, input_data, **kwargs) - Run automation
• list_playbooks(**kwargs) - List playbooks
• get_stats() - Get usage statistics
• get_debug_settings() - Get platform info

Knowledge Graph:

• extract_entities(text, **kwargs) - Extract entities
• get_graph_stats(**kwargs) - Get graph statistics

AsyncClient

Async version with same API using async/await.

Config(api_key, base_url, project_id, timeout)

Configuration object.

Get API Key

1. Sign up at cogniz.online
2. Go to Dashboard → API Keys
3. Create new API key
4. Copy key (starts with mp_)

Documentation

• Full Documentation
• API Reference
• Examples
• WordPress Plugin

Support

• Email: [e-mail]
• Discord: Join community
• Issues: GitHub Issues
• Documentation: docs.cogniz.online

Contributing

Contributions welcome! Please read CONTRIBUTING.md for guidelines.

License

MIT License - see LICENSE file for details.

Credits

Built by the Cogniz team. Powered by WordPress and httpx.