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Basic Information

Cognify is a developer-focused tool for automatically tuning, testing, and optimizing generative AI agents and workflow programs. It is designed to improve generation quality, reduce execution latency, and lower monetary cost for agents implemented with LangChain, LangGraph, DSPy, or Cognify's own Python workflow framework. The tool applies hierarchical, workflow-level optimization by experimenting with combinations of tuning methods across workflow components. Users run a simple CLI to optimize an agent source file and provide a configuration module that supplies a sample dataset, an evaluator for output quality, and optional optimization settings and model selections. Cognify iteratively evaluates candidate variations until a user-specified limit is reached and emits multiple optimized agent versions representing different quality, cost, and latency trade-offs on a Pareto frontier. The project includes installation via pip, documentation, a quickstart, and an API reference.

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App Details

Features
Automatic hierarchical workflow-level optimization that searches combinations of tuning methods across agent components. Command-line interface with commands to optimize an agent, evaluate optimization outputs, and resume earlier runs. Native support for unmodified LangChain and DSPy source code and compatibility with LangGraph and Cognify"s Python workflow framework. Config-driven workflow requiring a config file that defines a sample dataset, an evaluator for measuring final output quality, and optional optimization and model selection parameters. Provides built-in evaluator implementations and configurable search intensities such as light, medium, and heavy. Outputs a set of optimized agent versions and records the applied optimizations, producing a Pareto frontier of quality, cost, and latency trade-offs. Accompanied by documentation, a quickstart guide, and a research paper describing the approach.
Use Cases
Cognify helps developers and teams reduce the manual effort of tuning generative agents by automating experimentation and model-selection across workflow components. It can materially improve final generation quality while lowering runtime costs and end-to-end latency, with reported improvements up to 2.8x in quality, up to 10x cost reduction, and up to 2.7x latency reduction relative to baseline agents. By requiring a sample dataset and evaluator, Cognify evaluates candidate variants on real tasks, so optimizations are grounded in user-defined metrics. The tool produces multiple optimized versions so teams can choose desired trade-offs between accuracy, speed, and cost. It supports iterative development through resume functionality and integrates into existing agent codebases with minimal changes. Documentation and tutorials aid adoption.

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