# Together Cookbook: 30-odd Jupyter notebooks that assume you already have an API key

> The cookbook is a directory of notebooks rather than a library, so there is nothing to install and nothing to pin. That makes it fast to copy from and easy to copy carelessly.

**togethercomputer/together-cookbook** — A collection of notebooks/recipes showcasing usecases of open-source models with Together AI.

- Repository: https://github.com/togethercomputer/together-cookbook
- Stars: 1,190 · Forks: 222
- Language: Jupyter Notebook
- License: MIT
- Published: 2026-10-07 · Updated: 2026-10-07 · Language: en
- Canonical page: https://hysenlabs.com/projects/togethercomputer-together-cookbook

## A repository of notebooks, with no package to install

GitHub reports the primary language of togethercomputer/together-cookbook as Jupyter Notebook, and that single fact tells you what kind of artifact this is. There is no `setup.py`, no `package.json`, no build step and no library you import. What sits at the top level is a set of notebooks and a handful of directories:

```text
Agents/
Evals/
Finetuning/
Multimodal/
datasets/
```

The README states the intent plainly: the best way to use the recipes is to copy code snippets and integrate them into your own projects. That is a design decision with real consequences. You get working code immediately with no library churn, and in exchange you inherit whatever assumptions each notebook was written under, because nothing in the repository enforces one shared set of package versions across all of them.

## The Agents directory holds most of the useful patterns

The Agents table is the longest part of the README and the most legible, because each row pairs a pattern name with a one-line description and a Colab badge. The primitives are worth naming, since they are the vocabulary the other recipes borrow from.

Serial Chain Agent Workflow chains multiple LLM calls sequentially to process complex tasks. Conditional Router Agent Workflow creates an agent that routes tasks to specialized models. Parallel Agent Workflow runs multiple LLMs in parallel and aggregates their solutions. Orchestrator Subtask Agent Workflow breaks a task into parallel subtasks that LLMs then execute. Looping Agent Workflow builds an agent that iteratively improves its own responses.

Above those sit larger assemblies. Together Open Deep Research is described as an open source deep-research implementation with multi-step web search. Together Open Data Science Agent analyzes datasets end to end. Cross-Provider Advisor Agent Workflow puts a cheap Together executor in front and consults a frontier Claude advisor only when the cheap one gets stuck, which is the closest thing here to a cost-control strategy.

The directory also points outward a lot. Agno, Arcade.dev, Composio, DSPy, Klavis AI and LangGraph each get their own notebook, so a good share of the Agents directory is really a demonstration of someone else's framework running against Together models.

## The only documented setup step is an API key

The prerequisites section is short: to get the most out of the examples you need a Together AI API key, and you can sign up for free on the platform. After that, the README notes that the examples are primarily written in Python and JavaScript, and that the concepts can be adapted to any language that talks to the Together API.

There is no install command anywhere, which is not an oversight so much as a consequence of the notebook format. Each recipe carries its own cell-level setup, and the Colab badge on every row means you can open it and run without preparing a machine first. If you copy cells into your own repository instead, the dependency work transfers to you, and the README gives no lockfile to copy from.

What the prerequisites section does not say is anything about cost. Every notebook here makes API calls, some of them in loops, and a looping agent workflow is a good example of something that looks cheap per call and adds up quickly.

## Fine-tuning and evaluation notebooks sit beside the tutorials

Finetuning is its own directory, and the top level shows the specific jobs the cookbook covers: `LoRA_Finetuning&Inference.ipynb`, `LongContext_Finetuning_RepetitionTask.ipynb`, `Multiturn_Conversation_Finetuning.ipynb` and `Summarization_LongContext_Finetuning.ipynb`. Several are narrow tasks rather than general demos, which suggests the collection grew out of real problems someone needed solved rather than a curriculum.

Evaluation gets similar treatment. `Batch_Inference_Evals.ipynb` and `Summarization_Evaluation.ipynb` sit at the top level, with an `Evals/` directory for more. There is also `Flux_LoRA_Inference.ipynb`, which implies LoRA work extends beyond text.

Several notebooks target specific retrieval shapes: `Text_RAG.ipynb`, `Open_Contextual_RAG.ipynb`, `RAG_with_Reasoning_Models.ipynb`, `Semantic_Search.ipynb` and `Search_with_Reranking.ipynb`. That is a useful spread, because the interesting engineering decisions in RAG live in the gaps between those five, and having all of them written against one provider makes comparison cheaper.

## Multimodal, document and agentic work extends past text

Beyond RAG and fine-tuning, the tree shows work on documents and media. `MultiModal_RAG_with_Nvidia_Investor_Slide_Deck.ipynb` and `Multimodal_Search_and_Conditional_Image_Generation.ipynb` treat slide decks as a retrieval problem. `Structured_Text_Extraction_from_Images.ipynb` targets extraction, `PDF_to_Podcast.ipynb` turns documents into audio, and `Thinking_Augmented_Generation.ipynb` works on the reasoning side.

There are also two directories that look like experiments rather than tutorials. `OpenEnv_Code_Interpreter/` and `OpenEnv_GRPO_BlackJack/` suggest reinforcement-learning-flavored work, the second using a card game as a testbed. `contextual_rag_on_union/` is named after a specific corpus, which reads like a case study that grew into a directory.

The repository publishes no releases. So there is no version marker anywhere, and the README's cookbook table itself ends mid-row on its last visible entry. The full set of recipes is only knowable by opening the directory, which is a real limitation for anyone trying to judge whether this project is current.

## Contribution runs through Discord or a pull request

The contributing section is two sentences long. If you have a cookbook to add, reach out through the project's Discord or open a pull request. That is a low bar, and for a collection whose whole value is volume of examples it is the right bar.

The gap is review criteria. Nothing in the repository describes what a recipe has to satisfy to be accepted: no requirement that a notebook pin its model, no template, no rule about cost, no statement that a recipe must still run. Open issues sit at 4, and the last push was 2026-09-16, so the collection is being touched. Whether entries are being retired as models change is a different question, and the repository does not answer it.

The MIT license file is present at the top level, which is the one piece of governance here that is unambiguous. You can copy a cell into a commercial project without a licensing conversation, which is a meaningfully different position from most curated example sets.

## Conclusion

The Together Cookbook is a menu rather than a framework, and it is honest about that. It gives you working cells to read and copy, it points every recipe at a Colab badge, and it never asks you to install anything. That design makes it a good fit for someone deciding whether an agent pattern, a RAG shape or a fine-tuning recipe is worth building, and a poor fit for a team that needs one pinned, tested environment to reproduce in CI. The documentation does not settle what you actually pay per run, how the notebooks handle model deprecation, or which recipes are still current, since the repository publishes no releases to date them against. Read the top level of the repo, pick the one notebook that matches the pattern you want, and price a single run of it before you commit to a project.

## FAQ

### How do I install the Together Cookbook?

You do not install it. The repository has no package manifest and is a collection of Jupyter notebooks, each linked to a Colab badge. The only stated prerequisite is a Together AI API key, which you can create free on the platform.

### Which agent patterns does the Together Cookbook cover?

The Agents table lists serial chaining, conditional routing, parallel execution with aggregation, orchestrator subtask breakdown, looping self-improvement, and a cross-provider advisor that escalates from a cheap executor to a frontier Claude model when it gets stuck.

### Can I use these notebooks with a provider other than Together?

The README says the code examples are primarily Python and JavaScript, and that the concepts can be adapted to any programming language that supports interaction with the Together API. The translation work to another provider is left to you.

### What license is the Together Cookbook released under?

MIT. A LICENSE file sits at the top level of the repository, so you can copy cells into your own commercial projects without a licensing negotiation.

## Sources

- [Issues](https://github.com/togethercomputer/together-cookbook/issues)
- [License: MIT](https://github.com/togethercomputer/together-cookbook/blob/main/LICENSE)
- [README](https://github.com/togethercomputer/together-cookbook/blob/main/README.md)
- [togethercomputer/together-cookbook on GitHub](https://github.com/togethercomputer/together-cookbook)

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Hysen Labs editorial analysis, written from the project's own repository and release notes. Cite the canonical page: https://hysenlabs.com/projects/togethercomputer-together-cookbook