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base on AdalFlow: The library to build & auto-optimize LLM applications.  <h4 align="center"> <img alt="AdalFlow logo" src="https://raw.githubusercontent.com/SylphAI-Inc/AdalFlow/main/docs/source/_static/images/adalflow-logo.png" style="width: 100%;"> </h4> <h2> <p align="center"> ⚡ AdalFlow is a PyTorch-like library to build and auto-optimize any LM workflows, from Chatbots, RAG, to Agents. ⚡ </p> </h2> <p align="center"> <a href="https://colab.research.google.com/drive/1_YnD4HshzPRARvishoU4IA-qQuX9jHrT?usp=sharing"> <img alt="Try Quickstart in Colab" src="https://colab.research.google.com/assets/colab-badge.svg"> </a> </p> <h4 align="center"> <p> <a href="https://adalflow.sylph.ai/">View Documentation</a>  <p> </h4> <p align="center"> <a href="https://pypi.org/project/adalflow/"> <img alt="PyPI Version" src="https://img.shields.io/pypi/v/adalflow?style=flat-square"> </a> <a href="https://pypi.org/project/adalflow/"> <img alt="PyPI Downloads" src="https://static.pepy.tech/badge/adalflow"> </a> <a href="https://pypi.org/project/adalflow/"> <img alt="PyPI Downloads" src="https://static.pepy.tech/badge/adalflow/month"> </a> <a href="https://star-history.com/#SylphAI-Inc/AdalFlow"> <img alt="GitHub stars" src="https://img.shields.io/github/stars/SylphAI-Inc/AdalFlow?style=flat-square"> </a> <a href="https://github.com/SylphAI-Inc/AdalFlow/issues"> <img alt="Open Issues" src="https://img.shields.io/github/issues-raw/SylphAI-Inc/AdalFlow?style=flat-square"> </a> <a href="https://opensource.org/license/MIT"> <img alt="License" src="https://img.shields.io/github/license/SylphAI-Inc/AdalFlow"> </a> <a href="https://discord.gg/ezzszrRZvT"> <img alt="discord-invite" src="https://dcbadge.limes.pink/api/server/ezzszrRZvT?style=flat"> </a> </p>     # Why AdalFlow 1. **100% Open-source Agents SDK**: Lightweight and requires no additional API to setup ``Human-in-the-Loop`` and ``Tracing`` Functionalities. 2. **Say goodbye to manual prompting**: AdalFlow provides a unified auto-differentiative framework for both zero-shot optimization and few-shot prompt optimization. Our research, ``LLM-AutoDiff`` and ``Learn-to-Reason Few-shot In Context Learning``, achieve the highest accuracy among all auto-prompt optimization libraries. 3. **Switch your LLM app to any model via a config**: AdalFlow provides `Model-agnostic` building blocks for LLM task pipelines, ranging from RAG, Agents to classical NLP tasks.  <p align="center" style="background-color: #f0f0f0;"> <img src="https://raw.githubusercontent.com/SylphAI-Inc/AdalFlow/main/docs/source/_static/images/classification_opt_prompt.png" alt="AdalFlow Optimized Prompt" style="width: 80%;"> </p> <p align="center" style="background-color: #f0f0f0;"> <img src="https://raw.githubusercontent.com/SylphAI-Inc/AdalFlow/main/docs/source/_static/images/adalflow_tracing_mlflow.png" alt="AdalFlow MLflow Integration" style="width: 80%;"> </p>   View [Documentation](https://adalflow.sylph.ai) # Quick Start Install AdalFlow with pip: ```bash pip install adalflow ``` ## Hello World Agent Example ```python from adalflow import Agent, Runner from adalflow.components.model_client.openai_client import OpenAIClient from adalflow.core.types import ( ToolCallActivityRunItem, RunItemStreamEvent, ToolCallRunItem, ToolOutputRunItem, FinalOutputItem ) import asyncio # Define tools def calculator(expression: str) -> str: """Evaluate a mathematical expression.""" try: result = eval(expression) return f"The result of {expression} is {result}" except Exception as e: return f"Error: {e}" async def web_search(query: str="what is the weather in SF today?") -> str: """Web search on query.""" await asyncio.sleep(0.5) return "San Francisco will be mostly cloudy today with some afternoon sun, reaching about 67 °F (20 °C)." def counter(limit: int): """A counter that counts up to a limit.""" final_output = [] for i in range(1, limit + 1): stream_item = f"Count: {i}/{limit}" final_output.append(stream_item) yield ToolCallActivityRunItem(data=stream_item) yield final_output # Create agent with tools agent = Agent( name="MyAgent", tools=[calculator, web_search, counter], model_client=OpenAIClient(), model_kwargs={"model": "gpt-4o", "temperature": 0.3}, max_steps=5 ) runner = Runner(agent=agent) ``` ### 1. Synchronous Call Mode ```python # Sync call - returns RunnerResult with complete execution history result = runner.call( prompt_kwargs={"input_str": "Calculate 15 * 7 + 23 and count to 5"} ) print(result.answer) # Output: The result of 15 * 7 + 23 is 128. The counter counted up to 5: 1, 2, 3, 4, 5. # Access step history for step in result.step_history: print(f"Step {step.step}: {step.function.name} -> {step.observation}") # Output: # Step 0: calculator -> The result of 15 * 7 + 23 is 128 # Step 1: counter -> ['Count: 1/5', 'Count: 2/5', 'Count: 3/5', 'Count: 4/5', 'Count: 5/5'] ``` ### 2. Asynchronous Call Mode ```python # Async call - similar output structure to sync call result = await runner.acall( prompt_kwargs={"input_str": "What's the weather in SF and calculate 42 * 3"} ) print(result.answer) # Output: San Francisco will be mostly cloudy today with some afternoon sun, reaching about 67 °F (20 °C). # The result of 42 * 3 is 126. ``` ### 3. Async Streaming Mode ```python # Async streaming - real-time event processing streaming_result = runner.astream( prompt_kwargs={"input_str": "Calculate 100 + 50 and count to 3"}, ) # Process streaming events in real-time async for event in streaming_result.stream_events(): if isinstance(event, RunItemStreamEvent): if isinstance(event.item, ToolCallRunItem): print(f"🔧 Calling: {event.item.data.name}") elif isinstance(event.item, ToolCallActivityRunItem): print(f"📝 Activity: {event.item.data}") elif isinstance(event.item, ToolOutputRunItem): print(f"✅ Output: {event.item.data.output}") elif isinstance(event.item, FinalOutputItem): print(f"🎯 Final: {event.item.data.answer}") # Output: # 🔧 Calling: calculator # ✅ Output: The result of 100 + 50 is 150 # 🔧 Calling: counter # 📝 Activity: Count: 1/3 # 📝 Activity: Count: 2/3 # 📝 Activity: Count: 3/3 # ✅ Output: ['Count: 1/3', 'Count: 2/3', 'Count: 3/3'] # 🎯 Final: The result of 100 + 50 is 150. Counted to 3 successfully. ``` _Set your `OPENAI_API_KEY` environment variable to run these examples._ **Try the full Agent tutorial in Colab:** [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SylphAI-Inc/AdalFlow/blob/main/notebooks/agents/agent_tutorial.ipynb)  View [Quickstart](https://colab.research.google.com/drive/1_YnD4HshzPRARvishoU4IA-qQuX9jHrT?usp=sharing): Learn How `AdalFlow` optimizes LM workflows end-to-end in 15 mins. Go to [Documentation](https://adalflow.sylph.ai) for tracing, human-in-the-loop, and more.  # Research [Jan 2025] [Auto-Differentiating Any LLM Workflow: A Farewell to Manual Prompting](https://arxiv.org/abs/2501.16673) - LLM Applications as auto-differentiation graphs - Token-efficient and better performance than DsPy # Collaborations We work closely with the [**VITA Group** at University of Texas at Austin](https://vita-group.github.io/), under the leadership of [Dr. Atlas Wang](https://www.ece.utexas.edu/people/faculty/atlas-wang), who provides valuable support in driving project initiatives.  For collaboration, contact [Li Yin](https://www.linkedin.com/in/li-yin-ai/). # Hiring We are looking for a Dev Rel to help us build the community and support our users. If you are interested, please contact [Li Yin](https://www.linkedin.com/in/li-yin-ai/).  # Documentation AdalFlow full documentation available at [adalflow.sylph.ai](https://adalflow.sylph.ai/):  # AdalFlow: A Tribute to Ada Lovelace AdalFlow is named in honor of [Ada Lovelace](https://en.wikipedia.org/wiki/Ada_Lovelace), the pioneering female mathematician who first recognized that machines could go beyond mere calculations. As a team led by a female founder, we aim to inspire more women to pursue careers in AI. # Community & Contributors The AdalFlow is a community-driven project, and we welcome everyone to join us in building the future of LLM applications. Join our [Discord](https://discord.gg/ezzszrRZvT) community to ask questions, share your projects, and get updates on AdalFlow. To contribute, please read our [Contributor Guide](https://adalflow.sylph.ai/contributor/index.html). # Contributors [![contributors](https://contrib.rocks/image?repo=SylphAI-Inc/AdalFlow&max=2000)](https://github.com/SylphAI-Inc/AdalFlow/graphs/contributors) # Acknowledgements Many existing works greatly inspired AdalFlow library! Here is a non-exhaustive list: - 📚 [PyTorch](https://github.com/pytorch/pytorch/) for design philosophy and design pattern of ``Component``, ``Parameter``, ``Sequential``. - 📚 [Micrograd](https://github.com/karpathy/micrograd): A tiny autograd engine for our auto-differentiative architecture. - 📚 [Text-Grad](https://github.com/zou-group/textgrad) for the ``Textual Gradient Descent`` text optimizer. - 📚 [DSPy](https://github.com/stanfordnlp/dspy) for inspiring the ``__{input/output}__fields`` in our ``DataClass`` and the bootstrap few-shot optimizer. - 📚 [OPRO](https://github.com/google-deepmind/opro) for adding past text instructions along with its accuracy in the text optimizer. - 📚 [PyTorch Lightning](https://github.com/Lightning-AI/pytorch-lightning) for the ``AdalComponent`` and ``Trainer``.    ", Assign "at most 3 tags" to the expected json: {"id":"11559","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"

AI prompts