iterun 0.1.9

DSL-based intent execution system with iterative refinement, featuring AI-powered suggestions via Ollama, safe simulation, and automatic health validation.

ITERUN

DSL-based intent execution system with iterative refinement, ITERUN boundary, and AI-powered assistance

AI Cost Tracking

• 🤖 LLM usage: $2.3314 (14 commits)
• 👤 Human dev: ~$504 (5.0h @ $100/h, 30min dedup)

Generated on 2026-06-06 using openrouter/qwen/qwen3-coder-next

---

Overview

ITERUN is a system that allows you to:

1. Generate intents from prompts (LiteLLM / OpenRouter / Ollama) → iterun.yaml
2. Define intents manually in YAML DSL (sekcja INTENT:)
3. Simulate execution with dry-run planning
4. Deploy services in Docker with optional contract verify (TestQL + retry loop)
5. Get AI suggestions in the interactive shell (Ollama / LiteLLM)
6. Execute safely with the ITERUN boundary (explicit approval when enabled)

One-liner (prompt → running service):

iterun generate "Create a REST API for user management" \
  -o generated/ --execute --verify

Architecture

┌─────────────────────┐
│  CLI / Web / SDK    │  ← User interface
└─────────┬───────────┘
          ↓
┌─────────────────────┐
│ Generator (LLM)     │  ← prompt → iterun.yaml + intract + testql
└─────────┬───────────┘
          ↓
┌─────────────────────┐
│ Parser / Validator  │  ← DSL → IR
└─────────┬───────────┘
          ↓
┌─────────────────────┐
│ Planner / Simulator │  ← Dry-run → app.py, Dockerfile
└─────────┬───────────┘
          ↓
┌─────────────────────┐
│ Executor (Docker)   │  ← Deploy service
└─────────┬───────────┘
          ↓
┌─────────────────────┐
│ Contract verify       │  ← TestQL + expectations; retry on fail
└─────────┬───────────┘
          ↓
┌─────────────────────┐
│ session.json        │  ← Full session log in generated/
└─────────────────────┘

Quick Start

Installation

# Clone repository
git clone https://github.com/softreck/iterun.git
cd iterun

# Full setup (recommended)
make setup

# Editable install (recommended)
python3 -m venv venv && source venv/bin/activate
pip install -e ".[ai]"
cp .env.example .env

Configuration (.env)

Copy .env.example to .env and adjust:

# LLM for `iterun generate` (priority: --model > LLM_MODEL > DEFAULT_MODEL)
OPENROUTER_API_KEY=sk-or-...
LLM_MODEL=openrouter/deepseek/deepseek-v4-pro

# Local Ollama (shell suggest/chat fallback)
OLLAMA_BASE_URL=http://localhost:11434
DEFAULT_MODEL=llama3.2

# Server / execution
HOST=0.0.0.0
PORT=8080
SKIP_ITERUN_CONFIRMATION=true
CONTAINER_PORT=8000

Generate from prompt

source venv/bin/activate

# YAML only
iterun generate "Create a ping API" -o generated/

# Plan + artifacts
iterun generate "..." -o generated/ --run

# Docker + contract verify + repair loop
iterun generate "..." -o generated/ --execute --verify --max-verify-iterations 5

# Full JSON session log
iterun generate "..." -o generated/ --execute --verify --json

Output directory (generated/ by default) — see Session artifacts.

AI Gateway Setup (Ollama)

# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# Start and pull model
make ollama-start
make ollama-pull

# Or manually
ollama serve
ollama pull llama3.2

Using Makefile

make help          # Show all commands
make setup         # Full setup
make web           # Start web server
make shell         # Interactive shell
make execute       # Execute example intent
make test          # Run all tests
make ollama-models # List available models
make clean         # Clean temp files

Shell Interface

# Start interactive shell
make shell
# Or: python -m cli.main

# Generate + execute from prompt
make execute
# Or: iterun generate "$(cat examples/01-user-api/prompt.txt)" -o examples/01-user-api/generated/ --execute --verify

Interactive Shell Commands:

intent> new my-api          # Create new intent
intent> load iterun.yaml    # Load package (generated/iterun.yaml)
intent> plan                # Run dry-run
intent> suggest             # Get AI suggestions
intent> apply               # Auto-apply AI suggestions
intent> chat                # Chat with AI
intent> iterate             # Apply manual changes
intent> iterun                # Approve execution
intent> execute             # Execute approved intent
intent> show [json]         # Show current state
intent> models              # List AI models
intent> ai-health           # Check AI Gateway status
intent> help                # Show help
intent> exit                # Exit shell

Web Interface

# Start web server
python -m web.app

# Open browser at http://localhost:8080

AI Gateway

The AI Gateway uses LiteLLM for:

• iterun generate — cloud models via OpenRouter (OPENROUTER_API_KEY, LLM_MODEL)
• Shell suggest / chat — local Ollama (OLLAMA_BASE_URL, DEFAULT_MODEL)

Supported Models (≤12B parameters)

Model	Size	Description
llama3.2	3B	Default - Fast and efficient
llama3.2:1b	1B	Ultra lightweight
llama3.1:8b	8B	Balanced performance
mistral	7B	Fast inference
mistral-nemo	12B	Best quality under 12B
gemma2	9B	Google Gemma 2
gemma2:2b	2B	Lightweight
phi3	3.8B	Microsoft Phi-3
qwen2.5	7B	Alibaba Qwen 2.5
codellama	7B	Code generation
codegemma	7B	Google CodeGemma
deepseek-coder	6.7B	DeepSeek Coder

Configuration

Environment variables:

export OLLAMA_BASE_URL="http://localhost:11434"
export DEFAULT_MODEL="llama3.2"
export MAX_MODEL_PARAMS="12.0"

Package file: iterun.yaml

The canonical workspace filename is iterun.yaml (not intent.yaml). Full spec: docs/INTENT_DSL_SPEC.md.

INTENT:
  name: user-api
  goal: Create a REST API for user management

ENVIRONMENT:
  runtime: docker
  base_image: python:3.12-slim
  ports:
    - 8000

IMPLEMENTATION:
  language: python
  framework: fastapi
  actions:
    - api.expose GET /ping
    - api.expose GET /users
    - api.expose POST /users
    - api.expose DELETE /users/{id}

EXECUTION:
  mode: dry-run

Supported Actions

Action	Format	Description
api.expose	api.expose METHOD /path	Expose HTTP endpoint
db.create	db.create table_name	Create database table
db.add_column	db.add_column table column type	Add column to table
shell.exec	shell.exec command	Execute shell command
rest.call	rest.call METHOD url	Call external REST API
file.create	file.create path	Create file

API Reference

Pełna dokumentacja: docs/API.md — REST, SDK, MCP, STACK.
Rejestr usług/artefaktów: docs/REGISTRY.md — Backstage, OCI, OTel.
Runtime markpact+pactown: docs/RUNTIME.md — odchudzone uruchamianie.

Integration surfaces

Surface	Entry
REST	uvicorn web.app:app → /api/*, OpenAPI /docs
CLI	iterun generate, iterun plan, …
SDK	IterunClient() — local lub base_url="http://…"
MCP	iterun-mcp — narzędzia dla agentów LLM

REST Endpoints (skrót)

Method	Endpoint	Description
GET	/api/health	Liveness
GET	/api/interfaces	Lista powierzchni API
GET	/api/schema	JSON Schema for DSL
POST	/api/intents/validate-yaml	Validate YAML (is_stack)
POST	/api/intents/plan-yaml	Plan z YAML (STACK → compose)
POST	/api/pipeline/run	generate → plan → execute? → verify?
POST	/api/intents/generate	LLM → YAML
POST	/api/intents/generate-and-run	Alias /api/pipeline/run
GET	/api/intents	List all intents
POST	/api/intents/parse	Parse DSL and create intent
GET	/api/intents/{id}	Get intent by ID
POST	/api/intents/{id}/plan	Dry-run (compose_yaml dla STACK)
POST	/api/intents/{id}/execute	Execute approved intent

AI Gateway Endpoints

Method	Endpoint	Description
GET	/api/ai/status	Check AI Gateway status
GET	/api/ai/models	List available models
POST	/api/ai/complete	Generate AI completion
POST	/api/ai/chat	Chat with AI
POST	/api/intents/{id}/ai/suggest	Get AI suggestions
POST	/api/intents/{id}/ai/apply	Auto-apply suggestions

Python API

from generator.pipeline import run_pipeline
from parser import parse_dsl
from planner import plan_intent
from sdk import IterunClient

# Prompt → full pipeline
result = run_pipeline(
    "Create a REST API for user management",
    output_dir="generated",
    execute=True,
    verify=True,
)
print(result.yaml_path)       # generated/iterun.yaml
print(result.verification)    # testql + HTTP result

# Or SDK (local or remote REST)
client = IterunClient()
out = client.run_pipeline("Create a ping API", output_dir="generated", execute=True, verify=True)
# remote = IterunClient(base_url="http://localhost:8000")

# Manual DSL
ir = parse_dsl(open("generated/iterun.yaml").read())
plan = plan_intent(ir)
print(plan.generated_code)

Examples

Script	Opis
./examples/run-all.sh	01–08: prompt → iterun.yaml → plan
./examples/run-e2e.sh	09–12: execute + TestQL + Intract
./examples/run-resilience.sh	13–16: skrajne prompty, pętla naprawcza

Szczegóły: examples/README.md.

Session artifacts

Everything from one iterun generate run lands in --output-dir (default generated/):

File	Content
iterun.yaml	DSL package from LLM
session.json	Full session — prompt, generate attempts, plan, execute, verify
intract.yaml	Intract contract manifest
service.testql.toon.yaml	Auto-generated TestQL scenario
plan.result.json	Plan logs + IR
execution.json	Execute logs, endpoints, container id
container.log	Docker logs (tail)
verify.result.json	Contract verify result
verify.rounds.json	Repair loop history (--verify)
app.py / Dockerfile	Generated service

Testing

pytest
pytest tests/e2e/test_intent_generator.py -v
pytest tests/e2e/test_shell.py -v
pytest tests/e2e/test_web.py -v
pytest tests/e2e/test_ai_gateway.py -v

Project Structure

iterun/
├── generator/          # LLM generate, pipeline, testql, intract, verify loop
│   ├── intent_generator.py
│   ├── pipeline.py
│   ├── contract_verify.py
│   └── session.py
├── dsl/                # Pydantic schema for LLM validation
├── ir/                 # Intermediate Representation
├── parser/             # DSL parser
├── planner/            # Dry-run simulator
├── executor/           # Docker execution + HTTP validation
├── ai_gateway/         # LiteLLM (Ollama + OpenRouter)
├── cli/                # `iterun` CLI
├── web/                # FastAPI web UI
├── sdk/                # Python SDK client
├── iterun_mcp/         # MCP server (optional; not `mcp/` — conflicts with PyPI mcp)
├── examples/           # prompt.txt + run.sh → generated/
├── docs/               # DSL spec, docs index
├── tests/e2e/
├── config.py           # PACKAGE_FILENAME = "iterun.yaml"
└── README.md

Workflow

Prompt-first (recommended)

1. Prompt → iterun generate "..." -o generated/
2. Contracts → auto intract.yaml + service.testql.toon.yaml
3. Plan → app.py, Dockerfile, plan.result.json
4. Execute → Docker container
5. Verify → TestQL + HTTP (--verify); retry with error context on failure
6. Session → session.json aggregates all steps

Manual / interactive

1. Edit iterun.yaml or use shell new / load
2. Plan → dry-run
3. Suggest / iterate → AI or manual refinement
4. ITERUN → approve (unless SKIP_ITERUN_CONFIRMATION)
5. Execute → deploy + endpoint validation

Documentation: docs/README.md · docs/INTENT_DSL_SPEC.md

Validation & Auto-Fix

After container deployment, the system automatically:

1. Waits for container startup (configurable STARTUP_WAIT)
2. Validates all exposed endpoints with HTTP requests
3. Detects issues like connection refused, timeouts, HTTP errors
4. Auto-fixes common problems:
   • Missing __main__ block
   • Wrong port configuration
   • Missing dependencies
5. Restarts container with fixes
6. Re-validates until success or max iterations reached

Configuration

# In .env
VALIDATE_AFTER_EXECUTE=true
AUTO_FIX_ENABLED=true
MAX_FIX_ITERATIONS=3
STARTUP_WAIT=2
VALIDATION_TIMEOUT=10

Example Output

Execution Logs:
  [12:38:55] Container started: 8f35e0a2fb27
  [12:38:55] Waiting 2s for container startup...
  [12:38:57] ✓ http://localhost:8002 → 200
  [12:38:57] ✓ http://localhost:8002/ping → 200
  [12:38:57] ✓ http://localhost:8002/health → 200
  [12:38:57] ✓ All endpoints validated successfully
✓ Execution completed in 2.56s

Validation:
  ✓ All endpoints validated

API Endpoints

Method	Endpoint	Description
POST	/api/intents/{id}/validate	Validate running container
GET	/api/containers/{id}/logs	Get container logs

License

Licensed under Apache-2.0.