ollama-prompt 1.1.4

Ollama CLI prompt tool for local LLM code analysis

ollama-prompt

Local Ollama CLI Tool for Deep Analysis

Overview

ollama-prompt is a cross-platform Python command-line utility to interact with a local Ollama server for advanced code analysis, prompt evaluation, and cost tracking. Send custom prompts to your preferred Ollama model and receive a structured JSON response with all server-side metadata: prompt, output, token counts, durations, and much more.

Ideal for:

• AGI agent orchestration
• Cost-aware code review workflows
• Analytics on token usage
• Integrating structured LLM output into your developer pipeline

Features

• Flexible CLI flags: set prompt, model, temperature, and token count
• Prints full verbose JSON: includes response text, token usage (prompt_eval_count, eval_count), and engine stats
• Integrates easily into developer pipelines (PowerShell, Bash, agent loops)
• Works on Windows, Mac, Linux (Python 3.7+) with Ollama installed

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Installation

Recommended (PyPI):

pip install ollama-prompt

Requirements:

• Python 3.7 or higher
• Local Ollama server running (ollama serve)

Alternative: Development/Manual Install

Clone the repository and install in editable mode:

git clone https://github.com/dansasser/ollama-prompt.git
cd ollama-prompt
pip install -e .

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Usage

Quick Start:

You must have the Ollama server running locally:

ollama serve

Basic Example:

ollama-prompt --prompt "Summarize the architecture in src/modules." --model deepseek-v3.1:671b-cloud

Custom Flags:

ollama-prompt --prompt "Evaluate performance of sorting algorithms." --model deepseek-v3.1:671b-cloud --temperature 0.05 --max_tokens 4096

Output Example (JSON):

{
  "model": "deepseek-v3.1:671b-cloud",
  "prompt_eval_count": 38,
  "eval_count": 93,
  "response": "...",
  "total_duration": 13300000,
  "prompt_eval_duration": 1000000,
  "eval_duration": 12200000,
  "done": true
}

Advanced:

• Pipe results with jq:

  ollama-prompt --prompt "Critical design flaws in utils.py?" | jq .eval_count
  

• Integrate into agent loops or analytics dashboards via JSON output.

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Troubleshooting

• If you get ModuleNotFoundError: ollama, ensure you ran pip install ollama in the correct Python environment.
• Ollama server must be running locally for requests to succeed (ollama serve).
• For maximum context windows, check your model’s max token support.

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Development & Contributing

Editable Install:

git clone https://github.com/dansasser/ollama-prompt.git
cd ollama-prompt
pip install -e .

To contribute:

• Fork the repo, create a branch, submit PRs.
• Open issues for bugs/feature requests.

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License

MIT License (see Ollama license for server terms).

Credits

Developed by Daniel T Sasser II for robust code offload workflows, AGI agent orchestration, and token/cost analytics.

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