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openagent

OpenAgent is an open-source self-hosted AI assistant platform built in Go that combines large language models, retrieval-augmented generation (RAG), and autonomous agent capabilities. It ships as a single binary for Linux, macOS, and Windows, with support for 30+ model providers, browser automation, web search, shell execution, and MCP tool integration.

Source: GitHub — github.com/the-open-agent/openagent
5.4k
GitHub stars
616
Forks
Go
Primary language
Apache-2.0
License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

FieldValue
Repositorythe-open-agent/openagent
Ownerthe-open-agent
Primary languageGo
LicenseApache-2.0 — OSI-approved
Stars5.4k
Forks616
Open issues44
Latest releasev2.83.0 (2026-07-07)
Last updated2026-07-07
Sourcehttps://github.com/the-open-agent/openagent

What openagent is

Written in Go with a Node.js frontend, OpenAgent provides a unified platform for LLM inference, semantic search over document stores, and multi-step agent orchestration with tool calling. It includes a BPMN-style workflow builder, REST API, audit logging, and multi-tenancy support, deployable via binary, Docker, or source.

Quickstart

Get the openagent source

Clone the repository and explore it locally.

terminalbash
git clone https://github.com/the-open-agent/openagent.gitcd openagent# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Enterprise Knowledge Assistant

Organizations needing a self-hosted AI assistant that can search internal documents (PDFs, Word, Excel) and answer questions without sending data to external APIs. The RAG pipeline and isolated knowledge stores enable compliance-friendly deployments.

Autonomous Task Automation

Teams automating multi-step workflows: browser-based form filling, web scraping, shell command execution, and office document generation. The visual workflow builder and MCP integration allow complex orchestration without coding.

Multi-Model LLM Gateway

Engineering teams needing to switch between 30+ LLM providers (OpenAI, Claude, Gemini, local models) per conversation, with cost tracking and usage analytics per user, model, and organization.

Implementation considerations

  • Verify Go and Node.js versions (Go 1.25.0+, Node 20+) match your deployment environment before building from source.
  • Configure authentication (OIDC, OAuth2, LDAP, SAML) and audit logging from the start if multi-user or compliance is required.
  • Choose embedding and LLM providers early; switching after indexing documents requires re-embedding the knowledge base.
  • Test MCP tool integrations (SSE, Stdio, StreamableHTTP) and browser-use agent loop latency in your target network before production rollout.
  • Plan for token cost monitoring and rate-limiting by user/model to prevent budget overruns with pay-per-API-call providers.

When to avoid it — and what to weigh

  • Need Proven Enterprise SLA & Support — OpenAgent is community-driven open-source. No vendor SLA, guaranteed support tier, or commercial indemnity is stated in the data. Requires internal ops capability to maintain in production.
  • Require Minimal Infrastructure Footprint — While delivered as a single binary, the platform includes a backend server, frontend UI, database, and orchestration layer. Not suitable for edge-only or ultra-lightweight embedded deployments.
  • Strict Data Residency Without Review — Integrates with 30+ external model providers and embedding services. Requires explicit vetting of which providers are used and where data flows; not suitable for air-gapped environments without configuration audit.
  • Cannot Tolerate Open-Source Dependency Chain Risk — As an open-source platform, security patches and dependency updates depend on community contribution. Organizations with strict vulnerability SLAs should evaluate maintenance cadence and incident response practices.

License & commercial use

Licensed under Apache License 2.0, a permissive OSI-approved license allowing commercial use, modification, and distribution with attribution and liability disclaimer.

Apache 2.0 permits commercial deployment and redistribution. However, no commercial support, warranty, or indemnification is stated in the provided data. Organizations using this in production should: (1) review the full LICENSE file, (2) conduct their own security and compliance audit, (3) establish internal support capability or contract a third-party maintainer, and (4) ensure dependencies are also compatible with their commercial policy. Requires review if used in regulated industries.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

SignalAssessment
MaintenanceActive
DocumentationAdequate
License clarityClear
Deployment complexityLow
DEV.co fitStrong
Assessment confidenceHigh
Security considerations

Integrates with external LLM providers and embedding services; data flow must be audited per your compliance needs. Browser-use and shell-execution agents have elevated privilege; restrict to trusted use cases. OIDC/OAuth2/LDAP/SAML support present but configuration details not shown. Audit logging available but audit log retention and immutability not documented. No statement on vulnerability disclosure policy, security audit history, or SBOM. Supply-chain risk: Go and Node.js dependencies must be scanned. Recommend: conduct threat model for your threat profile before production use.

Alternatives to consider

LangChain / LlamaIndex

Popular Python frameworks for building custom LLM agents and RAG pipelines. More lightweight and flexible if you prefer code-first development; less opinionated UI and workflow builder than OpenAgent.

Hugging Face Transformers + FastAPI

Lower-level, fully customizable approach to building LLM applications. Suited for teams comfortable with Python and infrastructure; no pre-built UI, workflow automation, or multi-provider abstraction.

Claude API for inference, n8n for visual workflow automation and integrations. Vendor-locked to Anthropic but provides strong security model; lower operational overhead than self-hosted platforms.

Software development agency

Build on openagent with DEV.co software developers

Evaluate OpenAgent for your knowledge-base search, autonomous task automation, or multi-model LLM gateway use case. Start with the free demo, review the Apache 2.0 license and architecture, and plan your security audit before production.

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openagent FAQ

Can I use OpenAgent without connecting to external LLM providers?
Yes, via Ollama integration or local model support (mentioned in provider list). However, performance and model quality will depend on your local hardware.
Is my data stored locally or sent to external APIs?
OpenAgent runs on your infrastructure, but by default it sends prompts and queries to configured LLM/embedding providers (OpenAI, Anthropic, etc.) unless you use local models. You must choose and configure providers per your data policy.
What LLM models can I use?
Officially supports 30+ providers including OpenAI, Azure OpenAI, Anthropic Claude, Google Gemini, DeepSeek, Mistral, local models via Ollama, and others. You can add custom providers via the API.
How do I deploy this at scale for multiple teams?
Multi-tenancy is built-in with isolated workspaces, OIDC/OAuth2/LDAP/SAML auth, and RBAC. You'll need to provision a database backend, configure auth, and set up monitoring/logging for production.

Custom software development services

DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If openagent is part of your ai frameworks roadmap, our team can implement, customize, migrate, and maintain it.

Deploy a Self-Hosted AI Assistant Today

Evaluate OpenAgent for your knowledge-base search, autonomous task automation, or multi-model LLM gateway use case. Start with the free demo, review the Apache 2.0 license and architecture, and plan your security audit before production.