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The living loop for your AI workforce

Memory-grounded orchestration for AI agents and humans. Your agents execute, return artifacts to your knowledge base, and the next agent starts with the full context of what the last one did.

See how it works
● Local-first
● BYO models
● No vendor lock-in
conductloop.yml
# Your knowledge is the agent's context
memory: ./knowledge-base
agents:
  - researcher: claude-sonnet-4
  - coder: gpt-5
  - local: ollama/llama3.2

# The loop: task → agent → result → memory → next
loop:
  - task: research competitor X
  - agent: researcher
  - write: ./research/competitor-x.md
  - next: brief writer reads it

The problem

You have AI agents.
You don't have a system.

Most teams today run AI like a slot machine: prompt, hope, copy-paste the result, lose it. There's no memory, no compounding, no team. Just a faster way to do one-off tasks.

🧠

Your agents have amnesia

Every chat starts from zero. Every workflow forgets what the last one learned. You spend half your time re-explaining context to AI that should already know.

🗂️

Knowledge is scattered

Notion for docs. Slack for decisions. GitHub for code. Linear for tasks. Your brain for the actual why. Nothing is connected, and your agents can't see any of it.

🤹

Multi-project = multi-chaos

You're running 3-5 projects at once with mixed human + AI teams. Each project has its own stack, its own agents, its own graveyard of half-finished work.

💸

AI bills without compounding returns

You pay for ChatGPT, Claude, Cursor, ollama, plus three more. None of them talk to each other. None of them remember. The output never makes the next task easier.

How it works

Three steps from chaos
to compounding work.

01

Memory grounds the agents

Connect your Obsidian vault, Notion, GitHub, or any local folder. ConductLoop builds a queryable, semantic memory layer that every agent reads from. No more re-explaining context.

Bring your own storage. Local SQLite, Postgres, or D1. The knowledge never leaves your control.

02

Teams are human + AI

Define a project, assign humans and agents to it. Agents have roles (researcher, coder, reviewer). Humans have roles too. Everyone is on the same team graph.

ConductLoop ships with role templates but is fully extensible. Your AI stack is yours — we orchestrate around it.

03

The loop compounds

Task → agent executes → result writes back to memory → next agent reads it → result writes back → your knowledge base grows with every task. Nothing is throwaway work.

The system gets smarter as you use it. The more you run, the less you re-explain.

Why it's different

Static playbooks vs
a living system.

Most AI workflow tools give you a document. We give you an operating system. The difference: nothing in ConductLoop is read-once, throwaway work. Every output feeds the next task.

The old way
✗Static playbooks you read once
✗Agents forget after every task
✗One vendor's models only
✗Cloud-only, your data on their servers
✗Agents that suggest
✗You wire it together
ConductLoop
✓Living loop that runs continuously
✓Memory compounds across the whole team
✓Bring your own: Claude, GPT, Gemini, local Ollama
✓Local-first, your knowledge never leaves
✓Agents that execute and write back
✓It orchestrates itself once configured

FAQ

Questions, answered.

Most AI workflow tools are static: you write a playbook, the agent follows it once, the result is lost. ConductLoop is a closed loop. The agent executes, returns artifacts to your knowledge base, and the next agent starts with the full context of what the last one did. Memory compounds. The team gets smarter with every task. Nothing is throwaway work.

Ready to make your AI stack compound?

Join the waitlist. Be the first to try ConductLoop. Founder pricing for early signups. Your feedback will shape the product.

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