OpenAgent
A self-hostable personal assistant that combines models, knowledge retrieval, and executable agent tools.
Per-dimension scores and reasoning
Evidence shows: README claims support for shell execution, browser control, file read/write, etc., but provides no permission model, user confirmation mechanism, data flow transparency, sensitive data handling, dependency security audit, external effect control, rollback mechanism, or source attribution. Therefore all trust criteria score 0.
Evidence shows: README and go.mod are inconsistent on Go version (README requires 1.25.0+, go.mod specifies 1.25.0 but toolchain 1.25.8), and dependencies are numerous but no availability guarantee is provided. Thus self-consistency scores 1, dependency availability scores 1, failure messages score 0.
Evidence shows: README clearly targets personal AI assistant, supports multiple scenarios (browser, shell, office automation), but does not define capability boundaries or trigger precision. Therefore audience and scenarios score 2, capability boundaries score 1, trigger precision score 0, environment fit score 2 (multi-platform support).
Evidence shows: README is well-structured, provides installation instructions, examples, and documentation links, but lacks known limitations, changelog, and clear maintenance responsibility. Thus information architecture scores 2, install notes score 2, naming stability scores 1 (version exists but no stability statement), examples and FAQ score 1 (examples but no FAQ), known limitations score 0, license scores 2 (Apache-2.0), versioning and changelog score 1 (version but no changelog), maintenance responsibility scores 1 (community links but no explicit maintainer).
Evidence shows: README claims various features, but no actual output examples or user feedback, marginal value hard to assess, cost-benefit not mentioned. Therefore output usability scores 1, marginal value scores 2 (feature-rich), cost-benefit scores 1.
Evidence shows: README feature claims lack verifiable test results or third-party validation, and no separation of facts and inferences. Thus claim traceability scores 1, cross-source corroboration scores 1 (CI config exists), fact-inference separation scores 0.
- No permission model or user confirmation mechanism; shell execution and browser control may pose security risks.
- Numerous dependencies without security audit, posing supply chain risk.
- Inconsistency between README and go.mod on Go version may affect build.
What does this agent do, and when should you use it?
OpenAgent is an open-source, self-hostable personal AI assistant platform combining LLMs, RAG knowledge bases, and autonomous agent loops. It connects to multiple model providers and lets users switch models per conversation. Its agents can operate a browser, run shell commands, work with Office files, and call MCP-compatible tools. The platform also includes a visual workflow builder, scheduling, usage analytics, activity monitoring, and request logs. It ships as pre-built binaries for Linux, macOS, and Windows, with source-build and Docker Compose deployment paths.
After users upload PDFs, Word documents, Excel sheets, and other files, OpenAgent chunks, embeds, and indexes them; before each LLM response, it performs semantic retrieval and supplies relevant passages as context. Its agent loop can drive a real browser to navigate, click, fill forms, scrape pages, and take screenshots, while also performing web search and fetch, running shell commands and scripts, and reading or writing Word, Excel, and PowerPoint files. It connects to MCP servers over SSE, Stdio, or StreamableHTTP and exposes their tools to the agent. Work can be assembled in a BPMN-style drag-and-drop workflow editor with conditional branches, parallel execution, and recurring schedules; administration surfaces tool-call arguments and returns, request logs, and token and cost metrics.
- A team with internal PDFs, Word files, and spreadsheets can create searchable knowledge stores and assign separate stores to individual chats or applications.
- An operations user who needs website navigation, form completion, page scraping, or screenshots can use the Browser-Use capability for those steps.
- A technical user who needs commands or scripts run as part of an assistant interaction can use the agent's shell-execution capability.
- An organization with existing MCP services can expose their server tools to agents through SSE, Stdio, or StreamableHTTP.
- A business team automating multi-step work with decisions, independent tasks, or recurring runs can build and schedule workflows visually.
- An administrator tracking model, user, or application usage can inspect token and cost metrics, activity trends, and detailed request logs.
What are this agent's strengths and limitations?
- Combines document RAG, browser operation, shell execution, Office automation, and MCP tool calls in one self-hostable platform.
- Lists 30+ model providers and supports switching models per conversation without code changes.
- Supports MCP connections over SSE, Stdio, and StreamableHTTP.
- Provides pre-built binaries for Linux, macOS, and Windows, with native Windows support documented without WSL or Docker.
- Includes token and cost analytics, activity monitoring, centralized tool management, and detailed request logs.
- The supplied material does not specify credential setup, connection configuration, or security boundaries for model providers.
- Browser, web-search, shell, and MCP capabilities can act on external resources, so adopters need to assess permissions and network controls.
- Source builds require Go 1.25.0+, Node.js 20+, and Yarn 1.x, creating both backend and frontend toolchain requirements.
- The material does not document vector-store choices, document-ingestion failure handling, data retention, or backup behavior.
- A REST API and Swagger UI are stated, but authentication details, endpoints, and request examples are not provided.
How do you install or deploy this agent?
On macOS, Linux, or WSL:
curl -fsSL https://raw.githubusercontent.com/the-open-agent/openagent/master/scripts/install.sh | bashOn Windows PowerShell:
irm https://raw.githubusercontent.com/the-open-agent/openagent/master/scripts/install.ps1 | iexThe installer downloads the latest release and starts OpenAgent on port 14000; open http://localhost:14000. Optional environment variables are OPENAGENT_VERSION, INSTALL_DIR, and BIN_DIR. For a source build, the backend requires Go 1.25.0+ and the frontend requires Node.js 20+ and Yarn 1.x: run go build, then cd web && yarn install && yarn start. Docker deployment is available through docker-compose up. The supplied material does not document the exact setup for model-provider credentials.
How do you use this agent?
Open http://localhost:14000 after the service starts. Connect the needed model provider and select a model in a conversation; upload documents to create a knowledge base and assign a store to a chat or application. Configure browser, shell, Office, web-search, or MCP tools when the agent needs to act externally; the interface exposes tool calls, arguments, and returned values step by step. For repeatable multi-step work, compose the process in the visual workflow builder and add conditions, parallel paths, or a recurring schedule as needed. The supplied material does not provide field-level examples for model connections, MCP-server registration, or REST API calls.
How does this agent compare with similar options?
OpenAgent is positioned as a single self-hosted platform for multi-provider model access, RAG, and agent loops. It explicitly lists providers including OpenAI, Azure OpenAI, Anthropic Claude, Google Gemini, and Ollama, with model switching per conversation.
FAQ
Am I locked into one model provider?
Can it run in my own environment?
docker-compose up deployment options.