Framework Comparison

This guide compares AbstractCore with other popular LLM frameworks to help you choose the right tool for your needs.

Quick Comparison Table Detailed Comparisons Migration Guides Performance Comparison Use Case Recommendations Decision Matrix Combination Strategies Summary

Quick Comparison Table

Feature	AbstractCore	LiteLLM	LangChain	LangGraph	LlamaIndex
Primary Focus	Clean LLM infrastructure	API compatibility	Full LLM framework	Agent workflows	RAG/search
Size	~8k LOC	~15k LOC	100k+ LOC	~30k LOC	~80k LOC
Learning Curve	Minimal	Minimal	Steep	Medium	Medium
Provider Support	6 providers	100+ providers	Many via integrations	Via LangChain	Via LlamaHub
Tool Calling	✅ Universal execution	⚠️ Pass-through only	✅ Via integrations	✅ Native	⚠️ Limited
Streaming	✅ With tool support	✅ Basic	✅ Basic	❌ Limited	❌ Limited
Structured Output	✅ With retry logic	❌ None	⚠️ Via parsers	⚠️ Basic	⚠️ Basic
Production Ready	✅ Retry + circuit breakers	⚠️ Basic	✅ Via LangSmith	✅ Via LangSmith	⚠️ Depends
Memory/Sessions	✅ Simple sessions	❌ None	✅ Advanced	✅ Advanced	✅ Advanced
RAG Support	⚠️ Embeddings only	❌ None	✅ Full pipelines	⚠️ Basic	✅ Full pipelines
Agent Support	❌ Single calls	❌ None	✅ Via chains	✅ Native	⚠️ Query engines
Observability	✅ Built-in events	⚠️ Basic logging	✅ Via LangSmith	✅ Via LangSmith	⚠️ Basic

Detailed Comparisons

AbstractCore vs LiteLLM

LiteLLM provides API compatibility across providers. AbstractCore provides production infrastructure.

When to choose LiteLLM:

# LiteLLM is suitable for simple API compatibility
from litellm import completion

# Same API for all providers - that's it
response = completion(model="gpt-4", messages=[{"role": "user", "content": "Hello"}])

When to choose AbstractCore:

# AbstractCore for production features
from abstractcore import create_llm

llm = create_llm("openai", model="gpt-4o-mini")

# Tool calling that actually executes functions
response = llm.generate("What's the weather?", tools=weather_tools)

# Structured output with automatic retry
user = llm.generate("Extract user info", response_model=UserModel)

# Production reliability built-in
# - Automatic retries with circuit breakers
# - Comprehensive event system
# - Streaming with tool support

Summary: - LiteLLM = API compatibility layer - AbstractCore = Production LLM infrastructure

AbstractCore vs LangChain

LangChain is a comprehensive framework with many components. AbstractCore is focused infrastructure.

LangChain Strengths:

Ecosystem: Massive ecosystem with pre-built components
RAG: Complete RAG pipelines out of the box
Integrations: Hundreds of integrations with external services
Prompt Management: Advanced prompt templates and few-shot learning

AbstractCore Advantages:

Simplicity: Learn in 5 minutes vs days for LangChain
Reliability: Production-grade retry and error handling built-in
Performance: Lightweight with minimal dependencies
Tool Execution: Actually executes tools vs just formatting them

Code Comparison:

LangChain approach:

from langchain.llms import OpenAI
from langchain.agents import initialize_agent, AgentType
from langchain.tools import BaseTool

# More complex setup
llm = OpenAI(temperature=0)
tools = [CustomTool()]
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION)
response = agent.run("What's the weather?")

AbstractCore approach:

from abstractcore import create_llm

# Simple and direct
llm = create_llm("openai", model="gpt-4o-mini")
response = llm.generate("What's the weather?", tools=weather_tools)

Summary: - LangChain = Full framework with everything included - AbstractCore = Clean infrastructure you build on

AbstractCore vs LangGraph

LangGraph focuses on agent workflows and state management. AbstractCore focuses on reliable LLM calls.

LangGraph Strengths:

Agent Workflows: Multi-step agent reasoning with state
Graph Structure: DAG-based workflow definition
Human-in-the-loop: Built-in human approval workflows
State Persistence: Automatic state management

AbstractCore + AbstractAgent:

For complex agents, use AbstractAgent which builds on AbstractCore.

# LangGraph - workflow definition
from langgraph import StateGraph

workflow = StateGraph()
workflow.add_node("agent", agent_node)
workflow.add_node("tools", tool_node)
workflow.set_entry_point("agent")

# AbstractAgent - simpler agent creation
from abstract_agent import Agent
from abstractcore import create_llm

agent = Agent(
    llm=create_llm("openai", model="gpt-4o-mini"),
    tools=tools,
    memory=memory_system
)

Summary: - LangGraph = Complex workflow orchestration - AbstractCore = Foundation for building agents

AbstractCore vs LlamaIndex

LlamaIndex specializes in RAG and document processing. AbstractCore provides general LLM infrastructure.

LlamaIndex Strengths:

RAG Focus: Built specifically for retrieval-augmented generation
Document Processing: Advanced chunking and parsing strategies
Vector Stores: Integration with all major vector databases
Query Engines: Sophisticated query processing and routing

AbstractCore Approach:

AbstractCore provides the embeddings foundation - you build the RAG pipeline:

# LlamaIndex - full RAG out of the box
from llama_index import SimpleDirectoryReader, VectorStoreIndex

documents = SimpleDirectoryReader('data').load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
response = query_engine.query("What is the main topic?")

# AbstractCore - you build the pipeline
from abstractcore import create_llm
from abstractcore.embeddings import EmbeddingManager

embedder = EmbeddingManager()
llm = create_llm("openai", model="gpt-4o-mini")

# You implement: document chunking, vector storage, retrieval
relevant_docs = your_retrieval_system(query, embedder)
context = "\n".join(relevant_docs)
response = llm.generate(f"Context: {context}\nQuestion: {query}")

Summary: - LlamaIndex = Full RAG framework - AbstractCore = RAG building blocks

Migration Guides

From LiteLLM to AbstractCore

Before (LiteLLM):

import litellm

response = litellm.completion(
    model="gpt-3.5-turbo",
    messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)

After (AbstractCore):

from abstractcore import create_llm

llm = create_llm("openai", model="gpt-3.5-turbo")
response = llm.generate("Hello")
print(response.content)

# Plus you get: tool calling, structured output, streaming, reliability

From LangChain to AbstractCore

Before (LangChain):

from langchain.llms import OpenAI
from langchain.schema import HumanMessage

llm = OpenAI()
response = llm([HumanMessage(content="Hello")])

After (AbstractCore):

from abstractcore import create_llm

llm = create_llm("openai", model="gpt-3.5-turbo")
response = llm.generate("Hello")

# Simpler API, same functionality

From OpenAI SDK to AbstractCore

Before (OpenAI SDK):

import openai

client = openai.OpenAI()
response = client.chat.completions.create(
    model="gpt-3.5-turbo",
    messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)

After (AbstractCore):

from abstractcore import create_llm

llm = create_llm("openai", model="gpt-3.5-turbo")
response = llm.generate("Hello")
print(response.content)

# Plus: works with any provider, built-in reliability, tool calling

Performance Comparison

Memory Usage

Framework	Import Time	Memory (MB)	Dependencies
AbstractCore	~0.3s	~15MB	3 core
LiteLLM	~0.5s	~25MB	5+
LangChain	~2.0s	~150MB	50+
LangGraph	~1.0s	~80MB	20+
LlamaIndex	~1.5s	~120MB	40+

Feature Completeness vs Complexity

                    ┌─────────────────────┐
                 High│  LangChain     ●   │
 Feature Completeness│                     │
                     │  LlamaIndex ●      │
                     │                     │
                     │  LangGraph     ●   │
                Medium│                     │
                     │                     │
                     │  AbstractCore  ●   │
                     │                     │
                  Low│  LiteLLM       ●   │
                     └─────────────────────┘
                      Low        Medium   High
                           Complexity

● = Sweet spot for different use cases

Use Case Recommendations

Simple LLM Integration

Your App → AbstractCore → LLM Provider

Recommended for: AbstractCore Why: Simple, reliable, production-ready

API Compatibility Only

Your App → LiteLLM → Multiple Providers

Recommended for: LiteLLM Why: Massive provider support, minimal overhead

Complex RAG System

Documents → LlamaIndex → Vector DB → Query Engine

Recommended for: LlamaIndex Why: Full RAG pipeline with document processing

Agent Workflows

Task → LangGraph → Multi-step Agent → Tools

Recommended for: LangGraph or AbstractAgent Why: State management and workflow orchestration

Full Application Framework

Complex App → LangChain → Everything

Recommended for: LangChain Why: Comprehensive ecosystem

Decision Matrix

Choose AbstractCore if you want:

✅ Production-ready LLM infrastructure
✅ Universal tool calling across all providers
✅ Structured output with automatic retry
✅ Streaming with tool support
✅ Clean, simple API
✅ Built-in reliability (retry, circuit breakers)
✅ Comprehensive observability

Choose LiteLLM if you want:

✅ Simple API compatibility
✅ Maximum provider coverage (100+)
✅ Drop-in replacement for OpenAI SDK
❌ But no: tool execution, structured output, reliability features

Choose LangChain if you want:

✅ Pre-built components for everything
✅ Massive ecosystem and integrations
✅ Complex chain orchestration
❌ But accept: complexity, learning curve, many dependencies

Choose LangGraph if you want:

✅ Multi-step agent workflows
✅ State management and persistence
✅ Human-in-the-loop workflows
❌ But don't need: simple LLM calls, basic tool execution

Choose LlamaIndex if you want:

✅ Full RAG pipelines out of the box
✅ Advanced document processing
✅ Vector database integrations
❌ But don't need: general-purpose LLM calls, other use cases

Combination Strategies

Many projects benefit from combining tools:

AbstractCore + LlamaIndex

# Use LlamaIndex for RAG, AbstractCore for LLM calls
from llama_index import VectorStoreIndex
from abstractcore import create_llm

index = VectorStoreIndex.from_documents(docs)
relevant_docs = index.similarity_search(query)

llm = create_llm("anthropic", model="claude-3-5-sonnet-latest")
response = llm.generate(f"Context: {relevant_docs}\nQuestion: {query}")

AbstractCore + Custom Agents

# Use AbstractCore for reliable LLM calls in your agent
from abstractcore import create_llm

class MyAgent:
    def __init__(self):
        self.llm = create_llm("openai", model="gpt-4o-mini")

    def plan(self, task):
        return self.llm.generate(f"Plan steps for: {task}")

    def execute_step(self, step, tools):
        return self.llm.generate(step, tools=tools)

AbstractCore + LangChain Components

# Use LangChain for prompts, AbstractCore for execution
from langchain.prompts import PromptTemplate
from abstractcore import create_llm

template = PromptTemplate.from_template("Translate {text} to {language}")
llm = create_llm("openai", model="gpt-4o-mini")

prompt = template.format(text="Hello", language="French")
response = llm.generate(prompt)

Summary

AbstractCore is designed to be the reliable foundation that other tools can build on. It excels at:

Production reliability - Built-in retry, circuit breakers, error handling
Universal tool calling - Works across all providers
Structured output - Type-safe responses with validation
Clean architecture - Simple to use and integrate

For specialized needs, combine AbstractCore with: - LlamaIndex for RAG pipelines - AbstractAgent for complex agents - AbstractMemory for advanced memory - LangChain components for specific features

Choose the right tool for your specific needs, and don't hesitate to combine them when it makes sense.

The key insight: Every framework has trade-offs. AbstractCore prioritizes reliability and simplicity over feature completeness, making it an strong foundation for production LLM applications.

Framework Comparison

Table of Contents

Quick Comparison Table

Detailed Comparisons

AbstractCore vs LiteLLM

When to choose LiteLLM:

When to choose AbstractCore:

AbstractCore vs LangChain

LangChain Strengths:

AbstractCore Advantages:

Code Comparison:

AbstractCore vs LangGraph

LangGraph Strengths:

AbstractCore + AbstractAgent:

AbstractCore vs LlamaIndex

LlamaIndex Strengths:

AbstractCore Approach:

Migration Guides

From LiteLLM to AbstractCore

From LangChain to AbstractCore

From OpenAI SDK to AbstractCore

Performance Comparison

Memory Usage

Feature Completeness vs Complexity

Use Case Recommendations

Simple LLM Integration

API Compatibility Only

Complex RAG System

Agent Workflows

Full Application Framework

Decision Matrix

Choose AbstractCore if you want:

Choose LiteLLM if you want:

Choose LangChain if you want:

Choose LangGraph if you want:

Choose LlamaIndex if you want:

Combination Strategies

AbstractCore + LlamaIndex

AbstractCore + Custom Agents

AbstractCore + LangChain Components

Summary