Walk through what Mem0 does between two turns of the same user's session

Between two turns of the same user's session, Mem0 runs a small **extract then store then retrieve** pipeline. **Step 1: extraction.** After turn 1 finishes (the user message plus the assistant's reply), Mem0 runs an extraction LLM pass over that pair. The extraction prompt asks the model to identify any new facts about the user that are worth remembering, expressed as discrete declarative statements: 'user is a vegetarian,' 'user is moving to Berlin in July,' 'user prefers Python over JavaScript.' These are the **candidate memories**. **Step 2: dedup and merge.** For each candidate, Mem0 checks the existing memory store for similar entries using **embedding similarity**. If a near-duplicate exists, the new candidate is either merged with it (combining details) or discarded if it adds nothing. If the candidate contradicts an existing memory, say the store has 'user lives in NYC' and the candidate says 'user lives in Berlin', Mem0 applies a **contradiction-resolution rule**: usually 'the newer fact wins' or 'higher confidence wins.' The store is updated to reflect the new state. **Step 3: persist.** The merged, deduped fact is stored with its embedding so future retrieval can find it. **Step 4: retrieval at turn 2.** When the user sends turn 2, Mem0 takes the new user message as a query and runs a vector search over the memory store to find the most relevant facts (typically top-3 to top-10). These are formatted into a compact **memory block**, usually a few hundred tokens, and injected into the system prompt or context section before the model is invoked. The agent never sees the full archive. Only the per-turn salient slice reaches the model.

Mem0 operates as an **extract on write, retrieve on read** memory layer that sits between the agent and the conversation. The work happens in three phases, with the model-facing surface staying small and stable. **Write path (runs after turn 1 completes).** Mem0 takes the new user message plus the assistant's reply and runs an **extraction LLM** over the pair. The extraction prompt asks the model to surface declarative facts about the user, the task, or the world that are worth remembering long-term. The output is a list of candidate memories in structured form, each typically one sentence: 'user is vegetarian,' 'user is moving to Berlin in July,' 'user works at Anthropic as of 2026.' Each candidate is compared against the existing store via **embedding similarity**. Three outcomes are possible per candidate: - **New fact**: no near-duplicate exists, the candidate is stored as-is with its embedding. - **Duplicate or refinement**: a near-duplicate exists, and Mem0 either discards the candidate (if it adds nothing) or merges details into the existing entry. - **Contradiction**: a similar entry exists but the new candidate disagrees. Mem0 applies a **contradiction-resolution policy**, typically 'newer wins' with optional confidence weighting or temporal flagging. The old entry may be archived (kept with a timestamp) or replaced outright depending on configuration. The net effect is that the memory store maintains a curated set of facts about the user, free of duplicates, with contradictions resolved. **Read path (runs before turn 2 is sent to the model).** Mem0 takes the new user message as a query and runs **vector search** over the memory store, optionally filtered by user ID. The top-k results (commonly 3-10, tuned per workload) are formatted into a compact memory block: a labeled section in the system prompt or a user-message preamble.