ese-cli 0.1.3

Ensemble Software Engineering (ESE) CLI

Ensemble Software Engineering (ESE)

ESE is a lightweight framework for AI-assisted software development using specialized model roles.

Core pipeline

flowchart TD
  A[Human Scope] --> B[Architect]
  B --> C[Implementer]
  C --> D[Adversarial Reviewer]
  C --> E[Security Auditor]
  C --> F[Test Generator]
  C --> G[Performance Analyst]
  D --> H[Human Merge]
  E --> H
  F --> H
  G --> H

Quick start

1. Create a validated config (version: 1) with role assignments and constraints. ese init uses simple setup by default (goal profile + provider + model), with ese init --advanced for role-level configuration. It includes provider/model presets, alias-based model input, and a custom_api option for custom provider name/base URL/model IDs.
2. Run ese doctor --config ese.config.yaml to enforce ensemble separation.
3. Run ese start --config ese.config.yaml --artifacts-dir artifacts to execute the pipeline.
4. Review artifacts/ese_summary.md and artifacts/pipeline_state.json.

runtime.adapter defaults to dry-run and can be set to openai or a custom callable with module:function. Role execution is dynamic from roles, so custom roles are supported without source edits.

Role catalog

• architect: System design, decomposition, and interface contracts.
• implementer: Code changes and refactors.
• adversarial_reviewer: Bug/risk hunting and regression checks.
• security_auditor: Threat modeling and vulnerability review.
• test_generator: Unit/integration/e2e test generation.
• performance_analyst: Latency, memory, and scalability analysis.
• documentation_writer: README, API docs, and migration notes.
• devops_sre: CI/CD, deploy safety, and observability.
• database_engineer: Schema/index/migration correctness.
• release_manager: Go/no-go risk assessment and rollout checks.

Use ese roles to print this list directly in the CLI.

Provider and model presets

• Providers with built-in model pickers: openai, anthropic, google, xai, openrouter, huggingface, local.
• Each provider includes common model IDs plus custom (type model id).
• Model aliases are supported (for example g5, g5mini, sonnet, flash, pro).
• custom_api lets you enter:
  • custom provider name,
  • optional API base URL,
  • custom model ID,
  • custom API key env var.

Runtime adapters

• dry-run: no external API calls; generates deterministic placeholder artifacts.
• openai: calls the OpenAI Responses API with retry/timeout handling.
• module:function: custom Python callable adapter.

ese run remains available as a backward-compatible alias for ese start.

Example runtime config for real OpenAI execution:

provider:
  name: openai
  model: gpt-5-mini
  api_key_env: OPENAI_API_KEY
runtime:
  adapter: openai
  timeout_seconds: 60
  max_retries: 2
  retry_backoff_seconds: 1.0
  openai:
    base_url: https://api.openai.com/v1

GitHub Actions (optional)

Use .github/workflows/ese.yml to run ESE on pull requests.

Roadmap

See MILESTONE_1_0_0.md for the concrete 1.0.0 release checklist and PR plan.