标签 · Mcp — Glean

Recent picks

18picks · chronological

07-25

AI Agent Engineering in Depth: From Principles to Production

An open-source book providing a comprehensive guide to AI Agent engineering, from fundamental principles to production practices. Written by Li Bojie, it follows the core formula 'Agent = LLM + Context + Tools' across 10 chapters, covering context engineering, memory, tool use, coding agents, evaluation, post-training, continuous evolution, multimodal, and multi-agent collaboration. Includes 92 hands-on experiments (70+ runnable) on MCP, RAG, RL, etc. Ideal for engineers and researchers building production AI agents.

github.com · 15 min · Agents · AI Engineering · Context Engineering

07-23

Turn any codebase into a queryable knowledge graph, built for AI coding assistants

Graphify is an open-source tool that transforms codebases, docs, PDFs, images, etc. into a queryable knowledge graph. It uses tree-sitter for local deterministic AST parsing to extract code relationships (calls, imports, inheritance) without any LLM calls. Non-code files are semantically extracted via your AI assistant's model. The output includes an interactive HTML visualization, CLI queries (query/path/explain), and an MCP server for team use. Every edge is tagged EXTRACTED or INFERRED, providing transparency. Ideal for engineers navigating large monorepos, tracing dependencies, or understanding architecture.

github.com · 59 min · AI Agents · Developer Tools · Knowledge Graph

07-22

Local-first code intelligence graph for AI coding tools

code-review-graph is a local-first code intelligence graph that parses codebases with Tree-sitter and provides precise context to AI coding tools via the MCP protocol. It solves the problem of AI assistants re-reading large amounts of tokens during code review by performing blast-radius analysis to output only affected files. Features include 30+ MCP tools, incremental updates (<2s), a risk-scored GitHub Action for PR reviews, and support for custom languages. Benchmarks show median token reduction of 82x. Ideal for developers using AI coding assistants and teams maintaining large monorepos.

github.com · 36 min · Ai Tooling · Code Analysis · Code Intelligence

07-22

Kimi Code CLI: Terminal AI Coding Agent for Next-Gen Agents

Kimi Code CLI is an AI coding agent that runs in your terminal. It reads and edits code, runs shell commands, searches files, fetches web pages, and autonomously decides next steps based on feedback. It ships as a single binary with millisecond startup, features a purpose-built TUI, supports video input, AI-native MCP configuration, a rich plugin ecosystem, subagents for parallel tasks, lifecycle hooks, and ACP integration with editors like Zed and JetBrains. Ideal for developers seeking a powerful, extensible AI coding companion.

github.com · 4 min · AI Agents · CLI · Developer Tools

07-03

Local-first, agentic design workspace with 22 CLI agents and 150+ brand systems

Open Design is a local-first, open-source alternative to Claude Design. It is agent-native, meaning it doesn't ship its own agent but works with 22 coding-agent CLIs (Claude Code, Codex, Cursor, Copilot, etc.) already on your PATH. Using MCP, the agents read DESIGN.md brand systems, skills, and plugins to generate prototypes, live dashboards, decks, images, videos, and HyperFrames. Exports to HTML, PDF, PPTX, MP4. Supports BYOK for any OpenAI-compatible endpoint. Ships 100+ skills, 150+ brand-grade design systems, and 261 plugins. Ideal for engineers and designers who want brand fidelity and local control.

github.com · 35 min · Agent Engineering · Design Tools · Developer Tools

06-30

Browser Automation CLI for AI Agents

agent-browser is a native Rust CLI designed for AI agents to automate browser interactions. It uses a client-daemon architecture where the Rust daemon directly communicates with Chrome via CDP, eliminating the Node.js dependency. The tool offers a comprehensive command set covering navigation, element interaction (via ref/CSS/XPath/text selectors), snapshots, screenshots, network interception, session management, and authentication state persistence. It includes built-in safety features like domain allowlists, action policies, and encrypted state storage. It is optimized for AI workflows with accessibility tree snapshots, annotated screenshots, and MCP server support, making it ideal for engineers building AI agents, automated testing, web scraping, or enabling LLMs to control browsers reliably.

github.com · 64 min · AI Agents · Browser Automation · CDP

06-26

ECC: Cross-Harness Agent Operating System for Claude Code, Cursor & Beyond

ECC is a comprehensive agent harness operating system designed to enhance multiple AI coding assistants including Claude Code, Cursor, Codex, OpenCode, and GitHub Copilot. It's not a standalone AI tool but a collection of 260+ skills, 67+ agents, persistent memory, continuous learning, cost optimization, and security auditing features. By providing unified rules, hooks, and MCP configurations across platforms, it addresses issues of inconsistent agent behavior, context loss, and inadequate security. Ideal for professional developers deeply using AI coding assistants and teams seeking standardized agent engineering practices.

github.com · 94 min · Agent Engineering · Claude Code · Context Engineering

06-26

Human in the /loop

The author shares a practical workflow for coding with AI agents: define a verifiable 'definition of done' (model eval score, QA pass, green tests, performance benchmark), wrap it in a loop for the agent to iterate autonomously, and get notified via Slack when a decision is needed or the task completes. Loops run in the cloud, not on the local machine. The author runs 3-5 long loops concurrently plus shorter tasks. For engineers looking to level up from one-shot agent interactions to long-running autonomous optimization tasks.

x.com · 4 min · Agent Engineering · Ai Tooling · Context Engineering

06-24

Loop Engineering: How One Loop Ships 259 PRs a Month

This article breaks down the engineering of AI-driven development loops, contrasting a single engineer shipping 259 PRs in a month with a runaway loop that burned $47,000. It dissects six essential components—state file, automation/scheduling commands (e.g., /loop, /schedule, /goal), git worktrees, skills, MCP connectors, and sub-agents (writer vs. checker)—with concrete configuration examples for both Claude Code and OpenAI Codex. The piece provides a brake configuration template (max_turns, max_budget_usd, scope, circuit_breaker), describes four failure modes, and offers low-cost starting strategies. Aimed at engineers building or evaluating AI agent workflows.

x.com · 12 min · Agent Engineering · Ai Tooling · Claude Code

06-23

How To Use Loop Engineering To Build A Self-Improving Quant Trading System

Written by a backend developer working on quant trading systems, this article argues for moving beyond manual prompt-and-wait workflows to building self-running loops. It dissects six universal components of production loops: automation hooks, skill files (SKILL.md), state files (STATE.md), a separate verifier agent, Git worktrees for isolation, and MCP-based connectors. The author then wires these around the five-stage quant trading cycle (data ingestion, signal generation, verification, execution, risk monitoring), with a feedback mechanism that writes lessons back into the skill file for continuous improvement. Targeted at engineers building AI-driven or automated systems, especially in finance.

x.com · 13 min · Agents · Claude Code · Loop Engineering

06-23

30 Core Agentic Engineering Concepts Every Developer Should Know

This article distills 20 foundational concepts in agentic engineering, covering building blocks (Agent loop, Think-Act-Observe, state, patterns), configuration (config files, workflow files, prompt caching, context rot), capability (MCP, live document retrieval, persistent memory), orchestration (subagents, agent loops), guardrails (sandboxing, permissions, hooks, prompt injection defense, pre-commit gates), and observability (tracing, metrics). The author argues that frameworks change but these underlying ideas persist; understanding them makes any new tool familiar. Includes concrete config examples and practical advice (e.g., keep config files under 100 lines, distinguish proxy metrics from outcome metrics).

x.com · 24 min · Agent Architecture · Agents · AI Engineering

06-22

How to Build an AI Second Brain With Claude and Obsidian That Gets Smarter Every Day (Full Guide)

A step-by-step guide to building a persistent 'second brain' using Claude and Obsidian, based on Andrej Karpathy's LLM Wiki pattern. Obsidian stores all notes as local plain text files, while Claude (via MCP protocol) reads, organizes, and links the entire vault. Key steps: install Claude Desktop (paid plan), install Obsidian with Local REST API plugin, connect via MCP, create a CLAUDE.md profile via interview, structure projects with Inputs/Process/Outputs/Feedback folders, build reusable skills, wire in live data (calendar, email), and set up autopilot scheduling. The author stresses ownership (plain text, vendor-independent) and security ('keys, not prompts'). Aimed at engineers and knowledge workers tired of context loss.

x.com · 11 min · Ai-Memory · Claude Code · Context Engineering

06-17

Persistent Memory Engine for AI: Auto-Extract, Update, and Forget Intelligently

Supermemory is a memory and context layer for AI. It automatically extracts facts from conversations, builds and maintains user profiles, resolves contradictions, and intelligently forgets expired information. Combining hybrid search (RAG + memory), document processing, and live connectors (Google Drive, GitHub, etc.) into one API, it gives AI agents instant, personalized context. With plugins for Claude Code, Cursor, and more, it targets both developers integrating memory into apps and users wanting persistent AI memory across tools.

github.com · 14 min · Agent-Memory · Ai-Memory · Cloudflare

06-16

A Local-First Context Compression Layer for AI Agents: Library, Proxy, and MCP in One Stack

Headroom is a local-first context compression layer built specifically for AI coding agents. It slashes token consumption by 60-95% by compressing tool outputs, logs, files, and RAG results before they reach the LLM, all while maintaining answer accuracy. Usable as a Python/TypeScript library, a transparent proxy, a CLI wrapper for popular agents, or an MCP server, it fits into existing workflows without friction. Internally, it combines JSON structure-aware compression, AST-based code minification, and a custom fine-tuned model, grounded by a novel CCR reversible compression system that guarantees original data is never lost. This tool is ideal for engineers who rely heavily on coding agents and want to cut API costs without altering their current toolchain.

github.com · 18 min · Agents · Ast-Minification · Context Engineering

06-16

The Context Compression Layer for AI Agents: 60–95% Fewer Tokens, Zero Accuracy Loss

Headroom is a local-first context compression layer for AI agents that slashes token usage from tool outputs, logs, files, and RAG chunks by 60–95% before they reach the LLM, with preserved accuracy. It offers library, proxy, MCP server, and agent wrapper modes, using a content router to select the best compressor for JSON, code, or prose. Reversible compression ensures originals are retrievable on demand. With cross-agent memory and `headroom learn` for mining failed sessions, it is ideal for engineers running coding agents daily and anyone seeking to slash LLM costs without changing their workflow.

github.com · 18 min · Agent Architecture · Ai-Memory · Context Engineering

06-14

Hermes Agent: A Self-Improving, Multi-Platform AI Agent Runtime

Hermes Agent is a self-improving AI agent framework with a closed learning loop. It creates skills from experience, manages persistent memory across sessions, and operates over Telegram, Discord, Slack, and CLI via a single gateway. Any LLM backend can be used without code changes, and it runs on a $5 VPS or serverless infrastructure with near-zero idle cost. Built‑in cron scheduling, subagent delegation, and batch trajectory generation make it suitable for engineers and researchers who need an autonomous agent that evolves with use.

github.com · 11 min · Agent-Memory · Agents · CLI

06-13

Claude Agents & Skills for Investment Banking, Research, PE, and Wealth Management

Anthropic's official reference implementation of Claude agents for financial services, offering 9 end-to-end workflow agents for investment banking, research, PE, and wealth management, along with 8 vertical skill packs and 12+ MCP data connectors. Everything is file-based (Markdown/YAML), installable as Cowork plugins or deployable via Managed Agents API. Designed for technical teams who need ready-made finance AI workflows while retaining full customization.

github.com · 19 min · Agents · Anthropic · Financial-Services

06-12

How an Anthropic seller rebuilt his team's workflows with Claude Code

Jared Sires, a former account executive at Anthropic with no coding experience, used Claude Code to build CLAFTS, a Gmail-integrated tool that drafts customer emails in his voice while pulling context from live product documentation. The tool saves 10-15 hours per week. He expanded this into a sales plugin with skills for daily briefs, recaps, and pipeline management, wired into Salesforce, Gong, and other systems via MCP servers. About 80% of Anthropic's sales org now uses the plugin. The piece illustrates how non-technical practitioners can leverage AI coding tools to eliminate technical barriers and deliver workflow-specific software.

claude.com · 9 min · Agent Architecture · AI Engineering · Claude Code