Getting Started Quickstart

🚀 Quick Start — 30 Seconds to First Memory

Store and recall your first AI agent memory in seconds. Choose your preferred path below.

---

Path 1: Zero-Install AI Agent MCP (No Setup Required)

If you are connecting Spector to Claude Desktop, Cursor, Windsurf, or Claude Code, run the zero-install launcher:

npx -y @spectrayan/spector mcp

This connects directly to your local Spector node on :7070 if running, or automatically downloads spector.jar and runs an in-process memory kernel with embedded ONNX neural embeddings (requires OpenJDK 25+).

---

Path 2: Python Client SDK

Install the lightweight client SDK:

pip install spector-client

Store and recall memories with authentic cognitive verbs:

from spector_client import SpectorClient, MemoryTier

# Connect to running daemon or local test instance
client = SpectorClient.builder().with_rest("http://localhost:7070").build()

# 1. Remember
record = client.memory.remember(
    text="User is designing a low-latency RAG system with pgvector and Spector",
    tier=MemoryTier.SEMANTIC,
    tags=["rag", "architecture", "database"],
    interest=0.9,
    valence=1,
)
print(f"Memory recorded: {record.get('id', 'stored')}")

# 2. Recall with associative cognitive scoring
memories = client.memory.recall("database architecture preferences", top_k=3)
for memory in memories:
    print(f"[{memory.id}] score={memory.score:.4f} | {memory.text}")

---

Path 3: Universal TypeScript / Node.js SDK

Install via npm:

npm install @spectrayan/spector-client

Run in Node.js 18+, Bun, or Deno:

import { SpectorClient, MemoryTier } from '@spectrayan/spector-client';

const client = SpectorClient.createDefault('http://localhost:7070');

async function main() {
  // Store
  const record = await client.memory.remember({
    text: 'User prefers dark mode, high contrast, and TypeScript examples',
    tier: MemoryTier.SEMANTIC,
    tags: ['preferences', 'ui'],
  });

  // Recall
  const results = await client.memory.recall('user ui preferences', {
    topK: 5,
  });

  results.forEach(m => console.log(`[${m.id}] ${m.text}`));
}

main();

---

Path 4: Java Client SDK (spector-client)

Add the dependency to your pom.xml:

<dependency>
    <groupId>com.spectrayan</groupId>
    <artifactId>spector-client</artifactId>
    <version>0.1.0-alpha</version>
</dependency>

Connect and query with standard Java (no vector flags or preview options needed):

import com.spectrayan.spector.client.SpectorClient;
import java.util.List;

try (var client = SpectorClient.builder().baseUri("http://localhost:7070").build()) {
    // 1. Remember
    var record = client.memory().store(
        "User prefers concise responses with architectural diagrams",
        List.of("preferences", "formatting")
    );
    System.out.println("Stored engram: " + record.getId());

    // 2. Recall
    var results = client.memory().recall("user formatting preferences", 5);
    results.forEach(m -> System.out.println(m.getText()));
}

---

Path 5: Instant Local Server (Docker Compose)

Start the Spector memory daemon with a single command:

# Clone the repository
git clone https://github.com/spectrayan/spector.git
cd spector

# Start core daemon (REST + SSE on :7070) and Cortex Dashboard (on :7700)
docker compose up -d

# Check health
curl http://localhost:7070/actuator/health

# Open the 3D Neural Galaxy UI (Cortex) in your browser:
# http://localhost:7700

---

Path 6: One-Line CLI Installers

Install the standalone spector CLI binary on your machine:

Linux / macOS (POSIX)

```bash title="Terminal"
curl -fsSL https://raw.githubusercontent.com/spectrayan/spector/main/scripts/install.sh | sh
```

Windows (PowerShell)

```powershell title="Terminal"
irm https://raw.githubusercontent.com/spectrayan/spector/main/scripts/install.ps1 | iex
```

Verify your environment:

spector doctor

---

Next Steps

• 🔷 [[TypeScript SDK Guide|Sdk-Usage--Typescript-Sdk]] — Explore full async APIs and event streaming
• 🐍 [[Python SDK Guide|Sdk-Usage--Python-Sdk]] — Learn how to build agentic memory loops
• 🤖 [[MCP Server Configuration|Sdk-Usage--Mcp-Server]] — Configure Cursor, Claude, and Windsurf
• 🐳 [[Docker & Compose Guide|Deployment--Docker]] — Profiles, volumes, and GPU options