1cf1016e72
Bedrock rejects ``global-anthropic-claude-opus-4-7`` with ``HTTP 400:
The provided model identifier is invalid`` because its inference
profile IDs embed structural dots
(``global.anthropic.claude-opus-4-7``) that ``normalize_model_name``
was converting to hyphens. ``AIAgent._anthropic_preserve_dots`` did
not include ``bedrock`` in its provider allowlist, so every Claude-on-
Bedrock request through the AnthropicBedrock SDK path shipped with
the mangled model ID and failed.
Root cause
----------
``run_agent.py:_anthropic_preserve_dots`` (previously line 6589)
controls whether ``agent.anthropic_adapter.normalize_model_name``
converts dots to hyphens. The function listed Alibaba, MiniMax,
OpenCode Go/Zen and ZAI but not Bedrock, so when a user set
``provider: bedrock`` with a dotted inference-profile model the flag
returned False and ``normalize_model_name`` mangled every dot in the
ID. All four call sites in run_agent.py
(``build_anthropic_kwargs`` + three fallback / review / summary paths
at lines 6707, 7343, 8408, 8440) read from this same helper.
The bug shape matches #5211 for opencode-go, which was fixed in commit
f77be22c by extending this same allowlist.
Fix
---
* Add ``"bedrock"`` to the provider allowlist.
* Add ``"bedrock-runtime."`` to the base-URL heuristic as
defense-in-depth, so a custom-provider-shaped config with
``base_url: https://bedrock-runtime.<region>.amazonaws.com`` also
takes the preserve-dots path even if ``provider`` isn't explicitly
set to ``"bedrock"``. This mirrors how the code downstream at
run_agent.py:759 already treats either signal as "this is Bedrock".
Bedrock model ID shapes covered
-------------------------------
| Shape | Preserved |
| --- | --- |
| ``global.anthropic.claude-opus-4-7`` (reporter's exact ID) | ✓ |
| ``us.anthropic.claude-sonnet-4-5-20250929-v1:0`` | ✓ |
| ``apac.anthropic.claude-haiku-4-5`` | ✓ |
| ``anthropic.claude-3-5-sonnet-20241022-v2:0`` (foundation) | ✓ |
| ``eu.anthropic.claude-3-5-sonnet`` (regional inference profile) | ✓ |
Non-Claude Bedrock models (Nova, Llama, DeepSeek) take the
``bedrock_converse`` / boto3 path which does not call
``normalize_model_name``, so they were never affected by this bug
and remain unaffected by the fix.
Narrow scope — explicitly not changed
-------------------------------------
* ``bedrock_converse`` path (non-Claude Bedrock models) — already
correct; no ``normalize_model_name`` in that pipeline.
* Provider aliases (``aws``, ``aws-bedrock``, ``amazon``,
``amazon-bedrock``) — if a user bypasses the alias-normalization
pipeline and passes ``provider="aws"`` directly, the base-URL
heuristic still catches it because Bedrock always uses a
``bedrock-runtime.`` endpoint. Adding the aliases themselves to the
provider set is cheap but would be scope creep for this fix.
* No other places in ``agent/anthropic_adapter.py`` mangle dots, so
the fix is confined to ``_anthropic_preserve_dots``.
Regression coverage
-------------------
``tests/agent/test_bedrock_integration.py`` gains three new classes:
* ``TestBedrockPreserveDotsFlag`` (5 tests): flag returns True for
``provider="bedrock"`` and for Bedrock runtime URLs (us-east-1 and
ap-northeast-2 — the reporter's region); returns False for non-
Bedrock AWS URLs like ``s3.us-east-1.amazonaws.com``; canary that
Anthropic-native still returns False.
* ``TestBedrockModelNameNormalization`` (5 tests): every documented
Bedrock model-ID shape survives ``normalize_model_name`` with the
flag on; inverse canary pins that ``preserve_dots=False`` still
mangles (so a future refactor can't decouple the flag from its
effect).
* ``TestBedrockBuildAnthropicKwargsEndToEnd`` (2 tests): integration
through ``build_anthropic_kwargs`` shows the reporter's exact model
ID ends up unmangled in the outgoing kwargs.
Three of the new flag tests fail on unpatched ``origin/main`` with
``assert False is True`` (preserve-dots returning False for Bedrock),
confirming the regression is caught.
Validation
----------
``source venv/bin/activate && python -m pytest
tests/agent/test_bedrock_integration.py tests/agent/test_minimax_provider.py
-q`` -> 84 passed (40 new bedrock tests + 44 pre-existing, including
the minimax canaries that pin the pattern this fix mirrors).
CI-aligned broad suite: 12827 passed, 39 skipped, 19 pre-existing
baseline failures (all reproduce on clean ``origin/main``; none in
the touched code path).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.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), 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 | 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 — 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]"
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.