AgentToolDrop

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E2B

Runs AI-generated code in isolated cloud sandboxes you start and control from Python or JavaScript

Install the JavaScript SDK

npm i e2b

Copied from the project's docs at github.com/e2b-dev/E2B

Why you'd want it

E2B gives agent tasks their own isolated sandbox in the cloud, started with Sandbox.create() from the JavaScript or Python SDK, where code can run shell commands and programs. The Code Interpreter SDK adds runCode/run_code for executing snippets, and the Desktop SDK adds mouse, keyboard, screenshot and desktop streaming APIs for computer-use agents. The runtime is open source and can be self-hosted with Terraform on AWS, Google Cloud, Azure or a general Linux machine.

Best for

  • Running untrusted model-generated code
  • Code interpreter and data analysis features
  • Computer-use agents that need a desktop

Use it when

Your agent writes code or shell commands that must run somewhere isolated from your own machines and data.

Skip it if

You do not want to sign up for an E2B API key or operate the Terraform-based self-hosted runtime.

Related tools

Same category or shared tags, by drop score.

  1. Rank 57

    Daytona

    Sandbox

    Runs AI-generated code in hosted sandboxes with their own kernel, filesystem and network stack

    • score 43
    • No public repo
    • Hosted service

    Install the Python SDK

    pip install daytona
    Use it when, skip it if, stats for Daytona

    Why you'd want it

    Daytona sandboxes are full computers with a dedicated kernel, filesystem, network stack and allocated vCPU, RAM and disk, which agents create and control through SDKs for TypeScript, Python, Ruby, Go and Java, an API and a CLI. Operations cover sandbox lifecycle, file system, process and code execution and git, and stateful snapshots let agent work persist across sessions. The docs also cover computer use, an MCP server, and SSH, VNC and web terminal access, and the pricing page lists GPU options.

    Best for

    • Agents that need sandboxes that persist across sessions
    • SDK users in Ruby, Go or Java as well as Python and TypeScript
    • Sandboxed workloads that need GPUs

    Use it when

    You want hosted, isolated machines for agent code execution and prefer usage-based billing to running the infrastructure yourself.

    Skip it if

    You need to self-host or audit the platform source: core development moved to a private codebase and the public repo is archived. Billing is pay as you go per vCPU, memory and storage hour.

    Stats

    GitHub
    no public repo
    License
    Hosted service
    Pricing
    Paid
    Drop score
    43 of 100

    Full stat block for Daytona

  2. Rank 5

    Langfuse

    Evals

    Traces LLM calls and agent steps, then runs evals, datasets and prompt versioning on top of them

    • score 85
    • 35.8k stars
    • pushed
    • v4.59.0 on
    • MIT

    Install the Python SDK

    pip install langfuse
    Use it when, skip it if, stats for Langfuse

    Why you'd want it

    Langfuse instruments an app through Python and JS/TS SDKs or integrations such as the OpenAI SDK, LangChain, LlamaIndex, Vercel AI SDK and Mastra, and records LLM calls, retrieval and agent actions as traces you can inspect. The same platform covers prompt management with server and client caching, datasets for pre-deployment tests, and evaluations via LLM-as-a-judge, code evaluators, user feedback and manual labeling. You can use Langfuse Cloud or self-host it with Docker Compose, on a VM or on Kubernetes via Helm.

    Best for

    • Teams that want to self-host LLM observability
    • Tracing, prompts and evals in one place
    • Python and JS/TS stacks

    Use it when

    You need to see what your LLM app or agent did on each request and want to evaluate and iterate on prompts against the same traces.

    Skip it if

    You do not want to run a server stack yourself and cannot use a hosted account; self-hosting means running the Docker Compose or Kubernetes deployment. Code under the ee/ directories is not MIT licensed.

    Stats

    Stars
    35,832
    Last push
    Release
    v4.59.0,
    License
    MIT
    Pricing
    Open source
    Works with
    Docker
    Drop score
    85 of 100

    Full stat block for Langfuse

  3. Rank 19

    Mem0

    Memory

    Adds long-term user, session and agent memory to AI apps through a Python or TypeScript SDK

    • score 66
    • 67.2k stars
    • pushed
    • ts-v3.3.1 on
    • Apache-2.0

    Install with pip

    pip install mem0ai
    Use it when, skip it if, stats for Mem0

    Why you'd want it

    Mem0 extracts facts from conversations with an LLM call and stores them so later turns can search them by user, session or agent. Retrieval combines semantic search, BM25 keyword matching and entity linking, with time-aware ranking for questions about current state versus past events. You can run it as a library from pip or npm, as a self-hosted server with a dashboard and API keys through Docker Compose, or on the managed Mem0 Platform.

    Best for

    • Personalized assistants and support bots
    • Remembering user preferences across sessions
    • Starting with a library and moving to a managed service later

    Use it when

    Your agent should remember facts about users or its own past actions between conversations without you designing the storage and retrieval.

    Skip it if

    You want memory with no LLM or embedding calls: Mem0 requires an LLM and defaults to OpenAI models. The README notes its benchmark scores come from the managed platform, which includes optimizations not in the open-source SDK.

    Stats

    Stars
    67,205
    Last push
    Release
    ts-v3.3.1,
    License
    Apache-2.0
    Pricing
    Open source
    Drop score
    66 of 100

    Full stat block for Mem0

For agents

The same entry, as files an agent can read without scraping this page. 

# read one entry
curl -s https://agenttooldrop.com/tools/e2b.md

# list the MCP servers in the catalog
curl -s https://agenttooldrop.com/tools.json \
  | jq -r '.tools[] | select(.category=="mcp-server") | .slug'