Crusoe is now a Pydantic AI model provider

Crusoe is now a native model provider in Pydantic AI, enabling developers to run agents on Crusoe Managed Inference with a single string like 'crusoe:zai/GLM-5.2'. The integration, contributed upstream to the pydantic-ai repository, provides streaming, tool calling, structured output, and per-family model profiles for open models including GLM, Llama, DeepSeek, Qwen, Gemma, gpt-oss, Kimi, and NVIDIA Nemotron 3. Emmanuel Acheampong, Senior Developer Relations Manager at Crusoe, and Laís Carvalho, Developer Relations at Pydantic, co-authored the announcement.

The following is a guest post from Crusoe https://crusoe.ai/ , written by Emmanuel Acheampong https://www.linkedin.com/in/emmanuel-acheampong/ , Senior Developer Relations Manager. Co-authored by Laís Carvalho https://www.linkedin.com/in/laisbsc/ , Developer Relations at Pydantic. Crusoe is now a native model provider in Pydantic AI https://pydantic.dev/docs/ai/overview/ . One string, 'crusoe:zai/GLM-5.2' , and your agents run on Crusoe Managed Inference: streaming, tool calling, structured output, and the full open model catalog on the Crusoe Intelligence Foundry, all out of the box. This post covers who we are, why we built the integration, and how to use it. Why we built this Crusoe is the cloud for AI, however you build it. Open models are a fast-growing part of that picture, and they deserve infrastructure built for them. Crusoe Managed Inference serves the open catalog end to end on the Crusoe Intelligence Foundry: GLM, Llama, DeepSeek, Qwen, Gemma, gpt-oss, Kimi, and the NVIDIA Nemotron™ 3 family, with day-zero support for new releases. Serving open weights is half of that work. The other half is meeting developers in the open source tools they already use. Crusoe is an upstream provider in LiteLLM and now a native Pydantic AI provider. Each integration follows the same principle: contribute the code upstream, keep it maintained, and let the framework's own conventions handle configuration. Nothing in this stack locks you in. The models are open weights, the frameworks are open source, and the provider described in this post lives in the pydantic-ai repository https://github.com/pydantic/pydantic-ai , not in a Crusoe SDK. The ecosystem gets stronger when open and closed keep pushing each other forward, and builders match each workload to the right model. That is the outcome we are investing in. The problem Plenty of teams were already running Pydantic AI agents against Crusoe. It worked, but it meant wiring up OpenAIProvider with a custom base url , managing the API key by hand, and losing model profile inference along the way. Model profiles matter more than they sound: they tell Pydantic AI how each model family handles JSON schemas, tool definitions, and output formats. Point a generic OpenAI provider at a GLM or gpt-oss model and you get OpenAI defaults, which are not always the right ones. A native provider removes all of that. The endpoint, the key handling, and the per-family profiles ship in the framework. How it works The integration adds a CrusoeProvider to pydantic-ai , following the same pattern as other OpenAI-compatible providers. Install Pydantic AI, or the slim package with the openai group: uv add logfire "pydantic-ai-slim openai " Generate a key in the Crusoe Cloud console https://console.crusoecloud.com/ under Intelligence Foundry, then set it: export CRUSOE API KEY="cr ..." That is the whole setup. The shorthand string does the rest: python import logfire from pydantic ai import Agent logfire.configure logfire.instrument pydantic ai agent = Agent 'crusoe:zai/GLM-5.2' result = agent.run sync 'In one sentence: why do open agent stacks matter in 2026?' print result.output Structured output works the way you would expect from Pydantic AI, because the provider infers the right profile for the model family: python import logfire from pydantic import BaseModel from pydantic ai import Agent logfire.configure logfire.instrument pydantic ai class GpuSpec BaseModel : name: str memory gb: int interconnect: str agent = Agent 'crusoe:zai/GLM-5.2', output type=GpuSpec result = agent.run sync 'Summarize the NVIDIA HGX™ B200 as a spec.' print result.output name='NVIDIA HGX B200' memory gb=1536 interconnect='5th-Gen NVLink 1.8 TB/s per GPU , PCIe Gen5' If you need explicit control, construct the provider yourself: python import logfire from pydantic ai import Agent from pydantic ai.models.openai import OpenAIChatModel from pydantic ai.providers.crusoe import CrusoeProvider logfire.configure logfire.instrument pydantic ai model = OpenAIChatModel 'meta-llama/Llama-3.3-70B-Instruct', provider=CrusoeProvider api key='your-api-key' , agent = Agent model result = agent.run sync 'Be concise. Defend the Oxford comma.' print result.output The provider also accepts a custom httpx.AsyncClient or a preconfigured AsyncOpenAI client, so it fits whatever connection pooling or proxy setup you already run. Under the hood, CrusoeProvider maps the supported model families to their correct Pydantic AI profiles: meta-llama , deepseek-ai , qwen , google Gemma , moonshotai Kimi , zai GLM , and openai gpt-oss, which uses the harmony profile . Tool schemas and JSON output behave correctly per family without any configuration on your side. Families without an explicit profile fall back to OpenAI-compatible defaults. Results and learnings What you get from the pairing is a short list with a lot behind it. Pydantic AI brings the agent framework: type-safe outputs, tools, streaming, and evals through Pydantic Evals, with tracing through Pydantic Logfire /logfire . Crusoe brings the inference layer built for agent workloads. MemoryAlloy, our cluster-wide KV cache fabric, routes requests cache-aware, which matters for agents that re-send system prompts and accumulated history on every turn. Cached input pricing means the loop stays cheap as contexts grow. The main learning from building the provider: the OpenAI-compatible pattern in Pydantic AI is well factored. The whole integration is one provider class, a profile map, and tests that mirror the existing Nebius provider. If you serve open models behind an OpenAI-compatible endpoint, contributing a provider is a weekend project, and the maintainers' review process makes the result better than what you started with. Try it Two steps: grab a key from the Intelligence Foundry https://console.crusoecloud.com/request-foundry , then uv add logfire "pydantic-ai-slim openai " and point an Agent at crusoe: plus any model in the catalog https://docs.crusoecloud.com/managed-inference/overview/ . Once that first agent runs: - The Pydantic AI documentation https://pydantic.dev/docs/ai/overview/ covers what comes after a single run sync : tools, streaming, dependency injection, and multi-agent flows. - Every example above calls logfire.configure . That is Pydantic Logfire https://pydantic.dev/logfire , and it turns each run into a trace you can open: model calls, tool calls, retries, and token costs, queryable with SQL. The Pydantic AI integration docs https://pydantic.dev/docs/logfire/integrations/llms/pydanticai/ cover the setup, and the free tier is enough to watch your first agents work. Pydantic Evals https://pydantic.dev/docs/ai/evals/evals/ is worth reaching for when you start swapping models in the catalog and need to know whether the swap made things better. If you build something interesting on this stack, we would love to hear about it. Reach the Crusoe developer community at devcommunity@crusoe.ai mailto:devcommunity@crusoe.ai , and follow Crusoe for Developers on LinkedIn https://www.linkedin.com/showcase/crusoedev/ and @crusoedev on X https://x.com/crusoedev for model launches, cookbook drops, and more walkthroughs. An agent framework with validation at its core, and inference built for agents underneath it. That is the stack we wanted to use ourselves, so we wired it in.