Comparisons
How mnem stacks up against other agent-memory and knowledge-graph systems. Each comparison is honest: where they win, where mnem wins, when to pick which.
mnem is open source (Apache-2.0). Numbers come from public artefacts; where a competitor’s claim is closed-source we say so. Where a benchmark is not directly comparable, we say so rather than fabricate a single-number league table.
| Competitor | License | Server / Embedded | LLM at ingest | Bitemporal | Stars | Compare |
|---|---|---|---|---|---|---|
Graphiti (getzep/graphiti) | Apache-2.0 | server (Neo4j / Kuzu / FalkorDB / Neptune) | mandatory | yes | 25,409 | graphiti.md |
mem0 (mem0ai/mem0) | Apache-2.0 | library + cloud | default-on (opt-out) | no | 54,113 | mem0.md |
MemPalace (MemPalace/mempalace) | MIT | embedded (Python + ChromaDB) | no | partial | 49,768 | mempalace.md |
Supermemory (supermemoryai/supermemory) | MIT (repo) / closed (cloud) | hosted cloud | yes | no | 22,218 | supermemory.md |
Cognee (topoteretes/cognee) | Apache-2.0 | library + cloud | yes (cognify) | no | 16,807 | cognee.md |
Letta (letta-ai/letta) | Apache-2.0 | server + CLI | yes (agent is the writer) | partial | 22,305 | letta.md |
graphify (safishamsi/graphify) | MIT | one-shot CLI | yes (Claude subagents) | no | 35,262 | graphify.md |
| mnem | Apache-2.0 | embedded + four surfaces | no | no | small / pre-launch | (this repo) |
Star counts pulled from the GitHub API on 2026-04-26. License columns reflect the repository SPDX identifier; commercial / hosted layers above some of these projects ship under different terms.
mnem positioning
mnem is the substrate underneath the products in the table: a content- addressed, versioned, hybrid-retrieval graph that runs in-process, ingests without an LLM, and exposes token-budget telemetry on every retrieve. We are not building a memory product; we are building the thing the next memory product is built on.
Reading order
If you have read about agent memory before, the most useful first read is one of:
- mnem vs Graphiti if you have been thinking about bitemporal knowledge graphs.
- mnem vs mem0 if you have been using the LangChain / LlamaIndex / CrewAI defaults.
- mnem vs MemPalace if you care about no-LLM-on- write retrieval and reproducible benchmarks.
- mnem vs Supermemory if you have been weighing the closed cloud vs self-host trade-off.
- mnem vs Cognee if you have been looking at ECL- pipeline-shaped knowledge engines.
- mnem vs Letta if you have been looking at the MemGPT lineage of agent platforms.
- mnem vs graphify if you have been using one-shot folder-to-graph extractors.