ep114a-miadi-hermes-agent-coordinator-260706

@miadi/hermes-conductor ep114

it's supposed to be capable of coordinating multiple agents coordinate everything and do a really complex work in looking at only 3 or 4 pages. It sounds really like you're cutting corners on the quality of the analysis and the work that you're doing. I think you need to think a little bit more deeper and produce a revision again. Based on the fact that the agents will receive that in the terminal and they're going to go through our packages, and they're going to create a new package, and they're going to create something that we will be capable of using to coordinate all of the work, and it should also consider that we are going to use multiplexer which is something important, I think, to specify in here.

You’re right that the last revision was thin for something that’s meant to coordinate a whole multi‑agent workspace; below is a deeper spec that actually matches the complexity of what you’re building and the fact that agents will consume it from the terminal, traverse your existing packages, and create a new coordination package with multiplexer‑friendly workflows.

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Goals and constraints

• Hermes‑agent is the thinker, conductor is the orchestrator. Hermes‑agent remains the core reasoning agent, but orchestration, task routing, and multi‑agent coordination live in @miadi/hermes-conductor on top of LangGraph JS.¹²
• Multi‑agent, hierarchical, long‑running workflows. The conductor must support parallel builders, reviewers, scouts, and possibly nested “sub‑boards” for complex projects, using LangGraph’s durable execution and multi‑agent patterns.³⁴¹
• A2A contract‑first design. All payloads visible to agents in the terminal are defined in @miadi/a2a-contracts; the conductor never invents new ad‑hoc shapes. You evolve those contracts and then the conductor consumes them.⁵⁶
• Multiplexed terminal UX. Output is designed to be consumed in a terminal multiplexer (tmux/gmtermux/tide) with per‑agent panes/streams and clear event channels, not just a single log stream.⁵

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Runtime architecture with LangGraph JS

LangGraph JS is explicitly positioned as a low‑level orchestration runtime for long‑running, stateful, multi‑agent workflows, rather than just a chain builder. You can exploit three aspects:²¹

• StateGraph at the board level. A single StateGraph instance, ConductorGraph, represents the entire board, with channels for tasks, assignments, locks, board metrics, and terminal events, defined via Annotation.Root or Zod state schemas.⁷⁸
• Multiple specialized nodes (agents). Each node represents a role: Conductor (Hermes‑integrated), Builder, Reviewer, Scout, plus any future roles like “Researcher” or “Ops”, mirroring LangGraph’s multi‑agent examples.⁹³
• Hierarchical supervision when needed. For very complex workflows, you can layer this on top of LangGraph’s “supervisor” abstraction (@langchain/langgraph-supervisor) to manage sub‑graphs, i.e. a conductor that controls multiple local supervisors/boards.¹⁰¹¹

Board state (ConductorState)

Instead of a minimal state, define a richer board‑level state with multiple channels:

• plan: narrative description of the overall goal, plus a structured A2A PlanContract.
• tasks: list of A2A TaskContract objects, each with status, owner, related files, Notion/GitHub IDs.
• assignments: mapping agentId -> taskId, plus any agent capabilities/constraints from A2A.
• fileLocks: filepath -> { owner, lastModified, lockExpiry } with full lock history for debugging.
• boardMetrics: queue depth, latency per task, error counters, throughput per builder.
• terminalEvents: stream of TerminalEventContract objects that the CLI renders as multiplexed panes or feeds.

LangGraph’s Annotation.Root pattern lets you build this as strongly‑typed channels with reducers and defaults, or you can use the newer Zod‑based registry helpers to integrate with Studio/observability.¹²⁸

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Role design and coordination flow

You’re not just shuffling tasks; you need a detailed lifecycle that agents can trust when they read the spec in a terminal. Here’s a more explicit flow.

Conductor (Hermes‑driven)

• Responsibilities:
  • Take plan and current boardMetrics, decide on decomposition strategy.
  • Call Hermes‑agent via your connector to produce/adjust a TaskContract[] plan. Hermes is the “planner” and “strategist”; the conductor is the orchestrator.¹³¹⁴
  • Assign tasks to builders based on capabilities (from A2A), load, and queue depth.
  • Emit TerminalEventContract messages describing what changed: tasks added, reassigned, split, blocked.
• Graph behavior:
  • Node conductorNode(state) reads plan, tasks, boardMetrics and produces updates; edges route to builderNode for new tasks or back to conductorNode when the scout requests rebalancing.

Builders (one or many)

• Responsibilities:
  • Take ownership of tasks from assignments and fileLocks.
  • Execute work: create new packages (e.g. @miadi/hermes-conductor), edit existing packages, update A2A contracts, write docs.
  • Update TaskContract status, file lists, and completion artifacts (diffs, test results).
• Concurrency:
  • Multiple builders are parallel nodes or parallel instances of the same node; LangGraph supports this style of multi‑agent collaboration by letting nodes represent agents and edges represent communication/hand‑off.⁴³
  • Scout controls the number of active builders based on queue depth and error rates.

Reviewer

• Responsibilities:
  • Inspect completion artifacts, including diffs, test outputs, and A2A payloads.
  • Validate that the work adheres to contracts (e.g. board/terminal events match schema, packages build, tests pass).
  • Gate merges and publish events: review_requested, review_approved, review_rejected into terminalEvents.
• Human‑in‑loop:
  • LangGraph built‑in “interrupts”/human‑in‑the‑loop mechanisms give you a way to pause execution and ask for a terminal input.¹²
  • Reviewer node can emit an interrupt, the CLI prompts y/n or more structured feedback, then resumes with a review_approved or review_rejected event into state.

Scout (dynamic scaling / supervision)

• Responsibilities:
  • Observe boardMetrics and tasks to detect backlog, stalled tasks, or idle builders.
  • Spawn or retire builders (or ask an external supervisor) based on thresholds.
  • Emit events so humans can see scaling decisions in the multiplexer.

LangGraph’s supervisor library shows how to define a “supervisor agent” that controls multiple specialized agents, and even supervisors-of-supervisors, which maps well to Scout controlling builder groups.¹¹¹⁰

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Terminal and multiplexer integration

Because your agents will read this spec in the terminal and you already rely on multiplexers, the output needs to be structured explicitly around multi‑pane workflows.⁵

Event channels

Define at least three A2A event channels (and corresponding contracts):

• Board events: high‑level state changes: "plan_updated", "tasks_planned", "board_metrics_changed".
• Agent events: lifecycle: "builder_started_task", "builder_completed_task", "reviewer_requested_review", "scout_scaled_builders".
• File/lock events: "file_locked", "file_unlocked", "lock_conflict_detected", "lock_expired".

Each TerminalEventContract includes: event type, timestamp, agentId (if applicable), taskId/filepath, and a human‑readable message. The CLI subscribes to this stream and routes events to panes.

Multiplexer‑friendly CLI

Design the CLI so that:

• It can run as a standalone process or inside a multiplexer session (tmux attach, gmtermux, etc.), with flags to choose the layout (e.g. board summary pane + per‑agent panes + log pane).
• Each agent (Conductor, Builder N, Reviewer, Scout) has its own stream, making it easy to follow a single agent in a pane while keeping a global board summary visible.
• The CLI can export or replay event logs (A2A envelopes) so agents and humans can reconstruct decisions later.

You can take cues from multi‑agent examples where each agent’s messages are kept as distinct streams within a multi‑agent workflow, then map those streams onto terminal panes.¹⁵³⁴

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A2A contracts and package interaction

The conductor spec should explicitly say:

• All task and board payloads are A2A contracts.
  • Tasks, board snapshots, agent profiles, lock states, and terminal events are contract types defined in @miadi/a2a-contracts.
  • No “raw JSON” is exposed to agents; everything visible in the terminal is strongly typed and versioned.
• Conductor doesn’t own contract evolution.
  • When you realize you need new fields (e.g. multiplexerLayout, panePriority, traceId), you add them to @miadi/a2a-contracts, then update the conductor package to consume the new version via workspace/pnpm. Yarn metadata around @miadi/tide already demonstrates this pattern of using workspace dependencies for house packages.⁵
• Package creation flows are contract‑driven.
  • Builders use contracts like PackageCreationTaskContract and PackageRefactorTaskContract that encode which repository, workspace path, and build/test steps are required.
  • The conductor’s job is to keep the board and external tools (Notion, GitHub, CI) in sync via those contracts, not to hide them.

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Multi‑agent coordination pattern with LangGraph

To make this concrete for agents reading the spec, you can describe the coordination pattern in LangGraph terms, which is how the official examples teach multi‑agent systems.¹⁶³⁹

Core pattern

• State: ConductorState annotation or Zod schema with channels described above.⁸¹²
• Nodes: functions typed as GraphNode<typeof ConductorState> representing Conductor, Builder, Reviewer, Scout.⁷¹
• Edges:
  • START → conductor to bootstrap the board.
  • conductor → builder when tasks are ready to be executed.
  • builder → reviewer when tasks complete.
  • reviewer → conductor when plan needs adjustment.
  • scout → conductor|builder when scaling decisions are made.
  • Conditional edges from reviewer and scout to support re‑plan/re‑build loops until status === "done".

LangGraph’s documentation emphasizes that this is precisely the use case: long‑running, stateful, multi‑agent workflows with persistence, human‑in‑loop, and streaming; you are essentially building a higher‑level “Deep Agent” style harness for Hermes using those primitives.⁴²¹

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Implementation directives for agents (what they will read)

Given that agents will open this plan in a terminal and use it to create the conductor package, the spec they see should include explicit action directives, for example:

• Step 1: Inspect existing Miadi workspace and @miadi/a2a-contracts to list current contracts; do not introduce new payloads until those are understood.
• Step 2: Create @miadi/hermes-conductor package scaffold with LangGraph JS and LangChain JS, wired via workspace dependencies (@langchain/langgraph@^1.4.7, langchain@^0.3.x, @langchain/core@^1.1.x, @miadi/a2a-contracts@workspace:*).^1718195
• Step 3: Implement ConductorState and role nodes using current LangGraph state APIs (Annotation or Zod registry), with board, task, lock, metrics, and terminal channels.¹²⁸⁷
• Step 4: Implement connectors to Hermes‑agent, Notion, GitHub, and A2A endpoints, all speaking contract types. Hermes‑agent is the planner, conductor is the orchestrator.^6142013
• Step 5: Build the CLI around TerminalEventContract streams and design a default multiplexer layout that works well with tmux/gmtermux/tide.⁵
• Step 6: Add integration tests that simulate multi‑agent workflows with at least 3–5 builders, verify lock semantics, backlog scaling, human review gates, and external sync (Notion/GitHub). Use LangGraph’s persistence and interrupt APIs rather than ad‑hoc state.

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If you want, the next iteration can be a more formal “agent‑readable spec” (essentially a contract document) in your preferred style, with explicit JSON/Zod schemas and step‑by‑step build tasks that Hermes and friends can follow verbatim. <span style="display:none">²¹²²²³</span>

<div align="center">⁂</div>

https://www.perplexity.ai/search/69c944be-41e2-4524-bbc5-41cf977be3c1

Footnotes

1. https://docs.langchain.com/oss/javascript/langgraph/overview ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶

2. https://www.langchain.com/langgraph ↩ ↩² ↩³ ↩⁴

3. https://langchain-ai.github.io/langgraphjs/tutorials/multi_agent/multi_agent_collaboration/ ↩ ↩² ↩³ ↩⁴ ↩⁵

4. https://www.youtube.com/watch?v=hvAPnpSfSGo ↩ ↩² ↩³ ↩⁴

5. https://classic.yarnpkg.com/en/package/@miadi/tide ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶

6. https://agent2agent.info/docs/getting-started/setup/ ↩ ↩²

7. https://reference.langchain.com/javascript/langchain-langgraph/index/StateGraph/constructor ↩ ↩² ↩³

8. https://langchain-ai.github.io/langgraphjs/how-tos/define-state/ ↩ ↩² ↩³ ↩⁴

9. https://medium.com/@sushmita2310/building-multi-agent-systems-with-langgraph-a-step-by-step-guide-d14088e90f72 ↩ ↩²

10. https://www.youtube.com/watch?v=qSOzqN9pmTM ↩ ↩²

11. https://github.com/langchain-ai/langgraphjs-supervisor ↩ ↩²

12. https://medium.com/@mrcoffeeai/building-stateful-agents-with-langgraphs-annotated-559608c46d7e ↩ ↩² ↩³

13. https://github.com/NousResearch/hermes-agent ↩ ↩²

14. https://github.com/NousResearch/hermes-agent/blob/main/AGENTS.md ↩ ↩²

15. https://langchain-ai.github.io/langgraphjs/reference/variables/langgraph.MessagesAnnotation.html ↩

16. https://www.js-craft.io/blog/multi-agent-langgraph-javascript-1/ ↩

17. https://www.npmjs.com/package/@langchain/langgraph ↩

18. https://www.npmjs.com/package/langchain ↩

19. https://grounded-api.dev/npm/@langchain/core ↩

20. https://info.miadi.net/en/getit/framework/ ↩

21. https://langchain-ai.github.io/langgraphjs/reference/classes/langgraph.StateGraph.html ↩

22. https://langchain-ai.github.io/langgraphjs/concepts/low_level/ ↩

23. https://github.com/expectbugs/agents ↩