Top Open Source Tools for Managing AI Prompts

If you’ve ever lost track of which prompt version your team is using, you’re not alone. One’s in Notion, another’s in someone’s local file, and the “final” version? Who knows. That’s where prompt management tools come in. But if you want more control, transparency, or just prefer to keep things open, going with an open source option can make a lot of sense.

In this article, we’re diving into a few open source tools that let you manage, organize, and version your prompts without relying on a closed platform. Whether you’re building internal agents, testing prompts across models, or just trying to avoid chaos, these tools give you a starting point that’s flexible and actually usable.

1. Snippets AI

At Snippets AI, we’re trying to take the chaos out of open source prompt management and make it something that actually works across teams. Instead of stashing prompts in random docs, Slack messages, or personal notes, we’ve built a workspace where everything sits in one place and is easy to reuse. Whether you’re working solo, collaborating with a small team, or sharing knowledge across a bigger org, Snippets lets you save, search, organize, and insert prompts with just a few clicks or shortcuts. It’s a desktop-first tool with a focus on real-time collaboration, and we’ve tried to keep it simple without stripping away power features.

We support public and team workspaces, media previews, version tracking, and even things like syntax highlighting and voice input. You can drag and drop prompts, preview audio or video inside them, and share them with attached context to make your workflow smoother. If you’ve got multiple projects going, switching between workspaces is quick, and you can move or copy prompts across teams without friction. It’s not just about managing text snippets – it’s about giving your team a better way to work with AI prompts day to day.

Key Highlights:

• Quick access via keyboard shortcut (Ctrl + Space)
• Public and team workspaces for shared collaboration
• Built-in media, image, audio, and video previews
• Supports syntax highlighting and formatted code snippets
• Global favorites and advanced search across workspaces

Services:

• Prompt saving and sharing with teams
• Snippet organization using folders and tags
• Audio and video support within prompts
• Team-specific snippet libraries and workflows
• Import/export and prompt duplication across teams
• Public community workspaces for prompt discovery

Contact Information:

• Website: www.getsnippets.ai 
• E-mail: team@getsnippets.ai
• Twitter: x.com/getsnippetsai 
• LinkedIn: www.linkedin.com/company/getsnippetsai 
• Address: Skolas iela 3, Jaunjelgava, Aizkraukles nov., Latvija, LV-5134

2. Langfuse

Langfuse is an open source platform built for teams working on large language model (LLM) applications. It provides tools for tracing, evaluating, and managing prompts to help engineers understand how their AI systems behave and perform. Instead of juggling prompts in multiple files or re-deploying every time a change is made, Langfuse keeps everything connected through its unified prompt management system. It works with many popular LLM frameworks and offers SDKs for Python and TypeScript, making integration simple across different projects.

The platform is designed around visibility and control. Its observability tools allow developers to capture detailed traces of LLM interactions, inspect latency, and measure costs in real time. Prompt management supports versioning, comparisons, and rollback options, so teams can easily track progress and experiment safely. Langfuse can be self-hosted or used in the cloud, providing flexibility for both small teams and enterprise setups.

Key Highlights:

• Open source LLM engineering platform
• Works with popular frameworks like LangChain, Llama-Index, and Flowise
• Offers full prompt version control and rollback features
• Integrates tracing and metrics for better observability
• Supports both cloud and self-hosted deployments

Services:

• Prompt management and versioning
• LLM tracing and observability
• Evaluation and A/B testing tools
• Human annotation and dataset management
• SDKs for Python and TypeScript integration

Contact Information:

• Website: langfuse.com
• Twitter: x.com/langfuse
• LinkedIn: linkedin.com/company/langfuse

3. Latitude

Latitude is an open source platform built to help teams design, test, and deploy AI prompts and agents in one place. It blends a no-code setup with tools for managing prompts, running experiments, and tracking changes as they move from draft to production. Instead of relying on rigid logic or manual trial-and-error, Latitude supports batch testing, version control, and observability features that help teams iterate with more structure. It also comes with integrations that let users trigger prompts from external sources, or connect agents to tools like Slack, HubSpot, and Google Drive.

The system is designed around autonomous AI agents, meaning users can describe what they want in plain language, and Latitude figures out how to make it work using its prompt manager and workflow engine. There’s built-in support for refining prompts with human-in-the-loop or automated evals, along with version comparisons and full logs for debugging. Teams can host it themselves or use the cloud version for extra features. With its focus on prompt lifecycle management, it’s positioned more like an AI engineering studio than a simple prompt library.

Key Highlights:

• Open source and self-hostable with optional cloud version
• Visual prompt manager with batch testing and versioning
• Built-in observability for agent and prompt behavior
• LLM-as-judge and human-in-the-loop evaluation
• Many tool integrations for real-world workflows

Services:

• Prompt design and testing at scale
• Version control and deployment
• Real-time debugging and trace logs
• Dataset generation and experiment tools
• Agent orchestration using natural language setup
• Prompt improvement based on evaluation outcomes

Contact Information:

• Website: latitude.so
• Twitter: x.com/trylatitude
• LinkedIn: linkedin.com/company/trylatitude

4. Agenta

Agenta is an open source platform designed to support teams building LLM-based applications by offering built-in tools for prompt management, evaluation, and debugging. Instead of juggling prompts and test cases in disconnected tools, Agenta brings everything into one place. The platform helps teams track prompt versions, monitor how those prompts perform over time, and roll back if needed. There’s a web-based interface where users can experiment, compare prompts side by side, and deploy changes without digging into code.

What sets Agenta apart is its focus on closing the loop between prompt creation and quality control. Developers can run evaluations directly from the interface, trace unexpected outputs, and figure out where things are breaking down. It also supports organizing prompts with linked evaluations and traces, making it easier to spot edge cases or inconsistent behaviors. Overall, Agenta is built for teams that want to move fast but stay grounded in how their LLMs actually perform in production.

Key Highlights:

• Open source platform for managing LLM prompts
• Built-in web UI for evaluation and version control
• Supports rollback and linking prompts to trace logs
• Playground for testing different models and scenarios
• Focus on output quality with real-time evaluation

Services:

• Prompt versioning and registry
• Web-based prompt testing and comparison
• Output tracing and debugging tools
• Evaluation workflows with performance insights
• Production-ready deployment and rollback management

Contact Information:

• Website: agenta.ai
• Twitter: x.com/agenta_ai
• LinkedIn: linkedin.com/company/agenta-ai

5. E.D.D.I

E.D.D.I is an open source middleware framework built to handle prompt and conversation management for large language model APIs. Rather than being a traditional chatbot tool, it acts as the infrastructure layer connecting AI bots, APIs, and user interactions. It’s structured to support complex workflows, combining prompt engineering with runtime configuration, versioning, and rule-based logic. Developers can manage different bots and models in parallel, control how prompts are structured, and keep track of conversational context across sessions.

Built in Java with Quarkus, E.D.D.I focuses on being lightweight, scalable, and cloud-native. It integrates easily with Docker, Kubernetes, and OpenShift, and supports major LLM providers like OpenAI, Claude, Gemini, and Hugging Face through Langchain4j. Teams can customize behavior using configuration files and a flexible rules engine, while keeping authentication secure through OAuth2 and Keycloak. E.D.D.I is especially useful when you’re trying to maintain full control over conversational AI infrastructure without getting locked into proprietary tools.

Key Highlights:

• Middleware for prompt and conversation orchestration
• Built in Java with Quarkus for fast and lean deployment
• Supports multiple bots and versions at once
• Works with major LLM APIs via Langchain4j
• Runs on Docker with Kubernetes and OpenShift options

Services:

• Prompt templating and management
• Conversation state tracking and history
• API integration via runtime configuration
• Behavior control through rules engine
• Multi-bot and version orchestration
• OAuth2-based user authentication and access control

Contact Information:

• Website: eddi.labs.ai
• E-mail: contact@labs.ai
• LinkedIn: linkedin.com/company/eddi-labs-ai
• Phone: +43 699 1 930 62 50

6. AgentMark

AgentMark is a developer-first prompt management platform designed to fit neatly into existing workflows. It treats prompts like regular code artifacts, storing them as .prompt .mdx files in Git repositories. This setup lets developers track changes, collaborate through branches, and run prompt tests as part of their CI/CD pipelines. At the same time, the platform includes a web-based interface for non-technical teammates, making it easier to edit, test, and version prompts without needing to touch code directly.

Beyond just storing prompts, AgentMark supports different types of output generation, from text to structured data or images. Its format blends markdown and JSX, making it familiar for developers and flexible enough for templating. The CLI and SDK allow for local development, and integrations with editors like VS Code help teams test and refine prompts quickly. AgentMark also includes tools for tracing, metrics, and evaluations, giving teams better visibility into prompt behavior as they move from prototype to production.

Key Highlights:

• Prompts stored as code with full Git version control
• Markdown + JSX format with type safety
• Local and web-based editing options
• CLI, SDK, and VS Code extension support
• CI/CD-friendly with integrated prompt testing

Services:

• Prompt authoring with version history
• Web UI for non-developers to manage prompts
• Structured output support (text, object, image)
• Tracing and debugging tools
• Dataset and evaluation management
• Metrics tracking for latency, cost, and performance

Contact Information:

• Website: agentmark.co
• E-mail: hello@agentmark.co
• LinkedIn: linkedin.com/company/agentmark

7. Dakora

Dakora is a Python-based prompt management tool built around type safety, live updates, and a smooth development workflow. It uses a simple YAML format to organize templates and enforces input validation with clear typing, like string, number, and object. Prompts are hot-reloaded during development, so changes take effect without restarting your app. This setup works well for developers building LLM-driven APIs, especially when working with frameworks like FastAPI or tools like OpenAI’s API.

Templates are written using Jinja2, and Dakora provides a full CLI for managing them-listing, editing, bumping versions, and watching for changes. There’s also a browser-based playground that mirrors the local development environment, letting teams test and iterate quickly. For production use, the tool includes thread-safe caching and optional logging. While it’s clearly focused on developers, the structure it brings to managing prompts makes it practical and straightforward to use at scale.

Key Highlights:

• File-based YAML templates with input validation
• Hot-reload support for fast testing
• Jinja2 templating engine with custom filters
• Interactive playground (local and web-based)
• Semantic versioning and CLI management

Services:

• Prompt rendering with validated inputs
• Python SDK for integrating into apps
• Live template editing during development
• Version tracking and management
• Optional SQLite logging for debugging
• CLI for full prompt lifecycle control

Contact Information:

• Website: dakora.io

8. PromptLayer

PromptLayer is a collaborative, model-agnostic platform built for managing prompts across large language model workflows. It serves as a visual environment where teams can version, test, and monitor prompts without depending on code deployments. The system includes built-in tools for evaluation, A/B testing, and automated regression checks, making it easier to maintain consistency and quality across different model versions. Each prompt can be managed in isolation, deployed interactively, and tracked for cost, latency, and feedback, offering both technical and non-technical users a clear view of performance.

The platform is designed to bring prompt engineering into a structured workflow. Teams can compare versions, organize environments for production and development, and leave comments or commit messages for better collaboration. PromptLayer’s CMS-style approach helps businesses separate prompt logic from application code, allowing subject matter experts and engineers to iterate together. It supports Jinja2 and f-string templating, usage analytics, and automated testing pipelines. Combined with enterprise-grade security and compliance, it offers a practical framework for scaling AI development in a controlled and transparent way.

Key Highlights:

• Model-agnostic prompt management platform
• Visual versioning and deployment through a central dashboard
• Integrated evaluation, regression testing, and A/B comparison
• Usage analytics covering cost, latency, and performance
• Collaboration features with commenting and version notes
• SOC 2 Type 2 and HIPAA compliant for enterprise use

Services:

• Prompt version control and visual editing
• Automated regression and evaluation testing
• Model performance tracking and usage insights
• CMS for prompt organization and deployment
• Collaborative editing for technical and non-technical team
• Environment labeling for production and development workflows

Contact Information:

• Website: promptlayer.com
• E-mail: hello@promptlayer.com
• Twitter: x.com/promptlayer
• LinkedIn: linkedin.com/company/promptlayer
• Phone: +1 (201) 464-0959

Conclusion

If there’s one thing that becomes clear after exploring all these tools, it’s that prompt management has started to mature into its own discipline. What used to be a messy mix of copy-pasted text and untracked versions is now a structured process – and open source projects are leading that shift. Each tool takes a different angle: some focus on developer workflows, others on collaboration or experiment tracking, but they all share the same goal of bringing order and visibility to how teams use and improve prompts.

The nice part about going open source is the control it gives you. You’re not locked into a vendor, you can host things yourself, and you can adapt the system to fit however your team works. For teams working seriously with large language models, picking one of these tools isn’t just about convenience – it’s about building a foundation that scales. Whether you want a lightweight extension for personal use or a full platform that ties into CI/CD pipelines, the open source ecosystem already has the pieces. It’s just a matter of finding the one that fits your workflow best.