Unique User Memory

Persistent per-user memory for Unique AI agents.

unique_user_memory stores a compact Markdown profile for each user and updates it after every agent turn. The profile is loaded before the next turn so the assistant can remember stable user context such as communication preferences, work context, expertise, recent topics, and concrete future tasks.

What It Does

The package provides:

• UserMemoryConfig - Pydantic configuration for the consolidation model, profile token budget, and memory folder.
• load_user_memory(...) - resolves the user's private memory folder, downloads memory.md, and enforces the configured token budget. The language_model argument is used to tokenize memory.md when capping it, so it must be the same effective model the postprocessor uses for consolidation (see Integration below). Returns a UserMemoryState with the profile text and scope id.
• profile_body(...) - strips the YAML frontmatter and returns only the Markdown body. Use it whenever the profile is shown to a model; the frontmatter is bookkeeping for consolidation.
• UserMemoryMessageLogger - emits chat Steps (MessageLogs) for load and update, including typed UserMemory detail entries the chat frontend renders as a badge that opens Settings → Context Memory. Frontends that do not know the entry type render nothing, so the entries are safe to emit in any deploy order.
• UserMemoryPostprocessor - runs after the assistant response, consolidates the latest turn into the profile, and uploads the updated memory.md.

The memory file is intentionally small and structured. It is rewritten as a full Markdown profile rather than appended to as an event log.

Lifecycle

1. The orchestrator enables memory when space.allow_user_memory is true.
2. The orchestrator emits a Loading context memory Step, then load_user_memory(...) resolves the user's private memory folder — canonical /home-<user_id>/<root_folder>, falling back to the legacy /<root_folder>/<user_id> leaf for not-yet-migrated users, provisioning the home folder if neither exists — and downloads memory.md from it if present.
3. When load returns a UserMemoryState, that Step is completed with a Context memory detail entry (type: UserMemory) that the chat frontend renders as a badge opening Settings → Context Memory. A successful None return (soft skip) completes the Step without the entry; a raised exception marks the Step failed.
4. If memory was loaded, profile_body(...) of its text is passed into the agent context for the current turn — the prompt only gets the Markdown body, while the postprocessor keeps the full file because it needs the frontmatter to carry turn_count forward.
5. UserMemoryPostprocessor runs after the assistant response.
6. The package asks the configured language model to either return NOOP or a complete rewritten profile.
7. If a rewrite runs, an Updating your memory Step is shown while consolidating (no settings entry yet).
8. Every rewrite passes through a mandatory CID/PII scrub call (see Content Policy below) before it is assembled for upload. When the scrub cannot vouch for the rewrite, the existing memory is kept unchanged.
9. If the profile changed and memory.md uploads successfully (ingestion skipped, content hidden from chat), that Step is completed with a Review your context memory detail entry (same settings badge). On NOOP or failed upload the Step completes without the entry.

Content Policy — the current user's CID/PII only (UN-24886)

The profile belongs to exactly one person: the signed-in user. It may contain personal data (PII) of that user only. It must never contain client-identifying data (CID) or PII of any other person or private entity — not other users, not clients, not prospects, not counterparties. Facts about the user's own work that involve a client are stored only in a fully de-identified form ("runs quarterly portfolio reviews", never "reviews the portfolio of J. Muster"). Credentials, payment data, health records, and government IDs are never stored for anyone, including the user.

The policy text lives once, in _CID_POLICY in user_memory_prompts.py, and is embedded in every write-path stage so the rules cannot drift:

Storage-level isolation (per-user home folder with exclusive owner ACL, see Storage Model) already prevents cross-user access; this policy governs cross-user and cross-client content.

Note for follow-up client-memory work: this package is user memory only. A future client memory is a separate artefact with its own scoping and must not be built by loosening this policy — client-related durable knowledge is intentionally rejected here rather than stored under the user's profile.

Storage Model

Memory is stored in Unique content as Markdown, under each user's own root-level home folder (UN-24823):

/home-<user_id>/<root_folder>/memory.md

By default, root_folder is user-memory. There is no shared root folder — each user's home is created by node-ingestion (or, if missing, by this package) with an exclusive owner ACL, so memory is never company-writable (UN-24764). Users whose memory has not yet been migrated to their home folder are still read from the legacy location, /<root_folder>/<user_id>/memory.md.

Profile Format

Profiles contain YAML frontmatter followed by fixed Markdown sections:

---
user_id: user_123
schema_version: 1
last_updated: 2026-06-17T12:00:00+00:00
turn_count: 1
---

# User Memory

## Identity
_(empty)_

## Communication Preferences
- Prefers concise answers with concrete examples.

## Work Context
_(empty)_

## Skills & Expertise
_(empty)_

## Follow-ups
_(empty)_

## Recent Topics
_(empty)_

The consolidation prompt preserves the schema, keeps bullets short, and returns NOOP when a turn has no durable user facts.

Each fact lives in exactly one section. Recent Topics is a dated log reserved for discussion topics whose substance is not already captured as a fact elsewhere in the profile. Follow-ups only holds tasks the user explicitly committed to or asked to be reminded about — never the assistant's own offers or open questions — and entries are removed once completed or stale.

Configuration

Memory is activated by the orchestrator when space.allow_user_memory is true. UserMemoryConfig only configures how active memory is consolidated and stored.

from unique_user_memory import UserMemoryConfig

config = UserMemoryConfig(
    max_tokens=2000,
    root_folder="user-memory",
)

Observability

The package registers Prometheus metrics on the shared unique_toolkit.monitoring registry (namespace unique_user_memory). They appear on whatever host already scrapes GET /metrics (assistants-core). Labels are closed enums — no company or user ids. LLM series include the model name so latency can be split per deployed model.

QA dashboard: User Memory (unique-user-memory). The dashboard is provisioned by the assistants-core Helm chart.

Token usage for billing still flows through LanguageModelInvocationStats, not these series.

Integration

Typical orchestration code loads memory before the agent loop and registers the postprocessor for the same turn.

load_user_memory and UserMemoryPostprocessor must be given the same effective language model: the postprocessor consolidates memory with either the orchestrator model or the configured one depending on use_orchestrator_language_model, and load-time token capping must use that same model so the loaded baseline is tokenized the way consolidation expects. Resolve the effective model once and pass it to both:

from unique_toolkit.agentic.message_log_manager.service import MessageStepLogger
from unique_user_memory.user_memory import load_user_memory, profile_body
from unique_user_memory.user_memory_message_log import UserMemoryMessageLogger
from unique_user_memory.user_memory_postprocessor import UserMemoryPostprocessor

user_memory_config = config.agent.services.user_memory_config

# Resolve the effective model once and reuse it for load-time capping and
# consolidation so both use the same tokenizer.
memory_language_model = (
    config.space.language_model
    if user_memory_config.use_orchestrator_language_model
    else user_memory_config.language_model
)

message_step_logger = MessageStepLogger(chat_service)
memory_message_step_logger = UserMemoryMessageLogger(
    message_step_logger,
    logger=logger,
)
await memory_message_step_logger.log_loading_start()
user_memory_state = None
load_succeeded = False
try:
    user_memory_state = await load_user_memory(
        event=event,
        config=user_memory_config,
        language_model=memory_language_model,
        logger=logger,
    )
    load_succeeded = True
except Exception as exc:
    logger.warning(
        "[user-memory] load raised - running without memory: [%s] %s",
        type(exc).__name__,
        exc,
    )
finally:
    # Always close the RUNNING step — otherwise the chat Steps UI stays stuck
    # on "Loading context memory" for that turn when load raises.
    if not load_succeeded:
        await memory_message_step_logger.log_loading_failed()

if load_succeeded and user_memory_state is not None:
    await memory_message_step_logger.log_loading_complete(with_settings_entry=True)
    # The postprocessor keeps the full file (it needs the frontmatter to
    # carry turn_count forward); the prompt only gets the Markdown body.
    user_memory_text = profile_body(user_memory_state.text)
    postprocessor_manager.add_postprocessor(
        UserMemoryPostprocessor(
            config=user_memory_config,
            language_model=memory_language_model,
            event=event,
            state=user_memory_state,
            logger=logger,
            message_step_logger=memory_message_step_logger,
        )
    )
elif load_succeeded:
    await memory_message_step_logger.log_loading_complete(with_settings_entry=False)

Note that UserMemoryPostprocessor re-derives the effective model internally from use_orchestrator_language_model, so passing memory_language_model (rather than the raw orchestrator model) keeps its behavior identical while ensuring load_user_memory caps with the matching tokenizer.