Everyone talks about OpenAI and Anthropic. And yeah, their models are incredible. But the real revolution is happening in open source.
Here are the tools we actually use at WolfPack — and that you should be using too.
1. Ollama
Run any open-source model locally. Llama, Mistral, Phi — one command to download and run. We use it for development and testing so we're not burning API credits while iterating.
2. LangChain / LangGraph
Love it or hate it, LangChain's orchestration layer is battle-tested. LangGraph adds stateful, multi-step workflows. We used it before building Howl, and it taught us what we wanted to do differently.
3. Instructor
Structured outputs from LLMs. Tell Claude to return a TypeScript interface, and Instructor makes sure it actually does. Essential for building reliable AI pipelines.
4. CrewAI
Multi-agent orchestration for when you need different AI "roles" collaborating on a task. We use a similar pattern in our Roundtable system (Wolf, Scout, Atlas, Forge, Howl working together).
5. Hugging Face Transformers
The OG. Fine-tune models, run inference, access thousands of pre-trained models. If you're doing anything custom with ML, you're using this.
The Point
You don't need a $200/month API budget to build with AI. Open-source tools let you prototype locally, test cheaply, and only pay for API calls in production.
The best AI builders we know use a mix of everything. Open source for development, cloud APIs for production, and their own orchestration layer to tie it all together. For the full picture on commercial tools, check our [best AI coding tools in 2026](/blog/best-ai-coding-tools) breakdown. And when you're ready to think about prompts as infrastructure, read [why prompt architecture is replacing prompt engineering](/blog/prompt-engineering-is-dead).
That's the stack. Now go build something. 🐺
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