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AI Software
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Autonomous multi-agent engineering runtime · LangGraph · TypeScript · Hono

#multi-agent#langgraph#langchain#agentic-ai#tool-calling#reactflow#streaming#sse#hono#nextjs#typescript#llm#ai-runtime#ai-engineering
README.md
About

Simulates a real AI engineering organization where specialized agents collaborate to analyze repositories, retrieve code context, modify implementations, validate changes, and stream runtime activity live to the UI.

Key concepts
Multi-Agent SystemsTool CallingSemantic MemoryRuntime StreamingEvent-Driven ArchAutonomous LoopsHuman-in-the-loopVector RetrievalRepo IntelligenceAI Workflow Orch.
runtime-pipeline.ts
Execution flow
1
User Task
2
Intent Classifier
3
Strategic Planner
4
Repository Searcher
5
Task Classifier
6
Frontend / Backend Eng.
7
Reviewer
8
Validator
9
Change Summary
agents.config.ts
Active agents · 10 online
Intent ClassifierStrategic PlannerRepository SearcherTask ClassifierBackend EngineerFrontend EngineerReviewerValidatorChange Summary AgentAnswer Agent
graph-architecture.ts
LangGraph StateGraph
START
intent-classifier
planner → searcher → classifier
├── backend-engineer
└── frontend-engineer
reviewer → validator → change-summary
END

Routing via .addConditionalEdges("classifier", taskRouter) — dynamically determined by agent reasoning + runtime state.

memory-system.ts
Memory architecture
⬇
Extraction
⎔
Embedding generation
◈
Vector storage
⌕
Semantic retrieval
↯
Memory injection
Learns: repo architecture · implementation locations · framework conventions · recurring failures · engineering patterns
events.ts
Runtime events (SSE)
type RuntimeEvent =
  | "agent"
  | "tool"
  | "terminal"
  | "diff"
  | "approval"
  | "log"

Powers

live logs
graph visualization
terminal streaming
diff viewer
approval modals
tools.json
Tool system
⌕Repository Intelligence
search_code
read_file
grep / search_text
⚙Engineering
patch_file
git_diff
run_terminal
✓Validation
npm run build
npm run typecheck
lint / test execution
tech-stack.yml
Technology

Backend

  • TypeScript
  • LangGraph
  • LangChain
  • Hono
  • Node.js

Frontend

  • Next.js
  • React
  • Tailwind CSS
  • ReactFlow

AI / Runtime

  • Gemini
  • Tool Calling
  • Event Streaming
  • Multi-Agent Orch.

Memory

  • ChromaDB
  • Vector Embeddings
project-structure.ts
Source layout

server/src/

├── agents/
├── graph/
├── tools/
├── events/
├── runtime/
├── memory/
├── prompts/
├── server.ts
├── index.ts

web/

├── /app
├── /components
├── /lib
├── /types
setup.zsh
Getting started
$ install dependencies
npm install
$ run chromadb
docker run -p 8000:8000 chromadb/chroma
$ start backend
npm run dev
$ start frontend
cd web && npm install && npm run dev
tasks.txt
Example prompts
$Add auth request logging
$Fix Prisma validation issue
$Implement dark mode toggle
$Add Redis caching layer
$Refactor middleware architecture
approval-gates.ts
Human approval gates
Dangerous operations pause until user approval is confirmed.
⚠ rm commands
⚠ git push
⚠ migrations
⚠ docker operations
streaming.md
Real-time runtime
Server-Sent Events (SSE)
Event-driven architecture
Streaming terminal logs
Streaming tool execution
Live workflow updates
LangGraph features
◆ Conditional routing
◆ Autonomous loops
◆ Checkpointing
◆ Agent handoffs
◆ Dynamic execution flows
visualization.md
ReactFlow graph
Active agent highlighting
Real-time execution tracking
Dynamic graph rendering
Orchestration visibility
future.log
Roadmap
LangGraph Interrupts
Durable Workflow Resumption
Playwright Browser Agent
AST-safe Code Editing
Multi-Repository Intelligence
Distributed Worker Runtime
Monaco Diff Visualization
Kubernetes Runtime Workers
Autonomous PR Generation
goals.md
Project explores
Autonomous SW Engineering
AI Orchestration Systems
Multi-Agent Collaboration
Repository Intelligence
Durable AI Runtimes
Real-time AI Observability
Semantic Engineering Memory
Experimental system designed for learning advanced agentic AI architecture. Always review AI-generated code before production usage.

End of documentation — Autonomous AI Engineering System