Building Hermes Agent: A Layered Memory System for Personal AI Agents

Fajar M Reza — Mon, 08 Jun 2026 10:27:32 +0000

Technical Breakdown: How the 4-Tier Memory System Works

The Hermes Memory Pyramid is not only a visual concept. It is implemented as a practical memory architecture with four functional layers.
Each layer has a different role, storage format, access pattern, and benefit.
The goal is not to force every piece of information into one giant memory file.
The goal is to separate memory based on priority, speed, structure, and auditability.

Tier 0 — Core Memory
Tier 0 is the smallest and fastest memory layer.
This layer contains the most essential information that the agent must always know at the beginning of every session.
In Hermes, this layer is implemented as curated memory files such as:
MEMORY.md
USER.md
These files contain stable and high-priority information such as:
Agent identity.
User preferences.
Important operating rules.
System warnings.
Active project references.
Important folder paths.
Recurring configuration details.
Safety and workflow principles.
This layer is intentionally small.
It is not designed to store everything. It is designed to store what must never be forgotten.
The benefit of Tier 0 is instant orientation.
When a new session starts, Hermes does not need to rediscover who it is, who the user is, what projects matter, or what rules must be followed. The agent already has a compact operating memory.
This prevents the common “blank slate” problem in AI assistants.
Without Tier 0, every session starts from zero.
With Tier 0, every session starts with identity, direction, and rules already loaded.

Tier 1 — Daily Journal / Short-Term Context
Tier 1 is the short-term memory layer.
This layer captures what happened recently, usually within the last 24 to 72 hours.
In Hermes, this is implemented as daily journal files generated automatically from previous sessions.
The journal contains structured summaries such as:
What was discussed.
What files were mentioned.
What issues appeared.
What decisions were made.
What links or tools were used.
What projects were active that day.
This layer is useful because real work rarely happens in one session.
For example, when building an app, the user may debug an error at night, continue the next morning, test a new feature in the afternoon, and prepare a release the next day.
Without Tier 1, the agent needs to be reminded manually.
With Tier 1, Hermes can quickly understand the recent working context.
The benefit of Tier 1 is continuity.
It allows Hermes to answer questions like:
“What did we work on yesterday?”
“What was the last issue?”
“What should I continue today?”
“What decision did we make in the last session?”
Tier 1 acts like the agent’s short-term working memory.
It is more detailed than Tier 0, but still much smaller and easier to process than reading raw chat history.

Tier 2 — Structured Fact Store
Tier 2 is where Hermes becomes much more powerful.
This layer stores structured facts extracted from journals and conversation history.
Instead of saving everything as long text, Hermes converts important information into searchable facts.
A fact can be:
A project decision.
A user preference.
A known bug.
A chosen tool.
A deployment configuration.
A command that worked.
A recurring issue.
An entity connected to a project.
A technical rule that should be remembered.
In the Hermes implementation, this layer is stored in a structured database, such as SQLite, with fields like:
fact_id
content
category
tags
trust_score
retrieval_count
helpful_count
This makes memory queryable.
Instead of asking the model to scan a huge document, Hermes can search for relevant facts directly.
For example:
Search: “PromptLab deployment”
Result: facts about the PromptLab project, deployment setup, package name, Play Store progress, Supabase configuration, and previous decisions.
Search: “Hermes cron job”
Result: facts about scheduled jobs, backup tasks, daily reports, and memory extraction.
Search: “Rawajati PRIMA”
Result: facts about PRIMA, chatbot, QR code, landing page, and Kelurahan Rawajati workflow.
The benefit of Tier 2 is fast structured recall.
This is where the agent starts to feel like it actually remembers.
Not because it has a larger prompt, but because it can retrieve the right facts at the right time.
Tier 2 is also useful for ranking and filtering information. Since facts can have categories, tags, and trust scores, the agent can prioritize more reliable or more relevant information.
This is much better than relying only on long chat history.

Tier 3 — Raw Verbatim Logs
Tier 3 is the deepest memory layer.
This layer stores raw logs of conversations and events.
Unlike Tier 1 and Tier 2, Tier 3 does not try to summarize or structure everything immediately. It preserves the original interaction as closely as possible.
In Hermes, this layer is implemented as append-only raw log files.
A raw log can contain:
User messages.
Assistant responses.
Timestamps.
Session activity.
Important interaction details.
Exact wording from previous discussions.
The benefit of Tier 3 is auditability and recovery.
Why is this important?
Because summaries can miss details.
Fact extraction can be incomplete.
The model can misunderstand something.
A small sentence can become important later.
A technical decision may need to be traced back.
Tier 3 solves that by keeping the original source.
If Hermes forgets a detail, the raw log can be searched again.
If a structured fact was extracted incorrectly, the original message can be checked.
If a better extraction pipeline is built in the future, Hermes can re-process old logs and generate better facts.
This makes Tier 3 a forensic memory layer.
It is not the fastest layer, and it is not meant to be loaded all the time.
But it is extremely important for long-term reliability.
How the Layers Work Together
The strength of the system is not in one layer.
The strength is in the combination.
A simplified workflow looks like this:
User talks with Hermes.
The conversation is stored in the source database and raw logs.
At the end of the day, Hermes generates a daily journal.
Important facts are extracted from the journal and raw log.
Stable high-priority information can be promoted into core memory.
The next session starts with core memory and can retrieve structured facts when needed.

So the system moves information through layers:
Raw conversation → daily summary → structured facts → core memory if important.
This is important because not all information deserves the same treatment.
Some information is temporary.
Some information is useful for a few days.
Some information becomes a long-term fact.
Some information must be permanently remembered.
Some information only needs to exist for audit and recovery.

The Memory Pyramid gives each type of information the right place.
Practical Benefits of the 4-Tier System
The first benefit is lower context cost.
Hermes does not need to load every old conversation into the prompt. It can load only the core memory and retrieve relevant facts when needed.
The second benefit is faster recall.
Structured facts can be searched quickly, instead of asking the model to read huge chat histories.
The third benefit is better continuity.
The agent can continue yesterday’s work without forcing the user to repeat everything.
The fourth benefit is higher reliability.
If a summary misses something, raw logs can still be checked.
The fifth benefit is auditability.
Important decisions can be traced back to original conversations.
The sixth benefit is recoverability.
If one layer fails, the other layers can still help restore context.
The seventh benefit is better personalization.
The agent can remember the user’s long-term preferences, projects, workflows, and technical environment.
The eighth benefit is scalability.
As the user works on more projects, the memory does not become one messy file. It remains layered, searchable, and maintainable.

Example Scenario
Imagine the user asks:
“What was the last decision about the PRIMA project?”
Hermes does not need to read every previous chat.
It can follow a layered approach:
First, check Tier 0 for core project identity.
Then check Tier 1 for recent PRIMA activity.
Then query Tier 2 for structured facts related to PRIMA.
If something is unclear, search Tier 3 raw logs for the original conversation.
This gives the agent a practical reasoning path.
Fast first.
Structured second.
Forensic only when needed.
That is much more efficient than loading everything at once.
Why This Architecture Matters
Many AI agent projects focus heavily on tools.
Tool calling.
Browser automation.
Code execution.
APIs.
Workflows.
Multi-agent orchestration.
Those are important.
But without memory architecture, the agent remains shallow.
It may do tasks, but it cannot build long-term continuity.
Hermes shows that memory should be treated as a first-class system component.
Not as a side feature.
The technical lesson is simple:
A serious AI agent needs memory engineering, not just prompt engineering.
Prompting helps the model answer.
Memory engineering helps the agent continue.
That is the difference.
Strong Technical Summary

The Hermes Memory Pyramid can be summarized like this:
Tier 0 gives identity.
The agent knows who it is, who it serves, and what rules matter.
Tier 1 gives continuity.
The agent knows what happened recently.
Tier 2 gives retrieval.
The agent can search structured facts quickly.
Tier 3 gives auditability.
The agent can go back to the original raw source when needed.
Together, these layers turn Hermes from a simple chatbot into a persistent personal AI agent.
Not perfect.
Not magical.
But practical, inspectable, recoverable, and useful for real work.
That is the main value of the 4-tier memory system.

Most AI assistants are designed to answer.
Hermes is designed to continue.
That is the difference between a chatbot and a personal AI agent.
A chatbot responds to the current prompt.
A personal AI agent remembers the journey, tracks decisions, retrieves context, and helps the user move forward.
For me, that is the future of personal AI:
not just larger models,
but better memory,
better workflow,
better continuity,
and better control.

AI #AIAgents #AgenticAI #LLM #Automation #PersonalAI #IndieDev #BuildInPublic #ArtificialIntelligence #MemoryArchitecture #HermesAgent #PromptLab

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DEV Community: Fajar M Reza

Building Hermes Agent: A Layered Memory System for Personal AI Agents

AI #AIAgents #AgenticAI #LLM #Automation #PersonalAI #IndieDev #BuildInPublic #ArtificialIntelligence #MemoryArchitecture #HermesAgent #PromptLab