initrunner 1.4.0

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

InitRunner

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An AI assistant in your terminal with persistent memory, document search, and extensible tools. Scale up to autonomous agents, bots, and pipelines when you need them.

initrunner chat gives you an AI assistant that remembers across sessions, searches your documents, and discovers tools on demand. No config needed. When you need more, define agent roles in YAML: custom tools, triggers, guardrails, multimodal input, and RAG. Deploy as CLI tools, Telegram/Discord bots, cron daemons, or serve them as OpenAI-compatible APIs. Compose agents into pipelines. Manage and audit from a web dashboard or TUI.

v1.4.0 — Stable release. See the Changelog for details.

Try It

initrunner chat                    # start chatting, memory on by default
>>> remember that the deploy key is in 1Password
Noted.

>>> what's the deploy key location?
You told me the deploy key is in 1Password.

>>> /quit

No YAML, no config files. Add --ingest ./docs/ to search your documents, or --tool-profile all to enable every built-in tool.

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? pip install "initrunner[anthropic]" and set model: { provider: anthropic, name: claude-opus-4-6 }.

Interactive REPL — chat with any agent from the terminal

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. 16 built-in tools (filesystem, git, HTTP, Python, shell, SQL, search, email, MCP, 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.

Quickstart

1. Install

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

Or with a package manager:

pip install "initrunner[all]"       # everything included
pip install initrunner              # core only (OpenAI)
uv tool install initrunner          # or with uv

Common extras: anthropic (Claude), ingest (PDF/DOCX), dashboard (web UI), all (everything). See Installation docs for the full extras table and platform notes.

2. Set your API key

export OPENAI_API_KEY=sk-...          # OpenAI (default)
export ANTHROPIC_API_KEY=sk-ant-...   # Claude

You can also store keys in ~/.initrunner/.env — it's loaded automatically by all commands. Environment variables set in the shell take precedence over .env values.

Or run initrunner setup — it walks through provider, key, and first role interactively, and stores the key in ~/.initrunner/.env for you.

3. Start chatting

initrunner chat                        # zero-config chat with persistent memory
initrunner chat --resume               # resume previous session + auto-recall memories
initrunner chat --ingest ./docs/       # chat with your documents (instant RAG)
initrunner chat --tool-profile all     # chat with all tools enabled
initrunner chat --telegram             # one-command Telegram bot
initrunner chat --telegram --allowed-user-ids 123456789  # restrict access
initrunner run role.yaml -p "Hello!"   # one-shot prompt
initrunner run role.yaml -i            # interactive REPL

Memory is on by default — the agent remembers facts across sessions. Use --no-memory to disable. See Chat docs for all options, and CLI Reference for the full command list.

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

See Ingestion · Memory · RAG Quickstart.

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. Run initrunner init --skill my-skill to scaffold one. See Skills.

3. Add triggers

Turn it into a daemon that reacts to events — cron, file watch, webhook, 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.

4. Compose agents

Orchestrate multiple agents into a pipeline — one agent's output feeds into the next:

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 }

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

5. Serve as an API

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

initrunner serve support-agent.yaml --port 3000

See Server docs for client examples and Open WebUI integration.

6. Attach files and media

Send images, audio, video, and documents alongside your prompts:

initrunner run role.yaml -p "Describe this image" -A photo.png
initrunner run role.yaml -p "Compare these" -A before.png -A after.png

In the REPL, use /attach to queue files. See Multimodal Input.

7. Get structured output

Force the agent to return validated JSON matching a schema — ideal for pipelines and automation. Add an output section with a JSON schema and the agent's response is validated against it:

initrunner run classifier.yaml -p "Acme Corp invoice for $250"
# → {"status": "approved", "amount": 250.0}

See Structured Output for inline schemas, external schema files, and pipeline integration.

Community Roles

Browse, install, and run roles shared by the community:

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
initrunner run ~/.initrunner/roles/code-reviewer.yaml -i # run an installed role

Every install shows a security summary and asks for confirmation. See docs/agents/registry.md for details.

Docker

# One-shot prompt
docker run --rm -e OPENAI_API_KEY \
    -v ./roles:/roles ghcr.io/vladkesler/initrunner:latest \
    run /roles/my-agent.yaml -p "Hello"

# Web dashboard — open http://localhost:8420
docker run -d -e OPENAI_API_KEY \
    -v ./roles:/roles -v initrunner-data:/data \
    -p 8420:8420 ghcr.io/vladkesler/initrunner:latest \
    ui --role-dir /roles

Or use docker compose up with the included docker-compose.yml (copy examples/.env.example to .env first). Also available on Docker Hub: vladkesler/initrunner. See hello-world.yaml for a starter role.

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

Documentation

Area	Key docs
Getting started	Installation · Setup · Chat · RAG Quickstart · Tutorial · CLI Reference · Discord Bot · Telegram Bot
Agents & tools	Tools · Tool Creation · Tool Search · Skills · Structured Output · Providers
Knowledge & memory	Ingestion · Memory · Multimodal Input
Orchestration	Compose · Delegation · Autonomy · Triggers · Intent Sensing
Interfaces	Dashboard · TUI · API Server
Operations	Security · Guardrails · Audit · Reports · 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 covering code review, support bots, data analysis, web monitoring, and multi-agent orchestration.

Community & Support

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

If you find InitRunner useful, consider giving it a star — it helps others discover the project.

Contributing

Contributions welcome! See CONTRIBUTING.md for dev setup, PR guidelines, and quality checks. Share your roles by pushing to a public GitHub repo — anyone can install them with initrunner install user/repo. For security vulnerabilities, see SECURITY.md.

License

MIT — see LICENSE for details.