LangChain vs LlamaIndex for Postgres agents

LangChain and LlamaIndex appeared during the same period, in the wake of the release of ChatGPT at the end of 2022. Their starting point differs markedly. LangChain models an LLM application as a chain of composable steps: prompt, model call, tool, memory, all assembled via LCEL or an LangGraphgraph.

LlamaIndex first models data: documents, nodes, indexes, query engine. A VectorStoreIndex or SQLDatabase are first class citizens, not tools added to a generic agent. Both are open source (MIT license), available in Python and TypeScript, and today cover a largely overlapping scope: agents, RAG, tool call, SQL connection.

This convergence makes the comparison more useful on the architecture than on the list of features: both can, almost, do the same thing. What changes is how.

On the LangChain side, the langchain_community.utilities.SQLDatabase module encapsulates a SQLAlchemy engine. The create_sql_agent agent then exposes it as a set of tools: list tables, describe a schema, execute a query, check a query before execution.

from langchain_community.utilities import SQLDatabase
from langchain_community.agent_toolkits import create_sql_agent
from langchain_openai import ChatOpenAI

# Channel obtained from Studio → Settings → Connection.
# search_path route to the project schema (see SQLAlchemy/psycopg doc for
# the exact encoding of the "options" parameter depending on the driver used).
db = SQLDatabase.from_uri(
    "postgresql+psycopg2://aura:***@<host>:5432/aura_db_master?options=-csearch_path%3Dproject_<id>"
)

llm = ChatOpenAI(model="gpt-4o-mini")
agent = create_sql_agent(llm=llm, db=db, agent_type="tool-calling")

agent.invoke({"input": "Combien de commandes la semaine dernière ?"})

On the LlamaIndex side, the equivalent abstraction is llama_index.core.SQLDatabase, also built on a SQLAlchemy engine. The NLSQLTableQueryEngine query engine translates a natural language question into an SQL query, executes it, and then reformulates the result as a response.

from sqlalchemy import create_engine
from llama_index.core import SQLDatabase
from llama_index.core.query_engine import NLSQLTableQueryEngine
from llama_index.llms.openai import OpenAI

engine = create_engine(
    "postgresql+psycopg2://aura:***@<host>:5432/aura_db_master?options=-csearch_path%3Dproject_<id>"
)
sql_database = SQLDatabase(engine, include_tables=["orders", "customers"])

query_engine = NLSQLTableQueryEngine(
    sql_database=sql_database, llm=OpenAI(model="gpt-4o-mini"),
)
response = query_engine.query("Combien de commandes la semaine dernière ?")

Neither extract depends on Aurabase. A SQLAlchemy driver and a standard Postgres connection string are sufficient, just like for Supabase, RDS, or a self-hosted instance.

LangChain first proposed a classic agent loop (AgentExecutor, ReAct pattern). The project has since converged its agents towards LangGraph: an agent is represented there as an explicit graph of nodes and edges, with checkpointing and possible human intervention between two stages.

LlamaIndex responds with its Workflows: an event-driven orchestration, where each step emits and consumes typed events. An SQL or vector query engine plugs in directly as a step, without an additional adaptation layer, since these engines are already native primitives of the framework.

For an agent querying Postgres, the practical difference is this: LangGraph gives fine-grained control over branches and retries around an SQL call. LlamaIndex requires less linking code when the question first concerns data already indexed by the framework.

Go deeper: function calling tutorial for a Postgres agent

LlamaIndex was designed from the outset to connect an LLM to data sources, with a catalog of connectors (LlamaHub) and specialized indexes depending on the type of content. RAG remains the most direct use case of the framework, not a feature added after the fact.

LangChain covers the same need via its retrievers and fetch chains, with equally mature integration into the LangGraph ecosystem. The difference is less about capacity than where the business logic lives: integrated into the index on the LlamaIndex side, assembled explicitly in a chain on the LangChain side.

Both know how to use pgvector as a vector base: llama-index-vector-stores-postgres on the LlamaIndex side, the PGVector class of the langchain-postgres package on the LangChain side. On an Aurabase project, pgvector 0.8.6 is already present in the Postgres tenant image: both packages connect to it with the same connection string, without a separate activation step.

Dig Deeper: Building a Postgres/pgvector RAG Pipeline

CrewAI orchestrates several agents per role: each agent receives an objective, a context (backstory) and tools, grouped into Crew with Task executed in sequence or according to a hierarchy. It is a full-fledged orchestration framework, not an extension of LangChain.

AutoGen, a Microsoft research project, takes a different approach: agents that talk to each other (AssistantAgent, UserProxyAgent, GroupChat), with the ability to execute code in an isolated environment. Coordination looks like a conversation, not an explicit state graph like LangGraph.

Neither replaces the data connection layer. A CrewAI or AutoGen agent that needs to read Postgres calls, in practice, an SQL tool built with LangChain or LlamaIndex, or a simple Python function around psycopg2. CrewAI and AutoGen answer “who does what and in what order”, not “how to read the database”.

create_sql_agent and NLSQLTableQueryEngine execute the query generated by the model against the provided connection. Neither bounds the number of rows returned by default, nor blocks a write request: the actual guardrail is the Postgres role used in the connection string, not a framework option.

This is a structural difference with Aurabase's native NL2SQL, which translates a question into SQL on the server side, validates the generated query (SQL parsing, rejection of spoofed server fields) and bounds the LIMIT before execution. It's not the same brick: an NL2SQL endpoint responds in one turn, with guardrails installed by the platform; a LangChain or LlamaIndex agent reasons in several stages, with safeguards to assemble yourself.

In practice, the two approaches complement each other rather than exclude each other: a bounded NL2SQL endpoint for a simple question exposed to an end user, an agent for multi-step reasoning which combines several tools beyond SQL.

See also: NL2SQL tutorial on Postgres with Aurabase

Three steps are enough, regardless of the framework chosen, and they do not depend on any platform-specific connector.

# 1. Retrieve the project's Postgres connection string
#    (Studio → Settings → Connection, or any managed Postgres)
export AURA_DB_URL="postgresql+psycopg2://aura:***@<host>:5432/aura_db_master?options=-csearch_path%3Dproject_<id>"

# 2. Install the SQL driver and the chosen framework
pip install langchain langchain-community langchain-openai psycopg2-binary
# or, on the LlamaIndex side:
pip install llama-index llama-index-llms-openai psycopg2-binary

# 3. Create a dedicated Postgres role, read-only if the agent should only poll
CREATE ROLE agent_readonly LOGIN PASSWORD '***';
GRANT SELECT ON ALL TABLES IN SCHEMA project_<id> TO agent_readonly;

The third step matters more than the choice of framework. A Postgres role restricted to truly necessary rights remains the only reliable safeguard against a generated request that exceeds its scope, regardless of the agent executing it. See the AI documentation for the configuration of Aurabase's native LLM providers (OpenAI, Anthropic, Gemini) usable on the agent side.

Neither framework is strictly superior for an agent connected to Postgres. The starting context of the project is more decisive than the list of functionalities.

• LangChain: if the agent must combine several heterogeneous tools (SQL, external APIs, web search) with fine control of the flow via LangGraph, and if the team values the broadest integration ecosystem on the market.
• LlamaIndex: if the heart of the project is the RAG or the querying of already indexed data, with a strong need for source connectors and an index/query model that fits directly to the use case.
• CrewAI or AutoGen in addition: as soon as a single agent is no longer enough and work must be distributed between several specialized roles, above one or the other of the two data frameworks.

The two can also coexist in the same project: a LlamaIndex query engine exposed as a tool in a LangGraph agent is a common pattern. Maintaining two frameworks has a real complexity cost, to be weighed against the gain before adopting it by default.