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Iris is Redis's umbrella for AI-focused products. Use this skill when integrating with the Iris Redis Agent Memory (RAM) data plane on Redis Cloud — recording session events for an AI agent, creating or searching long-term memories, configuring a memory store, or tuning background memory promotion. Code examples use the official `redis-agent-memory` (Python) and `@redis-iris/agent-memory` (TypeScript) SDKs.

Use this Skill: https://skilld.dev/gh/redis/agent-skills/iris-development

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referencessetup-cloud-service.md

≈707 tokens on demand. Your agent reads this file only when SKILL.md points to it.

Create a Memory Service on Redis Cloud

Redis Cloud provisions the memory store, the backing Redis database, the background promotion worker, and the LLM/embedding provider credentials. Each store gets a unique storeId and a store API key used as a bearer token on every data-plane request.

Correct: Provision through the Redis Cloud Agent Memory console.

  1. Sign in at https://cloud.redis.io/#/agent-memory.
  2. Click New service and pick the correct settings for the user.
  3. After provisioning, copy three values from the console:
    • Server URL — the production data-plane URL is https://gcp-us-east4.memory.redis.io (your exact URL is shown in the Cloud console)
    • Store ID — 32-character UUID without dashes
    • Store API key — Bearer token (treat like a secret)
  4. Export them so the SDKs can read them from the environment:
export AGENT_MEMORY_BASE_URL="https://gcp-us-east4.memory.redis.io"
export AGENT_MEMORY_STORE_ID="<your-store-id>"
export AGENT_MEMORY_API_KEY="<your-store-api-key>"
  1. Install the SDK and run a smoke test.

Python — pip install redis-agent-memory:

import os
from datetime import datetime, timezone
from redis_agent_memory import AgentMemory, models

with AgentMemory(
    os.environ["AGENT_MEMORY_BASE_URL"],
    store_id=os.environ["AGENT_MEMORY_STORE_ID"],
    api_key=os.environ["AGENT_MEMORY_API_KEY"],
) as agent_memory:
    # Health check
    print(agent_memory.health())

    # Sanity write
    res = agent_memory.add_session_event(
        actor_id="user-42",
        role=models.MessageRole.USER,
        content=[{"text": "hello"}],
        created_at=datetime.now(timezone.utc),   # tz-aware UTC datetime
    )
    print(res.event.event_id)

TypeScript — npm add @redis-iris/agent-memory:

import { AgentMemory } from "@redis-iris/agent-memory";

const agentMemory = new AgentMemory({
  serverURL: process.env.AGENT_MEMORY_BASE_URL!,
  storeId:   process.env.AGENT_MEMORY_STORE_ID!,
  apiKey:    process.env.AGENT_MEMORY_API_KEY!,
});

async function smokeTest() {
  console.log(await agentMemory.health());

  const res = await agentMemory.addSessionEvent({
    actorId:   "user-42",
    role:      "USER",
    content:   [{ text: "hello" }],
    createdAt: new Date(),                       // SDK serializes to UTC ISO-8601
  });
  console.log(res.event.eventId);
}

smokeTest();

Store the API key in a secrets manager. It scopes access to a single store; rotating it requires regenerating from the Cloud console.

Reference: Redis Cloud Agent Memory · Python SDK · TypeScript SDK

Source: SKILL.md on GitHub

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  • Gen Agent Trust Hub1mo

    The skill provides comprehensive documentation and code examples for integrating with Redis Agent Memory (RAM) using official Python and TypeScript SDKs. It follows security best practices for credential management and relies on official Redis infrastructure.

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  • Snyk1mo

    Risk: MEDIUM · 1 issue

Signed by skilld at d20b723. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 3 days ago.

Activeupdated 4 months ago
metadata
{
  "author": "redis",
  "version": "1.0.0"
}
  • Python
  • TypeScript
  • redis
  • agent-memory
  • session-memory
  • long-term-memory
  • semantic-search
  • redis-cloud

README badge

README badge for redis/agent-skills/iris-development

Manages session and long-term memory for AI agents on Redis Cloud using the official `redis-agent-memory` (Python) and `@redis-iris/agent-memory` (TypeScript) SDKs. Covers recording conversation events, searching semantically indexed memories, configuring memory stores, and tuning background memory promotion between session and persistent tiers.

Generated from the current SKILL.md.

Does this skill work with both Python and TypeScript?
Yes. The skill provides code examples for both the official `redis-agent-memory` (Python) SDK and `@redis-iris/agent-memory` (TypeScript) SDK.
What is Redis Agent Memory (RAM)?
It is a managed persistent memory service for AI agents on Redis Cloud with two tiers: session memory for conversation history and long-term memory for semantically searchable facts. A background promotion worker extracts durable facts from sessions automatically.
Do I need to set up a Redis Cloud service first?
Yes. You must create a Memory service on Redis Cloud and obtain an API key and store ID, which the SDK reads from `AGENT_MEMORY_API_KEY` and `AGENT_MEMORY_STORE_ID` environment variables.
Can I search memories semantically?
Yes. The skill covers semantic search of long-term memory with filtering using the `search_long_term_memory()` (Python) or `searchLongTermMemory()` (TypeScript) method.
What is the difference between session events and long-term memory?
Session memory is append-only conversation history per session. Long-term memory stores semantically searchable records extracted from sessions by the background promotion worker or created directly; use the skill's session-when-to-use rule to decide which to use.

Generated from the current SKILL.md. These answers refresh after source changes.