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Llama Prompt Ops

https://github.com/meta-llama/prompt-ops
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Category
AI Tools
Rank
No. 660Tools index
Pricing
Open Source
Type
TOOL
Builder
meta-llama
GitHub
1.1k stars
Date

About

Open-source toolkit for LLM prompt optimization from Meta. Iterate on prompts with evals and structured experiments instead of vibes.

What it does

Llama Prompt Ops takes an existing system prompt, example inputs and expected outputs, plus YAML settings. It proposes a Llama-oriented replacement, measures both versions with selected metrics, and saves the prompt alongside comparative results.

Why it's ranked here

This is a focused, practical package for prompt migration. Project scaffolding, configurable dataset adapters, custom metrics, multiple inference backends, and a label-free duel optimizer give engineers meaningful control. The source-install recommendation and package-name transition make onboarding less tidy.

What's good

The workflow makes prompt changes testable against your own examples. Built-in support covers exact-match and structured JSON evaluation, while extension points accept custom datasets and metrics. LiteLLM routing supports hosted APIs and locally served models. Separate task and proposer models let teams control each role independently.

Tradeoffs

The standard workflow needs an existing prompt, a query-response dataset, YAML configuration, and working model access. The README suggests at least 50 examples. Installation from source is recommended because PyPI names are transitioning. DSPy is pinned to one exact release, which can constrain dependency resolution.

How to use it well

Use it when migrating a proven prompt to Llama or comparing prompt variants against representative examples. Start with generated project scaffolding, replace the sample data, choose a metric that matches the output contract, and inspect baseline comparisons. It does not provide model hosting, dataset creation, or production request monitoring.

Technical notes+

pyproject.toml defines a Python 3.10+ setuptools package, installs the prompt-ops CLI through prompt_ops.interfaces.cli:cli, and depends on Click, DSPy, LiteLLM, NumPy, SciPy, pandas, YAML, and dotenv support. setup.py discovers packages under src. src/prompt_ops/interfaces/cli.py implements Click commands, scaffolds projects, validates provider credentials through LiteLLM, and dynamically loads custom classes. src/prompt_ops/core/__init__.py exposes migrator, strategy, metric, and evaluator APIs. src/prompt_ops/core/pdo/__init__.py exposes Thompson-sampled duel selection and Copeland, Borda, win-rate, Elo, and TrueSkill-based ranking mechanisms. docs/inference_providers.md documents hosted and local backends.

Observed

License
MIT License
Primary language
Python
Python requirement
Python 3.10 or newer
Packaging
Setuptools build with wheel support; source installation is recommended, with a PyPI alternative during a package-name transition
Interfaces
Installable command-line interface and importable Python library
Platform support
Declared operating-system independent
Inference backends
LiteLLM-based configuration for OpenRouter, vLLM, NVIDIA NIMs, Together AI, and Groq

Read from README.md, setup.py, pyproject.toml, src/prompt_ops/__init__.py, src/prompt_ops/core/__init__.py, src/prompt_ops/debug/__init__.py, src/prompt_ops/interfaces/cli.py, src/prompt_ops/datasets/__init__.py, src/prompt_ops/templates/__init__.py, src/prompt_ops/interfaces/__init__.py, src/prompt_ops/core/pdo/__init__.py, src/prompt_ops/core/utils/__init__.py, src/prompt_ops/datasets/hotpotqa/__init__.py, docs/README.md, docs/inference_providers.md.

What it can do

  • Optimize LLM prompts through structured experiments

    Initial prompts and optimization parameters → Improved prompt versions with performance metrics

  • Run evaluations on prompt performance

    Prompts and test datasets → Evaluation scores and performance reports

  • Iterate on prompt versions systematically

    Current prompt and iteration criteria → New prompt variants for testing

  • Compare multiple prompt variations

    Set of prompt variants and evaluation criteria → Comparative analysis and ranking of prompts

  • Track prompt experiment results

    Experiment data and metrics → Structured experiment logs and history

  • Measure prompt effectiveness objectively

    Prompts and ground truth data → Quantitative performance measurements

Tags

llmpromptoptimizationmetallama

Tech Stack

Python

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