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-====== Large Language Model (LLM) Agents ======
+===== AgentWiki =====
-Welcome to the **LLM Agents Wiki**, your comprehensive resource for understanding and leveraging Large Language Model Agents in the realm of artificial intelligence. 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 ====
-===== Introduction =====
+🤖 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.
-**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.
+==== Agent System Overview ====
------
+🧠 In an LLM-powered autonomous agent system, the LLM functions as the agent's central processing unit, complemented by several key components:
-===== Key Features of LLM Agents =====
+  * **[[planning|Planning]]**
+    * Task Decomposition
+    * Self-Reflection
-  * **Reasoning and Planning:** LLM Agents analyze complex tasks, devise strategies, and plan sequences of actions to achieve specific goals.
+  * **[[memory|Memory]]**
-  * **Tool Utilization:** They interact with external tools, APIs, databases, and services to extend their capabilities beyond text generation, such as performing web searches, executing code, or manipulating data.
+    * Hierarchical Memory Systems
-  * **Memory and Context Management:** By maintaining context over interactions, LLM Agents can reference previous information and maintain coherent long-term objectives.
+    * Efficient Retrieval Mechanisms
-  * **Natural Language Understanding:** Advanced language comprehension allows LLM Agents to interpret and generate human-like text, making them effective for communication and instruction following.
-  * **Autonomy and Adaptability:** LLM Agents operate independently, making decisions and adapting to new information or changes in their environment.
------
+  * **[[tool_use|Tool Use]]**
+    * External API Integration
+    * Dynamic Tool Selection
-===== Types of LLM Agents =====
+  * **[[structured_outputs|Structured Outputs]]**
+    * Grammars
+    * Constrained Outputs
-==== Chain-of-Thought (CoT) Reasoning ====
+🚀 These components enable the agent to:
-An approach where the LLM generates a step-by-step reasoning process, enhancing its problem-solving capabilities by making intermediate reasoning steps explicit.
-==== ReAct (Reasoning and Acting) ====
+  * **Plan** complex tasks through decomposition and strategic reasoning.
-A framework that combines reasoning traces with actions, allowing the agent to reason about tasks and interact with external tools or environments in an interleaved manner.
+  * **Remember** past interactions using advanced memory architectures.
+  * **Utilize Tools** to extend capabilities beyond text generation.
-==== AutoGPT ====
+==== Key Features of LLM Agents ====
-An experimental open-source application demonstrating how LLMs like GPT-4 can autonomously achieve user-defined goals by iteratively planning, executing, and learning from actions.
-==== BabyAGI ====
+🌀 **[[advanced_reasoning_planning|Advanced Reasoning and Planning]]**:
-A simplified artificial general intelligence model that uses an LLM to create, prioritize, and execute tasks, aiming to autonomously achieve objectives.
+Employ sophisticated reasoning strategies to analyze complex tasks, devise multi-step plans, and sequence actions to achieve specific goals.
-==== AgentGPT ====
+🔧 **[[tool_utilization|Tool Utilization and API Interaction]]**:
-A platform enabling users to deploy autonomous AI agents that can carry out tasks in a web-based environment, combining planning and execution capabilities.
+Interface with external tools, APIs, databases, and services to perform actions such as web searches, code execution, and data manipulation.
-==== Plan-and-Execute Agents ====
+📚 **[[hierarchical_memory|Hierarchical Memory and Context Management]]**:
-Agents that first plan a sequence of actions to achieve a goal and then execute those actions, often revising the plan based on the outcomes of each step.
+Use multi-level memory architectures to maintain extensive context over interactions, enabling long-term coherence and adaptability.
-==== Conversational Agents ====
+💡 **[[natural_language_understanding|Natural Language Understanding and Generation]]**:
-Specialized in dialogue, these agents understand and generate human-like conversational responses, commonly used in customer support or virtual assistants.
+Interpret and generate human-like text, facilitating effective communication and instruction following.
-==== Tool-Using Agents ====
+🔄 **[[autonomy|Autonomy and Adaptive Behavior]]**:
-Agents capable of utilizing external tools or APIs (e.g., calculators, search engines, databases) to augment their capabilities and provide accurate or up-to-date information.
+Operate independently, making informed decisions and adapting to new information or changes in their environment through iterative learning processes.
------
+==== Workflows ====
-===== Design Patterns for LLM Agents =====
+Workflows in LLM Agent systems streamline the design, implementation, and orchestration of complex tasks by structuring multi-step processes for optimal performance.
-  * **Prompt Chaining:** Structuring complex tasks into a series of prompts that guide the LLM through a multi-step process.
+🌊 **Key Workflow Tools**
-  * **Reinforcement Learning from Human Feedback (RLHF):** Training LLMs using human feedback to improve responses and align them with desired outcomes.
+  * **[[flowise|Flowise]]**: A visual programming interface for designing agent workflows.
-  * **Agent Loop (Perception-Thought-Action Cycle):** An iterative process where the agent perceives inputs, thinks (reasoning/planning), and acts (produces outputs or takes actions).
+  * **[[promptflow|PromptFlow]]**: A framework for defining and testing prompt sequences in a systematic manner.
-  * **Context Window Management:** Techniques for managing the limited context window of LLMs, such as summarizing past interactions or retrieving relevant information.
-  * **Tool Integration Patterns:** Designing interfaces that allow the LLM to interact with external tools through well-defined APIs or action schemas.
-  * **Memory Augmentation:** Implementing mechanisms for the agent to store and retrieve information beyond the LLM's context window, such as external memory databases.
-  * **Modular Architecture:** Separating the agent's functionalities into modules (e.g., planning, memory, execution) to enhance maintainability and scalability.
------
+These tools enhance the modularity and reusability of task definitions, enabling seamless experimentation and deployment.
-===== Libraries and Frameworks =====
+==== Components of LLM Agents ====
-  * **LangChain:** A framework for developing applications powered by language models, providing tools for prompt chaining, memory management, and agent development.
+=== Planning ===
-  * **LlamaIndex (GPT Index):** A toolkit for connecting LLMs with external data sources and knowledge bases.
-  * **Hugging Face Transformers:** A library offering a wide range of pre-trained models and tools for natural language processing tasks, facilitating the development of LLM Agents.
-  * **OpenAI API:** Provides access to advanced language models like GPT-4, enabling the integration of LLM capabilities into custom applications and agents.
-  * **Microsoft Guidance:** A library for controlling LLM generation, allowing developers to specify desired behavior and constraints.
-  * **AutoGPT and BabyAGI Implementations:** Open-source projects demonstrating autonomous agents built on top of LLMs, serving as references for building similar systems.
-  * **Haystack:** An open-source framework for building search systems that combine LLMs with traditional search methods, useful for agents requiring information retrieval capabilities.
------
+🧩 Planning involves the strategic breakdown of complex tasks into manageable sub-tasks, devising algorithms, and sequencing actions based on logical reasoning and predicted outcomes.
-===== Applications of LLM Agents =====
+== Task Decomposition ==
-  * **Autonomous Task Execution:** Performing tasks such as data analysis, content generation, scheduling, and automation of workflows without human intervention.
+🌳 **[[chain_of_thought|Chain-of-Thought (CoT) Reasoning]]**
-  * **Customer Support and Virtual Assistants:** Providing personalized assistance, answering queries, and engaging in natural language conversations with users.
+🌲 **[[tree_of_thoughts|Tree of Thoughts]]**
-  * **Research Assistance:** Assisting researchers by summarizing papers, generating hypotheses, or exploring literature.
+⚙️ **[[llm_with_planning|LLM+P (LLM with Classical Planning)]]**
-  * **Education and Tutoring:** Offering personalized learning experiences, explanations, and educational content tailored to individual needs.
-  * **Content Creation:** Generating articles, reports, creative writing, or marketing materials based on user input or autonomous exploration.
-  * **Software Development Assistance:** Helping with code generation, debugging, documentation, and providing suggestions to developers.
-  * **Data Retrieval and Processing:** Collecting, processing, and analyzing data from various sources to provide insights or support decision-making.
------
+== Self-Reflection ==
-===== Recent Developments =====
+🔍 **[[react_framework|ReAct (Reasoning and Acting)]]**
+🔄 **[[reflexion_framework|Reflexion Framework]]**
+🪞 **[[chain_of_hindsight|Chain of Hindsight (CoH)]]**
+📉 **[[algorithm_distillation|Algorithm Distillation (AD)]]**
-The field of LLM Agents is rapidly evolving, with significant advancements including:
+=== Memory ===
-  * **Enhanced Reasoning Abilities:** Improvements in chain-of-thought prompting and reasoning strategies have led to better problem-solving capabilities.
+📦 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.
-  * **Tool Use Integration:** LLMs are increasingly able to interact with external tools, expanding functionality beyond text-based outputs.
-  * **Memory and Retrieval Augmented Models:** Incorporation of retrieval mechanisms allows agents to access relevant information from large datasets or knowledge bases as needed.
-  * **Ethical and Safe AI Practices:** Ongoing efforts ensure that LLM Agents operate within ethical guidelines, avoiding biases and harmful outputs.
-  * **Open-Source Agent Frameworks:** Projects like AutoGPT and BabyAGI accelerate experimentation and development in autonomous LLM Agents.
------
+== Hierarchical Memory Systems ==
-===== Getting Started =====
+🕒 **[[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]]**
-Embark on your journey with LLM Agents by exploring the following resources:
+== Efficient Retrieval Mechanisms ==
-  * **[[Introduction to LLM Agents|Introduction to LLM Agents]]:** An overview of the architecture and components of LLM-powered agent applications.
+🔎 **[[maximum_inner_product_search|Maximum Inner Product Search (MIPS)]]**
-  * **[[LangChain Documentation|LangChain Documentation]]:** Guides and tutorials on using LangChain for developing LLM applications and agents.
+    * 🧮 **[[locality_sensitive_hashing|Locality-Sensitive Hashing (LSH)]]**
-  * **[[OpenAI API Reference|OpenAI API Reference]]:** Documentation and examples for integrating OpenAI's language models into your projects.
+    * 📊 **[[approximate_nearest_neighbors|Approximate Nearest Neighbors (ANNOY)]]**
-  * **[[AutoGPT GitHub Repository|AutoGPT GitHub Repository]]:** Explore the code and documentation of AutoGPT to understand autonomous agent implementations.
+    * 🗺️ **[[hnsw_graphs|Hierarchical Navigable Small World (HNSW) Graphs]]**
-  * **[[ReAct Paper|ReAct: Synergizing Reasoning and Acting in Language Models]]:** A research paper introducing the ReAct framework.
+    * 🔍 **[[faiss|Facebook AI Similarity Search (FAISS)]]**
-  * **[[Hugging Face Transformers|Hugging Face Transformers]]:** Learn how to use pre-trained models and fine-tune them for your specific needs.
+    * 📈 **[[scann|Scalable Nearest Neighbors (ScaNN)]]**
------
+=== Tool Use ===
-===== Join the Community =====
+🔧 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.
-Stay updated with the latest trends, research, and developments in the field of LLM Agents by joining our community:
+  * 🧰 **[[mrkl_systems|MRKL Systems (Modular Reasoning, Knowledge, and Language)]]**
+  * 🛠️ **[[tool_augmented_language_models|Tool-Augmented Language Models (TALM)]]**
+  * 🤖 **[[toolformer|Toolformer]]**
-  * **Discussion Forums:** Engage with practitioners, share knowledge, and collaborate on projects.
+== API Integration ==
-  * **Contribute to Open-Source Projects:** Participate in the development of tools and frameworks related to LLM Agents.
-  * **Attend Workshops and Webinars:** Expand your knowledge through events focused on LLM technologies and applications.
------
+🔗 **[[openai_function_calling|OpenAI Function Calling and ChatGPT Plugins]]**
+🔗 **[[hugginggpt|HuggingGPT]]**
+🗂️ **[[api_bank_benchmark|API-Bank Benchmark]]**
-**Explore. Learn. Innovate.** Unlock the transformative potential of Large Language Model Agents and be at the forefront of the AI revolution.
+==== Types of LLM Agents ====
+🧠 **[[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]]**
+==== Design Patterns for LLM Agents ====
+🔗 **[[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]]**
+==== Libraries and Frameworks ====
+Explore a range of tools and platforms for developing LLM agents:
+**Frameworks & Platforms**
+⚙️ **[[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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