Cerebellum is an open-source Python framework designed to streamline the creation of intelligent AI agents. It provides declarative planning, dynamic tool orchestration, and persistent memory modules. Developers can define tasks as chains, invoke external APIs or utilities as tools, and maintain conversation context via memory stores. With extensible adapters for language models, Cerebellum simplifies building agents that reason, plan, and interact over extended sessions.
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Cerebellum
Cerebellum is an open-source Python framework designed to streamline the creation of intelligent AI agents. It provides declarative planning, dynamic tool orchestration, and persistent memory modules. Developers can define tasks as chains, invoke external APIs or utilities as tools, and maintain conversation context via memory stores. With extensible adapters for language models, Cerebellum simplifies building agents that reason, plan, and interact over extended sessions.
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What is Cerebellum?
Cerebellum offers a modular platform where developers define agents using declarative plans composed of sequential steps or tool invocations. Each plan can call built-in or custom tools—such as API connectors, retrievers, or data processors—through a unified interface. Memory modules allow agents to store, retrieve, and forget information across sessions, enabling context-aware and stateful interactions. It integrates with popular LLMs (OpenAI, Hugging Face), supports custom tool registration, and features an event-driven execution engine for real-time control flow. With logging, error handling, and plugin hooks, Cerebellum boosts productivity, facilitating rapid agent development for automation, virtual assistants, and research applications.
Who will use Cerebellum?
- AI researchers
- Machine learning engineers
- Software developers
- R&D teams
- Data scientists
How to use the Cerebellum?
- Step1: Install Cerebellum via pip: pip install cerebellum-core
- Step2: Import the library and register your custom tools
- Step3: Define a Plan class with sequential tasks or tool calls
- Step4: Configure Memory modules and select a language model adapter
- Step5: Instantiate an Agent with your Plan, tools, and memory settings
- Step6: Call agent.run(input ) to execute the agent pipeline
Platform
- Linux
- Mac
- Windows
Cerebellum's Core Features & Benefits
The Core Features
- Declarative planning framework
- Dynamic tool integration
- Persistent memory modules
- Multi-model adapter support
- Event-driven execution engine
The Benefits
- Accelerates AI agent development
- Maintains stateful interactions
- Simplifies tool orchestration
- Enhances reproducibility
- Open-source extensible design
Cerebellum's Main Use Cases & Applications
- Automating customer support workflows
- Building virtual personal assistants
- Orchestrating data processing pipelines
- Developing conversational bots with memory
- Conducting AI research and prototyping
FAQs of Cerebellum
Cerebellum Company Information
Cerebellum Reviews
5/5
Cerebellum's Main Competitors and alternatives?
LangChain
Haystack
Auto-GPT
LlamaIndex
AgentGPT
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