auxilia
auxilia is an open-source web MCP client for teams. It lets you host AI assistants backed by remote Model Context Protocol servers, share them across your organization, and keep every integration — tools, credentials, observability — under your own infrastructure.
Why auxilia
auxilia is more than a chat app. It’s a place for your team to design AI assistants, see what they can actually do with your company’s knowledge and tools, and put them to work wherever they’re needed.
- A sandbox for building better assistants — refine an assistant’s instructions, connect it to the right tools and documents (Notion pages, data models, internal guides), and quickly see what it can handle before rolling it out
- One source of truth for your team — when an assistant works well, share it with colleagues so everyone follows the same process instead of each person crafting their own prompts
- Works beyond the chat window — the same assistants can be plugged into automation tools, scheduled jobs, or embedded directly inside your existing products — customer support consoles, internal dashboards, spreadsheets, and more
- Freedom to choose the right AI model — pick the model that fits each assistant’s job and budget, and switch providers as the market evolves, without re-doing any of the setup
- Your data stays yours — auxilia runs on your own infrastructure, so conversations, credentials, and business context never leave your control
What you can build
- A CRM agent that queries HubSpot and drafts Slack replies
- A data analyst that runs BigQuery, produces charts in a sandbox, and posts results to a thread
- An on-call assistant that reads Sentry issues, searches Linear, and opens pull requests via GitHub
- A workspace-wide coordinator agent that dispatches work to specialized subagents
Architecture
auxilia is composed of three services plus an optional sandbox runtime:
| Service | Technology | Purpose |
|---|---|---|
| Backend | FastAPI + LangGraph | Agent runtime, MCP client, auth, API |
| Web | Next.js 16 + React 19 | User interface and backend proxy |
| Database | PostgreSQL 17 + Redis 7 | Persistence, checkpoints, OAuth token storage |
| Sandbox | OpenSandbox (optional) | Isolated code execution for agents |
┌─────────────┐ ┌─────────────┐ ┌──────────────────┐
│ Browser │────▶│ Next.js │────▶│ FastAPI │
│ (React) │◀────│ (proxy) │◀────│ (LangGraph) │
└─────────────┘ └─────────────┘ └────────┬─────────┘
│
┌────────────┼────────────┐
│ │ │
┌──────────▼──┐ ┌───────▼──────┐ ┌───▼──────────┐
│ Remote MCP │ │ PostgreSQL │ │ OpenSandbox │
│ Servers │ │ + Redis │ │ (optional) │
└─────────────┘ └──────────────┘ └──────────────┘Key concepts
Agents
An agent is defined by a system prompt, an avatar (emoji + color), a set of MCP server bindings, and optional subagents. Each agent can use tools from multiple MCP servers at once.
MCP servers
auxilia connects to remote MCP servers over HTTP (Streamable HTTP transport). Servers are registered at the workspace level by admins and can be bound to any agent.
Tools
Each tool exposed by an MCP server can be set to one of three states per agent: always allow, needs approval, or disabled. Approvals happen inline in the chat (or in Slack).
Sandbox
Turn on the sandbox for an agent to give it a Linux code-execution environment with ls, read_file, write_file, edit_file, glob, grep, and execute. Useful for data analysis, scripting, and file manipulation tasks.
Workspace roles
- admin — manage MCP servers, invites, personal access tokens, agent permissions
- editor — create and edit agents
- member — chat with agents the admin has shared with them
Supported LLM providers
| Provider | Models |
|---|---|
| Anthropic | Claude Haiku 4.5, Sonnet 4.6, Opus 4.6 |
| OpenAI | GPT-4o mini |
| Gemini 3 Flash Preview, Gemini 3 Pro Preview | |
| DeepSeek | DeepSeek Chat, DeepSeek Reasoner |
Configure one or more providers via environment variables. At least one is required.
Integrations
- Slack — invoke workspace agents, approve tool calls, and stream responses from Slack threads
- Langfuse — trace LLM calls, tool calls, and costs per agent and user
Where to go next
Pick the path that matches what you’re here for.
- Get Started — developers running auxilia locally with Docker Compose
- Deploy & Configure — DevOps / IT / AI engineers setting up auxilia for a team (Cloud Run, VM, environment, user management, MCP, sandbox, Langfuse, Slack)
- Build Agents — agent authors configuring instructions, tools, subagents, and permissions
- Tutorials — business-friendly how-tos (HTTP triggers, Slack, custom UIs, schedulers)
- Case Studies — real-world stories from teams using auxilia