Teknium dd60bcbfb7 feat: OpenAI-compatible API server + WhatsApp configurable reply prefix (#1756)
* feat: OpenAI-compatible API server platform adapter

Salvaged from PR #956, updated for current main.

Adds an HTTP API server as a gateway platform adapter that exposes
hermes-agent via the OpenAI Chat Completions and Responses APIs.
Any OpenAI-compatible frontend (Open WebUI, LobeChat, LibreChat,
AnythingLLM, NextChat, ChatBox, etc.) can connect by pointing at
http://localhost:8642/v1.

Endpoints:
- POST /v1/chat/completions  — stateless Chat Completions API
- POST /v1/responses         — stateful Responses API with chaining
- GET  /v1/responses/{id}    — retrieve stored response
- DELETE /v1/responses/{id}  — delete stored response
- GET  /v1/models            — list hermes-agent as available model
- GET  /health               — health check

Features:
- Real SSE streaming via stream_delta_callback (uses main's streaming)
- In-memory LRU response store for Responses API conversation chaining
- Named conversations via 'conversation' parameter
- Bearer token auth (optional, via API_SERVER_KEY)
- CORS support for browser-based frontends
- System prompt layering (frontend system messages on top of core)
- Real token usage tracking in responses

Integration points:
- Platform.API_SERVER in gateway/config.py
- _create_adapter() branch in gateway/run.py
- API_SERVER_* env vars in hermes_cli/config.py
- Env var overrides in gateway/config.py _apply_env_overrides()

Changes vs original PR #956:
- Removed streaming infrastructure (already on main via stream_consumer.py)
- Removed Telegram reply_to_mode (separate feature, not included)
- Updated _resolve_model() -> _resolve_gateway_model()
- Updated stream_callback -> stream_delta_callback
- Updated connect()/disconnect() to use _mark_connected()/_mark_disconnected()
- Adapted to current Platform enum (includes MATTERMOST, MATRIX, DINGTALK)

Tests: 72 new tests, all passing
Docs: API server guide, Open WebUI integration guide, env var reference

* feat(whatsapp): make reply prefix configurable via config.yaml

Reworked from PR #1764 (ifrederico) to use config.yaml instead of .env.

The WhatsApp bridge prepends a header to every outgoing message.
This was hardcoded to '⚕ *Hermes Agent*'. Users can now customize
or disable it via config.yaml:

  whatsapp:
    reply_prefix: ''                     # disable header
    reply_prefix: '🤖 *My Bot*\n───\n'  # custom prefix

How it works:
- load_gateway_config() reads whatsapp.reply_prefix from config.yaml
  and stores it in PlatformConfig.extra['reply_prefix']
- WhatsAppAdapter reads it from config.extra at init
- When spawning bridge.js, the adapter passes it as
  WHATSAPP_REPLY_PREFIX in the subprocess environment
- bridge.js handles undefined (default), empty (no header),
  or custom values with \\n escape support
- Self-chat echo suppression uses the configured prefix

Also fixes _config_version: was 9 but ENV_VARS_BY_VERSION had a
key 10 (TAVILY_API_KEY), so existing users at v9 would never be
prompted for Tavily. Bumped to 10 to close the gap. Added a
regression test to prevent this from happening again.

Credit: ifrederico (PR #1764) for the bridge.js implementation
and the config version gap discovery.

---------

Co-authored-by: Test <test@test.com>
2026-03-17 10:44:37 -07:00
2026-03-14 22:49:57 -07:00
2026-02-25 11:53:44 -08:00
2026-01-31 06:30:48 +00:00
2026-03-07 13:43:08 -08:00

Hermes Agent

Hermes Agent ☤

Documentation Discord License: MIT Built by Nous Research

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), z.ai/GLM, Kimi/Moonshot, MiniMax, OpenAI, or your own endpoint. Switch with hermes model — no code changes, no lock-in.

A real terminal interfaceFull TUI with multiline editing, slash-command autocomplete, conversation history, interrupt-and-redirect, and streaming tool output.
Lives where you doTelegram, Discord, Slack, WhatsApp, Signal, and CLI — all from a single gateway process. Voice memo transcription, cross-platform conversation continuity.
A closed learning loopAgent-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 automationsBuilt-in cron scheduler with delivery to any platform. Daily reports, nightly backups, weekly audits — all in natural language, running unattended.
Delegates and parallelizesSpawn 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 laptopSix 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-readyBatch 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, and WSL2. The installer handles everything — Python, Node.js, dependencies, and the hermes command. No prerequisites except git.

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

📖 Full documentation →

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
git submodule update --init mini-swe-agent   # required terminal backend
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]"
uv pip install -e "./mini-swe-agent"
python -m pytest tests/ -q

RL Training (optional): To work on the RL/Tinker-Atropos integration, also run:

git submodule update --init tinker-atropos
uv pip install -e "./tinker-atropos"

Community


License

MIT — see LICENSE.

Built by Nous Research.

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