Add Agent Memory

Before the model: recall what is known about the user. After the turn: write the exchange back. The service extracts long-term memories from sessions. What it remembers, shares, excludes and retains is a service setting, not an SDK argument: ask first, provision, then wire.

1. Discover

Read the project:

• Find the turn (where the model is called) and the user id and session or thread id in scope there.
• Identify the framework from imports.
• Find existing memory code (BaseStore, InMemoryStore, custom dict or DB). You will replace its reads and writes, not the graph.
• Already on Redis Agent Memory through its own client? Inventory before changing anything: adapters over the generated redis_agent_memory client, an MCP client exposing memory tools to the model, a namespace string written on every record, and any second runtime or framework plugin writing the same store. See "Already on Redis Agent Memory" in step 4.
• Async agent? Use the a-prefixed methods.

Then ask the developer these five questions, nothing more. A shrug takes the default.

Long sessions (support threads, multi-hour chats): keep summarization enabled with after_messages: 20, keep_recent: 10.

2. Get current facts from MCP

Do this before writing code. Never guess framework APIs from memory.

• Framework docs: query an available docs MCP (Context7, or the framework's own docs MCP such as LangChain's) for the installed version's dynamic system prompt or instructions, before/after model callbacks, and run context (how a tool or callback reads the user id).
• Redis docs: if the Redis docs MCP is connected, use search / fetch / ask for Agent Memory concepts (sessions, long-term memory, namespaces, custom memory types, exclusions).
• No MCP available: read the installed package source in the virtualenv. Say so in the report.

3. Configure

Write service.yaml from the answers. Commit it: it is the reviewable record of the service.

name: support-memory
# database: "1"                # the default on-prem; the registered database id the store lives in
short_term_ttl: 30d
long_term_ttl: 365d
# extraction_cadence: 60s      # the default (60s to 600s); raise it to spend less on extraction
summarization: {enabled: true, after_messages: 20, keep_recent: 10}
exclusions: {prompt: "Do not keep passwords, access tokens, recovery codes, payment card information, or booking confirmation codes in long-term memory."}
memory_types:                  # only when question 1 named structured data
  - name: trip_preference
    description: Structured requirements for a planned trip
    prompt: Extract trip requirements only when the user states a destination or travel plan.
    enabled: true
    fields: {destinations: "list[str]", travel_period: str, dietary_requirements: "list[str]"}

Provision from the terminal:

iris --json --no-input agent-memory service create --from service.yaml --dry-run
iris --json --no-input agent-memory service create --from service.yaml --write-env .env
iris --json --no-input agent-memory service check <storeId from the result> --env-file .env

Identity tokens. Each product chart can run its own Identity Service. Then set the product's own IRIS_AGENT_MEMORY_IDENTITY_TOKEN (and IRIS_AGENT_MEMORY_IDENTITY_URL, or the profile's identity_url), and leave IRIS_IDENTITY_URL unset: it overrides every product's identity_url in the profile. On a suite with one shared Identity Service, IRIS_IDENTITY_TOKEN and IRIS_IDENTITY_URL cover every product. The CLI takes the token from --identity-token, then IRIS_AGENT_MEMORY_IDENTITY_TOKEN, then IRIS_IDENTITY_TOKEN, then the product's service token, which only works when the two are the same secret. The dry run lists that fallback under warnings.

Start the project's iris.toml. 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_AGENT_MEMORY_URL, IRIS_AGENT_MEMORY_STORE_ID and IRIS_AGENT_MEMORY_API_KEY.

[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

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.

Run the CLI as an agent

Every iris call you make follows the same loop:

1. Always put --json --no-input before the product name. You get one JSON value on stdout, errors as one JSON value on stderr, and no prompts.
2. Dry-run first: service create ... --dry-run sends nothing and lists in missing every credential or URL the real run needs. If missing is not empty, stop and hand off (step 4) before any write. Report anything in warnings (a grant with no tenant, or the service token standing in for the Identity Service token) to the person before the real run.
3. Always deliver keys with --write-env PATH (or --write PATH on key). Never run create or key with --json and no file: that prints the key on stdout and into your transcript.
4. On credential_required, stop. Tell the person which variable to set (details.env), for which product and profile, and link details.obtain. On unauthenticated or forbidden, say which value was refused (details.source). Never ask for the secret in chat, pass it on the command line, or echo, cat or grep an env file.
5. After provisioning, run service check ID --env-file .env (the file create wrote; the key stays out of your shell) and report each step. On failure, report the failing hop (details.hop, details.step, details.cause), not a guess.
6. On resource_exists, ask the person whether to --reuse details.id or pick another name. Never delete to retry. If create fails with details.created: true, the resource exists but has no key yet: re-run with --reuse --write-env.

Every error is one JSON value on stderr with error.code; credential errors also carry details.env and details.obtain, never the value. Stop on the first failure and relay it:

--write-env .env writes IRIS_AGENT_MEMORY_URL, IRIS_AGENT_MEMORY_STORE_ID, IRIS_AGENT_MEMORY_API_KEY. Add the same three names to .env.example with empty values. Never commit .env. Later: iris agent-memory service list | show STORE_ID | env STORE_ID [--write PATH] (add --local for a local stack).

No access to the Agent Memory service, or on Redis Cloud? The person creates the service in the Redis Cloud console: Iris → Agent Memory → New service. They enter a Service name and pick a Database; Customize settings sets TTLs, extraction cadence, summarization, custom memory types and exclusions. Then they copy the Store ID and a URL from the URL List on the store's Configuration tab, and create an API key with access to the store in Iris → Access & Identity. The API key is shown once.

Create once at startup:

from redis_iris import AgentMemory

memory = AgentMemory.from_env(assistant_id="<agent-name>")
floor = memory.shared_namespace("<team-namespace>")  # question 2: memories every user may read

4. Wire the two seams

Before the model (context in):

known = memory.recall(question, owner=user_id, shared=[floor])  # await memory.arecall(...)
system_prompt = f"{system_prompt}\n\n{known.as_prompt()}"

After the turn (write-back):

convo = memory.conversation(owner=user_id, session_id=thread_id)  # session_id optional
convo.user(question)  # await convo.auser(...)
convo.assistant(answer)  # await convo.aassistant(...)

Explicit facts: memory.remember(text, owner=user_id, topics=[...]). Erasure (question 5): memory.forget(user_id) returns Forgotten(owner, sessions, memories). Anything else: memory.client.

Every row is the same two calls, with nothing framework-specific: hooks = memory.hooks(shared=[...]), then hooks.before(message, owner=user_id) into the system prompt and hooks.after(message, answer, owner=user_id, session=thread_id) after the reply (abefore / aafter in async code). Where they go (confirm names in step 2):

Remove the replaced memory code. Keep the checkpointer if the graph uses it for thread state.

Already on Redis Agent Memory

The store stays; the client changes. Keep the owner id and the session id derivation the project already uses, so existing sessions continue.

• Prior turns as messages. If the agent seeds the model with the session so far, memory.conversation(owner=user_id, session_id=thread_id).transcript() returns turns as (role, text) and the summary; put the summary first as a system message. If it only needs the facts, recall is enough.
• Two writers become one. Model-facing memory tools (create, search, edit, delete) and the service's own extraction both write long-term memory. Default to dropping the tools: recall before, record after, the service extracts. Keep a remember tool only if an evaluation shows explicit writes matter.
• A namespace string on records is the deprecated field. Reads are scoped by owner; sharing uses namespaces created with shared_namespace. Check that recall(owner=...) returns the existing records before deleting the constant, and never run the old and new paths in one environment: a client that filters on the string will not see what the new path writes.
• Values, not strings, when the agent needs them. hooks() renders prompt blocks. An agent that validates a cached answer against memory, or counts what was recalled, calls recall() and transcript() directly.
• Failure policy. The SDK raises; if the agent must answer during a memory outage, catch per call and continue with an empty block.

5. Verify

1. iris agent-memory service show $IRIS_AGENT_MEMORY_STORE_ID echoes the TTLs, summarization, exclusions and memory types from service.yaml.
2. Seed one memory: memory.remember("Prefers email.", owner="<test-user>").
3. Run the agent once as <test-user>.
4. Show memory.recall("contact", owner="<test-user>").as_prompt() lists the memory, and that it reached the model's prompt.
5. Show the session: memory.conversation(owner="<test-user>", session_id=...).transcript().turns is non-empty.
6. Question 5 was yes: memory.forget("<test-user>") reports the seeded counts, and a second recall returns nothing.
7. Unset a variable and confirm ConfigurationError names it (raised on first call).
8. Converted from an existing client: memory.recall(<a known topic>, owner=<an existing user>) returns memories written before the change.

Extracted memories appear after the extraction window; seeded memories are immediate.

6. Report

List the five answers and the service.yaml each produced, files changed, env vars added, where each seam went, verification output, and anything you could not confirm over MCP.