Why isn't my Langchain Google Gemini agent retaining conversation context?

Below is a set of suggestions to diagnose and fix your memory retention issue:

1. Use a Persistent MemorySaver Instance

The in‑memory MemorySaver instance (checkpointer) will only persist conversation context as long as the instance lives. If you’re recreating the Agent (or MemorySaver) between invocations, then the context won’t be retained. Make sure you’re reusing the same MemorySaver instance for a given conversation thread (using a persistent storage solution if needed).

2. Verify Consistent Thread IDs

The thread identifier is key for grouping conversation history. In your code, you pass a thread id ("test") as the configurable option. Double‑check that you:

• Always supply the same thread id when invoking the chat.
• Match the thread id with that used for loading and saving the memory.

For example, if you create a persistent MemorySaver that saves state to disk or a database, ensure that you load it using the same thread id before each call.

3. Explicitly Load and Save Memory

While the agent created by createReactAgent should use the checkpointSaver (MemorySaver) to automatically insert previous conversation history, you might need to insert manual calls in your debugging workflow. For instance, try:

TYPESCRIPTCopied
// Before invoking, load memory for the thread
await this.checkpointer.load({ thread_id: thread_id });

// Invoke the agent
const message = await this.agent.invoke(
  {
    messages: [{ role: "user", content: prompt }],
  },
  { configurable: { thread_id: thread_id } }
);

// Optionally, save memory explicitly
await this.checkpointer.save({ thread_id: thread_id });

This way, you verify that the context is actually being rehydrated and then updated.

4. Check How the Underlying Agent Uses the CheckpointSaver

Make sure that createReactAgent is designed to automatically attach the conversation history from the checkpointSaver into subsequent invocations (i.e., it appends the loaded conversation history to the new messages). You may want to review the documentation or the source code for createReactAgent to confirm that checkpointSaver is integrated as expected.

5. Debug Logging

Add debug logging inside your MemorySaver’s load and save methods to confirm:

• How many messages are being loaded from memory when you start a new invocation.
• Whether new messages are actually being saved to your MemorySaver.

This will help you confirm that the memory retention mechanism is working as expected.

Example Updated Code

Below is an example of how you could update your invoke method to explicitly load and then save the conversation state:

TYPESCRIPTCopied
public async invoke(prompt: string, thread_id?: string) {
  // Load previous conversation state (if any)
  await this.checkpointer.load({ thread_id });

  // Invoke the agent with the new message
  const message = await this.agent.invoke(
    {
      messages: [{ role: "user", content: prompt }],
    },
    {
      configurable: {
        thread_id,
      },
    }
  );

  // Save the updated conversation state
  await this.checkpointer.save({ thread_id });

  // Return the most recent response
  return message.messages[message.messages.length - 1].content;
}

Final Thoughts

Memory retention in Langchain with the Google Gemini (ChatGoogleGenerativeAI) model hinges on ensuring that:

• The same persistent MemorySaver instance is used across invocations.
• The conversation’s thread id is consistently provided and matches when loading and saving.
• The checkpointSaver integration in createReactAgent is being correctly used.

By double‑checking these points and adding explicit load/save as needed, you should be able to achieve the intended context retention across your chat sessions.