Engrami Documentation

What is Engrami?

Engrami is an enterprise-grade AI agent platform that enables organizations to build, deploy, and manage intelligent agents with persistent long-term memory. Unlike traditional AI systems that forget context between interactions, Engrami agents remember, learn, and evolve over time.

Our unique Control Plane + Data Plane architecture ensures that your sensitive data never leaves your infrastructure while providing a seamless management experience through our cloud-hosted control plane.

Key Features

Intelligent Agents

Create AI agents that can understand context, remember past interactions, and make intelligent decisions. Each agent can be customized with specific capabilities, personality traits, and access to different memory stores.

Memory Systems

Engrami provides four distinct memory types, each optimized for different use cases:

• Semantic Memory — Vector embeddings stored in ChromaDB for knowledge retrieval and similarity search. Perfect for FAQs, documentation, and knowledge bases.
• Episodic Memory — Conversation history and interaction logs stored in PostgreSQL with Redis caching. Enables agents to recall past conversations.
• Procedural Memory — Learned skills and behavioral patterns stored in Neo4j graph database. Allows agents to learn from experience.
• Decision Graph — Executable reasoning chains for complex decision-making processes with full auditability.

Workflow Automation

Build complex automated workflows that orchestrate multiple agents, integrate with external systems, and handle conditional logic. Our visual workflow builder makes it easy to create sophisticated automation without code.

Multi-Agent Collectives

Group agents into collectives for collaborative problem-solving. Collectives can use different orchestration strategies including hierarchical, democratic voting, and custom coordination patterns.

Architecture

Engrami uses a unique split architecture that separates the management layer from your data:

Getting Started

Follow these steps to get up and running with Engrami:

Quick Examples

Python SDK

from engrami import EngramiClient

# Initialize the client
client = EngramiClient(api_key="your-api-key")

# Create an agent
agent = client.agents.create(
    name="Customer Support Agent",
    type="support",
    memory_types=["semantic", "episodic"],
    config={
        "personality": "helpful and professional",
        "response_style": "concise"
    }
)

# Send a message and get a response
response = agent.chat("How do I reset my password?")
print(response.content)

TypeScript SDK

import { EngramiClient } from '@engrami/sdk';

// Initialize the client
const client = new EngramiClient({ apiKey: 'your-api-key' });

// Create an agent
const agent = await client.agents.create({
  name: 'Data Analyst',
  type: 'analyst',
  memoryTypes: ['semantic', 'procedural'],
  config: {
    capabilities: ['data_analysis', 'visualization'],
  },
});

// Query the agent
const response = await agent.chat('Analyze sales trends for Q4');
console.log(response.content);