LLM routing, MCP tooling, a single view of every project, goal, and the agents working on them, cross-machine, on one Cloudflare backend.
Pictured > Conduit Desktop - each center planet a Project, each child Session an in-progress goal.
Everything routes through a single Cloudflare Worker backed by D1, KV, R2, and Vectorize. No servers to patch, nothing to keep awake.
Live surfaces sit behind Cloudflare Access. Happy to walk through a demo.
Secure guidance, smart limits, trust supported.
A live map of every project and the independent sessions pushing its goals forward, across my Windows, Mac, and Linux machines. A coordinator keeps each project's plan; workers take tasks and hand back structured reports with test results, flagging anything that needs my input or sign-off.
No more endless terminal tabs - a companion on each machine starts, wakes, and streams those sessions into a secure web portal, with a cloud editor for any file on any machine.
Route the same prompt to OpenAI, xAI, Gemini, and Claude with provider-specific adapters. Compare responses side by side, pick the best output, switch providers without touching application code. The arena at tools.condi.dev makes this interactive.
Every send_to_llm call is traced: cost, tokens, and latency, stamped per provider and model. A live spend chart and a per-call ledger turn "what is this costing me" into a number, not a guess.
A slot-based, cron-driven information dashboard. Morning tasks, inbox triage, and an evening recap, each generated by a scheduled AI job and published to a named widget slot. ETag-cached polling keeps it snappy; a time-travel archive scrolls back through previous versions.
44 tools exposed via Model Context Protocol: task tracking, project management, context capture and hybrid search, cross-model workflows, email, dashboard and app publishing, media generation, and the session bridge. Connected to my AI coding tools and chat apps as a live integration, not a demo.
Content goes in messy - meeting notes, research dumps, raw captures. Conduit tags and distills it into LLM-ready context, chunks it for RAG retrieval, and stores relations as a first-class graph so references don't get lost. Retrieval is hybrid: full-text search and semantic vectors, fused and reranked, so a query finds the note that means it, not only the one that repeats its words.
Conduit handles task management, context capture, multi-provider LLM routing, and the coding sessions themselves across every project I run. Ten cron triggers run seven jobs: task triage, the evening recap, the voice assistant's daily brief and nightly journal, and cleanup.
The MCP server is connected to my AI coding tools and chat apps right now. Work flows through Conduit.
20+ years building defense software, simulation systems, and training platforms. Currently exploring what happens when one engineer treats AI tooling as infrastructure instead of a novelty.
Before Conduit, my AI setup was a pile of disjointed MCP servers - hard to update, capabilities scattered across terminals, apps, and browsers, nothing speaking to anything else. Conduit started as the cleanup. It turned into the system I work through every day: one place to capture context, track tasks, route prompts across providers, and publish back to a dashboard I actually read.
Want to talk AI-augmented workflows, MCP tooling, or Cloudflare architecture?
conduit@condi.dev