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by huggingface · MCP Server · ★ 476
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
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funes Durable memory for your AI coding agents. indexes your past sessions across Claude Code, Codex, pi, and Hermes and lets any agent recall the past decisions, rationale, and findings. Your memory is a dataset you can publish to the Hugging Face Hub — then any machine, teammate, or agent can recall from it. Put a question to a memory and borrow a coding agent to answer it: funes recalls the relevant sessions, hands them over, and you get one grounded answer that names the sessions it drew from — nothing installed. Here it reads the public dataset named right in the command. Features at a glance Your agent recalls your past work. The model spontaneously uses to recall prior decisions, rationale, and findings mid-task. One memory across your agents. Index Claude Code, Codex, pi, and Hermes into a single memory; recall spans all of them, and every hit shows which agent it came from. Your memory is a Hugging Face dataset. Publish it to the Hugging Face Hub; a teammate, another of your machines — or anyone, if you make it public — recalls from it with one flag.
| Stars | 476 |
| Forks | 39 |
| Language | Rust |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 63.9250419994617/100 |
| Open Issues | 17 |
| Last Updated | 2026-09-22 |
| Created | 2026-06-18 |
| Platforms | claude-code, codex, mcp, rust |
| Est. Tokens | ~24k |
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funes is Durable, searchable memory of your past agent sessions.. It is categorized as a MCP Server with 476 GitHub stars.
funes is primarily written in Rust. It covers topics such as agent-memory, ai-agents, claude-code.
You can find installation instructions and usage details in the funes GitHub repository at github.com/huggingface/funes. The project has 476 stars and 39 forks, indicating an active community.
funes is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to funes on Agent Skills Hub include vestige, agent-skills, maestro. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: