initrunner 1.24.0

Define AI agent roles in YAML and run them anywhere — CLI, API server, or autonomous daemon

InitRunner

Website · Docs · Discord · Issues

Define AI agents in YAML. Run them as CLI tools, Telegram bots, Discord bots, API servers, or autonomous daemons. Built-in RAG, persistent memory, 40+ tools, policy-based authorization. Any model.

One YAML file is all it takes to go from idea to running agent - with document search, persistent memory, and tools wired in automatically. Start with initrunner chat for a zero-config assistant, then scale to bots, pipelines, and API servers without rewriting anything.

v1.24.0 -- Replace zvec with LanceDB (pure-Python columnar vector store), add Python 3.13 support. See the Changelog.

Contents

• Quickstart
• Define an Agent in YAML
• User Interfaces
• Why InitRunner
• Features
• Security & Authorization
• Distribution & Deployment
• Documentation
• Community

Quickstart

curl -fsSL https://initrunner.ai/install.sh | sh -s -- --extras all
initrunner setup        # wizard: pick provider, model, API key
initrunner chat --ingest ./docs/   # chat with your docs, memory on by default

Or install with a package manager: uv tool install "initrunner[all]" / pipx install "initrunner[all]". See Installation, Setup, and Chat for details.

Or run with Docker, no install needed:

docker run --rm -it -e OPENAI_API_KEY \
    -v initrunner-data:/data ghcr.io/vladkesler/initrunner:latest chat

See the Docker guide for RAG, Telegram, API server, and more examples.

Define Agent Roles in YAML

When you need more control, define an agent as a 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-5-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. Using Claude? pipx install "initrunner[anthropic]" and set model: { provider: anthropic, name: claude-opus-4-6 }.

Quick Chat - ask a question, send the answer to Slack

User Interfaces

	Terminal UI (tui)	Web Dashboard (ui)
Launch	initrunner tui	initrunner ui
Install	pip install initrunner[tui]	pip install initrunner[dashboard]
Roles	Create from template, edit via forms	Form builder with live preview, AI generate
Chat	Streaming chat with token counts	SSE streaming with file attachments
Extras	Audit log, memory, daemon event log	Audit detail panel, memory, trigger monitor
Style	k9s-style keyboard-driven (Textual)	Server-rendered HTML (HTMX + DaisyUI)

See TUI docs · Dashboard docs · API Server docs

Why InitRunner

Zero config to start. initrunner chat gives you an AI assistant with persistent memory and document search out of the box. No YAML, no setup beyond an API key.

Config, not code. Define your agent's tools, knowledge base, and memory in one YAML file. No framework boilerplate, no wiring classes together. 20+ built-in tools (filesystem, git, HTTP, Python, shell, SQL, search, email, MCP, think, script, and more) 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.

How It Compares

	InitRunner	Build from scratch	LangChain
Setup	curl -fsSL https://initrunner.ai/install.sh | sh + API key	Install 5-10 packages, write glue code	pip install langchain + adapters
Agent config	One YAML file	Python classes + wiring	Python chains + config objects
RAG	--ingest ./docs/ (one flag)	Embed, store, retrieve, prompt - DIY	Loaders > splitters > vectorstore chain
Bot deployment	--telegram / --discord flag	Build bot framework integration	Separate bot framework + adapter
Model switching	--model flag, aliases, or change YAML	Rewrite client code	Swap LLM class + adjust prompts
Multi-agent	compose.yaml with delegation + auto-routing	Custom orchestration layer	Agent executor + custom routing

What Can You Build?

• A Telegram bot that answers questions about your codebase - point it at your repo, deploy with one flag
• A cron job that monitors competitors and sends daily digests - cron trigger + web scraper + Slack sink
• A document Q&A agent for your team's knowledge base - ingest PDFs and Markdown, serve as an API
• A code review bot triggered by new commits - file-watch trigger + git tools + structured output
• A multi-agent pipeline with auto-routing: intake > researcher / responder / escalator - sense routing picks the right target per message (initrunner examples copy support-desk)
• A personal assistant that remembers everything - persistent memory across sessions, no setup

Features

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

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
    semantic:
      max_memories: 1000

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

See Ingestion · Memory · RAG Quickstart.

Triggers

Turn it into a daemon that reacts to events - cron, file watch, webhook, heartbeat, Telegram, or Discord:

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

See Triggers · Telegram · Discord.

Compose agents

Orchestrate multiple agents into a pipeline - one agent's output feeds into the next. Use strategy: sense to auto-route messages to the right target:

apiVersion: initrunner/v1
kind: Compose
metadata: { name: email-pipeline }
spec:
  services:
    inbox-watcher:
      role: roles/inbox-watcher.yaml
      sink: { type: delegate, target: triager }
    triager:
      role: roles/triager.yaml
      sink: { type: delegate, strategy: sense, target: [researcher, responder] }
    researcher: { role: roles/researcher.yaml }
    responder: { role: roles/responder.yaml }

Run with initrunner compose up pipeline.yaml. See Compose · Delegation.

Security & Authorization

Built-in security with optional Cerbos policy-based authorization. JWT identity, per-route ABAC checks, tool-level authorization, PlanResources filtering, and full audit trail with principal tracking:

pip install initrunner[authz]
export INITRUNNER_CERBOS_ENABLED=true INITRUNNER_JWT_SECRET=my-secret
export INITRUNNER_CERBOS_TOOL_CHECKS=true  # per-tool identity checks
initrunner ui   # routes + tool calls checked against Cerbos policies

Also includes content filtering, PEP 578 sandboxing, Docker isolation, token budgets, and rate limiting out of the box. See Cerbos Authorization · Security · Guardrails.

More capabilities

Feature	Command / config	Docs
Skills - reusable tool + prompt bundles	spec: { skills: [../skills/web-researcher] }	Skills
Team mode - multi-persona on one task	kind: Team + spec: { personas: {…} }	Team Mode
API server - OpenAI-compatible endpoint	initrunner serve agent.yaml --port 3000	Server
Multimodal - images, audio, video, docs	initrunner run role.yaml -p "Describe" -A photo.png	Multimodal
Structured output - validated JSON schemas	spec: { output: { schema: {…} } }	Structured Output
Evals - test agent output quality	initrunner test role.yaml -s eval.yaml	Evals
MCP gateway - expose agents as MCP tools	initrunner mcp serve agent.yaml	MCP Gateway
MCP toolkit - tools without an agent	initrunner mcp toolkit	MCP Gateway

See Tutorial for a guided walkthrough.

Distribution & Deployment

Community roles

initrunner search "code review"                          # browse the community index
initrunner install code-reviewer                         # download, validate, confirm
initrunner install user/repo:roles/agent.yaml@v1.0       # install from any GitHub repo

See Registry.

OCI registry

Publish and install complete role bundles to any OCI-compliant container registry:

initrunner publish role.yaml oci://ghcr.io/org/my-agent --tag 1.0.0
initrunner install oci://ghcr.io/org/my-agent:1.0.0

See OCI Distribution.

Cloud deploy

Fly.io: See Cloud Deployment Guide.

Documentation

Area	Key docs
Getting started	Installation · Setup · Chat · RAG Quickstart · Tutorial · CLI Reference · Docker · Discord Bot · Telegram Bot
Agents & tools	Tools · Tool Creation · Tool Search · Skills · Structured Output · Providers
Knowledge & memory	Ingestion · Memory · Multimodal Input
Orchestration	Compose · Delegation · Team Mode · Autonomy · Triggers · Intent Sensing
Interfaces	Dashboard · TUI · API Server · MCP Gateway
Distribution	OCI Distribution · Shareable Templates
Operations	Security · Cerbos Authorization · Guardrails · Audit · Reports · Evals · Doctor · Observability · CI/CD

See docs/ for the full index.

Examples

initrunner examples list               # see all available examples
initrunner examples copy code-reviewer # copy to current directory

The examples/ directory includes 20+ ready-to-run agents, skills, and compose pipelines.

Community & Contributing

• Discord - InitRunner Hub - Chat, ask questions, share roles
• GitHub Issues - Bug reports and feature requests
• Changelog - Release notes and version history

Contributions welcome! See CONTRIBUTING.md for dev setup and PR guidelines. For security vulnerabilities, see SECURITY.md.

License

MIT - see LICENSE for details.

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_v1.24.0