Are there any agent frameworks that handle tool calling reliably for conversational search?

Yes — but “reliably” usually depends more on the model + tool contract + orchestration pattern than on the framework alone.

Frameworks commonly used for conversational search/tool calling

• LangChain / LangGraph
  Good ecosystem, lots of integrations, and LangGraph is especially useful if you want more reliable, stateful, multi-step tool use.
• LlamaIndex
  Strong for retrieval + query routing + RAG-style conversational search. Often simpler if your main problem is search over docs/data.
• Microsoft Semantic Kernel
  Nice for structured tool/plugin calling and enterprise-style orchestration.
• OpenAI Responses/Assistants-style tool calling
  Not a framework exactly, but often the most straightforward if you’re using OpenAI models and want native tool calling behavior.
• Haystack
  Solid for search/RAG pipelines, especially if you want a more traditional information retrieval setup.

What tends to make tool calling reliable

• Use strict tool schemas with clear inputs/outputs.
• Keep tools small and single-purpose.
• Add a router/orchestrator layer that decides when to search vs answer directly.
• Validate tool outputs and retry on malformed calls.
• Use a model with strong function/tool calling behavior.
• For conversational search, prefer RAG + tools over free-form agent loops when possible.

Practical recommendation

If your use case is conversational search over documents/data:

• Start with LlamaIndex or LangGraph
• Add retrieval tools, citation handling, and a simple state machine
• Avoid overly autonomous “agentic” loops unless you truly need multi-step reasoning

If you tell me your stack (Python/JS, OpenAI/Anthropic/open-source, docs vs web vs databases), I can recommend the best option more specifically.