Inference
Programmatic Tool Calling
A Claude API feature where the model writes Python code that calls your tools as functions inside a code execution container, instead of making one model round trip per tool call.
In depth
A Claude API feature where the model writes Python code that calls your tools as functions inside a code execution container, instead of making one model round trip per tool call. When the code calls a tool, execution pauses, your app returns the result, and the code continues; only the final output enters Claude's context, not the intermediate results. You opt a tool in by adding code execution to its allowed_callers field. It reduces latency and token use in workflows that loop over many calls or filter large results.
Primary source: platform.claude.com
Example
In practice, developers reach for Programmatic Tool Calling when they need the capability described above as part of an AI feature or workflow.
Go deeper at Developers Digest
Hands-on guides, comparisons, and tutorials that cover Inference.
FAQ
Common questions
What is Programmatic Tool Calling?
A Claude API feature where the model writes Python code that calls your tools as functions inside a code execution container, instead of making one model round trip per tool call.
Why does Programmatic Tool Calling matter for AI developers?
Programmatic Tool Calling sits in the Inference part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about Programmatic Tool Calling?
Developers Digest publishes tutorials and videos that cover Inference topics including Programmatic Tool Calling. Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
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Pretraining
The first and most expensive stage of building a language model, where it learns to predict the next token across a huge unlabeled corpus of web text, books, and code.
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Input vs Output Token Pricing
The split pricing model where LLM APIs charge one rate per million tokens you send (input) and a higher rate per million tokens the model generates (output).
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Speculative Decoding
An inference technique where a small, fast draft model proposes several tokens ahead and the large target model checks them all in one parallel forward pass.
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