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--- start [2024/11/26 22:48] – brad
+++ start [2024/12/13 18:26] (current) – brad
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-===== Large Language Model (LLM) Agents =====
+===== AgentWiki =====
-Welcome to the LLM Agents Wiki, a Wikipedia for understanding and leveraging Large Language Model Agents. Dive into the cutting-edge developments, explore various types and design patterns, and discover the libraries and tools that empower these intelligent systems to perform autonomously across diverse applications.
+Welcome to the **AgentWiki**, a comprehensive resource for understanding and leveraging Large Language Models (LLMs) for agent applications.
+Catch up on the latest developments, explore various architectures and design patterns, and discover the libraries and tools that empower these intelligent systems to perform autonomously across diverse domains.
 ==== Introduction ====
-Large Language Model (LLM) Agents are AI systems that utilize large language models to perform tasks autonomously. By understanding natural language, reasoning through complex problems, and interacting with external tools and environments, LLM Agents represent a significant advancement in artificial intelligence. They are capable of planning, executing, and adapting their actions based on given objectives and feedback from their environment.
+🤖 Large Language Model (LLM) Agents are sophisticated AI systems that utilize large-scale neural language models to perform tasks autonomously.
+By comprehending natural language, reasoning through complex problems, and interacting with external tools and environments, LLM Agents represent a significant advancement in artificial intelligence.
+They are capable of planning, executing, and adapting their actions based on given objectives and feedback from their environment.
 ==== Agent System Overview ====
-In an LLM-powered autonomous agent system, the LLM functions as the agent’s brain, complemented by several key components:
+🧠 In an LLM-powered autonomous agent system, the LLM functions as the agent's central processing unit, complemented by several key components:
-  * **Planning**
+  * **[[planning|Planning]]**
     * Task Decomposition
     * Self-Reflection
-  * **Memory**
-    * Types of Memory
-    * Maximum Inner Product Search (MIPS)
-  * **Tool Use**
-These components enable the agent to plan complex tasks, remember past interactions, and utilize external tools to extend their capabilities.
+  * **[[memory|Memory]]**
+    * Hierarchical Memory Systems
+    * Efficient Retrieval Mechanisms
-==== Key Features of LLM Agents ====
+  * **[[tool_use|Tool Use]]**
+    * External API Integration
+    * Dynamic Tool Selection
-  * **Reasoning and Planning**: LLM Agents analyze complex tasks, devise strategies, and plan sequences of actions to achieve specific goals.
+  * **[[structured_outputs|Structured Outputs]]**
-  * **Tool Utilization**: They interact with external tools, APIs, databases, and services to extend their capabilities beyond text generation.
+    * Grammars
-  * **Memory and Context Management**: By maintaining context over interactions, LLM Agents can reference previous information and maintain coherent long-term objectives.
+    * Constrained Outputs
-  * **Natural Language Understanding**: Advanced language comprehension allows LLM Agents to interpret and generate human-like text.
-  * **Autonomy and Adaptability**: LLM Agents operate independently, making decisions and adapting to new information or changes in their environment.
-==== Components of LLM Agents ====
+🚀 These components enable the agent to:
-=== Planning ===
+  * **Plan** complex tasks through decomposition and strategic reasoning.
+  * **Remember** past interactions using advanced memory architectures.
+  * **Utilize Tools** to extend capabilities beyond text generation.
-Planning involves breaking down complex tasks into manageable subgoals, devising strategies, and sequencing actions.
+==== Key Features of LLM Agents ====
-  * **Task Decomposition**
+🌀 **[[advanced_reasoning_planning|Advanced Reasoning and Planning]]**:
-    * Chain-of-Thought (CoT) Reasoning
+Employ sophisticated reasoning strategies to analyze complex tasks, devise multi-step plans, and sequence actions to achieve specific goals.
-    * Tree of Thoughts
-    * LLM+P (LLM plus Planning)
-  * **Self-Reflection**
-    * ReAct (Reasoning and Acting)
-    * Reflexion
-    * Chain of Hindsight (CoH)
-    * Algorithm Distillation (AD)
-=== Memory ===
+🔧 **[[tool_utilization|Tool Utilization and API Interaction]]**:
+Interface with external tools, APIs, databases, and services to perform actions such as web searches, code execution, and data manipulation.
-Memory allows agents to retain and recall information over extended periods, enhancing their ability to maintain context and learn from past interactions.
+📚 **[[hierarchical_memory|Hierarchical Memory and Context Management]]**:
+Use multi-level memory architectures to maintain extensive context over interactions, enabling long-term coherence and adaptability.
-  * **Types of Memory**
+💡 **[[natural_language_understanding|Natural Language Understanding and Generation]]**:
-    * Sensory Memory
+Interpret and generate human-like text, facilitating effective communication and instruction following.
-    * Short-Term Memory (STM) or Working Memory
-    * Long-Term Memory (LTM)
-      * Explicit/Declarative Memory
-      * Implicit/Procedural Memory
-  * **Maximum Inner Product Search (MIPS)**
-    * Locality-Sensitive Hashing (LSH)
-    * ANNOY (Approximate Nearest Neighbors Oh Yeah)
-    * Hierarchical Navigable Small World (HNSW)
-    * FAISS (Facebook AI Similarity Search)
-    * ScaNN (Scalable Nearest Neighbors)
-=== Tool Use ===
+🔄 **[[autonomy|Autonomy and Adaptive Behavior]]**:
+Operate independently, making informed decisions and adapting to new information or changes in their environment through iterative learning processes.
-Tool use extends the agent's capabilities by allowing interaction with external tools and APIs.
+==== Workflows ====
-  * **MRKL Systems**
+Workflows in LLM Agent systems streamline the design, implementation, and orchestration of complex tasks by structuring multi-step processes for optimal performance.
-  * **Tool Augmented Language Models (TALM)**
-  * **Toolformer**
-  * **ChatGPT Plugins and OpenAI API Function Calling**
-  * **HuggingGPT**
-  * **API-Bank**
-==== Types of LLM Agents ====
+🌊 **Key Workflow Tools**
+  * **[[flowise|Flowise]]**: A visual programming interface for designing agent workflows.
+  * **[[promptflow|PromptFlow]]**: A framework for defining and testing prompt sequences in a systematic manner.
-  * **Chain-of-Thought (CoT) Reasoning**
+These tools enhance the modularity and reusability of task definitions, enabling seamless experimentation and deployment.
-  * **ReAct (Reasoning and Acting)**
-  * **AutoGPT**
-  * **BabyAGI**
-  * **AgentGPT**
-  * **Plan-and-Execute Agents**
-  * **Conversational Agents**
-  * **Tool-Using Agents**
-==== Design Patterns for LLM Agents ====
+==== Components of LLM Agents ====
-  * **Prompt Chaining**
+=== Planning ===
-  * **Reinforcement Learning from Human Feedback (RLHF)**
-  * **Agent Loop (Perception-Thought-Action Cycle)**
-  * **Context Window Management**
-  * **Tool Integration Patterns**
-  * **Memory Augmentation**
-  * **Modular Architecture**
-==== Libraries and Frameworks ====
+🧩 Planning involves the strategic breakdown of complex tasks into manageable sub-tasks, devising algorithms, and sequencing actions based on logical reasoning and predicted outcomes.
-  * **LangChain**
+== Task Decomposition ==
-  * **LlamaIndex (GPT Index)**
-  * **Hugging Face Transformers**
-  * **OpenAI API**
-  * **Microsoft Guidance**
-  * **AutoGPT and BabyAGI Implementations**
-  * **Haystack**
-==== Applications of LLM Agents ====
+🌳 **[[chain_of_thought|Chain-of-Thought (CoT) Reasoning]]**
+🌲 **[[tree_of_thoughts|Tree of Thoughts]]**
+⚙️ **[[llm_with_planning|LLM+P (LLM with Classical Planning)]]**
-  * **Autonomous Task Execution**
+== Self-Reflection ==
-  * **Customer Support and Virtual Assistants**
-  * **Research Assistance**
-  * **Education and Tutoring**
-  * **Content Creation**
-  * **Software Development Assistance**
-  * **Data Retrieval and Processing**
-==== Case Studies ====
+🔍 **[[react_framework|ReAct (Reasoning and Acting)]]**
+🔄 **[[reflexion_framework|Reflexion Framework]]**
+🪞 **[[chain_of_hindsight|Chain of Hindsight (CoH)]]**
+📉 **[[algorithm_distillation|Algorithm Distillation (AD)]]**
-=== Scientific Discovery Agents ===
+=== Memory ===
-  * **ChemCrow**
+📦 Memory mechanisms allow agents to retain, retrieve, and utilize information over extended periods, significantly enhancing their ability to maintain context, learn from past experiences, and build upon accumulated knowledge.
-  * **Autonomous Scientific Research Agents**
-=== Generative Agents Simulation ===
+== Hierarchical Memory Systems ==
-  * **Generative Agents (Park et al. 2023)**
+🕒 **[[sensory_memory|Sensory Memory]]**
+⏳ **[[short_term_memory|Short-Term Memory (Working Memory)]]**
+📜 **[[long_term_memory|Long-Term Memory (Persistent Memory)]]**
+    * 📂 **[[explicit_memory|Explicit/Declarative Memory]]**
+    * 🤫 **[[implicit_memory|Implicit/Procedural Memory]]**
-=== Proof-of-Concept Examples ===
+== Efficient Retrieval Mechanisms ==
-  * **AutoGPT**
+🔎 **[[maximum_inner_product_search|Maximum Inner Product Search (MIPS)]]**
-  * **GPT-Engineer**
+    * 🧮 **[[locality_sensitive_hashing|Locality-Sensitive Hashing (LSH)]]**
+    * 📊 **[[approximate_nearest_neighbors|Approximate Nearest Neighbors (ANNOY)]]**
+    * 🗺️ **[[hnsw_graphs|Hierarchical Navigable Small World (HNSW) Graphs]]**
+    * 🔍 **[[faiss|Facebook AI Similarity Search (FAISS)]]**
+    * 📈 **[[scann|Scalable Nearest Neighbors (ScaNN)]]**
-==== Challenges ====
+=== Tool Use ===
-  * **Finite Context Length**
+🔧 Tool use extends the agent's functionality by enabling interaction with external systems, APIs, and tools, allowing the agent to perform actions beyond its inherent capabilities and access up-to-date information.
-  * **Long-Term Planning and Task Decomposition**
-  * **Reliability of Natural Language Interface**
-==== Recent Developments ====
+  * 🧰 **[[mrkl_systems|MRKL Systems (Modular Reasoning, Knowledge, and Language)]]**
+  * 🛠️ **[[tool_augmented_language_models|Tool-Augmented Language Models (TALM)]]**
+  * 🤖 **[[toolformer|Toolformer]]**
-The field of LLM Agents is rapidly evolving, with significant advancements including:
+== API Integration ==
-  * **Enhanced Reasoning Abilities**
+🔗 **[[openai_function_calling|OpenAI Function Calling and ChatGPT Plugins]]**
-  * **Tool Use Integration**
+🔗 **[[hugginggpt|HuggingGPT]]**
-  * **Memory and Retrieval Augmented Models**
+🗂️ **[[api_bank_benchmark|API-Bank Benchmark]]**
-  * **Ethical and Safe AI Practices**
-  * **Open-Source Agent Frameworks**
-==== Getting Started ====
+==== Types of LLM Agents ====
-Embark on your journey with LLM Agents by exploring the following resources:
+🧠 **[[chain_of_thought_agents|Chain-of-Thought Agents]]**
+🔄 **[[react_agents|ReAct Agents]]**
+🤖 **[[autonomous_agents|Autonomous Agents]]**
+    * 🤖 **[[autogpt|AutoGPT]]**
+    * 🤖 **[[babyagi|BabyAGI]]**
+    * 🤖 **[[agentgpt|AgentGPT]]**
+📋 **[[plan_and_execute_agents|Plan-and-Execute Agents]]**
+💬 **[[conversational_agents|Conversational Agents]]**
+🔧 **[[tool_using_agents|Tool-Using Agents]]**
-  * **Introduction to LLM Agents**
+==== Design Patterns for LLM Agents ====
-  * **LangChain Documentation**
-  * **OpenAI API Reference**
-  * **AutoGPT GitHub Repository**
-  * **ReAct Framework**
-  * **Hugging Face Transformers**
-==== Join the Community ====
+🔗 **[[prompt_chaining|Prompt Chaining and Orchestration]]**
+📈 **[[rlhf|Reinforcement Learning from Human Feedback (RLHF)]]**
+🔄 **[[agent_loop|Agent Loop (Perception-Thought-Action Cycle)]]**
+🗂️ **[[context_window_management|Context Window Management]]**
+🔧 **[[tool_integration_patterns|Tool Integration Patterns]]**
+📂 **[[memory_augmentation_strategies|Memory Augmentation Strategies]]**
+🏗️ **[[modular_architectures|Modular and Layered Architectures]]**
-Stay updated with the latest trends, research, and developments in the field of LLM Agents by joining our community:
+==== Libraries and Frameworks ====
-  * **Discussion Forums**
-  * **Contribute to Open-Source Projects**
-  * **Attend Workshops and Webinars**
-Explore. Learn. Innovate. Unlock the transformative potential of Large Language Model Agents and be at the forefront of the AI revolution.
+Explore a range of tools and platforms for developing LLM agents:
-==== Tags ====
+**Frameworks & Platforms**
-{{tag>nlp language-model agent steerability prompting}}
+⚙️ **[[agent_protocol|Agent Protocol]]**
+🤖 **[[anthropic_context_protocol|Anthropic Model Context Protocol]]**
+💻 **[[chatdev|ChatDev]]**
+⚡ **[[bolt_new|Bolt.new]]**
+🔗 **[[flowise|Flowise]]**
+📋 **[[instructor_framework|Instructor]]**
+🔎 **[[llamaindex|LlamaIndex]]**
+🧠 **[[autogpt|AutoGPT]]**
+🤖 **[[babyagi|BabyAGI]]**
+🔗 **[[langroid|Langroid]]**
+📊 **[[microsoft_graphrag|Microsoft GraphRAG]]**
+🌟 **[[lite_llm|LiteLLM]]**

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