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Toolhouse

★4.3
💬638
💲Free

Toolhouse provides a streamlined backend infrastructure for building and deploying AI agents. It eliminates the need to manage complex setups by offering ready-to-use tools such as Retrieval-Augmented Generation (RAG), evaluation systems, memory management, and edge functions—all accessible through a simple CLI or Agent Studio.

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Platform
web
AI agentsAPI deploymentAgent StudioBackend-as-a-ServiceEdge functionsEvalsFunction calling

What is Toolhouse?

Toolhouse is a complete cloud infrastructure designed to equip Large Language Models (LLMs) with actions and knowledge. It simplifies function calling, allowing developers to deploy smarter AI agents with minimal code. The platform offers built-in features like RAG, evals, memory, edge functions, and storage through a Backend-as-a-Service model.

Core Technologies

  • Large Language Models (LLMs)
  • Backend-as-a-Service
  • Function Calling
  • RAG
  • Evals
  • Memory
  • Edge Functions
  • Storage
  • API Deployment
  • Agent Studio

Key Capabilities

  • Deploy AI agents as APIs
  • Simplify agent development with pre-built components
  • Enable function calling and integrations
  • Provide debugging features and automated evaluations
  • Support for any LLM via SDK

Use Cases

  • Deploying AI agents as APIs
  • Building AI applications with pre-built infrastructure components
  • Automating tasks with function calling
  • Creating and sharing AI agents

Core Benefits

  • Reduces development time for AI agents
  • Offers a complete cloud infrastructure for LLMs
  • Provides built-in features like RAG, evals, and memory
  • Simplifies agent deployment with a CLI
  • Universally compatible with any LLM via SDK
  • Includes debugging features and automated evals

Key Features

  • Agentic Backend-as-a-Service
  • Built-in function calling and API integrations
  • Built-in RAG, evals, memory, edge functions, and storage
  • CLI for agent deployment
  • Agent Studio for building agents in written language

How to Use

  1. 1
    Define agents using natural language or code
  2. 2
    Use the Toolhouse CLI to deploy agents as APIs
  3. 3
    Leverage built-in features like RAG, memory, and evals
  4. 4
    Integrate with existing tools and authentication systems
  5. 5
    Scale automatically or opt for private instances

Frequently Asked Questions

Q.What languages does the Toolhouse SDK support?

A.The SDK is available in Python and TypeScript.

Q.What’s the pricing model?

A.There is a generous free tier. For scaling, monthly subscriptions provide full access to cloud components. Startup programs and enterprise options are also available.

Q.Who handles authentication?

A.Toolhouse manages authentication flows, token refresh, and scope management. You can also bring your own credentials or use third-party providers.

Q.How do I know you're production ready?

A.Millions of agent runs have been handled at scale. On-prem or dedicated private instance options are available for enterprise needs.

Pros & Cons (Reserved)

✓ Pros

  • Reduces development time for AI agents
  • Provides a complete cloud infrastructure for LLMs
  • Offers built-in features like RAG, evals, and memory
  • Simplifies agent deployment with a CLI
  • Universally compatible with any LLM via SDK
  • Includes debugging features and automated evals

✗ Cons

  • May require familiarity with the Toolhouse platform
  • Pricing may be a factor for some users
  • Reliance on Toolhouse infrastructure

Alternatives

No alternatives found.