Tessl

What if your AI agents could learn and adapt like seasoned developers, without breaking your codebase?

RecommendedTessl provides essential lifecycle management and governance for AI coding agent skills, transforming them from static artifacts into continuously validated and secure software components.

Tessl is a platform for managing context for AI coding agents, treating agent skills and context as software with a complete lifecycle: build, evaluate, distribute, and optimize. It provides a package manager, a registry for evaluated context bundles, and evaluations to measure and optimize how well context works with agents.

Visit Tessl

Visit Tessl

Key Features:

Package manager for installing, updating, and managing versioned, agent-agnostic skills and context.
Registry for discovering and distributing evaluated context bundles.
Evaluations to measure and optimize how well context works with agents, preventing regressions.
Private workspaces for defining internal proprietary skills, APIs, and compliance rules.
Security scanning and scoring of skills powered by Snyk before installation.

Pros

Software development teams using AI coding agents who need to maintain consistency and correctness across tools and projects.
Open-source maintainers and vendors who want to publish official, evaluated skills for their packages.
Organizations looking to enforce internal coding standards, security policies, and architectural conventions across all AI agents.

Cons

Requires integration with existing AI coding agents and development workflows.
The effectiveness relies on the quality and maintenance of the skills and context provided.
Adoption may require a shift in how teams manage and think about agent behavior and context.

Pricing

freemiumFree tier

Categories:

Code & Development AI Infrastructure Security & Privacy

Tags:

AI agents coding agents context management skill management developer tools software development AI native development registry evaluation security governance

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Quick Decision

Try if: You should try Tessl if your team uses AI coding agents for production code and you need a robust system to manage, evaluate, and secure the context and skills those agents rely on, especially across diverse tools and internal systems.

Skip if: Skip Tessl if you are not using AI coding agents, or if your current AI development workflow is simple enough that it doesn't require advanced context management, versioning, or security evaluations.

Not for: Individuals or small teams not using AI coding agents for production code.; Users primarily looking for a standalone AI coding assistant without context management needs.

Trust Signals

Team Size
small
Funding
Index Ventures, GV, boldstart, Accel

Notable Customers

ElevenlabsGoogleAnthropicCiscoOpenAI

Tech Details

Platforms

webcli

Integrations

Claude CodeCursorGeminiCodexCopilot CLICopilot in VSCodeGitHub ActionsSnyk

Open Source

Support

Channels

docsdiscordemail

Company

Name
Tessl AI Ltd
Location
London, UK

Tessl is building the pioneering AI Native Development platform, aiming to shape the future of software development by focusing on intent and taste rather than just code, and providing tools for a context-centric world.

FAQ

What problem does Tessl solve for AI coding agents?

AI agents often struggle to stay correct as libraries change, APIs evolve, and conventions drift. Tessl addresses this by providing a platform to manage, update, assess, and distribute context, ensuring agents remain effective and reliable.

How does Tessl ensure the quality and security of agent skills?

Tessl provides evaluations to measure skill performance and prevent regressions. It also integrates with Snyk for automated vulnerability scanning, providing security scores and audits for every skill in the registry before installation.

Can Tessl be used to manage internal coding standards and proprietary knowledge?

Yes, teams can create private context within Tessl to define internal proprietary skills, APIs, platform services, security rules, and naming conventions, ensuring all AI agents in the organization operate consistently and on-policy.

Use Cases

Software development teams using AI coding agents who need to maintain consistency and correctness across tools and projects.
Open-source maintainers and vendors who want to publish official, evaluated skills for their packages.
Organizations looking to enforce internal coding standards, security policies, and architectural conventions across all AI agents.