Shared environments for teams to develop, test, and manage prompts for large language models together.
Agenta is a Prompt Ops lifecycle manager and prompt management platform that decouples prompt engineering from application code. It serves as a centralized system for developing, versioning, and deploying prompt templates and model configurations across different environments. The platform functions as an AI agent orchestrator with a visual interface for building agent workflows and connecting models to external tools. It further acts as an evaluation framework and observability tool, utilizing OpenTelemetry to capture execution traces, monitor latency, and track token costs. The system cove
Agenta is a comprehensive prompt management and evaluation platform that provides the requested versioning, team collaboration, and testing features within a centralized workspace for LLM development.
promptfoo is an evaluation framework for measuring the performance of large language model prompts, agents, and retrieval augmented generation pipelines. It provides a suite of tools for conducting comparative benchmarking and executing automated quality and security regressions. The system features a benchmarking suite for running identical prompts across different model providers to compare output quality side-by-side. It also includes a dedicated red teaming tool for identifying security vulnerabilities and prompt injection risks through automated penetration testing. The framework suppor
This is a specialized evaluation and benchmarking framework for LLM prompts that supports comparative testing and shared reporting, though it functions more as a CLI-based testing tool than a full-featured collaborative workspace for prompt development.
Arize Phoenix is an LLM observability platform and evaluation framework designed to capture execution traces and monitor large language model applications. It serves as a prompt management system for versioning and testing templates, and as a self-hosted AI operations infrastructure for managing telemetry and experiments. The platform differentiates itself through a specialized embedding visualization tool used to detect data drift and optimize vector search. It provides a comprehensive evaluation suite that utilizes judge-based evaluators and ground-truth datasets to score model outputs, and
Arize Phoenix is an LLM observability and evaluation platform that provides the necessary infrastructure for prompt versioning, testing, and collaborative experimentation, making it a strong fit for managing the prompt engineering lifecycle.
Comet LLM is an observability platform and evaluation framework designed for large language model applications and agentic workflows. It functions as a system for tracing, monitoring, and debugging execution flows while providing tools for prompt optimization and the enforcement of AI safety guardrails. The platform distinguishes itself through a combination of model-based scoring and heuristic metrics to quantify output quality and detect hallucinations. It includes a dedicated prompt and agent optimizer with an interactive playground for refining templates and tool configurations. For retri
This platform provides an interactive playground for prompt optimization, version tracking, and evaluation, serving as a robust tool for teams to refine and test LLM prompts within a collaborative MLOps workflow.
This repository catalogs the system prompts used by Claude Code, organizing them into browsable categories with token-count estimates for each prompt. It functions as both a prompt library browser and a revision tracker, surfacing the size and complexity of individual prompts to support auditing and prompt engineering decisions. The project records prompt revisions by parsing git diffs between versions, capturing additions, removals, and token-count changes in a structured changelog. Token counts are approximated from character length using a fixed heuristic ratio, avoiding the need for API c
This repository is a collection and version-tracking tool for specific system prompts rather than a collaborative workspace for teams to develop, test, and evaluate LLM prompts in real-time.
Open WebUI is a self-hosted, web-based platform designed for interacting with local and remote artificial intelligence models. It functions as a unified interface and orchestration suite, enabling users to build, deploy, and manage specialized AI agents equipped with custom instructions, external tool access, and private knowledge bases. The platform distinguishes itself through a modular architecture that supports complex AI workflows. It features a plugin-based framework for custom logic and pipeline-based request processing, allowing developers to filter or transform data streams before th
This platform provides a collaborative interface for managing AI agents and workflows, though it focuses more on chat-based interaction and RAG than on the specific versioning and evaluation lifecycle required for prompt engineering.
This project is an automated prompt engineering and optimization tool designed to iteratively create, test, and refine prompts using a language model to improve output quality. It functions as a framework for generating candidate prompts and ranking their performance through correctness matching and ELO-based ratings. The system includes capabilities for model distillation, generating high-quality example pairs from frontier models to create training data for smaller models. It also provides tools to condense prompts for smaller models and transform instruction-tuned prompts into completion-b
This is an automated prompt optimization and benchmarking framework for individual developers rather than a collaborative workspace designed for team-based prompt management and shared development.
Instructor is a schema enforcement and validation library designed to transform language model outputs into structured, type-safe data formats. It functions as a validation layer that uses Pydantic to ensure model responses conform to specific data models, acting as a tool for forcing large language models to return data in predefined schemas. The project differentiates itself through a recursive error-feedback loop that automatically retries requests when structural errors occur, passing validation failure messages back to the model to guide corrections. It also includes a streaming parser c
This is a library for enforcing structured data schemas and validation in LLM responses, which serves as a technical building block rather than a collaborative workspace for managing and testing prompts.