Genai Toolkit

Genai Toolkit v2.0.0 — an AI toolkit for managing generative AI workflows from the command line. Log configurations, benchmarks, prompts, evaluations, fine-tuning runs, cost tracking, and optimization notes. Each entry is timestamped and persisted locally. Works entirely offline — your data never leaves your machine.

Why Genai Toolkit?

• - Works entirely offline — your data never leaves your machine
• Simple command-line interface with no GUI dependency
• Export to JSON, CSV, or plain text at any time for sharing or archival
• Automatic activity history logging across all commands
• Each domain command doubles as both a logger and a viewer

Commands

Domain Commands

Each domain command works in two modes: log mode (with arguments) saves a timestamped entry, view mode (no arguments) shows the 20 most recent entries.

Command	Description
INLINECODE0	Log a configuration note such as model parameters, API keys, or environment settings. Use this to record setup changes and track which configurations were active during experiments.
INLINECODE1

Log a benchmark result or performance observation. Record latency, throughput, accuracy, or other metrics to compare across runs and model versions. |
| genai-toolkit compare <input> | Log a comparison note between models, configurations, or approaches. Useful for side-by-side evaluations like GPT-4 vs Claude on specific tasks. |
| genai-toolkit prompt <input> | Log a prompt template or prompt engineering note. Track iterations on prompt design, record what worked, and document prompt versioning. |
| genai-toolkit evaluate <input> | Log an evaluation result or quality metric. Record accuracy scores, F1 metrics, human ratings, or any qualitative assessment of model outputs. |
| genai-toolkit fine-tune <input> | Log a fine-tuning run or hyperparameter note. Track epochs, learning rates, dataset sizes, and resulting model performance after fine-tuning. |
| genai-toolkit analyze <input> | Log an analysis observation or insight. Record patterns found in data, failure mode analysis, or trends across experiments. |
| genai-toolkit cost <input> | Log cost tracking data including API costs, compute expenses, and token consumption. Essential for budget monitoring across projects and providers. |
| genai-toolkit usage <input> | Log usage metrics or consumption data. Track request volumes, token counts, rate limit encounters, and daily/monthly consumption patterns. |
| genai-toolkit optimize <input> | Log optimization attempts or performance improvements. Record what was changed, the expected vs actual impact, and next steps. |
| genai-toolkit test <input> | Log test results or test case notes. Record pass/fail outcomes, edge cases discovered, and regression test results. |
| genai-toolkit report <input> | Log a report entry or summary finding. Capture weekly summaries, milestone reports, or executive-level findings from AI workflows. |

Utility Commands

Command	Description
INLINECODE12	Show summary statistics across all log files, including entry counts per category and total data size on disk.
INLINECODE13

Export all data to a file in the specified format. Supported formats: json, csv, txt. Output is saved to the data directory. | | genai-toolkit search <term> | Search all log entries for a term using case-insensitive matching. Results are grouped by log category for easy scanning. | | genai-toolkit recent | Show the 20 most recent entries from the unified activity log, giving a quick overview of recent work across all commands. | | genai-toolkit status | Health check showing version, data directory path, total entry count, disk usage, and last activity timestamp. | | genai-toolkit help | Show the built-in help message listing all available commands and usage information. | | genai-toolkit version | Print the current version (v2.0.0). |

Data Storage

All data is stored locally at ~/.local/share/genai-toolkit/. Each domain command writes to its own log file (e.g., configure.log, benchmark.log). A unified history.log tracks all actions across commands. Use export to back up your data at any time.

Requirements

• - Bash (4.0+)
• No external dependencies — pure shell script
• No network access required

When to Use

• - Tracking AI model benchmarks and comparisons across different providers and versions over time
• Logging prompt engineering iterations to understand what improvements actually moved the needle
• Monitoring API costs and token usage across multiple projects and billing periods
• Evaluating fine-tuning experiments with detailed hyperparameter and metric tracking
• Building a searchable knowledge base of optimization attempts and analysis insights

Examples

CODEBLOCK0

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