Session Memory

Session memory is short-lived context that exists during a single conversation, workflow run, or interaction, but disappears when the session ends. It holds temporary information needed for continuity within that interaction—like recent messages, intermediate results, or in-flight state—but does not persist for future sessions.

Session memory is sufficient for stateless tasks like answering a one-off question or completing a single workflow. However, for agents that need to remember past interactions, learn from experience, or maintain continuity across days or weeks, session memory is inadequate—persistent agent memory is required.

The outcome is knowing when session memory is enough (short, isolated tasks) and when you need durable memory (long-term collaboration, learning, multi-session workflows).

Why it matters

• Enables within-session continuity: Users can refer back to earlier in the conversation—"what did I ask two messages ago?"—without re-explaining.
• Reduces token usage: Recent context is readily available in session memory, avoiding redundant prompts or API calls.
• Supports multi-turn interactions: Clarifications, follow-ups, and refinements within a session work naturally with session memory.
• Sufficient for stateless use cases: Not every interaction needs persistent memory—one-off Q&A or single workflow runs are fine with session-only context.
• Prevents memory bloat: Short-lived sessions that don't need long-term recall avoid polluting durable memory with irrelevant context.
• Clarifies architectural choices: Understanding session vs. persistent memory helps teams choose the right storage and retrieval strategies.

How it works

Session memory operates through temporary storage tied to a session ID:

• Session Start → A conversation or workflow begins. A unique session ID is created. Session memory is initialized (empty or with starting context).
• Context Accumulation → As the user and agent interact, messages, intermediate results, and state are appended to session memory.
• Within-Session Retrieval → The agent references session memory to maintain continuity: "you asked about authentication earlier in this conversation."
• Session End → When the conversation ends (user closes tab, timeout, explicit end), session memory is discarded. No state carries forward.
• Next Session → A new session starts fresh with no memory of prior interactions—unless persistent agent memory is used.

This pattern works for chat interfaces, API workflows, and temporary tasks where history is not needed across sessions.

Comparison & confusion to avoid

Examples & uses

Customer support chatbot (session-only)
A user asks "how do I reset my password?" The bot provides steps. User follows up: "what was step 3 again?" Session memory holds prior messages, so the bot can reference them. When the conversation ends, the session is discarded—no need to remember this interaction long-term.

One-time data transformation workflow
An agent is given a CSV, asked to clean and transform it, then return results. Session memory holds intermediate state (parsed rows, transformations applied). Once the workflow completes, session memory is discarded—no future sessions need this context.

Short Q&A with documents (no learning required)
A user uploads a PDF and asks questions about it. Session memory holds the PDF content and prior questions/answers within this session. When the user moves on, session memory is cleared—the agent doesn't need to remember this interaction.

Best practices

• Use session memory for temporary tasks: If the interaction is self-contained and doesn't need future recall, session memory is sufficient and simpler.
• Expire sessions after inactivity: Implement timeouts (e.g., 30 min idle) to prevent session memory from living indefinitely.
• Don't persist sensitive data in session memory: If session memory is logged or cached, ensure it's purged promptly and doesn't leak sensitive information.
• Upgrade to persistent memory when needed: If users repeatedly ask "what did we discuss last time?" it's a signal that session-only memory is insufficient.
• Limit session memory size: Cap message history or state size to prevent unbounded growth within long sessions.
• Consider session replay for debugging: Store session memory temporarily for debugging or analytics, but separate this from production agent memory.

Common pitfalls

• Assuming session memory equals agent memory: Session memory resets—it's not durable memory. Don't confuse the two when designing agents.
• No session expiration: Without timeouts, session memory can linger and consume resources—implement TTLs.
• Storing session memory as persistent memory: If you save all sessions to a database, that's no longer session memory—it's becoming agent memory (intentionally or accidentally).
• Expecting cross-session continuity: Users will ask "what did we discuss yesterday?" If your design uses session-only memory, this fails.
• Over-engineering session memory: If the session is one API call, you don't need session memory—ephemeral context in the prompt is sufficient.

See also

• Agent Memory — Persistent memory that survives across sessions
• Stateful Agent — Agents that retain memory and learn over time
• Ephemeral Context — Temporary prompt-based state (even shorter-lived)
• Operating Memory — Shared memory for teams and systems
• Context Window — LLM's limited attention span per call

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See how Graphlit enables persistent Agent Memory beyond sessions → Agent Memory Platform