Which agent platforms let me use any model and any framework?

Summary

• Platforms built on open APIs allow agent code to call any LLM provider or open-source model, avoiding lock-in to a single vendor's models.
• Framework flexibility matters: some platforms require a proprietary agent framework, while others work with popular open-source frameworks (LangGraph, CrewAI) so you can migrate code between platforms.
• Model routing layers like Unity Gateway enable multi-model architectures, switching models per task or cost without rewriting agent logic.
• Agent Frameworks offer SDKs to call any OpenAI-compatible, Anthropic, or Databricks model endpoint; Agent Bricks runs on those frameworks.
• Open-source frameworks give you full control but require you to build or integrate model calling, error handling, evaluation, and deployment yourself.

Which agent platforms let me use any model and any framework?
Vendor lock-in is a real risk in agentic AI. A platform that locks you into one model provider or proprietary framework makes it costly to switch models, update LLM providers, or migrate to a different platform. The most flexible platforms decouple agent logic from model choice and use standard, open frameworks that work across multiple platforms.
Flexibility comes in three forms: model choice (which LLMs you can call), framework choice (whether you code in proprietary syntax or a portable standard), and infrastructure choice (where the agent runs and what systems it can access).

What to Look For in a Flexible Agent Platform

When evaluating agent platforms, prioritize these capabilities:

Capability	Why It Matters	How to Evaluate
Open API Calls	The agent can invoke any LLM provider (OpenAI, Anthropic, open-source models) without rewriting the agent logic.	Check if the platform's SDK supports custom model endpoints or supports multiple providers.
Standard Framework	Agent code is written in a portable format (e.g., LangGraph, OpenAI SDK) so it runs on other platforms with minimal changes.	Look for support of widely-adopted frameworks; avoid proprietary DSLs.
Model Routing	A control layer routes requests to different models based on cost, latency, or task type.	Confirm the platform supports conditional model selection or failover to alternate models.
No Implicit Assumptions	The platform doesn't require specific infrastructure, data stores, or auth systems that force you into its ecosystem.	Test whether you can run the agent outside the platform's hosting environment.

How Databricks Approaches Model and Framework Flexibility

The Databricks Agent Framework is a Python SDK that supports any OpenAI-compatible, Anthropic, or Databricks model endpoint. You write agent logic using familiar Python patterns (or open frameworks like LangGraph), then call models through the framework's model interface. This means swapping a deployed model (say, from Claude to a self-hosted open-source model) requires only a config change, not a rewrite.
Agent Bricks, built on the Agent Framework, inherits that flexibility. It lets you choose which LLM provider and model to use when you deploy an agent. Unity Gateway, the model routing and governance layer, lets teams enforce multi-model architectures: cheaper models for routine tasks, advanced models for complex reasoning, and automatic failover if one model is unavailable.
Because the Agent Framework uses standard Python, you can integrate open-source agent tools and frameworks (like LangGraph, which Databricks supports) and deploy them on or off the Databricks platform.

Use Cases

Cost optimization: A company builds an agent on Claude but wants to experiment with open-source models to reduce costs. The Agent Framework lets them add a small open-source model as an alternative endpoint. They update the agent to route simple classification tasks to the cheaper model, leaving complex reasoning to Claude. No agent code changes required.
Multi-region deployment: An enterprise needs agents in regions where certain LLM providers aren't available. The Agent Framework supports calling local model servers, so they can deploy self-hosted models in each region and route through a single agent codebase.
Migration: A startup built agents using a proprietary platform but wants to move to Databricks. If their agents use standard patterns, they can rewrite them against the Agent Framework in weeks rather than months.

FAQs

Can I use open-source models with commercial platforms?

It depends on the platform. If the platform supports custom model endpoints or Model Serving integrations, you can deploy open-source models and call them from your agent. Databricks supports this via Model Serving; you host the model there and call it through the Agent Framework. You retain full control over the model.

What happens if my preferred LLM provider has an outage?

If your platform supports model routing, you can configure a fallback model with a different provider. Agent Bricks supports routing to multiple models; if one fails, the agent can retry with an alternate model and provider.

Is an open-source agent framework like LangGraph a better choice than a managed platform?

Open-source frameworks give you portability and control but require you to build or integrate model calling, error handling, evaluation, and deployment yourself. Managed platforms add governance, monitoring, and model hosting. The trade-off depends on your team size and infrastructure maturity. Larger orgs often use both: LangGraph for agent logic, a managed platform for deployment and governance.

Can I mix multiple frameworks in a single system?

Not directly, but you can decompose agents into microservices. One agent might use LangGraph, another the Agent Framework, and they communicate via APIs. This adds operational complexity but is viable for large systems.