Give your AI agent eyes to see the entire internet. Read & search Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu — one CLI, zero API fees.
Install
$
git clone https://github.com/obot-platform/obotInstall command taken from the project's README or registry entry. Always review code before running it.
README excerpt
Read full README on GitHub →Obot
Obot is an open-source platform for organizations to manage, secure, and govern their AI ecosystems. It provides shared infrastructure for connecting AI clients to models and tools, distributing approved MCP servers and skills, managing agent access and credentials, running hosted AI workloads, and recording activity across hosted services and user devices.
Obot does not require an organization to standardize on a single AI client, model provider, or tool ecosystem. Desktop agents and tools such as Claude Code, Codex, Cursor, VS Code, and other IDEs and CLIs can use the parts of the platform that apply to them.
Architecture
The Obot Platform connects AI activity on user devices with services managed by or proxied through Obot Server.
On user devices, desktop agents and tools connect to Obot gateways, while Obot Sentry scans, audits, and enforces policy on AI activity taking place on the device. The Obot CLI lets users and AI clients discover, install, and manage approved MCP servers and skills.
Obot Server provides:
- MCP and LLM gateways for controlled access to MCP servers and model providers.
- Sandboxed execution for hosted MCP servers and agents.
- Platform services for identity and access control, including permissions and secrets.
- Correlated audit logs of AI activity across the platform.
- MCP and Skills registries built on curated Git-backed catalogs.
Obot integrates with remote MCP servers, LLM providers, S3-compatible object storage, Git providers, and auth providers.
Core Capabilities
MCP Gateway
The MCP Gateway is a single governed entry point to every MCP server a user is allowed to reach.
- Proxy MCP servers, whether hosted by Obot or running outside it.
- Create composite MCP servers that expose selected tools from multiple servers.
- Control server and tool access by user or identity-provider group.
- Manage MCP OAuth, user and shared credentials, and Kubernetes secret bindings.
- Inspect, reject, or modify MCP requests and responses with MCP or webhook filters.
LLM Gateway
The LLM Gateway presents provider-compatible endpoints that AI clients use to reach approved models.
- Connect external AI clients to OpenAI, Anthropic, Amazon Bedrock, Azure, and Generic Responses Compatible providers.
- Keep provider credentials in Obot instead of distributing them to individual users or clients.
- Authenticate clients with scoped Obot API keys.
- Restrict the models visible and callable by each user through Model Access Policies.
- Record requests and responses, client and session metadata, token usage, and estimated model cost.
Sandboxed MCP Servers and Agents
Obot can run agents and MCP servers itself, in isolated execution environments outside the main Obot Server process.
- Host
npx,uvx, and containerized MCP servers as Docker containers or Kubernetes workloads. - Apply domain-based egress rules to hosted MCP servers through a configured network-policy provider.
MCP and Skills Registries
The MCP and Skills Registries centralize the discovery, installation, and management of MCP servers and Agent Skills.
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