awesome-agent-cortex

# Awesome Agent Cortex [](https://awesome.re) <p align="center"> <img src="public/readme/agent-cortex-hero.jpeg" alt="Awesome Agent Cortex maps the sovereign agent stack" width="960"> </p> <p align="center"> <strong>A curated map of the tools, memory systems, identity rails, and operational patterns behind capable AI agents.</strong> </p> <p align="center"> <a href="LICENSE"><img src="https://img.shields.io/badge/license-CC0-FF6A2A.svg" alt="CC0 license"></a> <a href="https://github.com/0xNyk/awesome-agent-cortex/actions/workflows/docs-health.yml"><img src="https://github.com/0xNyk/awesome-agent-cortex/actions/workflows/docs-health.yml/badge.svg" alt="Documentation health"></a> <a href="https://github.com/0xNyk/awesome-agent-cortex"><img src="https://img.shields.io/github/stars/0xNyk/awesome-agent-cortex" alt="GitHub stars"></a> </p> --- Awesome Agent Cortex connects the layers that ordinary framework lists leave apart: agent runtimes, coding tools, MCP, evaluation, memory, knowledge graphs, on-chain identity, payments, security, and observability. Use it to find one component or to understand how the pieces fit into a complete agent system.  Choose a route from the contents below: - **Build:** frameworks, coding agents, MCP, skills, and prompt systems. - **Operate:** runtimes, harnessing, evaluation, security, and observability. - **Remember:** context engineering, knowledge graphs, neural memory, and Obsidian. - **Own:** identity, wallets, payments, DeFi, and Solana infrastructure.  Some resources appear in more than one section when they serve distinct workflows. Cross-listing is deliberate, limited, and reviewed under the contribution rules. ## Contents - [Agent Frameworks](#agent-frameworks) - [Coding Agents](#coding-agents) - [Voice and Multimodal Agents](#voice-and-multimodal-agents) - [Hermes Stack](#hermes-stack) - [CLI and TUI Tools](#cli-and-tui-tools) - [Agent Runtime Infrastructure](#agent-runtime-infrastructure) - [Agent Protocols and MCP Ecosystem](#agent-protocols-and-mcp-ecosystem) - [Prompt Engineering](#prompt-engineering) - [Agent Harnessing and Evaluation](#agent-harnessing-and-evaluation) - [ArXiv Deep Research Map](#arxiv-deep-research-map) - [Context Engineering](#context-engineering) - [Neural Networks and Neural Linking](#neural-networks-and-neural-linking) - [Obsidian Vault Architecture for Agents](#obsidian-vault-architecture-for-agents) - [Agent Security and Robustness](#agent-security-and-robustness) - [Agent Configs and Dotfiles](#agent-configs-and-dotfiles) - [Skill Engineering and Playbooks](#skill-engineering-and-playbooks) - [Knowledge Graphs and Memory](#knowledge-graphs-and-memory) - [Solana Agent Infrastructure](#solana-agent-infrastructure) - [Agent Identity and Wallets](#agent-identity-and-wallets) - [Agent Payments](#agent-payments) - [DeFi Agents](#defi-agents) - [Quant and Trading Agents](#quant-and-trading-agents) - [Agent Observability and Testing](#agent-observability-and-testing) - [Research Papers](#research-papers) - [Communities](#communities) ## Agent Frameworks Multi-agent orchestration, single-agent SDKs, and runtime frameworks. - [AG2](https://github.com/ag2ai/ag2) - Open-source AgentOS for building multi-agent systems (evolved from AutoGen). - [Agno](https://github.com/agno-agi/agno) - Framework for building and running agentic software at scale. - [AutoGen](https://github.com/microsoft/autogen) - Multi-agent conversation framework from Microsoft Research. - [Claude Agent SDK](https://github.com/anthropics/claude-agent-sdk-python) - Official Python SDK for building agents on the Claude Code runtime. - [CrewAI](https://github.com/crewAIInc/crewAI) - Role-based multi-agent orchestration framework. - [ElizaOS](https://github.com/elizaOS/eliza) - Multi-agent simulation framework for autonomous characters. - [Google ADK](https://github.com/google/adk-python) - Agent Development Kit for building agents with Gemini. - [Haystack](https://github.com/deepset-ai/haystack) - LLM orchestration framework for building search and RAG pipelines. - [Hermes Agent](https://github.com/NousResearch/hermes-agent) - Tool-using autonomous agent platform with memory, skills, delegation, and MCP support. - [Julep](https://github.com/julep-ai/julep) - Stateful agent platform with built-in persistence and task workflows. - [LangChain](https://github.com/langchain-ai/langchain) - Composable framework for building LLM-powered applications. - [LangGraph](https://github.com/langchain-ai/langgraph) - Library for building stateful multi-agent workflows as graphs. - [Letta](https://github.com/letta-ai/letta) - Stateful agents with long-term memory (formerly MemGPT). - [LlamaIndex](https://github.com/run-llama/llama_index) - Data framework for document agents, retrieval, and workflow orchestration. - [Magentic-One](https://github.com/microsoft/autogen/tree/main/python/packages/autogen-magentic-one) - Multi-agent team for complex web and file tasks. - [Mastra](https://github.com/mastra-ai/mastra) - TypeScript framework for building AI applications and agents. - [Microsoft Agent Framework](https://github.com/microsoft/agent-framework) - Framework for building, orchestrating, and deploying agents with Python and .NET support. - [OpenAI Agents SDK for Python](https://github.com/openai/openai-agents-python) - Official Python SDK for agent workflows, tools, handoffs, and guardrails. - [OpenAI Agents SDK for TypeScript](https://github.com/openai/openai-agents-js) - Official TypeScript SDK for agent workflows and voice agents. - [OpenClaw](https://github.com/openclaw/openclaw) - Self-hosted personal AI agent with multi-platform messaging and skill registry. - [PydanticAI](https://github.com/pydantic/pydantic-ai) - Type-safe agent framework built around Pydantic. - [Rig](https://github.com/0xPlaygrounds/rig) - Rust framework for building LLM-powered applications. - [Semantic Kernel](https://github.com/microsoft/semantic-kernel) - SDK for integrating LLMs into apps with plugin architecture. - [Smolagents](https://github.com/huggingface/smolagents) - Lightweight agent framework from Hugging Face. - [Swarm](https://github.com/openai/swarm) - Educational framework for multi-agent handoffs and routines. ## Coding Agents AI agents that write, review, and debug code. - [Aider](https://github.com/Aider-AI/aider) - AI pair programming in the terminal with git integration. - [Claude Code](https://docs.anthropic.com/en/docs/claude-code) - Anthropic's agentic CLI for code generation and editing. - [Cline](https://github.com/cline/cline) - Autonomous coding agent for VS Code with tool use. - [Codex CLI](https://github.com/openai/codex) - OpenAI's open-source coding agent for terminal workflows. - [Continue](https://github.com/continuedev/continue) - Open-source AI code assistant for VS Code and JetBrains. - [Cursor](https://cursor.com) - AI-first code editor built on VS Code. - [Devin](https://devin.ai) - Autonomous software engineering agent by Cognition. - [Goose](https://github.com/aaif-goose/goose) - Open-source developer agent governed by the Agentic AI Foundation. - [OpenHands](https://github.com/OpenHands/OpenHands) - Platform for AI software development agents (formerly OpenDevin). - [SWE-Agent](https://github.com/SWE-agent/SWE-agent) - Agent for resolving software engineering tasks from GitHub issues. - [Windsurf](https://codeium.com/windsurf) - AI-native IDE by Codeium with agentic flows. ### Claude Code Resources - [awesome-claude-code](https://github.com/hesreallyhim/awesome-claude-code) - Curated list of Claude Code resources. - [Claude Code Hooks](https://docs.anthropic.com/en/docs/claude-code/hooks) - Event-driven shell command automation. - [Claude Code Skills](https://docs.anthropic.com/en/docs/claude-code/memory#slash-commands-as-custom-skills) - Reusable prompt-driven workflows. - [claude-code-tips](https://github.com/ykdojo/claude-code-tips) - Community-sourced tips and tricks. - [CLAUDE.md Guide](https://docs.anthropic.com/en/docs/claude-code/memory) - Official documentation on memory files. - [Everything Claude Code](https://github.com/affaan-m/ECC) - Community collection of Claude Code skills, hooks, agents, and configuration patterns. ### Codex Resources - [AGENTS.md Guide (Codex)](https://developers.openai.com/codex/guides/agents-md) - Instruction hierarchy and scoping patterns for Codex. - [Codex CLI](https://developers.openai.com/codex/cli) - Guide to local Codex CLI workflows. - [Codex Docs](https://developers.openai.com/codex) - Official Codex documentation hub. - [Codex Non-Interactive Mode](https://developers.openai.com/codex/noninteractive) - Batch and CI automation with `codex exec`. - [Codex Optimization Playbook (this repo)](guides/codex-optimization-playbook.md) - Practical operator patterns for speed, safety, and quality. ## Voice and Multimodal Agents Agents with voice, vision, and multimodal capabilities. - [ElevenLabs](https://github.com/elevenlabs/elevenlabs-python) - Text-to-speech and voice cloning API for agent voice interfaces. - [LiveKit Agents](https://github.com/livekit/agents) - Framework for building real-time multimodal AI agents. - [Pipecat](https://github.com/pipecat-ai/pipecat) - Framework for building voice and multimodal conversational agents. - [TEN Framework](https://github.com/TEN-framework/ten-framework) - Open-source framework for conversational voice AI agents. - [Ultravox](https://github.com/fixie-ai/ultravox) - Fast multimodal LLM for real-time voice AI. - [Vapi](https://vapi.ai) - Platform for building and deploying voice AI agents. - [Vocode Core](https://github.com/vocodedev/vocode-core) - Modular open-source framework for building voice-based LLM agents. - [Whisper](https://github.com/openai/whisper) - Open-source speech recognition model from OpenAI. ## Hermes Stack Hermes Agent runtime, deployment rails, and operator resources. - [Hermes Agent](https://github.com/NousResearch/hermes-agent) - Open-source autonomous AI agent with CLI, gateway, memory, subagents, and broad tool integrations. - [Hermes Agent + hermes-fly Best Practices (this repo)](guides/hermes-agent-hermes-fly-playbook.md) - Practical setup, operations, security, and optimization playbook. - [Hermes Agent Optimization Playbook (this repo)](guides/hermes-agent-optimization-playbook.md) - Deep operator guide for context, delegation, memory, and execution tuning. - [Hermes Agent Self-Evolution](https://github.com/NousResearch/hermes-agent-self-evolution) - Evolutionary self-improvement framework for optimizing Hermes Agent prompts, skills, and code. - [Hermes Hub (this repo)](hermes/README.md) - Local operator knowledge base for Hermes setup, configuration, memory/skills workflows, and contribution orientation. - [Hermes Paperclip Adapter](https://github.com/NousResearch/hermes-paperclip-adapter) - Adapter for running Hermes Agent as a managed employee inside Paperclip. - [Hermes Stack Maturity Ladder (this repo)](guides/hermes-stack-maturity-ladder.md) - L1-L3 readiness model with upgrade paths and operational checklist. - [Hermes Stack Quickstart Recipes (this repo)](guides/hermes-stack-quickstart-recipes.md) - Copy/paste recipes for local dev, hosted production, secure mode, and CI operations. - [hermes-fly](https://github.com/alexfazio/hermes-fly) - Fly.io deployment and operations CLI for Hermes Agent with deploy, logs, doctor, and teardown workflows. ## CLI and TUI Tools Terminal-based agent interfaces and developer tools. - [aichat](https://github.com/sigoden/aichat) - All-in-one LLM CLI with chat, shell assistant, RAG, and agent features. - [Claude Code](https://docs.anthropic.com/en/docs/claude-code) - Agentic CLI that operates directly in the terminal. - [Codex CLI](https://github.com/openai/codex) - Open-source coding agent from OpenAI. - [Gemini CLI](https://github.com/google-gemini/gemini-cli) - Google's command-line interface for Gemini models. - [Glow](https://github.com/charmbracelet/glow) - Terminal Markdown renderer useful for agent output. - [Hermes Agent](https://github.com/NousResearch/hermes-agent) - CLI and gateway agent runtime with tools, memory, delegation, and automation support. - [hermes-fly](https://github.com/alexfazio/hermes-fly) - CLI wizard to deploy and operate Hermes Agent on Fly.io. - [lazygit](https://github.com/jesseduffield/lazygit) - Terminal UI for git commonly paired with coding agents. - [llm](https://github.com/simonw/llm) - CLI tool for interacting with LLMs from the terminal. - [sgpt](https://github.com/tbckr/sgpt) - Command-line productivity tool powered by LLMs. - [tmux](https://github.com/tmux/tmux) - Terminal multiplexer for running agents in persistent sessions. - [Warp](https://www.warp.dev) - Modern terminal with built-in AI assistance. - [Zellij](https://github.com/zellij-org/zellij) - Terminal workspace with plugin system for agent integration. ## Agent Runtime Infrastructure Execution sandboxes and runtime platforms for safely running agent actions and generated code. - [CUA](https://github.com/trycua/cua) - Open-source infrastructure for computer-use agents with sandboxes, SDKs, and benchmarks. - [Daytona](https://github.com/daytonaio/daytona) - Secure and elastic runtime infrastructure for AI-generated code execution. - [E2B](https://github.com/e2b-dev/E2B) - Open-source secure cloud sandbox environment for AI agents. - [Firecracker](https://github.com/firecracker-microvm/firecracker) - Secure and fast microVM technology for isolated agent execution. - [gVisor](https://github.com/google/gvisor) - Application kernel for containers that adds a strong isolation boundary. - [Kata Containers](https://github.com/kata-containers/kata-containers) - Lightweight VM-based container runtime for stronger workload isolation. - [Modal](https://modal.com) - Serverless compute platform often used for running agent workloads and tools. - [NemoClaw](https://github.com/NVIDIA/NemoClaw) - NVIDIA tooling for running OpenClaw inside an OpenShell sandbox with managed inference. - [RunPod Python SDK](https://github.com/runpod/runpod-python) - Python SDK for RunPod serverless and worker-based AI workloads. <a id="mcp-ecosystem"></a> ## Agent Protocols and MCP Ecosystem Open protocols, SDKs, servers, clients, and registries for connecting agents to tools, other agents, interfaces, and editors. - [AG-UI](https://github.com/ag-ui-protocol/ag-ui) - Event-based protocol for connecting agent backends to interactive user interfaces. - [Agent Client Protocol](https://github.com/zed-industries/agent-client-protocol) - Open protocol between coding agents and editors or IDEs. - [Agent2Agent (A2A)](https://github.com/a2aproject/A2A) - Linux Foundation protocol for communication between independent agent applications. - [Awesome MCP Servers](https://github.com/punkpeye/awesome-mcp-servers) - Curated list of MCP server implementations. - [Chrome DevTools MCP](https://github.com/ChromeDevTools/chrome-devtools-mcp) - Official Chrome DevTools MCP server for coding and browser automation agents. - [Context7 MCP](https://github.com/upstash/context7) - MCP server that retrieves current, version-specific library documentation. - [FastMCP](https://github.com/PrefectHQ/fastmcp) - Pythonic framework for building MCP servers and clients quickly. - [GitHub MCP Server](https://github.com/github/github-mcp-server) - Official MCP server for GitHub workflows and repository actions. - [MCP Agent](https://github.com/lastmile-ai/mcp-agent) - Framework patterns for building agents on top of MCP. - [MCP for Beginners](https://github.com/microsoft/mcp-for-beginners) - Cross-language curriculum and practical examples for learning MCP. - [MCP Go SDK](https://github.com/mark3labs/mcp-go) - Go implementation of the Model Context Protocol. - [MCP Inspector](https://github.com/modelcontextprotocol/inspector) - Official inspector and debugging tool for MCP servers. - [MCP Python SDK](https://github.com/modelcontextprotocol/python-sdk) - Official Python SDK for building MCP servers. - [MCP Reference Servers](https://github.com/modelcontextprotocol/servers) - Official reference implementations for Model Context Protocol servers. - [MCP Registry](https://github.com/modelcontextprotocol/registry) - Community registry service for discovering MCP servers. - [MCP Rust SDK](https://github.com/modelcontextprotocol/rust-sdk) - Official Rust SDK for building MCP servers. - [MCP Spec](https://modelcontextprotocol.io/specification) - Official Model Context Protocol specification. - [MCP Specification Repo](https://github.com/modelcontextprotocol/modelcontextprotocol) - Canonical specification and documentation repository. - [MCP TypeScript SDK](https://github.com/modelcontextprotocol/typescript-sdk) - Official TypeScript SDK for building MCP servers. - [Playwright MCP](https://github.com/microsoft/playwright-mcp) - MCP server for browser automation via Playwright. - [Smithery](https://smithery.ai) - Registry and hosting platform for MCP servers. ## Prompt Engineering Instruction-writing craft: system prompts, response framing, and reusable prompt templates. Focus here on *what to ask and how to phrase it* at the prompt layer. - [Anthropic Prompt Library](https://docs.anthropic.com/en/prompt-library) - Official prompt examples from Anthropic. - [awesome-chatgpt-prompts](https://github.com/f/prompts.chat) - Collection of prompt examples for ChatGPT. - [Claude System Prompts](https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering) - Guide to writing effective system prompts. - [DSPy](https://github.com/stanfordnlp/dspy) - Framework for programming with foundation models instead of prompting. - [fabric](https://github.com/danielmiessler/fabric) - Framework for augmenting humans using AI with curated prompts. - [LangChain Hub](https://smith.langchain.com/hub) - Community-driven prompt and chain sharing platform. - [OpenAI Prompt Engineering Guide](https://platform.openai.com/docs/guides/prompt-engineering) - Official guide to designing reliable prompts and instruction patterns. - [Promptfoo](https://github.com/promptfoo/promptfoo) - Testing and evaluation framework for LLM prompts. - [System Prompts](https://github.com/mustvlad/ChatGPT-System-Prompts) - Collection of system prompts for various AI models. ## Agent Harnessing and Evaluation Harnesses, benchmarks, and evaluation frameworks for measuring agent quality and reliability. ### Benchmark Reality Check (real-world tool use) - [MCPMark (paper)](https://arxiv.org/abs/2509.24002) - 127-task MCP benchmark; reports best pass@1 at 52.56% (gpt-5-medium), with several strong models below 30% pass@1. - [MCPMark (leaderboard)](https://mcpmark.ai/) - Live model comparisons for realistic MCP task execution. - [τ-bench](https://arxiv.org/abs/2406.12045) - Tool-agent-user benchmark; reports strong function-calling agents still below 50% task success in its setup. - [OSWorld](https://arxiv.org/abs/2404.07972) - Open-ended computer-use benchmark; reports best model 12.24% vs 72.36% human success in initial results. - [WebArena](https://arxiv.org/abs/2307.13854) - Realistic web-task benchmark; reports best GPT-4-based agent at 14.41% vs 78.24% human. - [GAIA](https://arxiv.org/abs/2311.12983) - General assistant benchmark; original framing reports large human-model gap on tool-heavy questions. - [AgentBench](https://github.com/THUDM/AgentBench) - Multi-domain benchmark suite for evaluating LLMs as agents. - [AgentEvals](https://github.com/langchain-ai/agentevals) - Evaluation utilities for scoring agent trajectories and outcomes. - [AutoGen agbench](https://github.com/microsoft/autogen/blob/main/python/packages/agbench/README.md) - Benchmark runner for AutoGen agent workflows. - [BrowserGym](https://github.com/ServiceNow/BrowserGym) - Gym-style environment for training and evaluating browser agents. - [browser-use](https://github.com/browser-use/browser-use) - Framework for browser task automation and agent web interaction loops. - [Inspect AI](https://github.com/UKGovernmentBEIS/inspect_ai) - Open-source framework for reproducible LLM and agent evaluations. - [JailbreakBench](https://github.com/JailbreakBench/jailbreakbench) - Open robustness benchmark for measuring jailbreak resistance in language models and agents. - [MCPMark](https://github.com/eval-sys/mcpmark) - Stress-testing benchmark for evaluating model and agent capability on MCP tasks. - [MLE-bench](https://github.com/openai/mle-bench) - Benchmark harness for autonomous ML engineering tasks. - [OSWorld](https://github.com/xlang-ai/OSWorld) - Open-ended benchmark environment for desktop computer-use agents. - [OpenCUA](https://github.com/xlang-ai/OpenCUA) - Open foundation stack for building and evaluating computer-use agents. - [Stagehand](https://github.com/browserbase/stagehand) - Browser automation framework for agentic web workflows and reproducible runs. - [SWE-bench](https://github.com/SWE-bench/SWE-bench) - Canonical benchmark for coding agents on real GitHub issue tasks. - [Tau-Bench](https://github.com/sierra-research/tau-bench) - Realistic interactive benchmark for measuring agent reliability. - [WebArena](https://github.com/web-arena-x/webarena) - Real-world web task benchmark environment for browser agents. - [WorkArena](https://github.com/ServiceNow/WorkArena) - Enterprise task benchmark for browser-based agent workflows. - [AgentDojo](https://github.com/ethz-spylab/agentdojo) - Security and robustness benchmark suite for tool-using agents. - [AppWorld](https://github.com/StonyBrookNLP/appworld) - Multi-application environment for benchmarking autonomous task completion. - [AgentLab](https://github.com/ServiceNow/AgentLab) - Research platform for developing and evaluating web agents. - [ALFWorld](https://alfworld.github.io/) - Interactive long-horizon benchmark environment for embodied planning agents. - [HELM](https://crfm.stanford.edu/helm/latest/) - Standardized evaluation framework for model and agent behavior comparison. - [GAIA Benchmark](https://huggingface.co/gaia-benchmark) - Realistic benchmark for tool-using, multi-step general assistant tasks. - [Agent Harnessing Playbook (this repo)](guides/agent-harnessing-playbook.md) - Practical framework for benchmark design, regression gates, and release readiness. ## ArXiv Deep Research Map Deep-dive reading map organized by the major categories in this repository. - [ArXiv Deep Research Map (this repo)](guides/arxiv-deep-research-map.md) - Curated paper paths with per-category must-reads, a recent watchlist, and a monthly refresh workflow across frameworks, coding, MCP/tool use, eval reliability, memory, security, multimodal, quant, and on-chain/DeFi-adjacent research. ## Context Engineering Systems-level context design: memory, retrieval, compression, routing, and long-horizon state management. Focus here on *what information the model gets, when, and in what form*. - [12-Factor Agents](https://github.com/humanlayer/12-factor-agents) - Engineering principles for building reliable, production-grade LLM agents. - [Anthropic: Building Effective Agents](https://www.anthropic.com/engineering/building-effective-agents) - Practical engineering patterns for agent design and execution loops. - [Anthropic: Contextual Retrieval](https://www.anthropic.com/engineering/contextual-retrieval) - Retrieval architecture guidance for improving grounding and precision. - [Anthropic: Effective Context Engineering for AI Agents](https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents) - Production guidance for context composition and lifecycle management. - [Anthropic: Effective Harnesses for Long-Running Agents](https://www.anthropic.com/engineering/effective-harnesses-for-long-running-agents) - Patterns for long-horizon orchestration and reliability. - [LangChain: Context Engineering for Agents](https://blog.langchain.com/context-engineering-for-agents/) - Practical taxonomy for writing, selecting, compressing, and isolating context. - [Manus: Context Engineering for AI Agents](https://manus.im/blog/Context-Engineering-for-AI-Agents-Lessons-from-Building-Manus) - Practitioner lessons from building production autonomous workflows. - [OpenAI Cookbook: Getting Started with Evals](https://developers.openai.com/cookbook/examples/evaluation/getting_started_with_openai_evals) - Practical eval setup walkthrough. - [OpenAI Evals Guide](https://platform.openai.com/docs/guides/evals) - Official framework for building eval loops and quality gates. - [RAG (Lewis et al., 2020)](https://arxiv.org/abs/2005.11401) - Foundational retrieval-augmented generation paper. - [Chain-of-Thought Prompting (Wei et al., 2022)](https://arxiv.org/abs/2203.02155) - Foundational reasoning/prompting technique paper. - [Lost in the Middle (Liu et al., 2023)](https://arxiv.org/abs/2307.03172) - Key long-context failure analysis paper. - [Context Engineering Playbook (this repo)](guides/context-engineering-playbook.md) - Practical context budget, memory, retrieval, and anti-drift checklist. - [Agent Operator Trend Signals (this repo)](guides/agent-operator-trend-signals-2026.md) - Synthesized practitioner themes for harness and context strategy. ## Neural Networks and Neural Linking Neural memory, retrieval, and graph-linking foundations relevant to advanced agent cognition. - [Neural Turing Machines (2014)](https://arxiv.org/abs/1410.5401) - Foundational differentiable external-memory architecture. - [End-to-End Memory Networks (2015)](https://arxiv.org/abs/1503.08895) - Multi-hop memory lookup architecture for iterative reasoning. - [Differentiable Neural Computer (2016)](https://arxiv.org/abs/1605.08582) - Enhanced neural memory addressing for long-horizon reasoning. - [Transformer-XL (2019)](https://arxiv.org/abs/1901.02860) - Segment-level recurrence for long-context memory reuse. - [Compressive Transformer (2019)](https://arxiv.org/abs/1911.05507) - Compressed memory tiers for scalable sequence retention. - [RAG (Lewis et al., 2020)](https://arxiv.org/abs/2005.11401) - Canonical retrieval-augmented generation architecture. - [kNN Language Models (2020)](https://arxiv.org/abs/1911.00172) - Non-parametric memory retrieval at inference time. - [RETRO (2021)](https://arxiv.org/abs/2112.04426) - Retrieval-heavy architecture for efficient knowledge access. - [Neural Bellman-Ford Networks (2021)](https://arxiv.org/abs/2106.06935) - Graph neural reasoning for multi-hop relational inference. - [DeepProbLog](https://github.com/ML-KULeuven/deepproblog) - Neural-symbolic framework combining perception models and logic rules. - [Neural Linking and Memory Playbook (this repo)](guides/neural-linking-memory-playbook.md) - Practical guide for agent memory architectures and neural-symbolic linking patterns. ## Obsidian Vault Architecture for Agents Obsidian-specific architecture patterns and APIs for using vaults as agent memory backends. - [Advanced URI](https://github.com/Vinzent03/obsidian-advanced-uri) - URI-based automation hooks for cross-tool workflows. - [Dataview](https://github.com/blacksmithgu/obsidian-dataview) - Query engine for structured note metadata and graph-aware retrieval. - [How Obsidian Stores Data](https://help.obsidian.md/Files+and+folders/How+Obsidian+stores+data) - Canonical vault-on-disk model and config layout. - [Juggl](https://github.com/HEmile/juggl) - Advanced graph exploration plugin for complex link topology workflows. - [Local REST API Plugin](https://github.com/coddingtonbear/obsidian-local-rest-api) - Local HTTP interface for external agent integrations. - [Obsidian Git](https://github.com/Vinzent03/obsidian-git) - Versioned vault operations for auditable agent writes. - [Obsidian Plugin Guide](https://docs.obsidian.md/Plugins/Getting+started/Build+a+plugin) - Official plugin architecture and lifecycle entrypoint. - [Obsidian Properties](https://help.obsidian.md/Editing+and+formatting/Properties) - Structured metadata schema for machine-readable note attributes. - [Obsidian TypeScript API (Vault)](https://docs.obsidian.md/Reference/TypeScript+API/Vault) - Programmatic CRUD layer for vault files. - [Obsidian Vault Architecture Playbook (this repo)](guides/obsidian-vault-architecture-playbook.md) - Reference architecture and operational patterns for agent-connected Obsidian systems. - [obsidian-api](https://github.com/obsidianmd/obsidian-api) - Official API type definitions for plugin development. ## Agent Security and Robustness Safety, red-teaming, and robustness tools for hardening agent behavior. - [garak](https://github.com/NVIDIA/garak) - LLM vulnerability scanning and red-teaming toolkit for security testing. - [Guardrails AI](https://github.com/guardrails-ai/guardrails) - Validation and safety guardrails framework for LLM outputs. - [Invariant](https://github.com/invariantlabs-ai/invariant) - Guardrails framework for secure and robust agent development. - [JailbreakBench](https://github.com/JailbreakBench/jailbreakbench) - Open robustness benchmark for measuring jailbreak resistance in language models and agents. - [llm-attacks](https://github.com/llm-attacks/llm-attacks) - Reference implementation and resources for adversarial jailbreak attack evaluation. - [MCP Security Best Practices](https://modelcontextprotocol.io/docs/tutorials/security/security_best_practices) - Official security guidance for MCP authorization flows, threats, and mitigations. - [NeMo Guardrails](https://github.com/NVIDIA-NeMo/Guardrails) - Toolkit for adding programmable safety and policy guardrails to LLM systems. - [Promptfoo](https://github.com/promptfoo/promptfoo) - Red-teaming and robustness testing toolkit for LLM systems. - [PyRIT](https://github.com/microsoft/PyRIT) - Python Risk Identification Tool for testing generative AI systems. ## Agent Configs and Dotfiles Configuration files and workflow examples for AI coding tools. - [awesome-cursorrules](https://github.com/PatrickJS/awesome-cursorrules) - Curated list of Cursor rule files. - [Claude Code Memory Files](https://docs.anthropic.com/en/docs/claude-code/memory) - Guide to CLAUDE.md and project memory. - [Claude Code Starter Configs](claude/) - Ready-to-use CLAUDE.md, rules, hooks, and skills for Claude Code projects. - [Codex CLI Starter Configs](codex/) - Ready-to-use AGENTS.md and config for OpenAI Codex CLI projects. - [Cursor Starter Configs](cursorrules/) - Ready-to-use .cursorrules and rule files for Cursor projects. - [CursorDirectory](https://cursor.directory) - Community-shared Cursor rules and configurations. - [dotfiles](https://dotfiles.github.io) - Guide to managing dotfiles including agent configurations. - [Trail of Bits Claude Code Config](https://github.com/trailofbits/claude-code-config) - Opinionated Claude Code defaults and workflows from a security-focused engineering team. ## Skill Engineering and Playbooks Hands-on resources for designing, testing, and shipping high-quality agent skills. - [Agent Skills Specification](https://github.com/agentskills/agentskills) - Open format and reference documentation for portable agent skill packages. - [Anthropic: The Complete Guide to Building Skills for Claude (PDF)](https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf) - Canonical end-to-end guide covering structure, triggering, testing, and distribution. - [anthropics/skills](https://github.com/anthropics/skills) - Official production-ready skill examples and reference implementations. - [Claude Skill Engineering Playbook (this repo)](guides/claude-skill-engineering-playbook.md) - Distilled patterns, anti-patterns, templates, and troubleshooting from the Anthropic guide. - [Claude Skills Quickstart Checklist (this repo)](guides/claude-skills-quickstart-checklist.md) - Build-test-ship checklist for repeatable skill quality. - [NVIDIA Agent Skills](https://github.com/NVIDIA/skills) - NVIDIA-maintained catalog of skills for CUDA-X libraries, blueprints, and platform tools. - [SkillsBench](https://github.com/benchflow-ai/skillsbench) - Benchmark for measuring how agents use skill packages across verifiable tasks. ## Knowledge Graphs and Memory Agent memory architectures, knowledge graphs, and second-brain integrations. - [Cognee](https://github.com/topoteretes/cognee) - Memory management layer for LLM apps using knowledge graphs. - [FalkorDB](https://github.com/FalkorDB/FalkorDB) - Ultra-fast graph database for AI agent knowledge. - [Graphiti](https://github.com/getzep/graphiti) - Real-time knowledge graph framework for AI agents. - [GraphRAG](https://github.com/microsoft/graphrag) - Graph-based retrieval augmented generation from Microsoft. - [Khoj](https://github.com/khoj-ai/khoj) - Personal AI assistant with long-term memory and knowledge search. - [LangMem](https://github.com/langchain-ai/langmem) - Memory management toolkit for building long-horizon agent systems. - [LightRAG](https://github.com/HKUDS/LightRAG) - Simple and fast RAG framework using graph structures. - [Mem0](https://github.com/mem0ai/mem0) - Memory layer for AI assistants and agents. - [Memgraph](https://github.com/memgraph/memgraph) - In-memory graph database for real-time agent queries. - [Neo4j](https://github.com/neo4j/neo4j) - Graph database platform widely used for agent knowledge stores. - [Obsidian](https://obsidian.md) - Knowledge base and note-taking app usable as agent memory backend. - [obsidian-graph-query](https://github.com/azuma520/obsidian-graph-query) - Query and traverse Obsidian vault graphs programmatically. - [ODIN](https://github.com/memgraph/odin) - Knowledge graph construction tool built on Memgraph. - [Pinecone](https://www.pinecone.io) - Vector database for semantic memory and retrieval. - [Qdrant](https://github.com/qdrant/qdrant) - High-performance vector search engine for agent memory. - [txtai](https://github.com/neuml/txtai) - All-in-one embeddings database for semantic search and workflows. - [Weaviate](https://github.com/weaviate/weaviate) - Vector database with built-in modules for AI workloads. - [Zep](https://github.com/getzep/zep) - Memory infrastructure and retrieval stack for AI assistants and agents. ## Solana Agent Infrastructure Tools and SDKs for building AI agents on Solana. - [Anchor](https://github.com/solana-foundation/anchor) - Core Solana framework for building and integrating smart contracts and clients. - [Awesome Solana AI](https://github.com/solana-foundation/awesome-solana-ai) - Solana Foundation's curated list of AI-Solana projects. - [GOAT SDK](https://github.com/goat-sdk/goat) - Open-source toolkit connecting AI agents to 200+ on-chain tools across Solana and EVM chains. - [Helius SDK](https://github.com/helius-labs/helius-sdk) - TypeScript SDK for Solana RPC, webhooks, and DAS API. - [Jito-Solana](https://github.com/jito-foundation/jito-solana) - MEV-aware Solana client infrastructure for advanced execution agents. - [Jupiter Swap API Docs](https://dev.jup.ag/docs/swap) - Official documentation for integrating Jupiter routing and swaps. - [LangChain Solana Agent Kit](https://github.com/sendaifun/solana-agent-kit) - LangChain tools for Solana agent operations. - [Light Protocol](https://github.com/Lightprotocol/light-protocol) - ZK compression for scalable on-chain agent state. - [Metaplex](https://github.com/metaplex-foundation/metaplex-program-library) - Solana programs for NFTs and digital assets used in agent identity. - [Pyth Crosschain](https://github.com/pyth-network/pyth-crosschain) - Oracle infrastructure for low-latency market data used by agent strategies. - [Solana Actions](https://github.com/solana-developers/solana-actions) - Spec and tools for blockchain-powered actions and blinks. - [Solana Agent Architecture Playbook (this repo)](guides/solana-agent-architecture-playbook.md) - Reference architecture, security controls, and ops checklist for production Solana agents. - [Solana Agent Kit](https://github.com/sendaifun/solana-agent-kit) - Toolkit for connecting AI agents to Solana protocols. - [Solana Kit](https://github.com/anza-xyz/kit) - Modern Solana client SDK stack for building high-quality applications and agents. - [Solana Web3.js](https://github.com/solana-foundation/solana-web3.js) - JavaScript SDK for interacting with the Solana blockchain. - [Switchboard Solana SDK](https://github.com/switchboard-xyz/solana-sdk) - Verifiable oracle and data-feed SDK for agent decision systems. - [Yellowstone gRPC](https://github.com/rpcpool/yellowstone-grpc) - High-throughput real-time Solana data streams for low-latency agents and indexers. ## Agent Identity and Wallets On-chain identity, wallets, and trust infrastructure for autonomous AI agents. - [Coinbase AgentKit](https://github.com/coinbase/agentkit) - Toolkit for giving AI agents programmable wallet capabilities. - [Crossmint](https://www.crossmint.com) - Wallet-as-a-service for agent-owned wallets and NFT minting. - [EIP-1271](https://eips.ethereum.org/EIPS/eip-1271) - Standard for contract wallet signature validation in dapps and agent auth flows. - [EIP-4337](https://eips.ethereum.org/EIPS/eip-4337) - Account abstraction standard enabling programmable smart accounts for agents. - [EIP-4361 (SIWE)](https://eips.ethereum.org/EIPS/eip-4361) - Sign-In with Ethereum standard for wallet-based authentication. - [EIP-7702](https://eips.ethereum.org/EIPS/eip-7702) - EOA delegation model for temporary smart-account-like behavior. - [ERC-7579](https://ercs.ethereum.org/ERCS/erc-7579) - Modular smart account standard for plugin-based permissions and execution. - [ERC-8004](https://eips.ethereum.org/EIPS/eip-8004) - Proposed standard for cross-chain agent identity. - [Lit Protocol](https://github.com/LIT-Protocol/lit-peer) - Decentralized key management and programmable signing infrastructure. - [Privy](https://www.privy.io) - Embedded wallet infrastructure for agent authentication. - [Safe](https://github.com/safe-fndn/safe-smart-account) - Multi-signature smart account for EVM agent treasuries. - [Sign-In With Solana](https://github.com/phantom/sign-in-with-solana) - Wallet-native authentication pattern for Solana apps and agents. - [Solana Agent Identity](https://github.com/sendaifun/solana-agent-kit) - Agent wallet and identity features in Solana Agent Kit. - [Squads Protocol](https://github.com/Squads-Protocol/v4) - Multisig and smart account protocol for Solana agents. - [Turnkey](https://www.turnkey.com) - Secure key infrastructure for programmatic wallet management. - [UCAN](https://github.com/ucan-wg/spec) - User-controlled authorization for decentralized agent capabilities. ## Agent Payments Payment protocols and infrastructure for autonomous agent transactions. - [Awesome Agentic Commerce](https://github.com/Merit-Systems/awesome-agentic-commerce) - Curated directory of agent payments and commerce protocols, including x402. - [Coinbase Agentic Wallets](https://www.coinbase.com/developer-platform/discover/launches/agentic-wallets) - Wallet infrastructure for AI agents with programmable spending limits. - [Google A2A x402 Extension](https://github.com/google-agentic-commerce/a2a-x402) - Cryptocurrency payments for the Agent-to-Agent protocol via x402. - [lobster.cash](https://www.lobster.cash) - Agent payment solution on Solana with Visa Intelligent Commerce integration by Crossmint. - [Request Network](https://request.network) - Crypto-native invoicing and payment request rails for agent billing workflows. - [Solana Pay](https://solanapay.com) - Open payments standard for Solana-based checkout and transfer flows. - [Superfluid](https://superfluid.org) - Streaming payment primitives for machine-to-machine and agent subscriptions. - [x402 Foundation](https://www.x402.org) - Open protocol foundation governing the x402 payment standard. - [x402 Protocol](https://github.com/coinbase/x402) - Open HTTP payment protocol using the 402 status code for agent-to-service payments. ## DeFi Agents AI agents for decentralized finance operations and strategy. - [Autonolas](https://github.com/valory-xyz/open-autonomy) - Framework for building autonomous agent services on-chain. - [DeFi Llama API](https://defillama.com/docs/api) - Open API for DeFi protocol data used by trading agents. - [Drift Protocol v2](https://github.com/drift-labs/protocol-v2) - On-chain perpetuals protocol infrastructure for autonomous trading agents. - [ElizaOS DeFi Plugins](https://github.com/elizaOS/eliza/tree/main/packages) - DeFi protocol integrations for ElizaOS agents. - [Gauntlet](https://www.gauntlet.xyz) - Risk management and simulation platform for DeFi agents. - [Griffain](https://griffain.com) - AI agent platform for Solana DeFi operations. - [Kamino KLend SDK](https://github.com/Kamino-Finance/klend-sdk) - Lending protocol SDK for credit and yield allocation agents. - [Lulo](https://lulo.fi) - Yield optimization protocol with agent-friendly APIs. - [Orca Whirlpools SDK](https://github.com/orca-so/whirlpools) - Solana concentrated liquidity SDK for agent strategies. - [Raydium SDK](https://github.com/raydium-io/raydium-sdk-V2) - Solana AMM SDK for agent-driven liquidity provision. - [Virtuals Protocol](https://www.virtuals.io) - Agent tokenization and autonomous commerce protocol tracking agentic GDP. - [Yearn Vaults](https://github.com/yearn/yearn-vaults-v3) - Automated yield vaults usable as agent strategy backends. ## Quant and Trading Agents Quantitative finance frameworks and AI-driven trading systems. - [BitQuant](https://github.com/OpenGradient/BitQuant) - Multi-agent quantitative analysis framework. - [DriftPy](https://github.com/drift-labs/driftpy) - Python SDK for building Solana-based perp and risk management agents. - [FinGPT](https://github.com/AI4Finance-Foundation/FinGPT) - Open-source financial LLM framework. - [FinRL](https://github.com/AI4Finance-Foundation/FinRL) - Deep reinforcement learning library for quantitative finance. - [Freqtrade](https://github.com/freqtrade/freqtrade) - Open-source algorithmic trading bot in Python. - [Hummingbot](https://github.com/hummingbot/hummingbot) - Open-source market making and arbitrage bot. - [Lean](https://github.com/QuantConnect/Lean) - Algorithmic trading engine by QuantConnect. - [NautilusTrader](https://github.com/nautechsystems/nautilus_trader) - High-performance algorithmic trading platform in Rust and Python. - [Phoenix v1](https://github.com/Ellipsis-Labs/phoenix-v1) - On-chain central limit order book protocol for low-latency execution agents. - [Qlib](https://github.com/microsoft/qlib) - AI-oriented quantitative investment platform from Microsoft. - [TradingAgents](https://github.com/TauricResearch/TradingAgents) - Multi-agent LLM framework simulating a trading firm. - [VectorBT](https://github.com/polakowo/vectorbt) - Fast backtesting and analysis library for trading strategies. - [Zipline](https://github.com/stefan-jansen/zipline-reloaded) - Pythonic algorithmic trading library for backtesting. ## Agent Observability and Testing Debugging, tracing, evaluation, and testing tools for AI agents. - [AgentOps](https://github.com/AgentOps-AI/agentops) - Monitoring, cost tracking, and benchmarking for agent workflows. - [Braintrust](https://www.braintrust.dev) - Evaluation and observability platform for AI products. - [DeepEval](https://github.com/confident-ai/deepeval) - Open-source LLM evaluation framework. - [Helicone](https://github.com/Helicone/helicone) - Open-source LLM observability and monitoring platform. - [LangFuse](https://github.com/langfuse/langfuse) - Open-source LLM engineering platform for tracing and evaluation. - [LangSmith](https://smith.langchain.com) - Platform for debugging, testing, and monitoring LLM applications. - [LiteLLM](https://github.com/BerriAI/litellm) - LLM gateway and proxy with logging, cost tracking, and routing controls. - [OpenAI Evals](https://github.com/openai/evals) - Framework and benchmark registry for evaluating LLM systems. - [OpenLLMetry](https://github.com/traceloop/openllmetry) - OpenTelemetry-based observability for LLM applications. - [Opik](https://github.com/comet-ml/opik) - Open-source platform for LLM and agent tracing, evaluation, and monitoring. - [Phoenix](https://github.com/Arize-ai/phoenix) - Open-source AI observability platform from Arize. - [Portkey](https://github.com/Portkey-AI/gateway) - AI gateway with observability, caching, and fallback routing. - [SigNoz](https://github.com/SigNoz/signoz) - OpenTelemetry-native observability platform for traces, logs, and metrics. - [TruLens](https://github.com/truera/trulens) - Open-source framework for evaluating and tracking LLM and agent experiments. - [Weave](https://github.com/wandb/weave) - Toolkit for tracking and evaluating LLM applications from W&B. ## Research Papers Curated papers on AI agents, multi-agent systems, and agent infrastructure. - [A Survey on Large Language Model based Autonomous Agents](https://arxiv.org/abs/2308.11432) - Comprehensive survey of LLM-based agent architectures. - [ArXiv Deep Research Map (this repo)](guides/arxiv-deep-research-map.md) - Category-by-category reading map spanning frameworks, coding, MCP/tool use, memory, security, multimodal, and quant/on-chain adjacent domains. - [Awesome AI Agent Papers](https://github.com/VoltAgent/awesome-ai-agent-papers) - Continuously updated collection of agent research papers. - [Chain-of-Thought Prompting](https://arxiv.org/abs/2201.11903) - Foundational paper on reasoning in language models. - [Generative Agents](https://arxiv.org/abs/2304.03442) - Simulating human behavior with LLM-driven agents in a sandbox. - [MemGPT](https://arxiv.org/abs/2310.08560) - OS-inspired memory management for LLM context windows. - [ReAct](https://arxiv.org/abs/2210.03629) - Synergizing reasoning and acting in language models. - [Reflexion](https://arxiv.org/abs/2303.11366) - Language agents with verbal reinforcement learning. - [The Landscape of Emerging AI Agent Architectures](https://arxiv.org/abs/2404.11584) - Survey of multi-agent design patterns. - [Toolformer](https://arxiv.org/abs/2302.04761) - Language models that learn to use tools autonomously. - [Voyager](https://arxiv.org/abs/2305.16291) - Open-ended embodied agent with LLM-powered curriculum. ## Communities Forums, Discord servers, newsletters, and social accounts. - [AI Agent Discord Servers](https://discord.gg/crewai) - CrewAI community Discord. - [Anthropic Discord](https://discord.gg/anthropic) - Official Anthropic community. - [ElizaOS Discord](https://discord.gg/elizaos) - Community for ElizaOS agent builders. - [LangChain Discord](https://discord.gg/langchain) - LangChain developer community. - [Latent Space Podcast](https://www.latent.space) - Podcast covering AI engineering and agents. - [r/artificial](https://www.reddit.com/r/artificial/) - Subreddit for AI discussions and news. - [r/LocalLLaMA](https://www.reddit.com/r/LocalLLaMA/) - Community for local LLM deployment and agent experimentation. - [Solana AI Discord](https://discord.gg/solana) - Solana developer community with AI channels. --- ## Contributing  Contributions are welcome. Read the [contribution guidelines](CONTRIBUTING.md) before opening a pull request. One focused resource per PR keeps review evidence clear. For broken links, outdated descriptions, or missing categories, use the repository's structured [issue forms](https://github.com/0xNyk/awesome-agent-cortex/issues/new/choose). [SUPPORT.md](SUPPORT.md) routes broader questions, and [SECURITY.md](SECURITY.md) covers private vulnerability reporting. Maintenance is best-effort. Inclusion does not mean endorsement, security certification, or a promise that a third-party project will remain available. ### Support the project If the directory saves you research time, you can [buy me a coffee](https://buymeacoffee.com/nyk_builderz) or support the repository with a star. Solana: `2k1oq9U99mwy4gm8P2hXPJoZusoXQCpFs35EEf5Ve73y` --- Built and maintained by [0xNyk](https://github.com/0xNyk). For agent infrastructure, trading systems, and Solana product work, visit [Builderz](https://builderz.dev). Released under [CC0 1.0 Universal](LICENSE).