How do I install Wavefront?

Copy the stdio config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.

Does Wavefront need an API key?

No required environment variable is listed on this record.

What transport does Wavefront use?

Wavefront runs locally over stdio.

When was Wavefront last checked?

Last checked 2026-10-28. 🔥🔥🔥 Enterprise AI Harness, connect your data to AI

Servidor MCP Wavefront

Potencialize seu agente

Adicionar este servidor

Install from the publisher. Not a PlugMCP endpoint.

Rodar no PlugMCP

Host a copy with our keys and logs.

No packaged release in the registry yet. Follow the repository README.

RepositoryPublisher installStep-by-step: add this in Claude

Remote URL or a command on your machine? How the two installs differ

Rodar no PlugMCP

Host a copy with our keys and logs.

A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.

Abrir

Server facts

Local

Transport: stdio
License: NOASSERTION
Last checked: 2026-10-28

Tools

No tool list yet. Remote servers get one after the first health check.

FAQ

Questions

What is Wavefront ?

Wavefront AI is an open-source middleware platform for building production AI applications on enterprise data. It lets you:

Connect to databases, APIs and cloud storage, and query them with access control and audit logging
Use any LLM or SLM, hosted or self-hosted
Build agents, multi-agent workflows, chatbots and voice agents on top of that data, configured in YAML and versioned
Add authentication, role-based access control, observability and evaluation to all of them

What people build with Wavefront ?

AI agents and workflows that audit, underwrite, supervise contact centers and automate business processes
Knowledge bases and RAG applications for internal enterprise use
Voice agents for collections and sales, over inbound and outbound phone calls
Chatbots that give each user a persistent conversation with a configured model
Workflows that combine multiple data sources, knowledge bases and services | Project Information | Details | |-----------|------------| |Release Status | Beta Release| |Wavefront License | GNU AFFERO GENERAL PUBLIC LICENSE 3.0 | |FloAI License | MIT LICENSE |

✨ Key Capabilities

🤖 Agents & Workflows
Define agents and multi-agent workflows in YAML, built on flo-ai. Every save creates a new version, validated by building the agent or compiling the workflow, and you can promote or roll back versions. Workflows can reference saved agents, define agents inline, or embed other workflows. Run them synchronously with streamed events, or asynchronously on background workers.
💬 Chatbots & Sessions
Configure a chatbot as a system prompt bound to a model. Each user gets persistent chat sessions with stored history and streaming replies. Sessions keep a snapshot of the prompt they started with, so editing a chatbot never rewrites past conversations.
🔊 Voice Agents
Voice-to-voice agents for inbound and outbound phone calls, built on Pipecat. They support multiple languages, tool calls during a call, smart turn detection, and LLM-based evaluation after each call.
- Telephony: Twilio, Exotel, Smartflo
- Speech-to-text: Deepgram, AssemblyAI, Whisper, Google, Azure, Sarvam, ElevenLabs
- Text-to-speech: ElevenLabs, Deepgram, Cartesia, Azure, Google, AWS, Sarvam
🌐 Data Connectivity
Connect to BigQuery, Redshift, PostgreSQL and SQL Server as datasources, and to S3, Google Cloud Storage and Azure Blob Storage for files. Write through an OData-style API, and read with parameterised dynamic queries defined in YAML, with row-level data permissions. Writes and file transfers can be recorded in an audit trail. API services connect any REST backend, with API key, Basic and Bearer authentication.
⚙️ Functions & Configurations
Sandboxed JavaScript functions for the deterministic steps of a workflow (computing, comparing, deciding), and namespaced JSON configurations that workflows read at run time, so thresholds, rule tables and scoring models can change without a redeploy. See Functions and Configurations.
🧠 Knowledge Bases & RAG
Upload documents to knowledge bases, with background ingestion and embedding. Retrieval combines vector (pgvector) and keyword search with reranking, and supports image search.
🛠️ Tools & Triggers
Agents can use datasources, knowledge bases, API services, email and your own functions as tools. Triggers start an agent or workflow from external events, starting with Gmail.

Architecture

Each application runs as its own deployment, and a single console manages all of them.

                ┌──────────────────────────────┐
                │  floconsole                  │  manages apps, builders and access
                └──────┬───────────────┬───────┘
                       │               │
          ┌────────────▼───┐     ┌─────▼──────────┐
          │ floware (app A)│     │ floware (app B)│  one core server per application
          └────────────────┘     └────────────────┘

Service	Role
floconsole	Creates and manages applications, their users and access, and proxies requests to each app's floware
floware	The core server for one application: agents, workflows, chatbots, datasources, knowledge bases, tools, RBAC
call_processing	The real-time voice runtime that handles phone calls for voice agents
Background jobs	Celery workers for async agent and workflow runs, RAG ingestion, and scheduled workflow jobs
inference_app	Hosts custom PyTorch models (experimental)

Because each application has its own floware, each gets its own database, storage and secrets.

Quick Start

Option 1: Schedule a demo and we help you build immediately.

Option 2: Self-host for maximum control and customization. Start with the Docker setup guide, then see Architecture and Environment and configuration in the documentation.

Option 3: Run Wavefront locally for development with the interactive scripts.

Local development setup

wavefront/dev-start.sh sets up and starts selected services on your machine. wavefront/dev-stop.sh stops them (running ones are pre-checked). Both are safe to re-run: services that are already running are left as they are on start.

Prerequisites: Python 3.11+, uv, Docker (running), Node.js 20+, pnpm (corepack enable) and openssl. dev-start.sh checks for all of them first.

cd wavefront
./dev-start.sh   # first-time setup + start selected services
./dev-stop.sh    # stop selected services

dev-start.sh:

Lets you choose which apps to start (floconsole, floware, web client, celery, RAG ingestion, inference, call processing).
Creates each selected service's .env from its checked-in sample (.env.example / .env.sample). Existing .env files are kept by default; turn on replace .env files to recreate them (with a backup). Secrets such as PASSTHROUGH_SECRET are generated or synced across services.
Starts Postgres (pgvector), Redis and LocalStack with server/docker-compose.yml. LocalStack provides S3, SQS and KMS locally, so no cloud account is needed.
Creates the databases, the S3 bucket, the RAG ingestion queue and the KMS keys used to sign tokens.
Installs the Python dependencies into server/.venv and the web client's dependencies (when selected).
Starts each selected service in its own terminal window, and waits until it is healthy.

Service	URL	Reloads on code changes
Web client	http://localhost:5173	Yes
floconsole	http://localhost:8002	Yes
floware	http://localhost:8001	Yes
inference_app (optional)	http://localhost:8003	Yes
call_processing (optional)	http://localhost:8004	Yes
Celery worker (optional)	Redis queue	No, restart it
RAG ingestion worker (optional)	LocalStack SQS queue	No, restart it

Optional services default to off in the checkbox. The inference app downloads its models (several GB) from Hugging Face on first run, and needs a Hugging Face token with access to the gated DINOv3 model. It runs on CPU on Linux and Apple Silicon Macs; with all three models loaded (CLIP, DINOv3 and BGE-M3) it needs roughly 4–5 GB of free memory. Intel Macs have no PyTorch build at the supported version (2.6 or later), so there it runs in mock mode: the same API returning synthetic embeddings, for integration testing, with no models to download. Set INFERENCE_MOCK_MODELS=true to use mock mode anywhere.

When the script finishes:

Open the web client at http://localhost:5173 and log in with the seed user from server/apps/floconsole/floconsole/.env (CONSOLE_EMAIL / CONSOLE_PASSWORD).
Add floware as an app: Apps → Create new app, with App Name localhost, Deployment Type Manual, and http://localhost:8001 as both the Public and Private URL. With VITE_APP_ENV=local in client/.env, you can tick “Create local app for development” to fill these in.
Start building: follow the Quickstart to create a model, an agent and a workflow, and run them from the console and the API.

To stop services, run ./dev-stop.sh (or close their terminal windows). To start again, re-run ./dev-start.sh.

Documentation

The full documentation is at wavefront.rootflo.ai. Where to start:

If you want to…	Read
Understand the building blocks	How Wavefront fits together, Concepts, Tour of the Wavefront Console
Build something in 15 minutes	Quickstart
Create agents	LLM Repository, Agents, Structured output, Tools, Images, documents and video
Build workflows	Workflows, How a workflow runs, Passing data between nodes, Sub-workflows, Routing and iteration, Function nodes, Versions
Work with data	Datasources, Dynamic queries, File storage, Configurations, Audit trail
Connect your application	Running workflows, , , ,

The documentation source lives in documentation/; run mint dev there to preview it locally.

Platform Components

Component	Description
flo-ai	FloAI library for Agent Building & A2A Orchestration. Detailed documentation is available here.
wavefront-server	The backend services: floconsole, floware, call processing and background workers. See Architecture.
wavefront-client	The Wavefront Console, for configuring agents, workflows, chatbots, voice agents, models, datasources and RBAC. See the console tour.
wavefront-cli	For configuring through the command line, for full developer control (Coming Soon)

Roadmap

See ROADMAP.md for detailed feature plans and contribution opportunities.

[!WARNING]

This project is under active development and APIs may change without notice. Please checkout the platform docs for the latest information.

The platform is not in the GA state, and there are unimplemented features. Checkout ROADMAP.md for the list of features, and what's missing.

⭐ Show Your Support

If you find Wavefront AI useful, please consider:

Starring this repository ⭐
Sharing with your network
Contributing to the project
Providing feedback and feature requests

Next Steps

Text us!