MCPInstall

neo4j-driver-python-skill

Neo4j Python Driver v6 — driver lifecycle, executequery, managed and explicit transactions, async (AsyncGraphDatabase), result handling, data type mapping, error handling, UNWIND batching, connection pool tuning, and causal consistency. Use when writing Python code that connects to Neo4j via GraphDatabase.driver, executequery, executeread, executewrite, AsyncGraphDatabase, neo4j.Result, or RoutingControl. Package name is neo4j (not neo4j-driver) since v6. Python =3.10 required. Does NOT handle Cypher query authoring — use neo4j-cypher-skill. Does NOT cover driver upgrades or breaking changes — use neo4j-migration-skill. Does NOT cover GraphRAG pipelines (neo4j-graphrag package) — use neo4j-graphrag-skill.

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984
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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/neo4j-contrib/neo4j-skills --skill neo4j-driver-python-skill
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads neo4j-driver-python-skill automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

When to Use

  • Writing Python code that connects to Neo4j
  • Setting up driver, sessions, transactions, or async patterns
  • Debugging result handling, serialization, or UNWIND batching
  • Reviewing Neo4j driver usage in Python code

When NOT to Use

  • Writing/optimizing Cypher → neo4j-cypher-skill
  • Driver version upgrades → neo4j-migration-skill
  • GraphRAG pipelines (neo4j-graphrag package) → neo4j-graphrag-skill

Installation

pip install neo4j                  # package name is `neo4j`, NOT `neo4j-driver` (deprecated since v6)
pip install neo4j-rust-ext         # optional: 3–10× faster serialization, same API

Python >=3.10 required for v6.x. Python 3.14 supported [6.1+]. Pandas 3 and PyArrow 23/24 supported [6.2+]. PyArrow 25 and Bolt 6.1 uuid.UUID values supported [6.3+]; driver-created SSL contexts honour SSLKEYLOGFILE [6.3+]. Python 3.15 supported [6.4+]. Pin neo4j>=6.4.1 — 6.4.0 can spin forever on pool acquisition and double-encodes bearer tokens over HTTP Query API.

Neo4j 2026.08+ UUID properties require neo4j>=6.3 to round-trip as uuid.UUID. Storing UUID needs block store format (Enterprise); Community (aligned format) fails: storing properties of type UUID is not supported in aligned store format — store str(uuid).


Environment Variables

Load connection config from environment — never hardcode credentials.

import os
from dotenv import load_dotenv   # pip install python-dotenv

load_dotenv(".env")   # reads NEO4J_URI / NEO4J_USERNAME / NEO4J_PASSWORD / NEO4J_DATABASE

URI      = os.getenv("NEO4J_URI",      "neo4j://localhost:7687")
USER     = os.getenv("NEO4J_USERNAME", "neo4j")
PASSWORD = os.getenv("NEO4J_PASSWORD", "")
DATABASE = os.getenv("NEO4J_DATABASE", "neo4j")

.env file format:

NEO4J_URI=neo4j+s://xxx.databases.neo4j.io
NEO4J_USERNAME=neo4j
NEO4J_PASSWORD=secret
NEO4J_DATABASE=neo4j

Add .env to .gitignore. Without python-dotenv, use export in shell or os.getenv directly.


Driver Lifecycle

Create one Driver per application. Thread-safe, expensive to create. Never create per-request.

from neo4j import GraphDatabase

URI  = "neo4j+s://xxx.databases.neo4j.io"   # Aura
AUTH = ("neo4j", "password")

# Context manager — preferred for scripts
with GraphDatabase.driver(URI, auth=AUTH) as driver:
    driver.verify_connectivity()
    # ... work ...

# Long-lived singleton (service / web app)
driver = GraphDatabase.driver(URI, auth=AUTH)
driver.verify_connectivity()
# on shutdown:
driver.close()

URI schemes:

SchemeUse
neo4j+s://TLS + cluster routing — Aura default
neo4j://Unencrypted + cluster routing
bolt+s://TLS, single instance
bolt://Unencrypted, single instance
https:// / http:// (e.g. https://host:7474/)HTTP Query API instead of Bolt [6.4+, preview] — pip install "neo4j[http]"

Auth options: ("user", "pass") tuple, basic_auth(), bearer_auth("jwt"), kerberos_auth("b64").

HTTP Query API [6.4+, preview — no semver guarantee]: pass database_= / database= on every call (no home-database resolution); auth basic (no realm) or bearer only. Ignored/unsupported: tx metadata + timeouts, fetch_size (no backpressure), notification filters; VECTOR, UUID, UnsupportedType cannot be exchanged. Needs Bolt for any of these → use neo4j+s:// / bolt+s://.


Choosing the Right API

APIUse whenAuto-retryStreaming
driver.execute_query()Most queries — simple, safe default✅❌ eager
session.execute_read/write()Large results / multiple queries in one tx✅✅
session.run()LOAD CSV, CALL {} IN TRANSACTIONS, scripts⚠️ one-shot [6.2+]✅
AsyncGraphDatabaseasyncio applications✅✅

session.run() retry [6.2+]: single immediate retry on DBMS-marked idempotent errors only (currently admission control). Disable with disable_auto_commit_retries=True at driver or session level.


execute_query — Default API

from neo4j import GraphDatabase, RoutingControl

# Tuple unpacking — most common
records, summary, keys = driver.execute_query(
    "MATCH (p:Person {name: $name})-[:KNOWS]->(f) RETURN f.name AS name",
    name="Alice",
    routing_=RoutingControl.READ,   # route reads to replicas
    database_="neo4j",              # always specify — saves a round-trip
)
for record in records:
    print(record["name"])
print(summary.result_available_after, "ms")

# Write — check counters
summary = driver.execute_query(
    "CREATE (p:Person {name: $name, age: $age})",
    name="Bob", age=30,
    database_="neo4j",
).summary
print(summary.counters.nodes_created)

Trailing-underscore convention — config kwargs end with _ (database_, routing_, auth_, result_transformer_, bookmark_manager_). No query parameter name may end with _; pass those via parameters_={"key_": val}.

Never f-string or format Cypher. Always $param — prevents injection and enables plan caching.

result_transformer_ — reshape before return:

import neo4j
df      = driver.execute_query("MATCH (p:Person) RETURN p.name, p.age", database_="neo4j",
                                result_transformer_=neo4j.Result.to_df)
record  = driver.execute_query("MATCH (p:Person {name:$n}) RETURN p", n="Alice", database_="neo4j",
                                result_transformer_=neo4j.Result.single)   # None if 0 rows; first record + warning if 2+

Result.single() defaults to strict=False: 0 rows → None; 2+ rows → first record + warning (no exception). Only single(strict=True) raises ResultNotSingleError (0 or 2+). Use strict=True when exactly one row required, e.g. result_transformer_=lambda r: r.single(strict=True); else check for None.


Managed Transactions (execute_read / execute_write)

Use for large results or multiple queries in one transaction.

with driver.session(database="neo4j") as session:

    def get_people(tx):
        result = tx.run("MATCH (p:Person) WHERE p.name STARTS WITH $pfx RETURN p.name AS name",
                        pfx="Al")
        return [r["name"] for r in result]   # consume INSIDE callback — Result invalid after tx closes

    names = session.execute_read(get_people)

    def create_person(tx):
        tx.run("CREATE (p:Person {name: $name})", name="Carol")

    session.execute_write(create_person)

Result lifetime — Result is a lazy cursor backed by the open transaction. Returning it unconsumed raises ResultConsumedError. Always collect to list inside the callback.

Callback may retry on transient failures — keep callbacks idempotent; move side effects (HTTP calls, emails) outside the callback.

Timeout/metadata via @unit_of_work (named functions only — cannot decorate lambdas):

from neo4j import unit_of_work

@unit_of_work(timeout=5.0, metadata={"app": "svc", "user": user_id})
def get_people(tx):
    return [r["name"] for r in tx.run("MATCH (p:Person) RETURN p.name AS name")]

session.execute_read(get_people)

Implicit Transactions (session.run)

Use only for LOAD CSV, CALL {} IN TRANSACTIONS, or quick scripts. session.run() does a single immediate retry on idempotent (DBMS-marked) errors only [6.2+]; other errors do not retry.

with driver.session(database="neo4j") as session:
    result = session.run("CREATE (p:Person {name: $name})", name="Alice")
    summary = result.consume()   # call consume() to guarantee commit before proceeding
    print(summary.counters.nodes_created)

# Opt out of one-shot retry [6.2+] — driver- or session-level
driver = GraphDatabase.driver(URI, auth=AUTH, disable_auto_commit_retries=True)
with driver.session(database="neo4j", disable_auto_commit_retries=True) as session:
    session.run("...")

Async API

Mirror of sync API — replace GraphDatabase with AsyncGraphDatabase, await every call.

from neo4j import AsyncGraphDatabase
import asyncio

# Singleton — same rule as sync: never create per-request
driver = AsyncGraphDatabase.driver(URI, auth=AUTH)

async def main():
    records, _, _ = await driver.execute_query(
        "MATCH (p:Person) RETURN p.name AS name",
        database_="neo4j", routing_=RoutingControl.READ,
    )
    print([r["name"] for r in records])
    await driver.close()

asyncio.run(main())

FastAPI lifespan pattern:

from contextlib import asynccontextmanager
from fastapi import FastAPI

_driver = None

@asynccontextmanager
async def lifespan(app: FastAPI):
    global _driver
    _driver = AsyncGraphDatabase.driver(URI, auth=AUTH)
    await _driver.verify_connectivity()
    yield
    await _driver.close()

app = FastAPI(lifespan=lifespan)

Parallel queries with asyncio.gather:

results = await asyncio.gather(
    driver.execute_query("MATCH (a:Artist) RETURN a.name AS name", database_="neo4j"),
    driver.execute_query("MATCH (v:Venue)  RETURN v.name AS name",  database_="neo4j"),
)

Never use sync GraphDatabase in asyncio — blocks the event loop.

Full async patterns → references/async.md


Error Handling

from neo4j.exceptions import (
    Neo4jError, ServiceUnavailable, TransientError,
    AuthError, ConstraintError,
)

try:
    driver.execute_query("...", database_="neo4j")
except AuthError:
    ...  # bad credentials
except ServiceUnavailable:
    ...  # no servers reachable
except ConstraintError as e:
    # unique/existence constraint violation — catch BEFORE Neo4jError (it's a subclass)
    print(e.code, e.message)
except TransientError as e:
    # raised only after retries exhausted (execute_query retries automatically)
    print(e.code)
except Neo4jError as e:
    print(e.code, e.message, e.gql_status)

Catch ConstraintError before Neo4jError — it is a subclass and will be swallowed otherwise.


Result Access & Null Safety

record = records[0]
record["name"]               # by key — KeyError if absent
record[0]                    # by index
record.get("name")           # None for absent key OR graph null
record.get("name", "Unknown")
d = record.data()            # dict — Node → dict of properties, Relationship → tuple, Path → list; temporal values stay driver objects

record.data(): Node → dict of properties, Relationship → (start_props, type, end_props) tuple (own properties dropped), Path → list. json.dumps accepts these but loses labels, element IDs, relationship properties. neo4j.time.Date/Time/DateTime stay driver objects → json.dumps raises TypeError. Project needed scalars in Cypher (toString() temporals); don't return whole entities.

# ❌ raises TypeError on json.dumps (temporal value)
records, _, _ = driver.execute_query("MATCH (p:Person) RETURN p.name AS name, p.created_at AS created_at", database_="neo4j")
json.dumps(records[0].data())

# ✅ project scalars
records, _, _ = driver.execute_query(
    "MATCH (p:Person) RETURN p.name AS name, p.age AS age, toString(p.created_at) AS created_at", database_="neo4j")
json.dumps(records[0].data())   # safe

Node/Relationship/temporal access:

node = record["p"]           # neo4j.graph.Node
node.element_id              # stable within this transaction only
node.labels                  # frozenset({'Person'})
dict(node)                   # all properties as plain dict

rel  = record["r"]           # neo4j.graph.Relationship
rel.type                     # 'KNOWS'

dt = record["created_at"]    # neo4j.time.DateTime
dt.to_native()               # datetime.datetime (loses sub-µs precision)

Full type mapping table → references/data-types.md


Batch Writes with UNWIND

Pass list[dict] — only shape the driver serializes correctly for UNWIND.

people = [{"name": "Alice", "age": 30}, {"name": "Bob", "age": 25}]
driver.execute_query(
    "UNWIND $rows AS row MERGE (p:Person {name: row.name}) SET p.age = row.age",
    rows=people,
    database_="neo4j",
)

Custom objects and dataclasses must be converted to dict before passing as parameters.


Performance

  • Always set database_ / database= — omitting triggers a home-database round-trip per call.
  • execute_read routes to replicas automatically; use routing_=RoutingControl.READ with execute_query.
  • Batch writes: one execute_write callback for the whole list > one tx per item.
  • Large results: stream lazily inside execute_read callback; execute_query is always eager.

Connection pool tuning:

driver = GraphDatabase.driver(URI, auth=AUTH,
    max_connection_pool_size=50,        # default 100
    connection_acquisition_timeout=30,  # seconds to wait for free connection
    max_connection_lifetime=3600,       # seconds; recycles stale connections
    connection_timeout=15,
    keep_alive=True,
)

Session exhaustion: each open session holds a connection. Always use with driver.session(...) as session.

Full performance patterns → references/performance.md


Common Errors

MistakeFix
f-string / .format() Cypher paramsUse $param placeholders always
Param name ending with _Pass via parameters_={"key_": val}
Omitting database_Always set — saves a round-trip every call
Returning Result from tx callbackConsume to list inside callback
Side effects in execute_read/write callbackMove outside — callback may retry
Passing dataclass/Pydantic as paramConvert to dict first
UNWIND with list of objectslist[dict] only
record.get() for absent-key detection"key" in record.keys() for absent; .get() returns None for both absent and graph null
No .consume() after session.run()Commit timing undefined; call .consume()
Sync driver inside asyncioUse AsyncGraphDatabase — sync blocks event loop
Async driver created per requestSingleton — create once at startup
Leaked sessionswith driver.session(...) as session always
json.dumps(record.data()) with temporal valuesTypeError — toString() in Cypher or convert. Whole nodes/relationships serialize but lose labels, IDs, relationship properties — project scalars
result["name"] on EagerResultIndex result.records[0]["name"] or unpack records, _, _ = ...
Assuming Result.single() raises on 0 or 2+ rowsDefault strict=False: None (0 rows) or first record + warning (2+). single(strict=True) raises
@unit_of_work on lambdaUse named function
Neo4jError caught before ConstraintErrorCatch ConstraintError first — it's a subclass
neo4j-driver package namePackage is neo4j since v6; neo4j-driver deprecated

References

Load on demand:

  • references/async.md — full async patterns: managed transactions, result methods, concurrency
  • references/data-types.md — complete Python↔Cypher type mapping, temporal conversion, graph object API, spatial types (CartesianPoint/WGS84Point)
  • references/performance.md — connection pool, lazy streaming, threading vs asyncio, bookmarks/causal consistency
  • references/transactions.md — explicit transactions, rollback, commit uncertainty, unit_of_work details

Docs:


Checklist

  • Package installed as neo4j (not neo4j-driver)
  • One Driver instance created at startup; shared everywhere
  • verify_connectivity() called at startup
  • database_ / database= set on every call
  • $param placeholders used — no f-strings or .format()
  • Result consumed inside tx callback (not returned raw)
  • Sessions used as context managers (with driver.session(...) as session)
  • ConstraintError caught before Neo4jError
  • AsyncGraphDatabase used in asyncio code (not sync driver)
  • Async driver created once at app startup (not per request)
  • Side effects outside execute_read/write callbacks
  • UNWIND batches use list[dict]

FAQ

What does the neo4j-driver-python-skill skill do?

Neo4j Python Driver v6 — driver lifecycle, executequery, managed and explicit transactions, async (AsyncGraphDatabase), result handling, data type mapping, error handling, UNWIND batching, connection pool tuning, and causal consistency. Use when writing Python code that connects to Neo4j via GraphDatabase.driver, executequery, executeread, executewrite, AsyncGraphDatabase, neo4j.Result, or RoutingControl. Package name is neo4j (not neo4j-driver) since v6. Python =3.10 required. Does NOT handle Cypher query authoring — use neo4j-cypher-skill. Does NOT cover driver upgrades or breaking changes — use neo4j-migration-skill. Does NOT cover GraphRAG pipelines (neo4j-graphrag package) — use neo4j-graphrag-skill.

How do I install neo4j-driver-python-skill?

Run npx skills add https://github.com/neo4j-contrib/neo4j-skills --skill neo4j-driver-python-skill in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/neo4j-driver-python-skill/ (personal) or .claude/skills/neo4j-driver-python-skill/ (project).

Which AI agents can use neo4j-driver-python-skill?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is neo4j-driver-python-skill safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains neo4j-driver-python-skill?

It is published by neo4j-contrib in neo4j-contrib/neo4j-skills. It has 984 recorded installs.

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