{"categories":[{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/13"},{"label":"Distributed Computing","url":"https://skillfed.io/packages/category/system-distributed-computing/2"}],"enrichment":{"capability":"Coordinates communication between multiple AI agents running in separate OpenCode sessions, enabling them to discover each other and exchange messages through a daemon.","skillfed_tags":["agent-orchestration","opencode-integration","daemon-service"],"use_cases":["Orchestrate collaborative problem-solving where multiple specialized agents work together on a single task without manual routing.","Build autonomous workflows where agents can request information or assistance from peers, reducing external API calls.","Monitor and debug multi-agent interactions using agent-hub-watch to see message flow and coordination in real time.","Control LLM API costs in high-frequency agent communication by enabling rate limiting to throttle message throughput.","Deploy a persistent agent hub as a system service to coordinate long-running agent sessions across multiple terminals."],"what_it_does":"opencode-agent-hub is a daemon that enables multiple AI agents running in separate OpenCode sessions to discover and communicate with each other. It works by polling OpenCode's shared SQLite database to find active sessions, facilitating introductions through a coordinator agent, and then routing messages between agents by watching for JSON files written to ~/.agent-hub/messages/ and injecting them into target sessions via async prompts. Agents do not poll for messages\u2014the daemon wakes them up when new communication arrives.\n\nThe package is built for developers who want to orchestrate autonomous multi-agent workflows within OpenCode. It includes a daemon service (installable as a systemd or launchd service), a watch utility for monitoring activity, and configurable rate limiting to control LLM API costs. The daemon relies on agent-hub-mcp being configured in OpenCode, and messages are validated with pydantic on receipt. Known limitations include that injected messages are not visible in the OpenCode TUI, and some models may flag orientation messages as prompt injections.","worth_installing":"Yes, with conditions. Install if you are actively using OpenCode and want to enable agent-to-agent communication\u2014the low install friction, active maintenance, and zero known vulnerabilities make it safe to try. However, be aware that it is still in Beta, the project is young, and injected messages are not visible in the OpenCode TUI, which may complicate debugging. Verify that agent-hub-mcp is properly configured before starting the daemon, and enable rate limiting if concerned about LLM API costs."},"id":"opencode-agent-hub","links":{"html":"https://skillfed.io/packages/opencode-agent-hub","md":"https://skillfed.io/packages/opencode-agent-hub.md","pypi":"https://pypi.org/project/opencode-agent-hub/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-27","license_spdx":"AGPL-3.0-only","license_treatment":"agpl","name":"opencode-agent-hub","python_support":"supports_current","summary":"Multi-agent coordination daemon and tools for OpenCode"},"popularity":{"monthly_downloads":308392,"position":7765,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.6.1"}