Yes, DeepSeek-V3.2 supports multi-step tool use through its OpenAI-compatible function calling interface and its reasoning-oriented deepseek-reasoner endpoint. The official docs describe function calling for deepseek-chat using the same JSON-based tool schema you may know from other ecosystems: you declare tools with names, arguments, and types, and the model returns tool_calls that your code executes. DeepSeek’s earlier V3.1 release notes also highlighted specific post-training on tool use and multi-step agent tasks, and public commentary around V3.2 mentions that these capabilities are now backed by the V3.2-Exp backbone, with better long-context handling and more stable reasoning.
Reliability for multi-step tool use comes from three pieces working together. First, the model has a 128K context window, so it can keep the history of several tool calls, intermediate results, and user confirmations in memory without truncating earlier steps. Second, the deepseek-reasoner endpoint produces an internal chain-of-thought (reasoning_content) that can plan tool usage over several steps, not just call one tool and stop. The streaming example in the docs shows how the model first reasons, then outputs the final answer, and the same pattern works when you inject tool results between turns. Third, DeepSeek and third-party tutorials document agent patterns where you wrap DeepSeek in a controller that loops until the model indicates it is done or a maximum number of tool steps is reached, which provides an outer safety net around the model.
In practice, a solid pattern is to use deepseek-chat for cheap, single-step tool calls and reserve deepseek-reasoner for flows where planning matters, such as research agents, data pipelines, or complex business workflows. If your agent is also doing retrieval, you can treat calls into a vector database such as Milvus or Zilliz Cloud as just another tool: the model decides when to search, your backend queries the vector store, and the results are appended as tool outputs. The Milvus and Zilliz tutorials that combine DeepSeek with vector search show this pattern: DeepSeek orchestrates the reasoning and tool sequencing, while the database provides fast, semantically relevant memory across steps.