Coding agent setup

Together AI publishes two complementary tools for coding agents:

Skills: 12 domain-specific skills that load on demand and teach your agent how to write correct Together AI code (right model IDs, SDK patterns, best practices).
Docs MCP server: Gives your agent live access to this documentation site so it can look up current information without leaving your editor.

Install both for the best experience: skills for code generation and MCP for documentation lookup.

Skills

When your agent detects a relevant task, it automatically loads the right skill. You can also call a skill explicitly with /<skill_name>.

Install skills

npx skills add togethercomputer/skills

# From the plugin marketplace
/plugin marketplace add togethercomputer/skills

# Or install a single skill
/plugin install together-chat-completions@togethercomputer/skills

# Or copy manually (project-level)
cp -r skills/together-* your-project/.claude/skills/

# Or copy manually (global, available in all projects)
cp -r skills/together-* ~/.claude/skills/

# Install via the Cursor plugin flow using the
# .cursor-plugin/ manifests in the repository:
# https://github.com/togethercomputer/skills

cp -r skills/together-* your-project/.agents/skills/

gemini extensions install https://github.com/togethercomputer/skills.git --consent

To verify the install, you should see one SKILL.md per installed skill (for example, ls your-project/.claude/skills/together-*/SKILL.md).

Available skills

Skill	What it covers
together-chat-completions	Serverless chat inference, streaming, multi-turn conversations, function calling (6 patterns), structured JSON outputs, and reasoning models.
together-images	Text-to-image generation, image editing with Kontext, FLUX model selection, LoRA-based styling, and reference-image guidance.
together-video	Text-to-video and image-to-video generation, keyframe control, model and dimension selection, and async job polling.
together-audio	Text-to-speech (REST, streaming, realtime WebSocket) and speech-to-text (transcription, translation, diarization, timestamps).
together-embeddings	Dense vector generation, semantic search, RAG pipelines, and reranking with dedicated model inference.
together-fine-tuning	LoRA, full, DPO preference, VLM, function-calling, and reasoning fine-tuning, plus BYOM uploads.
together-batch-inference	Async batch jobs with JSONL input, polling, result downloads, and up to 50% cost savings.
together-evaluations	LLM-as-a-judge workflows: classify, score, and compare evaluations with external provider support.
together-sandboxes	Remote sandboxed Python execution with session reuse, file uploads, and chart outputs.
together-dedicated-model-inference	Endpoints, deployments, and configs on dedicated GPUs, with autoscaling, traffic splitting, A/B tests, shadow experiments, and custom model or LoRA uploads.
together-dedicated-containers	Custom Dockerized inference workers using the Jig CLI, Sprocket SDK, and queue API.
together-gpu-clusters	On-demand and reserved GPU clusters (H100, H200, B200) with Kubernetes, Slurm, and shared storage.

Use a single skill

Each skill works on its own for focused tasks. Describe what you want and the right skill activates, or invoke a specific skill with /<skill_name>. If you prompt your agent with:

Build a multi-turn chatbot using Together AI with Kimi-K2.5
that can call a weather API and return structured JSON.

The agent uses together-chat-completions to generate correct SDK code with the right model ID, streaming setup, tool definitions, and the complete tool-call loop.

Chain skills together

Skills define hand-off boundaries between products, so the agent can chain them together for tasks that span multiple Together AI services. If you prompt your agent with:

Embed my document corpus with Together AI, build a retrieval pipeline
with reranking, then evaluate the answer quality with an LLM judge.

The agent chains three skills:

together-embeddings: Generates dense vectors and builds a cosine-similarity retriever with reranking.
together-chat-completions: Generates answers from the retrieved context.
together-evaluations: Scores answer quality with an LLM judge and downloads the per-row results.

See the skills repository for more workflow examples.

SDK compatibility

All generated code targets the Together Python v2 SDK (together>=2.0.0) and the Together TypeScript SDK (together-ai). If you’re upgrading from v1, see the Python v2 SDK migration guide.

Docs MCP server

Model Context Protocol (MCP) lets AI coding agents call external tools and pull in external data. The Together AI docs MCP server gives your agent direct access to this documentation site without leaving your editor.

Install

The fastest install is the universal npx add-mcp shortcut, which detects your active client and configures the server in one step. The other tabs cover client-specific install commands and manual configuration.

npx add-mcp https://docs.together.ai/mcp

claude mcp add --transport http "TogetherAIDocs" https://docs.together.ai/mcp

For manual configuration, add this to your Cursor MCP settings:

{
  "mcpServers": {
    "together-docs": {
      "url": "https://docs.together.ai/mcp"
    }
  }
}

Install in VS CodeFor manual configuration, add this to your VS Code settings.json:

{
  "mcp": {
    "servers": {
      "together-docs": {
        "type": "http",
        "url": "https://docs.together.ai/mcp"
      }
    }
  }
}

See the Codex repository for details. To connect to the remote server, add this to your Codex configuration:

[mcp_servers.together_docs]
type = "http"
url = "https://docs.together.ai/mcp"

Add this to your OpenCode configuration file:

{
  "mcp": {
    "together_docs": {
      "type": "remote",
      "url": "https://docs.together.ai/mcp",
      "enabled": true
    }
  }
}

Prompt examples

Once installed, your agent can answer prompts like:

“Write a script to process data with batch inference.”
“Build a simple chat app with Together AI’s chat completions API.”
“Find the best open-source model for frontier coding.”
“How do I fine-tune a model on my own data?”

The MCP server provides tools to search and retrieve documentation content, so your agent gets accurate answers without leaving your coding environment.

Resources

Skills repository on GitHub: Source code, full reference docs, and runnable scripts for all 12 skills.
Together AI cookbook: End-to-end examples and tutorials.
Python v2 SDK migration guide: Breaking changes between the v1 and v2 SDKs.
Agent Skills specification: The open standard these skills follow.

GET STARTED

SERVERLESS

INFERENCE APIS

DEDICATED MODEL INFERENCE

DEDICATED CONTAINER INFERENCE

GPU CLUSTERS

FINE-TUNING

CODE EXECUTION

ADMINISTRATION

Skills

Install skills

Available skills

Use a single skill

Chain skills together

SDK compatibility

Docs MCP server

Install

Prompt examples

Resources

​Skills

​Install skills

​Available skills

​Use a single skill

​Chain skills together

​SDK compatibility

​Docs MCP server

​Install

​Prompt examples

​Resources

Skills

Install skills

Available skills

Use a single skill

Chain skills together

SDK compatibility

Docs MCP server

Install

Prompt examples

Resources