Connect & automate
Bring in your files and tools, reuse useful instructions, and coordinate agents.
Connect your work tools
These are workflow examples, not a guarantee that every app supports every connector. Check your provider’s directory, your plan, and your company’s policy. Prefer official integrations; an MCP server is software you are trusting.
Files / Drive / Notion
One reference folder, read-only where supported.
Draft a brief with links back to the source documents.
Calendar / email
Read availability or draft replies first; approve sends and invites.
Prepare tomorrow’s agenda without contacting anyone.
Slack / Teams
Named channels only, if the connector supports it.
Summarize decisions; do not post automatically.
Browser
A separate work profile with only necessary logins.
Gather sources. Approve purchases, submissions, and account changes.
GitHub / terminal
One repository and a separate branch; no production secrets.
Make a small fix, run tests, and show a diff before publishing.
If the integration cannot limit access enough, upload selected copies instead. Never paste passwords or API secrets into a prompt. Check what data leaves your machine, revoke unused connections, and test write actions in a disposable destination first.
Parallel work, without the collision
Start with two independent tasks. More agents can mean more cost, conflicting edits, and a longer review queue—not more finished work.
01 / Separate
One thread per outcome. Put research in one thread and a draft from frozen inputs in another. Give each its own output folder.
02 / Assign
One owner per document. For code, use separate branches and worktrees. Do not ask two agents to rewrite the same file.
03 / Review
Ask for changed files, checks, and unresolved questions. Integrate one result at a time. Stop starting tasks when review becomes the bottleneck.
Manage several agents with Herdr
Not for one assistant. Consider Herdr when you already run several terminal agents and need a shared view of working, blocked, and finished sessions. It organizes sessions; it does not choose the best model or include model credits.
After following its install guide, run herdr from a project directory, create a workspace per project, and launch codex, claude, or opencode in a pane. Keep each agent’s own permissions. Detaching or closing the terminal can leave agents running: stop the session explicitly when finished. A workspace is not a security sandbox.
A faster daily loop: write a five-line brief → let the agent work → review an artifact → save the useful instructions. Reuse a tested template or skill before adding another agent. Use a second assistant for a targeted critique, not an endless debate.
Tools & reference guides
Optional references. Find a category when your current setup needs something more.
Agents, open-weight models, and orchestration tools for getting work done. These are existing directory entries, not newly verified recommendations. Review current setup instructions, licenses, and permissions before installing; start in a separate workspace.
Personal & general agents
4 tools
Self-hosted agent with persistent memory that writes its own reusable skills from experience, runs scheduled jobs, and connects to Telegram, Discord, Slack, and the terminal.
Local-first personal AI assistant that runs on your own devices, supports every major LLM, and connects to 20+ messaging channels. Crossed 100K GitHub stars within its first week (Jan 2026).
Extensible open agent framework from Block that automates engineering tasks end-to-end, built natively on MCP for tool connections.
Lets LLMs run code on your machine from natural language — a local, open counterpart to hosted code-execution sandboxes, with safeguards you control.
Coding agents
5 tools
Platform for AI software development agents that write code, run commands, browse the web, and fix issues end-to-end. Formerly OpenDevin.
Terminal pair-programmer that edits code in your local git repo, works with Claude, GPT, and local models, and commits with sensible messages.
Open-source autonomous coding agent inside VS Code — creates and edits files, runs commands, uses the browser, with human approval at each step.
Open-source terminal coding agent in the Claude Code mold — provider-agnostic, scriptable, with a polished TUI. Run it on Claude, GPT, or open-weight models.
Open IDE assistant for VS Code and JetBrains — chat, autocomplete, and edit with any model, plus shareable team configs. The bring-your-own-model default for IDE AI.
Agent infrastructure
5 tools
Makes websites accessible to AI agents — extracts interactive elements and lets any LLM click, type, and navigate. The de-facto standard library for browser agents.
High-throughput LLM serving engine with PagedAttention — the default way to serve open-weight models (including Korean models like EXAONE and Solar) in production.
One-command local model runtime — pull and run open-weight models (EXAONE and HyperCLOVA X SEED included) on a laptop, with an OpenAI-compatible local API.
Serving engine with RadixAttention prefix caching and a structured-generation language — the main open rival to vLLM, strong on agentic workloads with repeated prefixes.
Unified gateway that speaks the OpenAI format to 100+ providers — one proxy for keys, budgets, fallbacks, and routing across Claude, GPT, Gemini, and Korean APIs alike.
Korean open weights
4 tools
LG's frontier Korean-English model family, including reasoning and multimodal variants, under one of Korea's five MSIT sovereign-AI mandates.
Naver's open-weight SEED lineup — efficient Korean-first models for deployment, from small on-device sizes to the SEED Think reasoning line.
Upstage's Solar open models — the first Korean family recognized as frontier-class by Artificial Analysis, with open releases alongside the commercial API.
Kakao's Kanana model family with open-weight releases — compute-efficient bilingual models built for KakaoTalk-scale consumer deployment.
Multi-agent & orchestration frameworks
4 tools
Microsoft's multi-agent framework — agents that converse, critique, and delegate to each other, with the AgentChat API and no-code Studio on top.
Role-based agent teams — define a crew with roles, goals, and tools, and let them execute processes together. The fastest on-ramp from idea to working multi-agent flow.
Google's open framework behind Vertex AI agents — hierarchical agent composition, built-in evaluation, and deployment paths from laptop to cloud.
Agent framework from the Pydantic team — type-safe tools and structured outputs with the validation rigor Python backends already trust.
Build LLM-powered applications and graph-based agent workflows (LangGraph)
Claude Code & harness guides
Core guide checked · Individual sources carry their own dates.
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