Promptflow

Promptflow is a development framework and orchestrator for building applications powered by large language models. It functions as a suite of tools for designing, orchestrating, and deploying AI workflows by linking prompts, custom Python code, and language models into executable sequences.

The project is distinguished by a visual AI workflow designer that allows for the creation of directed acyclic graphs of logic nodes. It provides a dedicated prompt engineering environment for versioning and comparing templates, alongside stateful execution tracing to record function calls and variable values for step-by-step debugging.

The platform covers a broad capability surface including retrieval augmented generation via vector database lookups and metric-driven evaluation pipelines for batch testing and quality assurance. It handles the full lifecycle from development to production through containerized deployment, workflow endpoint serving, and secure connection management for API credentials.

A command-line interface and SDK are provided for workflow validation and integration into automated CI/CD pipelines.

Features

AI Application Orchestrators - Provides a runtime for visually designing and managing the orchestration of AI-powered application workflows.
AI Workflow Orchestrators - Orchestrates complex AI applications using directed graphs to manage data dependencies and execution sequences.
Graph-Based Workflow Orchestrators - Orchestrates AI application logic using a directed graph of interconnected nodes.
Containerized Deployments - Packages AI workflows and their dependencies into containers to ensure consistent execution across environments.
LLM Application Orchestration - Coordinates complex agentic workflows by chaining model calls and managing execution state.
Prompt Experimentation - Provides a dedicated environment for conducting controlled experiments on prompt variants and model hyperparameters.
LLM Application Development - Provides a comprehensive framework for building AI applications by linking prompts, Python code, and language models.
LLM Model Integrations - Integrates large language models into workflows to perform tasks like text summarization and response generation.
Retrieval Augmented Generation - Implements retrieval augmented generation pipelines that ground model responses in external proprietary data.
LLM Application Frameworks - Offers a comprehensive framework for building and deploying applications powered by large language models.
Prompt Engineering Environments - Provides a collaborative workspace for versioning, testing, and optimizing prompt templates with integrated execution traces.
Prompt Engineering Workflows - Provides an environment for iterating on prompt templates through versioning, batch testing, and side-by-side comparison.
Prompt Templates - Implements a system for defining and managing reusable prompt structures using markdown files and structured metadata.
Visual AI Workflow Builders - Includes a graphical interface for connecting language models and custom code into executable pipelines.
Prompt Metadata Schemas - Uses markdown files with structured front matter to separate model configurations from prompt text.
Custom Tool Definitions - Provides frameworks for defining, initializing, and validating the executable functions that AI agents invoke.
Visual Flow Editors - Provides a visual AI workflow designer to build directed acyclic graphs of logic nodes.
AI Application Hosting - Packages developed workflows into executable services for hosting on cloud platforms.
Model Serving Endpoints - Hosts AI workflows as active API endpoints to handle real-time inference requests.
Unified API Connection Management - Provides a unified system to securely store and manage API keys and credentials for multiple external service providers.
Directed Acyclic Graph Engines - Structures application logic as a sequence of modular nodes in a directed acyclic graph.
Agent Execution Trace Debugging - Records granular function calls and variable values to enable step-by-step debugging of agent behavior.
Pipeline Performance Evaluators - Runs evaluation workflows against datasets to compute quality metrics for LLM performance.
LLM Evaluation - Implements metric-driven evaluation pipelines to measure the quality and accuracy of LLM responses using synthetic datasets.
Workflow Logic Nodes - Allows the integration of custom Python functions to process data and call APIs within a workflow.
Workflow Parsers and Validators - Includes tools to check the structural integrity of workflow files and generate metadata for pipeline integration.
RAG Pipelines - Facilitates the implementation of retrieval augmented generation pipelines by connecting models to vector databases.
LLM Response Streaming - Implements token-by-token streaming of AI model responses to reduce latency.
Model Response Streaming - Delivers model outputs incrementally to the client to improve perceived responsiveness.
Prompt Variant Experimentation - Compares multiple prompt versions against a single dataset to analyze performance and token costs.
Observability and Tracing - Tracks the flow of data and model interactions to identify errors and iterate on prompt designs.
Prompt Batch Evaluators - Executes prompts across large datasets to evaluate response quality and accuracy using a collection of test entries.
Interactive Debugging and Testing - Offers an interactive environment to run workflows in real time for debugging and behavior validation.
CI/CD Pipeline Integrations - Provides native support for automating quality checks and evaluation metrics within continuous integration and deployment workflows.
Container Deployment - Packages developed AI workflows into portable container images for consistent cloud and local deployment.
LLM Observability - Combines containerized deployment with stateful execution tracing for debugging and monitoring scalable AI services.
Execution Tracing - Records the sequence of operations and function calls during runtime to enable detailed debugging of AI components.
Run Metadata Management - Implements a system to trigger workflow executions and manage historical run metadata and archives.
Automated Agent Quality Assurance - Integrates LLM evaluation and testing processes into automated pipelines for consistent output quality.
Graph Logic Validation - Validates the correctness of computational graphs and agent configurations through a CLI or SDK.
Reasoning Step Visualizers - Generates visual snapshots of workflow steps to allow developers to inspect the reasoning process of the AI.
Application Frameworks - Development suite for managing the lifecycle of AI applications.

genkit-ai/genkit

6,141View on GitHub

Genkit is an LLM application framework and generative AI developer toolkit designed for building production AI applications. It serves as an AI workflow orchestrator that coordinates model calls and agentic tool usage through type-safe execution flows. The project provides a unified model interface and plugin architecture to standardize access to diverse large language models, vector stores, and telemetry backends. It distinguishes itself with a dedicated observability suite for tracing execution steps and a developer toolkit for prompting, debugging, and evaluating AI logic via a local inter

Arize-ai/phoenix

8,605View on GitHub

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

tmc/langchaingo

9,416View on GitHub

langchaingo is an LLM application framework for Go designed for building language model-powered applications and autonomous agents. It serves as an orchestration library and tool integration framework that allows developers to link prompt sequences and model calls into complex, multi-step workflows. The project provides a toolkit for implementing retrieval-augmented generation pipelines by processing unstructured documents and retrieving relevant context via vector search. It includes a dedicated integration layer for indexing high-dimensional embeddings and performing similarity searches acr

cloudwego/eino

9,675View on GitHub

Eino is an AI agent development kit and LLM application framework designed for building autonomous agents and orchestrating complex language model workflows. It serves as a multi-agent orchestration engine and workflow orchestrator, providing a graph-based execution model to route data between models, tools, and retrievers. The framework distinguishes itself through a robust set of multi-agent coordination patterns, including supervisor-led management, sequential flows, and autonomous reasoning loops like ReAct. It features advanced agent execution controls such as active turn preemption, che

microsoftpromptflow

Features