Best Python Framework for Building AI Agents in 2026

Agent frameworks in Python handle the model loop, tool calls and structured output, and the choice between them is mostly about how much control you want over each step. Running an agent in production needs more than the loop, since conversations have to be stored, long tasks have to survive a restart, and the model API keys have to live somewhere other than the repository.

Developers are also cautious about the output, with 46 percent of respondents to the 2025 Stack Overflow Developer Survey saying they distrust the accuracy of AI tools against 33 percent who trust it, which is one reason agent backends tend to keep a full record of each run.

What to weigh when choosing:

• How tools are declared and whether their arguments are validated.
• Whether agent state can be checkpointed and resumed after a failure.
• How runs are traced, so you can see which tool call went wrong.
• Where conversations, queues and secrets live in production.

Python agent frameworks at a glance

Pydantic AI: typed agents

Pydantic AI

Pydantic AI comes from the Pydantic team and treats an agent as a typed object with tools declared as Python functions and outputs validated as Pydantic models.

Key features

• Tool arguments and outputs are validated, and validation errors go back to the model for a retry.
• Dependency injection for passing database handles or clients into tools.
• Support for many model providers behind one interface.

Good to know

Pydantic AI keeps orchestration light, so complex branching flows are written as ordinary Python or with its graph module, and state between requests is yours to store.

Try Pydantic AI →

LangGraph: agents as graphs

LangGraph

LangGraph models an agent as a graph of nodes with shared state, and checkpointers persist that state so a run can pause for human input and resume.

Key features

• Explicit control over loops, branches and retries.
• Checkpointing to Postgres or other stores.
• Interrupts for human approval steps.

Good to know

The graph model adds concepts to learn, and the checkpoint store is another database you run in each environment.

Try LangGraph →

OpenAI Agents SDK: handoffs and guardrails

OpenAI Agents SDK

The OpenAI Agents SDK provides agents, tools, handoffs between agents and guardrails, with tracing of each run.

Key features

• Handoffs let one agent delegate to another.
• Guardrails validate inputs and outputs in parallel with the run.
• Built-in tracing of model calls and tool use.

Good to know

The SDK is designed around OpenAI's APIs first, and its default tracing goes to OpenAI's dashboard, which matters if your data must stay in your own systems.

Try OpenAI Agents SDK →

Encore: infrastructure for the service that runs the agent

Encore combines the FastAPI or Django app that serves the agent with its infrastructure through an infrastructure SDK you install into the app, so the agent framework keeps the model loop while the service declares a Postgres database for runs and a Pub/Sub topic for work that takes longer than a request. encore run starts Postgres locally along with the topic, and a deploy provisions both in your own AWS or GCP account. Whichever option above you pick, Encore can add the database, topics and secrets to the same app.

The endpoint records a run and hands it to a background subscription that calls a Pydantic AI agent:

from contextlib import asynccontextmanager

import encore
from encore import pubsub
from encore.storage import postgres
from fastapi import FastAPI
from pydantic import BaseModel
from pydantic_ai import Agent

db = postgres.Database("agent", migrations="./migrations")


class RunRequested(BaseModel):
    run_id: int
    prompt: str


runs = pubsub.Topic[RunRequested]("agent-runs")
agent = Agent("openai:gpt-4o", instructions="Answer support questions briefly.")


async def execute(message: pubsub.Message) -> None:
    result = await agent.run(message.data.prompt)
    await db.exec(
        "UPDATE runs SET output = $1, done = true WHERE id = $2",
        result.output,
        message.data.run_id,
    )


worker = pubsub.Subscription[RunRequested](
    runs, "execute-run", handler=execute, max_retries=3
)


@asynccontextmanager
async def lifespan(app: FastAPI):
    await encore.start()
    yield


app = FastAPI(lifespan=lifespan)


@app.post("/runs", status_code=202)
async def create_run(prompt: str) -> dict:
    row = await db.query(
        "INSERT INTO runs (prompt) VALUES ($1) RETURNING id", prompt
    ).one()
    await runs.publish(RunRequested(run_id=row["id"], prompt=prompt))
    return {"run_id": row["id"]}


@app.get("/runs/{run_id}")
async def get_run(run_id: int) -> dict:
    row = await db.query("SELECT output, done FROM runs WHERE id = $1", run_id).one()
    return {"output": row["output"], "done": row["done"]}

Key features

• Runs survive restarts because the request and the work are separated by a topic with retries.
• Every run is stored in Postgres, which gives you an audit trail of prompts and outputs.
• A deploy provisions the database and topic in your own AWS or GCP account.
• A coding agent extending this service can add a bucket for uploaded documents in the same change, and the local MCP server lets it query the runs table to check its work.

Good to know

Encore does not call models or define tools, so the framework comparison below still decides how the agent itself is written. Agent output still needs review, and OpenTelemetry now defines semantic conventions for generative AI so model calls can be recorded in a standard shape, which makes the stored record of each run worth keeping. Cloud resources are created by Encore at deploy time, and the cloud access sits with Encore's IAM role, not the agent. Python support is in beta, and you can sign up for access.

Try Encore →

How to choose

Anthropic's guide to building effective agents reports that the most successful implementations used "simple, composable patterns" rather than complex frameworks, which favors starting small.

• Pydantic AI for typed tools and outputs with little abstraction.
• LangGraph for multi-step flows with checkpoints and approvals.
• OpenAI Agents SDK for several cooperating agents on OpenAI models.

For more on giving agents access to tools safely, see Encore's AI integration docs. Python support in Encore is in beta, and you can sign up for access.

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