Score breakdown
Popularity is tracked separately. Support, ads, sponsorships, and tips never affect these signals.
Why it matters
Useful for developer teams running AI agents in production who want durable, typed, searchable memory without depending on a hosted vector store, for platform teams that want a no-vendor-API-key, no-data-egress memory backend that plugs into the existing MCP stack, for anyone migrating off the typical hosted-agent-memory pattern where every message triggers an extraction / embedding call to a
Where this stands now
ThinkfleetAI/memmesh ranks #6 of 7 tracked Agent Memory items by composite score (6.9 against a section median of 8.2). The section currently carries 4 Silver, 3 Gold. RepoRadar has retained observations for this record since 2026-08-17 (54 days in the current window).
Who should use it
Who should skip it
Move on from ThinkfleetAI/memmesh if the licensing terms, language support, or platform requirements do not fit your project.
About this signal
ThinkfleetAI/memmesh is tracked by RepoRadar as a developer tool in the Agent Memory section. First seen 2026-08-17; the source record was last checked on 2026-08-17. The current verdict is 'try now' with a Silver tier and easy setup difficulty. Across RepoRadar's eight signals, ThinkfleetAI/memmesh is strongest on open-source/build quality (8.4) and workflow potential (8.4) and weakest on momentum (6.0) — a profile worth weighing against your own priorities. This page summarizes the evidence RepoRadar captured from https://github.com/ThinkfleetAI/memmesh.
How this item is evaluated
The ThinkfleetAI/memmesh record combines a 6.9/10 composite score with separate popularity (100.0), risk (low), and setup (easy) signals. See the scoring methodology for the current weights and evidence definitions.
Questions worth asking before you adopt this
Putting this into practice? Read How to vet an AI agent or MCP server before you wire it in for the checklist behind this score.
Risk explanation
The bge-small embedding model is downloaded on first observe (~130 MB into the cache) -- a user who skips the recommended path and runs [embeddings] provider = 'none' will lose semantic retrieval and fall back to pure keyword + recency; The supported tool list is finite (the README documents the supported tools) -- memmesh install will silently skip an unsupported agent host, so a multi-agent stack with non-canonical hosts may need to wire the MCP server block manually; Postgres is optional and the SQLite default is the canonical single-binary path -- a multi-user / multi-host deployment will need the Postgres backend and the documented connection-string configuration; The bi-temporal model (when it happened vs when you learned it) is a first-class primitive but the per-query syntax is not standardized in the README -- review the docs.memmesh.ai query reference for the canonical pattern.