Vestige – A queryless memory layer for multi agent coding fleets is a skill AI agent for Enable efficient collaboration and context sharing among multiple AI agents in coding systems without explicit queries. Pricing: not stated on the product page. As of 2026-11-06, KanonAgent records 3 upvotes. First indexed by KanonAgent on 2026-07-21.
Vestige enables seamless context sharing across multiple AI agents in coding workflows by providing a queryless memory layer.
3 upvotes
Tracked by Kanon since Jul 21, 2026
Status basis: npm_downloads_7d momentum fell to -158 per 3d from 78 (<= 40% of the prior window)
📈 Timelinewhat changed, and when
- Cooling npm_downloads_7d momentum fell to -158 per 3d from 78 (<= 40% of the prior window)
- Cooling npm_downloads_7d momentum fell to -183 per 3d from 78 (<= 40% of the prior window)
- Cooling npm_downloads_7d momentum fell to -192 per 3d from 132 (<= 40% of the prior window)
- Cooling npm_downloads_7d momentum fell to 0 per 3d from 132 (<= 40% of the prior window)
- Accelerating npm_downloads_7d gained 78 in the last 3d vs 47 in the 3d before (ratio 1.7x, threshold 1.5x)
- Cooling npm_downloads_7d momentum fell to 9 per 3d from 64 (<= 40% of the prior window)
- Accelerating npm_downloads_7d gained 47 in the last 3d after a flat 3d baseline (no prior movement to compare a ratio against)
- Reactivated npm_downloads_7d did not move for 7d, then gained 47 in the last 3d
- Cooling npm_downloads_7d momentum fell to -17 per 3d from 64 (<= 40% of the prior window)
- Accelerating npm_downloads_7d gained 47 in the last 3d after a flat 3d baseline (no prior movement to compare a ratio against)
- Accelerating npm_downloads_7d gained 64 in the last 3d after a flat 3d baseline (no prior movement to compare a ratio against)
- Accelerating npm_downloads_7d gained 64 in the last 3d after a flat 3d baseline (no prior movement to compare a ratio against)
- Cooling npm_downloads_7d momentum fell to 0 per 3d from 202 (<= 40% of the prior window)
- Cooling npm_downloads_7d momentum fell to 0 per 3d from 212 (<= 40% of the prior window)
- Cooling npm_downloads_7d momentum fell to 0 per 3d from 212 (<= 40% of the prior window)
- Accelerating npm_downloads_7d gained 202 in the last 3d vs 10 in the 3d before (ratio 20.2x, threshold 1.5x)
- Cooling npm_downloads_7d momentum fell to 0 per 3d from 212 (<= 40% of the prior window)
- Accelerating npm_downloads_7d gained 212 in the last 3d after a flat 3d baseline (no prior movement to compare a ratio against)
- Accelerating npm_downloads_7d gained 212 in the last 3d after a flat 3d baseline (no prior movement to compare a ratio against)
- Cooling npm_downloads_7d momentum fell to -241 per 3d from 11 (<= 40% of the prior window)
- Cooling npm_downloads_7d momentum fell to -209 per 3d from 10 (<= 40% of the prior window)
- Cooling npm_downloads_7d momentum fell to -210 per 3d from 11 (<= 40% of the prior window)
- Accelerating npm_downloads_7d gained 11 in the last 3d after a flat 3d baseline (no prior movement to compare a ratio against)
- Cooling npm_downloads_7d momentum fell to 0 per 3d from 11 (<= 40% of the prior window)
🔎 Known / Unknown
12 of 23 fields unknown
| Machine-callable | — | unknown | — |
| Open source | 1 | inferred | 1 quote(s) |
| Self-hostable | 1 | inferred | 1 quote(s) |
| Bring your own key | 1 | inferred | 1 quote(s) |
| Autonomy level | — | unknown | — |
| Pricing model | — | unknown | — |
| Integrations | mcp,claude-code,codex,cursor | inferred | 1 quote(s) |
“Unknown” means we have not verified it — it is not a “no”. Hard filters never treat unknown as false.
🤖 Agent teardown · skill
Job to be doneEnable efficient collaboration and context sharing among multiple AI agents in coding systems without explicit queries.
Who it is forAI coding assistants and automated development platforms.
Prerequisitesopen source · self-hostable · bring your own API key
Integrationsmcp · claude-code · codex · cursor
Traction · why it is risingEmerging as a foundational tool for complex, multi-agent development pipelines.
Why it matters
Solves a core challenge in multi-agent systems—memory consistency—making it essential for advanced AI-driven development.
Evidence quotesverbatim, from the product’s own materials
“public GitHub repository (README fetched)”— structural
“One 25MB Rust binary. No cloud. Your data never leaves your machine.”— readme
“After a one-time model download it works fully offline, with no API keys”— readme
“Local-first long-term memory for AI agents, delivered over MCP”— readme
“runs locally as an MCP server, so any MCP-capable agent (Claude Code, Claude Desktop, Codex, Cursor, and others)”— readme
Signal source: Show HN
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FAQ
What is Vestige?
Vestige enables seamless context sharing across multiple AI agents in coding workflows by providing a queryless memory layer.
What does Vestige do?
Enable efficient collaboration and context sharing among multiple AI agents in coding systems without explicit queries.
Why does Vestige matter?
Solves a core challenge in multi-agent systems—memory consistency—making it essential for advanced AI-driven development.
Is Vestige open source?
Yes — Vestige is open source.
Can I self-host Vestige?
Yes — Vestige can be self-hosted.
What does Vestige integrate with?
mcp,claude-code,codex,cursor
How popular is Vestige?
As tracked by KanonAgent: 3 upvotes (first indexed 2026-07-21).
What are the best Vestige alternatives?
Similar AI agents tracked by KanonAgent: Stealth Venture, archify, open-design, deer-flow, ruflo, career-ops.