Praktor – Multi-agent Claude Code orchestrator with Docker isolation

Hacker News

I built Praktor to run my own AI agents in isolated Docker containers, accessible from Telegram. It's a single Go binary: receives Telegram messages, routes them to named agents (each with its own role, model, and config), spins up Docker containers running Claude Code via the Agent SDK, and streams responses back. A Mission Control web UI provides real-time monitoring. Some highlights: - Named agents with smart routing (@agent_name or AI-powered) - Each agent in its own Docker container with isolated filesystem - Persistent per-agent memory via SQLite + MCP tools - Agent swarms: fan-out, pipeline, and collaborative patterns - Encrypted secrets vault (AES-256-GCM), never exposed to the LLM - Nix package manager — agents install tools on demand - Browser automation via playwright-cli - Per-agent MCP servers, plugins, and skills from the UI - Hot config reload, scheduled tasks, backup/restore Would love feedback.

Visit product View on Hacker NewsFeb 26, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, claude · Missing: mac, macos, cursor

97%97% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

44%44% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: filesystem, ide, pipe · Missing: https docs, excited, just released

43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

26%26% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

24%24% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: smart · Missing: web3, chat, crypto

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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