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Pre-release

This release is a pre-release and may not be stable for production use.

Redis Iris

One package for every Iris product: an opinionated Python API per product, and the iris CLI to set them all up.

CI Python 3.11+ Status: Alpha License: MIT

Install · Documentation · Releases · Contributing

Install

Requires Python 3.11+. uv is the recommended installer.

uv add redis-iris            # library, in your project
uv tool install redis-iris   # iris CLI, isolated
iris --help
pipx or pip
pipx install redis-iris
python -m pip install redis-iris   # inside an active virtual environment

From a source checkout, uv tool install --force . installs or replaces the iris tool, and python -m redis_iris runs the CLI wherever the package is installed.

Alpha releases may change commands and output contracts. Pin a version in automation and check the release notes before upgrading.

The stack

Iris is a suite of products. Use one, or all of them.

Product What your agent gets Instead of
Agent Memory Ordered session history, summarized automatically. Durable memories extracted in the background, typed to your domain and steered away from sensitive data. Recall across sessions, scoped to one customer plus shared team knowledge. Erase a customer in one call. an in-process store, a hand-built extraction chain, regex scrubbing
Playbook Answers only from published, versioned FAQs and Skills, scored. Rollback and withdrawal. Proposes an FAQ for human review when nothing covers the question. a vector store of unreviewed policy docs
Context Retriever (coming soon) Typed MCP tools generated from your data model: get, filter, search, per entity. Read-only agent keys scoped to one surface. Your Redis data stays where it is. hand-written @tool functions over your database
Lens (client publishing soon) One trace per turn: which memories and documents entered the prompt, tokens, cost. guessing why the agent said that

User guides

Your agent... Guide Product
forgets customers between sessions Remember customers Agent Memory
quotes policy nobody approved Answer from approved policy Agent Playbook
needs live account data, not documents Live account data Context Retriever
needs all three in one message One support turn Iris
needs to be configured Configuration all
needs an Agent Memory service created Set up Agent Memory Agent Memory
needs a Playbook and an agent key created Set up Agent Playbook Agent Playbook
has policy content to author and publish Agent Playbook CLI Agent Playbook

Integrate with an existing agent

Built your stack with a popular orchestration tool? A skill lets your coding agent do the wiring.

I want my agent to... Skill
figure out what it needs add-redis-iris
remember customers add-agent-memory
quote approved policy only add-agent-playbook
use live account data add-context-retriever

The CLI

iris <product> <command> creates services, mints keys, and manages content. Shared options go before the product name.

iris agent-memory service create --from service.yaml --write-env .env
iris agent-memory service key <store-id> --actions read --name inspector --write .env.inspector
iris agent-memory memories search "how does this customer want answers" --owner cust-1042 --prompt
iris agent-playbook extraction bind <store-id> --idle-seconds 60 --enable   # FAQ proposals from sessions
iris agent-playbook faqs validate ./faqs            # offline
iris agent-playbook faqs sync ./faqs --publish changed
iris --json --no-input agent-playbook faqs plan ./faqs   # unattended, one JSON value on stdout
iris --profile production agent-playbook health

Settings resolve from command options, environment variables, the selected TOML profile, shared TOML settings, then defaults. API keys belong in environment variables. Exit codes are 0 for success, 1 for operational failures, and 2 for invalid input or configuration. Help, version, completion, and local validation work without credentials or a network connection.

Guides: Set up Agent Memory, Set up Agent Playbook, Agent Playbook CLI, Configuration, example TOML.

Documentation

The docs are internal: read them in docs/, or render the site locally with task docs:serve.

Resource Start here to...
Core reference Every redis_iris class and setting
User guides One problem, one fix, per product
Skills Wire a product into an existing agent with a coding agent
llms.txt task docs:build writes site/llms.txt; hand that one file to a coding agent
Design What the package is, and why it is shaped this way
Release notes Check changes before upgrading

Contributing

Bug reports, documentation improvements, and new integrations are welcome. Open an issue or read the contributor guide.

task install
task check   # formatting, lint, types, offline tests, package build

Maintainers publish with the release runbook.

License

MIT.

Metadata

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