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https://github.com/fabro-sh/fabro.git
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Fabro
parent
220faba4be
commit
967a23cbff
5 changed files with 106 additions and 0 deletions
70
checkpoint.json
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70
checkpoint.json
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{
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"timestamp": "2026-04-16T17:14:26.515826Z",
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"current_node": "greet",
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"completed_nodes": [
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"start",
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"greet"
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],
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"node_retries": {},
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"context_values": {
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"internal.node_visit_count": 1,
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"graph.rankdir": "LR",
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"failure_class": "",
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"last_response": "Hello! 👋 I'm Claude, an AI coding assistant made by Anthropic. I'm here to help you with software engineering tasks — things like debugging, adding features, refactoring code, explaining how thin",
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"current_node": "greet",
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"internal.retry_count.start": 0,
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"internal.fidelity": "compact",
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"internal.thread_id": "start",
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"last_stage": "greet",
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"internal.run_id": "01KPBMJ9Z0NZ14SE6M1P8TVVRE",
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"outcome": "success",
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"graph.goal": "Say hello and demonstrate a basic Fabro workflow",
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"failure_signature": "",
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"response.greet": "Hello! 👋 I'm Claude, an AI coding assistant made by Anthropic. I'm here to help you with software engineering tasks — things like debugging, adding features, refactoring code, explaining how things work, and more.\n\nThis message is a **test of the Fabro workflow engine**! 🚀\n\nHere's what's happening under the hood:\n\n- **Fabro** is an AI-powered workflow orchestration platform\n- Workflows are defined as **Graphviz graphs**, where each node is a *stage* (agent, prompt, command, conditional, human, parallel, etc.)\n- The workflow engine parses those graphs and executes each stage in sequence (or in parallel, depending on the graph structure)\n- This \"say hello\" step is itself a stage in a workflow — a simple demonstration that the engine is running correctly\n\n### What Fabro can do:\n| Feature | Description |\n|---|---|\n| 🤖 AI Agents | Coding agents with tools: Bash, Read, Write, Edit, Glob, Grep, WebFetch |\n| 🔄 Checkpoints | Pause, checkpoint, and resume workflows at any stage |\n| 🌐 Multi-provider LLM | Anthropic, OpenAI, Gemini, and OpenAI-compatible backends |\n| 👤 Human-in-the-loop | Workflows can pause and wait for human input |\n| 🔗 Integrations | GitHub Apps, Slack, MCP (Model Context Protocol) |\n\nIf you'd like to explore the codebase, run a workflow, fix a bug, or add a feature — just ask! I'm ready to dive in. 🛠️",
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"internal.retry_count.greet": 0,
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"thread.start.current_node": "greet"
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},
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"node_outcomes": {
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"greet": {
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"status": "success",
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"context_updates": {
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"last_stage": "greet",
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"last_response": "Hello! 👋 I'm Claude, an AI coding assistant made by Anthropic. I'm here to help you with software engineering tasks — things like debugging, adding features, refactoring code, explaining how thin",
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"response.greet": "Hello! 👋 I'm Claude, an AI coding assistant made by Anthropic. I'm here to help you with software engineering tasks — things like debugging, adding features, refactoring code, explaining how things work, and more.\n\nThis message is a **test of the Fabro workflow engine**! 🚀\n\nHere's what's happening under the hood:\n\n- **Fabro** is an AI-powered workflow orchestration platform\n- Workflows are defined as **Graphviz graphs**, where each node is a *stage* (agent, prompt, command, conditional, human, parallel, etc.)\n- The workflow engine parses those graphs and executes each stage in sequence (or in parallel, depending on the graph structure)\n- This \"say hello\" step is itself a stage in a workflow — a simple demonstration that the engine is running correctly\n\n### What Fabro can do:\n| Feature | Description |\n|---|---|\n| 🤖 AI Agents | Coding agents with tools: Bash, Read, Write, Edit, Glob, Grep, WebFetch |\n| 🔄 Checkpoints | Pause, checkpoint, and resume workflows at any stage |\n| 🌐 Multi-provider LLM | Anthropic, OpenAI, Gemini, and OpenAI-compatible backends |\n| 👤 Human-in-the-loop | Workflows can pause and wait for human input |\n| 🔗 Integrations | GitHub Apps, Slack, MCP (Model Context Protocol) |\n\nIf you'd like to explore the codebase, run a workflow, fix a bug, or add a feature — just ask! I'm ready to dive in. 🛠️"
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},
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"notes": "Stage completed: greet",
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"usage": {
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"input": {
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"usage": {
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"model": {
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"provider": "anthropic",
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"model_id": "claude-sonnet-4-6"
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},
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"tokens": {
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"input_tokens": 356,
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"output_tokens": 390,
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"reasoning_tokens": 0,
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"cache_read_tokens": 0,
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"cache_write_tokens": 5616
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}
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},
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"facts": {
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"provider": "anthropic",
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"cache_write_5m_tokens": 5616,
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"cache_write_1h_tokens": 0
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}
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},
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"total_usd_micros": 27978
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}
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},
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"start": {
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"status": "success",
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"usage": null
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}
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},
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"next_node_id": "exit",
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"node_visits": {
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"start": 1,
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"greet": 1
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}
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}
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4
nodes/greet/prompt.md
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4
nodes/greet/prompt.md
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Goal: Say hello and demonstrate a basic Fabro workflow
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Say hello! Introduce yourself and explain that this is a test of the Fabro workflow engine.
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5
nodes/greet/provider_used.json
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5
nodes/greet/provider_used.json
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{
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"mode": "agent",
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"provider": "anthropic",
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"model": "claude-sonnet-4-6"
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}
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21
nodes/greet/response.md
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21
nodes/greet/response.md
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Hello! 👋 I'm Claude, an AI coding assistant made by Anthropic. I'm here to help you with software engineering tasks — things like debugging, adding features, refactoring code, explaining how things work, and more.
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This message is a **test of the Fabro workflow engine**! 🚀
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Here's what's happening under the hood:
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- **Fabro** is an AI-powered workflow orchestration platform
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- Workflows are defined as **Graphviz graphs**, where each node is a *stage* (agent, prompt, command, conditional, human, parallel, etc.)
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- The workflow engine parses those graphs and executes each stage in sequence (or in parallel, depending on the graph structure)
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- This "say hello" step is itself a stage in a workflow — a simple demonstration that the engine is running correctly
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### What Fabro can do:
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| Feature | Description |
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|---|---|
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| 🤖 AI Agents | Coding agents with tools: Bash, Read, Write, Edit, Glob, Grep, WebFetch |
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| 🔄 Checkpoints | Pause, checkpoint, and resume workflows at any stage |
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| 🌐 Multi-provider LLM | Anthropic, OpenAI, Gemini, and OpenAI-compatible backends |
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| 👤 Human-in-the-loop | Workflows can pause and wait for human input |
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| 🔗 Integrations | GitHub Apps, Slack, MCP (Model Context Protocol) |
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If you'd like to explore the codebase, run a workflow, fix a bug, or add a feature — just ask! I'm ready to dive in. 🛠️
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6
nodes/start/status.json
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6
nodes/start/status.json
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{
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"status": "success",
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"notes": null,
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"failure_reason": null,
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"timestamp": "2026-04-16T17:14:17.695723Z"
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}
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