{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/5"}],"enrichment":{"capability":"Evaluates and benchmarks large language models' ability to invoke functions correctly across diverse scenarios, from simple calls to multi-turn agentic interactions with web search and memory management.","skillfed_tags":["llm-evaluation","function-calling","benchmarking"],"use_cases":["Benchmark a new LLM variant's function-calling accuracy against the Berkeley leaderboard test suite","Evaluate locally-hosted open-source models using vllm or sglang backends to compare with proprietary APIs","Test multi-turn agentic workflows where models must chain function calls with web search and memory","Generate and evaluate function-calling responses for a custom subset of test cases using --run-ids","Integrate function-calling evaluation into a CI/CD pipeline to track model performance regressions"],"what_it_does":"BFCL is a comprehensive evaluation framework for assessing how well large language models can invoke functions correctly. It goes beyond simple function-calling tests to include parallel calls, multi-turn interactions, agentic scenarios with web search, and memory management. The package provides both a command-line interface and programmatic API to generate model responses and evaluate their correctness against executable test cases.\n\nThe framework integrates with major LLM providers (OpenAI, Anthropic, Mistral, Cohere, Google, Qwen) and supports locally-hosted models via vllm or sglang backends. It uses tree-sitter for code parsing, faiss-cpu for semantic retrieval, and sentence-transformers for embeddings. Results are stored in configurable directories and can be logged to Weights & Biases. The package requires substantial setup\u2014environment variables, API keys, and a project root directory\u2014but is designed for researchers and practitioners benchmarking model capabilities at scale.","worth_installing":"Yes, if you are benchmarking or comparing LLM function-calling capabilities. The package is actively maintained, well-integrated with major LLM providers, and covers realistic multi-turn and agentic scenarios beyond simple function calls. Install friction is low and no security vulnerabilities are known. Caveat: setup requires careful environment configuration (BFCL_PROJECT_ROOT, .env, API keys) and the 32 dependencies are substantial; use only if you actually need comprehensive function-calling evaluation."},"id":"bfcl-eval","links":{"html":"https://skillfed.io/packages/bfcl-eval","md":"https://skillfed.io/packages/bfcl-eval.md","pypi":"https://pypi.org/project/bfcl-eval/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-03-23","license_spdx":null,"license_treatment":"permissive","name":"bfcl-eval","python_support":"supports_current","summary":"Berkeley Function Calling Leaderboard (BFCL)"},"popularity":{"monthly_downloads":580493,"position":5911,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2026.3.23"}