LangGraph v0.2 Enhances Customization with New Checkpointer Libraries

LangChain
has
announced
the
stable
release
of
LangGraph
v0.2,
marking
a
significant
update
with
the
introduction
of
new
checkpointer
libraries.
These
libraries
aim
to
simplify
the
creation
and
customization
of
checkpointers,
enhancing
the
resilience
and
functionality
of
large
language
model
(LLM)
applications,
according
to
the

LangChain
Blog.

Why
LangGraph
v0.2
Was
Developed

LangGraph’s
core
feature
is
its
built-in
persistence
layer,
implemented
through
checkpointers.
These
checkpointers
save
the
state
of
the
graph
at
each
step,
enabling
capabilities
such
as
session
memory,
error
recovery,
human-in-the-loop
features,
and
time
travel.
Since
its
inception,
LangGraph
has
been
designed
to
be
database-agnostic,
allowing
users
to
implement
their
own
checkpointer
adapters.

However,
there
was
no
clear
blueprint
for
users
to
create
custom
checkpointers
for
popular
databases
like
Postgres,
Redis,
and
MongoDB.
LangGraph
v0.2
addresses
this
gap
by
providing
dedicated
checkpointer
libraries.

New
Checkpointer
Libraries
in
LangGraph
v0.2

The
new
release
includes
a
suite
of
checkpointer
libraries,
making
it
easier
to
create
and
customize
checkpointers:

• 
langgraph_checkpoint:
  The
  base
  interface
  for
  checkpointer
  savers
  and
  serialization/deserialization.
• 
langgraph_checkpoint_sqlite:
  An
  SQLite-based
  checkpointer
  ideal
  for
  local
  workflows
  and
  experimentation.
• 
langgraph_checkpoint_postgres:
  An
  optimized
  Postgres
  checkpointer
  for
  production
  environments,
  now
  open-sourced
  for
  community
  use.

These
implementations
can
be
used
interchangeably,
allowing
users
to
tailor
their
applications
to
their
specific
needs.

LangGraph
Postgres
Checkpointer
for
Production

The

langgraph_checkpoint_postgres
implementation
serves
as
a
blueprint
for
creating
optimized,
production-ready
checkpointers.
It
includes
several
optimizations,
such
as
using
Postgres
pipeline
mode
to
reduce
database
roundtrips
and
storing
each
channel
value
separately
to
minimize
storage
requirements.

Getting
Started
with
LangGraph
v0.2

To
get
started,
users
can
import
the
necessary
checkpointer
interfaces
and
implementations
using:

• 
from
langgraph.checkpoint.base
import
BaseCheckpointSaver
• 
from
langgraph.checkpoint.memory
import
MemorySaver
• 
from
langgraph.checkpoint.sqlite
import
SqliteSaver
• 
from
langgraph.checkpoint.postgres
import
PostgresSaver

SQLite
and
Postgres
checkpointers
require
separate
installations
via

pip
install
langgraph-checkpoint-sqlite
and

pip
install
langgraph-checkpoint-postgres,
respectively.
LangGraph
checkpoint
libraries
follow
semantic
versioning,
ensuring
that
breaking
changes
in
the
main
library
will
result
in
corresponding
major
version
updates
for
the
checkpointer
libraries.

Run
Agents
at
Scale
with
LangGraph
Cloud

LangGraph
v0.2
also
introduces
LangGraph
Cloud,
a
runtime
environment
designed
for
deploying
agents
at
scale.
LangGraph
Cloud
manages
task
queues,
servers,
and
includes
the
robust
Postgres
checkpointer
to
handle
concurrent
users
and
large
data
states.
It
supports
real-world
interaction
patterns
such
as
double-texting,
async
background
jobs,
and
cron
jobs.

LangGraph
Studio,
a
desktop
app
for
visualizing
and
debugging
agent
trajectories,
is
now
available
for
all
LangSmith
users.
LangGraph
Cloud
is
currently
in
open
beta
for
Plus
and
Enterprise
plan
users.

Additional
Changes
in
LangGraph
v0.2

The
latest
version
also
includes
several
breaking
changes
and
deprecations:

Breaking
Changes

• Renaming
  of
  
thread_ts
  and
  
parent_ts
  to
  
checkpoint_id
  and
  
parent_checkpoint_id.
• Re-exported
  imports
  are
  no
  longer
  possible
  due
  to
  the
  use
  of
  namespace
  packages.
• SQLite
  checkpointers
  have
  been
  moved
  to
  a
  separate
  library.

Deprecations

• Removal
  of
  
langgraph.prebuilt.chat_agent_executor.create_function_calling_executor.
• Removal
  of
  
langgraph.prebuilt.agent_executor.

Conclusion

LangChain
expresses
gratitude
to
its
community
for
their
feedback
and
support.
With
LangGraph
v0.2,
users
can
expect
easier
customization
and
maintenance
of
checkpointer
implementations,
paving
the
way
for
more
resilient
and
feature-rich
LLM
applications.

Image
source:
Shutterstock