By Sagar Shankaran, Founder of CallSphere
Letta uses an OS-inspired tiered memory; Mem0 ships a one-call API with hybrid vector-plus-graph retrieval. The 2026 production stack uses both — and a session buffer in front.
Key takeaways
TL;DR — Letta (formerly MemGPT) treats memory like an operating system — core memory always in context, archival memory in a vector store, recall memory for full conversation history. Mem0 ships a thin API:
add()andsearch(), with hybrid vector-plus-graph retrieval underneath. The 2026 winner is not either tool but the combination — episodic buffer, semantic facts, and graph relations, each handling what it does best.
Three memory scopes have become standard in 2026:
Letta enforces this through tiers: core memory (small, always in context, like RAM), archival memory (vector store, like disk), recall memory (full history, retrievable on demand). Mem0 enforces it through a flat API but routes internally to vector + graph + relational stores.
flowchart LR
T[Conversation turn] --> EX[Extractor]
EX --> EP[Episodic store]
EX --> SE[Semantic store]
EX --> PR[Procedural rules]
Q[Query] --> R{Router}
R -->|recent| EP
R -->|fact| SE
R -->|rule| PR
EP --> A[Agent]
SE --> A
PR --> A
Letta: every agent runs with a fixed-size core memory block ("user persona", "task state"). When core fills, the agent itself decides what to demote to archival. Archival memory is a vector store; recall memory is full conversation history. The agent can call memory_insert, memory_replace, archival_memory_search as tools.
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Mem0: mem0.add(messages, user_id="u1") takes a list of messages, runs an internal LLM extractor, and stores facts as embeddings + graph nodes/edges. mem0.search(query, user_id="u1") returns ranked facts. Hybrid vector + graph retrieval is the under-the-hood default.
The 2026 production pattern is a small stack: vector memory for fuzzy recall, episodic buffer for short-term coherence (last 6–10 turns), graph for entity-heavy queries.
CallSphere agents run a three-layer memory:
The Healthcare agent uses semantic memory for allergies, preferred pharmacy, current medications. UrackIT IT helpdesk uses episodic memory for recent ticket subjects ("the same error as Tuesday"). OneRoof real estate uses graph memory for buyer-broker-listing relationships.
37 agents · 90+ tools · 115+ tables · 6 verticals · $149/$499/$1499 · 14-day trial · 22% affiliate. Try multi-session memory on /demo.
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# Mem0 quick start
from mem0 import Memory
m = Memory()
# Write
m.add("User is allergic to penicillin", user_id="patient_4421")
m.add("User prefers Spanish for clinical conversations", user_id="patient_4421")
# Read at session start
facts = m.search("medication allergy", user_id="patient_4421")
# Letta agent with archival
from letta import create_client, AgentState
client = create_client()
agent = client.create_agent(
name="healthcare-receptionist",
persona="You are a HIPAA-compliant medical front desk agent.",
human="Patient ID 4421, Spanish-preferred, penicillin allergy",
tools=["archival_memory_insert", "archival_memory_search"],
)
Letta or Mem0? Letta if you want OS-style tiers and self-editing memory. Mem0 if you want one API and graph + vector hybrid out of the box.
Need a graph layer? Yes for entity-heavy verticals (real estate, healthcare). Optional for casual chat.
Self-host? Both have open-source cores. Letta has a hosted plan; Mem0 also has hosted.
Cost? Mem0 hosted starts free for hobby; Letta hosted starts at team-tier pricing.
See it on /demo? Yes — return tomorrow as the same user and notice the agent remembers.
Written by
Sagar Shankaran· Founder, CallSphere
Sagar Shankaran is the founder of CallSphere, where he builds production AI voice and chat agents deployed across healthcare, hospitality, real estate, and home services. He writes about agentic AI, LLM engineering, and shipping voice agents that handle real calls in production.
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