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9 tools

AI Agent Tools

Catalog of tools for developers building AI agent-based systems. Proven solutions with value descriptions for you and your team.

Below you will find carefully selected tools that help with context management, extending agent capabilities, and automating developer workflow.

🤖

HumanLayer

.claude/ directory with agents and workflow for Claude Code

WorkflowFreeAny size8,800
HumanLayer is a YC-backed startup building tools for AI agents. Their public repository (8.8k stars) contains a .claude/ directory that's a goldmine. **Agents in .claude/agents/** - each has one role: • codebase-analyzer - analyzes code for the task, context, dependencies, impact of changes • codebase-locator - finds places in code, returns file:line format (e.g. app/services/payment_service.rb:127) • codebase-pattern-finder - finds patterns, ask "how do we handle errors?" - get examples from entire codebase • thoughts-analyzer and thoughts-locator - search thoughts/ directory as project knowledge base • web-search-researcher - searches the web when local knowledge isn't enough **Commands in .claude/commands/** - here's the magic: • /create_plan - creates implementation plan. First uses agents for research, asks questions, gathers context. Plan lands in thoughts/shared/plans/. Linear tickets save to thoughts/shared/tickets/. Pauses after each phase waiting for confirmation. • /implement_plan - implements plan, each step has a commit. After automated verification pauses: "Phase N Complete - Ready for Manual Verification" • /validate_plan - checks if implementation matches the plan, compares git diff • /commit - commits without "Co-authored by AI". Has instruction: "NEVER add co-author information" • /founder_mode - for experimental features. Made a commit without a ticket? Creates Linear ticket, branch, cherry-pick, push, PR - entire flow after the fact • /research_codebase - documents code without suggesting changes. "DO NOT suggest improvements. ONLY describe what exists." • /ralph_impl - unsupervised loop: fetches highest priority SMALL/XS ticket from Linear, creates git worktree for isolation, implements, commits, creates PR and returns for next ticket **More commands:** /iterate_plan, /oneshot, /create_handoff, /resume_handoff, /create_worktree, /debug, /local_review, /linear, /ralph_plan, /ralph_research. Also _nt (no-thoughts) and _generic variants for projects without thoughts/ directory. How to adopt? Copy .claude/ to your project and restart Claude Code.

Features

  • 6 AI agents - each with one role (analyzer, locator, pattern-finder, thoughts, web-search)
  • 27+ slash commands with _nt and _generic variants
  • /create_plan - research → questions → plan with confirmation pauses
  • /implement_plan - phase by phase, each step = commit, pause for manual verification

Integrations

Claude CodeLinearGit WorktreesGitHub

Use Cases

  • • Standardizing Claude Code workflow in teams
  • • Automating cycle: Linear ticket → plan → code → PR
  • • Overnight development with /ralph_impl (agent works alone)
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🧠

Agent OS 2.0

Operating system for AI coding agents

Context ManagementFreeAny size3,400
Agent OS is a free, open-source markdown file system that transforms AI agents from "confused interns" into productive developers. You define directory structure, naming conventions, tech stack, and testing rules - all in one place. The agent starts a session, reads the configuration, and immediately knows how to work with your project. The key value is built-in workflow commands: • shape-spec - agent ASKS about requirements instead of guessing • write-spec - creates formal specification with use cases • create-tasks - generates task list • implement-tasks - single agent executes tasks • orchestrate-tasks - multiple agents for complex features This is spec-driven development - the agent doesn't start writing code until it understands what and why. In teams, this eliminates the problem of each developer having their own prompts and chaos.

Features

  • Markdown file system for AI context
  • Built-in workflow commands (shape-spec, write-spec, create-tasks)
  • Spec-driven development
  • Multi-agent orchestration

Integrations

Claude CodeCursorCodexGeminiAny AI coding tool

Use Cases

  • • Standardizing AI work in development teams
  • • Onboarding new developers to projects
  • • Automating spec-driven development
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⚡

Augment Code

Context Engine for development teams

Code AssistantFreemiumTeam
Augment Code is an AI platform for development teams with a unique Context Engine - a system that maintains current understanding of the entire tech stack: code, dependencies, architecture, and change history. Key components: • IDE Agents - agents integrated with VS Code and JetBrains, supporting task lists for complex work, automatic memory between sessions, converting prompts to pull requests • Code Review - automatic review that "thinks like a senior engineer" • CLI - full AI functionality available in terminal Integrations with Jira and Wiki mean the agent understands not just code, but also business context and team documentation. In tests on Elasticsearch (3.6M lines of Java), Augment agents outperformed Cursor and Claude Code: correctness +14.8%, completeness +18.2%.

Features

  • Context Engine - understands entire stack
  • Jira and Wiki integration
  • IDE Agents for VS Code and JetBrains
  • Automated Code Review

Integrations

VS CodeJetBrainsJiraConfluenceGitHubGitLab

Use Cases

  • • Teams working on large codebases
  • • Automating code review
  • • Integrating AI with existing workflow (Jira, Wiki)
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🔍

Glean

Enterprise AI for company knowledge

Enterprise SearchEnterpriseEnterprise
Glean is an enterprise AI platform that combines three key elements: AI assistant, automation agents, and intelligent search. Glean indexes over 100 business applications (Slack, Teams, GitHub, Salesforce, ServiceNow, Jira) and builds an "Enterprise Graph" - a context map containing all organizational knowledge. For developers this means: • Agent Builder - graphical constructor for building agents without coding • APIs and SDKs - ability to integrate Glean into any application • Model Hub - access to latest AI models Glean Agents can automate business processes, communication, and repetitive tasks. Unlike coding-only tools, Glean understands business context and documentation. ROI: return on investment in under 6 months, 93% employee adoption in 2 years, 20% reduction in IT queries.

Features

  • Indexing 100+ business applications
  • Enterprise Graph - company knowledge map
  • No-code Agent Builder
  • APIs and SDKs for integration

Integrations

SlackMicrosoft TeamsGitHubJiraSalesforceServiceNow+3

Use Cases

  • • Centralizing company knowledge
  • • Automating business processes
  • • Onboarding new employees
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🚀

Antigravity Awesome Skills

132 ready-to-use skills for AI agents

Skills LibraryFreeAny size
Antigravity Awesome Skills is a library of 132 ready-to-use skills for AI coding assistants. These are functionalities that extend AI agent capabilities. Universal SKILL.md format means skills work across all platforms without modification: • Claude Code (Anthropic CLI) • Gemini CLI (Google DeepMind) • Cursor • GitHub Copilot • Codex CLI (OpenAI) • Antigravity IDE Skill categories: • Cybersecurity (~50) - pentesting, ethical hacking, vulnerability testing • Development (~25) - TDD, debugging, React patterns, architecture • AI & LLM (~8) - prompt engineering, autonomous agent patterns • Autonomous Systems (~8) - planning, skill creator • Automation (~6) - GitHub automation, shell scripts Skills come from official Anthropic, Vercel Labs, and OpenAI repositories - these are battle-tested solutions, not amateur prompts.

Features

  • 132 ready-to-use skills
  • Universal SKILL.md format
  • Official skills from Anthropic, Vercel, OpenAI
  • Categories: Security, Development, AI, Automation

Integrations

Claude CodeGemini CLICursorGitHub CopilotCodex CLIAntigravity IDE+1

Use Cases

  • • Extending AI agent capabilities
  • • Security testing and pentesting
  • • Automating developer workflow
VisitGitHub
🔄

Ralph Wiggum

Automated iteration loop for Claude Code

WorkflowFreeAny size
Ralph Wiggum is an official Claude Code plugin that automates iterative AI development through persistent loops. The technique feeds prompts repeatedly to Claude until completion criteria are met. Core philosophy: rather than seeking perfection on the first attempt, the methodology leverages deterministic failures as learning data points. Key features: • Iterative development with self-referential prompts • Safety controls via --max-iterations parameter • Completion detection using --completion-promise • Compatible with Git worktrees for parallel development Real-world results: • 6 repositories generated at Y Combinator hackathon • $50K contract delivered for $297 in API costs • CURSED programming language developed over 3 months Ideal for well-defined tasks with clear success metrics, projects requiring refinement cycles, and greenfield development suitable for overnight automation.

Features

  • Automated iteration loops with safety controls
  • Completion detection via text matching
  • Git worktrees compatibility
  • Claude Code ecosystem integration

Integrations

Claude CodeGit WorktreesTest FrameworksLinters

Use Cases

  • • Automating repetitive developer tasks
  • • Overnight greenfield project development
  • • Iterative code refinement with tests
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đź“š

Context7

Up-to-date documentation for LLMs and AI editors

Context ManagementFreemiumAny size
Context7 is an MCP (Model Context Protocol) server from Upstash that delivers current, version-specific documentation directly into your prompt. The problem it solves: LLMs rely on outdated training data, leading to "hallucinated APIs that don't even exist" and obsolete code examples. How it works: 1. Write your coding prompt naturally 2. Append "use context7" to trigger documentation retrieval 3. Receive code solutions using current APIs Example: "Create a Next.js middleware validating JWT cookies. use context7" Key capabilities: • Library search and discovery • Context extraction from current documentation • Version-specific code examples • Private repository integration (premium tier) Free tier available, premium unlocks higher API limits and private repository support.

Features

  • Current documentation directly in your prompt
  • MCP server compatible with multiple clients
  • Library search and discovery
  • Version-specific code examples

Integrations

Claude CodeCursorOpenCodeAny MCP-compatible client

Use Cases

  • • Eliminating API hallucinations in AI-generated code
  • • Working with latest framework versions
  • • Rapid prototyping with current documentation
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📦

Repomix

Packs entire codebase into single AI-friendly file

Context ManagementFreeAny size
Repomix is an open-source CLI tool that converts entire code repositories into single files optimized for AI models. Packages code into XML, Markdown, JSON, or plain text formats - perfect for working with Claude, ChatGPT, and Gemini. Key features: • Git-aware - automatically respects .gitignore • Secret detection - built-in Secretlint for security • Token counting - per-file and repository-wide metrics • Code compression - Tree-sitter extracts signatures, ~70% token reduction • Remote repositories - process GitHub repos directly via URL Use cases: • Code reviews and refactoring analysis • Bug investigation with full context • Implementation planning • Security audits of third-party libraries Nominated for JSNation Open Source Awards 2025 in "Powered by AI" category.

Features

  • Convert repos to XML, Markdown, JSON, plain text
  • Automatic .gitignore respect
  • Secret detection via Secretlint
  • Code compression with Tree-sitter (~70% fewer tokens)

Integrations

ClaudeChatGPTGeminiGitHub ActionsDockerMCP Server

Use Cases

  • • Full codebase context for AI code review
  • • Debugging with entire repository as context
  • • Planning implementation of new features
VisitGitHub
đź“–

Google Code Wiki

AI-generated documentation that evolves with your code

Code AssistantFreemiumAny size
Google Code Wiki is a platform powered by Gemini AI that automatically generates and maintains comprehensive, always-up-to-date documentation for code repositories. The problem it solves: Developers spend 30-40% of their time understanding code they didn't write - compounded by poor, outdated, or missing documentation. How it works: • Architecture diagrams, class relationships, and sequence flows generated automatically • Documentation regenerated after each COMMIT - eliminates "documentation drift" • Built-in Gemini chat agent answers questions using current wiki as context Benefits: • New contributors can commit on Day 1 • Senior developers understand new libraries in minutes, not days • SREs and auditors get instant access to annotated code Currently in public preview - free for public repositories. Private repo pricing TBD. Gemini CLI extension in development for teams with legacy codebases.

Features

  • Automatic documentation generation by Gemini AI
  • Architecture and class relationship diagrams
  • Documentation updates after every commit
  • Built-in chat agent for code questions

Integrations

GitHubGemini AIGemini CLI (coming soon)

Use Cases

  • • Fast onboarding of new team members
  • • Understanding legacy codebase without tribal knowledge
  • • Maintaining up-to-date documentation effortlessly
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