From dd171fd096f3fccd484fcac191614e56ee2ffeee Mon Sep 17 00:00:00 2001 From: promptadmin Date: Tue, 14 Jul 2026 20:57:05 +0000 Subject: [PATCH] [upstream-sync] README.md from ARUNAGIRINATHAN-K/awesome-ai-agents-2026@8aed1b36 [catalogue] --- .../catalogue/README.md | 14 +++++++++++--- 1 file changed, 11 insertions(+), 3 deletions(-) diff --git a/upstream/ARUNAGIRINATHAN-K-awesome-ai-agents-2026/catalogue/README.md b/upstream/ARUNAGIRINATHAN-K-awesome-ai-agents-2026/catalogue/README.md index ed9dc9f..d2409c0 100644 --- a/upstream/ARUNAGIRINATHAN-K-awesome-ai-agents-2026/catalogue/README.md +++ b/upstream/ARUNAGIRINATHAN-K-awesome-ai-agents-2026/catalogue/README.md @@ -2,9 +2,9 @@ title: "Awesome AI Agents 2026" task: "" lineage_type: import -upstream_source: https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/blob/a00bf4c7/README.md -upstream_sha: a00bf4c7 -imported_at: 2026-07-08 +upstream_source: https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/blob/8aed1b36/README.md +upstream_sha: 8aed1b36 +imported_at: 2026-07-14 prompt_class: catalogue upstream_changes: accepted author: upstream @@ -139,6 +139,7 @@ validated: false - [Chroma](https://github.com/chroma-core/chroma) `🌱` `[Python]` `[Vector DB]` - Lightweight, embeddable vector store for building memory-augmented AI agents with fast semantic retrieval. - [cognee](https://github.com/topoteretes/cognee) `🌱` `[Python]` `[Graph-Based]` - Knowledge engine for AI agent memory, set up in 6 lines of code with graph-based knowledge extraction. - [Cortex Memory](https://github.com/prem-research/cortex) `🌱` `[Python]` `[Vector DB]` - Full-stack solution for agent memory covering extraction, vector search, and optimization. +- [Engrava](https://github.com/sovantica/engrava) `🔬` `[Python]` `[Graph-Based]` - Stores agent memory as a typed knowledge graph with hybrid search and a tamper-evident journal, embedded in SQLite. - [graphiti](https://github.com/getzep/graphiti) `🌱` `[Python]` `[Multi-Agent]` - Build real-time knowledge graphs for AI agents with automatic entity extraction and linking. - [Kage](https://github.com/kage-core/Kage) `🌱` `[TypeScript]` `[MCP]` - Git-native memory for coding agents that stores decisions and fixes as repo files and verifies them against the codebase, withholding stale knowledge. - [LanceDB](https://github.com/lancedb/lancedb) `🌱` `[Rust]` `[Vector DB]` - Serverless vector search database embedded directly in the agent process with no infrastructure needed. @@ -227,6 +228,7 @@ The protocol layer that enables agents to discover tools, communicate with each Sandboxes, web scrapers, browser automation, and networking layers that agents depend on. +- [Agent Bounties](https://github.com/NSPG13/agent-bounties) `🔬` `[Rust]` `[MCP]` - Coordinates verifiable digital bounty workflows designed for agents to post, fund, claim, solve, verify, and earn. - [AgentDock](https://github.com/agentdock/agentdock) `🚀` `[Python]` `[Docker]` - Framework for building and deploying production-ready AI agents with composable node architecture. - [Agent Starter](https://github.com/raintree-technology/agent-starter) `🌱` `[TypeScript]` `[MCP]` - Project-local config manager that syncs one `agent.json` manifest into Claude Code, Codex, Cursor, and MCP setup while preserving manual edits and detecting drift. - [codex-profiles](https://github.com/Ducksss/codex-profiles) `🚀` `[Python]` `[OpenAI]` - Bash CLI for switching OpenAI Codex CLI and Desktop profiles with isolated CODEX_HOME directories. @@ -256,6 +258,7 @@ Sandboxes, web scrapers, browser automation, and networking layers that agents d - [Flowise](https://github.com/FlowiseAI/Flowise) `🌱` `[TypeScript]` `[RAG]` - Open-source drag-and-drop UI to build customized LLM flows, RAG pipelines, and agent systems. - [Langflow](https://github.com/langflow-ai/langflow) `🌱` `[Python]` `[RAG]` - Visual drag-and-drop builder for LLM workflows, RAG agents, and multi-step pipelines. - [Lindy](https://www.lindy.ai) `🌱` `[Cloud]` `[No-Code]` - No-code AI agent platform with 3000+ app integrations for business workflow automation. +- [LLMGraph](https://llmgraph.ai) `🔬` `[Cloud]` `[RAG]` - Visual canvas for building RAG chatbots and AI agents with one-click deploy to REST API or chat widget. - [Make](https://www.make.com/en) `🌱` `[Cloud]` `[RAG]` - Visual workflow automation platform with AI capabilities and drag-and-drop scenario builder. - [n8n](https://github.com/n8n-io/n8n) `🌱` `[TypeScript]` `[Docker]` - Open-source workflow automation with AI agent nodes combining visual and code-based orchestration. - [Relevance AI](https://relevanceai.com) `🌱` `[Cloud]` `[No-Code]` - No-code AI agent builder for sales, support, and research use cases with team collaboration. @@ -300,6 +303,7 @@ Sandboxes, web scrapers, browser automation, and networking layers that agents d - [NeMo Guardrails](https://github.com/NVIDIA-NeMo/Guardrails) `🌱` `[Python]` `[Multi-Agent]` - NVIDIA programmable guardrails toolkit for controlling and securing LLM-powered agent conversations. - [Orchard Kit](https://github.com/OrchardHarmonics/orchard-kit) `🌱` `[Python]` `[Security]` - Modules for agent runtime security, self-audit trails, and collective cognition patterns. - [OWASP Top 10 for Agentic Apps](https://owasp.org/www-project-top-10-for-large-language-model-applications/) `🌱` `[Python]` `[Security]` - Security framework covering goal hijacking, tool misuse, and cascading failure mitigations for agents. +- [Pluribus](https://github.com/caioribeiroclw-pixel/pluribus) `🔬` `[TypeScript]` `[Observability]` - Generates cross-tool agent context and privacy-safe evidence receipts for loaded authority, handoffs, and skill use. - [Rebuff](https://github.com/protectai/rebuff) `🌱` `[Python]` `[Security]` - Self-hardening prompt injection detection system for securing agent inputs against adversarial attacks. - [ai-evaluation](https://github.com/future-agi/ai-evaluation) `🌱` `[Python]` `[Evaluation]` - LLM evaluation framework with 50+ metrics, LLM-as-Judge, and guardrail scanners (jailbreak, PII, injection). - [Future AGI](https://github.com/future-agi/future-agi) `🌱` `[Python]` `[Self-Hosted]` - Self-hostable end-to-end agent engineering platform with tracing, evals, guardrails, and gateway. @@ -397,6 +401,7 @@ Curated list of vertical agent solutions for finance, healthcare, legal, manufac - [Building Effective Agents](https://www.anthropic.com/engineering/building-effective-agents) `🚀` `[Python]` `[Anthropic]` - Anthropic's guide on agent design patterns, evaluation strategies, and production best practices. - [Hugging Face Agents Course](https://huggingface.co/learn/agents-course/unit0/introduction) `🚀` `[Python]` `[Multi-Agent]` - Open-source course on building AI agents using Hugging Face tools and models. - [Implicit Behavioral Alignment of Language Agents in High-Stakes Crowd Simulations](https://arxiv.org/abs/2509.16457) `🌱` `[Python]` `[Benchmark]` - EMNLP 2025 paper introducing PersonaEvolve, an LLM-based optimizer that refines agent personas so crowds of LLM agents behave realistically against expert benchmarks. +- [Jailbreaking LLMs' Safeguard with Universal Magic Words for Text Embedding Models](https://arxiv.org/abs/2501.18280) `🔬` `[Python]` `[Paper]` - Universal suffix that manipulates text-embedding similarity to bypass safety guardrails across ChatGPT, DeepSeek, and Qwen. - [LATS: Language Agent Tree Search](https://github.com/lapisrocks/LanguageAgentTreeSearch) `🌱` `[Python]` `[Paper]` - Combines Monte Carlo tree search with LLM reasoning for complex multi-step planning tasks. - [LLM Powered Autonomous Agents](https://lilianweng.github.io/posts/2023-06-23-agent/) `🌱` `[Python]` `[Multi-Agent]` - Deep breakdown of LLM-powered agent components: planning, memory, and tool use. - [Microsoft GenAI for Beginners](https://github.com/microsoft/generative-ai-for-beginners) `🚀` `[Python]` `[Microsoft]` - A 21-lesson course on generative AI concepts and agent development from Microsoft. @@ -431,6 +436,7 @@ Frameworks for orchestrating data transformations and long-running agent-driven - [Hamilton](https://github.com/apache/hamilton) `🌱` `[Python]` `[Pipeline]` - Defines modular data transformations as Python functions wired automatically into a DAG for agent pipelines. - [Hex AI](https://hex.tech) `🌱` `[Cloud]` `[Multi-Agent]` - Collaborative data platform with AI-powered analysis and notebook-based data exploration for teams. - [Julius AI](https://julius.ai) `🌱` `[Cloud]` `[Multi-Agent]` - Upload CSV or Excel files and analyze data using natural language questions for instant insights. +- [Nika](https://github.com/supernovae-st/nika) `🌱` `[Rust]` `[Workflow]` - Runs repeated AI work as reviewable YAML DAGs, statically checked for schema, permits, and an honest cost floor before any token is spent, with tamper-evident traces. - [PandasAI](https://github.com/sinaptik-ai/pandas-ai) `🌱` `[Python]` `[Multi-Agent]` - Chat with your data using natural language queries that convert to Pandas and SQL operations. - [Prefect](https://github.com/PrefectHQ/prefect) `🌱` `[Python]` `[Pipeline]` - Orchestrates agent workflows and data pipelines with retries, caching, and built-in observability. - [Signals CLI](https://signals.dev) `🌱` `[Cloud]` `[CLI]` - Intent signal CLI detecting LinkedIn engagers, keyword posters, and funding events with JSON output for agent pipelines. @@ -608,6 +614,7 @@ AI platforms that conduct autonomous multi-step research, synthesize findings fr | Gemini Research | 1M tokens | Google Search + KG | | Perplexity Pro | Variable | Real-time cited search | +- [Agon](https://github.com/AutoResearch-Factory/Agon) `🔬` `[Python]` `[Multi-Agent]` - Omnidisciplinary autonomous research system that replaces one-off prompts with Prompt Economy's reusable loops. - [CAJAL](https://github.com/Agnuxo1/CAJAL) `🌱` `[Python]` `[Local]` - Local AI agent that generates publication-ready IMRaD scientific papers with verified arXiv citations and AI tribunal scoring. - [ChatGPT Deep Research](https://openai.com/index/introducing-deep-research) `🚀` `[Cloud]` `[OpenAI]` - Conducts extended reasoning with web browsing to produce structured research reports with Canvas output. - [Claude Deep Research](https://www.anthropic.com/research) `🚀` `[Cloud]` `[Anthropic]` - Performs multi-step investigation with verified source citations and 200K token context window. @@ -635,6 +642,7 @@ Platforms that generate full-stack applications, components, or web apps from na - [PlayCode Agent](https://playcode.io) `🌱` `[Cloud]` `[Multi-Agent]` - Browser-only lightweight web app creation tool with AI-assisted code generation. - [Replit Agent](https://replit.com/agent4) `🌱` `[Cloud]` `[Multi-Agent]` - Builds and deploys full-stack projects from prompts with integrated hosting and collaboration. - [v0 by Vercel](https://v0.app) `🚀` `[Cloud]` `[CLI]` - Generates React and Tailwind UI components from text descriptions with one-click Vercel deployment. +- [VibeKit.bot](https://vibekit.bot) `🔬` `[Cloud]` `[Stateful]` - Builds and hosts full-stack apps from phone chat, with a persistent per-app agent and BYOK keys. ## Multi-Agent Consumer Platforms -- 2.54.0