Living Context Engine vs Mem0 vs Letta vs Zep: A Practical Comparison

Comparison · Updated May 2026

The Landscape in 2026

"AI memory" went from a research term to a product category between 2024 and 2026. Today, four projects dominate the discourse: Mem0, Letta (née MemGPT), Zep, and Feather DB's Living Context Engine. They are often discussed interchangeably. They are architecturally different in ways that matter for real production use cases.

This is the honest comparison. What each one is, what it optimizes for, when it fits, and where it falls short.

The Four at a Glance

System	Primary abstraction	Stack	Deploy model
Mem0	Conversation-tier memory layer	Python + vector DB + LLM-as-judge	SDK + managed cloud
Letta	Stateful agent runtime with memory tiers	Python server with Postgres	Self-hosted server
Zep	Conversation memory store with knowledge graph	Go service + Postgres + vector DB	Self-hosted or managed cloud
Feather DB (Living Context Engine)	Embedded vector-graph engine with adaptive decay	Rust core + Python binding	Embedded single-file

Mem0

Pitch: "The memory layer for AI agents." Mem0 sits on top of a vector DB and uses LLM judges to extract, dedupe, and update memories from conversation streams.

Strengths:

Great defaults for conversational use cases — chatbots, customer assistants.
Automatic memory extraction reduces application-layer work.
Managed cloud option for teams that don't want to operate infra.

Trade-offs:

Heavy reliance on LLM-as-judge means extraction quality depends on prompt design and is hard to reason about deterministically.
Memory is conversation-shaped — fits chat workloads better than structured business workflows.
No native graph traversal — memory is a flat space with similarity search.

Letta (formerly MemGPT)

Pitch: "Stateful agents with long-term memory." Letta is an agent runtime where memory is organized into tiers (core memory, archival memory) and the agent itself manages what gets moved between them.

Strengths:

Cleanest model of memory hierarchy — conscious of context window pressure.
Good fit for single-agent, long-running stateful sessions.
Active research community, strong ergonomics for agent loops.

Trade-offs:

Coupled to the agent runtime — using Letta for "just memory" pulls in an entire orchestration layer.
Requires a Postgres server in production. Operational footprint is non-trivial.
Less optimized for multi-agent, multi-tenant scenarios where each agent needs its own memory.

Zep

Pitch: "Memory for AI agents with built-in knowledge graphs." Zep stores conversational messages, summarizes them, and extracts a temporal knowledge graph from the conversation history.

Strengths:

Strongest graph story among the conversation-shaped systems — Zep's Graphiti graph layer is genuinely useful for relational queries.
Built-in summarization and fact extraction.
Good operational maturity — runs at production scale for several public customers.

Trade-offs:

Conversation-centric model — extending it to non-conversational context (briefs, documents, code) requires fitting your data into a chat shape.
Full service deployment with Postgres + vector store dependencies.
Graph extraction depends on LLM judges, with associated cost and latency.

Feather DB — Living Context Engine

Pitch: "The substrate underneath every Living Context Engine." Feather DB is an embedded engine — a single binary, a single file — that fuses HNSW vector search, a typed property graph, and adaptive decay scoring.

Strengths:

Embedded — no server, no Postgres, no network hop. Open a file, start writing context.
Per-agent / per-tenant isolation is filesystem-level. Cheap to spin up, cheap to discard.
Adaptive decay and typed-edge traversal are first-class in the retrieval kernel — not application-layer wrappers.
Single retrieval call (context_chain) returns a connected subgraph instead of a flat list.

Trade-offs:

Single-writer per file. Designed for many small per-agent stores, not one giant shared one.
No built-in LLM-judge extraction — write-back is the application's responsibility.
Less mature than Zep operationally; v0.10.0 as of May 2026.

Which One Fits Which Workload

"I'm building a chatbot and need persistent user memory"

Mem0 or Zep. Mem0 for the cleanest SDK; Zep if you want the conversation knowledge graph.

"I'm building a long-running stateful agent that needs to manage its own memory tiers"

Letta. The memory-tier abstraction matches the problem.

"I'm building a multi-agent system where each agent needs its own memory, with cross-agent edges"

Feather DB. The single-file model and typed-edge graph are the right primitive.

"I'm building a production AI that needs to compound — every output should improve the next decision"

Feather DB. The closed feedback loop is the architectural primitive Living Context Engines were named to encode.

"I have a static document corpus and need RAG"

None of the above. Use a plain vector DB and a wrapper layer. Don't pay for memory architecture you won't use.

The Architectural Difference That Matters Most

Mem0, Letta, and Zep treat memory as a tier above an existing retrieval store. Feather DB treats memory as a single fused primitive — vectors, graph, decay, and feedback all live in one kernel, in one file, in one process.

For workloads where memory is the application — agents that need to compound, multi-tenant systems that need isolation, decision systems that need substrate continuity — the fused-kernel model is the right architecture. For workloads where memory is a feature bolted onto a chat product, the tier-above-RAG model is usually enough.

The right question is not "which is best" — it is "which architectural shape matches my workload." Pick the one whose primary abstraction is the same shape as your problem.