My agent can't remember earlier steps, how do I debug that?

If your agent “can’t remember” earlier steps, it’s usually one of these issues:

1) The memory isn’t actually being passed back in

Check that previous messages/state are included in each new model call.

• Chat apps: verify the full conversation history is sent, not just the latest user message.
• Agent frameworks: confirm the scratchpad / memory / state object is injected into the prompt or tool call.
• Stateful workflows: ensure you’re loading the saved session/thread state before each step.

Debug tip: log the exact payload sent to the model before every call.

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2) You’re overwriting memory instead of appending it

Common bug: every step replaces the stored history.

• Use append semantics for message history.
• If you have structured memory, confirm you merge new state rather than reinitialize it.

Debug tip: print memory length before and after each step.

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3) Context window truncation

The agent may be remembering, but earlier content gets pushed out of the model’s context.

• Shorten prompts.
• Summarize older history.
• Keep only the most relevant turns.
• Store long-term memory externally and retrieve it on demand.

Debug tip: count tokens and compare against the model’s context limit.

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4) Session/thread identifiers are changing

If the agent uses separate threads or conversation IDs, a new ID means no prior memory.

• Confirm the same session/thread ID is reused.
• Check for accidental regeneration of IDs on each request.

Debug tip: log session ID, user ID, and conversation ID on every step.

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5) Tool/state outputs aren’t persisted

If the agent learns something from a tool call but doesn’t write it to memory/state, it’ll forget it next step.

• Save tool results explicitly if they matter later.
• Don’t assume the model will retain them unless they’re in the next prompt or state.

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6) Memory retrieval is failing

If you use vector DB / retrieval memory, the issue may be retrieval quality, not storage.

• Verify the item was stored.
• Verify retrieval queries match what you expect.
• Check embedding/model mismatch, filters, and top-k settings.

Debug tip: search the memory store directly for the forgotten fact.

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A simple debugging checklist

1. Log input prompt/messages for each step.
2. Log conversation/state length before and after each turn.
3. Verify session ID continuity.
4. Check token counts vs context window.
5. Inspect memory storage and retrieval results.
6. Run a minimal repro with 2–3 turns and one known fact.

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Minimal reproduction approach

Try this:

1. Step 1: tell the agent a fact like “My favorite color is teal.”
2. Step 2: ask it to repeat the fact.
3. If it fails, inspect:
   • Was step 1 stored?
   • Was it passed into step 2?
   • Was it truncated?
   • Did session ID change?

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If you tell me your stack, I can be more specific

For example:

• OpenAI Responses API / Assistants
• LangChain
• LangGraph
• AutoGen
• CrewAI
• custom Python/Node agent

If you share a small code snippet showing how you store and pass memory, I can help pinpoint the bug.