kshitij
db84a78e61
* fix(langfuse): reject placeholder credentials with one-shot warning When operators leave HERMES_LANGFUSE_PUBLIC_KEY / HERMES_LANGFUSE_SECRET_KEY at a template value like 'placeholder', 'test-key', or 'your-langfuse-key', the Langfuse SDK silently accepts the credentials at construction time and drops every trace at flush time. No warning, no error — just an empty Langfuse dashboard the operator only notices hours later. Add prefix-based validation in _get_langfuse() against the documented 'pk-lf-' / 'sk-lf-' prefixes that Langfuse always issues server-side. Anything else fires a single warning naming the offending env var(s) with a log-safe value preview (full string for short placeholders so the operator knows which template they left in place; truncated for long values so a real secret pasted into the wrong field never hits the log), then short-circuits via the existing _INIT_FAILED cache so the warning fires once per process, not once per hook invocation. The check sits after the 'Langfuse is None' SDK-installed guard so hosts without the optional langfuse SDK don't see misleading 'set real keys' hints when the actionable fix is 'pip install langfuse'. Missing credentials remains the documented opt-out path and stays silent — no log noise for unconfigured installs. Fixes #22763 Fixes #23823 * fix(langfuse): use actual API request messages for generation input on_pre_llm_request previously used the messages kwarg alone, which could be None when Hermes passes the payload via request_messages, conversation_history, or user_message instead. Add _coerce_request_messages to pick the first available list across all variants, falling back to a synthetic user message. Generations now show the real outbound payload rather than an empty input. * fix(langfuse): record tool call outputs in traces Tool observations showed input (arguments) but output was always undefined. Root cause: when tool_call_id is empty, pre_tool_call stored observations under a unique time-based key that post_tool_call could never reconstruct, so every tool span was closed without output by the _finish_trace sweep. Fix pre/post matching by routing empty-tool_call_id tools through a per-name FIFO queue (pending_tools_by_name) instead of the time-based key. Tools with a tool_call_id continue to use the id-keyed dict. Also: - Preserve OpenAI-style nested function shape in serialized tool calls so Langfuse renders name/arguments correctly - Keep name + tool_call_id on role:tool messages for proper pairing - Backfill tool results onto the matching turn_tool_calls entry so the generation's tool-call record carries the result alongside arguments - Coerce request messages from whichever field the runtime provides (request_messages, messages, conversation_history, user_message) * fix(langfuse): salvage-review polish — drop dead is_first_turn, shallow-copy request_messages, real threaded FIFO test Self-review of the combined #22345 + #23831 salvage surfaced three issues worth fixing in the same PR rather than as follow-ups: 1. Drop is_first_turn from the pre_api_request hook. The boolean expression `not bool(conversation_history)` was wrong: conversation_history is reassigned to None mid-run after compression (5 sites in run_agent.py), so the value flips False -> True mid-conversation on every post-compression API call. The langfuse plugin never consumed it, so the kwarg was both misleading AND dead. 2. Replace copy.deepcopy(request_messages) with shallow list() copy. The pre_api_request hook contract discards return values (invoke_hook never writes back to api_kwargs), and the langfuse plugin's _serialize_messages already builds its own snapshot dicts via _safe_value. A deepcopy on every API call would walk every tool result and base64 image — significant overhead for no real isolation benefit. Shallow copy of the outer list protects against later mutations of api_messages without paying for the inner-dict walk. 3. Rename test_empty_tool_call_id_concurrent_fifo_order -> test_empty_tool_call_id_observations_are_fifo_within_tool_name and add a real test_threaded_post_calls_preserve_fifo_under_lock that spawns 8 threads behind a barrier to actually exercise _STATE_LOCK on the pending_tools_by_name queue. The original test was sequential and only validated Python list semantics; this one validates the lock discipline. 4. Fix stale 'Cleared by reset_cache_for_tests()' comment on _INIT_FAILED — that function does not exist. Tests reload the module via sys.modules.pop + importlib.import_module instead. Tests: 37 langfuse plugin tests pass, 658 plugin tests overall pass. --------- Co-authored-by: xxxigm <tuancanhnguyen706@gmail.com> Co-authored-by: Brian Conklin <brian@dralth.com>
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), NovitaAI (AI-native cloud for Model API, Agent Sandbox, and GPU Cloud), NVIDIA NIM (Nemotron), 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 | Seven terminal backends — local, Docker, SSH, Singularity, Modal, Daytona, and Vercel Sandbox. 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, trajectory compression for training the next generation of tool-calling models. |
Quick Install
Linux, macOS, WSL2, Termux
curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash
Windows (native, PowerShell) — Early Beta
Heads up: Native Windows support is early beta. It installs and runs, but hasn't been road-tested as broadly as our Linux/macOS/WSL2 paths. Please file issues when you hit rough edges. For the most battle-tested Windows setup today, run the Linux/macOS one-liner above inside WSL2.
Run this in PowerShell:
irm https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.ps1 | iex
The installer handles everything: uv, Python 3.11, Node.js, ripgrep, ffmpeg, and a portable Git Bash (MinGit, unpacked to %LOCALAPPDATA%\hermes\git — no admin required, completely isolated from any system Git install). Hermes uses this bundled Git Bash to run shell commands.
If you already have Git installed, the installer detects it and uses that instead. Otherwise a ~45MB MinGit download is all you need — it won't touch or interfere with any system Git.
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 supported as an early beta — the PowerShell one-liner above installs everything, but expect rough edges and please file issues when you hit them. If you'd rather use WSL2 (our most battle-tested Windows path), the Linux command works there too. Native Windows install lives under
%LOCALAPPDATA%\hermes; WSL2 installs under~/.hermesas on Linux. The only Hermes feature that currently needs WSL2 specifically is the browser-based dashboard chat pane (it uses a POSIX PTY — classic CLI and gateway both run natively).
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> |
/<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 — clone and go with setup-hermes.sh:
git clone https://github.com/NousResearch/hermes-agent.git
cd hermes-agent
./setup-hermes.sh # installs uv, creates venv, installs .[all], symlinks ~/.local/bin/hermes
./hermes # auto-detects the venv, no need to `source` first
Manual path (equivalent to the above):
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]"
scripts/run_tests.sh
Community
- 💬 Discord
- 📚 Skills Hub
- 🐛 Issues
- 🔌 HermesClaw — Community WeChat bridge: Run Hermes Agent and OpenClaw on the same WeChat account.
License
MIT — see LICENSE.
Built by Nous Research.