Latitude is an open-source platform designed for developers and AI engineers to engineer, test, evaluate, and deploy custom AI agents and prompts at scale. It addresses the fragmentation in prompt engineering workflows by unifying version control, interactive testing, batch evaluations, and API deployment into a single platform. Whether you’re refining a simple prompt or building complex autonomous agents, Latitude provides the infrastructure to iterate quickly and collaborate effectively. It’s ideal for teams building production-grade AI applications who need visibility into performance, cost, and reliability across models and providers.
Latitude supports both cloud-hosted and self-hosted deployments, giving users flexibility in data governance and infrastructure control. With built-in integrations for 2,500+ tools and support for PromptL—a domain-specific language for structured prompt authoring—it enables precise control over AI behavior while maintaining developer-friendly workflows.
What You Get
- Collaborative Design - Version control prompts and AI agents with team-based collaboration, enabling rollbacks, branching, and shared editing through the web interface.
- Interactive Playground - Test prompts and agents in real time with dynamic inputs, temperature settings, and model choices to observe output variations instantly.
- Built-in Evaluations - Run automated evaluations using predefined metrics, LLM-as-judge scoring, or human-in-the-loop feedback to quantify prompt quality and model performance.
- AI Gateway - Deploy prompts or agents as production-ready API endpoints that auto-update when changes are published, with no manual re-deployment needed.
- Logs & Observability - Monitor real-time metrics including cost per request, latency, token usage, and error rates across all deployments.
- Experiments - Run controlled A/B tests across different LLM providers (e.g., GPT-4, Claude, open-source models) and configurations to identify optimal setups.
- Datasets for Testing - Manage curated test datasets for batch evaluations and regression testing, ensuring consistency across iterations.
- Integrations with 2,500+ Tools - Connect Latitude to external services like databases, CRM systems, and monitoring tools via pre-built or custom integrations.
Common Use Cases
- Building a customer support AI agent - A support team designs a prompt that routes inquiries based on sentiment and product category, tests it in the playground with real customer messages, evaluates accuracy using a dataset of 500 past tickets, then deploys as an API endpoint integrated with their helpdesk software.
- Optimizing RAG pipelines for enterprise knowledge bases - A data science team uses Latitude to test different prompt templates for retrieval-augmented generation, compares model performance across OpenAI and Mistral via experiments, and monitors latency to meet SLA requirements.
- Problem: Inconsistent prompt performance across teams → Solution: Centralized prompt registry with versioning - Multiple product teams were using ad-hoc prompts, leading to inconsistent AI behavior. Latitude provides a shared workspace where prompts are versioned, reviewed, and deployed with audit trails.
- DevOps teams managing multi-model AI deployments - Teams use Latitude’s AI Gateway to deploy and monitor prompts across GPT-4, Claude 3, and local LLMs, with cost tracking to optimize spend without sacrificing quality.
Under The Hood
The Latitude LLM project is a multi-language monorepo designed to support AI-powered agent systems, integrating TypeScript and Python services within a modular architecture. It emphasizes scalability, observability, and developer experience through modern frameworks and tooling.
Architecture
This system adopts a modular, multi-tiered architecture with distinct applications for console, engine, gateway, web, and workers. It follows a layered design that separates core logic from APIs and UI components.
- The monorepo structure enables shared libraries and consistent code organization across services
- Clear separation of concerns is maintained through well-defined app boundaries and responsibilities
- Middleware patterns are used for error handling, rate limiting, and telemetry integration
- Coupling exists between apps, introducing some complexity in dependency management
Tech Stack
The project leverages a hybrid TypeScript and Python stack, combining web-scale frameworks with AI processing capabilities.
- Built primarily with TypeScript for backend and frontend components, complemented by Python for AI reasoning tasks
- Uses Next.js and Hono to power web and API layers, ensuring fast and scalable delivery
- Integrates Drizzle ORM, Zod, React, and Hono for robust data handling and type safety
- Employs pnpm, Turbo, tsup, and Rollup for efficient monorepo management and builds
Code Quality
Code quality is well-maintained with strong testing practices and consistent style adherence.
- Extensive test coverage is present, particularly in the gateway service, ensuring reliability
- Error handling follows structured patterns using try/catch and centralized logging approaches
- The codebase enforces style consistency and modular structure to support long-term maintainability
- Technical debt is visible in reliance on mocking and missing core implementation files
What Makes It Unique
This project introduces a unique blend of web and AI technologies tailored for LLM agent systems.
- Combines TypeScript-based API gateways with Python-powered reasoning engines to support complex agent workflows
- Implements modular telemetry and state management for tracking agent trajectories and behavior
- Offers a scalable architecture that supports both high-volume web traffic and compute-intensive AI tasks
- Integrates observability tools like Datadog and OpenTelemetry to provide deep insights into system performance