Memveri Agent Operating System

Memveri vs the field

Memveri isn't another coding agent. It's the memory, code-intelligence, and safety layer that works on top of the agent you already use. Here's how the pieces compare, fact by fact.

Memveri — Agent OS layer Claude Code — Anthropic's agent OpenCode — open-source CLI agent MiMo Code — ByteDance coding agent
Capability
Memveri
Claude Code
OpenCode
MiMo Code
Cross-session memorysurvives restarts, searchable
Yes — semantic vector memory
Partial — markdown context files
No — stateless sessions
Partial — SQLite FTS5 files
Memory recall rankingrelevance scoring, not just full-text
Yes — embeddings + decay scoring
No
No
No — FTS5 keyword search
Code knowledge graphfunctions, classes, calls, imports
Yes — 2,268 nodes on Memveri's own repo
No
No
No
Guardrail decision ledgeraccept / reject / supersede, auditable
Yes — rules engine + ledger
Partial — permission rules & hooks
Partial — permissions config
Partial — permissions config
Works with any agentMCP server for external tools
Yes — 70+ MCP tools
No — Claude only
Partial — consumes MCP
Partial — consumes MCP
Team / multi-machine syncshare memory across machines
Yes — export/import bundles
No — machine-local files
No
No — machine-local files
Local-first datano cloud required
Yes — SQLite on your disk
Partial — local files, cloud login
Yes
Yes
Open sourcesource you can inspect and fork
Yes
No — proprietary
Yes — MIT
Yes — MIT

Measured on our own codebase

These are real numbers from Memveri's own benchmark scripts (scripts/benchmark_index.py, scripts/benchmark_recall.py), run on a laptop. Claude Code, OpenCode, and MiMo Code do not publish comparable indexing, recall, or latency numbers — so rather than inventing competitor scores, we show what we actually measured.

Indexing throughput

10 files0.19s
50 files1.03s
100 files2.51s
200 files5.70s

Full knowledge-graph build, synthetic tree.

Fastest re-index

Incremental updates

Single-file edit0.75–0.94ms
Memveri

Re-index after one save. No full reparse.

Real repo

Memveri itself92 files
Graph nodes2,268
Graph edges4,685

Full build 38.7s incl. embeddings + community detection; ~27 graph queries/sec.

Recall accuracy

R@10 (50-doc corpus)80–100%

Vector recall at k=10 across topics; matches brute-force cosine search.

The other tools are excellent agents. None of them give you a persistent, tool-agnostic memory with a guardrail ledger and a code knowledge graph.

Add Memveri on top of the one you already use — and keep it.

Download for Windows
How we scored this comparison

Feature matrix reflects public documentation as of July 2026. "Partial" means the capability exists in a limited form — e.g. Claude Code's memory is guidance loaded into context, not an enforced, searchable store (per Anthropic's own docs), and MiMo Code's memory is SQLite full-text search, not semantic recall.