Llm Flow

---
version: "1.0.0"
name: Langflow
description: "Langflow is a powerful tool for building and deploying AI-powered agents and workflows. llm-flow, python, agents, chatgpt, generative-ai."
---
# LLM Flow

An AI toolkit for configuring, benchmarking, comparing, prompting, evaluating, fine-tuning, analyzing, and optimizing LLM workflows. Each command logs timestamped entries to local files with full export, search, and statistics support.

## Commands

### Core AI Operations

| Command | Description |
|---------|-------------|
| `llm-flow configure <input>` | Record a configuration change (or view recent configs with no args) |
| `llm-flow benchmark <input>` | Log a benchmark run and its results |
| `llm-flow compare <input>` | Record a model or output comparison |
| `llm-flow prompt <input>` | Log a prompt template or prompt engineering note |
| `llm-flow evaluate <input>` | Record an evaluation result or metric |
| `llm-flow fine-tune <input>` | Log a fine-tuning session or parameters |
| `llm-flow analyze <input>` | Record an analysis observation |
| `llm-flow cost <input>` | Log cost tracking data (tokens, dollars, etc.) |
| `llm-flow usage <input>` | Record API usage metrics |
| `llm-flow optimize <input>` | Log an optimization attempt and outcome |
| `llm-flow test <input>` | Record a test case or test result |
| `llm-flow report <input>` | Log a report entry or summary |

### Utility Commands

| Command | Description |
|---------|-------------|
| `llm-flow stats` | Show summary statistics across all log files |
| `llm-flow export <fmt>` | Export all data in `json`, `csv`, or `txt` format |
| `llm-flow search <term>` | Search all entries for a keyword (case-insensitive) |
| `llm-flow recent` | Show the 20 most recent activity log entries |
| `llm-flow status` | Health check: version, entry count, disk usage, last activity |
| `llm-flow help` | Display full command reference |
| `llm-flow version` | Print current version (v2.0.0) |

## How It Works

Every core command accepts free-text input. When called with arguments, LLM Flow:

1. Timestamps the entry (`YYYY-MM-DD HH:MM`)
2. Appends it to the command-specific log file (e.g. `benchmark.log`, `cost.log`)
3. Records the action in a central `history.log`
4. Reports the saved entry and running total

When called with **no arguments**, each command displays the 20 most recent entries from its log file.

## Data Storage

All data is stored locally in plain-text log files:

```
~/.local/share/llm-flow/
├── configure.log     # Configuration changes
├── benchmark.log     # Benchmark results
├── compare.log       # Model comparisons
├── prompt.log        # Prompt templates & notes
├── evaluate.log      # Evaluation metrics
├── fine-tune.log     # Fine-tuning sessions
├── analyze.log       # Analysis observations
├── cost.log          # Cost tracking
├── usage.log         # API usage metrics
├── optimize.log      # Optimization attempts
├── test.log          # Test cases & results
├── report.log        # Report entries
├── history.log       # Central activity log
└── export.{json,csv,txt}  # Exported snapshots
```

Each log uses pipe-delimited format: `timestamp|value`.

## Requirements

- **Bash** 4.0+ with `set -euo pipefail`
- Standard Unix utilities: `wc`, `du`, `grep`, `tail`, `date`, `sed`
- No external dependencies — pure bash

## When to Use

1. **Building AI agent workflows** — log each step of your agent pipeline (configure → prompt → evaluate → optimize) with full traceability
2. **Tracking LLM costs and usage** — record per-request costs, token counts, and API usage to monitor spending across providers
3. **Benchmarking and comparing models** — log benchmark metrics side-by-side to make data-driven model selection decisions
4. **Fine-tuning experiment tracking** — capture hyperparameters, dataset details, and evaluation scores for every fine-tuning run
5. **Generating compliance reports** — export all logged activity to JSON/CSV for audits, SOC reviews, or stakeholder reporting

## Examples

```bash
# Configure a new workflow
llm-flow configure "workflow: summarize → classify → respond, model=claude-3.5"

# Benchmark a model
llm-flow benchmark "claude-3.5-sonnet: 94% accuracy, 0.8s p50 latency, $0.003/req"

# Log a prompt template
llm-flow prompt "system: You are a helpful assistant. Always cite sources."

# Track API costs
llm-flow cost "March week 3: 890k tokens in, 210k tokens out, $12.40 total"

# Evaluate output quality
llm-flow evaluate "human eval score: 4.2/5.0 across 50 samples"

# Search across all logs
llm-flow search "claude"

# Export to CSV for analysis
llm-flow export csv

# Quick health check
llm-flow status
```

## Configuration

Set the `DATA_DIR` variable in the script or modify the default path to change storage location. Default: `~/.local/share/llm-flow/`

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