#Introducing Hermes Swarm: Architectural Orchestration for Deep Research, Deep Tasks, Multi-Agents

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signal crest
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📂 Durable project state

  • toDo.txt stores the operational plan and progress
  • agents_team.json stores the reusable team structure

That means you can:

  • start fresh
  • resume an existing workflow
  • point at an existing repo/directory
  • continue from prior state
  • reuse a good agent team on future projects

🧠 Model and provider architecture

One of the biggest advantages is role-separated model routing.

Because the swarm rides on Hermes providers, it can use different models for different roles and different costs for different subflows.

So you can:

  • keep stronger models for orchestration and higher-level reasoning
  • use cheaper/faster or even non-agentic models for narrower utility flows
  • actively route support work like extraction, cleanup, and summarization to low-cost paths such as Hermes-4-70B

This gives Hermes a much more practical cost profile for long-running sessions.

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🌐 Deep research pipeline

I intentionally kept an advanced web pipeline instead of reducing everything to raw browsing.

The swarm uses Tavily for:

  • repeated deep-search loops
  • better credit rotation
  • more practical sustained research sessions

And on top of that it keeps structured search_web + extract_web_content flows:

search -> extract -> filter -> clean -> distill

That means raw/noisy evidence can be processed before it bloats the main reasoning agents.

🛰 Autonomic runtime

The swarm includes persistent daemons for:

  • semantic drift detection
  • topology tracking
  • milestone extraction
  • context refinement
  • novel-idea capture

So the system has an actual supervision layer around long-running agent work.

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📊 Data pipeline

Every session can also generate structured JSONL logs of:

  • messages
  • tool calls
  • LLM interactions
  • execution metadata

So the swarm is not just an execution runtime. It is also a data engine for future local training and evaluation.

🎛 Interfaces

You can use it through:

  • its own standalone CLI/runtime
  • the BIOS/CRT dashboard
  • Hermes skill integration

So Hermes can launch and supervise the swarm while the swarm keeps its own orchestration runtime.

🎯 In one line

hermes-swarm adds reusable teams, resumable workflows, support-model extraction, dynamic memory, autonomic supervision, deep web research, and training-grade observability to Hermes.

Happy to answer questions or show more logs/screenshots here.

Thanks to @acoustic depotesearch and @slim surge for the hackathon.

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