TL;DR: Yes. LingCode implements the same learning loop that made Nous Research's Hermes Agent popular — persistent memory, agent-proposed skills, and full-text search over past sessions — but inside a native macOS IDE instead of a headless server, across multiple AI providers, and with a human review step that keeps you in control.
Most AI coding agents are one-shot: they finish a task and forget everything. The Hermes Agent got attention in 2026 because it does the opposite — it learns. LingCode brings that same loop to its built-in agent so every session makes the next one cheaper, without sending your code or memory to someone else's server.
Hermes describes itself as "the only agent with a built-in learning loop — it creates skills from experience, improves them during use, nudges itself to persist knowledge, searches its own past conversations." That loop has three pillars:
Rather than fork Hermes, LingCode brings the same three pillars to its agent as first-class, on-device tools.
The agent has a memory_save tool that writes durable notes to a per-project memory file and a cross-project user memory file. It includes Hermes's self-nudge: after a configurable number of turns without saving (8 by default), LingCode reminds the agent to persist anything notable — a user preference, a non-obvious project decision, a recurring pain point. You can tune or disable the cadence in Settings.
When the agent notices it has run the same multi-step procedure more than once, it can call skill_propose to draft a new reusable skill (a slash-command). This is Hermes's "creates skills from experience" — with one deliberate difference: in LingCode the new skill lands as a draft you review and promote before it activates. You get compounding capability without an agent silently rewriting its own behavior.
LingCode indexes your past agent sessions and exposes a session_search tool backed by SQLite FTS5 full-text search. Ask about "the bug we hit last week" or "did I already fix this in another project," and the agent searches its own transcripts — returning matching snippets, session IDs, and the project each ran in — instead of guessing.
The memory, skill-proposal, and session tools live in LingCode's agent loop — so when you switch providers (DeepSeek, or any OpenAI-compatible model), the learning loop comes with you. The point of a multi-provider IDE is that capability shouldn't depend on which model you picked today.
| Capability | Hermes Agent | LingCode |
|---|---|---|
| Persistent memory + self-nudge | Yes | Yes |
| Agent-created skills | Yes (auto) | Yes — proposed as a draft, you approve |
| Session search | Yes (FTS5) | Yes (FTS5) |
| Runs where | Headless server / chat platforms | Native macOS IDE (and a web tab) |
| Skill format | agentskills.io frontmatter | agentskills.io-compatible |
| Models | Open-weights | Multi-provider (Claude + switchable) |
The headline distinction: Hermes lives on a server you SSH into; LingCode's learning loop lives inside the editor where you actually write code — with the project tree, build system, simulator, and diffs right there.
An agent that forgets is one you re-explain your codebase to every morning. The learning loop changes the economics: memory captures the "why" behind your project, proposed skills capture the "how" of your repeatable workflows, and session search means institutional knowledge doesn't evaporate when a chat ends.
Does LingCode use the Hermes Agent codebase?
No. LingCode independently implements the same learning-loop concepts as native tools, with a skill format compatible with the agentskills.io convention Hermes uses — so skills are portable.
Where does LingCode store agent memory?
In a per-project memory file and a cross-project user memory file on your machine, written through a dedicated memory_save tool so the IDE shows the save and applies size limits.
Do agent-created skills run automatically?
No. skill_propose writes a draft you review and promote — a deliberate safety step.
Is the learning loop in the web version?
Yes — it's available in both the Claude Code native tab and the web tab, and across LingCode's other providers.