<?xml version="1.0" encoding="UTF-8"?><rss version="2.0" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>AgentToolDrop: newest AI agent tools</title><description>A curated, ranked directory of AI agent tools: coding agents, MCP servers, agent frameworks, browser agents, memory, evals, sandboxes and model APIs. Each entry says what it does, when to use it, when to skip it and how to install it, with live GitHub signals.</description><link>https://agenttooldrop.com</link><language>en-us</language><atom:link href="https://agenttooldrop.com/rss.xml" rel="self" type="application/rss+xml"/><item><title>Agno: Builds Python agents, teams and workflows and serves them as an API with the AgentOS runtime</title><link>https://agenttooldrop.com/tools/agno</link><guid isPermaLink="true">https://agenttooldrop.com/tools/agno</guid><description>Builds Python agents, teams and workflows and serves them as an API with the AgentOS runtime</description><pubDate>Wed, 14 Oct 2026 12:00:00 GMT</pubDate><content:encoded>&lt;p&gt;Agno is a Python SDK with three primitives (agents, teams and workflows) plus a runtime, AgentOS, that serves them through a production API with 50+ endpoints over SSE and websockets. Sessions, memory, knowledge and traces are stored in your own database, and the runtime adds human approval, OpenTelemetry tracing, JWT-based RBAC with multi-tenant isolation, cron scheduling, and interfaces for Slack, Telegram, WhatsApp, Discord, AG-UI and A2A. Starter templates deploy it with Docker to Railway, AWS, GCP, Azure, Fly, Render, Modal or Helm.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Use it when:&lt;/strong&gt; You want to build agents and also run them as a multi-user service on your own infrastructure, with data kept in your own database.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Skip it if:&lt;/strong&gt; You are not on Python. Agno sends a telemetry event per agent run by default (prompts, messages and outputs are not sent); set AGNO_TELEMETRY=false to turn it off.&lt;/p&gt;&lt;p&gt;Install with pip:&lt;/p&gt;&lt;pre&gt;&lt;code&gt;pip install -U agno openai&lt;/code&gt;&lt;/pre&gt;&lt;p&gt;Agent frameworks. Open source. License: Apache-2.0.&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://docs.agno.com/&quot;&gt;Website&lt;/a&gt;, &lt;a href=&quot;https://github.com/agno-agi/agno&quot;&gt;GitHub&lt;/a&gt;, &lt;a href=&quot;https://agenttooldrop.com/tools/agno.md&quot;&gt;Markdown&lt;/a&gt;&lt;/p&gt;</content:encoded><category>Agent frameworks</category><category>python</category><category>multi-agent</category><category>runtime</category><category>self-hosted</category><category>workflows</category></item><item><title>Aider: Pair programs with an LLM in your terminal, editing files in your git repo and committing each change</title><link>https://agenttooldrop.com/tools/aider</link><guid isPermaLink="true">https://agenttooldrop.com/tools/aider</guid><description>Pair programs with an LLM in your terminal, editing files in your git repo and committing each change</description><pubDate>Wed, 14 Oct 2026 12:00:00 GMT</pubDate><content:encoded>&lt;p&gt;Aider builds a map of your whole codebase so it can work in larger projects, and it commits its changes to git with descriptive messages so you can diff and undo them with normal git tools. It connects to most cloud LLMs and to local models, can lint and test after every edit and fix what fails, and also takes requests from comments you add in your editor. Images, web pages and voice input can be added to the chat.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Use it when:&lt;/strong&gt; You want an agent that records every change as a git commit and lets you choose the model provider, including local models.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Skip it if:&lt;/strong&gt; You want an IDE-native agent with visual diffs, or you do not want to manage your own model API keys.&lt;/p&gt;&lt;p&gt;Install with pip and aider-install:&lt;/p&gt;&lt;pre&gt;&lt;code&gt;python -m pip install aider-install
aider-install&lt;/code&gt;&lt;/pre&gt;&lt;p&gt;Coding agents. Open source. License: Apache-2.0.&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://aider.chat/&quot;&gt;Website&lt;/a&gt;, &lt;a href=&quot;https://github.com/Aider-AI/aider&quot;&gt;GitHub&lt;/a&gt;, &lt;a href=&quot;https://agenttooldrop.com/tools/aider.md&quot;&gt;Markdown&lt;/a&gt;&lt;/p&gt;</content:encoded><category>Coding agents</category><category>cli</category><category>terminal</category><category>python</category><category>git</category><category>pair-programming</category></item><item><title>Anthropic Agent Skills: Provides Anthropic&apos;s example Agent Skills, the document skills behind Claude, and the skills spec</title><link>https://agenttooldrop.com/tools/anthropic-skills</link><guid isPermaLink="true">https://agenttooldrop.com/tools/anthropic-skills</guid><description>Provides Anthropic&apos;s example Agent Skills, the document skills behind Claude, and the skills spec</description><pubDate>Wed, 14 Oct 2026 12:00:00 GMT</pubDate><content:encoded>&lt;p&gt;Skills are folders with a SKILL.md file of instructions and metadata, plus optional scripts and resources, that Claude loads dynamically for specialized tasks. This repo collects Anthropic&apos;s examples across creative, technical and enterprise work (for example mcp-builder, webapp-testing, skill-creator and frontend-design), the docx, pdf, pptx and xlsx skills that power Claude&apos;s document features, a skill template and the Agent Skills specification. In Claude Code you can add the repo as a plugin marketplace and install document-skills or example-skills.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Use it when:&lt;/strong&gt; You are writing your own skills and want working patterns to copy, or you want Anthropic&apos;s document and example skills in Claude Code.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Skip it if:&lt;/strong&gt; You want production-ready behavior: Anthropic says these skills are for demonstration and education and may differ from what Claude does. The document skills&apos; license forbids copying them outside Anthropic&apos;s services or making derivative works.&lt;/p&gt;&lt;p&gt;Add as a Claude Code plugin marketplace:&lt;/p&gt;&lt;pre&gt;&lt;code&gt;/plugin marketplace add anthropics/skills
/plugin install document-skills@anthropic-agent-skills
/plugin install example-skills@anthropic-agent-skills&lt;/code&gt;&lt;/pre&gt;&lt;p&gt;Devtools. Open source. License: Set per skill: most are Apache-2.0; docx, pdf, pptx and xlsx are source-available under Anthropic&apos;s terms.&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://github.com/anthropics/skills&quot;&gt;Website&lt;/a&gt;, &lt;a href=&quot;https://github.com/anthropics/skills&quot;&gt;GitHub&lt;/a&gt;, &lt;a href=&quot;https://agenttooldrop.com/tools/anthropic-skills.md&quot;&gt;Markdown&lt;/a&gt;&lt;/p&gt;</content:encoded><category>Devtools</category><category>agent-skills</category><category>claude</category><category>claude-code</category><category>examples</category><category>documents</category></item><item><title>Arize Phoenix: Traces LLM and agent apps over OpenTelemetry and runs evals, datasets and experiments in a local UI</title><link>https://agenttooldrop.com/tools/arize-phoenix</link><guid isPermaLink="true">https://agenttooldrop.com/tools/arize-phoenix</guid><description>Traces LLM and agent apps over OpenTelemetry and runs evals, datasets and experiments in a local UI</description><pubDate>Wed, 14 Oct 2026 12:00:00 GMT</pubDate><content:encoded>&lt;p&gt;Phoenix collects traces through OpenTelemetry-based OpenInference instrumentation, with integrations for frameworks such as the OpenAI Agents SDK, Claude Agent SDK, LangGraph and Vercel AI SDK across Python, JavaScript, Java and Go. On top of traces it offers LLM-based evals, versioned datasets, experiments, a prompt playground and prompt management. It runs locally with `phoenix serve`, in a container or on Kubernetes, and exposes a remote MCP endpoint so Claude Code, Cursor and other MCP clients can query traces, datasets and experiments.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Use it when:&lt;/strong&gt; You want an observability and eval UI on your own machine or infrastructure, instrumented with OpenTelemetry rather than a vendor-specific SDK.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Skip it if:&lt;/strong&gt; You need an OSI open source license or want to offer it as a hosted service; the Elastic License 2.0 forbids that. For a managed platform Arize points to its separate Arize AX product.&lt;/p&gt;&lt;p&gt;Install with pip:&lt;/p&gt;&lt;pre&gt;&lt;code&gt;pip install arize-phoenix
phoenix serve&lt;/code&gt;&lt;/pre&gt;&lt;p&gt;Evals and observability. Free. License: Elastic License 2.0 (source-available).&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://arize.com/phoenix/&quot;&gt;Website&lt;/a&gt;, &lt;a href=&quot;https://github.com/Arize-ai/phoenix&quot;&gt;GitHub&lt;/a&gt;, &lt;a href=&quot;https://agenttooldrop.com/tools/arize-phoenix.md&quot;&gt;Markdown&lt;/a&gt;&lt;/p&gt;</content:encoded><category>Evals and observability</category><category>tracing</category><category>evals</category><category>opentelemetry</category><category>self-hosted</category><category>python</category></item><item><title>AutoGen: Builds multi-agent AI apps that act autonomously or alongside humans; now in maintenance mode</title><link>https://agenttooldrop.com/tools/autogen</link><guid isPermaLink="true">https://agenttooldrop.com/tools/autogen</guid><description>Builds multi-agent AI apps that act autonomously or alongside humans; now in maintenance mode</description><pubDate>Wed, 14 Oct 2026 12:00:00 GMT</pubDate><content:encoded>&lt;p&gt;AutoGen is Microsoft&apos;s multi-agent framework, layered into a Core API (message passing, event-driven agents, local and distributed runtimes), an AgentChat API for common patterns such as two-agent chat and group chat, and an Extensions API for model clients and code execution. AutoGen Studio adds a no-code GUI for prototyping multi-agent workflows, though the README says it is not meant to be a production-ready app. Existing users can keep using it, but it no longer receives new features.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Use it when:&lt;/strong&gt; You already run AutoGen and need it to keep working while you plan a migration.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Skip it if:&lt;/strong&gt; You are starting a new project: AutoGen is in maintenance mode and community managed, and Microsoft recommends its successor, Microsoft Agent Framework, which has a migration guide from AutoGen.&lt;/p&gt;&lt;p&gt;Install with pip:&lt;/p&gt;&lt;pre&gt;&lt;code&gt;pip install -U &amp;quot;autogen-agentchat&amp;quot; &amp;quot;autogen-ext[openai]&amp;quot;&lt;/code&gt;&lt;/pre&gt;&lt;p&gt;Agent frameworks. Open source. License: MIT for code (LICENSE-CODE); CC-BY-4.0 for docs and other content.&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://microsoft.github.io/autogen/&quot;&gt;Website&lt;/a&gt;, &lt;a href=&quot;https://github.com/microsoft/autogen&quot;&gt;GitHub&lt;/a&gt;, &lt;a href=&quot;https://agenttooldrop.com/tools/autogen.md&quot;&gt;Markdown&lt;/a&gt;&lt;/p&gt;</content:encoded><category>Agent frameworks</category><category>python</category><category>multi-agent</category><category>event-driven</category><category>microsoft</category><category>maintenance-mode</category></item><item><title>Browser Use: Lets an LLM drive a real browser to complete multi-step web tasks from Python code or a CLI</title><link>https://agenttooldrop.com/tools/browser-use</link><guid isPermaLink="true">https://agenttooldrop.com/tools/browser-use</guid><description>Lets an LLM drive a real browser to complete multi-step web tasks from Python code or a CLI</description><pubDate>Wed, 14 Oct 2026 12:00:00 GMT</pubDate><content:encoded>&lt;p&gt;Browser Use is an open-source browser agent: you give it a task in plain language and a model, and it opens a browser, works through the site and returns a result. It works with OpenAI, Anthropic and Google models or local models through Ollama, accepts custom tools registered as Python functions, and can drive a local Chrome profile, a remote browser over CDP or a Browser Use Cloud browser. A CLI gives existing agents such as Claude Code, Codex and Cursor browser access, and an integration implements Claude&apos;s browser toolset with Browser Use as the driver.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Use it when:&lt;/strong&gt; You want an agent that completes tasks on live websites, such as forms, bookings and lookups, from your own Python code with your choice of model.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Skip it if:&lt;/strong&gt; You only need fixed, scripted browser steps with no model in the loop. The library needs Python 3.11 or later, and stealth browsers, proxies and the BU2 model are paid Browser Use Cloud services.&lt;/p&gt;&lt;p&gt;Install with uv:&lt;/p&gt;&lt;pre&gt;&lt;code&gt;uv add browser-use&lt;/code&gt;&lt;/pre&gt;&lt;p&gt;Browser and computer use. Open source. License: MIT.&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://browser-use.com&quot;&gt;Website&lt;/a&gt;, &lt;a href=&quot;https://github.com/browser-use/browser-use&quot;&gt;GitHub&lt;/a&gt;, &lt;a href=&quot;https://agenttooldrop.com/tools/browser-use.md&quot;&gt;Markdown&lt;/a&gt;&lt;/p&gt;</content:encoded><category>Browser and computer use</category><category>python</category><category>browser-automation</category><category>web-agent</category><category>cli</category><category>cloud-browser</category></item><item><title>ccusage: Reports token usage and costs for Claude Code, Codex and other coding agent CLIs from local data</title><link>https://agenttooldrop.com/tools/ccusage</link><guid isPermaLink="true">https://agenttooldrop.com/tools/ccusage</guid><description>Reports token usage and costs for Claude Code, Codex and other coding agent CLIs from local data</description><pubDate>Wed, 14 Oct 2026 12:00:00 GMT</pubDate><content:encoded>&lt;p&gt;ccusage reads the usage data that coding agent CLIs store locally and turns it into daily, weekly, monthly and session reports, with per-model breakdowns and separate cache token counts. One report covers Claude Code, Codex, OpenCode, Gemini CLI, GitHub Copilot CLI and more than a dozen other tools, and there are Claude Code extras such as 5-hour billing window reports and a status line integration. Output is a table or JSON, and an offline mode uses cached pricing data.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Use it when:&lt;/strong&gt; You want to see how many tokens and dollars your coding agent sessions use, by day, model, session or project.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Skip it if:&lt;/strong&gt; You need team-wide spend tracking: it only reads local data on the machine where it runs. Costs are calculated from LiteLLM pricing data, not read from your provider bill.&lt;/p&gt;&lt;p&gt;Run with npx:&lt;/p&gt;&lt;pre&gt;&lt;code&gt;npx ccusage@latest&lt;/code&gt;&lt;/pre&gt;&lt;p&gt;Devtools. Open source. License: MIT.&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://ccusage.com&quot;&gt;Website&lt;/a&gt;, &lt;a href=&quot;https://github.com/ccusage/ccusage&quot;&gt;GitHub&lt;/a&gt;, &lt;a href=&quot;https://agenttooldrop.com/tools/ccusage.md&quot;&gt;Markdown&lt;/a&gt;&lt;/p&gt;</content:encoded><category>Devtools</category><category>cli</category><category>token-usage</category><category>cost-tracking</category><category>claude-code</category><category>codex</category></item><item><title>Chrome DevTools MCP: Lets a coding agent control and inspect a live Chrome browser through DevTools</title><link>https://agenttooldrop.com/tools/chrome-devtools-mcp</link><guid isPermaLink="true">https://agenttooldrop.com/tools/chrome-devtools-mcp</guid><description>Lets a coding agent control and inspect a live Chrome browser through DevTools</description><pubDate>Wed, 14 Oct 2026 12:00:00 GMT</pubDate><content:encoded>&lt;p&gt;It gives an agent the parts of Chrome DevTools a developer reaches for: recording performance traces with extracted insights, inspecting network requests, reading console messages with source-mapped stack traces and taking screenshots. Automation runs on Puppeteer and waits for action results automatically. A --slim mode trims the tool set for basic browser tasks, and a separate CLI works without MCP.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Use it when:&lt;/strong&gt; You want your coding agent to check its changes in a real Chrome instance, reading console, network and performance data the way you would in DevTools.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Skip it if:&lt;/strong&gt; You need Firefox or WebKit; only Chrome and Chrome for Testing are officially supported. Usage statistics go to Google by default (opt out with --no-usage-statistics), and performance tools may send trace URLs to the CrUX API unless you pass --no-performance-crux.&lt;/p&gt;&lt;p&gt;Add to your MCP client config:&lt;/p&gt;&lt;pre&gt;&lt;code&gt;{
  &amp;quot;mcpServers&amp;quot;: {
    &amp;quot;chrome-devtools&amp;quot;: {
      &amp;quot;command&amp;quot;: &amp;quot;npx&amp;quot;,
      &amp;quot;args&amp;quot;: [
        &amp;quot;-y&amp;quot;,
        &amp;quot;chrome-devtools-mcp@latest&amp;quot;
      ]
    }
  }
}&lt;/code&gt;&lt;/pre&gt;&lt;p&gt;MCP servers. Open source. License: Apache-2.0.&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://github.com/ChromeDevTools/chrome-devtools-mcp&quot;&gt;Website&lt;/a&gt;, &lt;a href=&quot;https://github.com/ChromeDevTools/chrome-devtools-mcp&quot;&gt;GitHub&lt;/a&gt;, &lt;a href=&quot;https://agenttooldrop.com/tools/chrome-devtools-mcp.md&quot;&gt;Markdown&lt;/a&gt;&lt;/p&gt;</content:encoded><category>MCP servers</category><category>browser</category><category>chrome</category><category>debugging</category><category>performance</category><category>puppeteer</category></item><item><title>Claude Agent SDK: Embeds Claude Code&apos;s agent loop, built-in tools and context management in Python or TypeScript apps</title><link>https://agenttooldrop.com/tools/claude-agent-sdk</link><guid isPermaLink="true">https://agenttooldrop.com/tools/claude-agent-sdk</guid><description>Embeds Claude Code&apos;s agent loop, built-in tools and context management in Python or TypeScript apps</description><pubDate>Wed, 14 Oct 2026 12:00:00 GMT</pubDate><content:encoded>&lt;p&gt;The Agent SDK is a library that runs the Claude Code binary, so your agent gets the same tools (read, write and edit files, run commands, search the web), agent loop and context management that power Claude Code. You can add custom tools as in-process MCP servers, run hooks at points in the agent lifecycle, spawn subagents, control which tools run automatically through permissions, and resume or fork sessions. It is available for Python (claude-agent-sdk) and TypeScript (@anthropic-ai/claude-agent-sdk).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Use it when:&lt;/strong&gt; You want Claude Code&apos;s tool set and permission model inside a process you operate, instead of writing the tool loop yourself.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Skip it if:&lt;/strong&gt; You want to use non-Claude models. It needs an Anthropic API key or Claude through Amazon Bedrock, Google Cloud or Microsoft Foundry, and third-party products may not offer claude.ai login unless approved by Anthropic.&lt;/p&gt;&lt;p&gt;Install with pip:&lt;/p&gt;&lt;pre&gt;&lt;code&gt;pip install claude-agent-sdk&lt;/code&gt;&lt;/pre&gt;&lt;p&gt;Agent frameworks. Paid. License: Python repo MIT; use governed by Anthropic Commercial Terms of Service.&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://code.claude.com/docs/en/agent-sdk/overview&quot;&gt;Website&lt;/a&gt;, &lt;a href=&quot;https://github.com/anthropics/claude-agent-sdk-python&quot;&gt;GitHub&lt;/a&gt;, &lt;a href=&quot;https://agenttooldrop.com/tools/claude-agent-sdk.md&quot;&gt;Markdown&lt;/a&gt;&lt;/p&gt;</content:encoded><category>Agent frameworks</category><category>python</category><category>typescript</category><category>claude</category><category>mcp</category><category>hooks</category><category>subagents</category></item><item><title>Claude API: Gives programmatic access to Claude models and Claude Managed Agents over a REST API with official SDKs</title><link>https://agenttooldrop.com/tools/anthropic-api</link><guid isPermaLink="true">https://agenttooldrop.com/tools/anthropic-api</guid><description>Gives programmatic access to Claude models and Claude Managed Agents over a REST API with official SDKs</description><pubDate>Wed, 14 Oct 2026 12:00:00 GMT</pubDate><content:encoded>&lt;p&gt;The Claude API at api.anthropic.com covers the Messages API, Message Batches (asynchronous, at a 50% cost reduction), token counting, a Files API and a Skills API, with beta Agents, Sessions and Environments APIs for running managed agents in cloud sandboxes. The official SDKs handle auth headers, retries, streaming and typed requests, and the Python SDK adds tool helpers and a tool runner. Claude is also available through Amazon Bedrock, Google Cloud, Claude Platform on AWS and Microsoft Foundry if you need cloud billing and IAM.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Use it when:&lt;/strong&gt; You are building on Claude and want direct access to the latest models and features with Anthropic billing and support.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Skip it if:&lt;/strong&gt; You need to run models on your own hardware or want one API across many vendors. It requires a Claude Console account and an API key, and usage is billed.&lt;/p&gt;&lt;p&gt;Install the Python SDK:&lt;/p&gt;&lt;pre&gt;&lt;code&gt;pip install anthropic&lt;/code&gt;&lt;/pre&gt;&lt;p&gt;Models and APIs. Paid. License: Proprietary API; Python SDK is MIT.&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://claude.com/platform/api&quot;&gt;Website&lt;/a&gt;, &lt;a href=&quot;https://github.com/anthropics/anthropic-sdk-python&quot;&gt;GitHub&lt;/a&gt;, &lt;a href=&quot;https://agenttooldrop.com/tools/anthropic-api.md&quot;&gt;Markdown&lt;/a&gt;&lt;/p&gt;</content:encoded><category>Models and APIs</category><category>claude</category><category>anthropic</category><category>rest-api</category><category>sdk</category><category>python</category><category>tool-use</category></item><item><title>Claude Code: Reads your codebase, edits files, runs commands and handles git work from the terminal, IDE, desktop or web</title><link>https://agenttooldrop.com/tools/claude-code</link><guid isPermaLink="true">https://agenttooldrop.com/tools/claude-code</guid><description>Reads your codebase, edits files, runs commands and handles git work from the terminal, IDE, desktop or web</description><pubDate>Wed, 14 Oct 2026 12:00:00 GMT</pubDate><content:encoded>&lt;p&gt;Claude Code runs in the terminal, VS Code, JetBrains IDEs, a desktop app and the browser, and every surface shares the same CLAUDE.md files, settings and MCP servers. It plans multi-file changes, runs tests, stages commits and opens pull requests, and it can read an existing AGENTS.md. Subagents, skills, hooks and a non-interactive `claude -p` mode let you script it in CI, and the Agent SDK exposes the same tools for building your own agents.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Use it when:&lt;/strong&gt; You want an agent that works across a whole repo, runs your tools and handles git, and you have a Claude subscription or Anthropic Console account.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Skip it if:&lt;/strong&gt; You need an open-source agent or have no Claude subscription or Anthropic Console account; Claude Code is proprietary and most surfaces require one of them.&lt;/p&gt;&lt;p&gt;Install with the native installer (macOS, Linux, WSL):&lt;/p&gt;&lt;pre&gt;&lt;code&gt;curl -fsSL https://claude.ai/install.sh | bash&lt;/code&gt;&lt;/pre&gt;&lt;p&gt;Coding agents. Paid. License: Proprietary; Anthropic Commercial Terms of Service.&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://claude.com/product/claude-code&quot;&gt;Website&lt;/a&gt;, &lt;a href=&quot;https://github.com/anthropics/claude-code&quot;&gt;GitHub&lt;/a&gt;, &lt;a href=&quot;https://agenttooldrop.com/tools/claude-code.md&quot;&gt;Markdown&lt;/a&gt;&lt;/p&gt;</content:encoded><category>Coding agents</category><category>cli</category><category>terminal</category><category>ide-extension</category><category>mcp</category><category>anthropic</category></item><item><title>Cline: Runs an approval-based coding agent in VS Code, JetBrains, the terminal or a desktop app with any model</title><link>https://agenttooldrop.com/tools/cline</link><guid isPermaLink="true">https://agenttooldrop.com/tools/cline</guid><description>Runs an approval-based coding agent in VS Code, JetBrains, the terminal or a desktop app with any model</description><pubDate>Wed, 14 Oct 2026 12:00:00 GMT</pubDate><content:encoded>&lt;p&gt;Cline edits files across a project, runs terminal commands and reacts to their output, and in VS Code and JetBrains each edit appears as a diff with checkpoints so you can undo its work. Plan mode explores the codebase and proposes a strategy before Act mode carries it out, and every edit and command needs your approval unless you turn on auto-approve. It works with Anthropic, OpenAI, Gemini, OpenRouter, Bedrock, Ollama, LM Studio and any OpenAI-compatible API, and extends through MCP servers, SDK plugins and .clinerules files.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Use it when:&lt;/strong&gt; You want an agent inside your editor that asks before each change, and you want to pick the model provider yourself.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Skip it if:&lt;/strong&gt; You need every client to be open source (the JetBrains plugin is not), or you want an agent that runs without per-step approvals by default.&lt;/p&gt;&lt;p&gt;Install the CLI with npm:&lt;/p&gt;&lt;pre&gt;&lt;code&gt;npm i -g cline&lt;/code&gt;&lt;/pre&gt;&lt;p&gt;Coding agents. Open source. License: Apache-2.0; the JetBrains plugin is not open source.&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://cline.bot/&quot;&gt;Website&lt;/a&gt;, &lt;a href=&quot;https://github.com/cline/cline&quot;&gt;GitHub&lt;/a&gt;, &lt;a href=&quot;https://agenttooldrop.com/tools/cline.md&quot;&gt;Markdown&lt;/a&gt;&lt;/p&gt;</content:encoded><category>Coding agents</category><category>vscode</category><category>jetbrains</category><category>cli</category><category>mcp</category><category>bring-your-own-model</category></item><item><title>Cognee: Turns documents, code and conversations into a knowledge graph that agents store to and recall from</title><link>https://agenttooldrop.com/tools/cognee</link><guid isPermaLink="true">https://agenttooldrop.com/tools/cognee</guid><description>Turns documents, code and conversations into a knowledge graph that agents store to and recall from</description><pubDate>Wed, 14 Oct 2026 12:00:00 GMT</pubDate><content:encoded>&lt;p&gt;Cognee ingests text, code and session data and builds connected memory: entities, relationships and searchable chunks for text, and a graph of symbols and dependencies for code. Its API centers on four operations (remember, recall, improve and forget), and with the gliner extra it builds and queries memory with small local CPU models and no API key, while adding an LLM key enables generated answers. It ships plugins for Claude Code and Codex, an MCP server for other clients, a REST API and Docker images for self-hosting.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Use it when:&lt;/strong&gt; You want agents to keep and query project knowledge, past decisions and fixes across sessions, on infrastructure you control.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Skip it if:&lt;/strong&gt; You are not on Python 3.10 to 3.14 for the library. Running the whole memory layer on a single Postgres instance is a demo feature; the production version is a licensed product.&lt;/p&gt;&lt;p&gt;Install with uv:&lt;/p&gt;&lt;pre&gt;&lt;code&gt;uv pip install &amp;quot;cognee[gliner]&amp;quot;&lt;/code&gt;&lt;/pre&gt;&lt;p&gt;Memory. Open source. License: Apache-2.0.&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://www.cognee.ai&quot;&gt;Website&lt;/a&gt;, &lt;a href=&quot;https://github.com/topoteretes/cognee&quot;&gt;GitHub&lt;/a&gt;, &lt;a href=&quot;https://agenttooldrop.com/tools/cognee.md&quot;&gt;Markdown&lt;/a&gt;&lt;/p&gt;</content:encoded><category>Memory</category><category>python</category><category>knowledge-graph</category><category>self-hosted</category><category>mcp</category><category>local-models</category></item><item><title>Container Use: Gives each coding agent its own container and git branch so several can work in parallel without conflicts</title><link>https://agenttooldrop.com/tools/container-use</link><guid isPermaLink="true">https://agenttooldrop.com/tools/container-use</guid><description>Gives each coding agent its own container and git branch so several can work in parallel without conflicts</description><pubDate>Wed, 14 Oct 2026 12:00:00 GMT</pubDate><content:encoded>&lt;p&gt;Container Use is an MCP server, run from a CLI and built on Dagger, that gives each agent a fresh container on its own git branch, so you can run several agents at once and discard failed attempts. You can see the full command history and logs of what an agent did, drop into its terminal to take over, and review its work with a normal git checkout of its branch. Setup is the same for any MCP-compatible agent, adding container-use stdio as an MCP server, and the docs have guides for Claude Code, Cursor, Windsurf, Zed, Codex, Gemini CLI and others.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Use it when:&lt;/strong&gt; You want coding agents to work in isolated, reviewable environments on your own machine instead of directly in your checkout.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Skip it if:&lt;/strong&gt; You do not have Docker and Git installed, which the quickstart requires. The project describes itself as experimental and in early development.&lt;/p&gt;&lt;p&gt;Install with Homebrew and add to Claude Code:&lt;/p&gt;&lt;pre&gt;&lt;code&gt;brew install dagger/tap/container-use
cd /path/to/repository
claude mcp add container-use -- container-use stdio&lt;/code&gt;&lt;/pre&gt;&lt;p&gt;Sandboxes. Open source. License: Apache-2.0.&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://container-use.com&quot;&gt;Website&lt;/a&gt;, &lt;a href=&quot;https://github.com/dagger/container-use&quot;&gt;GitHub&lt;/a&gt;, &lt;a href=&quot;https://agenttooldrop.com/tools/container-use.md&quot;&gt;Markdown&lt;/a&gt;&lt;/p&gt;</content:encoded><category>Sandboxes</category><category>mcp</category><category>containers</category><category>git</category><category>docker</category><category>parallel-agents</category><category>cli</category></item><item><title>Context7: Pulls current, version-specific library docs and code examples into your agent&apos;s context</title><link>https://agenttooldrop.com/tools/context7</link><guid isPermaLink="true">https://agenttooldrop.com/tools/context7</guid><description>Pulls current, version-specific library docs and code examples into your agent&apos;s context</description><pubDate>Wed, 14 Oct 2026 12:00:00 GMT</pubDate><content:encoded>&lt;p&gt;Context7 targets outdated training data and hallucinated APIs by fetching documentation and code examples for the library and version you name in the prompt. Its MCP server has two tools: resolve-library-id maps a library name to a Context7 ID, and query-docs returns docs relevant to a question. It can also run without MCP as the ctx7 CLI plus an agent skill, and npx ctx7 setup configures either mode for Cursor, Claude Code or OpenCode.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Use it when:&lt;/strong&gt; Your agent keeps writing code against outdated or nonexistent APIs for the libraries you depend on.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Skip it if:&lt;/strong&gt; You need a fully self-hosted setup: only the MCP server and CLI are in the repo, and requests go to the hosted Context7 API, with low rate limits unless you add an API key. Library docs are community-contributed and Context7 does not guarantee their accuracy.&lt;/p&gt;&lt;p&gt;Run the setup CLI:&lt;/p&gt;&lt;pre&gt;&lt;code&gt;npx ctx7 setup&lt;/code&gt;&lt;/pre&gt;&lt;p&gt;MCP servers. Free tier + paid. License: MIT (MCP server and CLI); the API backend, parsing and crawling engines are private.&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://context7.com&quot;&gt;Website&lt;/a&gt;, &lt;a href=&quot;https://github.com/upstash/context7&quot;&gt;GitHub&lt;/a&gt;, &lt;a href=&quot;https://agenttooldrop.com/tools/context7.md&quot;&gt;Markdown&lt;/a&gt;&lt;/p&gt;</content:encoded><category>MCP servers</category><category>docs</category><category>libraries</category><category>coding-agents</category><category>remote</category><category>cli</category></item><item><title>CrewAI: Orchestrates teams of role-based agents (Crews) and event-driven workflows (Flows) in Python</title><link>https://agenttooldrop.com/tools/crewai</link><guid isPermaLink="true">https://agenttooldrop.com/tools/crewai</guid><description>Orchestrates teams of role-based agents (Crews) and event-driven workflows (Flows) in Python</description><pubDate>Wed, 14 Oct 2026 12:00:00 GMT</pubDate><content:encoded>&lt;p&gt;CrewAI is a standalone Python framework with its own primitives for agents, tasks, crews, flows, tools and orchestration. Crews let role-based agents collaborate and delegate tasks autonomously, while Flows give event-driven control with state management and conditional branching, and the two can be combined. The crewai CLI scaffolds a project where agents and tasks are defined in JSONC files and run with crewai run.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Use it when:&lt;/strong&gt; You want several specialized agents with clear roles and tasks, wrapped in deterministic, event-driven workflows in regular Python.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Skip it if:&lt;/strong&gt; You are not on Python (it requires Python 3.10 to 3.13). It collects anonymous telemetry by default (disable with OTEL_SDK_DISABLED=true), and the managed control plane, CrewAI AMP, is a separate commercial product.&lt;/p&gt;&lt;p&gt;Install the CLI with uv:&lt;/p&gt;&lt;pre&gt;&lt;code&gt;uv tool install crewai&lt;/code&gt;&lt;/pre&gt;&lt;p&gt;Agent frameworks. Open source. License: MIT.&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://crewai.com/&quot;&gt;Website&lt;/a&gt;, &lt;a href=&quot;https://github.com/crewAIInc/crewAI&quot;&gt;GitHub&lt;/a&gt;, &lt;a href=&quot;https://agenttooldrop.com/tools/crewai.md&quot;&gt;Markdown&lt;/a&gt;&lt;/p&gt;</content:encoded><category>Agent frameworks</category><category>python</category><category>multi-agent</category><category>workflows</category><category>role-based</category><category>cli</category></item><item><title>Crush: Runs a terminal coding agent from Charm that works with your choice of LLM and uses LSPs for context</title><link>https://agenttooldrop.com/tools/crush</link><guid isPermaLink="true">https://agenttooldrop.com/tools/crush</guid><description>Runs a terminal coding agent from Charm that works with your choice of LLM and uses LSPs for context</description><pubDate>Wed, 14 Oct 2026 12:00:00 GMT</pubDate><content:encoded>&lt;p&gt;Crush lets you pick from many LLM providers, or add your own through OpenAI- or Anthropic-compatible APIs and local runtimes such as Ollama and LM Studio, and switch models mid-session without losing context. It keeps multiple sessions per project, pulls extra context from language servers, and extends through MCP servers over http, stdio and sse, plus agent skills and hooks. It runs in terminals on macOS, Linux, Windows, Android, FreeBSD, OpenBSD and NetBSD.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Use it when:&lt;/strong&gt; You want a terminal agent that is not tied to one model vendor and can use the language servers you already run.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Skip it if:&lt;/strong&gt; You need an OSI open-source license: Crush uses FSL-1.1-MIT, which bars competing commercial use until a version converts to MIT after two years.&lt;/p&gt;&lt;p&gt;Install with npm:&lt;/p&gt;&lt;pre&gt;&lt;code&gt;npm install -g @charmland/crush&lt;/code&gt;&lt;/pre&gt;&lt;p&gt;Coding agents. Free. License: FSL-1.1-MIT (source-available; each version becomes MIT two years after release).&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://github.com/charmbracelet/crush&quot;&gt;Website&lt;/a&gt;, &lt;a href=&quot;https://github.com/charmbracelet/crush&quot;&gt;GitHub&lt;/a&gt;, &lt;a href=&quot;https://agenttooldrop.com/tools/crush.md&quot;&gt;Markdown&lt;/a&gt;&lt;/p&gt;</content:encoded><category>Coding agents</category><category>cli</category><category>terminal</category><category>tui</category><category>mcp</category><category>lsp</category></item><item><title>Cursor: Gives you a code editor and CLI with a built-in agent that plans, edits and reviews changes across your repo</title><link>https://agenttooldrop.com/tools/cursor</link><guid isPermaLink="true">https://agenttooldrop.com/tools/cursor</guid><description>Gives you a code editor and CLI with a built-in agent that plans, edits and reviews changes across your repo</description><pubDate>Wed, 14 Oct 2026 12:00:00 GMT</pubDate><content:encoded>&lt;p&gt;Cursor is a desktop editor with an agent that searches and reads your repo, makes multi-file changes and shows them as diffs to review. Plan Mode researches the codebase, asks clarifying questions and waits for your approval before building, and the same Agent, Plan and Ask modes are available in the Cursor CLI. Premium and higher plans add Cloud Agents that keep running while you are away, Automations and Bugbot code review.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Use it when:&lt;/strong&gt; You want one editor with the agent built in, plus a CLI and cloud agents that share the same modes and subscription.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Skip it if:&lt;/strong&gt; You want Claude, GPT or Gemini models on a low tier: third-party models need Premium or higher and are billed at API list price from prepaid credit, and the Free plan has limited usage.&lt;/p&gt;&lt;p&gt;Download from cursor.com/downloads.&lt;/p&gt;&lt;p&gt;Coding agents. Free tier + paid. License: Proprietary.&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://cursor.com/&quot;&gt;Website&lt;/a&gt;, &lt;a href=&quot;https://agenttooldrop.com/tools/cursor.md&quot;&gt;Markdown&lt;/a&gt;&lt;/p&gt;</content:encoded><category>Coding agents</category><category>ide</category><category>editor</category><category>cli</category><category>cloud-agents</category><category>code-review</category></item><item><title>Daytona: Runs AI-generated code in hosted sandboxes with their own kernel, filesystem and network stack</title><link>https://agenttooldrop.com/tools/daytona</link><guid isPermaLink="true">https://agenttooldrop.com/tools/daytona</guid><description>Runs AI-generated code in hosted sandboxes with their own kernel, filesystem and network stack</description><pubDate>Wed, 14 Oct 2026 12:00:00 GMT</pubDate><content:encoded>&lt;p&gt;Daytona sandboxes are full computers with a dedicated kernel, filesystem, network stack and allocated vCPU, RAM and disk, which agents create and control through SDKs for TypeScript, Python, Ruby, Go and Java, an API and a CLI. Operations cover sandbox lifecycle, file system, process and code execution and git, and stateful snapshots let agent work persist across sessions. The docs also cover computer use, an MCP server, and SSH, VNC and web terminal access, and the pricing page lists GPU options.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Use it when:&lt;/strong&gt; You want hosted, isolated machines for agent code execution and prefer usage-based billing to running the infrastructure yourself.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Skip it if:&lt;/strong&gt; You need to self-host or audit the platform source: core development moved to a private codebase and the public repo is archived. Billing is pay as you go per vCPU, memory and storage hour.&lt;/p&gt;&lt;p&gt;Install the Python SDK:&lt;/p&gt;&lt;pre&gt;&lt;code&gt;pip install daytona&lt;/code&gt;&lt;/pre&gt;&lt;p&gt;Sandboxes. Paid. License: Hosted service; SDKs Apache-2.0 and CLI AGPL-3.0 (daytona/clients).&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://www.daytona.io&quot;&gt;Website&lt;/a&gt;, &lt;a href=&quot;https://agenttooldrop.com/tools/daytona.md&quot;&gt;Markdown&lt;/a&gt;&lt;/p&gt;</content:encoded><category>Sandboxes</category><category>code-execution</category><category>cloud</category><category>python</category><category>typescript</category><category>snapshots</category><category>computer-use</category></item><item><title>DeepEval: Unit tests LLM apps and agents in a Pytest-style workflow with LLM-as-a-judge and other metrics</title><link>https://agenttooldrop.com/tools/deepeval</link><guid isPermaLink="true">https://agenttooldrop.com/tools/deepeval</guid><description>Unit tests LLM apps and agents in a Pytest-style workflow with LLM-as-a-judge and other metrics</description><pubDate>Wed, 14 Oct 2026 12:00:00 GMT</pubDate><content:encoded>&lt;p&gt;DeepEval works like Pytest for LLM apps: you write test cases and assert them against metrics such as G-Eval, task completion, tool correctness, answer relevancy, faithfulness and hallucination, then run them with `deepeval test run`. Metrics cover agents, RAG, multi-turn chat, MCP use and multimodal output, can be powered by any LLM, and support both end-to-end and component-level evaluation. It also generates synthetic datasets, runs benchmarks like MMLU and GSM8K, and integrates with LangChain, LangGraph, CrewAI, OpenAI Agents, Pydantic AI and others.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Use it when:&lt;/strong&gt; You want LLM quality checks written as tests that run locally or in any CI environment.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Skip it if:&lt;/strong&gt; You are not working in Python (it requires Python 3.9+). Shared reports, iteration comparisons and production monitoring live in the separate Confident AI platform, which needs an account.&lt;/p&gt;&lt;p&gt;Install with pip:&lt;/p&gt;&lt;pre&gt;&lt;code&gt;pip install -U deepeval&lt;/code&gt;&lt;/pre&gt;&lt;p&gt;Evals and observability. Open source. License: Apache-2.0.&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://deepeval.com&quot;&gt;Website&lt;/a&gt;, &lt;a href=&quot;https://github.com/confident-ai/deepeval&quot;&gt;GitHub&lt;/a&gt;, &lt;a href=&quot;https://agenttooldrop.com/tools/deepeval.md&quot;&gt;Markdown&lt;/a&gt;&lt;/p&gt;</content:encoded><category>Evals and observability</category><category>evals</category><category>python</category><category>pytest</category><category>testing</category><category>rag</category><category>agents</category></item><item><title>DSPy: Builds LM programs from typed signatures and modules, then optimizes their prompts against a metric</title><link>https://agenttooldrop.com/tools/dspy</link><guid isPermaLink="true">https://agenttooldrop.com/tools/dspy</guid><description>Builds LM programs from typed signatures and modules, then optimizes their prompts against a metric</description><pubDate>Wed, 14 Oct 2026 12:00:00 GMT</pubDate><content:encoded>&lt;p&gt;DSPy replaces hand-written prompts with Python code: signatures declare typed inputs and outputs, and modules such as Predict, ChainOfThought and ReAct control how a signature runs, including tool use. You compose modules with plain Python control flow into pipelines, RAG systems or agent loops. Optimizers such as GEPA, MIPROv2 and BootstrapFewShot take examples and a scoring function and tune the program&apos;s prompts and weights, and the result can be saved and reloaded.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Use it when:&lt;/strong&gt; You want LLM behavior defined in code and improved systematically against a metric rather than by editing prompts by hand.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Skip it if:&lt;/strong&gt; You only need a single prompt or one structured extraction call. Optimization needs example data and a scoring function, and it requires Python 3.10 or later.&lt;/p&gt;&lt;p&gt;Install with pip:&lt;/p&gt;&lt;pre&gt;&lt;code&gt;pip install dspy&lt;/code&gt;&lt;/pre&gt;&lt;p&gt;Models and APIs. Open source. License: MIT.&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://dspy.ai&quot;&gt;Website&lt;/a&gt;, &lt;a href=&quot;https://github.com/stanfordnlp/dspy&quot;&gt;GitHub&lt;/a&gt;, &lt;a href=&quot;https://agenttooldrop.com/tools/dspy.md&quot;&gt;Markdown&lt;/a&gt;&lt;/p&gt;</content:encoded><category>Models and APIs</category><category>python</category><category>prompt-optimization</category><category>signatures</category><category>agents</category><category>rag</category></item><item><title>E2B: Runs AI-generated code in isolated cloud sandboxes you start and control from Python or JavaScript</title><link>https://agenttooldrop.com/tools/e2b</link><guid isPermaLink="true">https://agenttooldrop.com/tools/e2b</guid><description>Runs AI-generated code in isolated cloud sandboxes you start and control from Python or JavaScript</description><pubDate>Wed, 14 Oct 2026 12:00:00 GMT</pubDate><content:encoded>&lt;p&gt;E2B gives agent tasks their own isolated sandbox in the cloud, started with Sandbox.create() from the JavaScript or Python SDK, where code can run shell commands and programs. The Code Interpreter SDK adds runCode/run_code for executing snippets, and the Desktop SDK adds mouse, keyboard, screenshot and desktop streaming APIs for computer-use agents. The runtime is open source and can be self-hosted with Terraform on AWS, Google Cloud, Azure or a general Linux machine.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Use it when:&lt;/strong&gt; Your agent writes code or shell commands that must run somewhere isolated from your own machines and data.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Skip it if:&lt;/strong&gt; You do not want to sign up for an E2B API key or operate the Terraform-based self-hosted runtime.&lt;/p&gt;&lt;p&gt;Install the JavaScript SDK:&lt;/p&gt;&lt;pre&gt;&lt;code&gt;npm i e2b&lt;/code&gt;&lt;/pre&gt;&lt;p&gt;Sandboxes. Open source. License: Apache-2.0.&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://e2b.dev&quot;&gt;Website&lt;/a&gt;, &lt;a href=&quot;https://github.com/e2b-dev/E2B&quot;&gt;GitHub&lt;/a&gt;, &lt;a href=&quot;https://agenttooldrop.com/tools/e2b.md&quot;&gt;Markdown&lt;/a&gt;&lt;/p&gt;</content:encoded><category>Sandboxes</category><category>code-execution</category><category>cloud</category><category>python</category><category>typescript</category><category>computer-use</category><category>self-hosted</category></item><item><title>Exa MCP Server: Gives agents Exa web search and page fetching as MCP tools, with optional multi-step research runs</title><link>https://agenttooldrop.com/tools/exa-mcp</link><guid isPermaLink="true">https://agenttooldrop.com/tools/exa-mcp</guid><description>Gives agents Exa web search and page fetching as MCP tools, with optional multi-step research runs</description><pubDate>Wed, 14 Oct 2026 12:00:00 GMT</pubDate><content:encoded>&lt;p&gt;Exa hosts the server at mcp.exa.ai/mcp and it works without an API key under rate limits, so setup is a URL or a plugin install. The default tools are web_search_exa, which returns clean content for search results, and web_fetch_exa, which reads pages as markdown; advanced search with domain and date filters and Exa Agent runs for multi-step research can be turned on with the tools URL parameter. The repo also ships search and exa-agent skills that load with plugin installs.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Use it when:&lt;/strong&gt; Your agent needs to search the web or read pages as clean markdown and you want a hosted server with nothing to install.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Skip it if:&lt;/strong&gt; You need more than light anonymous use: the keyless server is rate-limited, and Exa Agent requires OAuth or an Exa API key.&lt;/p&gt;&lt;p&gt;Install the Claude Code plugin:&lt;/p&gt;&lt;pre&gt;&lt;code&gt;claude plugin install exa@claude-plugins-official&lt;/code&gt;&lt;/pre&gt;&lt;p&gt;MCP servers. Open source. License: MIT.&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://exa.ai/docs/get-started/exa-mcp&quot;&gt;Website&lt;/a&gt;, &lt;a href=&quot;https://github.com/exa-labs/exa-mcp-server&quot;&gt;GitHub&lt;/a&gt;, &lt;a href=&quot;https://agenttooldrop.com/tools/exa-mcp.md&quot;&gt;Markdown&lt;/a&gt;&lt;/p&gt;</content:encoded><category>MCP servers</category><category>web-search</category><category>web-fetch</category><category>research</category><category>hosted</category><category>agent-skills</category></item><item><title>FastMCP: Builds MCP servers, clients and interactive apps in Python from plain decorated functions</title><link>https://agenttooldrop.com/tools/fastmcp</link><guid isPermaLink="true">https://agenttooldrop.com/tools/fastmcp</guid><description>Builds MCP servers, clients and interactive apps in Python from plain decorated functions</description><pubDate>Wed, 14 Oct 2026 12:00:00 GMT</pubDate><content:encoded>&lt;p&gt;You declare a tool as a Python function and FastMCP generates the schema, validation and documentation; when you connect to a server by URL, it handles transport negotiation, authentication and the protocol lifecycle. Besides servers and clients it supports apps, which give tools interactive UIs rendered in the conversation. FastMCP 1.0 was incorporated into the official MCP Python SDK in 2024, and Prefect maintains a TypeScript counterpart.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Use it when:&lt;/strong&gt; You are writing an MCP server or client in Python and want schemas, transports and auth handled for you.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Skip it if:&lt;/strong&gt; Your server is not written in Python; the TypeScript version lives in a separate repo, PrefectHQ/fastmcp-ts.&lt;/p&gt;&lt;p&gt;Install with pip:&lt;/p&gt;&lt;pre&gt;&lt;code&gt;pip install fastmcp&lt;/code&gt;&lt;/pre&gt;&lt;p&gt;Devtools. Open source. License: Apache-2.0.&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://gofastmcp.com&quot;&gt;Website&lt;/a&gt;, &lt;a href=&quot;https://github.com/PrefectHQ/fastmcp&quot;&gt;GitHub&lt;/a&gt;, &lt;a href=&quot;https://agenttooldrop.com/tools/fastmcp.md&quot;&gt;Markdown&lt;/a&gt;&lt;/p&gt;</content:encoded><category>Devtools</category><category>python</category><category>mcp</category><category>framework</category><category>server</category><category>client</category></item><item><title>Fetch MCP Server: Fetches a URL and returns the page as markdown in chunks the model can page through</title><link>https://agenttooldrop.com/tools/fetch-mcp-server</link><guid isPermaLink="true">https://agenttooldrop.com/tools/fetch-mcp-server</guid><description>Fetches a URL and returns the page as markdown in chunks the model can page through</description><pubDate>Wed, 14 Oct 2026 12:00:00 GMT</pubDate><content:encoded>&lt;p&gt;The reference fetch server from the MCP project. Its single fetch tool converts HTML to markdown, truncates long pages and takes a start_index so the model can continue reading where it stopped, with a raw option for unconverted content. By default it obeys robots.txt for model-initiated requests and refuses private, loopback and cloud metadata addresses; flags let you set the user agent, use a proxy or allow private IPs.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Use it when:&lt;/strong&gt; Your agent needs to read web pages by URL and your client has no built-in web fetch tool.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Skip it if:&lt;/strong&gt; Your agent already has a web fetch tool, or you need to click through and interact with pages, which a browser automation server handles. It is a reference implementation, not a production-ready server.&lt;/p&gt;&lt;p&gt;Add to your MCP client config (uvx):&lt;/p&gt;&lt;pre&gt;&lt;code&gt;{
  &amp;quot;mcpServers&amp;quot;: {
    &amp;quot;fetch&amp;quot;: {
      &amp;quot;command&amp;quot;: &amp;quot;uvx&amp;quot;,
      &amp;quot;args&amp;quot;: [
        &amp;quot;mcp-server-fetch&amp;quot;
      ]
    }
  }
}&lt;/code&gt;&lt;/pre&gt;&lt;p&gt;MCP servers. Open source. License: Apache-2.0 (repo is transitioning from MIT; contributions not yet relicensed remain MIT).&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://github.com/modelcontextprotocol/servers/tree/main/src/fetch&quot;&gt;Website&lt;/a&gt;, &lt;a href=&quot;https://github.com/modelcontextprotocol/servers&quot;&gt;GitHub&lt;/a&gt;, &lt;a href=&quot;https://agenttooldrop.com/tools/fetch-mcp-server.md&quot;&gt;Markdown&lt;/a&gt;&lt;/p&gt;</content:encoded><category>MCP servers</category><category>web</category><category>http</category><category>markdown</category><category>reference</category><category>python</category></item><item><title>Filesystem MCP Server: Lets an agent read, write, edit, move and search files inside directories you allow</title><link>https://agenttooldrop.com/tools/filesystem-mcp-server</link><guid isPermaLink="true">https://agenttooldrop.com/tools/filesystem-mcp-server</guid><description>Lets an agent read, write, edit, move and search files inside directories you allow</description><pubDate>Wed, 14 Oct 2026 12:00:00 GMT</pubDate><content:encoded>&lt;p&gt;The reference filesystem server from the MCP project, published on npm as @modelcontextprotocol/server-filesystem. Access is limited to allowed directories passed on the command line or supplied by the client through MCP Roots, which can change at runtime without a restart. Every tool carries read-only, idempotent and destructive hints, so clients can tell a directory listing apart from an overwrite.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Use it when:&lt;/strong&gt; Your MCP client has no file access of its own and you want to grant it specific directories, optionally read-only through a Docker mount.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Skip it if:&lt;/strong&gt; Your coding agent already reads and edits files natively. The servers repo describes its servers as reference implementations and educational examples, not production-ready solutions.&lt;/p&gt;&lt;p&gt;Add to your MCP client config (npx):&lt;/p&gt;&lt;pre&gt;&lt;code&gt;{
  &amp;quot;mcpServers&amp;quot;: {
    &amp;quot;filesystem&amp;quot;: {
      &amp;quot;command&amp;quot;: &amp;quot;npx&amp;quot;,
      &amp;quot;args&amp;quot;: [
        &amp;quot;-y&amp;quot;,
        &amp;quot;@modelcontextprotocol/server-filesystem&amp;quot;,
        &amp;quot;/Users/username/Desktop&amp;quot;,
        &amp;quot;/path/to/other/allowed/dir&amp;quot;
      ]
    }
  }
}&lt;/code&gt;&lt;/pre&gt;&lt;p&gt;MCP servers. Open source. License: Apache-2.0 (repo is transitioning from MIT; contributions not yet relicensed remain MIT).&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://github.com/modelcontextprotocol/servers/tree/main/src/filesystem&quot;&gt;Website&lt;/a&gt;, &lt;a href=&quot;https://github.com/modelcontextprotocol/servers&quot;&gt;GitHub&lt;/a&gt;, &lt;a href=&quot;https://agenttooldrop.com/tools/filesystem-mcp-server.md&quot;&gt;Markdown&lt;/a&gt;&lt;/p&gt;</content:encoded><category>MCP servers</category><category>filesystem</category><category>files</category><category>reference</category><category>typescript</category><category>local</category><category>docker</category></item><item><title>Firecrawl MCP Server: Lets agents search, scrape, crawl and interact with web pages through Firecrawl, returning markdown or JSON</title><link>https://agenttooldrop.com/tools/firecrawl-mcp</link><guid isPermaLink="true">https://agenttooldrop.com/tools/firecrawl-mcp</guid><description>Lets agents search, scrape, crawl and interact with web pages through Firecrawl, returning markdown or JSON</description><pubDate>Wed, 14 Oct 2026 12:00:00 GMT</pubDate><content:encoded>&lt;p&gt;The server exposes Firecrawl scrape, map, crawl, search, parse, interact and agent tools, plus monitors for recurring page checks and a credit usage tool. A hosted endpoint works keyless for scrape, search and parse within rate limits, and signing in or adding an API key unlocks the full tool set. You can also run it locally with npx or Docker and point it at a self-hosted Firecrawl API with FIRECRAWL_API_URL.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Use it when:&lt;/strong&gt; Your agent needs content from known URLs, a whole site or a search query, in a format it can read directly.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Skip it if:&lt;/strong&gt; You want to drive a browser session step by step from your own client: each interact call runs to completion and returns control. Crawl, map and agent tools need a Firecrawl account or API key.&lt;/p&gt;&lt;p&gt;Add the hosted server (OAuth sign-in):&lt;/p&gt;&lt;pre&gt;&lt;code&gt;{
  &amp;quot;mcpServers&amp;quot;: {
    &amp;quot;firecrawl&amp;quot;: {
      &amp;quot;type&amp;quot;: &amp;quot;http&amp;quot;,
      &amp;quot;url&amp;quot;: &amp;quot;https://mcp.firecrawl.dev/v2/mcp-oauth&amp;quot;
    }
  }
}&lt;/code&gt;&lt;/pre&gt;&lt;p&gt;MCP servers. Open source. License: MIT.&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://docs.firecrawl.dev/mcp-server&quot;&gt;Website&lt;/a&gt;, &lt;a href=&quot;https://github.com/firecrawl/firecrawl-mcp-server&quot;&gt;GitHub&lt;/a&gt;, &lt;a href=&quot;https://agenttooldrop.com/tools/firecrawl-mcp.md&quot;&gt;Markdown&lt;/a&gt;&lt;/p&gt;</content:encoded><category>MCP servers</category><category>web-scraping</category><category>crawling</category><category>web-search</category><category>hosted</category><category>self-hosted</category></item><item><title>Framelink MCP for Figma: Gives a coding agent the layout and styling data behind a Figma file, frame or group link</title><link>https://agenttooldrop.com/tools/figma-context-mcp</link><guid isPermaLink="true">https://agenttooldrop.com/tools/figma-context-mcp</guid><description>Gives a coding agent the layout and styling data behind a Figma file, frame or group link</description><pubDate>Wed, 14 Oct 2026 12:00:00 GMT</pubDate><content:encoded>&lt;p&gt;Paste a Figma link into your agent&apos;s chat and the server fetches that design&apos;s metadata from the Figma API, then simplifies it to the layout and styling information before handing it to the model. The author&apos;s case is that this reduced, structured context lets the model implement designs more accurately than pasting screenshots. It runs locally through npx with your own Figma access token.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Use it when:&lt;/strong&gt; You build UI from Figma designs and want the agent to read the actual file data instead of a screenshot.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Skip it if:&lt;/strong&gt; You do not use Figma or cannot create a Figma personal access token. The README says it is designed specifically for Cursor, though the same config works in other MCP clients.&lt;/p&gt;&lt;p&gt;Add to your MCP client config (macOS / Linux):&lt;/p&gt;&lt;pre&gt;&lt;code&gt;{
  &amp;quot;mcpServers&amp;quot;: {
    &amp;quot;Framelink MCP for Figma&amp;quot;: {
      &amp;quot;command&amp;quot;: &amp;quot;npx&amp;quot;,
      &amp;quot;args&amp;quot;: [
        &amp;quot;-y&amp;quot;,
        &amp;quot;figma-developer-mcp&amp;quot;,
        &amp;quot;--figma-api-key=YOUR-KEY&amp;quot;,
        &amp;quot;--stdio&amp;quot;
      ]
    }
  }
}&lt;/code&gt;&lt;/pre&gt;&lt;p&gt;MCP servers. Open source. License: MIT.&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://www.framelink.ai/&quot;&gt;Website&lt;/a&gt;, &lt;a href=&quot;https://github.com/GLips/Figma-Context-MCP&quot;&gt;GitHub&lt;/a&gt;, &lt;a href=&quot;https://agenttooldrop.com/tools/figma-context-mcp.md&quot;&gt;Markdown&lt;/a&gt;&lt;/p&gt;</content:encoded><category>MCP servers</category><category>figma</category><category>design-to-code</category><category>frontend</category><category>cursor</category></item><item><title>Gemini CLI: Brings Gemini models into the terminal to query and edit codebases, run shell commands and search the web</title><link>https://agenttooldrop.com/tools/gemini-cli</link><guid isPermaLink="true">https://agenttooldrop.com/tools/gemini-cli</guid><description>Brings Gemini models into the terminal to query and edit codebases, run shell commands and search the web</description><pubDate>Wed, 14 Oct 2026 12:00:00 GMT</pubDate><content:encoded>&lt;p&gt;Gemini CLI is a terminal agent with built-in file, shell, web fetch and Google Search grounding tools, and it adds MCP servers configured in ~/.gemini/settings.json. It reads project context from GEMINI.md, can checkpoint and resume sessions, and has a headless mode with JSON and streaming JSON output for scripts. A separate GitHub Action uses it for PR reviews and issue triage.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Use it when:&lt;/strong&gt; Your organization has a Gemini Code Assist Standard or Enterprise license, or you pay for Gemini API or Vertex AI access, and you want a terminal agent on Gemini models.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Skip it if:&lt;/strong&gt; You planned to use it for free with a personal Google account or through Google AI Pro or Ultra: Gemini CLI stopped serving those users on June 18, 2026 and Google points them to Antigravity CLI.&lt;/p&gt;&lt;p&gt;Install globally with npm:&lt;/p&gt;&lt;pre&gt;&lt;code&gt;npm install -g @google/gemini-cli&lt;/code&gt;&lt;/pre&gt;&lt;p&gt;Coding agents. Open source. License: Apache-2.0.&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://geminicli.com/&quot;&gt;Website&lt;/a&gt;, &lt;a href=&quot;https://github.com/google-gemini/gemini-cli&quot;&gt;GitHub&lt;/a&gt;, &lt;a href=&quot;https://agenttooldrop.com/tools/gemini-cli.md&quot;&gt;Markdown&lt;/a&gt;&lt;/p&gt;</content:encoded><category>Coding agents</category><category>cli</category><category>terminal</category><category>google</category><category>gemini</category><category>mcp</category></item><item><title>GitHub Copilot: Suggests code, answers questions and works on assigned tasks across your IDE, terminal and GitHub</title><link>https://agenttooldrop.com/tools/github-copilot</link><guid isPermaLink="true">https://agenttooldrop.com/tools/github-copilot</guid><description>Suggests code, answers questions and works on assigned tasks across your IDE, terminal and GitHub</description><pubDate>Wed, 14 Oct 2026 12:00:00 GMT</pubDate><content:encoded>&lt;p&gt;Copilot is built into GitHub, so an issue can be picked up by an agent, come back as a pull request, get reviewed and be merged in one place. It runs in VS Code, Visual Studio, JetBrains IDEs, Eclipse and Xcode, in a terminal CLI, in the GitHub Copilot app, on github.com and in GitHub Mobile, and it accepts custom instructions, agent skills, custom agents and MCP servers. Organization admins control access, feature policies, file exclusions and audit logs.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Use it when:&lt;/strong&gt; Your code, issues and pull requests are on GitHub and you want agent work and code review tied into that workflow.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Skip it if:&lt;/strong&gt; Your code is not on GitHub; the CLI also requires an active Copilot subscription, and premium features and larger AI credit allowances need a paid plan.&lt;/p&gt;&lt;p&gt;Install Copilot CLI with npm:&lt;/p&gt;&lt;pre&gt;&lt;code&gt;npm install -g @github/copilot&lt;/code&gt;&lt;/pre&gt;&lt;p&gt;Coding agents. Free tier + paid. License: Proprietary.&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://github.com/features/copilot&quot;&gt;Website&lt;/a&gt;, &lt;a href=&quot;https://agenttooldrop.com/tools/github-copilot.md&quot;&gt;Markdown&lt;/a&gt;&lt;/p&gt;</content:encoded><category>Coding agents</category><category>ide</category><category>cli</category><category>github</category><category>code-review</category><category>agent</category></item><item><title>GitHub MCP Server: Connects agents to GitHub repos, issues, pull requests, Actions runs and security alerts</title><link>https://agenttooldrop.com/tools/github-mcp-server</link><guid isPermaLink="true">https://agenttooldrop.com/tools/github-mcp-server</guid><description>Connects agents to GitHub repos, issues, pull requests, Actions runs and security alerts</description><pubDate>Wed, 14 Oct 2026 12:00:00 GMT</pubDate><content:encoded>&lt;p&gt;GitHub&apos;s official MCP server lets an agent browse and search code, manage issues and pull requests, monitor Actions runs and review code scanning and Dependabot alerts. GitHub hosts a remote version at api.githubcopilot.com/mcp with OAuth or personal access token auth, and the same server runs locally from a Docker image or a Go binary. Tools are grouped into toolsets you can enable selectively, and a read-only mode removes every tool that makes changes.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Use it when:&lt;/strong&gt; Your agent needs to read or act on GitHub repositories, issues, pull requests or workflows as part of its task.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Skip it if:&lt;/strong&gt; You do not use GitHub. Remote OAuth depends on each host configuring a GitHub App or OAuth App, so some clients need a personal access token, and GitHub Enterprise Server needs extra setup.&lt;/p&gt;&lt;p&gt;Add the remote server to Claude Code:&lt;/p&gt;&lt;pre&gt;&lt;code&gt;claude mcp add-json github &apos;{&amp;quot;type&amp;quot;:&amp;quot;http&amp;quot;,&amp;quot;url&amp;quot;:&amp;quot;https://api.githubcopilot.com/mcp&amp;quot;,&amp;quot;headers&amp;quot;:{&amp;quot;Authorization&amp;quot;:&amp;quot;Bearer YOUR_GITHUB_PAT&amp;quot;}}&apos;&lt;/code&gt;&lt;/pre&gt;&lt;p&gt;MCP servers. Open source. License: MIT.&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://github.com/github/github-mcp-server&quot;&gt;Website&lt;/a&gt;, &lt;a href=&quot;https://github.com/github/github-mcp-server&quot;&gt;GitHub&lt;/a&gt;, &lt;a href=&quot;https://agenttooldrop.com/tools/github-mcp-server.md&quot;&gt;Markdown&lt;/a&gt;&lt;/p&gt;</content:encoded><category>MCP servers</category><category>github</category><category>issues</category><category>pull-requests</category><category>actions</category><category>go</category><category>remote</category></item><item><title>Google Agent Development Kit (ADK): Builds, evaluates and deploys Python agents and graph-based workflows, optimized for Gemini</title><link>https://agenttooldrop.com/tools/google-adk</link><guid isPermaLink="true">https://agenttooldrop.com/tools/google-adk</guid><description>Builds, evaluates and deploys Python agents and graph-based workflows, optimized for Gemini</description><pubDate>Wed, 14 Oct 2026 12:00:00 GMT</pubDate><content:encoded>&lt;p&gt;ADK is a code-first framework where you define agents, tools and orchestration in Python, then compose them into multi-agent hierarchies or graph-based workflows with routing, fan-out/fan-in, loops, retries and human-in-the-loop. It ships a CLI with an interactive runner, a local web UI for testing and debugging, an adk eval command, and deploy commands for Docker and Cloud Run, with Vertex AI Agent Engine for scaling. It is optimized for Gemini but model-agnostic, and tools can come from custom functions, OpenAPI specs or MCP.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Use it when:&lt;/strong&gt; You want agent definitions, a local testing UI, evaluation and deployment to Cloud Run in one toolkit.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Skip it if:&lt;/strong&gt; You are on Python older than 3.11. If Python is not your stack, ADK has separate Java, Kotlin, Go and TypeScript versions in other repos.&lt;/p&gt;&lt;p&gt;Install with pip:&lt;/p&gt;&lt;pre&gt;&lt;code&gt;pip install google-adk&lt;/code&gt;&lt;/pre&gt;&lt;p&gt;Agent frameworks. Open source. License: Apache-2.0.&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://adk.dev/&quot;&gt;Website&lt;/a&gt;, &lt;a href=&quot;https://github.com/google/adk-python&quot;&gt;GitHub&lt;/a&gt;, &lt;a href=&quot;https://agenttooldrop.com/tools/google-adk.md&quot;&gt;Markdown&lt;/a&gt;&lt;/p&gt;</content:encoded><category>Agent frameworks</category><category>python</category><category>multi-agent</category><category>workflows</category><category>gemini</category><category>evals</category><category>google-cloud</category></item><item><title>goose: Runs a general-purpose AI agent on your machine as a desktop app, CLI or embeddable API</title><link>https://agenttooldrop.com/tools/goose</link><guid isPermaLink="true">https://agenttooldrop.com/tools/goose</guid><description>Runs a general-purpose AI agent on your machine as a desktop app, CLI or embeddable API</description><pubDate>Wed, 14 Oct 2026 12:00:00 GMT</pubDate><content:encoded>&lt;p&gt;goose is a local agent for code as well as research, writing, automation and data analysis, available as a desktop app for macOS, Linux and Windows, a CLI and an API. It works with 15+ providers including Anthropic, OpenAI, Google, Ollama, OpenRouter, Azure and Bedrock, and can use existing Claude, ChatGPT or Gemini subscriptions through ACP. It connects to 70+ extensions over MCP, and you can build custom distributions with preconfigured providers, extensions and branding.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Use it when:&lt;/strong&gt; You want an open-source local agent that is not tied to one model vendor and that you extend with MCP servers.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Skip it if:&lt;/strong&gt; You want a coding agent built into your IDE; goose runs as its own desktop app or CLI.&lt;/p&gt;&lt;p&gt;Install the CLI with the install script:&lt;/p&gt;&lt;pre&gt;&lt;code&gt;curl -fsSL https://github.com/aaif-goose/goose/releases/download/stable/download_cli.sh | bash&lt;/code&gt;&lt;/pre&gt;&lt;p&gt;Coding agents. Open source. License: Apache-2.0.&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://goose-docs.ai/&quot;&gt;Website&lt;/a&gt;, &lt;a href=&quot;https://github.com/aaif-goose/goose&quot;&gt;GitHub&lt;/a&gt;, &lt;a href=&quot;https://agenttooldrop.com/tools/goose.md&quot;&gt;Markdown&lt;/a&gt;&lt;/p&gt;</content:encoded><category>Coding agents</category><category>cli</category><category>desktop-app</category><category>rust</category><category>mcp</category><category>acp</category></item><item><title>Graphiti: Builds temporal knowledge graphs from agent conversations and data, tracking how facts change over time</title><link>https://agenttooldrop.com/tools/graphiti</link><guid isPermaLink="true">https://agenttooldrop.com/tools/graphiti</guid><description>Builds temporal knowledge graphs from agent conversations and data, tracking how facts change over time</description><pubDate>Wed, 14 Oct 2026 12:00:00 GMT</pubDate><content:encoded>&lt;p&gt;Graphiti turns episodes (raw text or JSON) into entities and relationships and gives each fact a validity window, so when information changes the old fact is invalidated rather than deleted and you can query what was true at any point. New data is added incrementally without recomputing the graph, and retrieval combines semantic embeddings, BM25 keyword search and graph traversal. Every fact traces back to the episode that produced it, custom entity and edge types are defined with Pydantic models, and a bundled MCP server lets assistants add and search memory.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Use it when:&lt;/strong&gt; You need agent memory that answers questions about relationships and history, not just similar text chunks.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Skip it if:&lt;/strong&gt; You do not want to run a graph database: Graphiti needs Neo4j, FalkorDB or Amazon Neptune plus an LLM with structured output, OpenAI by default. Zep is the managed option from the same team, and anonymous telemetry is on by default (opt out with GRAPHITI_TELEMETRY_ENABLED=false).&lt;/p&gt;&lt;p&gt;Install with pip:&lt;/p&gt;&lt;pre&gt;&lt;code&gt;pip install graphiti-core&lt;/code&gt;&lt;/pre&gt;&lt;p&gt;Memory. Open source. License: Apache-2.0.&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://help.getzep.com/graphiti&quot;&gt;Website&lt;/a&gt;, &lt;a href=&quot;https://github.com/getzep/graphiti&quot;&gt;GitHub&lt;/a&gt;, &lt;a href=&quot;https://agenttooldrop.com/tools/graphiti.md&quot;&gt;Markdown&lt;/a&gt;&lt;/p&gt;</content:encoded><category>Memory</category><category>python</category><category>knowledge-graph</category><category>temporal</category><category>mcp</category><category>neo4j</category></item><item><title>Inspect: Defines and runs model and agent evaluations in Python from datasets, solvers and scorers</title><link>https://agenttooldrop.com/tools/inspect-ai</link><guid isPermaLink="true">https://agenttooldrop.com/tools/inspect-ai</guid><description>Defines and runs model and agent evaluations in Python from datasets, solvers and scorers</description><pubDate>Wed, 14 Oct 2026 12:00:00 GMT</pubDate><content:encoded>&lt;p&gt;Inspect, from the UK AI Security Institute and Meridian Labs, builds evaluations from composable datasets, solvers (from a single generate call to multi-turn agents with tools) and scorers, and runs them with `inspect eval`. It supports custom and MCP tools plus built-in bash, python, web browsing and computer tools, can evaluate external agents like Claude Code, Codex CLI and Gemini CLI, and sandboxes untrusted model code in Docker, Kubernetes, Modal and other systems. It ships over 200 pre-built benchmark evals, support for over 20 model providers, the Inspect View log viewer and a VS Code extension.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Use it when:&lt;/strong&gt; You want to measure how a model or agent performs on a task suite, including agentic tasks with tools and sandboxed code, and compare results across providers.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Skip it if:&lt;/strong&gt; You need tracing of a live production app; Inspect is an evaluation framework, and evals are written in Python.&lt;/p&gt;&lt;p&gt;Install with pip:&lt;/p&gt;&lt;pre&gt;&lt;code&gt;pip install inspect-ai&lt;/code&gt;&lt;/pre&gt;&lt;p&gt;Evals and observability. Open source. License: MIT.&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://inspect.aisi.org.uk/&quot;&gt;Website&lt;/a&gt;, &lt;a href=&quot;https://github.com/UKGovernmentBEIS/inspect_ai&quot;&gt;GitHub&lt;/a&gt;, &lt;a href=&quot;https://agenttooldrop.com/tools/inspect-ai.md&quot;&gt;Markdown&lt;/a&gt;&lt;/p&gt;</content:encoded><category>Evals and observability</category><category>evals</category><category>python</category><category>agents</category><category>benchmarks</category><category>sandbox</category><category>cli</category></item><item><title>Instructor: Returns validated Pydantic objects from LLM calls, retrying automatically when validation fails</title><link>https://agenttooldrop.com/tools/instructor</link><guid isPermaLink="true">https://agenttooldrop.com/tools/instructor</guid><description>Returns validated Pydantic objects from LLM calls, retrying automatically when validation fails</description><pubDate>Wed, 14 Oct 2026 12:00:00 GMT</pubDate><content:encoded>&lt;p&gt;Instructor lets you pass a Pydantic model as `response_model` and get back a typed, validated object instead of parsing JSON or tool calls yourself. `instructor.from_provider` uses the same API for OpenAI, Anthropic, Google, Ollama, Groq and other providers. Failed validations are retried with the error message, partial objects can be streamed as they are generated, and nested models work without extra code. Ports exist for TypeScript, Ruby, Go and Elixir.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Use it when:&lt;/strong&gt; You need reliable structured data from an LLM call and want validation and retries handled for you.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Skip it if:&lt;/strong&gt; You need full agent runs, built-in observability or shareable traces; the Instructor README itself points to PydanticAI for that.&lt;/p&gt;&lt;p&gt;Install with pip:&lt;/p&gt;&lt;pre&gt;&lt;code&gt;pip install instructor&lt;/code&gt;&lt;/pre&gt;&lt;p&gt;Models and APIs. Open source. License: MIT.&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://python.useinstructor.com/&quot;&gt;Website&lt;/a&gt;, &lt;a href=&quot;https://github.com/567-labs/instructor&quot;&gt;GitHub&lt;/a&gt;, &lt;a href=&quot;https://agenttooldrop.com/tools/instructor.md&quot;&gt;Markdown&lt;/a&gt;&lt;/p&gt;</content:encoded><category>Models and APIs</category><category>structured-outputs</category><category>pydantic</category><category>python</category><category>validation</category><category>extraction</category></item><item><title>LangChain: Provides one interface and a large integration library for models, embeddings, vector stores and tools</title><link>https://agenttooldrop.com/tools/langchain</link><guid isPermaLink="true">https://agenttooldrop.com/tools/langchain</guid><description>Provides one interface and a large integration library for models, embeddings, vector stores and tools</description><pubDate>Wed, 14 Oct 2026 12:00:00 GMT</pubDate><content:encoded>&lt;p&gt;LangChain is a framework for building agents and LLM apps from interoperable components, with a large library of integrations for model providers, tools, vector stores and retrievers. Its standard model interface lets you swap models as you experiment without rebuilding the app. An equivalent JS/TS library, LangChain.js, is available, and it pairs with LangGraph for lower-level orchestration and LangSmith for evals and tracing.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Use it when:&lt;/strong&gt; You want ready-made integrations for models, vector stores and retrievers, and a single interface for switching providers.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Skip it if:&lt;/strong&gt; You need fine-grained control over agent flow and state; the README points to LangGraph for advanced customization and agent orchestration.&lt;/p&gt;&lt;p&gt;Install with pip:&lt;/p&gt;&lt;pre&gt;&lt;code&gt;pip install -U langchain&lt;/code&gt;&lt;/pre&gt;&lt;p&gt;Agent frameworks. Open source. License: MIT.&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://docs.langchain.com/oss/python/langchain/overview&quot;&gt;Website&lt;/a&gt;, &lt;a href=&quot;https://github.com/langchain-ai/langchain&quot;&gt;GitHub&lt;/a&gt;, &lt;a href=&quot;https://agenttooldrop.com/tools/langchain.md&quot;&gt;Markdown&lt;/a&gt;&lt;/p&gt;</content:encoded><category>Agent frameworks</category><category>python</category><category>integrations</category><category>model-agnostic</category><category>rag</category><category>langchain</category></item><item><title>Langfuse: Traces LLM calls and agent steps, then runs evals, datasets and prompt versioning on top of them</title><link>https://agenttooldrop.com/tools/langfuse</link><guid isPermaLink="true">https://agenttooldrop.com/tools/langfuse</guid><description>Traces LLM calls and agent steps, then runs evals, datasets and prompt versioning on top of them</description><pubDate>Wed, 14 Oct 2026 12:00:00 GMT</pubDate><content:encoded>&lt;p&gt;Langfuse instruments an app through Python and JS/TS SDKs or integrations such as the OpenAI SDK, LangChain, LlamaIndex, Vercel AI SDK and Mastra, and records LLM calls, retrieval and agent actions as traces you can inspect. The same platform covers prompt management with server and client caching, datasets for pre-deployment tests, and evaluations via LLM-as-a-judge, code evaluators, user feedback and manual labeling. You can use Langfuse Cloud or self-host it with Docker Compose, on a VM or on Kubernetes via Helm.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Use it when:&lt;/strong&gt; You need to see what your LLM app or agent did on each request and want to evaluate and iterate on prompts against the same traces.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Skip it if:&lt;/strong&gt; You do not want to run a server stack yourself and cannot use a hosted account; self-hosting means running the Docker Compose or Kubernetes deployment. Code under the ee/ directories is not MIT licensed.&lt;/p&gt;&lt;p&gt;Install the Python SDK:&lt;/p&gt;&lt;pre&gt;&lt;code&gt;pip install langfuse&lt;/code&gt;&lt;/pre&gt;&lt;p&gt;Evals and observability. Open source. License: MIT; ee/ directories under a separate enterprise license.&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://langfuse.com&quot;&gt;Website&lt;/a&gt;, &lt;a href=&quot;https://github.com/langfuse/langfuse&quot;&gt;GitHub&lt;/a&gt;, &lt;a href=&quot;https://agenttooldrop.com/tools/langfuse.md&quot;&gt;Markdown&lt;/a&gt;&lt;/p&gt;</content:encoded><category>Evals and observability</category><category>tracing</category><category>evals</category><category>prompt-management</category><category>self-hosted</category><category>python</category><category>typescript</category></item><item><title>LangGraph: Orchestrates long-running, stateful agents as graphs with durable execution and human-in-the-loop</title><link>https://agenttooldrop.com/tools/langgraph</link><guid isPermaLink="true">https://agenttooldrop.com/tools/langgraph</guid><description>Orchestrates long-running, stateful agents as graphs with durable execution and human-in-the-loop</description><pubDate>Wed, 14 Oct 2026 12:00:00 GMT</pubDate><content:encoded>&lt;p&gt;LangGraph is a low-level orchestration layer for agents and workflows that need to persist through failures and resume exactly where they left off. It provides short-term working memory and long-term memory across sessions, and lets a human inspect and modify agent state at any point during execution. It is built by LangChain Inc. but can be used without LangChain, and an equivalent JS/TS library, LangGraph.js, is available.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Use it when:&lt;/strong&gt; You want explicit control over an agent&apos;s control flow and state, with checkpoints it can resume from after a failure or a human review.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Skip it if:&lt;/strong&gt; You want a quick agent with planning, subagents and file system use out of the box; the README points to Deep Agents, a higher-level package built on LangGraph, for that.&lt;/p&gt;&lt;p&gt;Install with pip:&lt;/p&gt;&lt;pre&gt;&lt;code&gt;pip install -U langgraph&lt;/code&gt;&lt;/pre&gt;&lt;p&gt;Agent frameworks. Open source. License: MIT.&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://www.langchain.com/langgraph&quot;&gt;Website&lt;/a&gt;, &lt;a href=&quot;https://github.com/langchain-ai/langgraph&quot;&gt;GitHub&lt;/a&gt;, &lt;a href=&quot;https://agenttooldrop.com/tools/langgraph.md&quot;&gt;Markdown&lt;/a&gt;&lt;/p&gt;</content:encoded><category>Agent frameworks</category><category>python</category><category>graph</category><category>stateful</category><category>durable-execution</category><category>human-in-the-loop</category><category>langchain</category></item><item><title>LangSmith: Traces, evaluates and monitors LLM apps and agents on LangChain&apos;s hosted platform via Python and JS SDKs</title><link>https://agenttooldrop.com/tools/langsmith</link><guid isPermaLink="true">https://agenttooldrop.com/tools/langsmith</guid><description>Traces, evaluates and monitors LLM apps and agents on LangChain&apos;s hosted platform via Python and JS SDKs</description><pubDate>Wed, 14 Oct 2026 12:00:00 GMT</pubDate><content:encoded>&lt;p&gt;LangSmith is LangChain&apos;s platform for debugging, evaluating and monitoring language model apps and agents; the MIT-licensed Python and JavaScript SDKs send traces to it, for example by wrapping the OpenAI client. It works with any LLM application and has a native integration with LangChain Python and LangChain JS. There is a free Developer plan with a monthly trace allowance and paid per-seat plans, with pay-as-you-go usage beyond the included traces.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Use it when:&lt;/strong&gt; You want managed tracing and evals with no servers to run, especially if your app already uses LangChain.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Skip it if:&lt;/strong&gt; You want to self-host without an enterprise contract; self-hosted and hybrid deployment are Enterprise plan options. Usage beyond the included traces is billed pay-as-you-go.&lt;/p&gt;&lt;p&gt;Install the Python SDK:&lt;/p&gt;&lt;pre&gt;&lt;code&gt;pip install -U langsmith
export LANGSMITH_TRACING=true
export LANGSMITH_API_KEY=ls_...&lt;/code&gt;&lt;/pre&gt;&lt;p&gt;Evals and observability. Free tier + paid. License: MIT (client SDKs); the LangSmith platform is a commercial hosted service.&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://www.langchain.com/langsmith/observability&quot;&gt;Website&lt;/a&gt;, &lt;a href=&quot;https://github.com/langchain-ai/langsmith-sdk&quot;&gt;GitHub&lt;/a&gt;, &lt;a href=&quot;https://agenttooldrop.com/tools/langsmith.md&quot;&gt;Markdown&lt;/a&gt;&lt;/p&gt;</content:encoded><category>Evals and observability</category><category>tracing</category><category>evals</category><category>monitoring</category><category>langchain</category><category>python</category><category>typescript</category></item><item><title>Letta: Runs stateful agents that keep memory and identity across sessions and rewrite their own memory over time</title><link>https://agenttooldrop.com/tools/letta</link><guid isPermaLink="true">https://agenttooldrop.com/tools/letta</guid><description>Runs stateful agents that keep memory and identity across sessions and rewrite their own memory over time</description><pubDate>Wed, 14 Oct 2026 12:00:00 GMT</pubDate><content:encoded>&lt;p&gt;Letta (formerly MemGPT) is now built as Letta Code, an agent harness whose agents keep memory, identity and conversation history and improve by rewriting their own memory blocks, skills and prompts. All context, including memory blocks, is tracked in git (MemFS), and agents can search past messages, call subagents and run on schedules. You reach the same agent from a terminal UI, a desktop app for macOS, Windows and Linux, chat.letta.com, or Slack, Telegram and Discord, with agent state stored in Letta Cloud or kept local.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Use it when:&lt;/strong&gt; You want a ready-made agent that remembers and adapts over long periods, rather than a memory library to wire into your own agent.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Skip it if:&lt;/strong&gt; You need the older Letta V1 API server, which is retired to the repo&apos;s archive branch. Letta Cloud is the default backend, and remote computers and secrets require signing in with Letta.&lt;/p&gt;&lt;p&gt;Install with npm:&lt;/p&gt;&lt;pre&gt;&lt;code&gt;npm install -g @letta-ai/letta-code&lt;/code&gt;&lt;/pre&gt;&lt;p&gt;Memory. Open source. License: Apache-2.0.&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://www.letta.com&quot;&gt;Website&lt;/a&gt;, &lt;a href=&quot;https://github.com/letta-ai/letta-code&quot;&gt;GitHub&lt;/a&gt;, &lt;a href=&quot;https://agenttooldrop.com/tools/letta.md&quot;&gt;Markdown&lt;/a&gt;&lt;/p&gt;</content:encoded><category>Memory</category><category>stateful-agents</category><category>cli</category><category>typescript</category><category>desktop-app</category><category>self-improving</category></item><item><title>LiteLLM: Calls 100+ LLM providers in the OpenAI format, as a Python SDK or a self-hosted AI gateway</title><link>https://agenttooldrop.com/tools/litellm</link><guid isPermaLink="true">https://agenttooldrop.com/tools/litellm</guid><description>Calls 100+ LLM providers in the OpenAI format, as a Python SDK or a self-hosted AI gateway</description><pubDate>Wed, 14 Oct 2026 12:00:00 GMT</pubDate><content:encoded>&lt;p&gt;LiteLLM gives one interface to OpenAI, Anthropic, Gemini, Bedrock, Azure and many other providers using the OpenAI request format, so you can swap providers without rewriting code. The Python SDK includes a Router with retry and fallback logic across deployments. The proxy server adds virtual keys, per-project spend tracking, guardrails, load balancing and an admin dashboard, and it can also act as an MCP gateway and an A2A agent gateway.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Use it when:&lt;/strong&gt; You call several model providers and want one OpenAI-format API, or a central gateway with keys, budgets and logging for a team.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Skip it if:&lt;/strong&gt; You use a single provider and its own SDK covers your needs. Features such as SSO sit under the commercial enterprise license.&lt;/p&gt;&lt;p&gt;Install with uv:&lt;/p&gt;&lt;pre&gt;&lt;code&gt;uv add litellm&lt;/code&gt;&lt;/pre&gt;&lt;p&gt;Models and APIs. Open source. License: MIT; enterprise/ directory under the BerriAI Enterprise license.&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://docs.litellm.ai&quot;&gt;Website&lt;/a&gt;, &lt;a href=&quot;https://github.com/BerriAI/litellm&quot;&gt;GitHub&lt;/a&gt;, &lt;a href=&quot;https://agenttooldrop.com/tools/litellm.md&quot;&gt;Markdown&lt;/a&gt;&lt;/p&gt;</content:encoded><category>Models and APIs</category><category>gateway</category><category>proxy</category><category>unified-api</category><category>openai-compatible</category><category>python</category><category>self-hosted</category></item><item><title>llama.cpp: Runs LLM and VLM inference locally in plain C/C++, with a CLI and an OpenAI-compatible server</title><link>https://agenttooldrop.com/tools/llama-cpp</link><guid isPermaLink="true">https://agenttooldrop.com/tools/llama-cpp</guid><description>Runs LLM and VLM inference locally in plain C/C++, with a CLI and an OpenAI-compatible server</description><pubDate>Wed, 14 Oct 2026 12:00:00 GMT</pubDate><content:encoded>&lt;p&gt;llama.cpp is a dependency-free C/C++ inference engine that runs on a wide range of hardware: Metal on Apple silicon, CUDA, HIP, Vulkan, SYCL, WebGPU and CPU builds, with CPU+GPU hybrid inference for models larger than your VRAM. It supports 1.5-bit to 8-bit quantization to cut memory use. `llama cli -hf &amp;lt;repo&amp;gt;` downloads and runs a model straight from Hugging Face, and `llama serve` starts an OpenAI-compatible API server with a built-in web UI that agents and editors can call.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Use it when:&lt;/strong&gt; You want direct control over local inference, quantization and hardware backends, or a lightweight OpenAI-compatible server.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Skip it if:&lt;/strong&gt; You only use hosted models, or you would rather not manage model files, quantization choices and hardware backends yourself.&lt;/p&gt;&lt;p&gt;Install with the install script:&lt;/p&gt;&lt;pre&gt;&lt;code&gt;curl -LsSf https://llama.app/install.sh | sh&lt;/code&gt;&lt;/pre&gt;&lt;p&gt;Models and APIs. Open source. License: MIT.&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://llama.app&quot;&gt;Website&lt;/a&gt;, &lt;a href=&quot;https://github.com/ggml-org/llama.cpp&quot;&gt;GitHub&lt;/a&gt;, &lt;a href=&quot;https://agenttooldrop.com/tools/llama-cpp.md&quot;&gt;Markdown&lt;/a&gt;&lt;/p&gt;</content:encoded><category>Models and APIs</category><category>local-models</category><category>inference</category><category>c-plus-plus</category><category>gguf</category><category>quantization</category><category>openai-compatible</category></item><item><title>LlamaIndex: Connects LLM agents to your data with connectors, indexes, query engines and event-driven workflows</title><link>https://agenttooldrop.com/tools/llamaindex</link><guid isPermaLink="true">https://agenttooldrop.com/tools/llamaindex</guid><description>Connects LLM agents to your data with connectors, indexes, query engines and event-driven workflows</description><pubDate>Wed, 14 Oct 2026 12:00:00 GMT</pubDate><content:encoded>&lt;p&gt;The LlamaIndex framework is a Python toolkit for context augmentation: data connectors ingest APIs, PDFs, docs and SQL, indexes structure the data, and query and chat engines retrieve context for the model. Agents can call a RAG pipeline as one tool among many, and Workflows add event-driven, multi-step processes with branching, retries and human-in-the-loop review. You can start with the llama-index starter package or install llama-index-core plus only the integrations you need from over 300 packages.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Use it when:&lt;/strong&gt; Your agent&apos;s main job is answering questions or taking actions over your own documents and data sources.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Skip it if:&lt;/strong&gt; You want a framework that is its maintainer&apos;s main focus: the README says the company&apos;s primary focus has shifted to LlamaParse, while the OSS framework stays available.&lt;/p&gt;&lt;p&gt;Install with pip:&lt;/p&gt;&lt;pre&gt;&lt;code&gt;pip install llama-index&lt;/code&gt;&lt;/pre&gt;&lt;p&gt;Agent frameworks. Open source. License: MIT.&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://developers.llamaindex.ai/python/framework/&quot;&gt;Website&lt;/a&gt;, &lt;a href=&quot;https://github.com/run-llama/llama_index&quot;&gt;GitHub&lt;/a&gt;, &lt;a href=&quot;https://agenttooldrop.com/tools/llamaindex.md&quot;&gt;Markdown&lt;/a&gt;&lt;/p&gt;</content:encoded><category>Agent frameworks</category><category>python</category><category>rag</category><category>data-connectors</category><category>workflows</category><category>integrations</category></item><item><title>LLM: Runs prompts against remote and local LLMs from the command line or Python, logging calls to SQLite</title><link>https://agenttooldrop.com/tools/llm-cli</link><guid isPermaLink="true">https://agenttooldrop.com/tools/llm-cli</guid><description>Runs prompts against remote and local LLMs from the command line or Python, logging calls to SQLite</description><pubDate>Wed, 14 Oct 2026 12:00:00 GMT</pubDate><content:encoded>&lt;p&gt;LLM is Simon Willison&apos;s CLI tool and Python library for OpenAI, Claude, Gemini and dozens of other models, via remote APIs or models installed on your own machine. Plugins such as llm-anthropic, llm-gemini and llm-ollama add providers, and `llm openai endpoint` targets any OpenAI-compatible Chat Completions server without configuration. Prompts and responses are stored in SQLite, and it also generates embeddings, extracts structured content with schemas and lets models execute tools.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Use it when:&lt;/strong&gt; You want quick model access from the terminal or a small Python library, with every prompt and response logged locally.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Skip it if:&lt;/strong&gt; You need a shared gateway with keys and spend tracking for a team; this is a CLI and Python library. Providers other than OpenAI need a plugin installed.&lt;/p&gt;&lt;p&gt;Install with pip:&lt;/p&gt;&lt;pre&gt;&lt;code&gt;pip install llm&lt;/code&gt;&lt;/pre&gt;&lt;p&gt;Models and APIs. Open source. License: Apache-2.0.&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://llm.datasette.io&quot;&gt;Website&lt;/a&gt;, &lt;a href=&quot;https://github.com/simonw/llm&quot;&gt;GitHub&lt;/a&gt;, &lt;a href=&quot;https://agenttooldrop.com/tools/llm-cli.md&quot;&gt;Markdown&lt;/a&gt;&lt;/p&gt;</content:encoded><category>Models and APIs</category><category>cli</category><category>python</category><category>plugins</category><category>sqlite</category><category>embeddings</category><category>local-models</category></item><item><title>Mastra: Builds TypeScript agents and graph-based workflows with memory, RAG, evals and model routing</title><link>https://agenttooldrop.com/tools/mastra</link><guid isPermaLink="true">https://agenttooldrop.com/tools/mastra</guid><description>Builds TypeScript agents and graph-based workflows with memory, RAG, evals and model routing</description><pubDate>Wed, 14 Oct 2026 12:00:00 GMT</pubDate><content:encoded>&lt;p&gt;Mastra is a TypeScript framework for agents and workflows that plugs into React, Next.js and Node apps or runs as a standalone server. It connects to 40+ model providers through one interface, has a graph-based workflow engine with .then(), .branch() and .parallel(), and can suspend an agent or workflow for human input and resume from stored state. It also covers conversation history, RAG, observational memory, MCP server authoring, evals and observability.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Use it when:&lt;/strong&gt; You want agents, workflows, memory and evals in one TypeScript framework instead of assembling separate libraries.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Skip it if:&lt;/strong&gt; You work in Python. Features in ee/ directories (for example auth) need a Mastra enterprise license for production use.&lt;/p&gt;&lt;p&gt;Create a project with npm:&lt;/p&gt;&lt;pre&gt;&lt;code&gt;npm create mastra@latest&lt;/code&gt;&lt;/pre&gt;&lt;p&gt;Agent frameworks. Open source. License: Apache-2.0; code in ee/ directories under the Mastra Enterprise License (source-available).&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://mastra.ai/&quot;&gt;Website&lt;/a&gt;, &lt;a href=&quot;https://github.com/mastra-ai/mastra&quot;&gt;GitHub&lt;/a&gt;, &lt;a href=&quot;https://agenttooldrop.com/tools/mastra.md&quot;&gt;Markdown&lt;/a&gt;&lt;/p&gt;</content:encoded><category>Agent frameworks</category><category>typescript</category><category>workflows</category><category>memory</category><category>rag</category><category>mcp</category><category>evals</category></item><item><title>MCP Inspector: Tests and debugs MCP servers from a web UI, a scriptable CLI or a terminal UI</title><link>https://agenttooldrop.com/tools/mcp-inspector</link><guid isPermaLink="true">https://agenttooldrop.com/tools/mcp-inspector</guid><description>Tests and debugs MCP servers from a web UI, a scriptable CLI or a terminal UI</description><pubDate>Wed, 14 Oct 2026 12:00:00 GMT</pubDate><content:encoded>&lt;p&gt;The Inspector is the reference developer tool from the MCP project for testing and debugging servers. One npx package gives you a web client that shows the JSON-RPC exchange as you call tools, a CLI with JSON output for CI, shell pipelines and coding agents, and a terminal UI; all three share the same transports, config files and OAuth state. It also installs mcpdo, an experimental client that keeps one connection open across many commands.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Use it when:&lt;/strong&gt; You are building or debugging an MCP server and want to list and call its tools directly, interactively or from a script.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Skip it if:&lt;/strong&gt; You only use MCP servers and never build one. It needs Node 22.19.0 or newer, and on machines without an OS keychain it stores secrets in an unencrypted file unless you supply a key.&lt;/p&gt;&lt;p&gt;Run with npx:&lt;/p&gt;&lt;pre&gt;&lt;code&gt;npx @modelcontextprotocol/inspector&lt;/code&gt;&lt;/pre&gt;&lt;p&gt;Devtools. Open source. License: Apache-2.0 for new contributions, MIT for older ones not yet relicensed; docs CC-BY-4.0.&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://modelcontextprotocol.io/docs/tools/inspector&quot;&gt;Website&lt;/a&gt;, &lt;a href=&quot;https://github.com/modelcontextprotocol/inspector&quot;&gt;GitHub&lt;/a&gt;, &lt;a href=&quot;https://agenttooldrop.com/tools/mcp-inspector.md&quot;&gt;Markdown&lt;/a&gt;&lt;/p&gt;</content:encoded><category>Devtools</category><category>mcp</category><category>testing</category><category>debugging</category><category>cli</category><category>typescript</category></item><item><title>Mem0: Adds long-term user, session and agent memory to AI apps through a Python or TypeScript SDK</title><link>https://agenttooldrop.com/tools/mem0</link><guid isPermaLink="true">https://agenttooldrop.com/tools/mem0</guid><description>Adds long-term user, session and agent memory to AI apps through a Python or TypeScript SDK</description><pubDate>Wed, 14 Oct 2026 12:00:00 GMT</pubDate><content:encoded>&lt;p&gt;Mem0 extracts facts from conversations with an LLM call and stores them so later turns can search them by user, session or agent. Retrieval combines semantic search, BM25 keyword matching and entity linking, with time-aware ranking for questions about current state versus past events. You can run it as a library from pip or npm, as a self-hosted server with a dashboard and API keys through Docker Compose, or on the managed Mem0 Platform.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Use it when:&lt;/strong&gt; Your agent should remember facts about users or its own past actions between conversations without you designing the storage and retrieval.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Skip it if:&lt;/strong&gt; You want memory with no LLM or embedding calls: Mem0 requires an LLM and defaults to OpenAI models. The README notes its benchmark scores come from the managed platform, which includes optimizations not in the open-source SDK.&lt;/p&gt;&lt;p&gt;Install with pip:&lt;/p&gt;&lt;pre&gt;&lt;code&gt;pip install mem0ai&lt;/code&gt;&lt;/pre&gt;&lt;p&gt;Memory. Open source. License: Apache-2.0.&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://mem0.ai&quot;&gt;Website&lt;/a&gt;, &lt;a href=&quot;https://github.com/mem0ai/mem0&quot;&gt;GitHub&lt;/a&gt;, &lt;a href=&quot;https://agenttooldrop.com/tools/mem0.md&quot;&gt;Markdown&lt;/a&gt;&lt;/p&gt;</content:encoded><category>Memory</category><category>python</category><category>typescript</category><category>memory-layer</category><category>self-hosted</category><category>personalization</category></item><item><title>Model Context Protocol: Defines an open standard for connecting AI applications to tools, data sources and workflows</title><link>https://agenttooldrop.com/tools/model-context-protocol</link><guid isPermaLink="true">https://agenttooldrop.com/tools/model-context-protocol</guid><description>Defines an open standard for connecting AI applications to tools, data sources and workflows</description><pubDate>Wed, 14 Oct 2026 12:00:00 GMT</pubDate><content:encoded>&lt;p&gt;MCP is the protocol behind every server in this category: a server exposes tools, resources and prompts once, and any MCP client, such as Claude, ChatGPT, VS Code or Cursor, can discover and call them. Messages are JSON-RPC 2.0 over stdio for local servers or Streamable HTTP for remote ones, with OAuth recommended for remote auth. Official SDKs cover TypeScript, Python, C#, Go, Rust, Ruby, Java, Swift, PHP and Kotlin, so you can build a server or client in the language you already use.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Use it when:&lt;/strong&gt; You want agents to reach an internal system or service, or you are building a client that should plug into existing MCP servers.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Skip it if:&lt;/strong&gt; You only want to use existing servers; the spec and SDKs are for building servers and clients, so pick a server entry instead.&lt;/p&gt;&lt;p&gt;Install the TypeScript server SDK:&lt;/p&gt;&lt;pre&gt;&lt;code&gt;npm install @modelcontextprotocol/server zod&lt;/code&gt;&lt;/pre&gt;&lt;p&gt;MCP servers. Open source. License: Apache-2.0 for new code and spec (transitioning from MIT); non-spec docs CC-BY-4.0.&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://modelcontextprotocol.io&quot;&gt;Website&lt;/a&gt;, &lt;a href=&quot;https://github.com/modelcontextprotocol/modelcontextprotocol&quot;&gt;GitHub&lt;/a&gt;, &lt;a href=&quot;https://agenttooldrop.com/tools/model-context-protocol.md&quot;&gt;Markdown&lt;/a&gt;&lt;/p&gt;</content:encoded><category>MCP servers</category><category>protocol</category><category>spec</category><category>sdk</category><category>json-rpc</category><category>typescript</category><category>python</category></item><item><title>Ollama: Runs open models locally behind a CLI and a REST API, with an OpenAI-compatible endpoint</title><link>https://agenttooldrop.com/tools/ollama</link><guid isPermaLink="true">https://agenttooldrop.com/tools/ollama</guid><description>Runs open models locally behind a CLI and a REST API, with an OpenAI-compatible endpoint</description><pubDate>Wed, 14 Oct 2026 12:00:00 GMT</pubDate><content:encoded>&lt;p&gt;Ollama downloads and runs open models such as Gemma with one command (`ollama run gemma4`) and serves them on a local REST API at localhost:11434. It supports a subset of the OpenAI API, so OpenAI clients work by changing the base URL, and it ships official Python and JavaScript libraries. `ollama launch` connects local models to coding agents including Claude Code, Codex, Copilot CLI and OpenCode, and the same API can reach Ollama&apos;s cloud models with an API key.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Use it when:&lt;/strong&gt; You want a local model server that existing OpenAI-style clients and coding agents can talk to with minimal setup.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Skip it if:&lt;/strong&gt; You only use hosted models from providers like Anthropic or OpenAI. Its OpenAI compatibility covers a subset of the OpenAI API, so check the docs if you depend on less common endpoints.&lt;/p&gt;&lt;p&gt;Install on macOS or Linux:&lt;/p&gt;&lt;pre&gt;&lt;code&gt;curl -fsSL https://ollama.com/install.sh | sh&lt;/code&gt;&lt;/pre&gt;&lt;p&gt;Models and APIs. Open source. License: MIT.&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;https://ollama.com&quot;&gt;Website&lt;/a&gt;, &lt;a href=&quot;https://github.com/ollama/ollama&quot;&gt;GitHub&lt;/a&gt;, &lt;a href=&quot;https://agenttooldrop.com/tools/ollama.md&quot;&gt;Markdown&lt;/a&gt;&lt;/p&gt;</content:encoded><category>Models and APIs</category><category>local-models</category><category>inference</category><category>cli</category><category>rest-api</category><category>openai-compatible</category><category>self-hosted</category></item></channel></rss>