AgentForge Blog Automation Service: Full Architecture - From AI Comments to Translation and Post Generation

Tue, 05 May 2026 00:30:00 +0900

Running a blog involves three of the most tedious tasks: replying to comments, maintaining English translations, and consistently writing new posts. The AgentForge project automates all three with AI agents.

This post outlines the complete architecture of our blog automation service, which operates across two servers.

System Topology

┌─────────────────────┐ HTTPS ┌─────────────────────┐
│ arm1 server │ ──────────────▶ │ ec1 server │
│ (Agent Operator) │ │ (Blog Hosting) │
├─────────────────────┤ ├─────────────────────┤
│ blog-agent (:8081) │ │ Hugo (nginx) │
│ ├─ CommentHandler │ │ Blog API (:8000) │
│ ├─ TranslateHandler│ │ ├─ translator.py │
│ └─ PostGenerator │ │ ├─ blog_manager.py │
│ │ │ └─ git_handler.py │
│ NATS / PostgreSQL │ │ │
│ Prometheus / Grafana │ │ Git (yarang/blogs) │
└─────────────────────┘ └─────────────────────┘

Server	Role	Core Services
arm1	Agent Operator	`blog-agent.service` — Flask + Scheduler + LLM Client
ec1	Blog Hosting + API	Hugo (nginx) + `blog-api.service` (FastAPI)

Communication between the two servers is restricted to HTTPS API calls only. SSH access from arm1 to ec1 is blocked, so all integrations are done through the Blog API.

arm1: Unified Blog Agent

Why Unified?

Initially, comment response, translation, and post generation operated as separate processes (three systemd services). The issues were:

Using Claude Code CLI (--print) for calls resulted in a response time of 9.7 seconds and consumed 688MB of disk space.
Managing six systemd units was burdensome.
No state sharing between processes was possible.

By unifying these into one process and switching to direct LLM API calls, we achieved the following:

Metric	Before	After
Response Time	9.7s	1.7s
Disk Usage	688MB	~50MB
systemd Units	6	1
Processes	3	1

Architecture

class BlogAgent:
 """1 Process = Flask (webhook) + Scheduler (timer) + LLM Client"""
 
 def __init__(self):
 self.config = AgentConfig.from_credentials()
 self.llm = LLMClient(self.config) # ZAI glm-4.7
 self.api = BlogAPIClient(self.config) # ec1 Blog API
 
 # Handlers
 self.comment = CommentHandler(self.llm, self.config)
 self.translate = TranslateHandler(self.api)
 self.post_gen = PostGenerator(self.llm, self.api)
 
 # Scheduler
 self.scheduler = Scheduler()
 self.scheduler.every(hours=6, task=self.translate.check_and_sync)
 self.scheduler.daily_at(hour=9, task=self.post_gen.generate_and_publish)

Module Operations

1. CommentHandler — AI Comment Response

Receives Webhook events from GitHub Discussions to automatically generate AI comments.

[User Comment] → GitHub Webhook → arm1 Flask → CommentHandler
 → LLM Call (ZAI glm-4.7) → Generate Reply → Post Comment via GitHub API

Trigger: Webhook event-based (real-time)
Filtering: Skips blog owner comments and AI-generated comments.
Security: HMAC-SHA256 Webhook secret verification, Flask-Limiter applied.

2. TranslateHandler — Automatic Translation Trigger

Requests translation synchronization from ec1’s Blog API every 6 hours.

[Scheduler 6h] → TranslateHandler.check_and_sync()
 → POST /translate/sync → ec1 Blog API performs actual translation

arm1 does not perform the translation itself; it only sends a trigger to the ec1 API. The actual translation logic resides in translator.py on ec1.

3. PostGenerator — Automatic Post Generation

Automatically generates technical blog posts every day at 9 AM.

[Scheduler 09:00 KST] → PostGenerator.generate_and_publish()
 → Collect existing topics → Refer to RSS trends → Generate content with LLM
 → Deduplication Check → Publish via Blog API

Deduplication is key. It compares the similarity between new titles and the last 100 existing titles using difflib.SequenceMatcher:

def _is_duplicate_title(self, new_title, existing_titles):
 """Considers it a duplicate if the ratio is >= 0.6"""
 new_lower = new_title.lower().strip()
 for title in existing_titles[-100:]:
 ex_lower = title.lower().strip()
 ratio = difflib.SequenceMatcher(None, new_lower, ex_lower).ratio()
 if ratio >= 0.6:
 return True
 return False

ec1: Blog API Translation System

Transition to Gemini

Initially, translations were performed using ZAI (glm-4.7), but a critical issue arose:

glm-4.7 is a reasoning model, which first consumes its max_tokens budget for reasoning_content (internal thought process). If max_tokens=256, it uses all 256 tokens for reasoning, leaving the actual content as an empty string.

This led to an incident where nine English posts were translated with empty string titles.

Solution: Replaced with Gemini 2.5 Flash Lite.

Item	ZAI (Previous)	Gemini (Current)
Model	glm-4.7 (reasoning)	gemini-2.5-flash-lite
Translation Time	~30s/post	~8s/post
Cost	Paid API	Free (1,500 requests/day)
Empty Response Issue	Occurred	None

OpenAI-Compatible Endpoint

Gemini provides an OpenAI-compatible API. The existing code can be used without any changes by simply switching the base URL:

LLM_BASE_URLS = {
 "GEMINI": "https://generativelanguage.googleapis.com/v1beta/openai",
 "ZAI": "https://api.z.ai/api/coding/paas/v4",
}

Translation Matching Logic

Pairing Korean↔English posts uses date prefix matching:

ko: 2026-05-04-001-개발-생산성-17배-극대화-deepseek-v4와-...
en: 2026-05-04-001-개발-생산성-17배-극대화-deepseek-v4와-...
 ↑ Same prefix = Same post

Although the slugs might differ in language, if the YYYY-MM-DD-NNN part is the same, it’s recognized as the same post. The prerequisite for this method is that no two posts with the same date and number exist.

Title-in-Body Translation Technique

Translating the title via a separate API call caused issues with empty results from the reasoning model. The solution is to include the title as the first line of the body:

# When requesting translation
prompt = f"# {original_title}\n\n{original_body}"

# Extracting the title from the translation result
if translated.lstrip().startswith("# "):
 lines = translated.lstrip().split("\n", 1)
 extracted_title = lines[0].lstrip("# ").strip()
 translated_body = lines[1].lstrip("\n")

This translates the title and body simultaneously in a single API call, preserving context and saving tokens.

LLM Strategy: Role-Based Model Separation

Not all tasks are handled by a single LLM. Models are separated based on the nature of the task.

Task	Server	Model	Reason
AI Comment Response	arm1	ZAI glm-4.7	Conversational, excellent Korean quality
Post Generation	arm1	ZAI glm-4.7	Long-form content generation, creativity required
Translation (ko→en)	ec1	Gemini Flash Lite	Non-reasoning, fast and free

Core Principle: Do not use reasoning models for translation. Reasoning models consume tokens for internal thought processes, making non-reasoning models more suitable for simple conversion tasks.

Monitoring and Operations

Health Check Endpoints

# arm1 agent
curl http://arm1:8081/health
# → {"status":"healthy","agent":"blog-agent","scheduler_jobs":2,"uptime_sec":...}

curl http://arm1:8081/status
# → {"scheduler":[{"name":"auto-translate","last_run":...},{"name":"post-generator","last_run":"2026-05-04"}]}

# ec1 Blog API
curl https://blog.example.com/api/health
# → {"status":"healthy","version":"2.0.0"}

Observability Points

Metric	Normal Range	Alert Condition
arm1 uptime	>0	Service Down
scheduler_jobs	2	≠ 2
Translation Sync	ko post count = en post count	Discrepancy occurs
Post Generation	1 post daily	No posts for over 24 hours

Lessons Learned and Operational Tips

1. The Pitfall of Reasoning Models

It’s often not explicitly stated in documentation that max_tokens combines reasoning and content. If you get an empty response, check the finish_reason—if it’s "length", it indicates insufficient token budget.

2. Value of the OpenAI-Compatible Pattern

When switching translation providers from ZAI to Gemini, the code change was just one line for the base URL. Abstracting to an OpenAI-compatible interface from the start dramatically reduces LLM replacement costs.

3. Constraints of Date Prefix Matching

In the YYYY-MM-DD-NNN pattern, if two or more posts share the same date and number, translation matching will break. The PostGenerator must include logic to check the last number for that date and increment it when generating new posts.

4. Benefits of Process Consolidation

Consolidating three independent services into one resulted in:

State Sharing (LLM clients, configurations, API clients initialized only once)
Simplified Deployment (one systemd unit)
Easier Debugging (logs consolidated in one place)

Future Plans

Review the integration of arm1 agent’s LLM with Gemini.
Comment Quality Evaluation Pipeline (monitoring the appropriateness of auto-generated comments).
Automatic Translation Quality Verification (comparing with back-translation).
Expanding inter-agent collaboration through the AgentForge framework.

Blog automation aims not for “complete automation,” but for “minimal human intervention.” A structure where AI generates content, humans review it, and the system alerts operators to anomalies is the key to stable operation.

[Discord MCP] Gateway Architecture Design

Sun, 01 Mar 2026 01:13:00 +0900

The Claude Code team designed a Discord Gateway Service for user communication via Discord. This article summarizes the key architecture decisions and user_comm Agent design.

1. Overall Architecture

Components

Layer	Component	Role
Discord	Bot, Channel, Thread	User interface
Gateway	WebSocket, REST API, SSE	Message routing
MCP	gcp-mcp, oci-mcp, db-mcp	Tool execution
user_comm	Discord Agent	User communication

Message Flow

flowchart LR
 subgraph D[Discord]
 U[User]
 end
 subgraph G[Gateway :8081]
 WS[WebSocket]
 API[REST API]
 SSE[SSE]
 end
 subgraph A[Claude Code Team]
 UC[user_comm]
 GCP[gcp-mcp]
 OCI[oci-mcp]
 DB[db-mcp]
 end

 U --> WS
 WS --> UC
 UC --> API
 API --> GCP
 API --> OCI
 API --> DB
 GCP --> UC
 OCI --> UC
 DB --> UC
 UC --> SSE
 SSE --> U

2. user_comm Agent (User Communication Handler)

Role and Responsibilities

The user_comm Agent is a member of the Claude Code team, communicating with users through Discord channels and collaborating with other agents.

Function	Description
Input Reception	Receive Discord messages and route to appropriate agent
Opinion Request	Query user opinions at other agents’ requests
Notification/Report	Send system status, warnings, reports
Team Communication	Exchange messages with other agents

Internal Structure

flowchart TB
 subgraph UC[user_comm Agent]
 DBot[Discord Bot]
 Agent[UserCommAgent]
 TComm[TeamCommunicator]
 DBot <--> Agent
 Agent <--> TComm
 end
 subgraph DAPI[Discord API]
 M[Messages]
 B[Buttons]
 T[Threads]
 end
 subgraph O[Other Agents]
 GCP[gcp-mcp]
 OCI[oci-mcp]
 AL[alert-mcp]
 end

 DBot <--> M
 DBot <--> B
 DBot <--> T
 TComm --> GCP
 TComm --> OCI
 TComm --> AL

Message Types

class MessageType(Enum):
 TASK_REQUEST = "task_request" # Task request
 NOTIFICATION = "notification" # Notification
 OPINION_REQUEST = "opinion_request" # Opinion request
 OPINION_RESPONSE = "opinion_response" # Opinion response
 STATUS_REPORT = "status_report" # Status report
 ERROR = "error" # Error

Key Function Flows

Input Reception (Discord -> Agent)

User: "@gcp-monitor check server status"
 -> DiscordBot.on_message
 -> UserCommAgent._parse_command (target: gcp-monitor)
 -> TeamCommunicator.send_to_agent
 -> Discord: "Request forwarded to gcp-monitor."

Opinion Request (Agent -> Discord)

gcp-monitor: "Clean up disk?" -> user_comm
 -> DiscordBot.ask_opinion (wait for button or reply)
 -> User response
 -> TeamCommunicator.send_to_agent (forward response)

Notification/Report (Agent -> Discord)

oci-monitor: "Disk 92% warning" -> user_comm
 -> DiscordBot.send_message (Embed format)

3. Lightweight Architecture Without Redis

Why Remove Redis?

Item	With Redis	With In-Memory
Thread Lock	Redis SET NX	Python dict
Event Distribution	Redis Streams	Direct SSE
State Storage	Redis Cache	Memory cache

Conclusion: In-memory is sufficient for single-instance environments

Gateway Structure

flowchart TB
 subgraph GW[Gateway Service]
 Bot[Discord Bot]
 subgraph C[Core Components]
 Lock[Thread Lock]
 Cache[Message Cache]
 SSEMgr[SSE Manager]
 end
 end
 DAPI[Discord API] <--> Bot
 Bot --> Lock
 Bot --> Cache
 Bot --> SSEMgr
 SSEMgr --> Clients[WebSocket Clients]

Component Details

Module	Role	Features
Discord Bot	WebSocket connection	Auto-reconnect
Thread Lock	Concurrency control	5min timeout
Message Cache	Message storage	Max 1000 items
SSE Manager	Real-time delivery	Broadcast to all MCPs

4. MCP Selection: 4-Stage Hybrid

Selection Priority

Rank	Method	Example	Description
1	Slash Command	`/gcp status`	Most explicit
2	@Mention	`@gcp-monitor status`	Natural conversation
3	Keyword Detection	`gcp server status`	Auto keyword recognition
4	Channel Assignment	#gcp-monitoring	Channel default MCP

Fallback Order

flowchart TD
 A[Message Received] --> B{Slash Command?}
 B -->|Yes| C[Route to MCP]
 B -->|No| D{Mention?}
 D -->|Yes| C
 D -->|No| E{Keyword?}
 E -->|Yes| C
 E -->|No| F{Channel Default?}
 F -->|Yes| C
 F -->|No| G[Broadcast]

Slash Command List

Command	MCP	Description
`/gcp status [server]`	gcp-mcp	GCP server status
`/gcp list`	gcp-mcp	GCP instance list
`/oci status [server]`	oci-mcp	OCI server status
`/oci list`	oci-mcp	OCI instance list
`/db query <sql>`	db-mcp	Execute DB query
`/db list`	db-mcp	DB list
`/alert check`	alert-mcp	Check alerts

5. Thread Lock Rules

Lock Behavior

sequenceDiagram
 participant M1 as gcp-mcp
 participant GW as Gateway
 participant M2 as oci-mcp
 participant DT as Discord Thread

 Note over M1,DT: Lock Acquisition
 M1->>+GW: Lock request
 GW-->>-M1: Lock acquired
 M1->>DT: Send response

 Note over M2,GW: Lock Conflict
 M2->>GW: Lock request
 GW-->>M2: Lock failed

 Note over M1,GW: 5min timeout
 GW->>GW: Auto-release lock

 Note over M2,GW: Lock Retry
 M2->>+GW: Lock request
 GW-->>-M2: Lock acquired

Lock API

Method	Endpoint	Description
`POST`	`/api/threads/{id}/acquire`	Acquire lock
`POST`	`/api/threads/{id}/release`	Release lock
`GET`	`/api/threads/{id}/lock`	Check lock status

Request/Response Example

Lock Acquire Request

POST /api/threads/123456/acquire
{
 "agent_name": "gcp-mcp",
 "timeout": 300
}

Lock Acquire Response

{
 "acquired": true,
 "thread_id": "123456",
 "agent": "gcp-mcp"
}

6. MCP Tools (8)

Tool List

Tool	Description	Key Parameters
`discord_send_message`	Send message	channel_id, content
`discord_get_messages`	Get messages	channel_id, limit
`discord_wait_for_message`	Wait for message	channel_id, timeout
`discord_create_thread`	Create thread	channel_id, message_id
`discord_list_threads`	List threads	channel_id
`discord_archive_thread`	Archive thread	thread_id
`discord_acquire_thread`	Acquire lock	thread_id, agent_name
`discord_release_thread`	Release lock	thread_id, agent_name

Tool Relationships

flowchart LR
 subgraph Lock[Lock Tools]
 Acquire[acquire_thread]
 Release[release_thread]
 end
 subgraph Thread[Thread Tools]
 Create[create_thread]
 List[list_threads]
 Archive[archive_thread]
 end
 subgraph Msg[Message Tools]
 Send[send_message]
 Get[get_messages]
 Wait[wait_for_message]
 end

 Acquire --> Create
 Create --> Send
 Send --> Release

Usage Examples

# Send message
discord_send_message(
 channel_id="123456789",
 content="Server status: OK"
)

# Create thread
discord_create_thread(
 channel_id="123456789",
 message_id="987654321",
 name="Status Check"
)

# Acquire lock
discord_acquire_thread(
 thread_id="111222333",
 agent_name="gcp-mcp",
 timeout=300
)

7. File Structure

flowchart TB
 subgraph P[server_monitor/]
 subgraph Gw[gateway/]
 M1[main.py]
 M2[discord_ws.py]
 M3[thread_lock.py]
 M4[sse.py]
 end
 subgraph Uc[user_comm/]
 A1[agent.py]
 A2[discord_bot.py]
 A3[team_comm.py]
 end
 subgraph Mcp[discord_mcp/]
 S1[server.py]
 end
 subgraph Sh[mcp_shared/]
 MO[monitor/]
 end
 subgraph Doc[docs/]
 D1[MCP_SELECTION_STRATEGY.md]
 D2[MCP_ROUTING_POLICY.md]
 end
 end

Directory Description

Path	Description
`gateway/`	Gateway Service (FastAPI)
`user_comm/`	User Communication Agent
`discord_mcp/`	MCP Server (8 tools)
`mcp_shared/`	Shared MCP tools
`docs/`	Documentation

8. How to Run

Local Execution

# Start Gateway Service
uvicorn gateway.main:app --host 0.0.0.0 --port 8081

# Start user_comm Agent (standalone mode)
python main.py --standalone

# Start user_comm Agent (team mode)
python main.py --team server-monitor

# Health check
curl http://localhost:8081/health

# Response
{"status": "healthy", "discord_connected": true}

Claude Code MCP Configuration

// ~/.claude/settings.json
{
 "mcpServers": {
 "discord-gateway": {
 "command": "python3",
 "args": ["/path/to/discord_mcp/server.py"],
 "env": {
 "GATEWAY_URL": "http://localhost:8081"
 }
 }
 }
}

9. Roadmap

Phase 1: Complete

FastAPI Gateway Service
Discord WebSocket connection
Thread Lock (In-Memory)
SSE broadcast
MCP Server (8 tools)

Phase 2: In Progress

user_comm Agent implementation
Slash command implementation
Channel default MCP settings
Keyword auto-detection
Routing configuration file

Phase 3: Optional

API authentication (API Key)
Rate Limiting
Message persistence (SQLite)

Conclusion

We chose a strategy of starting with a lightweight architecture and expanding when needed.

Item	Current	Future
State Storage	In-Memory	SQLite (if needed)
Distributed Lock	Not used	Redis (multi-instance)
Authentication	None	API Key (if needed)
User Communication	Gateway direct	user_comm Agent

The current structure is sufficient for single instances, and we plan to expand incrementally as traffic grows. The user_comm Agent systematizes user communication and improves collaboration with other agents on the team.

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