Remote Python execution in managed sandboxes on Together AI with stateful sessions, file uploads, data analysis, chart generation, and notebook-like runs via the Sandboxes API. Reach for it whenever the user wants managed remote Python execution instead of local execution, raw clusters, or full model hosting.
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Added November 7, 2026
developmenttypescriptpythonshellapibackend
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api
mcp
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---
name: together-sandboxes
description: "Remote Python execution in managed sandboxes on Together AI with stateful sessions, file uploads, data analysis, chart generation, and notebook-like runs via the Sandboxes API. Reach for it whenever the user wants managed remote Python execution instead of local execution, raw clusters, or full model hosting."
---
# Together 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-clusters` for full infrastructure control or larger distributed jobs
- Use `together-dedicated-containers` for custom containerized runtime logic
- Use `together-chat-completions` if the user only wants generated code, not executed code
## Quick Routing
- **Remote execution with session reuse**
- Start with [scripts/execute_with_session.py](scripts/execute_with_session.py) or [scripts/execute_with_session.ts](scripts/execute_with_session.ts)
- **Response schema and session listing**
- Read [references/api-reference.md](references/api-reference.md)
- **MCP-style access for agent workflows**
- Read [references/api-reference.md](references/api-reference.md)
## Workflow
1. Decide whether the task needs code execution or only code generation.
2. Start a session with `client.code_interpreter.execute()`.
3. Reuse `session_id` when the workflow depends on prior state.
4. Inspect `stdout`, `stderr`, structured outputs, and display outputs separately.
5. 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_id` as part of the workflow state.
- Inspect `response.errors` before assuming a run succeeded.
- `plt.show()` with the Agg backend does not reliably produce `display_data` outputs. To retrieve charts, save the figure to a `BytesIO` buffer with `fig.savefig()`, base64-encode it, and print the encoded string to stdout. Parse it from the `stdout` output on the client side. See the chart example in [scripts/execute_with_session.py](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](references/api-reference.md)
- **Alternative access patterns**: [references/api-reference.md](references/api-reference.md)
- **Python workflow**: [scripts/execute_with_session.py](scripts/execute_with_session.py)
- **TypeScript workflow**: [scripts/execute_with_session.ts](scripts/execute_with_session.ts)
## Official Docs
- [Together Sandboxes](https://docs.together.ai/docs/together-code-interpreter)
- [Sandboxes API](https://docs.together.ai/reference/tci-execute)