How Hermes Enables AI to Remember, Learn, and Grow Autonomously

What “autonomous learning” means in Hermes source code

The official documentation defines a learning loop that extracts abilities from experience, continuously optimises usage, actively memorises key information, searches past dialogues, and gradually learns user habits across sessions.

The implementation consists of six concrete actions:

Store immutable critical facts in long‑term memory.

Organise repeatable procedures as dedicated skills .

Run a background review to decide whether to persist information or skills.

Retrieve previously stored items in the next conversation, with full‑text search support.

Modify skills immediately when problems are discovered.

Record the entire operation trace for later analysis or optimisation.

How Hermes actually learns

1. Learning stable facts – the MEMORY.md and USER.md files

Two markdown files are used: MEMORY.md records system environment, project conventions, and tool quirks. USER.md records user preferences, communication style, work habits, and expectations.

At the start of each session these files are injected into the system prompt as a fixed snapshot. New entries are written to the files immediately but become visible only in the next session.

2. Learning reusable methods – the skill store

Skills act as procedural memory for repeated tasks and are kept separate from factual memory. The code supports creating, modifying, deleting, and persisting skills as files, forming an AI‑specific skill library that can be invoked directly for similar tasks.

3. Learning from past experience – session search

Historical sessions are stored in a SQLite database with FTS5 full‑text indexing. When needed, Hermes quickly retrieves matching records, extracts concise summaries, and excludes the current session to avoid context contamination.

When does learning get triggered?

1. Defining the rules

Memory : only persistent facts and user preferences are stored.

Search : historical dialogues are consulted before processing new input.

Skill : complex tasks, problem‑solving methods, and efficient workflows are saved as skills; errors trigger immediate updates.

2. Runtime counters that fire the review

Memory trigger : counts user dialogue rounds and reminds to review memory.

Skill trigger : counts tool‑call iterations and reminds to review skills after task completion.

Core feedback loop – asynchronous background review

The function _spawn_background_review() launches a background thread that clones the current AI branch, runs a dedicated review prompt, and writes results into the memory and skill stores without blocking the main task.

The review performs three checks:

Is there user information worth persisting?

Is there a reusable method?

If a skill exists, update it; otherwise create a new one.

Where does the learned knowledge reside?

Memory : MEMORY.md / USER.md with file locking, atomic writes, and safety scanning.

Skills : a dedicated skill directory that automatically clears cached prompts after saving.

Session history : the SQLite session_search database.

Operation trace : JSONL files for downstream training analysis.

How the knowledge is used later

Memory : loaded as a snapshot in the next session; mid‑session changes do not affect the current dialogue.

History : retrieved, deduplicated, and summarised to quickly re‑establish context.

Skills : loaded when a matching task appears; problems trigger immediate modification for continuous iteration.

Pre‑compression memory rescue mechanism

Before context compression, Hermes invokes flush_memories() to force‑save key information such as long‑term preferences, corrected facts, stable habits, recurring patterns, and reusable knowledge. This prevents loss of valuable data that compression would otherwise discard.

The rescue follows three steps:

Pause compression and ask the model to identify information worth remembering.

Provide a special internal prompt that restricts the model to the memory tool only.

If the model deems information valuable, it writes it to persistent memory; otherwise it discards it.

After the rescue, any temporary traces are cleared so they do not pollute the official dialogue history.

Reliability guarantees

Sequential guarantee: rescue always occurs before compression.

Tool restriction: only the memory tool is available during rescue, preventing drift.

Persistent write: saved data survives subsequent compressions.

Prompt stability: the rescue prompt is not overwritten mid‑session.

Temporary trace cleanup: ensures no contamination of normal conversation flow.

External memory integration

Hermes defines a MemoryProvider abstract interface and a MemoryManager that orchestrates built‑in and external memory sources, allowing a single external backend to participate in the learning loop.

Layered architecture of Hermes autonomous learning

Rule layer : system‑prompt definitions for memory, skill, and search rules.

Trigger layer : counters for memory, skill, and rescue activation.

Execution layer : memory tool, skill management, background review, and history search.

Storage layer : skill directory, memory files, SQLite database, and operation‑trace files.

Overall closed‑loop flow

Fact → Method → History → Background review → Future reuse → External extension.

Design highlights and takeaways

Learning rules are written into the system prompt in advance.

Fact memory and skill memory are stored separately.

Session history is decoupled from persistent memory.

Background review runs asynchronously, not blocking the main task.

Memory snapshots protect prompt stability.

Skills are mutable and continuously iterated.

Separate counters trigger precise memory and skill updates.

Pre‑compression rescue prevents loss of key information.

Unified interface for external memory makes the design extensible.