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Show HN: Nerve – self hosted runtime for AI agents

Authors
animetyan
Source
Hacker News
Published
Collection
Software Intelligence
AI Agent RuntimeSelf Hosted AgentsMulti Agent Software EngineeringCode IntelligenceHuman AI Interaction
Open GitHubMarkdown source

Summary

Nerve is a self-hosted runtime that turns Claude Agent SDK agents into long-running personal or worker agents with memory, tasks, skills, crons, and human approval flows. It matters for agent-based software work because it packages persistence, scheduling, source ingestion, and approval gates into one deployable service.

Problem

  • Long-running agents need state across sessions, scheduled work, and ways to ask humans for decisions; a plain chat UI does not cover these needs.
  • Team worker agents also need audit trails, plan approval, and safe handling of untrusted inputs before they monitor CI, review PRs, or edit code.
  • The project targets self-hosting, so users can run the runtime with their own Claude API key or Claude subscription through a CLI proxy.

Approach

  • The runtime wraps the Claude Agent SDK in a single Python process with a FastAPI gateway, React web UI, Telegram channel, APScheduler jobs, and optional Claude OAuth proxy.
  • It uses two memory layers: MEMORY.md for curated hot facts injected into prompts, and memU in SQLite for semantic recall over conversations, facts, preferences, and events.
  • It stores tasks and skills as Markdown backed by SQLite indexes, so agents can read, create, update, and reuse procedures during later sessions.
  • Worker mode starts from a plain-English mission, writes its own TASK.md, creates skills, sets cron jobs, proposes plans, and waits for human approval before implementation.
  • Source ingestion pulls Telegram, Gmail, GitHub notifications, and GitHub events into a shared inbox with prompt-injection warnings and independent consumer cursors.

Results

  • The excerpt reports no benchmark, user study, ablation, or task-completion metric, so there is no quantitative evidence of agent accuracy or productivity gain.
  • Claimed system scale: about 30 custom MCP tools, up to 4 concurrent agent sessions by default configuration, and 3 session modes for scheduled AI jobs.
  • Claimed memory controls: 4 memory types (profile, event, knowledge, behavior), 3-level quality filtering, semantic deduplication at cosine similarity 0.85, and mutation audit logs.
  • Claimed automation cadence: inbox processing every 15 minutes, task planning every 4 hours, skill extraction every 12 hours, and skill revision weekly in personal mode.
  • Claimed safety and review features include plan approval, decline, and revision workflow, file snapshot diffs without git, 30-second script timeout for skill scripts, and full plan/session logs.

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