equitrcoder 2.3.0

Modular AI coding assistant supporting single and multi-agent workflows

EQUITR Coder

Modular AI coding assistant supporting single and multi-agent workflows and an ML-focused researcher mode. Includes an advanced TUI.

Quick Start

• Install (latest from PyPI):

  </code></pre>
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  <p>pip install equitrcoder</p>
  <pre><code>- Optional extras:
  - API server: `pip install "equitrcoder[api]"`
  - TUI (Textual+Rich): `pip install textual rich`
  
  ## Configure API keys (env vars)
  
  Set whatever providers you plan to use:
  - `OPENAI_API_KEY`
  - `ANTHROPIC_API_KEY`
  - `OPENROUTER_API_KEY`
  - `MOONSHOT_API_KEY`
  - `GROQ_API_KEY`
  
  You can also set `CLAUDE_AGENT_MODEL`, `CLAUDE_AGENT_BUDGET`, and `CLAUDE_AGENT_PROFILE` to override defaults.
  
  Export examples (macOS/Linux):
  ```bash
  export OPENAI_API_KEY=sk-...
  export ANTHROPIC_API_KEY=...
  export OPENROUTER_API_KEY=...
  

  TUI (Interactive)

  • Launch TUI:

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    <p>equitrcoder tui --mode single   # or multi, research</p>
    <pre><code>- Keys:
    - Enter: execute task in current mode (requires task in input field)
    - m: open model selector
    - Ctrl+C: exit
    - Research mode fields:
    - Datasets: comma-separated paths
    - Experiments: `name:command; name:command; ...`
    
    Troubleshooting:
    - If you see a Textual widget error, ensure `textual` and `rich` are installed.
    - If you have no API keys, you can still launch the TUI, but model listings will be minimal and execution may fail when contacting providers.
    
    ## CLI
    
    - Single:
    ```bash
    equitrcoder single "Build a small API" --model moonshot/kimi-k2-0711-preview
    

    • Multi:

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      <p>equitrcoder multi "Ship a feature" --supervisor-model moonshot/kimi-k2-0711-preview <br />
      --worker-model moonshot/kimi-k2-0711-preview --workers 3 --max-cost 15</p>
      <pre><code>- Research (ML only):
      ```bash
      equitrcoder research "Evaluate model X on dataset Y" \
      --supervisor-model moonshot/kimi-k2-0711-preview --worker-model moonshot/kimi-k2-0711-preview \
      --workers 3 --max-cost 12
      

      Programmatic Usage

      from equitrcoder import EquitrCoder, TaskConfiguration
      
      coder = EquitrCoder(mode="single", repo_path=".")
      config = TaskConfiguration(description="Refactor module X", max_cost=2.0, max_iterations=20)
      result = await coder.execute_task("Refactor module X", config)
      print(result.success, result.cost, result.iterations)
      

      • Multi-agent and researcher programmatic configs are available via MultiAgentTaskConfiguration and ResearchTaskConfiguration.

      API Server

      • Start server (requires extras):

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        <p>equitrcoder api --host 0.0.0.0 --port 8000</p>
        <pre><code>- Endpoints:
        - `GET /` root
        - `GET /health`
        - `GET /tools`
        - `POST /single/execute`
        - `POST /multi/create`
        - `POST /multi/{id}/execute`
        - `GET /multi/{id}/status`
        - `DELETE /multi/{id}`
        
        ## Configuration
        
        - Default config lives in `equitrcoder/config/default.yaml`.
        - User overrides: `~/.EQUITR-coder/config.yaml`.
        - Env overrides supported for selected keys (see code and docs).
        - `session.max_context: "auto"` is supported and normalized automatically.
        
        ## Examples
        
        See `examples/` for patterns:
        - `create_react_website.py`
        - `mario_parallel_example.py`
        - `research_programmatic_example.py`
        
        ## Troubleshooting
        
        - Missing models or keys: ensure relevant env vars are set. The TUI will still load, but execution may fail when contacting providers.
        - Textual errors: install TUI deps: `pip install textual rich`.
        - Git integration issues: run inside a git repo or disable with `git_enabled=False` in programmatic usage.