8d7b7feb0d
* fix(gateway): bound _agent_cache with LRU cap + idle TTL eviction
The per-session AIAgent cache was unbounded. Each cached AIAgent holds
LLM clients, tool schemas, memory providers, and a conversation buffer.
In a long-lived gateway serving many chats/threads, cached agents
accumulated indefinitely — entries were only evicted on /new, /model,
or session reset.
Changes:
- Cache is now an OrderedDict so we can pop least-recently-used entries.
- _enforce_agent_cache_cap() pops entries beyond _AGENT_CACHE_MAX_SIZE=64
when a new agent is inserted. LRU order is refreshed via move_to_end()
on cache hits.
- _sweep_idle_cached_agents() evicts entries whose AIAgent has been idle
longer than _AGENT_CACHE_IDLE_TTL_SECS=3600s. Runs from the existing
_session_expiry_watcher so no new background task is created.
- The expiry watcher now also pops the cache entry after calling
_cleanup_agent_resources on a flushed session — previously the agent
was shut down but its reference stayed in the cache dict.
- Evicted agents have _cleanup_agent_resources() called on a daemon
thread so the cache lock isn't held during slow teardown.
Both tuning constants live at module scope so tests can monkeypatch
them without touching class state.
Tests: 7 new cases in test_agent_cache.py covering LRU eviction,
move_to_end refresh, cleanup thread dispatch, idle TTL sweep,
defensive handling of agents without _last_activity_ts, and plain-dict
test fixture tolerance.
* tweak: bump _AGENT_CACHE_MAX_SIZE 64 -> 128
* fix(gateway): never evict mid-turn agents; live spillover tests
The prior commit could tear down an active agent if its session_key
happened to be LRU when the cap was exceeded. AIAgent.close() kills
process_registry entries for the task, tears down the terminal
sandbox, closes the OpenAI client (sets self.client = None), and
cascades .close() into any active child subagents — all fatal if
the agent is still processing a turn.
Changes:
- _enforce_agent_cache_cap and _sweep_idle_cached_agents now look at
GatewayRunner._running_agents and skip any entry whose AIAgent
instance is present (identity via id(), so MagicMock doesn't
confuse lookup in tests). _AGENT_PENDING_SENTINEL is treated
as 'not active' since no real agent exists yet.
- Eviction only considers the LRU-excess window (first size-cap
entries). If an excess slot is held by a mid-turn agent, we skip
it WITHOUT compensating by evicting a newer entry. A freshly
inserted session (zero cache history) shouldn't be punished to
protect a long-lived one that happens to be busy.
- Cache may therefore stay transiently over cap when load spikes;
a WARNING is logged so operators can see it, and the next insert
re-runs the check after some turns have finished.
New tests (TestAgentCacheActiveSafety + TestAgentCacheSpilloverLive):
- Active LRU entry is skipped; no newer entry compensated
- Mixed active/idle excess window: only idle slots go
- All-active cache: no eviction, WARNING logged, all clients intact
- _AGENT_PENDING_SENTINEL doesn't block other evictions
- Idle-TTL sweep skips active agents
- End-to-end: active agent's .client survives eviction attempt
- Live fill-to-cap with real AIAgents, then spillover
- Live: CAP=4 all active + 1 newcomer — cache grows to 5, no teardown
- Live: 8 threads racing 160 inserts into CAP=16 — settles at 16
- Live: evicted session's next turn gets a fresh agent that works
30 tests pass (13 pre-existing + 17 new). Related gateway suites
(model switch, session reset, proxy, etc.) all green.
* fix(gateway): cache eviction preserves per-task state for session resume
The prior commits called AIAgent.close() on cache-evicted agents, which
tears down process_registry entries, terminal sandbox, and browser
daemon for that task_id — permanently. Fine for session-expiry (session
ended), wrong for cache eviction (session may resume).
Real-world scenario: a user leaves a Telegram session open for 2+ hours,
idle TTL evicts the cached AIAgent, user returns and sends a message.
Conversation history is preserved via SessionStore, but their terminal
sandbox (cwd, env vars, bg shells) and browser state were destroyed.
Fix: split the two cleanup modes.
close() Full teardown — session ended. Kills bg procs,
tears down terminal sandbox + browser daemon,
closes LLM client. Used by session-expiry,
/new, /reset (unchanged).
release_clients() Soft cleanup — session may resume. Closes
LLM client only. Leaves process_registry,
terminal sandbox, browser daemon intact
for the resuming agent to inherit via
shared task_id.
Gateway cache eviction (_enforce_agent_cache_cap, _sweep_idle_cached_agents)
now dispatches _release_evicted_agent_soft on the daemon thread instead
of _cleanup_agent_resources. All session-expiry call sites of
_cleanup_agent_resources are unchanged.
Tests (TestAgentCacheIdleResume, 5 new cases):
- release_clients does NOT call process_registry.kill_all
- release_clients does NOT call cleanup_vm / cleanup_browser
- release_clients DOES close the LLM client (agent.client is None after)
- close() vs release_clients() — semantic contract pinned
- Idle-evicted session's rebuild with same session_id gets same task_id
Updated test_cap_triggers_cleanup_thread to assert the soft path fires
and the hard path does NOT.
35 tests pass in test_agent_cache.py; 67 related tests green.
Hermes Agent ☤
The self-improving AI agent built by Nous Research. It's the only agent with a built-in learning loop — it creates skills from experience, improves them during use, nudges itself to persist knowledge, searches its own past conversations, and builds a deepening model of who you are across sessions. Run it on a $5 VPS, a GPU cluster, or serverless infrastructure that costs nearly nothing when idle. It's not tied to your laptop — talk to it from Telegram while it works on a cloud VM.
Use any model you want — Nous Portal, OpenRouter (200+ models), Xiaomi MiMo, z.ai/GLM, Kimi/Moonshot, MiniMax, Hugging Face, OpenAI, or your own endpoint. Switch with hermes model — no code changes, no lock-in.
| A real terminal interface | Full TUI with multiline editing, slash-command autocomplete, conversation history, interrupt-and-redirect, and streaming tool output. |
| Lives where you do | Telegram, Discord, Slack, WhatsApp, Signal, and CLI — all from a single gateway process. Voice memo transcription, cross-platform conversation continuity. |
| A closed learning loop | Agent-curated memory with periodic nudges. Autonomous skill creation after complex tasks. Skills self-improve during use. FTS5 session search with LLM summarization for cross-session recall. Honcho dialectic user modeling. Compatible with the agentskills.io open standard. |
| Scheduled automations | Built-in cron scheduler with delivery to any platform. Daily reports, nightly backups, weekly audits — all in natural language, running unattended. |
| Delegates and parallelizes | Spawn isolated subagents for parallel workstreams. Write Python scripts that call tools via RPC, collapsing multi-step pipelines into zero-context-cost turns. |
| Runs anywhere, not just your laptop | Six terminal backends — local, Docker, SSH, Daytona, Singularity, and Modal. Daytona and Modal offer serverless persistence — your agent's environment hibernates when idle and wakes on demand, costing nearly nothing between sessions. Run it on a $5 VPS or a GPU cluster. |
| Research-ready | Batch trajectory generation, Atropos RL environments, trajectory compression for training the next generation of tool-calling models. |
Quick Install
curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash
Works on Linux, macOS, WSL2, and Android via Termux. The installer handles the platform-specific setup for you.
Android / Termux: The tested manual path is documented in the Termux guide. On Termux, Hermes installs a curated
.[termux]extra because the full.[all]extra currently pulls Android-incompatible voice dependencies.Windows: Native Windows is not supported. Please install WSL2 and run the command above.
After installation:
source ~/.bashrc # reload shell (or: source ~/.zshrc)
hermes # start chatting!
Getting Started
hermes # Interactive CLI — start a conversation
hermes model # Choose your LLM provider and model
hermes tools # Configure which tools are enabled
hermes config set # Set individual config values
hermes gateway # Start the messaging gateway (Telegram, Discord, etc.)
hermes setup # Run the full setup wizard (configures everything at once)
hermes claw migrate # Migrate from OpenClaw (if coming from OpenClaw)
hermes update # Update to the latest version
hermes doctor # Diagnose any issues
CLI vs Messaging Quick Reference
Hermes has two entry points: start the terminal UI with hermes, or run the gateway and talk to it from Telegram, Discord, Slack, WhatsApp, Signal, or Email. Once you're in a conversation, many slash commands are shared across both interfaces.
| Action | CLI | Messaging platforms |
|---|---|---|
| Start chatting | hermes |
Run hermes gateway setup + hermes gateway start, then send the bot a message |
| Start fresh conversation | /new or /reset |
/new or /reset |
| Change model | /model [provider:model] |
/model [provider:model] |
| Set a personality | /personality [name] |
/personality [name] |
| Retry or undo the last turn | /retry, /undo |
/retry, /undo |
| Compress context / check usage | /compress, /usage, /insights [--days N] |
/compress, /usage, /insights [days] |
| Browse skills | /skills or /<skill-name> |
/skills or /<skill-name> |
| Interrupt current work | Ctrl+C or send a new message |
/stop or send a new message |
| Platform-specific status | /platforms |
/status, /sethome |
For the full command lists, see the CLI guide and the Messaging Gateway guide.
Documentation
All documentation lives at hermes-agent.nousresearch.com/docs:
| Section | What's Covered |
|---|---|
| Quickstart | Install → setup → first conversation in 2 minutes |
| CLI Usage | Commands, keybindings, personalities, sessions |
| Configuration | Config file, providers, models, all options |
| Messaging Gateway | Telegram, Discord, Slack, WhatsApp, Signal, Home Assistant |
| Security | Command approval, DM pairing, container isolation |
| Tools & Toolsets | 40+ tools, toolset system, terminal backends |
| Skills System | Procedural memory, Skills Hub, creating skills |
| Memory | Persistent memory, user profiles, best practices |
| MCP Integration | Connect any MCP server for extended capabilities |
| Cron Scheduling | Scheduled tasks with platform delivery |
| Context Files | Project context that shapes every conversation |
| Architecture | Project structure, agent loop, key classes |
| Contributing | Development setup, PR process, code style |
| CLI Reference | All commands and flags |
| Environment Variables | Complete env var reference |
Migrating from OpenClaw
If you're coming from OpenClaw, Hermes can automatically import your settings, memories, skills, and API keys.
During first-time setup: The setup wizard (hermes setup) automatically detects ~/.openclaw and offers to migrate before configuration begins.
Anytime after install:
hermes claw migrate # Interactive migration (full preset)
hermes claw migrate --dry-run # Preview what would be migrated
hermes claw migrate --preset user-data # Migrate without secrets
hermes claw migrate --overwrite # Overwrite existing conflicts
What gets imported:
- SOUL.md — persona file
- Memories — MEMORY.md and USER.md entries
- Skills — user-created skills →
~/.hermes/skills/openclaw-imports/ - Command allowlist — approval patterns
- Messaging settings — platform configs, allowed users, working directory
- API keys — allowlisted secrets (Telegram, OpenRouter, OpenAI, Anthropic, ElevenLabs)
- TTS assets — workspace audio files
- Workspace instructions — AGENTS.md (with
--workspace-target)
See hermes claw migrate --help for all options, or use the openclaw-migration skill for an interactive agent-guided migration with dry-run previews.
Contributing
We welcome contributions! See the Contributing Guide for development setup, code style, and PR process.
Quick start for contributors:
git clone https://github.com/NousResearch/hermes-agent.git
cd hermes-agent
curl -LsSf https://astral.sh/uv/install.sh | sh
uv venv venv --python 3.11
source venv/bin/activate
uv pip install -e ".[all,dev]"
python -m pytest tests/ -q
RL Training (optional): To work on the RL/Tinker-Atropos integration:
git submodule update --init tinker-atropos uv pip install -e "./tinker-atropos"
Community
- 💬 Discord
- 📚 Skills Hub
- 🐛 Issues
- 💡 Discussions
- 🔌 HermesClaw — Community WeChat bridge: Run Hermes Agent and OpenClaw on the same WeChat account.
License
MIT — see LICENSE.
Built by Nous Research.