* fix(amazon-bedrock): add known model context windows to discovery Bedrock's ListFoundationModels API does not expose token limits. Discovery was hardcoding contextWindow: 32000 for every model, causing Claude (1M), Nova (300K), and other models to hit premature 'Context limit exceeded' errors and unnecessary session resets. Adds a lookup table of known context windows for Bedrock models: - Anthropic Claude: 200K-1M - Amazon Nova: 128K-1M - Meta Llama: 128K - Mistral: 32K-128K - DeepSeek: 128K - Cohere: 128K - AI21 Jamba: 256K Inference profile prefixes (us., eu., ap., global.) are stripped before lookup, so us.anthropic.claude-opus-4-6-v1 correctly resolves to 1M. Also raises the default fallback from 32K to 128K for unknown models — most modern models have at least 128K context. Single file change, no type system modifications. Complementary to #65030 (provenance flag for warning on unknown models). Fixes #64919 Related: #64250 * add KNOWN_MAX_TOKENS map and expand model coverage - Add KNOWN_MAX_TOKENS lookup table with Bedrock-optimized values that balance response quality against quota burndown (5x rate for Claude 3.7+) - Add missing models to KNOWN_CONTEXT_WINDOWS: Opus 4.7 (1M), Opus 4.1/4.5, Sonnet 4, Claude 3/3.5 Haiku, DeepSeek V3/V3.2, Google Gemma 3 - Refactor prefix-stripping into shared resolveKnownValue() helper - Fix: use !== undefined instead of truthy check for table lookups - Wire resolveKnownMaxTokens into toModelDefinition and resolveInferenceProfiles Quota burndown context: Bedrock reserves input_tokens + max_tokens from TPM at request start. For Claude 3.7+, output burns at 5x. The values in KNOWN_MAX_TOKENS are intentionally conservative (8-16K for Claude) to maximize concurrent throughput while still allowing useful responses. Thinking budget is added separately by the runtime. * remove KNOWN_MAX_TOKENS — maxTokens should be handled upstream Remove the KNOWN_MAX_TOKENS map. Hardcoding maxTokens values in discovery is the wrong layer to solve this — any explicit value still gets reserved against Bedrock's TPM quota at request start. The correct fix is upstream in pi's Bedrock provider: omit maxTokens from inferenceConfig when not explicitly set, letting the model use its internal default. This avoids quota waste entirely. See: badlogic/pi-mono#3399 and badlogic/pi-mono#3400 Keep the expanded KNOWN_CONTEXT_WINDOWS (context windows ARE the right thing to set in discovery — they affect compaction thresholds and session management, not API-level quota reservation). * docs: clarify why hardcoded context windows are needed Bedrock's ListFoundationModels and GetFoundationModel APIs return no token limit information — there is no Bedrock API to discover context windows or max output tokens programmatically. Note that this table should become a fallback if AWS adds token metadata in the future. * fix: add au and apac to inference profile prefix regex Add missing geo prefixes discovered by querying inference profiles across multiple regions: - au. (Australia/NZ, used in ap-southeast-2/4/6) - apac. (Asia-Pacific, used for older models in ap-northeast-1) Both resolveKnownContextWindow and resolveBaseModelId now handle all known prefixes: us, eu, ap, apac, au, jp, global. * test: port au. prefix test from #65449 by @alickgithub2, add apac. coverage Port the Australia/NZ inference profile test from PR #65449 (credit: @alickgithub2) and extend it to also cover the apac. prefix discovered in ap-northeast-1. * expand model coverage: Llama 4, MiniMax, NVIDIA, Mistral 3, GLM, Qwen Cross-referenced KNOWN_CONTEXT_WINDOWS against live list-foundation-models API. Added missing models: - Llama 4 Maverick (1M) and Scout (512K) - MiniMax M2/M2.1/M2.5 (1M) - NVIDIA Nemotron Super/Nano variants (128K) - Mistral Large 3 675B (128K) - GLM 4.7/4.7-flash/5 (128K) - Qwen3 Coder/32B/VL (128-256K) Removed deprecated deepseek.v3-v1:0 and claude-opus-4-20250514 (not in active foundation models list). * raise default context window from 128K to 200K 200K matches the floor for all current Claude models (the most popular on Bedrock). Every other active model with a lower actual limit is already in the explicit table. This ensures new Claude models get a correct default without requiring a table update. * test: update discovery test expectations for known context window values * test: fix remaining contextWindow expectation (default 200K) * fix(amazon-bedrock): keep conservative context fallback * docs(changelog): note Bedrock context window fix * fix(amazon-bedrock): normalize known context fallback --------- Co-authored-by: Vincent Koc <vincentkoc@ieee.org>
🦞 OpenClaw — Personal AI Assistant
EXFOLIATE! EXFOLIATE!
OpenClaw is a personal AI assistant you run on your own devices. It answers you on the channels you already use. It can speak and listen on macOS/iOS/Android, and can render a live Canvas you control. The Gateway is just the control plane — the product is the assistant.
If you want a personal, single-user assistant that feels local, fast, and always-on, this is it.
Supported channels include: WhatsApp, Telegram, Slack, Discord, Google Chat, Signal, iMessage, BlueBubbles, IRC, Microsoft Teams, Matrix, Feishu, LINE, Mattermost, Nextcloud Talk, Nostr, Synology Chat, Tlon, Twitch, Zalo, Zalo Personal, WeChat, QQ, WebChat.
Website · Docs · Vision · DeepWiki · Getting Started · Updating · Showcase · FAQ · Onboarding · Nix · Docker · Discord
New install? Start here: Getting started
Preferred setup: run openclaw onboard in your terminal.
OpenClaw Onboard guides you step by step through setting up the gateway, workspace, channels, and skills. It is the recommended CLI setup path and works on macOS, Linux, and Windows (via WSL2; strongly recommended).
Works with npm, pnpm, or bun.
Sponsors
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Subscriptions (OAuth):
- OpenAI (ChatGPT/Codex)
Model note: while many providers and models are supported, prefer a current flagship model from the provider you trust and already use. See Onboarding.
Install (recommended)
Runtime: Node 24 (recommended) or Node 22.16+.
npm install -g openclaw@latest
# or: pnpm add -g openclaw@latest
openclaw onboard --install-daemon
OpenClaw Onboard installs the Gateway daemon (launchd/systemd user service) so it stays running.
Quick start (TL;DR)
Runtime: Node 24 (recommended) or Node 22.16+.
Full beginner guide (auth, pairing, channels): Getting started
openclaw onboard --install-daemon
openclaw gateway --port 18789 --verbose
# Send a message
openclaw message send --to +1234567890 --message "Hello from OpenClaw"
# Talk to the assistant (optionally deliver back to any connected channel: WhatsApp/Telegram/Slack/Discord/Google Chat/Signal/iMessage/BlueBubbles/IRC/Microsoft Teams/Matrix/Feishu/LINE/Mattermost/Nextcloud Talk/Nostr/Synology Chat/Tlon/Twitch/Zalo/Zalo Personal/WeChat/QQ/WebChat)
openclaw agent --message "Ship checklist" --thinking high
Upgrading? Updating guide (and run openclaw doctor).
Models config + CLI: Models. Auth profile rotation + fallbacks: Model failover.
Security defaults (DM access)
OpenClaw connects to real messaging surfaces. Treat inbound DMs as untrusted input.
Full security guide: Security
Default behavior on Telegram/WhatsApp/Signal/iMessage/Microsoft Teams/Discord/Google Chat/Slack:
- DM pairing (
dmPolicy="pairing"/channels.discord.dmPolicy="pairing"/channels.slack.dmPolicy="pairing"; legacy:channels.discord.dm.policy,channels.slack.dm.policy): unknown senders receive a short pairing code and the bot does not process their message. - Approve with:
openclaw pairing approve <channel> <code>(then the sender is added to a local allowlist store). - Public inbound DMs require an explicit opt-in: set
dmPolicy="open"and include"*"in the channel allowlist (allowFrom/channels.discord.allowFrom/channels.slack.allowFrom; legacy:channels.discord.dm.allowFrom,channels.slack.dm.allowFrom).
Run openclaw doctor to surface risky/misconfigured DM policies.
Highlights
- Local-first Gateway — single control plane for sessions, channels, tools, and events.
- Multi-channel inbox — WhatsApp, Telegram, Slack, Discord, Google Chat, Signal, BlueBubbles (iMessage), iMessage (legacy), IRC, Microsoft Teams, Matrix, Feishu, LINE, Mattermost, Nextcloud Talk, Nostr, Synology Chat, Tlon, Twitch, Zalo, Zalo Personal, WeChat, QQ, WebChat, macOS, iOS/Android.
- Multi-agent routing — route inbound channels/accounts/peers to isolated agents (workspaces + per-agent sessions).
- Voice Wake + Talk Mode — wake words on macOS/iOS and continuous voice on Android (ElevenLabs + system TTS fallback).
- Live Canvas — agent-driven visual workspace with A2UI.
- First-class tools — browser, canvas, nodes, cron, sessions, and Discord/Slack actions.
- Companion apps — macOS menu bar app + iOS/Android nodes.
- Onboarding + skills — onboarding-driven setup with bundled/managed/workspace skills.
Security model (important)
- Default: tools run on the host for the
mainsession, so the agent has full access when it is just you. - Group/channel safety: set
agents.defaults.sandbox.mode: "non-main"to run non-mainsessions inside sandboxes. Docker is the default sandbox backend; SSH and OpenShell backends are also available. - Typical sandbox default: allow
bash,process,read,write,edit,sessions_list,sessions_history,sessions_send,sessions_spawn; denybrowser,canvas,nodes,cron,discord,gateway. - Before exposing anything remotely, read Security, Sandboxing, and Configuration.
Operator quick refs
- Chat commands:
/status,/new,/reset,/compact,/think <level>,/verbose on|off,/trace on|off,/usage off|tokens|full,/restart,/activation mention|always - Session tools:
sessions_list,sessions_history,sessions_send - Skills registry: ClawHub
- Architecture overview: Architecture
Docs by goal
- New here: Getting started, Onboarding, Updating
- Channel setup: Channels index, WhatsApp, Telegram, Discord, Slack
- Apps + nodes: macOS, iOS, Android, Nodes
- Config + security: Configuration, Security, Sandboxing
- Remote + web: Gateway, Remote access, Tailscale, Web surfaces
- Tools + automation: Tools, Skills, Cron jobs, Webhooks, Gmail Pub/Sub
- Internals: Architecture, Agent, Session model, Gateway protocol
- Troubleshooting: Channel troubleshooting, Logging, Docs home
Apps (optional)
The Gateway alone delivers a great experience. All apps are optional and add extra features.
If you plan to build/run companion apps, follow the platform runbooks below.
macOS (OpenClaw.app) (optional)
- Menu bar control for the Gateway and health.
- Voice Wake + push-to-talk overlay.
- WebChat + debug tools.
- Remote gateway control over SSH.
Note: signed builds required for macOS permissions to stick across rebuilds (see macOS Permissions).
iOS node (optional)
- Pairs as a node over the Gateway WebSocket (device pairing).
- Voice trigger forwarding + Canvas surface.
- Controlled via
openclaw nodes ….
Runbook: iOS connect.
Android node (optional)
- Pairs as a WS node via device pairing (
openclaw devices ...). - Exposes Connect/Chat/Voice tabs plus Canvas, Camera, Screen capture, and Android device command families.
- Runbook: Android connect.
From source (development)
Prefer pnpm for builds from source. Bun is optional for running TypeScript directly.
For the dev loop:
git clone https://github.com/openclaw/openclaw.git
cd openclaw
pnpm install
# First run only (or after resetting local OpenClaw config/workspace)
pnpm openclaw setup
# Optional: prebuild Control UI before first startup
pnpm ui:build
# Dev loop (auto-reload on source/config changes)
pnpm gateway:watch
If you need a built dist/ from the checkout (for Node, packaging, or release validation), run:
pnpm build
pnpm ui:build
pnpm openclaw setup writes the local config/workspace needed for pnpm gateway:watch. It is safe to re-run, but you normally only need it on first setup or after resetting local state. pnpm gateway:watch does not rebuild dist/control-ui, so rerun pnpm ui:build after ui/ changes or use pnpm ui:dev when iterating on the Control UI. If you want this checkout to run onboarding directly, use pnpm openclaw onboard --install-daemon.
Note: pnpm openclaw ... runs TypeScript directly (via tsx). pnpm build produces dist/ for running via Node / the packaged openclaw binary, while pnpm gateway:watch rebuilds the runtime on demand during the dev loop.
Development channels
- stable: tagged releases (
vYYYY.M.DorvYYYY.M.D-<patch>), npm dist-taglatest. - beta: prerelease tags (
vYYYY.M.D-beta.N), npm dist-tagbeta(macOS app may be missing). - dev: moving head of
main, npm dist-tagdev(when published).
Switch channels (git + npm): openclaw update --channel stable|beta|dev.
Details: Development channels.
Agent workspace + skills
- Workspace root:
~/.openclaw/workspace(configurable viaagents.defaults.workspace). - Injected prompt files:
AGENTS.md,SOUL.md,TOOLS.md. - Skills:
~/.openclaw/workspace/skills/<skill>/SKILL.md.
Configuration
Minimal ~/.openclaw/openclaw.json (model + defaults):
{
agent: {
model: "<provider>/<model-id>",
},
}
Full configuration reference (all keys + examples).
Star History
Molty
OpenClaw was built for Molty, a space lobster AI assistant. 🦞 by Peter Steinberger and the community.
Community
See CONTRIBUTING.md for guidelines, maintainers, and how to submit PRs. AI/vibe-coded PRs welcome! 🤖
Special thanks to Mario Zechner for his support and for pi-mono. Special thanks to Adam Doppelt for the lobster.bot domain.
Thanks to all clawtributors: