Added Antropic AI Model Support - #188
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- Introduced tests for prompt caching, token counting, server tools, and batch processing in `test_anthropic_features.py`. - Implemented provider detection, client construction, and request assembly tests in `test_anthropic_provider.py`. - Created tests for pure Anthropic request translation in `test_anthropic_request.py`. - Enhanced LLM call tests to include Anthropic model dispatching and parameter handling in `test_call_llm.py`.
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This pull request introduces native Anthropic Claude support to the codebase, making Claude models first-class citizens alongside OpenAI and Google providers. It updates documentation, installation instructions, and the core agent implementation to support Anthropic's APIs, including advanced features like server tools, prompt caching, and token counting. The changes also clarify provider selection, add new extras for Anthropic backends, and correct prior documentation that implied but did not provide native Claude support.
Major Anthropic Claude Integration
agentflow/core/graph/agent_internal/anthropic.py, including request assembly, parameter mapping, and token counting.Documentation and Installation Updates
README.md,CLAUDE.md,.github/copilot-instructions.md,CONTRIBUTING.md) to reflect native Anthropic support, new extras (anthropic,anthropic-vertex,anthropic-bedrock,all), and correct prior misleading statements about Claude support. [1] [2] [3] [4] [5] [6] [7]Provider Detection and Selection Improvements
anthropic/andclaude/model prefixes, and clarified the use of theanthropic_backendselector for different Claude backends. [1] [2]Bug Fixes and Breaking Changes
use_vertex_ai=Trueincorrectly routed Claude models to Google GenAI, and where model prefixes were not stripped before sending requests.anthropic/andclaude/prefixes now select the native Anthropic provider by default; explicit provider override is required to use OpenAI-compatible endpoints.Extras and Development Workflow
These changes make Anthropic Claude models fully supported and configurable, improve the clarity and accuracy of documentation, and ensure provider selection is robust and explicit.