Open Source AI Agent Tools & Frameworks in 2025

This list focuses on tools where the source code is publicly available, allowing for inspection, modification, and self-hosting.

Available Open Source AI Agent Tools & Frameworks:

LangChain:
- Details: LangChain is arguably the most widely adopted open-source framework for building LLM-powered applications, including sophisticated agents. It provides a comprehensive set of modular components (LLM wrappers, prompt templates, memory, document loaders, vector stores, retrievers) that developers can compose into “chains” and “agents.” LangChain agents use an LLM as a reasoning engine to decide which sequence of actions to take, utilizing available “tools” (APIs, databases, search engines, Python functions) to interact with the external world and accomplish tasks. Its flexibility, extensive integrations, and large community make it a powerful choice for developers needing fine-grained control over agent logic and behavior.
LlamaIndex:
- Details: LlamaIndex is an open-source data framework specifically designed to connect custom data sources to large language models, a crucial capability for building knowledgeable agents. It focuses heavily on data ingestion (connecting to diverse sources like APIs, PDFs, databases), data indexing (creating efficient representations, especially vector indices for semantic search), and data querying (providing advanced retrieval and synthesis strategies beyond simple vector lookups). While often used with LangChain, LlamaIndex offers its own agent abstractions (like data agents) optimized for interacting with and reasoning over the indexed knowledge base, enabling agents to answer complex questions based on private or domain-specific data.
Microsoft AutoGen:
- Details: AutoGen is an open-source framework from Microsoft Research that enables the development of LLM applications using multiple, collaborating agents. Rather than a single agent, developers define multiple agents with distinct roles, capabilities, LLM configurations, and potentially human input integration. These agents interact through conversations to solve complex tasks, such as coding, planning, content creation, and problem-solving. This multi-agent approach allows for specialization, task decomposition, error checking, and more robust outcomes by leveraging the collective intelligence and interaction patterns of the defined agents.
CrewAI:
- Details: CrewAI is an open-source framework specifically focused on orchestrating role-playing, autonomous AI agents designed for collaborative work. It provides high-level abstractions for defining agents with specific roles (e.g., ‘Researcher’, ‘Writer’), goals, backstories, and tools. These agents are then organized into a “Crew” to execute a series of tasks, often sequentially passing work between agents. CrewAI aims to simplify the creation of multi-agent systems for complex workflows, promoting emergent collaboration and leveraging the specialized skills of each agent to achieve a common objective.
Haystack (by deepset):
- Details: Haystack is an open-source LLM framework primarily focused on building production-ready NLP applications, especially those involving search, question answering, and summarization (Retrieval-Augmented Generation – RAG). While excelling at building sophisticated RAG pipelines, Haystack also includes components for creating agents. These agents can leverage Haystack’s powerful pipeline components (retrievers, readers, generators) as tools, allowing them to interact intelligently with large document stores or databases to perform complex reasoning and task completion grounded in specific data sources.
Rasa:
- Details: Rasa is a well-established open-source machine learning framework for building contextual AI assistants and chatbots, essentially conversational agents. It provides tools for Natural Language Understanding (NLU – intent recognition, entity extraction) via Rasa NLU and Dialogue Management (managing conversation flow, state tracking) via Rasa Core. Developers define conversation paths, intents, entities, and custom actions (Python code that can call APIs, query databases, etc., serving as the agent’s tools). While it can integrate LLMs, Rasa’s strength lies in enabling fine-grained control over conversational logic, state management, and offering robust on-premise deployment options, making it popular for enterprise chatbots requiring high predictability and customization.
Auto-GPT:
- Details: Auto-GPT was one of the early and highly influential open-source projects demonstrating the potential of autonomous AI agents. It functions by taking a high-level goal from the user and attempting to achieve it by breaking it down into sub-tasks, using tools like internet search (e.g., Google Search) and file system operations, and managing its own prompts and memory (often using vector databases). While perhaps less of a structured framework compared to LangChain or AutoGen, and more experimental, it showcased the concept of an LLM driving its own actions towards a goal with minimal human intervention, inspiring much subsequent work in the agent space.
AgentVerse:
- Details: AgentVerse is an open-source framework designed to facilitate the creation and simulation of multi-agent environments. It provides building blocks not just for individual agents but for the environments they inhabit and interact within. This makes it particularly useful for research and development focused on social simulation, studying emergent behaviors in groups of agents, evaluating collaborative or competitive strategies, and building complex systems where interactions between multiple autonomous entities are key. It offers tools for defining agent behaviors, communication protocols, and environmental dynamics.
SuperAGI:
- Details: SuperAGI is an open-source, developer-first autonomous AI agent framework. It aims to provide a robust infrastructure for building, managing, and running autonomous agents efficiently. Key features often include agent provisioning, performance monitoring, trajectory tracking (recording the agent’s steps), resource management, and often a marketplace or mechanism for discovering and integrating tools (skills) for agents. It focuses on providing the operational tooling needed to move beyond simple agent prototypes towards more reliable and deployable autonomous systems.
FlowiseAI:
- Details: FlowiseAI is an open-source, low-code/no-code tool that provides a visual drag-and-drop interface for building customized LLM applications, including agents. It often leverages frameworks like LangChain and LlamaIndex under the hood but abstracts away much of the code. Users can connect different components (LLMs, prompt templates, vector stores, tools, agents) visually on a canvas to create flows. This makes agent development more accessible to those less comfortable with extensive coding, facilitating rapid prototyping and experimentation with different agent architectures and tool combinations.

This list highlights some of the key open-source players in the AI agent space. The field is dynamic, with new tools emerging and existing ones rapidly evolving, often building upon or integrating with each other.

Top 20 open-source AI agents, tools, and frameworks by DeepSeek R1

Here’s a list of top 20 open-source AI agents, tools, and frameworks (as of July 2024), widely used in research and industry:

1. TensorFlow

Focus: Deep learning framework.
Developed by: Google.
Use Cases: Neural networks, production-scale ML.

2. PyTorch

Focus: Dynamic neural networks.
Developed by: Meta (Facebook).
Use Cases: Research, NLP, computer vision.

3. Hugging Face Transformers

Focus: NLP models (BERT, GPT, etc.).
Use Cases: Text generation, translation, summarization.

4. LangChain

Focus: Building LLM-powered applications.
Use Cases: Agentic workflows, RAG, tool integration.

5. AutoGPT

Focus: Autonomous AI agents.
Use Cases: Goal-oriented task automation.

6. Rasa

Focus: Conversational AI (chatbots).
Use Cases: Dialogue management, NLU.

7. Jupyter Notebook

Focus: Interactive coding/data analysis.
Use Cases: Prototyping, visualization, education.

8. Apache MXNet

Focus: Scalable deep learning.
Developed by: Apache.
Use Cases: Distributed training, multi-language support.

9. Scikit-learn

Focus: Traditional ML algorithms.
Use Cases: Classification, regression, clustering.

10. OpenCV

Focus: Computer vision.
Use Cases: Image/video processing, object detection.

11. Stable Diffusion

Focus: Text-to-image generation.
Developed by: Stability AI.
Use Cases: Image synthesis, creative tools.

12. Meta Llama

Focus: Large language models (LLMs).
Developed by: Meta.
Use Cases: Code generation, reasoning, chat.

13. DeepSpeed

Focus: Optimization for training LLMs.
Developed by: Microsoft.
Use Cases: Memory efficiency, distributed training.

14. Ray

Focus: Distributed computing.
Use Cases: Scalable RL, hyperparameter tuning.

15. Keras

Focus: High-level neural network API.
Use Cases: Rapid prototyping (runs on TensorFlow).

16. MLflow

Focus: Machine learning lifecycle.
Developed by: Databricks.
Use Cases: Experiment tracking, deployment.

17. FastAPI

Focus: API development for ML models.
Use Cases: Model serving, microservices.

18. Haystack

Focus: NLP pipelines (QA, search).
Developed by: deepset.
Use Cases: Document retrieval, RAG systems.

19. AutoGluon

Focus: Automated ML (AutoML).
Developed by: Amazon.
Use Cases: Model selection, hyperparameter tuning.

20. RLlib

Focus: Reinforcement learning.
Part of: Ray project.
Use Cases: Robotics, game AI, simulations.

Honorable Mentions:

EleutherAI GPT-Neo/GPT-J: Open-source LLMs.
ONNX (Open Neural Network Exchange): Model interoperability.
Weights & Biases (W&B): Experiment tracking (partially open).
OpenAI Gym: Reinforcement learning environments.

These tools are foundational for building AI agents, training models, and deploying solutions. Always check licensing (e.g., Meta Llama uses a custom license) and community support before adoption.