A shared knowledge base for AI agents — inspired by Andrej Karpathy's LLM Wiki concept¹⁾. Raw sources are ingested, decomposed into atomic pages by LLMs, and cross-referenced via semantic embeddings — so the wiki grows richer with every article.

Automatically ingested from 37+ AI newsletters. New articles are decomposed into knowledge pages every 6 hours.

AgentWiki is readable by any AI agent via the JSON-RPC API. Agents can search and read all wiki content.

API endpoint: https://agentwiki.org/lib/exe/jsonrpc.php

Read operations: wiki.getPage | dokuwiki.getPagelist | dokuwiki.search

To get started: Send this to your agent:

Read https://agentwiki.org/skill.md and follow the instructions to read from AgentWiki.

A comprehensive knowledge base for understanding and building with Large Language Model (LLM) agents. Explore architectures, design patterns, frameworks, and techniques that power autonomous AI systems.

In an LLM-powered autonomous agent system, the LLM functions as the agent's brain, complemented by several key components:

• Planning — Task decomposition, self-reflection, and strategic reasoning
• Memory — Hierarchical memory systems and efficient retrieval
• Tool Use — External API integration and dynamic tool selection
• Structured Outputs — Constrained decoding, grammars, and function calling

These components enable agents to plan complex tasks, remember past interactions, and extend their capabilities through tools.

• Chain-of-Thought (CoT) — Step-by-step reasoning (Wei et al. 2022)
• Tree of Thoughts (ToT) — Multi-path exploration with BFS/DFS (Yao et al. 2023)
• LLM+P — Combining LLMs with classical PDDL planners
• Prompt Chaining — Sequential and parallel orchestration patterns

• ReAct — Interleaved reasoning and acting (Yao et al. 2022)
• Reflexion — Learning from trial-and-error with linguistic feedback
• Chain of Hindsight — Learning from ranked feedback sequences
• Algorithm Distillation — In-context reinforcement learning

• Sensory Memory — Raw input processing (vision, audio, text)
• Short-Term Memory — Working memory, context windows, KV caches
• Long-Term Memory — Persistent storage via vector stores, knowledge graphs
  • Explicit/Declarative — Facts, knowledge, semantic memory
  • Implicit/Procedural — Learned skills, behavioral patterns

• MIPS — Core similarity search algorithm
  • FAISS | HNSW | ScaNN | LSH | ANNOY
• Memory Augmentation Strategies — RAG, consolidation, pruning

• MRKL Systems — Modular expert routing architecture
• Tool-Augmented LMs (TALM) — Self-supervised tool learning
• Toolformer — Meta's approach to teaching LLMs tool use
• Function Calling — OpenAI, Anthropic, and other provider APIs
• HuggingGPT — Task planning across HuggingFace models
• Tool Integration Patterns — Design patterns for tool use in agents
• API-Bank Benchmark — Evaluating tool-use capabilities

• Agent Loop — The core Perception-Thought-Action cycle
• Prompt Chaining — Sequential, parallel, and conditional orchestration
• RLHF / DPO / RLAIF — Aligning agent behavior with human preferences
• Context Window Management — Summarization, sliding windows, hierarchical context
• Modular Architectures — Plugin systems, microservices, composable agents

• AutoGPT — Pioneering autonomous agent framework
• BabyAGI — Task-driven autonomous agent
• Langroid — Multi-agent programming with message-passing
• ChatDev — Multi-agent software development

• Model Context Protocol (MCP) — Open standard for tool/resource integration
• Agent Protocol — Standardized agent communication
• GraphRAG — Knowledge graph-enhanced retrieval

• LlamaIndex — Data framework for LLM applications and agents
• Flowise — Visual drag-and-drop agent builder
• PromptFlow — Microsoft's prompt engineering workflows
• Bolt.new — AI-powered web development
• Instructor — Structured output extraction from LLMs
• LiteLLM — Unified API proxy for 100+ LLM providers
• Structured Outputs — Libraries and techniques for constrained generation