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Add Redis Iris

RedisOfficialCreated by redis

  • Skill
  • Workflow
  • Automation and integrations
  • MIT
  • No issues found

From the author

Add Redis Iris to an existing AI agent in any framework (LangGraph, LangChain, OpenAI Agents SDK, Google ADK, Pydantic AI, CrewAI, or a plain loop). Use when the user asks to add Iris, Redis Agent Memory, Agent Playbooks, Context Gateway, Intent Router, or Lens to their agent, or asks which Iris product their agent needs. Detects the framework and routes to the product skill.
— the description by redis

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a structured procedure for identifying, installing, and configuring Redis Iris components within an existing AI agent codebase. It guides the user through detecting the current framework (such as LangGraph, CrewAI, or Pydantic AI), identifying missing agent capabilities, installing the necessary library, and routing to specific product-level integration skills. The process emphasizes minimal code disruption, maintaining existing agent logic, and secure configuration management via environment variables and TOML profiles.

What it does

  • Analyzes existing code to detect the AI framework and identify current agent architecture.
  • Maps specific agent limitations to the appropriate Redis Iris product (Memory, Playbooks, Gateway, Router, or Lens).
  • Provides instructions for installing the redis-iris package via standard package managers.
  • Guides the setup of configuration profiles for either Redis Cloud or self-managed suite environments.
  • Ensures secure handling of service URLs and API keys by separating configuration from application code.
  • Facilitates the integration of multiple Iris products into a single agent project.

When to use it

  • When you need to add Redis Iris capabilities like Agent Memory, Playbooks, or Intent Routing to an existing agent.
  • When you are unsure which Redis Iris product is required to solve a specific agent limitation.
  • When you need to standardize the configuration of Redis services across different agent frameworks.
  • When you want to implement observability or tracing using Lens in an existing agent.

Advantages

  • Framework-agnostic approach that respects existing agent logic and prompts.
  • Clear, step-by-step methodology for identifying and resolving agent infrastructure gaps.
  • Supports both cloud-managed and self-managed deployment models.
  • Promotes secure configuration practices by avoiding hardcoded secrets.

Usage examples

“I have a LangGraph agent and I want to add Redis Agent Memory to it. How do I start?”
The agent will analyze your project structure to confirm the LangGraph implementation, guide you through installing redis-iris, and direct you to the specific memory integration steps.
“My agent is getting too complex with many tools. Which Iris product should I use?”
The agent will evaluate your current tool configuration and recommend the Context Gateway to manage your MCP servers and tool schemas efficiently.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/redis/add-redis-iris/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip add-redis-iris.zip -d .agents/skills

Documentation by redis

Add Redis Iris

Inspect, install, route. Do not rewrite the agent.

1. Find the agent

Locate:

  • The turn: where the model is called (invoke, Runner.run, generate_content, agent.run, kickoff, chat.completions.create, messages.create).

  • Tool registration: where tools are defined and handed to the model.

  • User and session ids: where the request's user id and thread or session id live.

  • More than one loop (a hand-written loop and a framework, or two frameworks)? Create the product objects once in a module with no framework imports, wire each loop at its own seam, and convert one loop at a time. Do not pick the loop for the user.

2. Detect the framework

Search imports:

ImportFramework
langgraph, langchain, langchain_coreLangGraph / LangChain
agents (package openai-agents)OpenAI Agents SDK
google.adkGoogle ADK
pydantic_aiPydantic AI
crewaiCrewAI
only openai, anthropic, or another model clientPlain loop

Note the version from the lockfile. Product skills look up that version's API over MCP.

3. Find what it lacks

Symptom in the codeProductSkill
No long-term memory, InMemoryStore, checkpointer only, memory in a dict or local DBAgent Memoryadd-agent-memory
Already on Redis Agent Memory through its own REST or MCP client, hand-written adapters, or model-facing memory toolsAgent Memoryadd-agent-memory, "Already on Redis Agent Memory"
Policy or procedures hardcoded in the prompt, tool usage guidance in the prompt, or a vector store over policy docsAgent Playbooksadd-agent-playbooks
A semantic response cache: model answers stored and re-served to later users, curated by handAgent Playbooksadd-agent-playbooks, "Replacing a response cache"
Several MCP servers configured one by one in the agent, each with its own secret, or too many tool schemas in the promptContext Gatewayadd-context-gateway
A keyword if/else router, or an LLM call just to pick a routeIntent Routeradd-intent-router
No per-turn tracing: no way to see which LLM calls, tools and retrievals one answer used, or what it costLensadd-lens

If the user named a product, use that. If unclear, ask once, listing what you found.

Lens goes next to any product, or on its own: it traces the turn, and every Iris product traces itself inside it. Offer it alongside whatever else you add.

4. Install

  • uv.lock or [tool.uv] present: uv add redis-iris.

  • Poetry: poetry add redis-iris.

  • Otherwise add redis-iris to the project's requirements file.

If resolution fails on pre-releases, retry with uv add --prerelease=allow redis-iris. Confirm: uv run python -c "import redis_iris; print(redis_iris.__version__)".

5. Route

Follow each chosen product skill. More than one product: each skill adds its own calls. Optionally build them from one config with Iris.from_env() (iris.agent_memory(), iris.agent_playbooks(), iris.context_gateway(), iris.intent_router()).

Ask once whether the services run on Redis Cloud or a self-managed suite, then write the project's iris.toml profile. Only the iris CLI reads iris.toml; the app reads env vars. It holds URLs only: a token in TOML makes the CLI exit 2. Select the profile with IRIS_CONFIG=./iris.toml and IRIS_PROFILE=cloud or IRIS_PROFILE=suite.

Redis Cloud. The person fills .env from the console: IRIS_<PRODUCT>_URL, its ID and _API_KEY per product.

[profiles.cloud]
kind = "cloud"
console_url = "https://cloud.redis.io"

[profiles.cloud.agent_memory]
url = "https://gcp-us-east4.memory.redis.io"  # the data-plane URL from the console

Self-managed suite. The operator's tokens go in .env.admin, never the app's .env; service create --write-env .env writes the app's .env.

[profiles.suite]
kind = "self-managed"

[profiles.suite.agent_memory]
service_url = "https://memory-admin.example.com"  # control plane: the CLI calls it for service create and check
url = "https://memory.example.com"  # data plane: what the agent calls
identity_url = "https://memory-identity.example.com"  # Identity Service that mints the agent's keys

Add one table per product the app uses: agent_memory, agent_playbooks, context_gateway, intent_router. [profiles.suite.context_gateway] has no url: service create --write-env writes the Gateway's endpoint. A suite with one shared Identity Service sets [profiles.suite.identity] url once instead of each identity_url. IRIS_<PRODUCT>_SERVICE_URL, IRIS_<PRODUCT>_URL and IRIS_<PRODUCT>_IDENTITY_URL override the profile.

Rules

  • Keep the user's framework, graph, and prompts. Smallest diff.

  • Never guess framework APIs from memory. Look them up over MCP (see each product skill).

  • Only use APIs in redis_iris. redis_iris.integrations.langgraph.attach (extra redis-iris[langgraph]) and redis_iris.integrations.adk.attach (no extra) exist; there is no IrisTool, iris.tools(), or iris.turn().

  • The SDK raises on a missing setting or an outage. Ask whether a product failure may fail the turn; most production agents catch it and continue with an empty block.

  • Never commit secrets.

Files

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs an MCP server, Needs Python

Security

No issues found by our static scan· No issues found

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
redis
Publisher
Redis (Official)
Source
redis/agent-skills · skills/add-redis-iris · commit 74708da
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced
Also distributed in
redis/agent-skills

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Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download add-redis-iris.zip