OpenFang Hands Configuration Guide: Mastering the Seven Automation Modules

Slug: openfang-hands-config-guide Category: usage-guides Target Keywords: OpenFang Hands, OpenFang automation modules, OpenFang Agent configuration Search Intent: Informational (users looking to understand and use OpenFang Hands) Target Word Count: ~2000 words Language: en

What are Hands?

Hands are a core concept in OpenFang—each Hand represents an independent, composable automation module. Like the tools on a Swiss Army knife, you can enable, disable, and combine different Hands based on your specific needs.

Unlike monolithic Agent designs, OpenFang's Hands architecture runs each automation task in an isolated sandbox, ensuring they do not interfere with one another. If one Hand crashes, it won't affect other running tasks. This design provides enterprise-grade stability while remaining lightweight.

OpenFang currently offers seven built-in Hands, covering the entire automation lifecycle from data collection to content production.

Overview of the Seven Hands

General Configuration Structure

All Hands share a common configuration syntax:

[hands.clip]enabled = true                    # Enable/Disableschedule = "0 */6 * * *"         # Cron schedule expressiontimeout = 300                     # Timeout in secondsretry = 3                         # Number of retries on failurenotify_on = ["error", "complete"] # Notification policyworkspace = "./hands/clip"        # Dedicated workspace directory

Hand 1: Clip — Video & Audio Automation

Clip is a powerful tool for content creators to automatically process video and audio content:

[hands.clip]enabled = trueschedule = "0 9 * * 1-5"        # 9 AM on weekdays[hands.clip.sources]youtube_channels = ["@techreview", "@aiexplained"]podcast_feeds = ["https://example.com/feed.xml"][hands.clip.outputs]formats = ["summary", "transcript", "key_points"]languages = ["zh", "en"]max_duration = 3600              # Process videos up to 1 hour

Clip's core capability is transforming long-form video/audio into structured text. It automatically pulls new content and generates summaries, key points, and full transcripts. It is ideal for teams that need to track numerous video channels.

Hand 2: Lead — Sales Lead Generation

Lead focuses on identifying and extracting potential customer information from public data sources:

[hands.lead]enabled = trueschedule = "0 */8 * * *"         # Runs every 8 hours[hands.lead.filters]industries = ["SaaS", "Fintech", "HealthTech"]company_size = "10-500"signals = ["job_posting:AI", "funding_round", "tech_stack_change"]exclude_existing = true           # Exclude existing customers[hands.lead.outputs]format = "crm_ready"             # Output format compatible with major CRMsmax_leads_per_run = 50

Lead works by scanning multiple signal sources—job postings, funding news, tech stack changes—to identify potential customers with purchase intent. All leads are output in a CRM-ready format, ready for direct import into Salesforce or HubSpot.

Hand 3: Collector — Intelligence Monitoring

Collector is a continuous, customizable information gathering system:

[hands.collector]enabled = trueschedule = "0 * * * *"            # Runs hourly[hands.collector.targets]competitors = ["competitor-a.com", "competitor-b.com"]keywords = ["agent operating system", "AI automation platform"]news_sources = ["techcrunch.com", "theverge.com"][hands.collector.alerts]threshold = "high_confidence"     # Filter out low-confidence resultsdedup_window = "24h"              # No duplicate notifications within 24h

The difference between Collector and Researcher is that Collector acts as a continuous "radar"—scanning constantly and notifying you of changes—while Researcher acts as a targeted "probe" for deep dives into specific topics.

Hand 4: Predictor — Trend Forecasting

Predictor is the most computationally intensive of the seven Hands, analyzing historical data to forecast trends:

[hands.predictor]enabled = trueschedule = "0 2 * * *"            # Runs at 2 AM daily (compute-intensive)[hands.predictor.models]time_series = "prophet"           # Time-series forecasting modelsentiment = "finbert"             # Sentiment analysis model[hands.predictor.data_sources]reddit = ["r/MachineLearning", "r/technology"]arxiv = ["cs.AI", "cs.CL", "stat.ML"]github = ["trending"]

Predictor outputs a structured trend report, including heat curves for emerging topics, sentiment analysis, and confidence intervals. It is perfect for investment analysts and market researchers.

Hand 5: Researcher — Deep Research

Researcher is the most flexible Hand, capable of conducting multi-source, multi-turn deep research on any topic:

[hands.researcher]enabled = truetimeout = 600                     # 10-minute timeout (deep research takes time)[hands.researcher.methods]search_depth = "deep"             # shallow / medium / deepsource_types = ["academic", "news", "social", "code"]cross_validation = true           # Cross-source validationdiversity_lenses = 3              # Multi-perspective analysis

Researcher outputs a structured report with full citations, including key findings, opposing viewpoints, confidence assessments, and further research directions.

Hand 6: Twitter + Browser

This combined Hand provides automation capabilities for social media and web browsing:

[hands.twitter]enabled = trueschedule = "0 */4 * * *"[hands.twitter.monitors]accounts = ["@OpenFangOS", "@AnthropicAI", "@rustlang"]hashtags = ["#AgentOS", "#RustLang", "#AIAutomation"]reply_policy = "manual_review"    # manual_review / auto / off[hands.browser]enabled = trueheadless = trueproxy = "${PROXY_URL}"user_agent = "OpenFang-Browser/1.0"

The Twitter Hand monitors specified accounts and hashtags, while the Browser Hand provides full headless browser capabilities for web data scraping.

Hand 7: Multi-Agent Orchestration

The Multi-Agent Hand can orchestrate multiple Agents into complex workflows. This represents the leap from a "single Agent" to an "Agent team":

[hands.multi_agent]enabled = true[[hands.multi_agent.teams]]name = "content_pipeline"agents = ["researcher", "writer", "reviewer"]pipeline = [    { agent = "researcher", action = "research_topic" },    { agent = "writer", action = "draft_article", depends_on = ["researcher"] },    { agent = "reviewer", action = "fact_check", depends_on = ["writer"] },]

Each sub-Agent can be configured with a different model: Researcher uses a deep analysis model, Writer uses a creative model, and Reviewer uses a rigorous model.

Combined Use Cases

In production, you rarely use just a single Hand. Here are two common combination examples:

Case 1: Automated Content Operations

Collector (Monitor trends) → Researcher (Deep dive) → Clip (Generate content) → Twitter (Publish/Promote)

This combination enables full automation from topic discovery to content publishing. The Collector scans industry trends hourly; once a noteworthy topic is found, it triggers the Researcher for a deep dive. The report is then passed to Clip to generate an article/video summary, which is finally published automatically via Twitter.

Case 2: Sales Intelligence System

Lead (Find leads) → Researcher (Company research) → Collector (Monitor updates) → Notify sales team

Lead discovers potential customers from job postings and funding news daily, Researcher conducts company background checks on high-priority leads, and Collector continuously monitors key changes for existing leads. The entire process outputs a list of "Top 10 Sales Opportunities for Today."

FAQ

Next Steps

• OpenFang Installation & Setup Guide: Deploy OpenFang from scratch
• OpenFang Automation Workflow Guide: Build complex multi-Agent automation processes