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Migration
The OpenAI Assistants API provides managed thread storage, file retrieval, and tool calling without infrastructure overhead. At scale and with specific requirements, those managed layers become constraints: thread storage costs compound, retrieval quality is opaque and unadjustable, and the architecture is tightly coupled to one provider.
We migrate Assistants API integrations to custom agent architectures, your own memory store, your retrieval pipeline, model-agnostic tool definitions, and multi-model routing if your use case benefits from it.
Tell us about your assistant.
Four Assistants API constraints that custom architecture resolves.
OpenAI charges for thread and file storage in the Assistants API. At low usage the cost is negligible. At production scale (hundreds of thousands of threads accumulating over months) thread storage becomes a material ongoing cost. A custom memory store (PostgreSQL, Redis, or a purpose-built vector store) costs orders of magnitude less for the same data volume.
The Assistants API's file_search tool uses OpenAI's retrieval implementation. You can't see what chunks are retrieved for a given query, can't adjust the retrieval strategy, and can't add reranking or hybrid search. When retrieval quality is the problem, the Assistants API gives you no levers to fix it.
An Assistants API integration is tightly coupled to OpenAI's infrastructure, API schema, and pricing. If OpenAI raises prices, changes the API, or experiences outages, your system has no fallback. A custom architecture can be built model-agnostic from the start, routing to different providers based on cost, capability, or availability.
Different tasks in your agent workflow benefit from different models. Simple classification and routing tasks don't need GPT-5. Complex reasoning tasks might benefit from o1 or Claude. The Assistants API ties you to a single model per assistant. A custom agent architecture routes each step to the appropriate model.
A custom agent with your own memory store, retrieval pipeline, and model-agnostic tool definitions.
Conversation history and context stored in a database you own and control: PostgreSQL for structured conversation data, a vector store for semantic retrieval, or Redis for session-scoped working memory. No thread storage fees, full schema control, and the ability to query conversation history directly.
If you're using file_search in the Assistants API, we replace it with a retrieval pipeline you control, chunking strategy, embedding model, reranking, and hybrid search options. Every retrieval decision is visible in code and adjustable.
Tool definitions that work across OpenAI, Anthropic, and Google model APIs, not locked to OpenAI's function calling schema. The same tools work regardless of which model is handling a given request.
Route different agent tasks to the model that makes sense for each, a cheaper model for classification, a more capable model for generation, different providers for different compliance requirements. The routing logic is explicit in code and adjustable without touching the tool or prompt definitions.
The migration makes sense when Assistants API constraints are genuinely limiting you. We'll tell you if they're not.
Teams who haven't hit Assistants API limits
If the Assistants API is working well for your use case and you haven't hit thread costs, retrieval quality limits, or vendor lock-in concerns, the migration isn't worth it yet. The custom architecture is better long-term, but the migration has a cost and the Assistants API is genuinely good for many use cases at moderate scale.
Teams who want multi-model routing without first stabilising their current agent
Multi-model routing adds complexity. If your current agent has reliability issues, adding routing across multiple providers makes those issues harder to diagnose. We migrate to custom architecture first, stabilise on one provider, then add multi-model routing as a distinct step.