Long term memory for LLM

The LLM Memory Bottleneck

• Stateless requests mean every conversation starts from zero.
• Context windows are capped and expensive to fill.
• History truncation loses preferences and prior decisions.

The LifeLongMemory.dev Difference

• Up to 90% token reduction via condensation.
• Relevant recall instead of full dumps.
• Persistent memory across sessions and devices.

1) Capture

SDKs and webhooks ingest chat, events, and product telemetry with optional PII redaction.

2) Condense

Summarizers and schema mappers form semantic and episodic memories with embeddings.

3) Retrieve

Hybrid search + recency/importance ranking returns compact memory bundles for prompts.

4) Learn

Feedback updates relevance. Decay and re‑summarization keep memory fresh and bounded.

Founders & Product

Personalization without context bloat. Higher retention and LTV.

AI Engineers

Drop‑in SDKs, SLAs, and observability. Focus on prompts and agents.

Enterprises

VPC deploys, SSO, RBAC, audit logs, and region pinning for compliance.

Customer Support AI

Starts in context with prior tickets, configs, and sentiment. Lower AHT, higher CSAT.

Sales & CRM Copilots

Surfaces threads, notes, and objections. Proposes next‑best actions.

Compliance & Audit

Immutable logs of reads/writes with purpose tags and retention rules.

Knowledge Retention

Captures tribal knowledge so agents persist through team changes.

Use cases

• PFM copilots remember bill cycles and savings goals for timely nudges.
• Lending flows pre‑fill from prior docs and intents to cut drop‑off.
• Wealth advisors recall risk profile, household, and tax events.
• Support bots spot recurring merchant disputes and advise early.

Controls & compliance

• PCI scope isolation and tokenization hooks.
• PII tags and purpose‑limited retrieval.
• Region pinning (EU/IN/US) and right‑to‑forget.

Intelligent condensation

Extracts meaning without bloating context windows—up to 90% token savings.

Semantic & episodic recall

Combines durable facts with time‑ordered episodes for accuracy.

Low‑latency vector storage

Modern vector DBs tuned for speed and scale.

Universal integration

Works with OpenAI, Anthropic, Gemini, Claude, and more.

Fintech

PFM, lending, wealth. Risk‑aware personalization.

Healthcare

Care continuity, triage memory, auditability.

E‑commerce

Preference memory and dynamic merchandising.

Education

Adaptive tutoring with durable learner profiles.

B2B SaaS

CRM/support copilots and onboarding recall.

Healthcare

PHI workflows and purpose‑limited retrieval.

E‑commerce

Memory‑driven recommendations and support.

Education

Long‑term student models for personalization.

B2B SaaS

Account history, stakeholder maps, ticket lineage.

Travel

Itinerary and loyalty memory for proactive help.

Fintech

PCI‑aware, risk‑sensitive memory for finance.

example.py

# 1. Store context
lifelongmemory.save_context(
    user_id="user_123",
    context="User loves coffee, not tea."
)

# 2. Retrieve relevant memory
mems = lifelongmemory.retrieve(
    user_id="user_123",
    query="User asks for a drink recommendation"
)

# 3. Augment your LLM prompt
# f"Relevant memories: {mems.text}"

Steps

1. Store Context: Save interactions and preferences.
2. Retrieve Memory: Get relevant context for new queries.
3. Augment Prompt: Personalize the LLM with memory.

Integrates with