Web browser automation with AI-optimized snapshots for claude-flow agents
インストール方法を見るインストールする前に、エージェントに与えられる指示の中身を確認できます。
Web browser automation using agent-browser with AI-optimized snapshots. Reduces context by 93% using element refs (@e1, @e2) instead of full DOM.
# 1. Navigate to page
agent-browser open <url>
# 2. Get accessibility tree with element refs
agent-browser snapshot -i # -i = interactive elements only
# 3. Interact using refs from snapshot
agent-browser click @e2
agent-browser fill @e3 "text"
# 4. Re-snapshot after page changes
agent-browser snapshot -i
| Command | Description |
|---|---|
open <url> | Navigate to URL |
back | Go back |
forward | Go forward |
reload | Reload page |
close | Close browser |
| Command | Description |
|---|---|
snapshot | Full accessibility tree |
snapshot -i | Interactive elements only (buttons, links, inputs) |
snapshot -c | Compact (remove empty elements) |
snapshot -d 3 | Limit depth to 3 levels |
screenshot [path] | Capture screenshot (base64 if no path) |
| Command | Description |
|---|---|
click <sel> | Click element |
fill <sel> <text> | Clear and fill input |
type <sel> <text> | Type with key events |
press <key> | Press key (Enter, Tab, etc.) |
hover <sel> | Hover element |
select <sel> <val> | Select dropdown option |
check/uncheck <sel> | Toggle checkbox |
scroll <dir> [px] | Scroll page |
| Command | Description |
|---|---|
get text <sel> | Get text content |
get html <sel> | Get innerHTML |
get value <sel> | Get input value |
get attr <sel> <attr> | Get attribute |
get title | Get page title |
get url | Get current URL |
| Command | Description |
|---|---|
wait <selector> | Wait for element |
wait <ms> | Wait milliseconds |
wait --text "text" | Wait for text |
wait --url "pattern" | Wait for URL |
wait --load networkidle | Wait for load state |
| Command | Description |
|---|---|
--session <name> | Use isolated session |
session list | List active sessions |
# Get refs from snapshot
agent-browser snapshot -i
# Output: button "Submit" [ref=e2]
# Use ref to interact
agent-browser click @e2
agent-browser click "#submit"
agent-browser fill ".email-input" "test@test.com"
agent-browser find role button click --name "Submit"
agent-browser find label "Email" fill "test@test.com"
agent-browser find testid "login-btn" click
agent-browser open https://example.com/login
agent-browser snapshot -i
agent-browser fill @e2 "user@example.com"
agent-browser fill @e3 "password123"
agent-browser click @e4
agent-browser wait --url "**/dashboard"
agent-browser open https://example.com/contact
agent-browser snapshot -i
agent-browser fill @e1 "John Doe"
agent-browser fill @e2 "john@example.com"
agent-browser fill @e3 "Hello, this is my message"
agent-browser click @e4
agent-browser wait --text "Thank you"
agent-browser open https://example.com/products
agent-browser snapshot -i
# Iterate through product refs
agent-browser get text @e1 # Product name
agent-browser get text @e2 # Price
agent-browser get attr @e3 href # Link
# Session 1: Navigator
agent-browser --session nav open https://example.com
agent-browser --session nav state save auth.json
# Session 2: Scraper (uses same auth)
agent-browser --session scrape state load auth.json
agent-browser --session scrape open https://example.com/data
agent-browser --session scrape snapshot -i
All browser operations are available as MCP tools with browser/ prefix:
browser/openbrowser/snapshotbrowser/clickbrowser/fillbrowser/screenshot# Store successful patterns
npx @claude-flow/cli memory store --namespace browser-patterns --key "login-flow" --value "snapshot->fill->click->wait"
# Retrieve before similar task
npx @claude-flow/cli memory search --query "login automation"
# Pre-browse hook (get context)
npx @claude-flow/cli hooks pre-edit --file "browser-task.ts"
# Post-browse hook (record success)
npx @claude-flow/cli hooks post-task --task-id "browse-1" --success true
-i flag - Gets only interactive elements, smaller outputまだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Add descriptions for new models from the HuggingFace router to chat-ui configuration. Use when new models are released on the router and need descriptions added to prod.yaml and dev.yaml. Triggers on requests like "add new model descriptions", "update models from router", "sync models", or when explicitly invoking /add-model-descriptions.
日本語の概要は準備中です。原文の説明を表示しています。
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
日本語の概要は準備中です。原文の説明を表示しています。
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
日本語の概要は準備中です。原文の説明を表示しています。
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
日本語の概要は準備中です。原文の説明を表示しています。
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
日本語の概要は準備中です。原文の説明を表示しています。
Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.
日本語の概要は準備中です。原文の説明を表示しています。