llama-index-llms-anthropic
llama-index llms anthropic integration
Decision gist · record as of 2026-08-14
Yes. The package has low install friction, active maintenance, permissive MIT licensing, no known vulnerabilities, and integrates cleanly into LlamaIndex workflows. Install it if you are building LlamaIndex applications and want to use Claude models; skip it if you are using Anthropic's SDK directly without LlamaIndex.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires a valid Anthropic API key set in the ANTHROPIC_API_KEY environment variable or passed directly to the Anthropic constructor.
- Low friction install with only two runtime dependencies (anthropic and llama-index-core).
- Package is actively maintained with a recent release.
License · maintenance · safety
MIT (permissive) — MIT license permits unrestricted use, modification, and distribution with minimal restrictions.
last release 2026-08-04 (10 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 418,469 downloads/mo, #6,803 on PyPI
Alternatives
Verify before relying
pip install llama-index-llms-anthropic
import os
from llama_index.llms.anthropic import Anthropic
os.environ["ANTHROPIC_API_KEY"] = "your-key"
llm = Anthropic(model="claude-3-opus-20240229")
resp = llm.complete("Paul Graham is ")
print(resp)- Whether the 180k token limit mentioned for Claude 3 in the description applies to all Claude models or only specific versions.
- Current status of Claude 3 tokenizer updates since the description notes it has not been updated yet.
- Specific performance characteristics or latency expectations when used with LlamaIndex.
What it is and what it does
This package provides a LlamaIndex integration for Anthropic's Claude language models, allowing developers to use Claude within the LlamaIndex framework for text generation tasks. It wraps the anthropic client library and exposes Claude through LlamaIndex's standard LLM interface, supporting both synchronous and asynchronous operations.
The integration handles model configuration, API key management, and token counting specific to Claude's tokenizer. It supports multiple interaction patterns: simple text completion, multi-turn chat with message history, streaming responses for real-time output, structured prediction to extract typed objects from model responses, and tool use including web search capabilities. Developers can also route requests through Vertex AI if preferred.
Use it for
- Build a LlamaIndex RAG pipeline that uses Claude for retrieval-augmented generation over custom documents.
- Create a chatbot with multi-turn conversation history using Claude through LlamaIndex's chat interface.
- Extract structured data from unstructured text by using Claude's structured prediction to generate typed Pydantic models.
- Stream Claude responses in real-time to a user interface while processing long-form text generation.
- Integrate web search capabilities into Claude completions for fact-checking or current-information queries.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
The package has low install friction, active maintenance, permissive MIT licensing, no known vulnerabilities, and integrates cleanly into LlamaIndex workflows. Install it if you are building LlamaIndex applications and want to use Claude models; skip it if you are using Anthropic's SDK directly without LlamaIndex.
Install
llama-index-llms-anthropic on PyPI
Before you install
Low friction install with only two runtime dependencies (anthropic and llama-index-core). Package is actively maintained with a recent release.
Requires a valid Anthropic API key set in the ANTHROPIC_API_KEY environment variable or passed directly to the Anthropic constructor.
License in practice
MIT license permits unrestricted use, modification, and distribution with minimal restrictions.
Quickstart
pip install llama-index-llms-anthropic
import os
from llama_index.llms.anthropic import Anthropic
os.environ["ANTHROPIC_API_KEY"] = "your-key"
llm = Anthropic(model="claude-3-opus-20240229")
resp = llm.complete("Paul Graham is ")
print(resp)Verify before relying
- Whether the 180k token limit mentioned for Claude 3 in the description applies to all Claude models or only specific versions.
- Current status of Claude 3 tokenizer updates since the description notes it has not been updated yet.
- Specific performance characteristics or latency expectations when used with LlamaIndex.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release <4.0,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagesanthropicllama-index-core |
| Maintenance | Actively maintained 10 days since the last release |
| First released | |
| Downloads | 418,469 / month, #6,803 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: llama_index_llms_anthropic-0.11.10-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “anthropic llm llamaindex”
- llama-index-llms-anthropicIntegrates Anthropic's Claude language models into LlamaIndex…
- traceloop-sdkTraceloop SDK instruments LLM applications to capture execution…
- openlitOpenLIT provides OpenTelemetry-native auto-instrumentation for…
Give your agent the search over MCP, or paste the wish link into any chat.
More Artificial Intelligence packages
LiteLLM provides a unified Python interface to call 100+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and others) using OpenAI-compatible API format, available as both a Python SDK and a self-hosted AI Gateway proxy server.
Install it if you need to work with multiple LLM providers or want to centralize LLM routing in your organization.
Client library and CLI tool for downloading, uploading, and managing models, datasets, and repositories on the Hugging Face Hub platform.
Install it if you work with Hugging Face Hub models or datasets.
LangChain provides a framework for building agents and LLM-powered applications by composing language models, tools, and memory through a unified API that abstracts over multiple model providers.
hf-xet provides chunk-based deduplication and efficient file transfer for the Hugging Face Hub, enabling faster uploads and downloads of large files with local disk caching.
Tokenizers converts raw text into token sequences for NLP models, with support for training custom vocabularies and using pre-built tokenizers (BPE, WordPiece) optimized for speed via Rust.
Transformers provides a unified framework for loading, fine-tuning, and running state-of-the-art pretrained models across text, vision, audio, video, and multimodal tasks using PyTorch, JAX, or TensorFlow.
Install it if you need to run or train any transformer-based model for NLP, vision, audio, or multimodal tasks.
See also anthropic · llm-anthropic · llama-index-llms-openai · llama-index-llms-bedrock · llama-index-llms-bedrock-converse · llama-index · llama-index-llms-vertex · langchain-anthropic · llama-index-llms-ollama · llama-index-llms-azure-openai