# Langchain4J

> LangChain4j is an open-source Java library that simplifies the integration of LLMs into Java applica llm-chain, java, anthropic, chatgpt, chroma, embeddings.

- Skill: `bytesagain/langchain4j` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add bytesagain/langchain4j`
- Raw SKILL.md: https://api.skillmd.com/api/skills/bytesagain/langchain4j/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: bytesagain (https://skillmd.com/u/bytesagain)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/bytesagain/langchain4j

---

# LLM Chain

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

### Utility Commands

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

## How It Works

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

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-chain/
├── 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. **Tracking LLM experiments** — log benchmark results, prompt variations, and evaluation scores as you iterate on model configurations
2. **Cost monitoring** — record token usage, API costs, and billing data to keep spending under control across multiple models
3. **Comparing models side-by-side** — use `compare` and `benchmark` to log performance differences between GPT-4, Claude, Gemini, etc.
4. **Fine-tuning documentation** — capture fine-tuning parameters, dataset info, and results for reproducibility
5. **Generating operational reports** — export all logged data to JSON/CSV for dashboards, audits, or stakeholder reviews

## Examples

```bash
# Log a configuration change
llm-chain configure "switched to gpt-4o, temperature=0.7, max_tokens=2048"

# Record a benchmark result
llm-chain benchmark "gpt-4o MMLU=87.2% latency=1.3s cost=$0.012/req"

# Track a cost entry
llm-chain cost "2024-03-18: 142k tokens, $4.26 total (gpt-4o)"

# Compare two models
llm-chain compare "claude-3.5 vs gpt-4o: claude wins on reasoning, gpt wins on speed"

# Log a prompt engineering note
llm-chain prompt "added chain-of-thought prefix: 'Let me think step by step...'"

# Search all logs for a keyword
llm-chain search "gpt-4o"

# Export everything to JSON
llm-chain export json

# Check health and disk usage
llm-chain status
```

## Configuration

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

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