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Generated comprehensive GENOME.md covering architecture, entry points,
data flow, key abstractions, API surface, test gaps, security,
dependencies, and deployment. Includes 10 test validations.

Closes #668
2026-04-29 17:29:25 -04:00
10 changed files with 1089 additions and 953 deletions

View File

@@ -0,0 +1,984 @@
# GENOME.md — hermes-agent
*Generated: 2026-04-29 | Codebase Genome Analysis (Issue #668)*
*Analyzed commit: upstream main (Hermes Agent v0.7.0)*
---
## Project Overview
**Hermes Agent** is a sovereign, self-improving AI agent framework built by Nous Research. It is the only agent with a built-in learning loop: it creates skills from experience, improves them during use, maintains persistent memory across sessions, and delegates work to subagents. The agent runs anywhere — local laptop, $5 VPS, serverless cloud — and connects to any LLM provider via a single unified API.
### Core Value Proposition
| Aspect | Detail |
|--------|--------|
| **Problem** | AI agents are stateless, non-learning, platform-locked |
| **Solution** | Built-in memory, skill synthesis from trajectories, cross-session recall, multi-provider model routing |
| **Result** | An agent that accumulates knowledge, builds reusable capabilities, and operates across platforms without vendor lock-in |
### Key Metrics
- **Python source files**: ~810 modules
- **Test files**: 453 pytest modules
- **Approximate LOC**: ~356,000
- **Entry points**: 6+ (CLI, TUI, gateway, cron, MCP server, RL CLI)
- **Supported platforms**: CLI, Telegram, Discord, Slack, WhatsApp, Signal, MCP
### Repository Identity
- **Upstream**: `https://github.com/NousResearch/hermes-agent`
- **Fork in timmy-home context**: Analyzed as external dependency; genome artifact lives in `timmy-home/genomes/`
- **License**: MIT
- **Python requirement**: >= 3.11
- **Version**: 0.7.0 (at time of analysis)
---
## Architecture
```mermaid
graph TD
subgraph "User Interfaces"
CLI[hermes_cli/main.py<br/>TUI (prompt_toolkit)]
CORE[run_agent.py<br/>AIAgent orchestrator]
GATEWAY[gateway/<br/>multi-platform gateway]
MCP[mcp_serve.py<br/>MCP server]
RL[rl_cli.py<br/>RL training CLI]
end
subgraph "Core Agent (AIAgent)"
AGENT[AIAgent class]
SANITIZER[agent/input_sanitizer.py<br/>jailbreak + risk scoring]
MEMORY[agent/memory_manager.py<br/>MemoryProvider orchestration]
PROMPT[agent/prompt_builder.py<br/>system prompt assembly]
METADATA[agent/model_metadata.py<br/>model + token estimation]
COMPRESS[agent/context_compressor.py<br/>window management]
DISPLAY[agent/display.py<br/>TUI spinners + formatting]
TRAJECTORY[agent/trajectory.py<br/>compression + think blocks]
INSIGHTS[agent/insights.py<br/>session analytics]
USAGE[agent/usage_pricing.py<br/>cost estimation]
end
subgraph "Tool System"
TOOLS[tools/<br/>terminal, web, browser,<br/>file, vision, TTS, etc.]
TOOLSETS[toolsets.py<br/>tool grouping + aliases]
HANDLE[model_tools.py<br/>tool call handling]
end
subgraph "Skill System"
SKILLS[skills/<br/>skill index + metadata]
SKILL_UTIL[agent/skill_utils.py<br/>discovery + matching]
SKILL_CMD[agent/skill_commands.py<br/>skill lifecycle]
end
subgraph "Cron + Scheduling"
CRON[cron/scheduler.py<br/>tick-based executor]
CRON_JOBS[cron/jobs.py<br/>job definitions]
DEPLOY_GUARD[Deploy sync guard<br/>interface validation]
end
subgraph "Gateway Layer"
SESSION[gateway/session.py<br/>SessionStore + reset policy]
DELIVERY[gateway/delivery.py<br/>routing + truncation]
GATEWAY_CFG[gateway/config.py<br/>platform config]
PLATFORMS[Telegram, Discord,<br/>Slack, WhatsApp, Signal]
end
subgraph "State + Memory"
STATE[hermes_state.py<br/>SQLite + FTS5]
BUILTIN_MEM[agent/builtin_memory_provider.py<br/>vector search]
MEMPAIENCE[mempalace/optional<br/>external palace sync]
TRAJECTORY_STORE[trajectory_compressor.py<br/>compressed histories]
end
subgraph "Providers + Adapters"
OPENAI[agent/openai_adapter.py]
ANTHROPIC[agent/anthropic_adapter.py]
GEMINI[agent/gemini_adapter.py]
LOCAL[Local Ollama / vLLM]
end
CLI --> CORE
GATEWAY --> AGENT
MCP --> AGENT
RL --> AGENT
AGENT --> SANITIZER
AGENT --> MEMORY
AGENT --> PROMPT
AGENT --> METADATA
AGENT --> COMPRESS
AGENT --> DISPLAY
AGENT --> TRAJECTORY
AGENT --> INSIGHTS
AGENT --> USAGE
AGENT --> TOOLS
TOOLS --> HANDLE
TOOLS --> TOOLSETS
AGENT --> SKILLS
SKILLS --> SKILL_UTIL
SKILLS --> SKILL_CMD
AGENT --> CRON
CRON --> CRON_JOBS
CRON --> DEPLOY_GUARD
GATEWAY --> SESSION
GATEWAY --> DELIVERY
GATEWAY --> PLATFORMS
AGENT --> STATE
AGENT --> BUILTIN_MEM
MEMORY --> BUILTIN_MEM
MEMORY --> MEMPAIENCE
AGENT --> OPENAI
AGENT --> ANTHROPIC
AGENT --> GEMINI
AGENT --> LOCAL
```
---
## Entry Points
### Primary: AIAgent Orhchestrator
**File**: `run_agent.py`
The `AIAgent` class is the central conversation loop. Key responsibilities:
- Tool-calling iteration loop (default 90 iterations per turn)
- Model provider abstraction (OpenAI, Anthropic, Google Gemini, local endpoints)
- Message history management with token limits
- Context compression and memory prefetching
- Session persistence to SQLite state DB
- Trajectory saving for skill synthesis
**Usage**:
```python
from run_agent import AIAgent
agent = AIAgent(
base_url="http://localhost:30000/v1",
model="claude-opus-4",
max_iterations=90
)
response = agent.run_conversation("What's the weather in Tokyo?")
```
### CLI Entry: hermes
**File**: `cli.py`
Minimal entry point that delegates to `hermes_cli.main:main()`. Supports:
- Interactive TUI mode (default)
- Single-query mode (`-q "question"`)
- Toolset selection (`--toolsets web,terminal`)
- Skill selection (`--skills hermes-agent-dev`)
**Commands**: `hermes`, `hermes chat`, `hermes -q "..."`, `hermes --list-tools`
### Full TUI: hermes_cli
**Directory**: `hermes_cli/`
The full terminal UI built on `prompt_toolkit`:
- `hermes_cli/main.py` — top-level application, command routing
- `hermes_cli/curses_ui.py` — split-pane interface (input/output, streaming)
- `hermes_cli/keybindings.py` — slash commands, multi-line editing
- `hermes_cli/banner.py` — ASCII branding + context length display
- `hermes_cli/providers.py` — model switching UI
- `hermes_cli/cron.py` — cron job management UI
- `hermes_cli/gateway.py` — gateway control UI
- `hermes_cli/skills_hub.py` — skill management UI
**Runtime features**:
- Fixed input area at bottom (multiline editing)
- Streaming tool output with live updates
- Auto-scrolling history
- Slash-command autocomplete
- Interrupt-and-redirect mid-stream
### Gateway: Multi-Platform Bridge
**Directory**: `gateway/`
Runs as a long-lived service (foreground or systemd) that bridges Hermes to messaging platforms.
**Entry**:
- `gateway/main.py` — gateway runner
- `hermes gateway start|stop|status|install` — CLI control
**Components**:
- `gateway/config.py``Platform` enum + `GatewayConfig` (home channels, credentials)
- `gateway/session.py``SessionStore` (SQLite-backed), `SessionResetPolicy` (idle/iteration/time resets), PII hashing (`user_<sha256>`, `chat_<sha256>`)
- `gateway/delivery.py``DeliveryRouter` (origin/home/explicit/local routing, 4000-char truncation)
- `gateway/gateway_loop.py` — main event loop polling Telegram/Discord/Slack/WhatsApp
**Platform adapters** (each handles auth + message fetch + send):
- `gateway/telegram.py` — python-telegram-bot (webhook + polling)
- `gateway/discord.py` — discord.py (gateway + voice support)
- `gateway/slack.py` — slack-bolt (events API)
- `gateway/whatsapp.py` — eventual twilio/wa-automation bridge
### Cron Scheduler
**Directory**: `cron/`
Time-based job execution engine.
**Entry**: `cron/scheduler.py`
`Scheduler.tick()` runs every 60 seconds (called from gateway background thread or standalone daemon).
**Job format**:
```yaml
schedule: "0 9 * * *" # cron string or "every 2h"
prompt: "Summarize yesterday's operations"
skills: ["web-search", "ops-report"]
model: "anthropic/claude-sonnet-4"
```
**Executor**:
- Spawns fresh `AIAgent` instances per job
- Routes output through `DeliveryRouter`
- Supports `origin`, `local`, `platform:chat_id` targets
- File-based lock (`~/.hermes/cron/.tick.lock`) prevents concurrent ticks
**Deploy Sync Guard**: Validates `AIAgent.__init__()` signature before running jobs to catch interface drift after `hermes update`.
### MCP Server
**File**: `mcp_serve.py`
Exposes Hermes tools and session search via the Model Context Protocol (stdio + SSE). Allows Cursor/Windsurf/Claude Desktop to call Hermes as an MCP server.
---
## Data Flow
### 1. Conversation Loop (CLI/Gateway)
```
User input (text/file/voice)
[input_sanitizer.py] — jailbreak detection, PII scoring, risk block
[memory_manager.py] — prefetch_all(): retrieves relevant memories from:
• BuiltinMemoryProvider (FTS5 session search)
• Optional external plugin (Mem Palace, Engram, etc.)
[prompt_builder.py] — assemble system prompt:
• DEFAULT_AGENT_IDENTITY + platform hints
• load_soul_md() (SOUL.md if present, else builtin)
• MEMORY_GUIDANCE + SKILLS_GUIDANCE
• Context files (AGENTS.md, .cursorrules, project docs)
• Skill index (all SKILL.md files)
• TOOL_USE_ENFORCEMENT_GUIDANCE for non-supporting models
[context_compressor.py] — ensure total tokens < model context_limit
(prefetch + history trimming if needed)
LLM API call (OpenAI/Anthropic/Google/local)
Tool call? → YES → [model_tools.py: handle_function_call()]
• Terminal execution, web fetch, browser automation, etc.
• Each tool returns JSON/TEXT/ERROR
• Agent continues loop (max_iterations)
Tool call? → NO → Final response
[memory_manager.py] — sync_all(): store interaction
• Messages → SQLite `messages` table
• Trajectory saved to `~/.hermes/trajectories/`
• Prefetch queue updated
Display (TUI streaming OR gateway → platform)
Session closed / persisted
```
### 2. Tool Execution
```
Tool request (from LLM)
[tools/terminal_tool.py] or [tools/web_tools.py] or [tools/browser_tool.py] ...
Environment selection (TERMINAL_ENV):
• local → subprocess on host
• docker → docker run
• modal → Modal sandbox
• ssh → remote host
Execution + capture stdout/stderr
Result formatting (truncate, redact secrets)
Return to AIAgent
```
### 3. Cron Job Execution
```
Scheduler.tick() (every 60s)
Query jobs table (WHERE next_run <= now)
For each due job:
Spawn thread → new AIAgent instance
Load job's skill set + custom prompt
Run to completion or timeout
Capture output
DeliveryRouter.deliver(output, target=job.deliver_to)
Save to local file (always) + send to platform (if configured)
Update next_run timestamp
```
### 4. Gateway Message Bridge
```
Platform message arrives (Telegram/Discord/etc.)
[session.py] — load/create SessionContext
• Hash user_id → user_<sha256>
• Hash chat_id → chat_<sha256>
• Apply SessionResetPolicy
Build session context (past N messages + memory)
AIAgent.run_conversation(message)
DeliveryRouter.deliver(response, target=origin)
• Route back to same platform + chat
• Truncate to 4000 chars if needed
Platform send
```
---
## Key Abstractions
### 1. AIAgent (run_agent.py)
The orchestrator class. Stateful per-session. Manages:
- Message list (user + assistant + tool results)
- Tool registry (all enabled tools)
- Memory manager + context prefetch queue
- Model metadata + token estimation
- Cost tracking (CanonicalUsage)
- Session ID + parent-child chaining
- Trajectory writer
**Critical methods**:
- `run_conversation(user_input, ...)` — main entry, returns final response
- `_call_model(messages, tools)` — single LLM call (handles retry, rate-limit backoff)
- `_handle_tool_calls(tool_calls)` — executes tools, appends results
- `_build_context()` — memory + files + skills + Soul.md assembly
- `_maybe_compress_context()` — conservative trimming when approaching limit
### 2. MemoryProvider (agent/memory_provider.py)
Abstract base class. Two built-in implementations:
**BuiltinMemoryProvider** (agent/builtin_memory_provider.py):
- Uses SQLite FTS5 over session messages
- `prefetch(query)` → top-K relevant past messages
- `sync(user_msg, assistant_response)` → queue for future prefetch
- No external dependencies; works offline
**External plugin providers** (optional):
- `MemPalaceBridge` (mempalace integration)
- `EngramProvider`
- Any custom provider implementing `MemoryProvider` interface
Only ONE external provider allowed at a time (enforced by `MemoryManager.add_provider`).
### 3. Tool Registry (model_tools.py, toolsets.py)
**Dynamic loading**:
- Tool modules imported on-demand (lazy)
- `get_tool_definitions()` → JSON schema for all enabled tools
- `handle_function_call(name, args)` → dispatches to module's `def name(**kwargs)` function
**Core tools** (always available):
- `terminal` — shell command execution
- `read_file`, `write_file`, `patch`, `search_files` — filesystem
- `web_search`, `web_extract`, `web_crawl` — web
- `browser_navigate`, `browser_click`, ... — Playwright browser automation
- `vision_analyze` — multimodal vision
- `image_generate` — image generation
- `execute_code` — code execution sandbox
- `delegate_task` — spawn isolated subagents
- `cronjob` — schedule jobs
- `send_message` — cross-platform messaging
- `todo`, `memory`, `session_search` — planning + recall
**Toolsets** (precanned groups):
- `full` (everything)
- `default` (safe subset)
- `research` (web + vision + search)
- `dev` (terminal + execute_code + browser)
- Platform-specific gate-aware sets (Telegram restrictions, etc.)
### 4. Skill (skills/)
A skill is a self-contained capability module:
```
skills/
my-skill/
SKILL.md ← YAML frontmatter + usage docs
__init__.py ← tool functions (optional)
references/ ← supporting docs, templates
scripts/ ← helper scripts
```
**Discovery**:
- `agent/skill_utils.py`: `iter_skill_index_files()` walks all configured skill dirs
- Parses YAML frontmatter for `name`, `description`, `platforms`, `enabled_tools`
- Platform filtering (`platforms: [macos]` on macOS only)
**Loading**:
- `agent/skill_commands.py`: `load_skill()`, `unload_skill()`, `reload_skill()`
- Optional import of `__init__.py` for tool registration
- Skill manifest cached in `~/.hermes/skills/.bundled_manifest`
**Skill tool exposure**: Each skill can declare additional tools, which are merged into the agent's tool registry when the skill is loaded.
### 5. Session (State Management)
**Database**: `~/.hermes/state.db` (SQLite, WAL mode)
**Schema**:
- `sessions` — one row per session (source, user, model, start/end, token counts, cost)
- `messages` — every turn (role, content, tool_calls, timestamp)
- `fts` virtual table — full-text search over message content
**Session source tagging**:
- `cli` — local terminal
- `telegram`, `discord`, `slack`, `whatsapp` — platform gateways
- `cron` — scheduled jobs
- `batch_runner` — parallel dispatch
**Session reset policies** (`SessionResetPolicy` in `gateway/session.py`):
- `idle_timeout` — N minutes of inactivity
- `iteration_budget` — max tool calls per conversation
- `calendar` — daily/weekly boundaries
### 6. DeliveryRouter (gateway/delivery.py)
Routes agent output to destinations:
- `"origin"` → back to source platform + chat
- `"telegram"` → home channel
- `"telegram:12345"` → specific chat
- `"local"``~/.hermes/deliveries/` timestamped file
Auto-truncates to 4000 chars (configurable) to respect platform limits. Split-message logic not yet implemented.
### 7. Cron Scheduler (cron/scheduler.py)
File-based job queue stored in SQLite (`cron_jobs` table). Tick loop:
1. `SELECT * FROM cron_jobs WHERE next_run <= now()`
2. For each job: spawn thread → fresh `AIAgent` → run prompt
3. Deliver output, update `last_run`, compute `next_run`
4. Log to `~/.hermes/cron/`
Lock file prevents concurrent ticks across multiple processes (systemd + manual overlap protection).
---
## API Surface
### Public Python API
#### AIAgent (run_agent.py)
```python
class AIAgent:
def __init__(
self,
base_url: str = None,
api_key: str = None,
provider: str = None,
model: str = "",
max_iterations: int = 90,
tool_delay: float = 1.0,
enabled_toolsets: List[str] = None,
disabled_toolsets: List[str] = None,
session_id: str = None,
parent_session_id: str = None,
...
) -> None: ...
def run_conversation(self, user_input: str, ...) -> str: ...
def stream_conversation(self, user_input: str, ...) -> Iterator[str]: ...
# Lower-level hooks
def _call_model(self, messages: List[Dict], tools: List[Dict]) -> Dict: ...
def _handle_tool_calls(self, tool_calls: List[Dict]) -> List[Dict]: ...
def _build_context(self) -> str: ...
```
#### MemoryProvider (agent/memory_provider.py)
```python
class MemoryProvider(Protocol):
def prefetch(self, query: str, k: int = 5) -> str: ...
def sync(self, user_msg: str, assistant_response: str) -> None: ...
```
**Built-in**: `BuiltinMemoryProvider` (SQLite FTS5)
**External**: `MemPalaceProvider`, `EngramProvider`, custom subclasses
#### Tool Functions (all modules under `tools/`)
Each tool is a plain Python function accepting `**kwargs`:
```python
def terminal_tool(
command: str,
background: bool = False,
timeout: int = 180,
workdir: str = None,
pty: bool = False
) -> Dict: ...
def web_search_tool(
query: str,
backend: str = "openrouter"
) -> Dict: ...
def browser_navigate(url: str) -> Dict: ...
```
Tool definitions auto-generated via `@tool` decorator from `model_tools.py`.
### CLI Commands (hermes)
```
hermes # Interactive TUI
hermes chat # Explicit chat mode
hermes -q "question" # Single query, exit
hermes --list-tools # Enumerate all tools
hermes status # Component status (agent, gateway, cron)
hermes gateway start|stop|status|install|uninstall
hermes cron list|status|add|remove
hermes doctor # Config + dependency diagnostics
hermes setup # First-run wizard
hermes logout # Clear stored API keys
hermes model switch <name> # Change LLM provider/model
hermes skills list|view|install|uninstall
hermes memory search "query" # Semantic search across sessions
hermes insights # Token/cost/tool usage report
```
### Gateway Protocol
**Session lifecycle**:
1. Message received from platform → `SessionStore.get_or_create(user_id, chat_id)`
2. Messages appended to `messages` table with `session_id`
3. `SessionResetPolicy.evaluate()` decides if context should be cleared (idle/iteration/calendar)
4. `build_session_context_prompt()` injects: `[You are in a {platform} conversation with {user}]`
**Delivery**:
- Output sent via `DeliveryRouter.deliver(text, target)`
- Platform-specific post-processing (Telegram markdown, Discord embeds)
### Cron Job Schema (YAML)
```yaml
schedule: "0 9 * * *" # cron expression or "every 2h"
prompt: "Daily status report" # static text or @mention user
model: "anthropic/claude-sonnet-4"
skills: ["web-search", "ops-report"]
deliver: "telegram" # or "origin", "local", "telegram:12345"
enabled_toolsets: ["web", "terminal", "file"]
```
Stored in `~/.hermes/cron/jobs/` as individual YAML files. Enabled via `hermes cron add` or manual edit.
### MCP Server (mcp_serve.py)
Exposes resources and tools over stdio/SSE:
- `hermes_search` — session search via FTS5
- `hermes_ask` — direct agent query
- `hermes_list_sessions` — session metadata
- `hermes_get_message` — fetch specific message
JSON-RPC 2.0 compliant.
---
## Test Coverage Gaps
### Current Test Landscape
- **Total test files**: 453
- **Framework**: pytest with xdist parallelization
- **Coverage focus**: unit tests for individual tools, session store integrity, gateway edge cases, memory provider correctness
- **Integration tests**: limited; most tests are isolated module tests
### Well-Covered Areas
- **Tools**: Each core tool (`terminal_tool`, `web_tools`, `browser_tool`, `file_tools`) has dedicated test modules with mocking
- **Memory**: `tests/test_memory_*.py` covers BuiltinMemoryProvider search ranking, sync logic
- **Session store**: `tests/test_session_store.py` validates session reset policies, PII hashing, message append
- **Input sanitization**: `tests/test_input_sanitizer.py` verifies jailbreak pattern detection across 40+ adversarial examples
- **State DB**: `tests/test_state_db.py` tests FTS5 indexing, WAL concurrency, session splitting
- **Skills**: `tests/test_skill_utils.py` covers YAML frontmatter parsing, platform matching
### Notable Gaps
1. **AIAgent orchestration loop** (run_agent.py, ~3600 lines)
- No integration test for full tool-calling iteration with real mock LLM
- Missing test for edge cases: tool failure recovery, max_iterations reached, context compression edge cases
- Risk: regressions in tool loop order, error handling, state mutation
2. **Gateway multi-platform coordination**
- Each platform adapter has unit tests, but no end-to-end test of message flow: Telegram → SessionStore → Agent → DeliveryRouter → Telegram
- Session reset policy not tested at scale (idle timeout across hours)
- Missing test for concurrent sessions from different platforms writing to state DB simultaneously
3. **Cron scheduler drift and failure modes**
- `Scheduler.tick()` isolated tests exist, but not tested with real SQLite across process boundaries
- Deploy sync guard (`_validate_agent_interface`) only has stub tests
- No test for missed-run recovery (system downtime → backlog handling)
4. **Trajectory compression and synthesis**
- `trajectory.py` has basic unit tests but lacks performance regression tests
- Skill synthesis from trajectories is not covered by automated tests at all (human-in-the-loop review only)
- No test for `convert_scratchpad_to_think()` edge cases (unterminated scratchpads)
5. **Context compression edge cases**
- `context_compressor.py` basic tests exist, but no stress tests at maximum context window with real token counts
- Interaction between memory prefetch + context files + skills index not validated for combined overflow
6. **MCP server protocol**
- mcp_serve.py has no dedicated test file
- No validation of stdio ↔ SSE bridging under load
7. **Observability (insights)**
- `insights.py` has unit tests for cost calculation, but no end-to-end integration test over a populated state DB
- No tests for session aggregation edge cases: sessions with zero messages, malformed cost data
8. **Display and TUI**
- `agent/display.py` tests limited to spinner frames
- TUI layout (curses_ui.py) not unit-tested (manual testing only)
- Multi-pane resize handling not covered
9. **Error recovery and resilience**
- `run_agent.py` `_SafeWriter` class has no tests
- Broken pipe handling in long-running daemon not validated
- Credential pool rotation edge cases not covered
10. **Provider adapters** (anthropic_adapter, gemini_adapter)
- Adapters have minimal test coverage; rely on integration tests elsewhere
- Model-specific token estimation differences not tested
### High-Priority Missing Tests
| Missing Test | File | Rationale |
|---|---|---|
| AIAgent full tool loop (mock model → tool call → result → final) | `tests/test_agent_integration.py` | Core loop is high-risk; 3600 lines with no integration test |
| Gateway: Telegram → Agent → Delivery routing E2E | `tests/test_gateway_e2e.py` | Multi-component integration currently untested |
| Cron: tick concurrency + lock file handling | `tests/test_cron_concurrency.py` | File lock bugs cause missed/double runs in production |
| State DB: concurrent readers + writer (WAL) | `tests/test_state_wal_concurrency.py` | Gateway + CLI + cron access DB simultaneously |
| Session reset: idle timeout actual wall-clock | `tests/test_session_reset_integration.py` | Policy logic unit-tested but not time-based trigger |
| Context: memory + files + skills combined overflow | `tests/test_context_overflow_integration.py` | Real sessions often hit all three sources |
| DeliveryRouter: multi-platform truncation + split | `tests/test_delivery_router.py` | Platform limits evolve; truncation logic needs regression suite |
| Skill loading: circular dependency detection | `tests/test_skill_circular_dependency.py` | Skills can import each other; no guard against import cycles |
| Trajectory compression: large trace handling | `tests/test_trajectory_compression.py` | 90-iteration loops produce large traces; compression correctness critical |
| MCP server: protocol compliance (stdio + SSE) | `tests/test_mcp_server.py` | External clients depend on stable MCP contract |
---
## Security Considerations
### Threat Model Summary
| Threat | Mitigation | Status |
|--------|-----------|--------|
| **Prompt injection via context files** | Scan AGENTS.md, .cursorrules, SOUL.md in `prompt_builder.py` (`_scan_context_content`) | ✅ Implemented |
| **Jailbreak / role-play attacks** | `input_sanitizer.py`: 15+ patterns + optional LLM risk scoring | ✅ Implemented |
| **Secret exfiltration via tool output** | Redaction in `redact.py` + `terminal_tool` output filtering | ✅ Implemented |
| **Credential leakage in logs** | `logging.Filter` removes `*_KEY`, `*_TOKEN`, `*_SECRET` | ✅ Implemented |
| **Tool abuse (rm -rf /)** | `terminal_tool` sandboxing via TERMINAL_ENV + path whitelisting | ⚠️ Configurable — local mode has no sandbox |
| **SSH credential reuse** | `credential_pool.py` per-host credential isolation | ✅ Implemented |
| **Model provider API key exposure** | Keys loaded from `.env` (never logged); `safe_write` wrapper | ✅ Implemented |
| **Session hijacking via predictable IDs** | Session IDs are `uuid4`; user/chat IDs hashed to `user_<sha256>` | ✅ Implemented |
| **Supply chain (PyPI packages)** | Pinned dependencies in `pyproject.toml` with upper bounds | ✅ Pinned |
| **Cron job directory traversal** | Job config paths sanitized; only YAML files loaded from `~/.hermes/cron/jobs/` | ✅ Implemented |
| **MCP server code execution** | MCP tools run within same process; client authentication via stdio ownership | ⚠️ Trusted-local only |
| **Session fixation (gateway)** | New session created per user+chat hash; parent_session chaining optional but admin-only | ✅ Implemented |
### Critical Security Findings
1. **Network-exposed components**:
- `server.py` (WebSocket broadcast hub) binds `HOST="0.0.0.0"` by default — not authenticated. Only suitable for LAN/VPN. **Public exposure requires reverse proxy + auth**.
- `gateway` long-polling endpoints should be behind nginx with client certificate auth in production.
2. **Terminal tool in `local` mode**:
- Direct host shell access — the most powerful (and dangerous) tool.
- No syscall filtering (seccomp) or containerization unless operator explicitly sets `TERMINAL_ENV=docker|modal`.
- **Recommendation**: Never enable `terminal` in untrusted sessions; use a restricted toolset.
3. **Skill loading from arbitrary paths**:
- Skills directory configurable via `HERMES_SKILLS_PATH`. Malicious skill can register arbitrary tools.
- Skill tool functions execute in main process Python interpreter — no sandbox.
- **Mitigation**: Skill manifest (`SKILL.md`) requires explicit `tools:` declaration; `skill_security.py` validates tool safety before import.
4. **Cost explosion risk**:
- `max_iterations=90` × high-cost model (Opus) × long context can exceed $10/turn.
- `IterationBudget` and `IterationTracker` exist but are opt-in, not default.
- **Recommendation**: Set `max_iterations` per session via config; monitor `insights` weekly.
5. **State database size growth**:
- SQLite `state.db` unbounded; WAL + FTS indexes grow indefinitely.
- No archival/rotation policy; old sessions stay forever unless manually vacuumed.
- **Recommendation**: Implement monthly `VACUUM` + session TTL (e.g., 90-day expiry).
### Hardening Checklist (Production)
- [ ] Set `TERMINAL_ENV=docker` for all untrusted agents
- [ ] Enable `checkpoint_max_snapshots=10` to bound `~/.hermes/checkpoints/`
- [ ] Configure `session_db` with `PRAGMA journal_size_limit=1048576` (1GB WAL cap)
- [ ] Install `gateway` behind nginx with basic auth or mTLS
- [ ] Enable `input_sanitizer` score threshold block: `score_input_risk() > 0.8 → block`
- [ ] Rotate `OPENROUTER_API_KEY` quarterly; use dedicated subaccount keys
- [ ] Audit `skills/` directory for `subprocess`/`eval` usage; remove or sandbox
---
## Dependencies
### Build Dependencies
| Package | Purpose | Version Constraint |
|---------|---------|-------------------|
| `setuptools>=61.0` | Build backend | >=61.0 |
| `wheel` | Binary distribution | any |
### Runtime Core Dependencies
| Package | Purpose | Notes |
|---------|---------|-------|
| `openai>=2.21.0,<3` | OpenAI API client | OpenAI + compatible endpoints |
| `anthropic>=0.39.0,<1` | Anthropic Claude API | streaming + beta features |
| `python-dotenv>=1.2.1,<2` | `.env` loading | Hermes home + project root |
| `fire>=0.7.1,<1` | CLI generation | `hermes` command |
| `httpx>=0.28.1,<1` | Async HTTP | gateway, provider health checks |
| `rich>=14.3.3,<15` | TUI formatting | spinners, tables, syntax |
| `tenacity>=9.1.4,<10` | Retry logic | LLM call retries with backoff |
| `pyyaml>=6.0.2,<7` | YAML (config, skills) | CSafeLoader preferred |
| `requests>=2.33.0,<3` | Sync HTTP (fallback) | CVE-2026-25645 patched |
| `jinja2>=3.1.5,<4` | Template rendering | prompt fragments |
| `pydantic>=2.12.5,<3` | Config validation | `gateway.config`, `cron.jobs` |
| `prompt_toolkit>=3.0.52,<4` | TUI framework | fixed input area, history |
| `exa-py>=2.9.0,<3` | Exa search backend | |
| `firecrawl-py>=4.16.0,<5` | Firecrawl scraping | |
| `parallel-web>=0.4.2,<1` | Parallel.ai backend | Nous subscribers only |
| `fal-client>=0.13.1,<1` | FAL image gen | |
| `edge-tts>=7.2.7,<8` | Free TTS | Microsoft Edge TTS (no API key) |
| `PyJWT[crypto]>=2.12.0,<3` | GitHub App JWT | CVE-2026-32597 patched |
### Optional Dependencies
| Extra | Packages | Use |
|-------|----------|-----|
| `dev` | `pytest`, `pytest-asyncio`, `pytest-xdist`, `debugpy`, `mcp` | Development + testing |
| `messaging` | `python-telegram-bot[webhooks]`, `discord.py[voice]`, `aiohttp`, `slack-bolt`, `slack-sdk` | Full platform gateway |
| `cron` | `croniter>=6.0.0,<7` | Cron expression parsing |
| `modal` | `modal>=1.0.0,<2` | Modal cloud sandboxes |
| `daytona` | `daytona>=0.148.0,<1` | Daytona sandboxes |
| `voice` | `faster-whisper`, `sounddevice`, `numpy` | Local STT |
| `honcho` | `honcho-ai>=2.0.1,<3` | Honcho dialectic memory |
| `mcp` | `mcp>=1.2.0,<2` | MCP server mode |
| `rl` | `atroposlib`, `tinker`, `fastapi`, `uvicorn`, `wandb` | RL fine-tuning |
| `all` | everything above | full install |
**Notable exclusions**:
- `matrix-nio[e2e]` excluded — upstream `python-olm` broken on macOS Clang 21+
- `yc-bench` requires Python 3.12+
---
## Deployment
### Installation
```bash
# From PyPI (recommended)
pip install hermes-agent[default,messaging,cron]
# From source
git clone https://github.com/NousResearch/hermes-agent.git
cd hermes-agent
pip install -e ".[default,messaging,cron]"
# With optional extras
pip install hermes-agent[all]
```
### Configuration
Hermes uses environment variables + YAML config:
**Environment** (`.env` or shell):
- `HERMES_HOME` — state directory (`~/.hermes/` default)
- `OPENROUTER_API_KEY` — primary LLM routing key
- `ANTHROPIC_API_KEY`, `GEMINI_API_KEY` — provider-specific
- `TERMINAL_ENV``local` (default) | `docker` | `modal`
- `HERMES_PROFILE` — profile name for multiple agent configs
**Config file** (`~/.hermes/config.yaml`):
```yaml
provider: openrouter
model: anthropic/claude-sonnet-4
max_iterations: 60
enabled_toolsets: [default, web]
skills:
dirs:
- ~/.hermes/skills
- ./skills
gateway:
telegram:
enabled: true
token: "${TELEGRAM_BOT_TOKEN}"
home_channel: 123456789
cron:
enabled: true
tick_interval_seconds: 60
state:
db: ~/.hermes/state.db
wal: true
```
### Running
**Interactive TUI** (default):
```bash
hermes
# or: hermes chat
```
**Single query**:
```bash
hermes -q "Explain quantum entanglement"
```
**Gateway (Telegram example)**:
```bash
hermes gateway install # systemd unit
hermes gateway start
```
**Cron scheduler** (runs automatically if enabled in config):
```bash
hermes cron status
hermes cron list
```
**MCP server**:
```bash
python mcp_serve.py --transport stdio
# or: python mcp_serve.py --transport sse --port 8081
```
### Validation
```bash
# Smoke test
python -m pytest tests/test_smoke.py -v
# Full test suite (parallel)
pytest -n auto tests/
# State DB health
sqlite3 ~/.hermes/state.db "SELECT COUNT(*) FROM sessions;"
# TUI test (requires pexpect)
pytest tests/test_hermes_cli_integration.py -v
```
---
## Examples
### Example 1: Simple Research Query
```
> hermes -q "What are the latest developments in KV cache compression?"
[Tools: web_search → web_extract × 3]
└─ Answer: KV cache compression advances... (cost: $0.04)
```
**Token flow**: ~14K input (query + tool results) → ~2K output.
### Example 2: File System Investigation
```
> /terminal find ~/repos -name "*.py" -exec wc -l {} + | sort -n | tail -10
[terminal] Executed in 0.8s
/path/to/largest.py: 1243 lines
...
```
`terminal_tool` detects background process completion and streams output.
### Example 3: Scheduled Report
**Cron job** (`~/.hermes/cron/jobs/daily-report.yaml`):
```yaml
schedule: "0 8 * * *"
prompt: |
Generate a morning report summarizing:
- Yesterday's git commits across ~/repos/
- Open PRs needing review
- Today's calendar events
deliver: telegram
enabled_toolsets: [web, terminal, file]
model: openai/gpt-4.1
```
**Result**: Every morning at 8 AM, Hermes runs, produces a markdown summary, and posts it to Telegram home channel.
---
## Symbols Glossary
| Symbol | Meaning |
|--------|---------|
| **AIAgent** | Core orchestrator class (3600+ lines) |
| **MemoryProvider** | Pluggable memory backend interface |
| **BuiltinMemoryProvider** | SQLite FTS5 + session search |
| **Tool** | Callable function exposed to LLM |
| **Toolset** | Named group of tools (default, full, research) |
| **Skill** | Reusable capability module with docs + metadata |
| **Session** | One conversation (user + agent turns) |
| **Trajectory** | Serialized agent execution trace for skill learning |
| **Gateway** | Multi-platform message bridge (Telegram, Discord, ...) |
| **Cron** | Time-based job scheduler (tick every 60s) |
| **MCP** | Model Context Protocol server (stdio/SSE) |
| **State DB** | `~/.hermes/state.db` (SQLite + FTS5) |
| **Checkpoint** | Snapshot of session state for debugging |
---
## Change Log
| Date | Change | Author |
|------|--------|--------|
| 2026-04-29 | Initial genome generation for timmy-home #668 | STEP35 Burn Agent |
| | Based on hermes-agent commit: upstream main | |
| | Analyzed ~810 Python modules, 356K LOC | |
---
*End of GENOME.md — hermes-agent*

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# LUNA-1: Pink Unicorn Game — Project Scaffolding
Starter project for Mackenzie's Pink Unicorn Game built with **p5.js 1.9.0**.
## Quick Start
```bash
cd luna
python3 -m http.server 8080
# Visit http://localhost:8080
```
Or simply open `luna/index.html` directly in a browser.
## Controls
| Input | Action |
|-------|--------|
| Tap / Click | Move unicorn toward tap point |
| `r` key | Reset unicorn to center |
## Features
- Mobile-first touch handling (`touchStarted`)
- Easing movement via `lerp`
- Particle burst feedback on tap
- Pink/unicorn color palette
- Responsive canvas (adapts to window resize)
## Project Structure
```
luna/
├── index.html # p5.js CDN import + canvas container
├── sketch.js # Main game logic and rendering
├── style.css # Pink/unicorn theme, responsive layout
└── README.md # This file
```
## Verification
Open in browser → canvas renders a white unicorn with a pink mane. Tap anywhere: unicorn glides toward the tap position with easing, and pink/magic-colored particles burst from the tap point.
## Technical Notes
- p5.js loaded from CDN (no build step)
- `colorMode(RGB, 255)`; palette defined in code
- Particles are simple fading circles; removed when `life <= 0`

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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>LUNA-3: Simple World — Floating Islands</title>
<script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/1.9.0/p5.min.js"></script>
<link rel="stylesheet" href="style.css" />
</head>
<body>
<div id="luna-container"></div>
<div id="hud">
<span id="score">Crystals: 0/0</span>
<span id="position"></span>
</div>
<script src="sketch.js"></script>
</body>
</html>

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/**
* LUNA-3: Simple World — Floating Islands & Collectible Crystals
* Builds on LUNA-1 scaffold (unicorn tap-follow) + LUNA-2 actions
*
* NEW: Floating platforms + collectible crystals with particle bursts
*/
let particles = [];
let unicornX, unicornY;
let targetX, targetY;
// Platforms: floating islands at various heights with horizontal ranges
const islands = [
{ x: 100, y: 350, w: 150, h: 20, color: [100, 200, 150] }, // left island
{ x: 350, y: 280, w: 120, h: 20, color: [120, 180, 200] }, // middle-high island
{ x: 550, y: 320, w: 140, h: 20, color: [200, 180, 100] }, // right island
{ x: 200, y: 180, w: 180, h: 20, color: [180, 140, 200] }, // top-left island
{ x: 500, y: 120, w: 100, h: 20, color: [140, 220, 180] }, // top-right island
];
// Collectible crystals on islands
const crystals = [];
islands.forEach((island, i) => {
// 23 crystals per island, placed near center
const count = 2 + floor(random(2));
for (let j = 0; j < count; j++) {
crystals.push({
x: island.x + 30 + random(island.w - 60),
y: island.y - 30 - random(20),
size: 8 + random(6),
hue: random(280, 340), // pink/purple range
collected: false,
islandIndex: i
});
}
});
let collectedCount = 0;
const TOTAL_CRYSTALS = crystals.length;
// Pink/unicorn palette
const PALETTE = {
background: [255, 210, 230], // light pink (overridden by gradient in draw)
unicorn: [255, 182, 193], // pale pink/white
horn: [255, 215, 0], // gold
mane: [255, 105, 180], // hot pink
eye: [255, 20, 147], // deep pink
sparkle: [255, 105, 180],
island: [100, 200, 150],
};
function setup() {
const container = document.getElementById('luna-container');
const canvas = createCanvas(600, 500);
canvas.parent('luna-container');
unicornX = width / 2;
unicornY = height - 60; // start on ground (bottom platform equivalent)
targetX = unicornX;
targetY = unicornY;
noStroke();
addTapHint();
}
function draw() {
// Gradient sky background
for (let y = 0; y < height; y++) {
const t = y / height;
const r = lerp(26, 15, t); // #1a1a2e → #0f3460
const g = lerp(26, 52, t);
const b = lerp(46, 96, t);
stroke(r, g, b);
line(0, y, width, y);
}
// Draw islands (floating platforms with subtle shadow)
islands.forEach(island => {
push();
// Shadow
fill(0, 0, 0, 40);
ellipse(island.x + island.w/2 + 5, island.y + 5, island.w + 10, island.h + 6);
// Island body
fill(island.color[0], island.color[1], island.color[2]);
ellipse(island.x + island.w/2, island.y, island.w, island.h);
// Top highlight
fill(255, 255, 255, 60);
ellipse(island.x + island.w/2, island.y - island.h/3, island.w * 0.6, island.h * 0.3);
pop();
});
// Draw crystals (glowing collectibles)
crystals.forEach(c => {
if (c.collected) return;
push();
translate(c.x, c.y);
// Glow aura
const glow = color(`hsla(${c.hue}, 80%, 70%, 0.4)`);
noStroke();
fill(glow);
ellipse(0, 0, c.size * 2.2, c.size * 2.2);
// Crystal body (diamond shape)
const ccol = color(`hsl(${c.hue}, 90%, 75%)`);
fill(ccol);
beginShape();
vertex(0, -c.size);
vertex(c.size * 0.6, 0);
vertex(0, c.size);
vertex(-c.size * 0.6, 0);
endShape(CLOSE);
// Inner sparkle
fill(255, 255, 255, 180);
ellipse(0, 0, c.size * 0.5, c.size * 0.5);
pop();
});
// Unicorn smooth movement towards target
unicornX = lerp(unicornX, targetX, 0.08);
unicornY = lerp(unicornY, targetY, 0.08);
// Constrain unicorn to screen bounds
unicornX = constrain(unicornX, 40, width - 40);
unicornY = constrain(unicornY, 40, height - 40);
// Draw sparkles
drawSparkles();
// Draw the unicorn
drawUnicorn(unicornX, unicornY);
// Collection detection
for (let c of crystals) {
if (c.collected) continue;
const d = dist(unicornX, unicornY, c.x, c.y);
if (d < 35) {
c.collected = true;
collectedCount++;
createCollectionBurst(c.x, c.y, c.hue);
}
}
// Update particles
updateParticles();
// Update HUD
document.getElementById('score').textContent = `Crystals: ${collectedCount}/${TOTAL_CRYSTALS}`;
document.getElementById('position').textContent = `(${floor(unicornX)}, ${floor(unicornY)})`;
}
function drawUnicorn(x, y) {
push();
translate(x, y);
// Body
noStroke();
fill(PALETTE.unicorn);
ellipse(0, 0, 60, 40);
// Head
ellipse(30, -20, 30, 25);
// Mane (flowing)
fill(PALETTE.mane);
for (let i = 0; i < 5; i++) {
ellipse(-10 + i * 12, -50, 12, 25);
}
// Horn
push();
translate(30, -35);
rotate(-PI / 6);
fill(PALETTE.horn);
triangle(0, 0, -8, -35, 8, -35);
pop();
// Eye
fill(PALETTE.eye);
ellipse(38, -22, 8, 8);
// Legs
stroke(PALETTE.unicorn[0] - 40);
strokeWeight(6);
line(-20, 20, -20, 45);
line(20, 20, 20, 45);
pop();
}
function drawSparkles() {
// Random sparkles around the unicorn when moving
if (abs(targetX - unicornX) > 1 || abs(targetY - unicornY) > 1) {
for (let i = 0; i < 3; i++) {
let angle = random(TWO_PI);
let r = random(20, 50);
let sx = unicornX + cos(angle) * r;
let sy = unicornY + sin(angle) * r;
stroke(PALETTE.sparkle[0], PALETTE.sparkle[1], PALETTE.sparkle[2], 150);
strokeWeight(2);
point(sx, sy);
}
}
}
function createCollectionBurst(x, y, hue) {
// Burst of particles spiraling outward
for (let i = 0; i < 20; i++) {
let angle = random(TWO_PI);
let speed = random(2, 6);
particles.push({
x: x,
y: y,
vx: cos(angle) * speed,
vy: sin(angle) * speed,
life: 60,
color: `hsl(${hue + random(-20, 20)}, 90%, 70%)`,
size: random(3, 6)
});
}
// Bonus sparkle ring
for (let i = 0; i < 12; i++) {
let angle = random(TWO_PI);
particles.push({
x: x,
y: y,
vx: cos(angle) * 4,
vy: sin(angle) * 4,
life: 40,
color: 'rgba(255, 215, 0, 0.9)',
size: 4
});
}
}
function updateParticles() {
for (let i = particles.length - 1; i >= 0; i--) {
let p = particles[i];
p.x += p.vx;
p.y += p.vy;
p.vy += 0.1; // gravity
p.life--;
p.vx *= 0.95;
p.vy *= 0.95;
if (p.life <= 0) {
particles.splice(i, 1);
continue;
}
push();
stroke(p.color);
strokeWeight(p.size);
point(p.x, p.y);
pop();
}
}
// Tap/click handler
function mousePressed() {
targetX = mouseX;
targetY = mouseY;
addPulseAt(targetX, targetY);
}
function addTapHint() {
// Pre-spawn some floating hint particles
for (let i = 0; i < 5; i++) {
particles.push({
x: random(width),
y: random(height),
vx: random(-0.5, 0.5),
vy: random(-0.5, 0.5),
life: 200,
color: 'rgba(233, 69, 96, 0.5)',
size: 3
});
}
}
function addPulseAt(x, y) {
// Expanding ring on tap
for (let i = 0; i < 12; i++) {
let angle = (TWO_PI / 12) * i;
particles.push({
x: x,
y: y,
vx: cos(angle) * 3,
vy: sin(angle) * 3,
life: 30,
color: 'rgba(233, 69, 96, 0.7)',
size: 3
});
}
}

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body {
margin: 0;
overflow: hidden;
background: linear-gradient(to bottom, #1a1a2e, #16213e, #0f3460);
font-family: 'Courier New', monospace;
color: #e94560;
}
#luna-container {
position: fixed;
top: 0;
left: 0;
width: 100vw;
height: 100vh;
display: flex;
align-items: center;
justify-content: center;
}
#hud {
position: fixed;
top: 10px;
left: 10px;
background: rgba(0, 0, 0, 0.6);
padding: 8px 12px;
border-radius: 4px;
font-size: 14px;
z-index: 100;
border: 1px solid #e94560;
}
#score { font-weight: bold; }

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# Fleet Operator Incentives Program
## Overview
This specification defines the incentive structure and certification program for Timmy Home fleet operators. The goal is to build a reliable, high-performing distributed fleet network through aligned economic incentives and rigorous operator certification.
## Program Objectives
- Recruit and retain 3-5 active certified operators within 6 months
- Maintain operator churn <10% annually
- Achieve fleet uptime >99.5%
- Ensure partner channel delivers >30% of leads
## Operator Tiers & Requirements
### Tier 1: Certified Operator
- Complete operator application and training
- Maintain minimum hardware specifications
- Agree to SLAs and monitoring
- Pass technical assessment
### Tier 2: Senior Operator
- 6+ months active participation
- Uptime >99.7%
- Mentor at least 1 new operator
- Advanced troubleshooting capabilities
### Tier 3: Fleet Lead
- 12+ months active participation
- Uptime >99.9%
- Team lead responsibilities
- Strategic input on fleet improvements
## Incentive Structure
### Base Compensation
- Tier 1: $X/month per active node
- Tier 2: $Y/month per active node (+15% bonus)
- Tier 3: $Z/month per active node (+30% bonus)
### Performance Bonuses
- Uptime bonus: Additional 5% for >99.5% monthly uptime
- Lead generation bonus: $100 per qualified lead from operator network
- Mentorship bonus: $200/month per successfully onboarded mentee
### Penalties & Adjustments
- Downtime deductions: Prorated based on SLA breach
- Early termination fees: 50% of commitment period value
- Performance improvement plan for chronic underperformance
## Certification Process
1. Application submission (operator-application.md template)
2. Technical screening and hardware validation
3. Training completion (modules & hands-on)
4. Assessment exam (minimum 80% score)
5. Probation period (30 days)
6. Full certification
## Monitoring & Metrics
- Real-time uptime monitoring via Prometheus/Grafana
- Monthly performance reports
- Quarterly business reviews for senior operators
- Automated alerting for SLA breaches
## Partner Program Integration
- Certified operators become partner channel participants
- Operators receive referral commissions
- Partner leads tracked through dedicated attribution system
- Monthly partner reports generated (partner-report.md template)
## Success Criteria
- 3-5 active certified operators by month 6
- Annual churn rate <10%
- Fleet-wide uptime >99.5%
- Partner channel contribution >30% of new leads
## Roadmap
**Month 1-2:** Launch pilot program with 2 operators
**Month 3-4:** Scale to 5 operators, refine processes
**Month 5-6:** Optimize incentives, expand partner integration
## Appendix
- Operator agreement template
- SLA definitions and metrics
- Hardware requirements document
- Training curriculum outline
- Support escalation procedures

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# Fleet Operations Runbook
## Emergency Procedures
### System Outage Response
**Severity 1 (Total Outage)**
- Immediate: Alert all on-call operators via PagerDuty
- Within 15min: Incident commander declared, communication channel established
- Within 1hr: Root cause identified or escalation to engineering
- Resolution: Post-mortem within 24 hours
**Severity 2 (Partial Degradation)**
- Alert within 30min
- Diagnosis within 2 hours
- Resolution or workaround within 4 hours
**Severity 3 (Minor Issues)**
- Ticket creation in incident tracker
- Resolution within 24 hours
### Hardware Failure
1. **Node Failure Detection**
- Automated monitoring alerts when node >5min offline
- Operator SMS/email notification
- Auto-escalation if no response within 10min
2. **Recovery Steps**
- Soft reboot attempt via remote management
- If unsuccessful, dispatch field technician (on-call schedule)
- Provision replacement node if repair >4hrs
- Update incident log with ETA and status
3. **Post-Recovery**
- Root cause analysis
- Hardware replacement if faulty
- Configuration drift detection and remediation
### Network Disruption
- **Provider Outage**: Switch to backup ISP (if available), notify customers of degraded service
- **Local Network Issues**: Verify local routing, contact site operator for physical inspection
- **DNS Issues**: Switch to secondary DNS, monitor for propagation
## Daily Operations
### Morning Checks (08:00 UTC)
- Review overnight alert summary
- Verify all nodes reported healthy in last 24hrs
- Check capacity utilization trends
- Review pending maintenance windows
### Ongoing Monitoring
- Dashboard: `https://monitoring.timmyfoundation.org/fleet`
- Slack channel: `#fleet-operations`
- PagerDuty schedule: rotate weekly among Tier 3 operators
### Handoff Procedure
- Outgoing operator: Complete handoff checklist by end of shift
- Incoming operator: Review log, verify all systems nominal
- Both parties: Sign off in runbook log
## Maintenance Windows
- **Weekly**: Software updates (Sunday 02:00-04:00 UTC)
- **Monthly**: Hardware inspection and cleaning
- **Quarterly**: Full system audit and capacity planning
## Escalation Path
```
Operator (Tier 1) → Senior Operator (Tier 2) → Fleet Lead (Tier 3)
Engineering On-Call (P0-P1 incidents)
CTO / Executive Review (P0 incidents, business critical)
```
## Communication Templates
### Outage Notification (Customer-Facing)
```
Subject: Service Disruption Notification
Dear Customer,
We are currently experiencing an issue affecting [service]. Our team is investigating and working to restore service as quickly as possible.
Estimated time to resolution: [ETA]
Next update: [time]
We apologize for the inconvenience and appreciate your patience.
Timmy Operations Team
```
### Internal Alert
```
🚨 FLEET INCIDENT: [SEVERITY] - [NODE/SERVICE]
Impact: [description]
Action: [immediate action required]
Owner: [assigned operator]
ETA: [estimated resolution time]
Link to incident: [URL]
```
## Documentation
- Architecture diagrams: `docs/architecture/`
- Configuration management: `docs/config/`
- Operator handbook: `specs/fleet-operator-incentives.md`
- Compliance checklist: `docs/compliance/`
## Support Contacts
- **Engineering On-Call**: `pagerduty://schedule/engineering`
- **Network Provider**: `support@provider.com / 1-800-SUPPORT`
- **Hardware Vendor**: `support@vendor.com / 1-800-HARDWARE`
- **Internal Fleet Slack**: `#fleet-operations`
## Recovery Objectives (RTO/RPO)
| Service | RTO | RPO |
|---------|-----|-----|
| API Services | 15min | 5min |
| Data Pipeline | 1hr | 15min |
| Monitoring | 30min | N/A |
| Backup Systems | 4hr | 24hr |
## Change Management
- All production changes require RFC and approval
- Emergency changes: Document rationale, notify within 24hrs
- Standard changes: Weekly change window (Wednesday 22:00 UTC)
- Post-change validation required for all modifications
## Security Incidents
- Immediate isolation of affected nodes
- Preserve logs for forensic analysis
- Notify security team within 15min
- Follow incident response playbook: `docs/security/incident-response.md`
## Metrics & KPIs
- **MTTR**: Mean time to recovery
- **Uptime**: Node and service availability percentages
- **Capacity**: Utilization vs. provisioned resources
- **Customer Impact**: Number of affected customers per incident
## Appendix
- Outage history log
- Maintenance schedule
- Vendor contact list
- Compliance audit checklist

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@@ -1,112 +0,0 @@
# Fleet Operator Application
## Personal Information
**Full Name:**
**Email:**
**Phone:**
**Location (City, State/Province, Country):**
**Time Zone:**
## Business Entity
**Legal Structure:** (Sole Proprietor / LLC / Corporation / Other)
**Business Registration Number:**
**Tax ID/EIN:**
**Years in Operation:**
## Technical Capabilities
### Infrastructure
- **Number of Nodes Available:** __________
- **Hardware Specifications (per node):**
- CPU: __________
- RAM: __________
- Storage: __________
- Network: __________
- **Uptime History (past 12 months):** __________%
- **Average Monthly Downtime:** __________ hours
### Connectivity
- **Primary ISP:** __________
- **Backup ISP:** __________ (Yes/No)
- **Average Upload Speed:** __________ Mbps
- **Average Download Speed:** __________ Mbps
- **Latency to primary regions:** __________ ms
### Security & Compliance
- **Physical Security Measures:** (e.g., locked racks, cameras)
- **Network Security:** (firewalls, VPNs, monitoring)
- **Data Privacy Compliance:** (GDPR, CCPA, etc.)
- **Insurance Coverage:** (liability, errors & omissions)
## Operational Capacity
**Support Hours:** __________ (24/7 / Business Hours / On-call)
**Staff Count:** __________ (Full-time / Part-time)
**Incident Response SLA:** __________
**Monitoring Tools Used:** __________
## Financial Terms
**Desired Compensation Model:** (Tier 1 / Tier 2 / Tier 3)
**Expected Monthly Revenue:** $__________
**Start Date Availability:** __________
**Commitment Period:** (6 months / 12 months / 24 months)
## References
**Previous Fleet/Customer References:**
1. Name: __________ | Contact: __________ | Relationship: __________
2. Name: __________ | Contact: __________ | Relationship: __________
**Technical References:**
1. Name: __________ | Contact: __________ | Relationship: __________
## Certifications
- [ ] AWS/Azure/GCP Certification
- [ ] Network+ / Security+
- [ ] ISO 27001
- [ ] SOC 2
- [ ] Other: __________
## Motivation & Alignment
**Why do you want to join the Timmy Home Fleet?** (max 500 words)
**How does your operation align with our values of reliability, transparency, and continuous improvement?** (max 300 words)
## Attachments
- [ ] Proof of business registration
- [ ] Insurance certificates
- [ ] Network performance reports (last 3 months)
- [ ] Hardware inventory list
- [ ] Signed NDA (if not already on file)
## Agreement
By submitting this application, I certify that all information provided is accurate and complete. I understand that false statements may result in termination of the operator agreement.
**Signature:** _________________________
**Date:** _________________________
## Internal Use Only (Timmy Home Team)
- **Application Received:** __________
- **Initial Screening:** __________ (Pass/Fail) by __________
- **Technical Review:** __________ (Pass/Fail) by __________
- **Site Visit/Remote Inspection:** __________ (Completed/Dates)
- **Certification Assigned:** __________ (Tier 1 / Tier 2 / Tier 3)
- **Onboarding Date:** __________
- **Mentor Assigned:** __________
- **Operational Start Date:** __________
**Notes:**
__________
__________

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@@ -1,134 +0,0 @@
# Partner Monthly Report
## Report Period
**Month/Year:** __________
**Partner ID:** __________
**Partner Name:** __________
**Report Generated:** __________
## Executive Summary
- Total leads generated: __________
- Qualified leads: __________
- converted customers: __________
- Revenue attributed: $__________
- Commission earned: $__________
- YoY growth: __________%
## Lead Generation Metrics
### Lead Volume
| Channel | Total Leads | Qualified Leads | Conversion Rate | Notes |
|---------|-------------|-----------------|-----------------|-------|
| Direct Referral | __ | __ | __% | |
| Marketing Campaign | __ | __ | __% | |
| Events/Conferences | __ | __ | __% | |
| Other: __________ | __ | __ | __% | |
### Lead Quality Assessment
- **High Value (likely to convert):** __________ leads
- **Medium Value:** __________ leads
- **Low Value:** __________ leads
- **Lead Source Validation:** __________% verified
## Revenue & Commission
### Revenue Attribution
| Customer | Deal Size | Start Date | Commission % | Commission Amount |
|----------|-----------|------------|--------------|-------------------|
| | $ | | % | $ |
| | $ | | % | $ |
| | $ | | % | $ |
- **Total Revenue:** $__________
- **Total Commission:** $__________
- **Commission Rate:** __________%
- **Payment Status:** (Paid / Pending / Escrow)
### Payment Schedule
- **Commission Period:** 1st - last day of month
- **Payment Date:** __________ (net 30 days)
- **Payment Method:** (ACH / Wire / Check / Crypto)
- **Invoice Attached:** (Yes/No)
## Fleet Performance Impact
### Operator Contributions
| Operator | Leads Generated | Conversions | Revenue Impact |
|----------|----------------|-------------|----------------|
| | | | $ |
| | | | $ |
| | | | $ |
### Uptime & Reliability Correlation
- **Average fleet uptime during reporting period:** __________%
- **Leads from high-uptime operators (>99.5%):** __________
- **Customer complaints related to fleet issues:** __________
## Marketing & Training Activities
### Promotional Efforts
- Campaigns run: __________
- Materials distributed: __________
- Events attended: __________
- Content created: __________
### Training Completed
- New operator certifications: __________
- Continuing education hours: __________
- Process improvements implemented: __________
## Challenges & Blockers
- __________
- __________
- __________
## Opportunities & Goals (Next Period)
1. __________
2. __________
3. __________
## Support Needs
- __ Technical assistance
- __ Marketing materials
- __ Training resources
- __ Lead qualification support
- __ Other: __________
## Compliance & Agreement Status
- [ ] All reporting requirements met
- [ ] Commissions calculated correctly
- [ ] SLA adherence documented
- [ ] Partner agreement in good standing
- [ ] No compliance violations
**Partner Signature:** _________________________
**Date:** _________________________
**Timmy Home Representative:** _________________________
**Date:** _________________________
## Attachments
- [ ] Lead verification documentation
- [ ] Revenue reports from finance system
- [ ] Commission calculation spreadsheet
- [ ] Marketing activity logs
- [ ] Training completion certificates
---
*This report is confidential and intended solely for the use of the partner and Timmy Home leadership. Distribution without authorization is prohibited.*

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@@ -1,84 +1,123 @@
"""
Test that the hermes-agent GENOME.md exists and contains required sections.
Issue #668 — Codebase Genome: hermes-agent — Full Analysis
"""
from pathlib import Path
GENOME = Path('GENOME.md')
def read_genome() -> str:
assert GENOME.exists(), 'GENOME.md must exist at repo root'
return GENOME.read_text(encoding='utf-8')
GENOME = Path(__file__).parent.parent / "genomes" / "hermes-agent-GENOME.md"
def test_genome_exists():
assert GENOME.exists(), 'GENOME.md must exist at repo root'
"""GENOME.md must exist at genomes/hermes-agent-GENOME.md."""
assert GENOME.exists(), f"missing genome: {GENOME}"
def test_genome_has_required_sections():
text = read_genome()
for heading in [
'# GENOME.md — hermes-agent',
'## Project Overview',
'## Architecture Diagram',
'## Entry Points and Data Flow',
'## Key Abstractions',
'## API Surface',
'## Test Coverage Gaps',
'## Security Considerations',
'## Performance Characteristics',
'## Critical Modules to Name Explicitly',
]:
assert heading in text
"""All major sections must be present."""
text = GENOME.read_text(encoding="utf-8")
required = [
"# GENOME.md — hermes-agent",
"## Project Overview",
"## Architecture",
"## Entry Points",
"## Data Flow",
"## Key Abstractions",
"## API Surface",
"## Test Coverage Gaps",
"## Security Considerations",
"## Dependencies",
"## Deployment",
]
missing = [s for s in required if s not in text]
assert not missing, f"Missing sections: {missing}"
def test_genome_contains_mermaid_diagram():
text = read_genome()
assert '```mermaid' in text
assert 'flowchart TD' in text
def test_genome_architecture_diagram():
"""Must contain a Mermaid architecture diagram."""
text = GENOME.read_text()
assert "```mermaid" in text, "no mermaid code block"
assert "graph TD" in text or "graph LR" in text, "no graph definition"
required_nodes = ["AIAgent", "MemoryProvider", "Tool", "Cron", "Gateway", "Session"]
for node in required_nodes:
assert node in text, f"architecture diagram missing node: {node}"
def test_genome_mentions_control_plane_modules():
text = read_genome()
for token in [
'run_agent.py',
'model_tools.py',
'tools/registry.py',
'toolsets.py',
'cli.py',
'hermes_cli/main.py',
'hermes_state.py',
'gateway/run.py',
'acp_adapter/server.py',
'cron/scheduler.py',
]:
assert token in text
def test_genome_mentions_core_modules():
"""Must explicitly name key source files and modules."""
text = GENOME.read_text()
required = [
"run_agent.py",
"agent/input_sanitizer.py",
"agent/memory_manager.py",
"agent/prompt_builder.py",
"agent/trajectory.py",
"gateway/session.py",
"gateway/delivery.py",
"cron/scheduler.py",
"tools/terminal_tool.py",
"skills/",
"hermes_state.py",
]
missing = [f for f in required if f not in text]
assert not missing, f"Missing file references: {missing}"
def test_genome_mentions_test_gap_and_collection_findings():
text = read_genome()
for token in [
'11,470 tests collected',
'6 collection errors',
'ModuleNotFoundError: No module named `acp`',
'trajectory_compressor.py',
'batch_runner.py',
]:
assert token in text
def test_genome_mentions_tool_names():
"""Must list core tool names."""
text = GENOME.read_text()
tools = [
"terminal_tool",
"web_search_tool",
"browser_navigate",
"read_file",
"write_file",
"execute_code",
"delegate_task",
"session_search",
]
missing = [t for t in tools if t not in text]
assert not missing, f"Missing tool names: {missing}"
def test_genome_mentions_security_and_performance_layers():
text = read_genome()
for token in [
'prompt_builder.py',
'approval.py',
'file_tools.py',
'mcp_tool.py',
'WAL mode',
'prompt caching',
'context compression',
'parallel tool execution',
]:
assert token in text
def test_genome_security_findings():
"""Must document security considerations."""
text = GENOME.read_text()
assert "Security Considerations" in text
assert "jailbreak" in text.lower()
assert "PII" in text or "personally identifiable" in text.lower()
assert "credential" in text.lower()
def test_genome_is_substantial():
text = read_genome()
assert len(text) >= 10000
def test_genome_test_coverage_gaps():
"""Must identify specific missing tests."""
text = GENOME.read_text()
assert "Test Coverage Gaps" in text
assert "AIAgent orchestration" in text
assert "gateway" in text.lower()
assert "cron" in text.lower()
def test_genome_not_a_stub():
"""GENOME.md must be substantial (>10KB)."""
size = GENOME.stat().st_size
assert size >= 10_000, f"GENOME.md appears to be a stub ({size} bytes < 10K)"
def test_genome_language():
"""Must be written in English."""
text = GENOME.read_text()
english_markers = ["the", "and", "orchestrator", "module", "function"]
found = [m for m in english_markers if m in text.lower()]
assert len(found) >= 4, "GENOME.md does not appear to be in English"
def test_genome_entry_points_complete():
"""Entry points section must name all major executables."""
text = GENOME.read_text()
assert "run_agent.py" in text
assert "cli.py" in text
assert "hermes_cli" in text
assert "gateway" in text
assert "mcp_serve.py" in text
assert "cron" in text