Repository intelligence

mario-andreschak/FLUJO

An open-source, local-first orchestration platform for visually building, debugging, and automating private AI agents with multi-model support and Model Context Protocol (MCP) integrations.

CLOUDM0N decision

REVIEW BEFORE ADOPTION

Trust REVIEW · 20/100

Good fit if

Developers and power users who want to build custom, private AI agents visually or through step-by-step recipes on their local machines

Watch out for

Does not feature any built-in authentication layer, requiring manual reverse-proxy setup for public network exposure

Practical intelligence

What matters before you adopt it

Decision first · detail on demand

Problem it solves

The complexity of building, debugging, orchestrating, and automating multi-model AI agents and MCP servers locally while keeping API keys and private data secure and under the user's control.

Best for

Developers and power users who want to build custom, private AI agents visually or through step-by-step recipes on their local machines

Teams and individuals utilizing Model Context Protocol (MCP) servers who want a centralized proxy to configure once and re-expose to clients like Cursor or Claude Desktop

Users with Claude Pro/Max subscription plans who want to build agentic workflows without paying for metered API keys

Main trade-offs

Does not feature any built-in authentication layer, requiring manual reverse-proxy setup for public network exposure

Does not expose an Ollama-compatible server endpoint, meaning external integrations must connect via its OpenAI-compatible endpoint

Uninstalling the software completely and permanently deletes all local workspace data, including flows, configurations, and chat history

Why it stands out

Flexible multi-model support including OpenAI, Anthropic, Google Gemini, Ollama, Azure OpenAI, Grok, and OpenRouter

Robust local-first security featuring at-rest encryption of keys/secrets and masked server-side variable storage

Comprehensive automation triggers supporting cron-style scheduling, authenticated webhooks, file watchers, URL watching, and MCP tool polling

Trust & CVEs

Security evidence without the noise

Trust remains a decision signal; CVEs and scanner evidence explain what is driving the risk.

Security findings

CLOUDM0N scanner findings

Critical

High

Medium

Low

View trust evidence & security findings▼

Why this score

No trust rationale was stored for this scan.

CLOUDM0N findings

HIGH

Remote download piped or chained into a shell requires manual review. 5 sample match(es) found.

HIGH

1 high severity dependency vulnerability/vulnerabilities reported by npm audit.

MEDIUM

Dynamic code execution pattern detected. 3 sample match(es) found.

MEDIUM

The project can spawn operating-system processes; review command construction and input handling. 5 sample match(es) found.

MEDIUM

Broad permission or elevated-command pattern detected. 5 sample match(es) found.

Architecture from code18 modules · 9 edges▼

Structural evidence

Modules and dependency edges extracted from repository code. This is code evidence, not README inference.

Code files

1928

Modules

Dependency edges

Core modules

(root)

8 files

__tests__

tests

743 files

bin

executables

1 files

githubpages

4 files

mcp-servers

20 files

scripts

14 files

src

3 files

src/app

221 files

src/backend

backend

340 files

src/config

configuration

1 files

src/frontend

frontend

409 files

src/shared

shared code

78 files

Dependency flow

__tests__→(root)

__tests__→mcp-servers

__tests__→scripts

__tests__→src/backend

scripts→src/backend

src/backend→mcp-servers

src/frontend→src/utils

src/utils→src/backend

src/utils→src/shared

Detected languages

TypeScript · JavaScript · Shell · Python

Detected frameworks

Axios · Jest · MCP SDK · Next.js · OpenAI SDK · React · Webpack · Zod

Architecture evidence details

flowchart TD
    %% flujo-ai — high-level architecture (DRAFT, refine me)
    n0["(root) · 8 files"]
    n1["__tests__ · tests · 743 files"]
    n2["bin · executables · 1 file"]
    n3["githubpages · 4 files"]
    n4["mcp-servers · 20 files"]
    n5["scripts · scripts · 14 files"]
    n6["src · 3 files"]
    n7["src/app · 221 files"]
    n8["src/backend · backend · 340 files"]
    n9["src/config · configuration · 1 file"]
    n10["src/frontend · frontend · 409 files"]
    n11["src/shared · shared code · 78 files"]
    n12["src/types · 1 file"]
    n13["src/utils · utility helpers · 85 files"]
    n1 --> n0
    n1 --> n4
    n1 --> n5
    n1 --> n8
    n5 --> n8
    n8 --> n4
    n10 --> n13
    n13 --> n8
    n13 --> n11
    class n8 api
    class n2,n5,n9 infra
    class n11,n13 shared
    class n1 test
    class n10 ui
    classDef api fill:#af7aa1,color:#ffffff,stroke:#8a5f7f
    classDef infra fill:#b35c00,color:#ffffff,stroke:#8f4a00
    classDef shared fill:#79706e,color:#ffffff,stroke:#5d5654
    classDef test fill:#499894,color:#ffffff,stroke:#397975
    classDef ui fill:#4e79a7,color:#ffffff,stroke:#3a5b80

Evidence, security & integrations▼

Integrations

OpenAIAzure OpenAIAnthropicClaude Agent SDKClaude Code CLIGoogle GeminiX.ai (Grok)OpenRouter

Security notes

• Has no built-in authentication layer, binding strictly to localhost (127.0.0.1) by default to prevent unauthorized command execution

• Encrypts all API keys and secrets at rest, using an optional custom password for extra storage protection

• Secrets are processed server-side and never sent to the browser, displaying as masked placeholders in DevTools

• Network exposure can be manually expanded to 'Local Network' or 'Public' in Settings, but public exposure requires an authenticating reverse proxy

Still unknown

• The README does not provide system performance stats or limits regarding execution concurrency for high-frequency triggers

• Does not specify exact memory or CPU footprint recommendations for running multiple local models via Ollama

Adoption guidance▼

Adopt if

+ You need a visually designed, local-first agent orchestrator that can combine multiple AI models and local tools safely

+ You want to re-expose complex MCP configurations as a single proxy endpoint to multiple local IDEs or clients

+ You require deep debugging controls like breakpoints and manual step execution over autonomous tool flows

Avoid if

– You need an out-of-the-box system with built-in multi-user authentication and role-based access control

– You must run the orchestrator completely offline but cannot configure or install Node.js, git, or Docker manually on your host

How it works & getting started▼

How it works

1.The user installs FLUJO locally via the automated installer, npx, or Docker Compose

2.Global variables and API keys are saved once and encrypted at rest in local storage

3.AI connections are established with providers like OpenAI, Anthropic, Gemini, or local Ollama instances

4.MCP tools and servers are integrated from local paths, GitHub repos, or the official registry

5.The user designs agents visually in expert mode or sequentially in simple mode

Getting started

→ Install FLUJO on Windows by running the flow setup executable, or execute the one-line bash install script for Linux and macOS

→ Alternatively, launch using Docker with: docker compose up --build

→ Or start instantly from npm with: npx flujo-ai@latest

→ Open the browser interface at: http://localhost:4200

Agent handoff

Use with any agent

JSON API

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