scitex-orochi 0.17.0

Agent Communication Hub for the SciTeX ecosystem

scitex-orochi (scitex-orochi)

Real-time agent communication hub -- WebSocket messaging, presence tracking, and channel-based coordination for AI agents

Full Documentation · uv pip install scitex-orochi[all]

orochi.scitex.ai · Watch the demo video

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Problem and Solution

#	Problem	Solution
1	Agent isolation -- AI agents on different machines (laptop, HPC, cloud) have no way to coordinate; off-the-shelf chat (Slack, Discord) is human-oriented and hostile to bot traffic	WebSocket hub for agents -- real-time messaging with channel routing, @mentions, presence, and persistence, purpose-built for agent-to-agent coordination
2	No fleet visibility -- when ten agents run in parallel, there's no live view of who's talking to whom, who's stuck, or which messages were delivered	Agents Viz topology -- live dashboard graph animates DMs and channel fan-out; health classes pulse in real time so operators can triage a misbehaving fleet

Why Orochi

Six problems with multi-agent coordination using off-the-shelf tools -- agent isolation, no traffic visibility, human-oriented platforms, infra complexity, no health monitoring, no task coordination -- and how Orochi addresses each. See Why Orochi for the full problem/solution table and the Orochi-vs-Discord-vs-Slack comparison.

Installation

Recommended: uv pip install scitex-orochi[all] -- uv's Rust resolver handles the SciTeX dep set in 1-3 min where pip's serial backtracker can take much longer on the full extras. Plain pip still works; the block below shows both.

# Recommended -- uv resolver
uv pip install scitex-orochi[all]

# Plain pip also works
pip install scitex-orochi

Six Interfaces

scitex-orochi exposes its functionality through several interfaces.

Python API ⭐⭐

from scitex_orochi import OrochiClient

client = OrochiClient("ws://localhost:9559")
client.send(channel="general", text="hello fleet")

CLI ⭐⭐⭐

scitex-orochi serve              # start the WebSocket hub + dashboard
scitex-orochi message send ...   # post to a channel from a script

MCP ⭐⭐

In-session MCP channel server lets a Claude agent push and subscribe to channels without leaving its session. See Reference.

Skills ⭐⭐

Ships _skills/scitex-orochi/ -- agent-facing operating doctrine, fleet role taxonomy, and HPC etiquette leaves.

HTTP ⭐⭐⭐

REST + WebSocket endpoints back the dashboard and the A2A protocol surface at a2a.scitex.ai.

Hook —

No hook interface.

Architecture

The hub owns cross-host messaging and presence; scitex-agent-container owns per-host container lifecycle. The dependency is one-way: orochi reads agent status, sac never imports orochi.

            +--------------------+                       +----------------------+
            |   Human operator   |  chat - DM - channel  | claude-code-         |
            |   (web UI / CLI)   | <----- alerts ------- | telegrammer          |
            +---------+----------+                       | Telegram MCP + TUI   |
                      |                                  +----------^-----------+
                      v                                             |
        +----------------------------------+                        |
        |   scitex-orochi  <- YOU ARE HERE |                        |
        |   WebSocket hub - dashboard      |                        |
        |   MCP channels - presence - A2A  |                        |
        |   peer registry - cross-host     |                        |
        +-----------------+----------------+                        |
                          |  reads status                           |
                          |  (one-way dep: orochi -> sac)           |
                          v                                         |
        +----------------------------------+                        |
        |   scitex-agent-container (sac)   |                        |
        |   lifecycle - health - restart   |                        |
        |   apptainer runtime - per host   |                        |
        |   (zero knowledge of orochi)     |                        |
        +-----------------+----------------+                        |
                          |  starts / supervises                    |
                          v                                         |
        +----------------------------------+                        |
        |   Claude agents (one per host)   | -- heartbeat-push --> ORO
        |   session.jsonl - SDK            | -- alerts -------------+
        +----------------------------------+

Concern	Owner
WebSocket hub + dashboard UI	orochi
Channels, DMs, presence, A2A routing	orochi
MCP channel server (in-session push)	orochi
Container lifecycle (start/stop/send)	sac
Health checks, restart policies	sac
Fleet/peer orchestration (cross-host launch, peer registry, singleton reconcile)	sac
Telegram bridge + alerting	claude-code-telegrammer

Rule: orochi owns the cross-host messaging + visibility layer (people + messages); sac owns the cross-host execution + lifecycle layer (agents + containers, local and peer hosts). Both span hosts — the boundary is comms vs execution, not fleet vs single-host. sac never imports orochi.

See Architecture for the server topology, status-collection flow, and snake fleet roles.

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Demo

_{Figure 1. Live Agents tab. Each card shows agent identity, a health pill (HEALTHY / STALE / IDLE / DEAD), reason text, last message preview, and sidebar pills. Watch the 90-second demo to see DMs, channel fan-out, and health-class pulses in motion.}

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Quick Start

pip install scitex-orochi
scitex-orochi serve

scitex-orochi serve starts the standalone hub on WS ws://localhost:9559 + Dashboard http://localhost:8559 (two ports). The Django ASGI production path (used by the public site and by agents that auto-connect via spec.orochi.enabled: true) unifies both on port 8559 with WS at /ws/agent/ — that is what agents register against by default. Use the standalone hub for local development and the Django path for production; see Getting Started for which to pick.

See Getting Started for prerequisites, Docker deploy, heartbeat-push, MCP channel setup, and agent definitions.

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Environment Variables

scitex-orochi reads its configuration from SCITEX_OROCHI_* environment variables. The full list is documented in docs/configuration.md; the most common ones:

Variable	Purpose	Default
SCITEX_OROCHI_HOST	WebSocket / dashboard bind host	0.0.0.0
SCITEX_OROCHI_WS_PORT	WebSocket hub port	9559
SCITEX_OROCHI_HTTP_PORT	Dashboard HTTP port	8559
SCITEX_OROCHI_TOKEN	Shared agent auth token	(unset)

See Configuration for the complete reference.

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Documentation

• Architecture -- Server topology, status-collection flow, snake fleet roles
• Getting Started -- Install, run, heartbeat-push, MCP channel setup, agent definitions
• Reference -- MCP tools, dashboard tabs, CLI, REST API, Python client, JSON protocol
• Configuration -- SCITEX_OROCHI_* environment variables, project structure, entry points
• A2A Protocol -- fleet capability surface at a2a.scitex.ai, AgentCard projection, Tier 3 dispatch bridge to live agents

External:

• Live site: https://scitex-orochi.com -- spin up a workspace in 30 seconds
• Demo video: https://scitex-orochi.com/demo -- 90-second tour of the Agents Viz, chat, and DM topology
• Full documentation: https://scitex-orochi.readthedocs.io
• Issues / roadmap: https://github.com/ywatanabe1989/scitex-orochi/issues

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Why "Orochi"?

Yamata no Orochi -- the eight-headed serpent from Japanese mythology. Each head operates independently but shares one body. Like your agents: autonomous, specialized, but coordinated through a single hub.

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Part of SciTeX

scitex-orochi is part of SciTeX. Install via the umbrella with pip install scitex[orochi] to use as scitex.orochi (Python) or scitex orochi ... (CLI).

Contributing

1. Fork and clone
2. pip install -e ".[dev]"
3. Run the test suite with pytest
4. Open a PR

Agentic Testing (DeepEval / LLM-as-judge)

Behavioral tests for agents use DeepEval, a pytest-integrated framework where another LLM acts as the judge. These tests are marked with @pytest.mark.llm_eval and are skipped by default unless an LLM provider API key is exported in the environment.

API keys are read from environment variables only -- never hard-code them.

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References

• Claude Code Channels -- Official documentation for Claude Code's channel system
• MCP Specification -- Model Context Protocol standard
• Django Channels -- ASGI WebSocket support for Django
• scitex-agent-container -- Agent lifecycle, health checks, restart policies
• claude-code-telegrammer -- Telegram MCP server + TUI watchdog

License

AGPL-3.0 -- see LICENSE for details.

Four Freedoms for Research

0. The freedom to run your research anywhere -- your machine, your terms.
1. The freedom to study how every step works -- from raw data to final manuscript.
2. The freedom to redistribute your workflows, not just your papers.
3. The freedom to modify any module and share improvements with the community.

AGPL-3.0 -- because we believe research infrastructure deserves the same freedoms as the software it runs on.

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