Modernizing a legacy TypeScript-based agentic system to a modern Python LangChain v1 architecture, achieving 5-10x throughput improvements and 50-70% reduction in token costs.
Industry: Developer Tools / AI SaaS
Duration: 3 months
2025
Throughput Improvement
5-10x
Request handling capacity
Cache Hit Rate
90%+
Up from ~10%
Token Cost Reduction
50-70%
Via systematic caching
Warm Request Latency
<100ms
Down from 1-2s
Concurrent User Handling
25x
Improvement
Deployment Time
<2min
Down from 5-10min
A fast-growing AI development platform sought to modernize their agentic AI architecture to improve performance, reduce costs, and enable multi-model support. Our consultancy led the migration from a legacy TypeScript-based system to a modern Python LangChain v1 architecture, achieving 5-10x throughput improvements, 90%+ cache hit rates, and 50-70% reduction in token costs.
The client operated an AI-powered coding assistant serving thousands of developers. Their platform enables users to build applications through natural language instructions.
| Challenge | Business Impact |
|---|---|
| Single Model Lock-in | Couldn't leverage cost-effective models for routine tasks |
| Poor Cache Utilization | Token costs growing linearly with user growth |
| Build Complexity | TypeScript compilation adding deployment friction |
| Limited Concurrency | Serial request handling creating bottlenecks |
| Tight Coupling | Sandbox provider changes required extensive refactoring |
Before: Legacy TypeScript
TypeScript Runtime
LangGraph v0.4.9
DeepAgent Framework
PostgreSQL Checkpointer
Single Model (Claude Sonnet)
No runtime flexibility
Tightly Coupled Sandbox
Hard to change providers
No caching
Build complexity
Serial requests
Model lock-in
After: Python LangChain v1
Python Runtime
create_agent API (LangChain v1)
Composable Middleware Stack
Claude Sonnet 4.5
Gemini 3 Flash
GPT-4.1 mini
Modal
Docker
E2B
90%+ cache rate
Runtime switching
Parallel requests
Provider agnostic
We designed a composable middleware stack that intercepts requests before and after model execution:
Pre-Processing
Input Processing
Token Optimization
Model Routing
Cache Integration
+ additional layers
Model Execution
LLM Call
Post-Processing
Response Parsing
State Management
Persistence Layer
+ validation
A critical insight: preventing cross-contamination between user messages in long conversations:
Conversation
Shared context
Per-Run Isolation
Run 1
files
commands
calls
Run 2
files
commands
calls
Run 3
files
commands
calls
No Cross-talk
Clean isolation
Systematic caching with fixed breakpoints for predictable behavior:
1
System Prompt
~15K tokens
100%
2
Tool Definitions
~5K tokens
100%
3
First 2 Messages
~2K tokens
100%
Key: Middleware appends without cache_control, preserving static cache while enabling dynamic content
Tools decoupled from infrastructure providers through a clean interface:
Provider-Agnostic Tools
write_file()
read_file()
execute_cmd()
SandboxProvider Protocol
Clean interface contract
Modal
Current
Docker
Local dev
E2B
Alternative
| Metric | Before | After | Improvement |
|---|---|---|---|
| Cache Hit Rate | ~10% | ~90% | 9x |
| Warm Request Latency | 1-2s | <100ms | 10-20x faster |
| Single User Throughput | 0.5 req/s | 100+ req/s | 200x |
| Concurrent Users | 2 req/s (10 users) | 50+ req/s (100 users) | 25x |
| Token Costs | $X/1K requests | $0.3-0.5X/1K requests | 50-70% |
| Deployment Time | 5-10 minutes | <2 minutes | 5x faster |
Python Over TypeScript
LangChain and LangGraph are Python-native. Access to v1 Alpha features, native async, direct SDK integration.
Middleware Over Custom Nodes
Separation of concerns, testable components, future-proof as LangChain evolves.
Run-Scoped State
Accurate workflow tracking, reliable turn limits, correct state snapshots per interaction.
Fixed Cache Breakpoints
90%+ cache hit rate, 50-70% token cost reduction, consistent performance.
Incremental migration minimized risk
Middleware pattern enabled independent testing
Provider abstraction paid dividends mid-project
Per-run isolation solved long conversation bugs
Start with state design before features
Instrument token usage, cache rates, latency
Abstract providers early (even with one)
Test middleware components independently
| Layer | Technology | Purpose |
|---|---|---|
| Framework | LangChain v1, LangGraph | Agent orchestration |
| Primary Model | Claude Sonnet 4.5 | Complex reasoning |
| Secondary Model | Gemini 3 Flash | Simple tasks |
| State | PostgreSQL | Checkpointing |
| Sandbox | Modal | Code execution |
| Monitoring | LangSmith | Tracing |
Confidentiality Notice
This case study has been anonymized to protect client confidentiality. Specific metrics and architecture details have been generalized while preserving technical accuracy.
Let's discuss how we can modernize your AI architecture for better performance and lower costs.
Case Study Prepared by: Frenxt Consultancy
Date: 2025