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Session Storage
Hermes Agent uses a SQLite database (~/.hermes/state.db) to persist session
metadata, full message history, and model configuration across CLI and gateway
sessions. This replaces the earlier per-session JSONL file approach.
Source file: hermes_state.py
Architecture Overview
~/.hermes/state.db (SQLite, WAL mode)
├── sessions — Session metadata, token counts, billing
├── messages — Full message history per session
├── messages_fts — FTS5 virtual table for full-text search
└── schema_version — Single-row table tracking migration state
Key design decisions:
- WAL mode for concurrent readers + one writer (gateway multi-platform)
- FTS5 virtual table for fast text search across all session messages
- Session lineage via
parent_session_idchains (compression-triggered splits) - Source tagging (
cli,telegram,discord, etc.) for platform filtering - Batch runner and RL trajectories are NOT stored here (separate systems)
SQLite Schema
Sessions Table
CREATE TABLE IF NOT EXISTS sessions (
id TEXT PRIMARY KEY,
source TEXT NOT NULL,
user_id TEXT,
model TEXT,
model_config TEXT,
system_prompt TEXT,
parent_session_id TEXT,
started_at REAL NOT NULL,
ended_at REAL,
end_reason TEXT,
message_count INTEGER DEFAULT 0,
tool_call_count INTEGER DEFAULT 0,
input_tokens INTEGER DEFAULT 0,
output_tokens INTEGER DEFAULT 0,
cache_read_tokens INTEGER DEFAULT 0,
cache_write_tokens INTEGER DEFAULT 0,
reasoning_tokens INTEGER DEFAULT 0,
billing_provider TEXT,
billing_base_url TEXT,
billing_mode TEXT,
estimated_cost_usd REAL,
actual_cost_usd REAL,
cost_status TEXT,
cost_source TEXT,
pricing_version TEXT,
title TEXT,
FOREIGN KEY (parent_session_id) REFERENCES sessions(id)
);
CREATE INDEX IF NOT EXISTS idx_sessions_source ON sessions(source);
CREATE INDEX IF NOT EXISTS idx_sessions_parent ON sessions(parent_session_id);
CREATE INDEX IF NOT EXISTS idx_sessions_started ON sessions(started_at DESC);
CREATE UNIQUE INDEX IF NOT EXISTS idx_sessions_title_unique
ON sessions(title) WHERE title IS NOT NULL;
Messages Table
CREATE TABLE IF NOT EXISTS messages (
id INTEGER PRIMARY KEY AUTOINCREMENT,
session_id TEXT NOT NULL REFERENCES sessions(id),
role TEXT NOT NULL,
content TEXT,
tool_call_id TEXT,
tool_calls TEXT,
tool_name TEXT,
timestamp REAL NOT NULL,
token_count INTEGER,
finish_reason TEXT,
reasoning TEXT,
reasoning_details TEXT,
codex_reasoning_items TEXT
);
CREATE INDEX IF NOT EXISTS idx_messages_session ON messages(session_id, timestamp);
Notes:
tool_callsis stored as a JSON string (serialized list of tool call objects)reasoning_detailsandcodex_reasoning_itemsare stored as JSON stringsreasoningstores the raw reasoning text for providers that expose it- Timestamps are Unix epoch floats (
time.time())
FTS5 Full-Text Search
CREATE VIRTUAL TABLE IF NOT EXISTS messages_fts USING fts5(
content,
content=messages,
content_rowid=id
);
The FTS5 table is kept in sync via three triggers that fire on INSERT, UPDATE,
and DELETE of the messages table:
CREATE TRIGGER IF NOT EXISTS messages_fts_insert AFTER INSERT ON messages BEGIN
INSERT INTO messages_fts(rowid, content) VALUES (new.id, new.content);
END;
CREATE TRIGGER IF NOT EXISTS messages_fts_delete AFTER DELETE ON messages BEGIN
INSERT INTO messages_fts(messages_fts, rowid, content)
VALUES('delete', old.id, old.content);
END;
CREATE TRIGGER IF NOT EXISTS messages_fts_update AFTER UPDATE ON messages BEGIN
INSERT INTO messages_fts(messages_fts, rowid, content)
VALUES('delete', old.id, old.content);
INSERT INTO messages_fts(rowid, content) VALUES (new.id, new.content);
END;
Schema Version and Migrations
Current schema version: 6
The schema_version table stores a single integer. On initialization,
_init_schema() checks the current version and applies migrations sequentially:
| Version | Change |
|---|---|
| 1 | Initial schema (sessions, messages, FTS5) |
| 2 | Add finish_reason column to messages |
| 3 | Add title column to sessions |
| 4 | Add unique index on title (NULLs allowed, non-NULL must be unique) |
| 5 | Add billing columns: cache_read_tokens, cache_write_tokens, reasoning_tokens, billing_provider, billing_base_url, billing_mode, estimated_cost_usd, actual_cost_usd, cost_status, cost_source, pricing_version |
| 6 | Add reasoning columns to messages: reasoning, reasoning_details, codex_reasoning_items |
Each migration uses ALTER TABLE ADD COLUMN wrapped in try/except to handle
the column-already-exists case (idempotent). The version number is bumped after
each successful migration block.
Write Contention Handling
Multiple hermes processes (gateway + CLI sessions + worktree agents) share one
state.db. The SessionDB class handles write contention with:
- Short SQLite timeout (1 second) instead of the default 30s
- Application-level retry with random jitter (20-150ms, up to 15 retries)
- BEGIN IMMEDIATE transactions to surface lock contention at transaction start
- Periodic WAL checkpoints every 50 successful writes (PASSIVE mode)
This avoids the "convoy effect" where SQLite's deterministic internal backoff causes all competing writers to retry at the same intervals.
_WRITE_MAX_RETRIES = 15
_WRITE_RETRY_MIN_S = 0.020 # 20ms
_WRITE_RETRY_MAX_S = 0.150 # 150ms
_CHECKPOINT_EVERY_N_WRITES = 50
Common Operations
Initialize
from hermes_state import SessionDB
db = SessionDB() # Default: ~/.hermes/state.db
db = SessionDB(db_path=Path("/tmp/test.db")) # Custom path
Create and Manage Sessions
# Create a new session
db.create_session(
session_id="sess_abc123",
source="cli",
model="anthropic/claude-sonnet-4.6",
user_id="user_1",
parent_session_id=None, # or previous session ID for lineage
)
# End a session
db.end_session("sess_abc123", end_reason="user_exit")
# Reopen a session (clear ended_at/end_reason)
db.reopen_session("sess_abc123")
Store Messages
msg_id = db.append_message(
session_id="sess_abc123",
role="assistant",
content="Here's the answer...",
tool_calls=[{"id": "call_1", "function": {"name": "terminal", "arguments": "{}"}}],
token_count=150,
finish_reason="stop",
reasoning="Let me think about this...",
)
Retrieve Messages
# Raw messages with all metadata
messages = db.get_messages("sess_abc123")
# OpenAI conversation format (for API replay)
conversation = db.get_messages_as_conversation("sess_abc123")
# Returns: [{"role": "user", "content": "..."}, {"role": "assistant", ...}]
Session Titles
# Set a title (must be unique among non-NULL titles)
db.set_session_title("sess_abc123", "Fix Docker Build")
# Resolve by title (returns most recent in lineage)
session_id = db.resolve_session_by_title("Fix Docker Build")
# Auto-generate next title in lineage
next_title = db.get_next_title_in_lineage("Fix Docker Build")
# Returns: "Fix Docker Build #2"
Full-Text Search
The search_messages() method supports FTS5 query syntax with automatic
sanitization of user input.
Basic Search
results = db.search_messages("docker deployment")
FTS5 Query Syntax
| Syntax | Example | Meaning |
|---|---|---|
| Keywords | docker deployment | Both terms (implicit AND) |
| Quoted phrase | "exact phrase" | Exact phrase match |
| Boolean OR | docker OR kubernetes | Either term |
| Boolean NOT | python NOT java | Exclude term |
| Prefix | deploy* | Prefix match |
Filtered Search
# Search only CLI sessions
results = db.search_messages("error", source_filter=["cli"])
# Exclude gateway sessions
results = db.search_messages("bug", exclude_sources=["telegram", "discord"])
# Search only user messages
results = db.search_messages("help", role_filter=["user"])
Search Results Format
Each result includes:
id,session_id,role,timestampsnippet— FTS5-generated snippet with>>>match<<<markerscontext— 1 message before and after the match (content truncated to 200 chars)source,model,session_started— from the parent session
The _sanitize_fts5_query() method handles edge cases:
- Strips unmatched quotes and special characters
- Wraps hyphenated terms in quotes (
chat-send→"chat-send") - Removes dangling boolean operators (
hello AND→hello)
Session Lineage
Sessions can form chains via parent_session_id. This happens when context
compression triggers a session split in the gateway.
Query: Find Session Lineage
-- Find all ancestors of a session
WITH RECURSIVE lineage AS (
SELECT * FROM sessions WHERE id = ?
UNION ALL
SELECT s.* FROM sessions s
JOIN lineage l ON s.id = l.parent_session_id
)
SELECT id, title, started_at, parent_session_id FROM lineage;
-- Find all descendants of a session
WITH RECURSIVE descendants AS (
SELECT * FROM sessions WHERE id = ?
UNION ALL
SELECT s.* FROM sessions s
JOIN descendants d ON s.parent_session_id = d.id
)
SELECT id, title, started_at FROM descendants;
Query: Recent Sessions with Preview
SELECT s.*,
COALESCE(
(SELECT SUBSTR(m.content, 1, 63)
FROM messages m
WHERE m.session_id = s.id AND m.role = 'user' AND m.content IS NOT NULL
ORDER BY m.timestamp, m.id LIMIT 1),
''
) AS preview,
COALESCE(
(SELECT MAX(m2.timestamp) FROM messages m2 WHERE m2.session_id = s.id),
s.started_at
) AS last_active
FROM sessions s
ORDER BY s.started_at DESC
LIMIT 20;
Query: Token Usage Statistics
-- Total tokens by model
SELECT model,
COUNT(*) as session_count,
SUM(input_tokens) as total_input,
SUM(output_tokens) as total_output,
SUM(estimated_cost_usd) as total_cost
FROM sessions
WHERE model IS NOT NULL
GROUP BY model
ORDER BY total_cost DESC;
-- Sessions with highest token usage
SELECT id, title, model, input_tokens + output_tokens AS total_tokens,
estimated_cost_usd
FROM sessions
ORDER BY total_tokens DESC
LIMIT 10;
Export and Cleanup
# Export a single session with messages
data = db.export_session("sess_abc123")
# Export all sessions (with messages) as list of dicts
all_data = db.export_all(source="cli")
# Delete old sessions (only ended sessions)
deleted_count = db.prune_sessions(older_than_days=90)
deleted_count = db.prune_sessions(older_than_days=30, source="telegram")
# Clear messages but keep the session record
db.clear_messages("sess_abc123")
# Delete session and all messages
db.delete_session("sess_abc123")
Database Location
Default path: ~/.hermes/state.db
This is derived from hermes_constants.get_hermes_home() which resolves to
~/.hermes/ by default, or the value of HERMES_HOME environment variable.
The database file, WAL file (state.db-wal), and shared-memory file
(state.db-shm) are all created in the same directory.