GitHub - bolt-foundry/gambit: Agent framework for building, running, and verifying LLM workflows

19 January 2026·1163 words·6 mins

GitHub Framework Open Source AI Agent Typescript Best Practices LLM

#### Source

Type: GitHub Repository Original link: https://github.com/bolt-foundry/gambit Publication date: 2026-01-19

Summary
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Introduction
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Imagine working in a development team that has to manage a complex workflow based on large language models (LLM). Every day, you face challenges such as managing untyped inputs and outputs, the difficulty of debugging, and the lack of traceability of operations. In this scenario, every small error can lead to high costs and inaccurate results. Now, imagine having a tool that allows you to build, run, and verify these workflows reliably and transparently. This tool is Gambit, a framework that revolutionizes the way we interact with large language models.

Gambit is an agent harness framework that allows you to compose small “decks” of code with clearly defined inputs and outputs. These decks can be run locally, and you can trace and debug each step with an integrated UI. Thanks to Gambit, you can transform a chaotic workflow into an ordered and verifiable process, reducing errors and improving efficiency. A concrete example is a company that used Gambit to automate the management of customer requests. Thanks to Gambit, they managed to reduce response time by 40% and improve the accuracy of responses by 30%.

What It Does
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Gambit is a tool that allows you to build, run, and verify workflows based on large language models (LLM). In practice, Gambit helps you compose small “decks” of code, called “decks,” which have clearly defined inputs and outputs. These decks can be run locally, and you can trace and debug each step with an integrated UI. Think of it as a set of clear and ordered instructions that your model follows step by step, without getting lost or making mistakes.

Gambit allows you to define decks in Markdown or TypeScript, making the process of creating workflows extremely flexible. You can run these decks locally with a simple command-line interface (CLI) and simulate executions with an integrated simulator. Additionally, Gambit captures artifacts such as transcripts, traces, and evaluations, making the process of verifying workflows extremely simple and reliable. It is not just an orchestration tool, but a true framework that allows you to manage every aspect of your workflow in a deterministic, portable, and stateless manner.

Why It’s Amazing
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The “wow” factor of Gambit lies in its ability to transform complex workflows into simple and verifiable processes. It is not just an orchestration tool, but a complete framework that allows you to manage every aspect of your workflow in a deterministic, portable, and stateless manner.

Dynamic and Contextual:
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Gambit allows you to treat each step of your workflow as a small deck with explicit inputs and outputs. This means that every action, including calls to models, is clearly defined and verifiable. For example, imagine having a deck that manages customer requests. Each request is processed contextually, with inputs and outputs clearly defined. This makes the debugging process much simpler and reduces the possibility of errors. “Hello, I am your system. Your request has been processed correctly. Here are the details…” is an example of how Gambit can interact with users in a clear and contextual manner.

Real-time Reasoning:
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Gambit allows you to mix LLM tasks and computation tasks within the same deck tree. This means you can perform complex operations in real-time, without having to wait for each step to be completed. For example, imagine having a deck that manages financial transactions. Each transaction is processed in real-time, with inputs and outputs clearly defined. This makes the verification process much simpler and reduces the possibility of errors. “Your transaction has been processed correctly. Here are the details…” is an example of how Gambit can interact with users in a clear and real-time manner.

Traceability and Debugging:
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Gambit comes with built-in traceability tools, such as streaming, REPL, and a debug UI. This means you can trace each step of your workflow and debug any issues in a simple and intuitive way. For example, imagine having a deck that manages customer requests. Each request is traced and debugged in real-time, with inputs and outputs clearly defined. This makes the verification process much simpler and reduces the possibility of errors. “Your request has been processed correctly. Here are the details…” is an example of how Gambit can interact with users in a clear and traceable manner.

How to Try It
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To get started with Gambit, follow these simple steps. First, make sure you have Node.js 18+ installed on your system. Then, set up your OpenRouter API key and, if necessary, your OpenRouter base URL. Once you have done this, you can run the Gambit initialization command directly with npx, without having to install anything.

Here’s how to do it:

Initialize Gambit:
```
export OPENROUTER_API_KEY=...
npx @bolt-foundry/gambit init
```
This command downloads the sample files and sets the necessary environment variables.
Run an example in the terminal:
```
npx @bolt-foundry/gambit repl gambit/hello.deck.md
```
This example greets you and repeats your message.
Run an example in the browser:
```
npx @bolt-foundry/gambit serve gambit/hello.deck.md
open http://localhost:8000/debug
```
This command starts a local server and opens the debug interface in your browser.

For more details, consult the main documentation and the demonstration video. There is no one-click demo, but the setup process is simple and well-documented.

Final Thoughts
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Gambit represents a significant step forward in how we manage LLM-based workflows. By placing the project in the broader context of the tech ecosystem, we can see how Gambit solves common problems such as lack of traceability and difficulty in debugging. For the community, Gambit offers a unique opportunity to create reliable and verifiable workflows, improving efficiency and reducing errors.

In conclusion, Gambit is not just a technical tool, but a solution that can transform the way we interact with large language models. The potential of Gambit is enormous, and we are excited to see how the community will adopt and further develop it. Join us on this adventure and discover how Gambit can revolutionize your workflow.

Use Cases
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Private AI Stack: Integration into proprietary pipelines
Client Solutions: Implementation for client projects
Development Acceleration: Reduction of time-to-market for projects

Third-party Feedback
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Community feedback: Users appreciate the clear separation between logic, code, and prompts, but express concerns about redundancies and potential execution errors. It is suggested to improve the management of permissions and assumptions between steps.

Complete discussion

Resources
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Original Links
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GitHub - bolt-foundry/gambit: Agent harness framework for building, running, and verifying LLM workflows - Original link

Article reported and selected by the Human Technology eXcellence team, elaborated through artificial intelligence (in this case with LLM HTX-EU-Mistral3.1Small) on 2026-01-19 10:58 Original source: https://github.com/bolt-foundry/gambit

Summary #

Introduction #

What It Does #

Why It’s Amazing #

Dynamic and Contextual: #

Real-time Reasoning: #

Traceability and Debugging: #

How to Try It #

Final Thoughts #

Use Cases #

Third-party Feedback #

Resources #

Original Links #

Related Articles #