LLM Frameworks & Agent Orchestration
Build production-grade LLM applications using LangChain, LlamaIndex, and custom agent frameworks with proper error handling, observability, and scalability.
Frameworks We Specialize In
LangChain
Chains, agents, and memory systems for complex multi-step workflows, document Q&A, and conversational AI with structured tool calling.
LlamaIndex
Data ingestion, indexing, and retrieval pipelines for RAG applications with support for diverse document formats and metadata filtering.
Custom Agent Frameworks
Elixir/Phoenix-based agent orchestration for real-time, fault-tolerant LLM applications with OTP supervision trees and distributed task execution.
Observability & Tracing
LangSmith, Phoenix, and custom logging for debugging agent behavior, tracking token usage, and identifying performance bottlenecks.
Our Three-Layer Approach with LLM Frameworks
Advisory & Governance
Framework selection, architecture design, and guardrails for LLM agent systems.
- • LangChain vs LlamaIndex vs custom framework evaluation
- • Agent system architecture (ReAct, Plan-and-Execute, multi-agent)
- • Tool safety and permission boundaries for autonomous agents
- • Memory and context management strategies
- • Error handling and fallback mechanism design
Example Deliverable:
Agent system architecture with tool registry and safety controls
Build & Integrate
Production-ready LLM applications with proper chain orchestration, tool integration, and error recovery.
- • RAG pipelines with LlamaIndex for enterprise document search
- • Multi-agent systems for complex workflow automation
- • Conversational memory with chat history and context injection
- • Tool-calling agents for API integration and database queries
- • Custom chains for domain-specific processing pipelines
Example Deliverable:
RAG-powered customer support system with LlamaIndex and GPT-4
Operate & Scale
Monitor agent behavior, optimize chain performance, and scale LLM applications to production traffic.
- • LangSmith tracing for debugging agent decision-making
- • Chain performance profiling and bottleneck identification
- • Prompt version control and A/B testing for chains
- • Token usage optimization across multi-step workflows
- • Horizontal scaling of agent pools for high concurrency
Example Deliverable:
LangSmith observability dashboard with chain performance metrics
Real-World Implementations
Voice Agent with Real-Time Tool Orchestration
Custom Elixir-based agent framework for coordinating GPT-4.5 inference, voice synthesis, telephony integration, and CRM updates in real-time recruiting interviews.
Multi-Agent Healthcare Outreach System
Coordinated agents for appointment confirmation, rescheduling logic, and escalation to human agents using LangChain with Azure OpenAI and custom tools for EHR integration.
Field Service Knowledge Assistant
LlamaIndex-powered RAG system for technicians to query service manuals, troubleshooting guides, and historical job data via natural language.
Why Not Just Use the OpenAI API Directly?
LLM frameworks provide essential abstractions for production applications:
Without Frameworks
- • Manual prompt template management
- • Custom retry logic and error handling
- • Building tool-calling infrastructure from scratch
- • Complex state management for conversations
- • No observability or debugging tools
With Frameworks
- • Prompt templates with variable injection
- • Built-in retry, fallback, and caching
- • Tool registry and structured calling
- • Memory systems and conversation history
- • Tracing, logging, and performance monitoring
Ready to build production LLM applications?
Let's discuss your use case and select the right framework and architecture for your LLM application.
Schedule Consultation