binex 0.5.1

Debuggable runtime for AI agent pipelines

Binex

Open-source visual orchestrator for AI agent workflows
Build, run, debug, and replay multi-agent pipelines — 100% locally.

Demo · Install · Web UI · Features · Docs · Issues

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Demo

Full workflow: drag & drop nodes, configure models and prompts, run with human input, see results, debug, trace, and lineage — all in the browser.

_{Click to watch full demo video}

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What is Binex?

Binex is an open-source, fully local runtime for AI agent workflows. No cloud. No telemetry. No vendor lock-in.

pip install binex
binex ui

That's it. Browser opens. You're building AI workflows.

Why Binex?

• 100% local — your data never leaves your machine
• 100% open source — MIT licensed, audit every line
• Zero telemetry — no tracking, no analytics, no surprises
• Full debuggability — every input, output, prompt, and cost is visible
• Any model — OpenAI, Anthropic, Google, Ollama, OpenRouter, DeepSeek, and 40+ more via LiteLLM

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Installation

pip install binex

With extras:

pip install binex[langchain]    # LangChain Runnables
pip install binex[crewai]       # CrewAI Crews
pip install binex[autogen]      # AutoGen Teams
pip install binex[telemetry]    # OpenTelemetry tracing
pip install binex[rich]         # Rich colored CLI output

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Web UI

Launch the visual workflow editor:

binex ui

Visual Drag & Drop Editor

Build workflows visually — drag nodes from the palette, connect them, configure models and prompts inline.

6 node types: LLM Agent, Local Script, Human Input, Human Approve, Human Output, A2A Agent

• 20+ preset models including 8 free OpenRouter models
• Built-in prompt library (Planner, Researcher, Analyzer, Writer, Reviewer, Summarizer)
• Switch between Visual and YAML modes — changes sync both ways
• Real-time cost estimation as you build
• Custom model input — use any litellm-compatible model

18 Pages — Full CLI Parity

Category	Pages
Workflows	Browse, Visual Editor, Scaffold Wizard
Runs	Dashboard, RunLive (SSE), RunDetail
Analysis	Debug (input/output artifacts), Trace (Gantt timeline), Diagnose (root-cause), Lineage (artifact graph)
Comparison	Diff (side-by-side), Bisect (find divergence)
Costs	Cost Dashboard (charts), Budget Management
System	Doctor (health), Plugins, Gateway, Export

Replay

Debug any node → click Replay → swap the model or prompt → re-run just that node. No re-running the entire pipeline.

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Quickstart

CLI

# Zero-config demo
binex hello

# Run a workflow
binex run examples/simple.yaml

# Inspect the run
binex debug latest
binex trace latest

Web UI

binex ui

Create a Workflow

name: research-pipeline
nodes:
  input:
    agent: "human://input"
    outputs: [output]

  planner:
    agent: "llm://gemini/gemini-2.5-flash"
    system_prompt: "Break this topic into research questions"
    depends_on: [input]
    outputs: [output]

  researcher:
    agent: "llm://openrouter/google/gemma-3-27b-it:free"
    system_prompt: "Investigate and report findings"
    depends_on: [planner]
    outputs: [output]

  output:
    agent: "human://output"
    depends_on: [researcher]
    outputs: [output]

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Features

Agent Adapters

Prefix	Description
local://	In-process Python callable
llm://	LLM via LiteLLM (40+ providers)
a2a://	Remote agent via A2A protocol
human://input	Free-text input from user
human://approve	Approval gate with conditional branching
human://output	Display results to user
langchain://	LangChain Runnable (plugin)
crewai://	CrewAI Crew (plugin)
autogen://	AutoGen Team (plugin)

CLI Commands

Command	Description
binex run	Execute a workflow
binex ui	Launch Web UI
binex debug	Post-mortem inspection
binex trace	Execution timeline
binex replay	Re-run with agent swaps
binex diff	Compare two runs
binex cost show	Cost breakdown per node
binex explore	Interactive TUI dashboard
binex scaffold	Generate workflow from DSL
binex export	Export to CSV/JSON
binex doctor	System health check
binex hello	Zero-config demo

LLM Providers

OpenAI · Anthropic · Google Gemini · Ollama · OpenRouter · Groq · Mistral · DeepSeek · Together AI

Built With

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Examples

Example	What it demonstrates
simple.yaml	Minimal two-node pipeline
diamond.yaml	Diamond dependency pattern
fan-out-fan-in.yaml	Parallel execution with aggregation
human-in-the-loop.yaml	Approval gates and conditional branching
multi-provider-demo.yaml	Multiple LLM providers in one workflow
ollama-research.yaml	Full research pipeline with Ollama + OpenRouter
langchain-summarizer.yaml	LangChain Runnable in a pipeline
crewai-research-crew.yaml	CrewAI Crew as a workflow node
autogen-coding-team.yaml	AutoGen Team for code generation

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Architecture

src/binex/
├── adapters/        # Agent backends (local, LLM, A2A, human, frameworks)
├── cli/             # Click CLI commands
├── graph/           # DAG construction + topological scheduling
├── models/          # Pydantic v2 domain models
├── plugins/         # Plugin registry for custom adapters
├── prompts/         # 121 built-in prompt templates
├── runtime/         # Orchestrator, dispatcher, replay engine
├── stores/          # SQLite execution + filesystem artifacts
├── trace/           # Debug, lineage, timeline, diffing
├── ui/              # FastAPI backend + React frontend
│   ├── api/         # 20 REST endpoints
│   └── static/      # Pre-built React app
└── workflow_spec/   # YAML loader + validator

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Documentation

Full docs at alexli18.github.io/binex

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Contributing

Contributions are welcome! If you find this useful:

• Star the repo — it takes 1 second and helps more than you know
• Open issues — tell me what's broken or what you need
• Submit PRs — let's build this together

I'm a solo developer building this in the open. Every star, issue, and PR makes a real difference.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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License

Distributed under the MIT License. See LICENSE for more information.

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_{Built by a solo dev who believes AI agents shouldn't be black boxes.
No cloud. No telemetry. No surprises. Just debuggable AI workflows.}