Archestra
Enterprise AI Platform with guardrails, MCP registry, gateway & orchestrator
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What is Archestra?
Archestra is an open-source enterprise MCP (Model Context Protocol) platform designed to enable non-technical users to safely build and deploy Autonomous AI agents with access to corporate data. It acts as a secure agentic gateway sitting between your application and Language Models, functioning as a smart, security-aware router for AI-driven workflows. The platform solves critical enterprise AI adoption needs by removing MCP security risks while allowing companies to unleash well-connected agentic workflows.
Key features include deterministic agentic guardrails that prevent data exfiltration and prompt injection attacks, a ChatGPT-like internal UI for both technical and non-technical users, built-in RAG knowledge base connecting to Jira, Confluence, GitHub, Notion, SharePoint, Google Drive, and Salesforce, and a private MCP registry with full governance. The platform offers Kubernetes-native MCP orchestration for isolated pods, cost monitoring with dynamic optimization reducing costs up to 96%, Prometheus metrics and OpenTelemetry tracing for observability, and multi-model support working with Claude, GPT-4, Gemini, and open-source models.
Archestra is designed for organizations where software engineers and non-technical teams need to work together with AI agents. Technical users can build agents using LangChain, n8n, pure Python or other stacks leveraging the MCP orchestrator, guardrails and observability. Non-technical users enjoy the simple ChatGPT-like UI and get immediate results. The platform integrates with existing infrastructure including n8n, Vercel AI, Pydantic AI, Mastra, OpenWebUI, and supports communication via web UI, Slack, MS Teams, and Email.
The platform is self-hostable with Docker deployable in seconds, open-source under MIT license, and includes Terraform provider and Helm chart for Infrastructure as Code deployment. It provides per-team, per-agent, or per-organization cost monitoring and limitations, automatic model switching to cheaper models for simpler tasks, and granular budget limits.
Archestra pricing
Pricing model: Freemium
Self-Host for Free - Archestra is open-source under MIT license. The platform can be deployed with Docker using 'docker pull archestra/platform:latest' and 'docker run -p 9000:9000 -p 3000:3000' command. Full enterprise demo is available by booking. The company raised $3.3M pre-seed funding. No paid tier pricing details are publicly listed on the website - enterprise customers need to contact sales for custom enterprise plans.
Archestra pros
- Open-source platform with MIT license
- Self-host for free with Docker
- Deterministic guardrails prevent data exfiltration
- Protects against prompt injection attacks
- ChatGPT-like UI for non-technical users
- Built-in RAG without external vector database
- Connects to Jira, Confluence, GitHub, Notion, SharePoint
- Kubernetes-native MCP orchestrator with isolation
- Cost optimization reducing expenses up to 96%
- Per-team cost tracking and budget limits
- Private MCP registry with governance
- Works with Claude, GPT-4, Gemini, open-source models
- Integrates with n8n, LangChain, Vercel AI, Pydantic AI
- Prometheus metrics and OpenTelemetry tracing
- 45ms 95th percentile latency
- Slack, MS Teams, and Email agent communication
- Terraform provider for IaC deployment
- Helm chart for Kubernetes deployment
- No-code autonomous agent builder
- Local-first architecture for privacy
Archestra cons
- Requires Docker for deployment
- Kubernetes knowledge needed for full orchestration
- Self-hosting requires infrastructure management
- New platform with limited community size
- 129 GitHub stars indicates early adoption stage
- Complex setup for enterprise features
- Learning curve for MCP concepts
- PostgreSQL and data volumes required
Frequently asked questions about Archestra
What is Archestra?
Archestra is an open-source enterprise MCP platform for AI agents. It is a centralized AI Platform designed for organizations where software engineers and non-technical teams all need to work with AI agents. It acts as an agentic gateway that sits between your application and Language Models, functioning as a smart, security-aware router for AI-driven workflows with deterministic guardrails.
Is Archestra free to use?
Yes, Archestra is open-source under MIT license and you can self-host for free. Deploy Archestra in seconds with Docker using the quick start command. The platform includes all core features for free self-hosting. Enterprise demo is available for organizations needing additional support.
How does Archestra protect against prompt injection?
Archestra provides deterministic agentic guardrails that prevent agents from leaking sensitive data, corrupting systems, and following prompt injection. It uses a Dynamic Tool Engine that dynamically scopes which tools an agent can access, isolating untrusted contexts from high-privilege actions like execute_code or send_email. It also features Dual LLM Sanitization where a separate model sanitizes inbound data before reaching the primary agent.
What MCP servers does Archestra support?
Archestra supports full OSS MCPs - compatible with thousands of existing MCP servers. You can connect to any MCP server from your private registry with a single click. The platform includes a Private MCP Registry where you can add MCPs to share with your team, including self-hosted and remote, self-built and third-party servers with version control and access management.
Can non-technical users build agents with Archestra?
Yes, Archestra is specifically designed to enable non-technical users to safely build Autonomous AI agents. Non-technical users enjoy a simple ChatGPT-like UI and get immediate results. The Agent Runtime provides a no-code builder for autonomous agents where you can define system prompts, assign MCP tools and sub-agents, and configure triggers without coding.
What data sources can agents connect to?
Archestra's built-in RAG Knowledge Base connects to Jira, Confluence, GitHub, Notion, SharePoint, Google Drive, Salesforce, and more. The full RAG stack including chunking, embedding, hybrid search, and reranking runs inside Archestra without external vector database or separate retrieval service required. Agents can answer from your own company data.
How does cost optimization work in Archestra?
Archestra provides per-team, per-agent, or per-organization cost monitoring and limitations. The dynamic optimizer automatically reduces costs up to 96% by intelligently switching to cheaper models for simpler tasks. Features include real-time cost tracking with per-token granularity, granular budget limits, and automatic tool call and result compression to reduce token usage.
What observability features does Archestra include?
Archestra works with your observability stack by exporting metrics to Prometheus, traces to OpenTelemetry, and visualization in Grafana. It tracks LLM tokens_total, LLM request duration seconds, HTTP request duration seconds, time to first token, tokens per second, and blocked tool calls. Pre-configured Grafana dashboards are ready for monitoring AI infrastructure.
How do I deploy Archestra?
Deploy Archestra in seconds with Docker using 'docker pull archestra/platform:latest' and 'docker run -p 9000:9000 -p 3000:3000 -e ARCHESTRA_QUICKSTART=true -v /var/run/docker.sock:/var/run/docker.sock archestra/platform'. Then open http://localhost:3000. For Kubernetes, use the production-ready Helm chart with 'helm install archestra'. Terraform provider is also available for Infrastructure as Code.
Which AI models does Archestra support?
Archestra has multi-model support and works with Claude, GPT-4, Gemini, and open-source models. The LLM proxy acts as a drop-in proxy between your apps and LLM providers. The MCP Gateway provides a single endpoint for all MCP tools and works with any framework including n8n, LangChain, Vercel AI, Pydantic AI, and Mastra.