unique-user-memory 2026.30.0.dev0

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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 open follow-ups.

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).
• 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. load_user_memory(...) resolves the pre-provisioned root folder, ensures a private child folder for the current user, and downloads /user-memory/<user_id>/memory.md if it exists.
3. The loaded memory text is passed into the agent context for the current turn.
4. UserMemoryPostprocessor runs after the assistant response.
5. The package asks the configured language model to either return NOOP or a complete rewritten profile.
6. If the profile changed, memory.md is uploaded back to the user's folder with ingestion skipped and the content hidden from chat.

Storage Model

Memory is stored in Unique content as Markdown:

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

By default, root_folder is user-memory. The root folder must already exist. The package creates the per-user child folder when needed.

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)_

## Recent Topics
_(empty)_

## Open Questions / Follow-ups
_(empty)_

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

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",
)

Field	Default	Description
use_orchestrator_language_model	True	When true, consolidation and load-time token capping use the model the orchestrator passes in and language_model is ignored. Set to False to use the configured language_model for both.
language_model	DEFAULT_GPT_4o	Model used to consolidate the latest turn and to tokenize memory.md at load time when use_orchestrator_language_model is False.
max_tokens	2000	Maximum profile size. Must be between 500 and 8000 tokens.
root_folder	user-memory	Root KB folder that contains per-user memory folders.

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_user_memory.user_memory import load_user_memory
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
)

user_memory_state = await load_user_memory(
    event=event,
    config=user_memory_config,
    language_model=memory_language_model,
    logger=logger,
)

if user_memory_state is not None:
    user_memory_text = 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,
        )
    )

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.