A LangChain chatbot does not remember earlier API calls automatically. In the current Python architecture, add a LangGraph checkpointer and reuse a stable thread_id on every request. That provides short-term memory for one conversation. Use a database-backed checkpointer for durable history, and a separate LangGraph store for user facts that must survive across conversations.
What “memory” means in LangChain
An LLM only sees the messages and other state your application sends for the current invocation. Your application must retrieve earlier state and include the relevant parts in the next model call.
Conversation history
This is the ordered sequence of messages in one conversation:
User: My name is Maya.
Assistant: Nice to meet you, Maya.
User: What is my name?Short-term memory
Short-term memory is thread-scoped state used to continue a conversation. It can contain messages, tool results, uploaded files, retrieved documents, and other graph state. LangGraph persists that state as checkpoints. See LangChain’s memory concepts.
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Long-term memory
Long-term memory is information shared across separate threads, such as a user’s preferred language, dietary restriction, or an account fact. LangGraph stores these memories as JSON documents organized by namespace and key. A checkpointer preserves one thread; it is not automatically a user-profile database. See the long-term memory guide.
What you will build
The example uses Python, the current create_agent API, one chat model, no tools, and an in-memory checkpointer. The same thread recalls facts; a different thread does not.
Prerequisites and installation
Current LangChain Python documentation requires Python 3.10 or newer. Install the core packages and a provider integration in a virtual environment. Provider setup details are in the installation guide and quickstart.
python -m venv .venv
source .venv/bin/activate
# Windows PowerShell: .venvScriptsactivate
pip install -U langchain langgraph langchain-openai
export OPENAI_API_KEY="your-api-key"
# Windows PowerShell:
# $env:OPENAI_API_KEY="your-api-key"openai:gpt-5.4 is the model identifier shown in current documentation, not a guarantee that it is available for every account or region. Replace it with a model supported by your provider. LangChain also documents integrations for Anthropic, Google, Azure, AWS Bedrock, OpenRouter, Fireworks, Ollama, and others. For Anthropic, install langchain-anthropic and set ANTHROPIC_API_KEY; see the Anthropic integration page.
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Run this complete example:
from langchain.agents import create_agent
from langgraph.checkpoint.memory import InMemorySaver
checkpointer = InMemorySaver()
agent = create_agent(
model="openai:gpt-5.4",
tools=[],
system_prompt=(
"You are a helpful chatbot. "
"Use the conversation history to answer follow-up questions."
),
checkpointer=checkpointer,
)
thread_a = {"configurable": {"thread_id": "user-42-chat-1"}}
thread_b = {"configurable": {"thread_id": "user-42-chat-2"}}
agent.invoke(
{
"messages": [
{"role": "user", "content": "I prefer concise answers and my name is Maya."}
]
},
thread_a,
)
answer = agent.invoke(
{
"messages": [
{"role": "user", "content": "What answer style do I prefer, and what is my name?"}
]
},
thread_a,
)
print(answer["messages"][-1].content)
new_conversation = agent.invoke(
{"messages": [{"role": "user", "content": "What is my name?"}]},
thread_b,
)
print(new_conversation["messages"][-1].content)The second call on thread_a can answer “Maya” and “concise.” The call on thread_b has no history from thread_a. This is thread memory, not a cross-session user-memory system.
How the thread ID contract works
checkpointer=... enables persistence, while configurable.thread_id selects which checkpointed conversation to load. Reuse the same logical ID for follow-up requests; generating a new random ID on every request creates a new conversation. LangChain’s reference requires a thread ID when invoking a graph with a checkpointer; see the checkpointer reference.
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A thread normally represents one conversation, not an entire user. A practical identifier is:
thread_id = application_user_id + ":" + conversation_idStore the conversation ID in your application database or authenticated session. On every request, verify that the authenticated user owns it. Never let possession of an arbitrary client-supplied ID expose another customer’s history.
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Many search results show ConversationBufferMemory, ConversationChain, or LLMChain. Those examples may target older releases. In the current agent architecture, conversational state is graph state persisted by a checkpointer. Use version-specific legacy documentation if you maintain an older chain; do not mix old memory classes into a current-agent tutorial. The current quickstart uses from langchain.agents import create_agent.
Make conversation memory durable
What InMemorySaver is—and is not
InMemorySaver is appropriate for tutorials, local experiments, unit tests, and disposable sessions. Its data disappears when the process exits, a container is replaced, or a request reaches a different worker that has its own memory. It is not durable production storage.
PostgreSQL checkpointer
Install the documented PostgreSQL saver:
pip install langgraph-checkpoint-postgresfrom langchain.agents import create_agent
from langgraph.checkpoint.postgres import PostgresSaver
DB_URI = (
"postgresql://postgres:postgres@localhost:5432/postgres"
"?sslmode=disable"
)
with PostgresSaver.from_conn_string(DB_URI) as checkpointer:
checkpointer.setup()
agent = create_agent(
model="openai:gpt-5.4",
tools=[],
checkpointer=checkpointer,
)
config = {"configurable": {"thread_id": "production-conversation-1"}}
result = agent.invoke(
{"messages": [{"role": "user", "content": "Remember that I prefer email."}]},
config,
)
print(result["messages"][-1].content)setup() creates the required schema in this documented pattern. Confirm migrations and APIs against the exact saver version you install. In production, keep the connection string in a secret manager, enable TLS, use a least-privileged database role, configure pooling, and back up the database. LangChain also documents SQLite, PostgreSQL, and Azure Cosmos DB persistence options; choose according to durability, concurrency, and operations. See short-term memory documentation.
Add memory that survives new conversations
Starting thread-2 does not automatically expose thread-1 messages. Add a LangGraph store for user-scoped facts:
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namespace = ("users", authenticated_user_id)A record could be:
{
"name": "Maya",
"response_style": "concise",
"language": "English"
}Retrieve authorized records and deliberately provide them to the agent as data. Store only facts that are explicitly requested, stable, safe to retain, correctly attributed, and easy to inspect or delete.
Hot-path writes
Save a confirmed memory during the request when it must be available immediately. This is deterministic but adds latency and requires extraction and validation.
Background writes
Queue extraction after responding to keep chat latency lower. The next request may not see the memory, so use a reliable worker, retries, deduplication, and idempotency.
Control long conversation history
Appending every message forever increases latency and input-token cost, can exceed the model context window, and leaves stale instructions competing with current requests. LangChain’s memory guidance covers context limits and memory management.
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Keep the newest messages while preserving complete assistant/tool-call boundaries. This suits casual or task-focused bots but can drop older facts.
Summarize
Replace old turns with a compact summary containing goals, decisions, constraints, important facts, unresolved questions, and durable tool results. A summary is model-generated state and may omit or distort information.
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Hybrid memory
Use recent raw messages, a compact summary, explicit long-term facts, and retrieved application records. This usually gives better relevance than sending the full transcript indefinitely. The LangGraph history-management guide covers trimming, deletion, summarization, state inspection, and checkpoint deletion.
Secure and test chatbot memory
- Bind every thread to an authenticated user and authorize every read and write.
- Use user-scoped store namespaces such as
("users", user_id). - Define retention, deletion, correction, and export procedures for personal data.
- Record provenance and timestamps for durable facts; distinguish explicit memories from inferences.
- Treat stored messages, summaries, memories, and retrieved documents as untrusted data, not authorization instructions.
- Keep system and developer instructions separate from user history and revalidate permissions on every request.
- Test that the same thread recalls context, a different thread does not, one user cannot read another’s thread, restarts behave differently for in-memory and durable savers, and trimming preserves valid tool-call message boundaries.
Common failures and recovery
The bot forgets everything
Check for a new ID on every request, a missing invocation config, a process restart, worker-local memory, an uninitialized database saver, or a frontend that failed to persist the conversation ID. Log the authenticated user and thread ID, inspect checkpoint existence, then move to shared durable storage.
Users see one another’s memory
Look for a global hard-coded ID, missing ownership checks, unscoped long-term namespaces, or shared test and production data. Generate unique conversation IDs, bind them to users, authorize every operation, and add cross-tenant tests.
Requests fail as history grows
Trim or summarize old turns, avoid repeatedly storing retrieved documents, refresh summaries, define checkpoint retention, and monitor token budgets before invocation.
The chatbot remembers a wrong fact
Require explicit confirmation for durable memories, retain provenance and timestamps, let users correct or delete records, and prefer current application records over generated recollections.
Old instructions override the current request
Never use remembered text as an authorization decision. Revalidate permissions and apply prompt-injection defenses to stored memories and retrieved documents.
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Tool calls break after trimming
Preserve valid message ordering and complete assistant/tool-call pairs. Keep important tool results in structured state and test provider-specific message formats.
Production architecture and choices
Frontend
│ authenticated user ID + conversation ID
API service
├── LangChain agent
├── checkpointer ── PostgreSQL (or another durable saver)
├── long-term store ── user memories
└── optional tracing/evaluation| Requirement | Recommended approach | Trade-off |
|---|---|---|
| Quick demo or unit test | InMemorySaver |
Data disappears on restart |
| Durable conversations | Database-backed checkpointer | Database operations and cost |
| Preferences across chats | Long-term store | More modeling and privacy responsibility |
| Very long chats | Trim, summarize, or use a hybrid | Possible context loss or distortion |
| Tracing and evaluation | Optional LangSmith | Hosted-service and usage considerations |
Provider integrations standardize interfaces but not capabilities. Tool calling, structured output, streaming, multimodal input, limits, latency, and pricing vary by model and provider; consult the provider integration overview. LangSmith is optional; its current pricing is listed at langchain.com/pricing.
When not to use LangChain memory
- Use a stateless completion for a genuinely single-turn request.
- Use your existing application database directly when it already owns chat messages and you do not need agent graph state.
- Use a conventional workflow record when no agent or tool state is involved.
- Use process-local memory only when losing state on restart is acceptable.
Frequently Asked Questions
Does a checkpointer remember a user across every conversation?
No. It preserves state for one thread. Cross-conversation preferences require a separate LangGraph store and explicit retrieval.
Why does a new thread ID appear to erase memory?
A new thread ID selects a different conversation checkpoint. Reuse the authorized conversation’s stable ID for follow-up requests.
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Is InMemorySaver suitable for production?
Only for disposable sessions, tests, or prototypes. Use a shared durable checkpointer when history must survive restarts or multiple workers.
The Bottom Line
For a current LangChain chatbot, configure create_agent with a checkpointer and pass the same authorized thread_id on follow-up requests. Upgrade to a durable saver for production, and add a user-scoped store only when you need memory across separate conversations.
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