A single Go binary that runs your AI assistant stack locally

What it does

OpenAgent is a self-hostable platform that wires together LLM chat, document-based RAG, and autonomous agent loops capable of driving browsers, executing shell commands, and calling MCP-compatible tools. It ships as a single Go binary with no runtime dependencies, runs natively on Windows without WSL, and includes a visual workflow builder, admin dashboards, and SSO auth.

The interesting bit

The project treats “single binary” as a genuine deployment philosophy rather than a build artifact — backend, frontend, and all integrations compile into one executable that starts on port 14000. That is unusual for a stack this broad, and it sidesteps the typical Docker-compose tax for teams that just want to run something on a laptop or small server.

Key highlights

• Supports 30+ model providers (OpenAI, Anthropic, Gemini, Ollama, OpenRouter, etc.) with per-conversation switching
• Agent loops include real browser automation, web search, shell execution, Office file manipulation, and MCP server integration
• RAG pipeline with pluggable embeddings, automatic chunking, and isolated knowledge stores per workspace
• Built-in BPMN-style workflow editor with conditional branching, parallel execution, and scheduled triggers
• Admin dashboards for token/cost analytics, activity monitoring, tool management, and full request/response audit logs
• Multi-tenancy with OIDC/OAuth2/LDAP/SAML and a REST API with Swagger UI

Caveats

• The README is heavy on feature lists and light on architecture details — it is unclear how the RAG embedding stores are persisted or whether vector search runs in-process
• “No installation needed” is technically true for the binary, but building from source requires separate Go and Node/Yarn toolchains
• The demo environments are read-only or reset every 5 minutes, so evaluating persistence or multi-user behavior is limited

Verdict

Worth a look if you want a consolidated, self-hosted alternative to cloud AI platforms and prefer shipping a single file over orchestrating containers. Skip it if you need deep visibility into the retrieval pipeline or are already invested in a mature LangChain/LLamaIndex deployment you can modify.