ollama-prompt 1.0.0

Ollama CLI prompt tool for local LLM code analysis

ollama-prompt.py – 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. It sends a user-defined prompt to a chosen Ollama model, offloads analysis, and returns a complete JSON response with all server-side metadata: prompt, output, token counts, durations, and more.

This tool is ideal for:

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

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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.
• Matches PowerShell API workflows and allows easy parsing for downstream analysis, reporting, or archiving.
• Works on Windows, Mac, Linux with Python 3.7+ and Ollama installed.

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Installation

1. Install Python ≥ 3.7
   Windows: Python Downloads Mac/Linux: Preinstalled or use your package manager.

2. Install Ollama Python client

   pip install ollama
   

3. Ensure the Ollama server is running locally

   ollama serve
   

4. Download/copy the script Save the following code as ollama-prompt.py:

import ollama
import argparse
import json

def main():
    parser = argparse.ArgumentParser(description="Send a prompt to local Ollama and get full verbose JSON response (tokens, durations, output).")
    parser.add_argument('--prompt', required=True, help="Prompt to send to the model")
    parser.add_argument('--model', default="deepseek-v3.1:671b-cloud", help="Model name")
    parser.add_argument('--temperature', type=float, default=0.1, help="Sampling temperature")
    parser.add_argument('--max_tokens', type=int, default=2048, help="Maximum tokens for response")
    args = parser.parse_args()
    result = ollama.generate(
        model=args.model,
        prompt=args.prompt,
        options={
            "temperature": args.temperature,
            "num_predict": args.max_tokens
        },
        stream=False
    )
    result_dict = result.model_dump() if hasattr(result, "model_dump") else dict(result)
    print(json.dumps(result_dict, indent=2))

if __name__ == "__main__":
    main()

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Usage

Basic Analysis

python3 ollama-prompt.py --prompt "Summarize the architecture in src/modules." --model deepseek-v3.1:671b-cloud

Custom Temperature & Tokens

python3 ollama-prompt.py --prompt "Evaluate performance of the sorting algorithms." --temperature 0.05 --max_tokens 4096

Full JSON Output for Agent/Log Use

python3 ollama-prompt.py --prompt "Critical design flaws in utils.py?" > analysis.json

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Output

Returns a single JSON object. Example fields:

• model: Model name used
• prompt_eval_count: Tokens in your prompt
• eval_count: Tokens generated/used in completion
• response: The full answer text from Ollama
• total_duration: Full query latency (ns)
• prompt_eval_duration: Prompt token eval time
• eval_duration: Output token eval time
• done: Completion status

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Advanced Tips

• Pipe results to jq or similar for further processing:

  python3 ollama-prompt.py --prompt "..." | jq .eval_count
  

• Integrate in agent loops or cost dashboards:
  • Use eval_count to track cost and offload efficiency.
  • Use prompt_eval_count for context window analytics.
• Change model for any Ollama-supported LLM.

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Troubleshooting

• If you get ModuleNotFoundError: ollama, ensure pip install ollama matches the Python interpreter used.
• Ollama must be running locally for API requests to succeed.
• For maximum context windows, check your model’s max token support.

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License

MIT (see Ollama license)

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Credits

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

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For help or suggestions, open an issue or contact the developer.