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Readme

0agent

A persistent, learning AI agent that runs on your machine.

npx 0agent@latest

That's it. 0agent installs, walks you through a 4-step setup, and starts a daemon that gets smarter with every task you run.

What it does

# Sprint workflow
0agent /office-hours "I want to build a Slack bot"
0agent /plan-ceo-review
0agent /plan-eng-review
0agent /build
0agent /review
0agent /qa --url https://staging.myapp.com
0agent /ship
0agent /retro

# One-off tasks
0agent /research "Acme Corp Series B funding"
0agent /debug "TypeError at auth.ts:47"
0agent /test-writer src/payments/
0agent /refactor src/api/routes.ts

# Plain language
0agent run "fix the auth bug Marcus reported"
0agent run "research Acme Corp and draft a follow-up email to Sarah"

# Entity-scoped (learns who you are)
0agent run "pull auth metrics" --entity sarah_chen

How it learns

Every time you run a task, 0agent records which strategy it chose and whether it worked. After 50 interactions, it converges to your optimal workflow — measurably, provably, via a weighted knowledge graph.

Edge weights start at 0.5 (neutral)
Positive outcomes push them toward 1.0
Negative outcomes push them toward 0.0
After 100 traces, plan selection is noticeably better

Requirements

Node.js ≥ 20
API key for Anthropic, OpenAI, or a local Ollama instance
Docker (optional but recommended — enables sandboxed subagents)

Install

# One-liner
npx 0agent@latest

# Global install
npm install -g 0agent
0agent init

# Or via brew (coming soon)
brew install 0agent

Local development

git clone https://github.com/0agent-oss/0agent
cd 0agent
pnpm install
pnpm build

# Run the wizard
node bin/0agent.js init

# Start daemon
node bin/0agent.js start

# Check status
node bin/0agent.js status

# Open dashboard
open http://localhost:4200

Architecture

You → 0agent CLI → Daemon (port 4200) → Knowledge Graph
                                      → Subagents (sandboxed)
                                      → MCP Tools (filesystem, browser, shell)
                                      → Learning Engine (weight propagation)

Knowledge graph — weighted, multimodal. SQLite + HNSW. Persists to ~/.0agent/graph.db
Subagents — sandboxed (Docker/Podman/process). Zero-trust capability tokens. Never write to the graph.
MCP — connects to any MCP server. Built-in: filesystem, shell, browser, memory.
Skills — 15 built-in YAML-defined skills. Add your own in ~/.0agent/skills/custom/
Self-improvement — weekly analysis of skill gaps, workflow optimization, prompt refinement.

Entity nesting

0agent can learn individual personalities within an organization:

# One-time setup in config
entity_nesting:
  enabled: true
  visibility_policy:
    allow_work_context: true       # company sees projects/tasks
    allow_personality_profile: false  # company can't see communication style

After 3+ interactions with Sarah, responses automatically match her style:

Terse? Leads with numbers, no preamble.
Bullet-point user? Bullets.
Exploratory? More context and options.

The company graph sees [from member] Sarah used /build — not the raw conversations.

Config

~/.0agent/config.yaml — created by 0agent init, edit anytime:

llm_providers:
  - provider: anthropic
    model: claude-sonnet-4-6
    api_key: sk-ant-...
    is_default: true

sandbox:
  backend: docker   # docker | podman | process | firecracker

entity_nesting:
  enabled: true

self_improvement:
  schedule: weekly

License

Apache 2.0