Show HN: Add AI code interpreter to any LLM via SDK

This page summarizes the projects mentioned and recommended in the original post on news.ycombinator.com

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  1. code-interpreter

    Python & JS/TS SDK for running AI-generated code/code interpreting in your AI app

    Hi, I'm the CEO of the company that built this SDK.

    We're a company called E2B [0]. We're building and open-source [1] secure environments for running untrusted AI-generated code and AI agents. We call these environments sandboxes and they are built on top of micro VM called Firecracker [2].

    You can think of us as giving small cloud computers to LLMs.

    We recently created a dedicated SDK for building custom code interpreters in Python or JS/TS. We saw this need after a lot of our users have been adding code execution capabilities to their AI apps with our core SDK [3]. These use cases were often centered around AI data analysis so code interpreter-like behavior made sense

    The way our code interpret SDK works is by spawning an E2B sandbox with Jupyter Server. We then communicate with this Jupyter server through Jupyter Kernel messaging protocol [4].

    We don't do any wrapping around LLM, any prompting, or any agent-like framework. We leave all of that on users. We're really just a boring code execution layer that sats at the bottom that we're building specifically for the future software that will be building another software. We work with any LLM. Here's how we added code interpreter to Claude [5].

    Our long-term plan is to build an automated AWS for AI apps and agents.

    Happy to answer any questions and hear feedback!

    [0] https://e2b.dev/

    [1] https://github.com/e2b-dev

    [2] https://github.com/firecracker-microvm/firecracker

    [3] https://e2b.dev/docs

    [4] https://jupyter-client.readthedocs.io/en/latest/messaging.ht...

    [5] https://github.com/e2b-dev/e2b-cookbook/blob/main/examples/c...

  2. InfluxDB

    InfluxDB – Built for High-Performance Time Series Workloads. InfluxDB 3 OSS is now GA. Transform, enrich, and act on time series data directly in the database. Automate critical tasks and eliminate the need to move data externally. Download now.

    InfluxDB logo
  3. E2B

    Open-source, secure environment with real-world tools for enterprise-grade agents.

    Hi, I'm the CEO of the company that built this SDK.

    We're a company called E2B [0]. We're building and open-source [1] secure environments for running untrusted AI-generated code and AI agents. We call these environments sandboxes and they are built on top of micro VM called Firecracker [2].

    You can think of us as giving small cloud computers to LLMs.

    We recently created a dedicated SDK for building custom code interpreters in Python or JS/TS. We saw this need after a lot of our users have been adding code execution capabilities to their AI apps with our core SDK [3]. These use cases were often centered around AI data analysis so code interpreter-like behavior made sense

    The way our code interpret SDK works is by spawning an E2B sandbox with Jupyter Server. We then communicate with this Jupyter server through Jupyter Kernel messaging protocol [4].

    We don't do any wrapping around LLM, any prompting, or any agent-like framework. We leave all of that on users. We're really just a boring code execution layer that sats at the bottom that we're building specifically for the future software that will be building another software. We work with any LLM. Here's how we added code interpreter to Claude [5].

    Our long-term plan is to build an automated AWS for AI apps and agents.

    Happy to answer any questions and hear feedback!

    [0] https://e2b.dev/

    [1] https://github.com/e2b-dev

    [2] https://github.com/firecracker-microvm/firecracker

    [3] https://e2b.dev/docs

    [4] https://jupyter-client.readthedocs.io/en/latest/messaging.ht...

    [5] https://github.com/e2b-dev/e2b-cookbook/blob/main/examples/c...

  4. firecracker

    Secure and fast microVMs for serverless computing.

    Hi, I'm the CEO of the company that built this SDK.

    We're a company called E2B [0]. We're building and open-source [1] secure environments for running untrusted AI-generated code and AI agents. We call these environments sandboxes and they are built on top of micro VM called Firecracker [2].

    You can think of us as giving small cloud computers to LLMs.

    We recently created a dedicated SDK for building custom code interpreters in Python or JS/TS. We saw this need after a lot of our users have been adding code execution capabilities to their AI apps with our core SDK [3]. These use cases were often centered around AI data analysis so code interpreter-like behavior made sense

    The way our code interpret SDK works is by spawning an E2B sandbox with Jupyter Server. We then communicate with this Jupyter server through Jupyter Kernel messaging protocol [4].

    We don't do any wrapping around LLM, any prompting, or any agent-like framework. We leave all of that on users. We're really just a boring code execution layer that sats at the bottom that we're building specifically for the future software that will be building another software. We work with any LLM. Here's how we added code interpreter to Claude [5].

    Our long-term plan is to build an automated AWS for AI apps and agents.

    Happy to answer any questions and hear feedback!

    [0] https://e2b.dev/

    [1] https://github.com/e2b-dev

    [2] https://github.com/firecracker-microvm/firecracker

    [3] https://e2b.dev/docs

    [4] https://jupyter-client.readthedocs.io/en/latest/messaging.ht...

    [5] https://github.com/e2b-dev/e2b-cookbook/blob/main/examples/c...

  5. e2b-cookbook

    Examples of using E2B

    Hi, I'm the CEO of the company that built this SDK.

    We're a company called E2B [0]. We're building and open-source [1] secure environments for running untrusted AI-generated code and AI agents. We call these environments sandboxes and they are built on top of micro VM called Firecracker [2].

    You can think of us as giving small cloud computers to LLMs.

    We recently created a dedicated SDK for building custom code interpreters in Python or JS/TS. We saw this need after a lot of our users have been adding code execution capabilities to their AI apps with our core SDK [3]. These use cases were often centered around AI data analysis so code interpreter-like behavior made sense

    The way our code interpret SDK works is by spawning an E2B sandbox with Jupyter Server. We then communicate with this Jupyter server through Jupyter Kernel messaging protocol [4].

    We don't do any wrapping around LLM, any prompting, or any agent-like framework. We leave all of that on users. We're really just a boring code execution layer that sats at the bottom that we're building specifically for the future software that will be building another software. We work with any LLM. Here's how we added code interpreter to Claude [5].

    Our long-term plan is to build an automated AWS for AI apps and agents.

    Happy to answer any questions and hear feedback!

    [0] https://e2b.dev/

    [1] https://github.com/e2b-dev

    [2] https://github.com/firecracker-microvm/firecracker

    [3] https://e2b.dev/docs

    [4] https://jupyter-client.readthedocs.io/en/latest/messaging.ht...

    [5] https://github.com/e2b-dev/e2b-cookbook/blob/main/examples/c...

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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