Does GLM-5 support function calling or tool use?

Yes, GLM-5 supports function calling / tool use, meaning you can define tools (functions) with a JSON schema and allow the model to request tool invocations in a structured way. This is the cleanest way to build “agent” workflows where the model needs external information (docs retrieval, database lookup, running tests) instead of guessing. In a typical setup, your app provides a list of tools (name, description, JSON parameter schema). When GLM-5 decides a tool is needed, it returns a tool call (function name + arguments), your app executes it, then you send the tool result back to the model to produce the final answer. The official reference for this is the Z.ai documentation on Function Calling and the GLM-5 migration notes describing streaming tool-call parameter output with tool_stream=true: Migrate to GLM-5.

To make tool use reliable, treat tools like an API contract, not an “optional feature.” Keep tools small, deterministic, and well-scoped. For example:

• search_docs(query, top_k, filters) → returns chunk IDs + text + URLs

• get_file(path) → returns file contents

• run_tests(command) → returns exit code + logs

• create_patch(diff) → applies diff in a sandbox and returns results

Then add guardrails: validate tool arguments, cap tool outputs, and log every tool call (inputs/outputs). If a tool can do something risky (write files, run shell commands), enforce allowlists and require human approval for sensitive actions. This makes GLM-5 tool use auditable, which matters for production debugging (“why did it do that?”) and for safety (“what did it run?”). If you want framework-level support, Z.ai also documents integrations such as LangChain: LangChain integration.

Tool calling pairs extremely well with vector search. A very standard pattern is: implement search_docs on top of a vector database such as Milvus or Zilliz Cloud. Your tool runs vector similarity search with metadata filters (product/version/lang), returns top-k chunks, and then GLM-5 writes an answer using only those chunks. This avoids the most common failure mode in LLM apps: the model inventing details that weren’t in your docs. It also makes your system measurable: you can track retrieval hit rate, similarity score distributions, and how often answers are supported by retrieved text. In practice, “GLM-5 + tools + Milvus/Zilliz Cloud retrieval” is the most dependable recipe for documentation assistants and developer-facing FAQ bots.