This content originally appeared on DEV Community and was authored by Sunil Kumar Dash
I have been building AI applications for the past 4 years and contributing to major AI tooling platforms for a while now.
Over this period, I have used many tools and frameworks for building;
- AI agents that actually work in the real world.
- Tools for AI agents.
- End-to-end RAG applications.
I have curated a coveted list of open-source tools and frameworks that will help you craft robust and reliable AI applications. 🔥
/ some GIF here
Feel free to explore their GitHub repositories, contribute to your favourites, and support them by starring the repositories.
1. Composio 👑
I have tried building many agents, and honestly, while it is easy to create them, it is an entirely different ball game to get them right.
Building efficient AI agents that actually work requires efficient toolsets. This is where Composio comes into the picture.
Composio lets you augment your AI agents with robust tools and integrations to accomplish AI workflows.
Get started with the following pip command.
pip install composio-core
Add a GitHub integration.
composio add github
Composio handles user authentication and authorization on your behalf.
Here is how you can use the GitHub integration to star a repository.
from openai import OpenAI
from composio_openai import ComposioToolSet, App
openai_client = OpenAI(api_key="******OPENAIKEY******")
# Initialise the Composio Tool Set
composio_toolset = ComposioToolSet(api_key="**\*\***COMPOSIO_API_KEY**\*\***")
## Step 4
# Get GitHub tools that are pre-configured
actions = composio_toolset.get_actions(actions=[Action.GITHUB_ACTIVITY_STAR_REPO_FOR_AUTHENTICATED_USER])
## Step 5
my_task = "Star a repo ComposioHQ/composio on GitHub"
# Create a chat completion request to decide on the action
response = openai_client.chat.completions.create(
model="gpt-4-turbo",
tools=actions, # Passing actions we fetched earlier.
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": my_task}
]
)
For more information, visit the official docs, and also, for even complex examples, visit the example sections of the repository.
Star the Composio.dev repository ⭐
2. Julep
Developing AI applications, especially those requiring long-term memory, presents significant challenges.
Julep is solving for this problem. It is an open-source framework for building production-ready stateful AI agents.
They provide a built-in state management system that helps in efficient context storage and retrieval.
Context storage helps maintain conversation continuity, ensuring that interactions with the AI remain coherent and contextually relevant over time.
3. E2B
If I am building an AI app with code execution capabilities, such as an AI tutor or AI data analyst, E2B's Code Interpreter will be my go-to tool.
E2B Sandbox is a secure cloud environment for AI agents and apps.
It allows AI to run safely for long periods, using the same tools as humans, such as GitHub repositories and cloud browsers.
They offer native Code Interpreter SDKs for Python and Javascript/Typescript.
The Code Interpreter SDK allows you to run AI-generated code in a secure small VM - E2B sandbox - for AI code execution. Inside the sandbox is a Jupyter server you can control from their SDK.
4. Camel-ai
Solving for scalable multi-agent collaborative systems can unlock many potential in building AI applications.
Camel is well-positioned for this. It is an open-source framework offering a scalable approach to studying multi-agent systems' cooperative behaviours and capabilities.
If you intend to build a multi-agent system, Camel can be one of the best choices available in the open-source scene.
Star the camel-ai repository ⭐
5. CopilotKit
If you want to include AI capabilities in your existing application, look no further. The CopilotKit lets you use GPT models to automate interaction with your application's front and back end.
It is a ready-made Copilot that you can integrate with your application or any code you can access (OSS).
It offers React components like text areas, popups, sidebars, and chatbots to augment any application with AI capabilities.
Star the CopilotKit repository ⭐
6. Aider
Aider is an AI-powered pair programmer that can start a project, edit files, or work with an existing Git repository from the terminal.
It works with leading LLMs like GPT4o, Sonnet 3.5, DeepSeek Coder, Llama 70b, etc.
7. Haystack
There are plenty of frameworks for building AI pipelines, but if I want to integrate production-ready end-to-end search pipelines into my application, Haystack is my go-to.
Whether it's RAG, Q&A, or semantic searches, Haystack's highly composable pipelines make development, maintenance, and deployment a breeze.
Their clean and modular approach is what sets them apart.
Haystack lets you effortlessly integrate rankers, vector stores, and parsers into new or existing pipelines, making it easy to turn your prototypes into production-ready solutions.
Star the Haystack repository ⭐
8. Pgvectorscale
Modern RAG applications are incomplete without vector databases. These store documents (texts, images) as embeddings, enabling users to search for semantically similar documents.
Pgvectorscale is an extension of PgVector--vector database from PostgreSQL.
If you are building an application with vector stores, this is a no-brainer.
Pgvectorscale has outperformed Pinecone's storage-optimized index (s1). And it costs 75% less.
Star the Pgvectorscale repository ⭐
GIF
9. GPTCache
LLMs are expensive.
If you are building an app that requires more extended conversations with chat models and do not want to max out credit cards, you need caching.
However, traditional caching is of no use here. This is where GPTCache comes into the picture.
It is a semantic caching tool from Zilliz, the parent organization of the Milvus vector store.
It lets you store conversations in your preferred vector stores.
Before sending a query to the LLM, it searches the vector store; if there is a hit, it fetches it. Otherwise, it routes the request to the model.
Star the GPTCache repository ⭐
GIF
10. Mem0 (formerly EmbedChain)
Mem0 provides a smart, self-improving memory layer for Large Language Models,
It lets you add persistent memory for users, agents, and sessions.
If you are building a chatbot or Q&A system on custom data, you may consider Mem0.
Star the Mem0 (formerly Embedchain) repository ⭐
GIF
11. FastEmbed
Speed of execution is paramount in software development, and it is even more important when building an AI application.
Usually, embedding generation can take a long time, slowing down the entire pipeline. However, this should not be the case.
FastEmbed from Qdrant is a fast, lightweight Python library built for embedding generation.
It uses ONNX runtime instead of Pytorch, making it faster. It also supports most of the state-of-the-art open-source embedding models.
Star the FastEmbed repository ⭐
GIF
12. Instructor
If you have played with LLM outputs, you know it can be challenging to validate structured responses.
Instructor is an open-source tool that streamlines the validation, retry, and streaming of LLM outputs.
It uses Pydantic underneath and also supports various model providers beyond openAI.
13. LiteLLM
Let's be honest; we all have screamed at some point because a new model provider does not follow the OpenAI SDK format for text, image, or embedding generation.
However, with LiteLLM, using the same implementation format, you can use any model provider (Claude, Gemini, Groq, Mistral, Azure AI, Bedrock, etc.) as a drop-in replacement for OpenAI models.
Do you use or have built some other cool tool or framework?
Let me know about them in the comments :)
This content originally appeared on DEV Community and was authored by Sunil Kumar Dash
Sunil Kumar Dash | Sciencx (2024-07-15T11:23:00+00:00) 13 Open-source Frameworks to Become an AI Wizard 🧙♂️🪄. Retrieved from https://www.scien.cx/2024/07/15/13-open-source-frameworks-to-become-an-ai-wizard-%f0%9f%a7%99%e2%99%82%ef%b8%8f%f0%9f%aa%84/
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