About together-sandboxes
together-sandboxes is a skill for running Python remotely in a managed Together sandbox. It is intended for workflows that need reusable session state, remote files, or outputs such as charts without custom infrastructure.
Key features
- 01Execute Python in a remote sandbox
- 02Reuse stateful sessions
- 03Upload files to the runtime
- 04Inspect text and structured outputs
- 05Retrieve display outputs
Use cases
- 01Perform remote data analysis
- 02Generate charts from Python
- 03Run agent-generated code
- 04Continue work across stateful sessions
MCP support
The skill describes MCP-style access for agent workflows and points to the API reference for access patterns. It exposes remote sandbox execution through Together's code interpreter workflow.
Install
Copy the command and run it in a terminal.
$
npx skills add https:/ / github.com/ togethercomputer/ skills --skill together-sandboxesAfter installing, type/together-sandboxesin Claude Code to confirm it loaded.
Bundled files
References and scripts installed along with SKILL.md.
agents/openai.yamlreferences/api-reference.mdscripts/execute_with_session.pyexecute_with_session.tsDetails
From the project READMETogether Sandboxes
Overview
Use Together Sandboxes when the user wants to execute Python remotely in a managed sandbox.
Typical fits:
- stateful Python sessions
- data analysis and chart generation
- agent-generated code execution
- file uploads into a remote runtime
When This Skill Wins
- The user wants remote execution rather than local shell execution
- Session state needs to persist across multiple calls
- The result may include display outputs such as charts
- A lightweight managed runtime is enough; no custom infra is required
Hand Off To Another Skill
- Use
together-gpu-clustersfor full infrastructure control or larger distributed jobs - Use
together-dedicated-containersfor custom containerized runtime logic - Use
together-chat-completionsif the user only wants generated code, not executed code
Quick Routing
- Remote execution with session reuse
- Response schema and session listing
- MCP-style access for agent workflows
Workflow
- Decide whether the task needs code execution or only code generation.
- Start a session with
client.code_interpreter.execute(). - Reuse
session_idwhen the workflow depends on prior state. - Inspect
stdout,stderr, structured outputs, and display outputs separately. - List sessions only when the user needs operational visibility or cleanup.
High-Signal Rules
- Python scripts require the Together v2 SDK (
together>=2.0.0). If the user is on an older version, they must upgrade first:uv pip install --upgrade "together>=2.0.0". - Treat
session_idas part of the workflow state. - Inspect
response.errorsbefore assuming a run succeeded. plt.show()with the Agg backend does not reliably producedisplay_dataoutputs. To retrieve charts, save the figure to aBytesIObuffer withfig.savefig(), base64-encode it, and print the encoded string to stdout. Parse it from thestdoutoutput on the client side. See the chart example in scripts/execute_with_session.py.- Use this skill when the user benefits from remote stateful execution, not just because Python is involved.
- If the task outgrows the sandbox model, hand off to GPU clusters or dedicated containers.
Resource Map
- API reference: references/api-reference.md
- Alternative access patterns: references/api-reference.md
- Python workflow: scripts/execute_with_session.py
- TypeScript workflow: scripts/execute_with_session.ts
Official Docs
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