Ross ROSS = Recommend OSS · open-source software intelligence for agents

vectorize-io/self-driving-agents

None observed · 2026-08-28

github.com/vectorize-io/self-driving-agents · homepage · TypeScript observed · 2026-08-28

Health v2 · maintenance only

78/100

  • Activity 99
  • Release rhythm 89
  • Longevity 9

Flags: young no_license

How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.

  • gap_med: 0
  • age_days: 127
  • days_rel: 79
  • days_push: 7
  • n_releases_24m: 18

Full methodology

Adoption not part of the score

2730 stars · 806 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A collection of 179 ready-to-use, self-learning AI agent definitions organized into 13 departments (design, engineering, marketing, sales, testing, etc.), installable via an npm CLI into harnesses like Claude Code, Claude Chat, OpenClaw, and NemoClaw. Agents retain durable memory through Hindsight, auto-updating their own knowledge pages after each conversation so they improve over time.

Use cases

  • install prebuilt AI agents for marketing, SEO, or engineering into Claude Code
  • give my AI assistant persistent memory across sessions
  • find a collection of specialized AI agent prompts for design and UX work
  • build a custom AI agent that learns from conversations
  • set up an AI workforce covering sales, support, and product departments
  • create agents that maintain their own knowledge pages and playbooks
  • install agents from a local directory or GitHub repo into a chat harness

When to choose

  • you use Claude Code, Claude Chat, OpenClaw, or a compatible harness and want curated, domain-specific agents
  • you want agents with persistent, self-maintaining memory powered by Hindsight
  • you need a broad roster of role-based agents (design, finance, marketing, testing) without writing prompts from scratch

When to avoid

  • you need a production agent runtime or orchestration backend rather than agent definitions and an installer
  • you work outside the supported harnesses (e.g., custom LangChain pipelines or other LLM tooling)
  • you require a license file or formal support guarantees — the repo has no explicit license listed in metadata

Facets

library · maturity active

agent-framework cli prompt-engineering chatbot large-language-models developer-tools cli cross-platform ai-workforce agent-templates agent-memory hindsight claude-code self-learning-agents npm-package mit-license ai-agents automation nodejs

4 sources

Member repositories

RepositoryRoleHealth v2
vectorize-io/self-driving-agentsmain78

For agents

markdown · JSON · MCP: product_card(name="vectorize-io/self-driving-agents")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem