Persistent Memory

Hermes Agent has bounded, curated memory that persists across sessions. This lets it remember your preferences, your projects, your environment, and things it has learned.

How It Works

Two files make up the agent's memory:

File	Purpose	Char Limit
MEMORY.md	Agent's personal notes — environment facts, conventions, things learned	2,200 chars (~800 tokens)
USER.md	User profile — your preferences, communication style, expectations	1,375 chars (~500 tokens)

Both are stored in ~/.hermes/memories/ and are injected into the system prompt as a frozen snapshot at session start. The agent manages its own memory via the memory tool — it can add, replace, or remove entries.

info

Character limits keep memory focused. When memory is full, the agent consolidates or replaces entries to make room for new information.

How Memory Appears in the System Prompt

At the start of every session, memory entries are loaded from disk and rendered into the system prompt as a frozen block:

══════════════════════════════════════════════
MEMORY (your personal notes) [67% — 1,474/2,200 chars]
══════════════════════════════════════════════
User's project is a Rust web service at ~/code/myapi using Axum + SQLx
§
This machine runs Ubuntu 22.04, has Docker and Podman installed
§
User prefers concise responses, dislikes verbose explanations

The format includes:

A header showing which store (MEMORY or USER PROFILE)
Usage percentage and character counts so the agent knows capacity
Individual entries separated by § (section sign) delimiters
Entries can be multiline

Frozen snapshot pattern: The system prompt injection is captured once at session start and never changes mid-session. This is intentional — it preserves the LLM's prefix cache for performance. When the agent adds/removes memory entries during a session, the changes are persisted to disk immediately but won't appear in the system prompt until the next session starts. Tool responses always show the live state.

Memory Tool Actions

The agent uses the memory tool with these actions:

add — Add a new memory entry
replace — Replace an existing entry with updated content (uses substring matching via old_text)
remove — Remove an entry that's no longer relevant (uses substring matching via old_text)

There is no read action — memory content is automatically injected into the system prompt at session start. The agent sees its memories as part of its conversation context.

Substring Matching

The replace and remove actions use short unique substring matching — you don't need the full entry text. The old_text parameter just needs to be a unique substring that identifies exactly one entry:

# If memory contains "User prefers dark mode in all editors"
memory(action="replace", target="memory",
       old_text="dark mode",
       content="User prefers light mode in VS Code, dark mode in terminal")

If the substring matches multiple entries, an error is returned asking for a more specific match.

Two Targets Explained

`memory` — Agent's Personal Notes

For information the agent needs to remember about the environment, workflows, and lessons learned:

Environment facts (OS, tools, project structure)
Project conventions and configuration
Tool quirks and workarounds discovered
Completed task diary entries
Skills and techniques that worked

`user` — User Profile

For information about the user's identity, preferences, and communication style:

Name, role, timezone
Communication preferences (concise vs detailed, format preferences)
Pet peeves and things to avoid
Workflow habits
Technical skill level

What to Save vs Skip

Save These (Proactively)

The agent saves automatically — you don't need to ask. It saves when it learns:

User preferences: "I prefer TypeScript over JavaScript" → save to user
Environment facts: "This server runs Debian 12 with PostgreSQL 16" → save to memory
Corrections: "Don't use sudo for Docker commands, user is in docker group" → save to memory
Conventions: "Project uses tabs, 120-char line width, Google-style docstrings" → save to memory
Completed work: "Migrated database from MySQL to PostgreSQL on 2026-01-15" → save to memory
Explicit requests: "Remember that my API key rotation happens monthly" → save to memory

Skip These

Trivial/obvious info: "User asked about Python" — too vague to be useful
Easily re-discovered facts: "Python 3.12 supports f-string nesting" — can web search this
Raw data dumps: Large code blocks, log files, data tables — too big for memory
Session-specific ephemera: Temporary file paths, one-off debugging context
Information already in context files: SOUL.md and AGENTS.md content

Capacity Management

Memory has strict character limits to keep system prompts bounded:

Store	Limit	Typical entries
memory	2,200 chars	8-15 entries
user	1,375 chars	5-10 entries

What Happens When Memory is Full

When you try to add an entry that would exceed the limit, the tool returns an error:

{
  "success": false,
  "error": "Memory at 2,100/2,200 chars. Adding this entry (250 chars) would exceed the limit. Replace or remove existing entries first.",
  "current_entries": ["..."],
  "usage": "2,100/2,200"
}

The agent should then:

Read the current entries (shown in the error response)
Identify entries that can be removed or consolidated
Use replace to merge related entries into shorter versions
Then add the new entry

Best practice: When memory is above 80% capacity (visible in the system prompt header), consolidate entries before adding new ones. For example, merge three separate "project uses X" entries into one comprehensive project description entry.

Practical Examples of Good Memory Entries

Compact, information-dense entries work best:

# Good: Packs multiple related facts
User runs macOS 14 Sonoma, uses Homebrew, has Docker Desktop and Podman. Shell: zsh with oh-my-zsh. Editor: VS Code with Vim keybindings.

# Good: Specific, actionable convention
Project ~/code/api uses Go 1.22, sqlc for DB queries, chi router. Run tests with 'make test'. CI via GitHub Actions.

# Good: Lesson learned with context
The staging server (10.0.1.50) needs SSH port 2222, not 22. Key is at ~/.ssh/staging_ed25519.

# Bad: Too vague
User has a project.

# Bad: Too verbose
On January 5th, 2026, the user asked me to look at their project which is
located at ~/code/api. I discovered it uses Go version 1.22 and...

Duplicate Prevention

The memory system automatically rejects exact duplicate entries. If you try to add content that already exists, it returns success with a "no duplicate added" message.

Security Scanning

Memory entries are scanned for injection and exfiltration patterns before being accepted, since they're injected into the system prompt. Content matching threat patterns (prompt injection, credential exfiltration, SSH backdoors) or containing invisible Unicode characters is blocked.

Session Search

Beyond MEMORY.md and USER.md, the agent can search its past conversations using the session_search tool:

All CLI and messaging sessions are stored in SQLite (~/.hermes/state.db) with FTS5 full-text search
Search queries return relevant past conversations with Gemini Flash summarization
The agent can find things it discussed weeks ago, even if they're not in its active memory

hermes sessions list    # Browse past sessions

session_search vs memory

Feature	Persistent Memory	Session Search
Capacity	~1,300 tokens total	Unlimited (all sessions)
Speed	Instant (in system prompt)	Requires search + LLM summarization
Use case	Key facts always available	Finding specific past conversations
Management	Manually curated by agent	Automatic — all sessions stored
Token cost	Fixed per session (~1,300 tokens)	On-demand (searched when needed)

Memory is for critical facts that should always be in context. Session search is for "did we discuss X last week?" queries where the agent needs to recall specifics from past conversations.

Configuration

# In ~/.hermes/config.yaml
memory:
  memory_enabled: true
  user_profile_enabled: true
  memory_char_limit: 2200   # ~800 tokens
  user_char_limit: 1375     # ~500 tokens

Honcho Integration (Cross-Session User Modeling)

For deeper, AI-generated user understanding that works across tools, you can optionally enable Honcho by Plastic Labs. Honcho runs alongside existing memory — USER.md stays as-is, and Honcho adds an additional layer of context.

When enabled:

Prefetch: Each turn, Honcho's user representation is injected into the system prompt
Sync: After each conversation, messages are synced to Honcho
Query tool: The agent can actively query its understanding of you via query_user_context

Setup:

# 1. Install the optional dependency
uv pip install honcho-ai

# 2. Get an API key from https://app.honcho.dev

# 3. Create ~/.honcho/config.json
cat > ~/.honcho/config.json << 'EOF'
{
  "enabled": true,
  "apiKey": "your-honcho-api-key",
  "peerName": "your-name",
  "hosts": {
    "hermes": {
      "workspace": "hermes"
    }
  }
}
EOF

Or via environment variable:

hermes config set HONCHO_API_KEY your-key

tip

Honcho is fully opt-in — zero behavior change when disabled or unconfigured. All Honcho calls are non-fatal; if the service is unreachable, the agent continues normally.

How It Works​

How Memory Appears in the System Prompt​

Memory Tool Actions​

Substring Matching​

Two Targets Explained​

memory — Agent's Personal Notes​

user — User Profile​

What to Save vs Skip​

Save These (Proactively)​

Skip These​

Capacity Management​

What Happens When Memory is Full​

Practical Examples of Good Memory Entries​

Duplicate Prevention​

Security Scanning​

Session Search​

session_search vs memory​

Configuration​

Honcho Integration (Cross-Session User Modeling)​