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magentic

Appears in 4 awesome lists

Seamlessly integrate LLMs as Python functions. Use type annotations to specify structured output. Mix LLM queries and function calling with regular Python code to create complex LLM-powered functionality.

Open github.comjackmpcollins/magentic

Found in these lists

awesome-ChatGPT-repositories

Section: Prompts · Seamlessly integrate LLMs as Python functions

FreshScore 87

Awesome LangChain

Section: Other LLM Frameworks · Seamlessly integrate LLMs as Python functions

FreshScore 90

Awesome LLM JSON List

Section: Python Libraries · (MIT) call LLMs from Python using 3 lines of code. Simply use the @prompt decorator to create functions that return structured output from the LLM, powered by Pydantic.

StaleScore 55

Awesome LLMOps

Section: LLMOps · Seamlessly integrate LLMs as Python functions. Use type annotations to specify structured output. Mix LLM queries and function calling with regular Python code to create complex LLM-powered functionality.

ActiveScore 75

LangChain

Langchain integrates various providers like Anthropic, AWS, and OpenAI, and offers tools for components such as LLMs, chat models, and data analysis, supporting functionalities from Alpha Vantage to YouTube github | docs

In 20 listsDetails

LiteLLM

Unified proxy and SDK that routes to 100+ LLM providers behind a single OpenAI-compatible interface, with a Router handling retry/fallback across deployments, per-project cost and rate-limit tracking, and OTEL callback integrations. The right infrastructure layer when your harness needs provider…

In 16 listsDetails

LlamaIndex

(MIT) provides modules for structured outputs at different levels of abstraction, including output parsers for text completion endpoints, Pydantic programs for mapping prompts to structured outputs using function calling or output parsing, and pre-defined Pydantic programs for specific output types.

In 14 listsDetails

Haystack

Open-source AI orchestration framework for building context-engineered, production-ready LLM applications. Design modular pipelines and agent workflows with explicit control over retrieval, routing, memory, and generation. Built for scalable agents, RAG, multimodal applications, semantic search,…

In 13 listsDetails

AutoGen

Microsoft's multi-agent conversation framework with a complete AgentChat layer covering agent loop, tool integration, termination conditions, and human-in-the-loop. The most comprehensive open-source reference for large-scale multi-agent harness design.

In 14 listsDetails

PydanticAI

June 2026 harness-first redesign built around the Capability primitive: a single composable unit bundling instructions, tools, lifecycle hooks, and model settings. The split between a small stable core and a fast-moving pydantic-ai-harness lets capabilities graduate as they prove essential, while…

In 12 listsDetails

DSPy

(MIT) is a framework for algorithmically optimizing LM prompts and weights. DSPy introduced typed predictor and signatures to leverage Pydantic for enforcing type constraints on inputs and outputs, improving upon string-based fields.

In 11 listsDetails

promptfoo

Test your prompts, models, RAGs. Evaluate and compare LLM outputs, catch regressions, and improve prompt quality. LLM evals for OpenAI/Azure GPT, Anthropic Claude, VertexAI Gemini, Ollama, Local & private models like Mistral/Mixtral/Llama with CI/CD

In 11 listsDetails