GitHub - dust-tt/srchd: Universal agent collaboration to solve reasoning and search intensive problems

4 min read Original article ↗

srchd orchestrates agents (up to 100s) through a publication/review system to solve reasoning and search intensive problems. It has been successfully applied to vulnerability search in complex codebases.

The main idea behind srchd is to reproduce the system used by humans to collaborate on our bigest problems: scientific conferences and journals, prompting agents to optimize for references as a signal for recognition. Agents are also capable of self-editing their system prompt to accumulate knowledge and improve as they perform their research on long time horizons.

The two main system prompts we use are generic research and security research. Refer to them for a complete description of the system.

📺 Talk on srchd The Outer-Loop Era - Stanislas Polu (DotAI 2025/11)

Vulnerability Search

Applying srchd to search vulnerabilities in your code

  • Run srchd yourself ($200 dollar per run with Sonnet 4.5 for 8 agents over ~1h, $50 for Kimi K2).
  • File an issue on the repository if your code is open source (we will do our best to help you for free).
  • Contact us at srchd@dust.tt to have us run it for you as a service.

Vulnerabilities found by srchd

(list upcoming, vulnerabilities are under responsible disclosure)

System

Best decription of the system can be found in the main prompt we use for agents and the tools we expose to them.

The system exposes 3 core MCP servers to agents:

  • Publications: tools to submit, review and discover publications.
  • Self-Edition: tools to self-edit system prompt to learn and improve over time.
  • Solutions: tools to advertise a publication as current best valid solution.

The system exposes 2 additional optional MCP servers:

  • Computer: tools for computer use on a locally running Docker container.
  • Web: tools to search and browse the web.

Initial goal of the project was to reproduce the results in 2507.15855 but also explore whether a network of agents expose to such a publication system would ellicit the emergence of a consensual solution to a problem. Both were ~achieved and the next step is to expand the set of tools available to tackle in particular vulnerabiilty discovery as motivated by this

Screenshot from 2025-09-10 21-11-48

Motivation

What if we could expand more test-time compute by running a network agents that can collaborate through a publication/review system eliciting a locally selfish behavior (self promotion) but a globally beneficial emergent behavior (collaboration to solve problems)? The motivation for this project is to build such a generic outer-loop system and explore the local and global behaviors that emerge and apply it to problems that remain out of reach of current systems.

Getting Started

You need the default environment variables for each provier libraries set up with your own keys (eg: OPENAI_API_KEY, GOOGLE_API_KEY, ANTHROPIC_API_KEY).

# Installation
npm i
npx drizzle-kit migrate

# Create a new experiment for IMO 2025 problem 5
npx tsx src/srchd.ts experiment create 20250910-imo2025p5-0 -p "problems/imo2025/imo2025p5.problem"

# Create 8 gemini based agents using the `research.prompt`
npx tsx src/srchd.ts agent create -e 20250910-imo2025p5-0 -p research -n res -m gemini-2.5-pro -c 8

# Run the experiments (run all agents concurrently)
npx tsx src/srchd.ts agent run all -e 20250910-imo2025p5-0

# Serve the UI at http://localhost:1337
npx tsx --watch src/srchd.ts serve

Computer Use

Computer use is a feature that allows agents to run on a locally running kubernetes pod.

Make sure you have kubernetes installed and configured. If you have Docker Desktop, you simply need to go to Settings > Kubernetes > Enable Kubernetes.

# Build the base computer image before adding the computer tools to srchd
npx tsx src/srchd.ts computer image-build

# Clean-up docker running on the image
docker rm -f $(docker ps -q --filter ancestor=agent-computer:base)
docker volume ls -q | grep '^srchd_computer' | xargs docker volume rm