Evan
d43077afa9
* fix(providers): remove ~anthropic, skip ~ prefixes in sync script
OpenRouter uses ~ prefixes for internal auto-routing aliases (e.g. ~anthropic).
These are not real providers — they already route through openrouter.toml.
The generated ~anthropic.toml was confusing (looked like a stale backup)
and redundant with the existing openrouter provider.
- Delete providers/~anthropic.toml
- Skip provider IDs starting with ~ in sync-pricing.py --create-missing
* fix(providers): remove morph, aider, kwaipilot
- morph: specialized code-editing/patching tool, not a general LLM provider
- aider: CLI meta-tool wrapper (base_url empty), redundant with claude-code/codex-cli/gemini-cli/qwen-code
- kwaipilot: Kwai internal coding assistant routed via OpenRouter, niche
* fix(sync): add morph/aider/kwaipilot to SKIP_PROVIDERS to prevent re-creation
* feat(sync): merge OpenRouter-only providers into openrouter.toml
Instead of generating standalone .toml files that just wrap the OpenRouter
endpoint, merge their models directly into openrouter.toml with the
standard 'openrouter/{provider}/{model}' ID convention.
- Add _build_model_fields() and _model_lines() helpers to deduplicate
model rendering between standalone and merged paths
- Add merge_into_openrouter() that appends new models idempotently
- generate_provider_toml() now only runs for providers in PROVIDER_API
- --create-missing routes OpenRouter-only providers to merge_into_openrouter
* fix(providers): remove 14 OpenRouter-only standalone files
These providers have no direct public API and all route through
openrouter.ai/api/v1. Per the new sync-pricing.py policy, their models
will be merged into openrouter.toml on the next CI run instead of
living in separate files that just wrap the OpenRouter endpoint.
Removed: allenai, deepcogito, essentialai, inclusionai, inflection,
liquid, meituan, nex-agi, nousresearch, prime-intellect, relace,
switchpoint, tngtech, writer
* fix(providers): remove 7 niche providers with no driver support
No dedicated LLM driver code exists for these providers — they rely
purely on OpenAI-compatible passthrough with no special handling.
Removing them reduces registry noise; users can still reach them via
openrouter.toml if needed.
Removed: microsoft, ibm-granite, xiaomi, upstage, inception, aion-labs, arcee-ai
* fix(providers): remove ai21, chutes, venice
All three use ApiFormat::OpenAI with no special handling — pure passthrough.
No registry entry needed; users can reach them via openrouter.toml or by
adding a custom provider.
* docs(providers): rewrite README with full provider catalog and inclusion criteria
- List all 46 providers grouped by category with descriptions
- Document why each provider exists (direct API, unique endpoint, dedicated driver, local, CLI)
- Add inclusion criteria section explaining when to create standalone files vs merging into openrouter.toml
- Document sync script routing logic
- Update model counts: 49→46 providers, 339→232 models
* docs: add comprehensive READMEs for all registry sections + deepinfra provider
- agents/README.md: 32 agents across 7 categories with capability field reference
- channels/README.md: 44 channels across 5 categories with protocol reference table
- hands/README.md: 18 hands across 5 categories with HAND.toml format guide
- mcp/README.md: 33 MCP servers across 5 categories with transport/auth format
- plugins/README.md: 12 plugins with hook protocol documentation
- skills/README.md: 60 skills across 9 categories with SKILL.md format guide
- providers/deepinfra.toml: add DeepInfra serverless inference (5 models)
304 lines
11 KiB
Markdown
304 lines
11 KiB
Markdown
# LibreFang Registry
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Community-maintained content registry for [LibreFang](https://github.com/librefang/librefang) — the open-source Agent Operating System.
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This repository is the **single source of truth** for all installable content definitions. Anyone can submit a PR to add new agents, hands, MCP servers, skills, or provider models — no changes to the LibreFang binary required.
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## Overview
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| Type | Count | Description |
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|------|------:|-------------|
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| [Hands](#hands) | 14 | User-facing "apps" — agent + tools + settings + dashboard |
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| [Agents](#agents) | 32 | Autonomous agent definitions with model config and tools |
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| [MCP Servers](#mcp-servers) | 25 | MCP server connections (GitHub, Slack, DBs, etc.) |
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| [Providers](#providers) | 46 | LLM provider & model metadata with pricing |
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| [Models](#providers) | 232 | Individual model definitions across all providers |
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| [Aliases](#aliases) | 70 | Short names mapped to canonical model IDs |
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| [Plugins](#plugins) | 10 | Memory, guardrails, and conversation plugins |
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| [Skills](#skills) | 2 | Reusable prompt templates and Python scripts |
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| [Workflows](#workflows) | 9 | Pre-built multi-agent workflow definitions |
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| [Templates](#templates) | 6 | Starter templates for each content type |
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## Repository Structure
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```
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librefang-registry/
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├── agents/ # Agent definitions (TOML manifests)
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│ ├── hello-world/
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│ │ └── agent.toml
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│ ├── researcher/
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│ │ └── agent.toml
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│ └── ... (32 agents)
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├── hands/ # Hand definitions (app bundles)
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│ ├── browser/
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│ │ ├── HAND.toml # Metadata, tools, settings, i18n (6 languages)
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│ │ └── SKILL.md # Domain expert knowledge injected at runtime
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│ ├── trader/
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│ │ ├── HAND.toml
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│ │ └── SKILL.md
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│ └── ... (14 hands)
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├── mcp/ # MCP server templates
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│ ├── github.toml
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│ ├── slack.toml
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│ └── ... (25 MCP servers)
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├── providers/ # LLM provider & model metadata
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│ ├── anthropic.toml
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│ ├── openai.toml
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│ └── ... (46 providers, 232 models)
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├── plugins/ # Memory, guardrails, and utility plugins
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│ ├── episodic-memory/
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│ ├── guardrails/
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│ └── ... (10 plugins)
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├── skills/ # Reusable skill definitions
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│ ├── custom-skill-prompt/skill.toml
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│ └── custom-skill-python/
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├── workflows/ # Pre-built multi-agent workflow definitions
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│ ├── code-review.toml
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│ ├── research.toml
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│ └── ... (9 workflows)
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├── templates/ # Starter templates for each content type
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│ ├── agent.toml
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│ ├── HAND.toml
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│ └── ... (6 templates)
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├── docs/ # Additional documentation
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│ └── content-guide.md # Content contribution guidelines
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├── aliases.toml # Global model alias mappings (70 aliases)
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├── schema.toml # Provider/model schema reference
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├── scripts/
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│ └── validate.py # Content validation script
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├── CONTRIBUTING.md
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└── LICENSE # MIT
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```
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## Content Types
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### Hands
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Hands are the **user-facing "apps"** in LibreFang. Each hand bundles an agent, tools, user-configurable settings, dashboard metrics, dependency checks, and i18n translations into a single deployable unit.
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Every hand includes a `SKILL.md` — domain-specific expert knowledge that is injected into the agent's context at runtime, giving it deep expertise in its domain.
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| Icon | Hand | Category | Description |
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|:----:|------|----------|-------------|
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| 📈 | analytics | data | Data collection, analysis, visualization, dashboards, and automated reporting |
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| 🔌 | apitester | development | Endpoint discovery, request validation, load testing, and regression detection |
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| 🌐 | browser | productivity | Web navigation, form filling, and multi-step web tasks with user approval |
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| 🎬 | clip | content | Turns long-form video into viral short clips with captions and thumbnails |
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| 🔍 | collector | data | Intelligence collection, change detection, and knowledge graphs |
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| 👷 | devops | development | CI/CD management, infrastructure monitoring, deployment, and incident response |
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| 📊 | lead | data | Lead generation, enrichment, scoring, and scheduled delivery |
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| 💼 | linkedin | communication | Profile optimization, content creation, networking, and engagement |
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| 🔮 | predictor | data | Signal collection, calibrated predictions, and accuracy tracking |
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| 📢 | reddit | communication | Subreddit monitoring, content posting, and engagement tracking |
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| 🧪 | researcher | productivity | Deep research, cross-referencing, fact-checking, and structured reports |
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| 🎯 | strategist | productivity | Market research, competitive analysis, and strategic planning |
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| 📈 | trader | data | Multi-signal analysis, adversarial reasoning, and risk management |
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| 𝕏 | twitter | communication | Content creation, scheduled posting, engagement, and analytics |
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**HAND.toml format:**
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```toml
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id = "browser"
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name = "Browser Hand"
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description = "Autonomous web browser"
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category = "productivity"
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icon = "🌐"
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tools = ["browser_navigate", "browser_click", "browser_type"]
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[routing]
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aliases = ["browse", "open website"]
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weak_aliases = ["web", "url"]
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[[requires]]
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key = "chromium"
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requirement_type = "binary"
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check_value = "chromium"
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[[settings]]
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key = "headless"
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setting_type = "toggle"
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default = "true"
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[agent]
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name = "browser-hand"
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module = "builtin:chat"
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system_prompt = """You are an autonomous web browser agent..."""
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[dashboard]
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[[dashboard.metrics]]
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label = "Pages Visited"
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memory_key = "pages_visited"
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format = "number"
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# i18n — 6 languages supported: zh, ja, ko, es, fr, de
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[i18n.zh]
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name = "浏览器 Hand"
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description = "自主网页浏览器"
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category = "生产力"
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[i18n.zh.settings.headless]
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label = "无头模式"
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description = "在后台运行浏览器"
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```
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### Agents
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Agent definitions describe autonomous agents with model configuration, tools, capabilities, and routing aliases.
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```toml
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name = "hello-world"
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description = "A friendly greeting agent"
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module = "builtin:chat"
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[model]
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provider = "default"
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model = "default"
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system_prompt = "You are a helpful assistant."
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[capabilities]
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tools = ["web_search", "file_read"]
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```
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**32 built-in agents:** academic-researcher, analyst, architect, assistant, code-reviewer, coder, customer-support, data-scientist, debugger, devops-lead, doc-writer, email-assistant, health-tracker, hello-world, home-automation, legal-assistant, meeting-assistant, ops, orchestrator, personal-finance, planner, recipe-assistant, recruiter, researcher, sales-assistant, security-auditor, social-media, test-engineer, translator, travel-planner, tutor, writer
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### MCP Servers
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MCP server templates define [MCP](https://modelcontextprotocol.io/) server connections with transport configuration, required environment variables, and setup instructions.
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```toml
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id = "github"
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name = "GitHub"
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category = "devtools"
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[transport]
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type = "stdio"
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command = "npx"
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args = ["-y", "@modelcontextprotocol/server-github"]
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[[required_env]]
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name = "GITHUB_PERSONAL_ACCESS_TOKEN"
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is_secret = true
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```
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**25 MCP servers across 6 categories:**
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| Category | MCP Servers |
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|----------|-------------|
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| DevTools | bitbucket, github, gitlab, jira, linear, sentry |
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| Data | elasticsearch, mongodb, postgresql, redis, sqlite |
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| Productivity | dropbox, gmail, google-calendar, google-drive, notion, todoist |
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| Communication | discord, slack, teams |
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| Cloud | aws, azure, gcp |
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| AI Search | brave-search, exa-search |
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### Providers
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Provider files define LLM providers and their models with pricing, context windows, and capability flags. See [schema.toml](schema.toml) for the full field reference.
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**49 providers** including: Anthropic, OpenAI, Google Gemini, DeepSeek, Groq, Mistral, Cohere, xAI, Together, Fireworks, Ollama (local), LM Studio (local), vLLM (self-hosted), Alibaba Coding Plan, and many more.
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**339 models** with metadata for each: pricing (input/output per token), context window size, capability flags (vision, function calling, streaming), and tier classification.
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### Aliases
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Global model alias mappings in [aliases.toml](aliases.toml) let users reference models by short names:
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```toml
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"sonnet" = "claude-sonnet-4-6"
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"gpt4" = "gpt-4o"
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"flash" = "gemini-2.5-flash"
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"deepseek" = "deepseek-chat"
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```
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Models can also define aliases directly in their provider TOML files, which are auto-registered at load time.
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### Plugins
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Plugins extend agent capabilities with memory systems, safety guardrails, and conversation utilities.
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**10 plugins:** auto-summarizer, context-decay, conversation-logger, episodic-memory, guardrails, keyword-memory, sentiment-tracker, todo-tracker, topic-memory, user-profile
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### Skills
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Reusable prompt templates or Python scripts that agents can invoke.
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```toml
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[skill]
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name = "meeting-agenda"
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description = "Generate a structured meeting agenda"
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[runtime]
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type = "promptonly"
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[prompt]
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template = "Create a meeting agenda for: {{topic}}"
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```
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### Workflows
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Pre-built multi-agent workflow definitions in `workflows/<name>.toml` orchestrate multiple agents for complex tasks.
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**9 workflows:** brainstorm, code-review, content-pipeline, content-review, customer-support, data-pipeline, research, translate-polish, weekly-report
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### Templates
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Starter templates in `templates/` for creating new content. Copy a template to get started quickly:
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```bash
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cp templates/agent.toml agents/my-agent/agent.toml
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cp templates/HAND.toml hands/my-hand/HAND.toml
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```
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**6 templates:** agent.toml, HAND.toml, integration.toml, plugin.toml, provider.toml, skill.toml
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See also [docs/content-guide.md](docs/content-guide.md) for naming conventions and contribution guidelines.
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## Usage
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### Install from Registry
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```bash
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# Update all registry content
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librefang catalog update
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# Install a specific hand
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librefang hand install browser
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# Install a specific MCP server
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librefang mcp install github
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```
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### Custom Local Content
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Create custom content locally without submitting to this registry:
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```bash
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# Custom agent
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mkdir -p ~/.librefang/agents/my-agent
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# Edit ~/.librefang/agents/my-agent/agent.toml
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# Custom model aliases
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# Add to ~/.librefang/model_catalog.toml
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```
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## Validation
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```bash
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python scripts/validate.py
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```
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Validates all content files for correctness: required fields, valid types, non-negative costs, no duplicate IDs.
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## Contributing
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1. Fork this repository
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2. Add or edit content in the appropriate directory
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3. Run validation: `python scripts/validate.py`
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4. Submit a Pull Request
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See [CONTRIBUTING.md](CONTRIBUTING.md) for detailed instructions for each content type.
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## License
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MIT License. See [LICENSE](LICENSE).
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