Symbol-LLM is an open-source Python framework that combines large language models with symbolic memory and reasoning to build intelligent agents. It provides a planner-executor-memory architecture alongside built-in tools for search, calculation, code execution and API integration. Developers can design custom tools, manage long-term context, and orchestrate multi-step tasks for automation, information retrieval and decision support.
AI Platforms & Frameworks
Tool Libraries
#AI Agents
#modular architecture
#symbolic memory
#task planning
#Automation Tools
#custom integrations
#language model assistants
#Rapid Prototyping
#context management
#web search tool
#calculation tool
#Code Execution
#Developer Toolkit
#workflow automation
#long-term context
#LLM enhancement
#conversational AI
#Research Applications
#customer support solution
#Open-source AI
...
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Symbol-LLM
Symbol-LLM is an open-source Python framework that combines large language models with symbolic memory and reasoning to build intelligent agents. It provides a planner-executor-memory architecture alongside built-in tools for search, calculation, code execution and API integration. Developers can design custom tools, manage long-term context, and orchestrate multi-step tasks for automation, information retrieval and decision support.
AI Platforms & Frameworks
Tool Libraries
#AI Agents
#modular architecture
#symbolic memory
#task planning
#Automation Tools
#custom integrations
#language model assistants
#Rapid Prototyping
#context management
#web search tool
#calculation tool
#Code Execution
#Developer Toolkit
#workflow automation
#long-term context
#LLM enhancement
#conversational AI
#Research Applications
#customer support solution
#Open-source AI
...
Featured
What is Symbol-LLM?
Symbol-LLM offers a modular architecture for constructing AI agents powered by large language models augmented with symbolic memory stores. It features a planner module to break down complex tasks, an executor to invoke tools, and a memory system to maintain context across interactions. With built-in toolkits like web search, calculator and code runner, plus simple APIs for custom tool integration, Symbol-LLM enables developers and researchers to rapidly prototype and deploy sophisticated LLM-based assistants for various domains including research, customer support, and workflow automation.
Who will use Symbol-LLM?
- AI developers and researchers
- Software engineers building conversational agents
- Data scientists automating analysis workflows
- Technical enthusiasts exploring LLM tooling
How to use the Symbol-LLM?
- Step1: Install via pip: pip install symbol_llm
- Step2: Configure your preferred LLM credentials (OpenAI, Azure, etc.)
- Step3: Import Symbol-LLM modules and initialize planner, executor, and memory
- Step4: Define or register custom tools using the tool API
- Step5: Call agent.plan_and_execute with user prompts
- Step6: Process agent outputs and integrate into your application
Platform
- mac
- windows
- linux
Symbol-LLM's Core Features & Benefits
The Core Features
- Planner module for multi-step task decomposition
- Executor for invoking built-in and custom tools
- Symbolic memory management for long-term context
- Prebuilt tools: web search, calculator, code execution
- API for integrating custom tools and services
The Benefits
- Modular and extensible agent architecture
- Enhances LLM reasoning and automation
- Maintains conversation state across sessions
- Rapid prototyping with built-in toolkits
- Open-source and customizable to any domain
Symbol-LLM's Main Use Cases & Applications
- Automated research assistant for literature review
- Data analysis pipelines with LLM-driven toolchains
- Customer support chatbots with knowledge integration
- Workflow automation across APIs
- Interactive tutoring and decision-support systems
FAQs of Symbol-LLM
Symbol-LLM Company Information
- xufangzhi
Symbol-LLM Reviews
5/5
Symbol-LLM's Main Competitors and alternatives?
- LangChain
- Auto-GPT
- Llama-Index
- Haystack
- AgentLLM
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