Beyond LangChain: When You Need Production-Ready AI Agents

The Prototyping to Production Gap

LangChain, LlamaIndex, and similar frameworks excel at getting something working quickly. They provide abstractions that let developers chain together LLM calls, retrieval systems, and tools with minimal code. For demonstrations and proof-of-concepts, this is valuable.

Production systems have different requirements:

• Predictable behavior — You need to understand exactly what the system will do, not debug through layers of abstraction
• Performance control — You need to optimize specific bottlenecks, not work around framework overhead
• Maintainability — Code must be readable and debuggable by your team, not dependent on framework internals
• Flexibility — Requirements change; you need to modify behavior without fighting the framework

Common Problems with Framework-Based Agents

Teams that scale LangChain projects to production often encounter:

• Abstraction leakage — When something breaks, you need to understand both your code and the framework internals
• Version fragility — Framework updates frequently break existing code, requiring constant maintenance
• Performance overhead — Generic abstractions add latency and memory usage that matter at scale
• Limited customization — Doing something the framework was not designed for requires awkward workarounds
• Debugging complexity — Stack traces through framework code obscure the actual problem

These are not criticisms of LangChain specifically—they are inherent trade-offs of general-purpose frameworks when applied to production systems with specific requirements.

Our Approach: Purpose-Built Agents

We build AI agents from first principles, tailored to your use case:

• Direct LLM integration — We call model APIs directly with prompts optimized for your domain
• Custom retrieval — Vector search, keyword search, or hybrid approaches designed for your data characteristics
• Explicit control flow — Clear, readable code that shows exactly how decisions are made
• Focused functionality — Only the features you need, without framework bloat

The result is a system your team can understand, maintain, and extend without framework expertise.

Technical Comparison

Prompt Management

Frameworks often template prompts dynamically. We prefer explicit prompt versioning—prompts are treated as configuration, stored in version control, and updated through your deployment process. Changes are reviewable, testable, and reversible.

Chain Composition

LangChain chains are implicit data pipelines. Our agents use explicit function calls with clear inputs and outputs. When debugging, you can step through the code and see exactly what data flows where.

Tool Integration

Framework tool definitions require conforming to specific interfaces. We define tools as standard functions with typed parameters, integrated through dependency injection. Adding or modifying tools does not require understanding framework conventions.

State Management

Agent state (conversation history, context, intermediate results) is managed explicitly. You choose the storage mechanism (Redis, PostgreSQL, in-memory) based on your requirements, not framework assumptions.

When Frameworks Make Sense

We are not against frameworks categorically. They are appropriate when:

• You are building a quick prototype to test feasibility
• Your use case matches the framework's designed patterns exactly
• You have team members with deep framework expertise
• The project is experimental and may not reach production

But if you need a production system that your team can maintain long-term, purpose-built often wins.

Migration Path

Many clients come to us with existing LangChain prototypes. We do not ask you to discard that work. Instead:

1. We analyze your prototype to understand the intended behavior
2. We document the prompts, tools, and logic that make it work
3. We rebuild the core functionality in maintainable, tested code
4. We run both systems in parallel to verify equivalent behavior
5. We transition to the production system with full documentation

The prototype was valuable for proving the concept. The production system delivers it reliably.

What You Get

Our delivered agents include:

• Clean codebase — No framework dependencies, just standard libraries and your infrastructure
• Documentation — Architecture overview, API reference, deployment guide
• Test suite — Unit tests, integration tests, and example scenarios
• Deployment artifacts — Docker images, Kubernetes manifests, CI/CD configuration
• Monitoring setup — Metrics, logging, and alerting configuration

You own the code completely. No vendor lock-in, no framework dependencies, no surprise licensing issues.

Learn More

Explore how we build AI agents for a deeper look at our development process. For specific deployment requirements, see our self-hosted AI agents page.