initrunner 0.2.0

A lightweight AI agent runner

InitRunner — The AI Agent Runner

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Define AI agents like CI pipelines. YAML in, autonomous agents out.

Your agent is a YAML file. Its tools, knowledge base, memory, and triggers — all config, not code. Deploy it as a CLI tool, a cron-driven daemon, or an OpenAI-compatible API. Compose agents into pipelines. RAG and long-term memory come batteries-included. Manage, chat, and audit from a web dashboard or terminal TUI.

Note: InitRunner is currently in beta (v0.2.0b1). APIs may change between releases.

Table of Contents

• See It in Action
• Why InitRunner
• From Simple to Powerful
• Community Roles
• Install & Quickstart
• Core Concepts
• CLI Quick Reference
• Documentation
• Examples
• Community & Support
• Contributing
• License

See It in Action

A code reviewer that can read your files and inspect git history — one YAML file:

apiVersion: initrunner/v1
kind: Agent
metadata:
  name: code-reviewer
  description: Reviews code for bugs and style issues
spec:
  role: |
    You are a senior engineer. Review code for correctness and readability.
    Use git tools to examine changes and read files for context.
  model: { provider: openai, name: gpt-4o-mini }
  tools:
    - type: git
      repo_path: .
    - type: filesystem
      root_path: .
      read_only: true

initrunner run reviewer.yaml -p "Review the latest commit"

That's it. No Python, no boilerplate. The same file also runs as an interactive chat (-i), a trigger-driven daemon, or an OpenAI-compatible API server.

Why InitRunner

Config, not code — Define your agent's tools, knowledge base, and memory in one YAML file. No framework boilerplate, no wiring classes together. 13+ built-in tools (filesystem, git, HTTP, Python, shell, SQL, MCP) work out of the box. Need a custom tool? One file, one decorator.

Version-control your agents — Agent configs are plain text. Diff them, review them in PRs, validate in CI, reproduce anywhere. Your agent definition lives next to your code.

Prototype to production — Same YAML runs as an interactive chat, a one-shot CLI command, a trigger-driven daemon, or an OpenAI-compatible API. No rewrite when you're ready to deploy.

From Simple to Powerful

Start with the code-reviewer above. Each step adds one capability — no rewrites, just add a section to your YAML.

1. Add knowledge & memory

Point at your docs for RAG — a search_documents tool is auto-registered. Add memory for persistent recall across sessions:

spec:
  ingest:
    sources: ["./docs/**/*.md", "./docs/**/*.pdf"]
  memory:
    store_path: ./memory.db
    max_memories: 1000

initrunner ingest role.yaml   # extract | chunk | embed | store
initrunner run role.yaml -i --resume   # search_documents + memory ready

2. Add skills

Compose reusable bundles of tools and prompts. Each skill is a SKILL.md file — reference it by path:

spec:
  skills:
    - ../skills/web-researcher
    - ../skills/code-tools.md

The agent inherits each skill's tools and prompt instructions automatically.

3. Add triggers

Turn it into a daemon that reacts to events:

spec:
  triggers:
    - type: cron
      schedule: "0 9 * * 1"
      prompt: "Generate the weekly status report."
    - type: file_watch
      paths: [./src]
      prompt_template: "File changed: {path}. Review it."

initrunner daemon role.yaml   # runs until stopped

4. Compose agents

Orchestrate multiple agents into a pipeline. One agent's output feeds into the next:

apiVersion: initrunner/v1
kind: Compose
spec:
  services:
    inbox-watcher:
      role: roles/inbox-watcher.yaml
      sink: { type: delegate, target: triager }
    triager:
      role: roles/triager.yaml

initrunner compose up pipeline.yaml

5. Serve as an API

Turn any agent into an OpenAI-compatible endpoint. Drop-in for Open WebUI, Vercel AI SDK, or any OpenAI-compatible client:

initrunner serve support-agent.yaml --port 3000

from openai import OpenAI

client = OpenAI(base_url="http://localhost:3000/v1", api_key="unused")
response = client.chat.completions.create(
    model="support-agent",
    messages=[{"role": "user", "content": "How do I reset my password?"}],
)

Community Roles

Browse, install, and run roles shared by the community — no copy-paste needed:

initrunner search "code review"       # browse the community index
initrunner install code-reviewer      # download, validate, confirm
initrunner run ~/.initrunner/roles/code-reviewer.yaml -i

Install directly from any GitHub repo:

initrunner install user/repo:roles/support-agent.yaml@v1.0

Every install shows a security summary (tools, model, author) and asks for confirmation before saving. See docs/agents/registry.md for source formats, the community index, and update workflows.

Install & Quickstart

1. Install

curl -fsSL https://initrunner.ai/install.sh | sh

Or install directly with pip install initrunner, uv tool install initrunner, or pipx install initrunner. Common extras:

Extra	What it adds
initrunner[anthropic]	Anthropic provider (Claude)
initrunner[ingest]	PDF, DOCX, XLSX ingestion
initrunner[dashboard]	Web dashboard (FastAPI + Next.js)

See docs/getting-started/installation.md for the full extras table, dev setup, and environment configuration.

2. Create your first agent (code-reviewer.yaml)

apiVersion: initrunner/v1
kind: Agent
metadata:
  name: code-reviewer
  description: An experienced code review agent
spec:
  role: |
    You are a senior engineer. Review code changes for correctness,
    readability, and potential bugs. Be constructive.
  model: { provider: openai, name: gpt-4o-mini }
  tools:
    - type: git
      repo_path: .
      read_only: true
    - type: filesystem
      root_path: .
      read_only: true

3. Set your API key and run

export OPENAI_API_KEY=sk-...
initrunner run code-reviewer.yaml -p "Review the last 3 commits"

Start an interactive chat with -i, or use initrunner setup for a guided wizard. See docs/getting-started/setup.md for provider setup.

Core Concepts

Role files

Every agent is a YAML file with four top-level keys:

apiVersion: initrunner/v1
kind: Agent
metadata:
  name: my-agent
  description: What this agent does
spec:
  role: "System prompt goes here."
  model: { provider: openai, name: gpt-4o-mini }
  tools: [...]
  guardrails: { max_tool_calls: 20, timeout_seconds: 300 }

Validate with initrunner validate role.yaml or scaffold one with initrunner init --name my-agent.

Tools

Tools are configured in spec.tools. The most common ones:

Type	What it does
filesystem	Read/write files within a root directory
git	Git log, diff, blame, show (read-only by default)
http	HTTP requests to a base URL
python	Run Python in an isolated subprocess
shell	Run shell commands with allowlist/blocklist
mcp	Connect to MCP servers (stdio, SSE, streamable-http)
sql	Query SQLite databases (read-only by default)
delegate	Hand off to other agents

See docs/agents/tools.md for the full reference.

Creating tools

Add a built-in tool by creating a single file in initrunner/agent/tools/ with a config class and a @register_tool decorated builder function. The tool is auto-discovered and immediately available in role YAML — no other files need editing. Tools use PydanticAI's FunctionToolset under the hood. See docs/agents/tool_creation.md for the full guide, including custom tools loaded from external modules, declarative API tools, and the plugin registry.

Skills

Skills are reusable bundles of tools and prompts packaged as SKILL.md files. They let you compose capabilities across agents without duplicating configuration. Reference them from a role with spec.skills:

spec:
  skills:
    - ../skills/web-researcher
    - ../skills/code-tools.md

See docs/agents/skills_feature.md for skill file format, resolution rules, and examples.

Run modes

Mode	Command	Use case
Single-shot	initrunner run role.yaml -p "prompt"	One question, one answer
Interactive	initrunner run role.yaml -i	Multi-turn chat (REPL)
Daemon	initrunner daemon role.yaml	Trigger-driven (cron, file watch, webhook)
API server	initrunner serve role.yaml	OpenAI-compatible HTTP API

RAG & Memory

Document ingestion — add spec.ingest to your role pointing at your documents, then run initrunner ingest role.yaml to extract, chunk, embed, and store locally in SQLite (powered by sqlite-vec). A search_documents tool is automatically registered so the model can query the knowledge base at runtime. See docs/core/ingestion.md for custom embedding models, URL sources, and chunking strategies.

Long-term memory — add spec.memory to your role and the agent gets remember(), recall(), and list_memories() tools. Use --resume to continue previous REPL sessions. See docs/core/memory.md.

Triggers & Compose

Triggers let daemons react to events — cron schedules, file changes, or webhooks. See docs/core/triggers.md.

Compose orchestrates multiple agents as a pipeline with delegate sinks, health checks, and restart policies. Deploy with compose up (foreground) or compose install (systemd). See docs/orchestration/agent_composer.md.

Providers

The default provider is OpenAI (gpt-4o-mini). Switch providers by changing spec.model in your role file and installing the relevant extra (e.g. pip install initrunner[anthropic]). See docs/configuration/providers.md for Ollama, OpenRouter, Azure, custom endpoints, and the full provider table.

CLI Quick Reference

Command	Description
run <role.yaml> -p "..."	Single-shot prompt
run <role.yaml> -i	Interactive REPL
validate <role.yaml>	Validate a role definition
init --name <name>	Scaffold a new role
setup	Guided provider setup wizard
ingest <role.yaml>	Ingest documents into vector store
daemon <role.yaml>	Run in trigger-driven daemon mode
serve <role.yaml>	Serve as OpenAI-compatible API
tui	Launch terminal dashboard
ui	Launch web dashboard
compose up <compose.yaml>	Run multi-agent orchestration
install <source>	Install a community role from GitHub
uninstall <name>	Remove an installed role
search <query>	Search the community role index
info <source>	Inspect a role before installing
list	Show installed roles
update [name] / --all	Update installed roles

See docs/getting-started/cli.md for the full command list and all options.

Documentation

Getting Started

• Installation — Install methods, extras, dev setup, environment
• Setup — First-time setup wizard and provider configuration
• CLI Reference — All commands and options

Agent Development

• Tools — Full tool reference with configuration examples
• Tool Creation — Building custom tools and plugins
• Skills — Reusable skill definitions
• Providers — Provider configs, custom endpoints, model reference
• Ollama — Local model setup with Ollama

Advanced Features

• Ingestion — Document extraction, chunking, and embedding
• Memory — Session persistence and semantic memory
• Triggers — Cron, file watcher, and webhook triggers
• Compose — Multi-agent orchestration and delegate sinks
• Delegation — Agent-to-agent delegation patterns
• Pipelines — Sequential and parallel agent pipelines
• Sinks — Output sinks (webhook, file, custom)
• Server — OpenAI-compatible API server

Operations

• Dashboard — Web dashboard setup and API reference
• TUI — Terminal dashboard key bindings and screens
• Audit — Audit trail queries, export, and pruning
• Security — Security model, sandboxing, and content policies
• Guardrails — Guardrails config and token budgets
• Token Control — Token budgets and usage limits
• Testing — Test suites and assertions
• CI/CD — Continuous integration and deployment
• Registry — Installing and sharing community roles

Examples

The examples/ directory includes 20+ ready-to-run agents, skills, and compose pipelines covering real-world scenarios:

Role definitions (examples/roles/) — single-agent configs for support bots, code reviewers, changelog generators, deploy notifiers, web monitors, data analysts, and more.

Skills (examples/skills/) — reusable capability bundles:

• web-researcher/ — web research tools (fetch pages, HTTP requests)
• code-tools.md — code execution and file browsing tools

See examples/roles/skill-demo.yaml for a role composing multiple skills.

Compose pipelines (examples/compose/) — multi-agent orchestration:

• email-pipeline/ — cron-driven email triage with fan-out to researcher and responder
• content-pipeline/ — file-watch-driven content creation with process_existing startup scan
• ci-pipeline/ — webhook-driven CI build analysis with notifications

Community & Support

• GitHub Issues — Bug reports and feature requests
• GitHub Discussions — Questions and community help
• Changelog — Release notes and version history

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

Run local quality checks before pushing:

uv sync --dev
uv run pre-commit install
uv run pre-commit run --all-files

Manual Ruff commands (equivalent to CI formatting/lint checks):

uv run ruff format .
uv run ruff check .

Share a role

Push your role.yaml to a public GitHub repo — anyone can install it with initrunner install user/repo. To list it in the community index so users can initrunner install my-role by name, open a PR to vladkesler/community-roles adding an entry to index.yaml. See docs/agents/registry.md for details.

For security vulnerabilities, please see SECURITY.md.

License

MIT — see LICENSE for details.