
Skill
together-sandboxes
execute Python code in Together AI 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.
SKILL.md
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-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
More skills from the skills repository
View all 14 skillstogether-audio
process audio with Together AI
Jul 26AudioSpeechText-to-SpeechTranscriptiontogether-batch-inference
run asynchronous batch inference jobs
Jul 17AutomationLLMPerformancetogether-chat-completions
generate text with Together AI
Jul 26AIAPI DevelopmentLLMtogether-dedicated-containers
deploy custom inference containers on Together AI
Jul 26AI InfrastructureDeploymentDockerMachine Learningtogether-dedicated-model-inference
deploy and operate Together AI models
Jul 26AI InfrastructureDeploymentLLMtogether-embeddings
generate embeddings and build RAG pipelines
Jul 26AILLMRAGSearch
More from Together AI
View publisherblog-post
write structured blog posts
together-cookbook
Jul 17Content CreationMarketingSEOWritingsocial-media
create social media content
together-cookbook
Jul 17Content CreationMarketingSocial MediaWritingtogether-evaluations
evaluate LLM outputs with Together AI
skills
Jul 26BenchmarkingEvalsLLMtogether-fine-tuning
fine-tune and adapt models on Together AI
skills
Jul 26AILLMMachine Learningtogether-gpu-clusters
orchestrate GPU clusters on Together AI
skills
Jul 26AI InfrastructureCloudKubernetesMachine Learningtogether-images
generate and edit images with Together AI
skills
Jul 17CreativeDesignImage GenerationMultimodal