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* feat(video_gen): unified video_generate tool with pluggable provider backends One core video_generate tool, every backend a plugin. Mirrors the image_gen + memory_provider + context_engine architecture: ABC, registry, plugin-context registration hook, and per-plugin model catalogs surfaced through hermes tools. Surface (one schema, every backend): - operation: generate / edit / extend - modalities: text-to-video (prompt only), image-to-video (prompt + image_url), video edit (prompt + video_url), video extend (video_url) - reference_image_urls, duration, aspect_ratio, resolution, negative_prompt, audio, seed, model override - Providers ignore unknown kwargs and declare what they support via VideoGenProvider.capabilities() — backend-specific quirks stay in the backend, the agent learns one tool Backends shipped: - plugins/video_gen/xai/ — Grok-Imagine, full generate/edit/extend + image-to-video + reference images (salvaged from PR #10600 by @Jaaneek, reshaped into the plugin interface) - plugins/video_gen/fal/ — Veo 3.1 (t2v + i2v), Kling O3 i2v, Pixverse v6 i2v with model-aware payload building that drops keys a model doesn't declare Wiring: - agent/video_gen_provider.py — VideoGenProvider ABC, normalize_operation, success_response / error_response, save_b64_video / save_bytes_video, $HERMES_HOME/cache/videos/ - agent/video_gen_registry.py — thread-safe register/get/list + get_active_provider() reading video_gen.provider from config.yaml - hermes_cli/plugins.py — PluginContext.register_video_gen_provider() - hermes_cli/tools_config.py — Video Generation category in hermes tools, plugin-only providers list, model picker per plugin, config write to video_gen.{provider,model} - toolsets.py — new video_gen toolset - tests: 31 new tests covering ABC, registry, tool dispatch, both plugins - docs: developer-guide/video-gen-provider-plugin.md (parallel to the image-gen guide), sidebar + toolsets-reference + plugin guides updated Supersedes: #25035 (FAL), #17972 (FAL), #14543 (xAI), #13847 (HappyHorse), #10458 (provider categories), #10786 (xAI media+search bundle), #2984 (FAL duplicate), #19086 (Google Veo standalone — easy port to plugin interface). Co-authored-by: Jaaneek <Jaaneek@users.noreply.github.com> * feat(video_gen): dynamic schema reflects active backend's capabilities Address the 'capability variance' question — instead of one tool with a static schema that lies about what every backend supports, the video_generate tool now rebuilds its description at get_definitions() time based on the configured video_gen.provider and video_gen.model. The agent sees backend-specific guidance up-front: - 'fal-ai/veo3.1/image-to-video': 'image-to-video only — image_url is REQUIRED; text-only prompts will be rejected' - 'fal-ai/veo3.1' (t2v): no image_url restriction shown - xAI grok-imagine-video: 'operations: generate, edit, extend; up to 7 reference_image_urls' - Backends without edit/extend: 'not supported on this backend — surface that they need to switch backends via hermes tools' This is the same pattern PR #22694 used for delegate_task self-capping — documented in the dynamic-tool-schemas skill. Cache invalidation is free: get_tool_definitions() already memoizes on config.yaml mtime, so a mid-session backend swap rebuilds the schema automatically. Tested: - Empirical FAL OpenAPI schema check confirms image-to-video models require image_url (FAL returns HTTP 422 otherwise) — client-side rejection in FALVideoGenProvider.generate() now prevents the wasted round-trip - Live E2E: fal-ai/veo3.1/image-to-video + prompt-only → clean missing_image_url error; fal-ai/veo3.1 + prompt-only → dispatches - 6 new tests cover the builder (no config / image-only / full-surface / text-only / unknown provider / registry wiring), all passing - 37/37 in the slice, 134/134 in the broader regression set * test(video_gen/xai): full surface integration tests + cleaner schema Verified end-to-end that the xAI plugin handles every documented mode from PR #10600's surface: text-to-video, image-to-video, reference-images-to-video, video edit, video extend (with and without prompt). All five modes route to the correct xAI endpoint (/videos/generations, /videos/edits, /videos/extensions) with the right payload shape (image / reference_images / video keys), and all five client-side rejections fire before the network: edit-without-prompt, extend-without-video_url, image+refs conflict, >7 references, and duration/aspect_ratio clamping. 15 new integration tests grouped into four classes (endpoint routing, modalities, validation, clamping). httpx is stubbed via a small fake AsyncClient that records POSTs so the tests assert the actual payload the plugin would send to xAI — not just the success/error envelope. Also cleaned up a description redundancy: when a model's operations match the backend's overall set, we no longer print the duplicate 'operations supported by this model' line. xAI's description now reads: Active backend: xAI . model: grok-imagine-video - operations supported by this backend: edit, extend, generate - modalities supported by this backend: image, reference_images, text - aspect_ratio choices: 16:9, 1:1, 2:3, 3:2, 3:4, 4:3, 9:16 - resolution choices: 480p, 720p - duration range: 1-15s - reference_image_urls: up to 7 images Co-authored-by: Jaaneek <Jaaneek@users.noreply.github.com> * feat(video_gen): collapse surface to t2v + i2v, family-based auto-routing Two design changes per Teknium: 1) Drop edit/extend from the tool surface entirely. Only text-to-video and image-to-video remain. The agent sees a clean tool with two modalities; backend-specific quirks like xAI's edit/extend endpoints stay out of the unified schema. 2) FAL: pick a model FAMILY once, the plugin routes between the family's text-to-video and image-to-video endpoints based on whether image_url was passed. Users no longer pick 'fal-ai/veo3.1' AND 'fal-ai/veo3.1/image-to-video' as separate options — they pick 'veo3.1', and the plugin handles the rest. Catalog rewritten as families: veo3.1 fal-ai/veo3.1 / fal-ai/veo3.1/image-to-video pixverse-v6 fal-ai/pixverse/v6/text-to-video / fal-ai/pixverse/v6/image-to-video kling-o3-standard fal-ai/kling-video/o3/standard/text-to-video / fal-ai/kling-video/o3/standard/image-to-video xAI uses a single endpoint (/videos/generations) for both modes, routed by the presence of the 'image' field in the payload — no edit/extend exposure. Schema changes: - VIDEO_GENERATE_SCHEMA: drop operation, drop video_url. Final params: prompt (required), image_url, reference_image_urls, duration, aspect_ratio, resolution, negative_prompt, audio, seed, model. - VideoGenProvider ABC: drop normalize_operation, VALID_OPERATIONS, DEFAULT_OPERATION. capabilities() drops 'operations' key. - success_response: add 'modality' field ('text' | 'image') so the agent and logs can see which endpoint was actually hit. Dynamic schema builder simplified — no operations bullet, no 'switch backends if you need edit/extend' guidance. When the active backend supports both modalities (the common case), description reads: Active backend: FAL . model: pixverse-v6 - supports both text-to-video (omit image_url) and image-to-video (pass image_url) - routes automatically - aspect_ratio choices: 16:9, 9:16, 1:1 - resolution choices: 360p, 540p, 720p, 1080p - duration range: 1-15s - audio: pass audio=true to enable native audio (pricing tier) - negative_prompt: supported Tests: 51 in the video_gen slice, 216 across the broader image+video sweep, all passing. New FAL routing tests prove pixverse-v6 + no image hits text-to-video endpoint, pixverse-v6 + image_url hits image-to-video endpoint, same for veo3.1 and kling-o3-standard. Docs updated: developer-guide page rewrites the 'model families' pattern as a first-class section so external plugin authors know the convention. toolsets-reference and toolsets.py descriptions match the new surface. Co-authored-by: Jaaneek <Jaaneek@users.noreply.github.com> * feat(video_gen/fal): expand catalog to 6 families, cheap + premium tiers Catalog now covers everything Teknium specced from FAL: Cheap tier: ltx-2.3 fal-ai/ltx-2.3-22b/text-to-video / image-to-video pixverse-v6 fal-ai/pixverse/v6/text-to-video / image-to-video Premium tier: veo3.1 fal-ai/veo3.1 / fal-ai/veo3.1/image-to-video seedance-2.0 bytedance/seedance-2.0/text-to-video / image-to-video kling-v3-4k fal-ai/kling-video/v3/4k/text-to-video / image-to-video happy-horse fal-ai/happy-horse/text-to-video / image-to-video DEFAULT_MODEL moved from veo3.1 (premium) to pixverse-v6 (cheap, sane defaults, both modalities) — better first-run UX for users who haven't explicitly picked a model. New family-entry knob: image_param_key. Kling v3 4K's image-to-video endpoint expects start_image_url instead of image_url; declaring image_param_key='start_image_url' on the family lets _build_payload remap correctly. Other families default to plain image_url. Per-family capability flags reflect each model's docs: - LTX 2.3 + Happy Horse: minimal payloads (no duration/aspect/resolution enum exposed by FAL — let endpoint apply defaults) - Seedance: 6 aspect ratios incl 21:9, durations 4-15, audio supported, negative prompts NOT supported per docs - Kling v3 4K: 16:9/9:16/1:1, 3-15s, audio + negative - Veo 3.1: unchanged, 16:9/9:16, 4/6/8s Tests: +5 covering the new families (full catalog, Kling 4K start_image_url remap, Seedance routing, LTX payload minimality, Happy Horse minimality). 56/56 in the slice green. Note: I did NOT add the FAL-hosted xAI Grok-Imagine variant. Hermes already has a direct xAI plugin that talks to xAI's own API; routing the same model through FAL's wrapper would duplicate the surface without adding capabilities. Users on FAL who want Grok-Imagine should use the xAI plugin directly; flag if you want both routes available. * test(video_gen): tool-surface routing matrix — every model x modality End-to-end matrix test driven through _handle_video_generate() — the actual function the agent's video_generate tool call lands in. Writes config.yaml, invokes the registered handler with a raw args dict, then asserts the outbound HTTP/SDK call hit the right endpoint with the right payload shape. Parametrized over FAL_FAMILIES.keys() so the matrix auto-discovers new families as they're added (add a family to FAL_FAMILIES and you get both modalities tested for free). Coverage: - All 6 FAL families x {text-only, text+image} = 12 cases - xAI x {text-only, text+image} = 2 cases - tool-level model= arg overrides config = 2 cases For each case, verifies: - result['success'] is True - result['modality'] matches input shape ('text' if no image_url, 'image' otherwise) - outbound endpoint URL matches the family's text_endpoint or image_endpoint - text-only payloads carry no image-shaped keys - text+image payloads carry the family's image key (image_url for most, start_image_url for kling-v3-4k, wrapped 'image' object for xAI) All 16 cases passing. Confirms the tool surface routes every (provider, model, modality) combination correctly with zero leakage. * feat(video_gen): keep video_gen out of first-run setup, surface in status Two changes: 1. video_gen joins _DEFAULT_OFF_TOOLSETS, so it is NOT pre-selected in the first-run toolset checklist. Video gen is niche, paid, and slow — most users don't want it nagging them during initial setup. Anyone who wants it opts in via 'hermes tools' -> Video Generation, which already routes to the provider+model picker. 2. The 'hermes setup' status panel learns about video_gen — but only shows the row when a plugin reports available. Users without FAL_KEY/XAI_API_KEY see nothing about video gen; users with one of those keys see 'Video Generation (FAL) ✓' as confirmation it's wired. Verified live: - Fresh install (no creds): zero video_gen mentions in wizard. - With FAL_KEY: status row appears with active backend name. - 160/160 in the setup + tools_config + video_gen test slice. Rationale: image_gen is on by default because it's a featured creative tool used in casual chat (telegrams, etc). Video gen is heavier — long wait, paid per-second pricing. Default-off matches user intent better. --------- Co-authored-by: Jaaneek <Jaaneek@users.noreply.github.com> |
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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), 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, Atropos RL environments, 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
RL Training (optional): The RL/Atropos integration (
environments/) — seeCONTRIBUTING.mdfor the full setup.
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.