yatulandClaude Opus 5
0e3eb57317
* feat(delegate): add action=list, scoped to the originating chat Fixes #1545. A delegation result was addressable only by the UUID returned once in a tool result, which the calling model had to carry forward by hand. One mistyped character orphaned a completed, durably stored result with no way back: `get` answers "delegation result not found", and there was nothing else to ask. Observed in production with a 31B-class caller — one flipped character, and separately a splice of the previous delegation's tail onto the next one's prefix. `spawn`, the sibling async mechanism over the same table, has had list/wait/cancel all along; `delegate` had delegate/get. Scope is tenant and calling agent, as get already resolves, plus the origin chat. The chat rather than the session, for three reasons: - It survives a session reset. Deferring long work, clearing the context and coming back to ask for status is ordinary use; a SessionKey predicate would return nothing exactly then — when the handle is most likely already lost. - It keeps chats apart, which is the enumeration boundary #1525 is about: there spawn's list filters on the parent agent key alone and ignores the session, so one chat reads another chat's task text. - It does not carry a conversation between chats. A delegation raised in a team chat stays visible in that team chat and does not surface in someone's DM with the same agent. History stays where it began. In a direct chat that separates users as well, since the chat ID is per person. Group chats deliberately show the group what the group started. get is left as it was, deliberately. #1525 is an enumeration defect — no prior knowledge needed and task text is disclosed. get is access through an unguessable handle, and adding a predicate there would break fetching a result by an ID kept across a reset, which is the very failure this fixes. No schema change: the origin fields are already persisted by createDelegateCompletion. ListByParent is filtered in Go behind a cap of 20, which suits handle recovery; a dedicated predicate would be the next step if this ever needs to page. Tests pin the chat boundary, the session-reset case, refusal when there is no chat to scope to (without querying the store), and the cap. The fake store leaves ListBySession embedded and nil, so a refactor back to session scoping panics rather than passing quietly. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> * fix(delegate): list needs its own store query — ListByParent excludes delegations The action shipped in 506ecba4 always returned an empty list. It read through SubagentTaskStore.ListByParent, whose SQL carries AND COALESCE(metadata->>'completion_kind', 'subagent') <> 'delegate' ListBySession carries the same clause. Both serve spawn and filter delegations out on purpose, so no listing in the store could return one — only Get by ID reaches a delegation. The feature was a no-op in production while its unit tests were green, because the fake store returned whatever rows the fixture supplied and never reproduced the predicate that does the damage. Found by running it against a live cluster: an async delegation was created, `get` returned it completed with its result, and `list` reported zero. Adds ListDelegationsByChat to the interface and to both implementations, with the inverse predicate plus origin_chat_id, and points the tool at it. Chat scope and delegate-only selection now live in the query rather than in a Go filter over whatever the store happened to return; an empty chat yields no rows instead of falling back to everything. Tests are where the fix matters most: - internal/store/sqlitestore exercises the real SQL. It pins that ListDelegationsByChat returns the chat's delegation, that a spawn in the same chat is not one, that another tenant's identically named chat stays invisible, and — the part that would have caught this — that ListByParent and ListBySession still do not return delegations, so the complementarity is documented rather than assumed. - the tool's fake now panics if ListByParent or ListBySession is called, so a regression to either fails loudly instead of quietly listing nothing, and it applies the chat and kind predicates itself so fixtures behave like the store. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 5 <noreply@anthropic.com>
Multi-Tenant AI Agent Platform
Multi-agent AI gateway built in Go. 20+ LLM providers. 7 channels. Multi-tenant PostgreSQL.
Single binary. Production-tested. Agents that orchestrate for you.
Documentation • Quick Start • Twitter / X
🌐 Languages: 🇻🇳 Tiếng Việt · 🇨🇳 简体中文 · 🇯🇵 日本語 · 🇰🇷 한국어 · 🇵🇭 Tagalog · 🇪🇸 Español · 🇧🇷 Português · 🇮🇹 Italiano · 🇩🇪 Deutsch · 🇫🇷 Français · 🇸🇦 العربية · 🇮🇳 हिन्दी · 🇷🇺 Русский · 🇧🇩 বাংলা · 🇮🇱 עברית · 🇵🇱 Polski · 🇨🇿 Čeština · 🇳🇱 Nederlands · 🇹🇷 Türkçe · 🇺🇦 Українська · 🇮🇩 Bahasa Indonesia · 🇹🇭 ไทย · 🇵🇰 اردو · 🇷🇴 Română · 🇸🇪 Svenska · 🇬🇷 Ελληνικά · 🇭🇺 Magyar · 🇫🇮 Suomi · 🇩🇰 Dansk · 🇳🇴 Norsk
Core Features
- 8-Stage Agent Pipeline — context → history → prompt → think → act → observe → memory → summarize. Pluggable stages, always-on execution
- 4-Mode Prompt System — Full / Task / Minimal / None with section gating, cache boundary optimization, and per-session mode resolution
- 3-Tier Memory — Working (conversation) → Episodic (session summaries) → Semantic (knowledge graph). Progressive loading L0/L1/L2
- Knowledge Vault — Document registry with wikilinks, hybrid search (FTS + pgvector), filesystem sync
- Agent Teams & Orchestration — Shared task boards, inter-agent delegation (sync/async), 3 orchestration modes (auto/explicit/manual)
- Self-Evolution — Metrics → suggestions → auto-adapt with guardrails. Agents refine their own communication style
- Multi-Tenant PostgreSQL — Per-user workspaces, per-user context files, encrypted API keys (AES-256-GCM), RBAC, isolated sessions
- 20+ LLM Providers — Anthropic (native HTTP+SSE with prompt caching), OpenAI, OpenRouter, Groq, DeepSeek, Gemini, Mistral, xAI, MiniMax, DashScope, Claude CLI, Codex, ACP, and any OpenAI-compatible endpoint
- 7 Messaging Channels — Telegram, Discord, Slack, Zalo OA, Zalo Personal, Feishu/Lark, WhatsApp
- Production Security — 5-layer permission system, rate limiting, prompt injection detection, SSRF protection, AES-256-GCM encryption
- Single Binary — ~25 MB static Go binary, no Node.js runtime, <1s startup, runs on a $5 VPS
- Observability — Built-in LLM call tracing with spans and prompt cache metrics, optional OpenTelemetry OTLP export
Desktop Edition (GoClaw Lite)
A native desktop app for local AI agents — no Docker, no PostgreSQL, no infrastructure.
macOS:
curl -fsSL https://raw.githubusercontent.com/nextlevelbuilder/goclaw/main/scripts/install-lite.sh | bash
Windows (PowerShell):
irm https://raw.githubusercontent.com/nextlevelbuilder/goclaw/main/scripts/install-lite.ps1 | iex
What's Included
- Single native app (Wails v2 + React), ~30 MB
- SQLite database (zero setup)
- Chat with agents (streaming, tools, media, file attachments)
- Agent management (max 5), provider config, MCP servers, skills, cron
- Team tasks with Kanban board and real-time updates
- Auto-update from GitHub Releases
Lite vs Standard
| Feature | Lite (Desktop) | Standard (Server) |
|---|---|---|
| Agents | Max 5 | Unlimited |
| Teams | Max 1 (5 members) | Unlimited |
| Database | SQLite (local) | PostgreSQL |
| Memory | FTS5 text search | pgvector semantic |
| Channels | — | Telegram, Discord, Slack, Zalo, Feishu, WhatsApp |
| Knowledge Graph | — | Full |
| RBAC / Multi-tenant | — | Full |
| Auto-update | GitHub Releases | Docker / binary |
Building from Source
# Prerequisites: Go 1.26+, pnpm, Wails CLI (go install github.com/wailsapp/wails/v2/cmd/wails@latest)
make desktop-build # Build .app (macOS) or .exe (Windows)
make desktop-dmg VERSION=0.1.0 # Create .dmg installer (macOS only)
make desktop-dev # Dev mode with hot reload
Desktop Releases
Desktop uses independent versioning with lite-v* tags:
git tag lite-v0.1.0 && git push origin lite-v0.1.0
# → GitHub Actions builds macOS (.dmg + .tar.gz) + Windows (.zip)
# → Creates GitHub Release with all assets
Architecture
Quick Start
Prerequisites: Go 1.26+, PostgreSQL 18 with pgvector, Docker (optional)
From Source
git clone -b main https://github.com/nextlevelbuilder/goclaw.git && cd goclaw
make build
./goclaw onboard # Interactive setup wizard
source .env.local && ./goclaw
Note: The default branch is
dev(active development). Use-b mainto clone the stable release branch.
With Docker
# Generate .env with auto-generated secrets
chmod +x prepare-env.sh && ./prepare-env.sh
# Add at least one GOCLAW_*_API_KEY to .env, then:
make up
# If Postgres fails to start ("port 5432 already allocated"), set another host
# port in .env, e.g. POSTGRES_PORT=5433 (see .env.example).
# Web Dashboard at http://localhost:18790 (built-in)
# Health check: curl http://localhost:18790/health
# Optional: separate nginx for custom SSL/reverse proxy
# make up WITH_WEB_NGINX=1 → Dashboard at http://localhost:3000
make up creates a Docker network, embeds the correct version from git tags, builds and starts all services, and runs database migrations automatically.
Common commands:
make up # Start all services (build + migrate)
make down # Stop all services
make logs # Tail logs (goclaw service)
make reset # Wipe volumes and rebuild from scratch
Operator CLI:
The main goclaw binary can also inspect local or remote gateways:
goclaw traces list --status error
goclaw traces get <trace-id> -o json
goclaw --server https://goclaw.example.com --token "$GOCLAW_GATEWAY_TOKEN" traces follow --session <session-key>
Optional services — enable with WITH_* flags:
| Flag | Service | What it does |
|---|---|---|
WITH_BROWSER=1 |
Headless Chrome | Enables browser tool for web scraping, screenshots, automation |
WITH_OTEL=1 |
Jaeger | OpenTelemetry tracing UI for debugging LLM calls and latency |
WITH_SANDBOX=1 |
Docker sandbox | Isolated container for running untrusted code from agents |
WITH_TAILSCALE=1 |
Tailscale | Expose gateway over Tailscale private network |
WITH_REDIS=1 |
Redis | Redis-backed caching layer |
Flags can be combined and work with all commands:
# Start with browser automation and tracing
make up WITH_BROWSER=1 WITH_OTEL=1
# Stop everything including optional services
make down WITH_BROWSER=1 WITH_OTEL=1
When GOCLAW_*_API_KEY environment variables are set, the gateway auto-onboards without interactive prompts — detects provider, runs migrations, and seeds default data.
Docker image variants:
Image Description latestBackend + embedded web UI + Python (recommended) latest-baseBackend API-only, no web UI, no runtimes latest-fullAll runtimes + skill dependencies pre-installed latest-otelLatest + OpenTelemetry tracing goclaw-webStandalone nginx + React SPA (for custom reverse proxy) For custom builds (Tailscale, Redis):
docker build --build-arg ENABLE_TSNET=true ...See the Deployment Guide for details.
Updating
Docker
docker compose pull && docker compose up -d
Binary (with embedded web UI)
goclaw update --apply # Downloads, verifies SHA256, swaps binary, restarts
Web Dashboard
Open About dialog → click Update Now (admin only). The update includes both backend and web dashboard when using the default latest image.
Multi-Agent Orchestration
Each agent runs with its own identity, tools, LLM provider, and context files.
Agent Links define outbound, inbound, or bidirectional permission edges.
Delegation can run synchronously or asynchronously and exchanges files through
an isolated delegation workspace; validated outputs are published back under
the caller's .delegations/<delegation-id>/ directory.
Details: Agent Teams docs
Knowledge Vault
Document registry with [[wikilinks]] for bidirectional linking. Hybrid search combines full-text (BM25) and semantic (pgvector) for precise retrieval. Filesystem sync keeps vault in sync with on-disk files.
Self-Evolution
Agents improve themselves through a 3-stage guardrailed pipeline: metrics collection → suggestion analysis → auto-adaptation. Can refine communication style and domain expertise (CAPABILITIES.md) — but never change identity, name, or core purpose.
Provider Adapters
20+ LLM providers unified through a single adapter interface. Capability-based routing, encrypted API keys (AES-256-GCM), extended thinking support per-provider, and prompt caching for Anthropic + OpenAI.
Event-Driven Architecture
Typed domain events power the consolidation pipeline — session summaries, knowledge graph extraction, and dreaming promotion all run asynchronously via worker pools with dedup and retry.
Built-in Tools
30+ tools across 8 categories:
| Category | Tools | Description |
|---|---|---|
| Filesystem | read_file, write_file, edit_file, list_files, search, glob |
File operations with virtual FS routing |
| Runtime | exec, browser |
Shell commands (approval workflow) + browser automation |
| Web | web_search, web_fetch |
Search (Brave, DuckDuckGo) + content extraction |
| Memory | memory_search, memory_get, knowledge_graph_search |
3-tier memory + KG traversal |
| Media | create_image, create_audio, create_video, read_*, tts |
Generation + analysis (multi-provider) |
| Skills | skill_search, use_skill, skill_manage |
BM25 + semantic hybrid search |
| Teams | team_tasks, spawn, delegate, message |
Task board + orchestration + messaging |
| Automation | cron, heartbeat, sessions_* |
Scheduling + session management |
Full tool reference at docs.goclaw.sh
Webhook API
Trigger agents or send channel messages from external systems without the gateway token.
# Bearer auth — sync LLM call
curl -X POST https://example.com/v1/webhooks/llm \
-H "Authorization: Bearer wh_..." \
-H "Content-Type: application/json" \
-d '{"input":"Summarize today metrics","mode":"sync"}'
# HMAC auth — sign with hmac_signing_key from create response
TS=$(date +%s); BODY='{"input":"hi","mode":"sync"}'
SIG=$(echo -n "${TS}.${BODY}" | openssl dgst -sha256 -mac HMAC \
-macopt "hexkey:${WEBHOOK_HMAC_KEY}" | awk '{print $2}')
curl -X POST https://example.com/v1/webhooks/llm \
-H "Content-Type: application/json" \
-H "X-Webhook-Id: ${WEBHOOK_ID}" \
-H "X-GoClaw-Signature: t=${TS},v1=${SIG}" \
-d "$BODY"
See docs/webhooks.md for the full reference: auth, async callbacks, retry schedule, HMAC examples, channel matrix.
Documentation
Full documentation at docs.goclaw.sh — or browse the source in goclaw-docs/
| Section | Topics |
|---|---|
| Getting Started | Installation, Quick Start, Configuration, Web Dashboard Tour |
| Core Concepts | Agent Loop, Sessions, Tools, Memory, Multi-Tenancy |
| Agents | Creating Agents, Context Files, Personality, Sharing & Access |
| Providers | Anthropic, OpenAI, OpenRouter, Gemini, DeepSeek, +15 more |
| Channels | Telegram, Discord, Slack, Feishu, Zalo, WhatsApp, WebSocket |
| Agent Teams | Teams, Task Board, Messaging, Delegation & Handoff |
| Advanced | Custom Tools, MCP, Skills, Cron, Sandbox, Hooks, RBAC |
| Deployment | Docker Compose, Database, Security, Observability, Tailscale |
| Reference | CLI Commands, REST API, WebSocket Protocol, Environment Variables |
Testing
go test ./... # Unit tests
go test -v ./tests/integration/ -timeout 120s # Integration tests (requires running gateway)
Project Status
See CHANGELOG.md for detailed feature status including what's been tested in production and what's still in progress.
Acknowledgments
GoClaw was originally inspired by the OpenClaw project architecture.
License
CC BY-NC 4.0 — Creative Commons Attribution-NonCommercial 4.0 International