docs(readme): update benchmark numbers and add temporal reasoning

- LongMemEval: 93.4 → 94.8 (top_50 with temporal reasoning)
- Research Highlights: update LongMemEval from +26 to +27 pts
- Add Temporal Reasoning to "What Changed" feature list

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

README.md

@@ -47,7 +47,7 @@
 | Benchmark | Old | New  | Tokens  | Latency p50  |
 | --- | --- | --- | --- | --- |
 | **LoCoMo** | 71.4 | **91.6** | 7.0K  | 0.88s  |
-| **LongMemEval** | 67.8 | **93.4** | 6.8K  | 1.09s  |
+| **LongMemEval** | 67.8 | **94.8** | 6.8K  | 1.09s  |
 | **BEAM (1M)** | — | **64.1** | 6.7K  | 1.00s  |
 | **BEAM (10M)** | — | **48.6** | 6.9K  | 1.05s  |
 
@@ -58,12 +58,13 @@ All benchmarks run on the same production-representative model stack. Single-pas
 - **Agent-generated facts are first-class** -- when an agent confirms an action, that information is now stored with equal weight.
 - **Entity linking** -- entities are extracted, embedded, and linked across memories for retrieval boosting.
 - **Multi-signal retrieval** -- semantic, BM25 keyword, and entity matching scored in parallel and fused.
+- **Temporal Reasoning** -- time-aware retrieval that ranks the right dated instance for queries about current state, past events, and upcoming plans.
 
 See the [migration guide](https://docs.mem0.ai/migration/oss-v2-to-v3) for upgrade instructions. The [evaluation framework](https://github.com/mem0ai/memory-benchmarks) is open-sourced so anyone can reproduce the numbers.
 
 ## Research Highlights
 - **91.6 on LoCoMo** -- +20 points over the previous algorithm
-- **93.4 on LongMemEval** -- +26 points, with +53.6 on assistant memory recall
+- **94.8 on LongMemEval** -- +27 points, with +53.6 on assistant memory recall
 - **64.1 on BEAM (1M)** -- production-scale memory evaluation at 1M tokens
 - [Read the full paper](https://mem0.ai/research)
 

docs/api-reference/events/get-event.mdx

@@ -5,3 +5,5 @@ openapi: get /v1/event/{event_id}/
 ---
 
 Retrieve details about a specific event by passing its `event_id`. This endpoint is particularly helpful for tracking the status, payload, and completion details of asynchronous memory operations.
+
+For `POST /v3/memories/add/`, the event confirms that the write pipeline completed. Temporal reasoning enrichment runs asynchronously by default, so the event may be `SUCCEEDED` slightly before temporal ranking signals are available to subsequent `search` calls.

docs/api-reference/memory/add-memories.mdx

@@ -83,4 +83,3 @@ The request is queued for background processing. The response contains an `event
 <Info>
 Poll the event status via `GET /v1/event/{event_id}/`. Status will be `SUCCEEDED` or `FAILED` once processing completes.
 </Info>
-

docs/api-reference/memory/get-memories.mdx

@@ -64,4 +64,3 @@ memories = client.get_all(
 <Info>
 The response is a paginated envelope with `count`, `next`, `previous`, and `results`. Use `page` and `page_size` query params to step through results.
 </Info>
-

docs/api-reference/memory/search-memories.mdx

@@ -49,6 +49,7 @@ related_memories = client.search(
     {
       "id": "ea925981-272f-40dd-b576-be64e4871429",
       "memory": "Likes to play cricket and plays cricket on weekends.",
+      "user_id": "alice",
       "metadata": {
         "category": "hobbies"
       },

docs/changelog/highlights.mdx

@@ -4,6 +4,21 @@ description: "Major product launches, headline features, and milestones for Mem0
 mode: "wide"
 ---
 
+<Update label="2026-05-13" description="Temporal Reasoning for Mem0 Platform v3">
+
+**Temporal Reasoning — Time-Aware Retrieval for Platform v3**
+
+Mem0 Platform v3 can now interpret time-aware memories and queries so assistants retrieve the right information for questions about the past, upcoming plans, and current state.
+
+- **Time-aware search intent** — Queries like `last week`, `upcoming`, `right now`, and `as of March 2025` return contextually appropriate results automatically
+- **Enabled by default** — No per-request toggle required for v3 writes or searches
+- **Anchored relative queries** — `reference_date` anchors relative search phrases for tests, backfills, and reproducible demos
+- **Normal response shape** — Temporal reasoning affects ranking while preserving existing client response patterns
+
+See [Temporal Reasoning](/platform/features/temporal-reasoning) for usage details.
+
+</Update>
+
 <Update label="2026-05-08" description="Memory Decay">
 
 **Memory Decay — Recently-Used Memories Surface Higher, Automatically**
@@ -112,4 +127,4 @@ Major expansion of the provider ecosystem:
 
 First skill launch — a dedicated Mem0 skill providing platform API reference, quickstart patterns, and integration examples directly inside agent sessions. Available on [skills.sh](https://skills.sh) for any compatible AI coding agent.
 
-</Update>
+</Update>

docs/changelog/platform.mdx

@@ -4,6 +4,17 @@ description: "Release notes for the Mem0 hosted platform — backend, dashboard,
 mode: "wide"
 ---
 
+<Update label="2026-05-13" description="">
+
+**New Features:**
+- **Memory:** Added Temporal Reasoning for Platform v3 to improve ranking for time-aware queries such as `last week`, `upcoming`, `right now`, and `as of ...`
+- **Search:** Added `reference_date` support to anchor relative temporal queries for tests, backfills, and reproducible demos
+
+**Improvements:**
+- **API:** Temporal reasoning preserves the normal client response shape for search and get-all results
+
+</Update>
+
 <Update label="2026-05-04" description="">
 
 **New Features:**
@@ -301,4 +312,3 @@ mode: "wide"
 - **Core:** Fixed unicode error in user_id, agent_id, run_id and app_id
 
 </Update>
-

docs/changelog/sdk.mdx

@@ -55,7 +55,7 @@ mode: "wide"
 **Breaking Changes:**
 - **`add()` returns ADD-only events** — No more `"UPDATE"` or `"DELETE"` events. Memories accumulate; nothing is overwritten ([#4805](https://github.com/mem0ai/mem0/pull/4805))
 - **`search()` default `threshold` is now `0.1`** — Pass `threshold=0.0` for previous behavior ([#4805](https://github.com/mem0ai/mem0/pull/4805))
-- **`search()` `score` is now a combined multi-signal score** — The top-level `score` fuses semantic similarity, BM25 keyword match, and entity boost into one value. Absolute numbers shift versus the old raw cosine score; retune any hard thresholds against representative queries. Per-signal scores are not exposed on the response ([#4805](https://github.com/mem0ai/mem0/pull/4805), [#4836](https://github.com/mem0ai/mem0/pull/4836))
+- **`search()` `score` is now a combined multi-signal score** — The top-level `score` fuses semantic similarity, BM25 keyword match, entity signals, and temporal boosts into one value. Absolute numbers shift versus the old raw cosine score; retune any hard thresholds against representative queries ([#4805](https://github.com/mem0ai/mem0/pull/4805), [#4836](https://github.com/mem0ai/mem0/pull/4836))
 - **`search()` default `rerank` is now `False`** — Pass `rerank=True` for previous behavior ([#4805](https://github.com/mem0ai/mem0/pull/4805))
 - **`top_k` default changed 100 → 20** in `Memory.get_all()` and `Memory.search()` (sync + async). Pass `top_k=100` explicitly to restore the old behavior ([#4843](https://github.com/mem0ai/mem0/pull/4843))
 - **Entity ID validation:** `user_id` / `agent_id` / `run_id` are trimmed; empty-string and whitespace-only values now raise `ValueError` ([#4843](https://github.com/mem0ai/mem0/pull/4843))

docs/core-concepts/memory-operations/search.mdx

@@ -156,6 +156,10 @@ const memories = memory.search("food preferences", {
   Expect an array of memory documents. Platform responses include vectors, metadata, and timestamps; OSS returns your stored schema.
 </Info>
 
+<Note>
+  On Mem0 Platform v3, time-aware queries use Temporal Reasoning internally while preserving the normal search response shape. See <Link href="/platform/features/temporal-reasoning">Temporal Reasoning</Link>.
+</Note>
+
 ## Filter patterns
 
 Filters help narrow down search results. Common use cases:
@@ -251,4 +255,4 @@ For the full list of filter logic, comparison operators, and optional search par
     icon="rocket"
     href="/cookbooks/operations/support-inbox"
   />
-</CardGroup>
+</CardGroup>

docs/docs.json

@@ -72,7 +72,8 @@
                           "platform/features/entity-scoped-memory",
                           "platform/features/async-client",
                           "platform/features/multimodal-support",
-                          "platform/features/custom-categories"
+                          "platform/features/custom-categories",
+                          "platform/features/temporal-reasoning"
                         ]
                       },
                       {
@@ -1144,4 +1145,4 @@
       "destination": "/introduction"
     }
   ]
-}
+}

docs/llms.txt

@@ -185,6 +185,7 @@ If the user is on a pre-current major (Python < 2, TS < 3, or Platform `output_f
 ### Features - Advanced Retrieval
 - [Advanced Retrieval](https://docs.mem0.ai/platform/features/advanced-retrieval) [Platform]: Use when the user needs keyword search, reranking, or hybrid retrieval.
 - [Criteria-Based Retrieval](https://docs.mem0.ai/platform/features/criteria-retrieval) [Platform]: Use when targeting memories by custom criteria, not just semantic similarity.
+- [Temporal Reasoning](https://docs.mem0.ai/platform/features/temporal-reasoning) [Platform]: Use when time-aware searches like last week, upcoming, or right now need better result ordering.
 - [Contextual Add](https://docs.mem0.ai/platform/features/contextual-add) [Platform]: Use when `add()` should consider the surrounding conversation, not just the latest turn.
 - [Custom Instructions](https://docs.mem0.ai/platform/features/custom-instructions) [Platform]: Use when tailoring what Mem0 extracts and stores on Platform.
 - [Memory Decay](https://docs.mem0.ai/platform/features/memory-decay) [Platform]: Use when search results should boost recently-reinforced memories and dampen stale ones — opt-in per project, search-time only, never filters candidates out.

docs/migration/platform-v2-to-v3.mdx

@@ -42,7 +42,7 @@ Previously, when an agent said something like "I've booked your flight for March
 
 ### Retrieval is hybrid now
 
-Search now uses hybrid retrieval, which improves ranking quality — especially for queries involving exact keywords, proper nouns, or entities that appear across multiple memories. The response shape is unchanged:
+Search now uses hybrid retrieval, which improves ranking quality — especially for queries involving exact keywords, proper nouns, entities that appear across multiple memories, and time-aware queries (via Temporal Reasoning). The response shape is unchanged:
 
 ```json
 {
@@ -58,7 +58,7 @@ Search now uses hybrid retrieval, which improves ranking quality — especially
 }
 ```
 
-The top-level `score` remains a `[0, 1]` value. Relative ranking between results stays comparable to v2, but absolute numbers shift since the scoring method changed — retune any hard thresholds in your app against representative queries.
+The top-level `score` remains a `[0, 1]` value. Relative ranking between results stays comparable to v2, but absolute numbers shift since the scoring method changed — retune any hard thresholds in your app against representative queries. Temporal signals are applied internally during ranking and are not returned as extra client-facing fields.
 
 ## API Changes
 
@@ -289,6 +289,7 @@ If your application previously read graph relations from the API response (`rela
 - **V1 and V2 endpoints continue to work.** There is no requirement to migrate to V3 endpoints immediately.
 - **Existing memories are preserved.** The new algorithm does not modify or re-process previously stored memories.
 - **Search response shape is unchanged.** The top-level `score` and `results[]` array are the same; existing code that reads `score` continues to work. What changed is the scoring method behind the number (multi-signal fusion instead of pure cosine), so the absolute values shift even when ranking stays comparable.
+- **Search remains backward-compatible at the top level.** Existing code that reads `results[]` and `score` continues to work. Temporal signals are applied internally during retrieval and do not change the client response shape.
 - **List response shape changed.** `get_all` now returns a paginated envelope (`{count, next, previous, results}`) instead of a bare `{results: [...]}`. Update code that reads `response["results"]` to continue working, or switch to the client SDKs which handle both shapes.
 
 ## Performance Improvements

docs/openapi.json

@@ -419,7 +419,7 @@
 													},
 													"results": {
 														"type": "array",
-														"description": "Array of results produced by the event."
+														"description": "Array of results produced by the event. For add events, this confirms the write completed; temporal reasoning enrichment runs asynchronously by default."
 													},
 													"created_at": {
 														"type": "string",
@@ -2071,7 +2071,7 @@
 					"memories"
 				],
 				"summary": "Search memories (V3)",
-				"description": "Relevance-ranked search across stored memories. V3 uses hybrid retrieval — the returned `score` is a combined `[0, 1]` value; per-signal component scores are not exposed on the response. Entity IDs **must** be passed inside the `filters` object — top-level `user_id` / `agent_id` / `run_id` are rejected with 400. At least one entity ID is required.",
+				"description": "Relevance-ranked search across stored memories. V3 uses hybrid retrieval and can also apply temporal reasoning for time-aware queries. Entity IDs **must** be passed inside the `filters` object — top-level `user_id` / `agent_id` / `run_id` are rejected with 400. At least one entity ID is required.",
 				"operationId": "memories_search_v3",
 				"requestBody": {
 					"required": true,
@@ -2112,6 +2112,21 @@
 										"type": "boolean",
 										"default": false,
 										"description": "Apply the managed reranker for better ordering (adds latency)."
+									},
+									"reference_date": {
+										"oneOf": [
+											{
+												"type": "integer"
+											},
+											{
+												"type": "number"
+											},
+											{
+												"type": "string"
+											}
+										],
+										"nullable": true,
+										"description": "Optional query anchor time for relative temporal interpretation. Accepts Unix epoch, YYYY-MM-DD, or ISO datetime."
 									}
 								}
 							},

docs/platform/features/temporal-reasoning.mdx

@@ -0,0 +1,145 @@
+---
+title: Temporal Reasoning
+description: "Time-aware memory retrieval for Mem0 Platform v3 so queries like 'last week', 'upcoming', and 'right now' return the right memories."
+icon: "clock"
+badge: "v3"
+---
+
+Some memories matter because of **when** they happened, not just because they sound similar. Temporal Reasoning lets Mem0 Platform v3 understand time-aware queries and return the most contextually appropriate results.
+
+<Info>
+  **Use Temporal Reasoning when…**
+  - Users ask questions like "what happened last week?" or "what do I have coming up?"
+  - Your app stores both past events and future plans for the same person
+  - You want time-aware retrieval without building your own date-parsing layer
+</Info>
+
+<Warning>
+  Temporal Reasoning is a **Mem0 Platform v3** feature. It is not available on OSS memory stores or older Platform endpoints.
+</Warning>
+
+## Configure access
+
+Confirm your `MEM0_API_KEY` is set and that you are using the v3 Platform client:
+
+```python
+from mem0 import MemoryClient
+
+client = MemoryClient(api_key="your-api-key")
+```
+
+## How it works
+
+When a memory describes an event, a future plan, or an ongoing state, Temporal Reasoning recognizes the time context so the right results surface at search time.
+
+A query like `what did I do last week?` should return a completed past event — not an upcoming appointment and not a stable fact that hasn't changed. Temporal Reasoning handles that distinction automatically.
+
+### Memory types Temporal Reasoning handles
+
+| Type | What it represents | Example |
+| --- | --- | --- |
+| Dated occurrence | Something that happened at a known time | "I finished the Q1 review on March 10, 2025." |
+| Future plan | A future commitment or scheduled item | "I have a dentist appointment on March 18, 2025." |
+| Ongoing state | A fact that remains true over time | "I am the product lead at Acme Corp." |
+| Relationship | A durable connection between people or entities | "Priya manages Jordan." |
+| Preference | A stable preference or habit | "I prefer morning meetings." |
+
+Results come back in the normal search response shape — Temporal Reasoning affects ranking, not the response format.
+
+## Configure it
+
+Temporal Reasoning is enabled by default for all v3 searches and writes. There is no per-request toggle.
+
+Two parameters give you precise control when you need it:
+
+- `timestamp` on `add()` — anchors an imported memory to the time it actually happened, rather than the time it was added to Mem0
+- `reference_date` on `search()` — resolves relative phrases like `last week` against a fixed point in time
+
+<CodeGroup>
+```python Python
+from datetime import datetime, timezone
+from mem0 import MemoryClient
+
+client = MemoryClient(api_key="your-api-key")
+
+# Import a historical memory anchored to when it happened
+client.add(
+    [{"role": "user", "content": "I finished the Q1 review on March 10, 2025."}],
+    user_id="jordan",
+    timestamp=int(datetime(2025, 3, 10, tzinfo=timezone.utc).timestamp()),
+)
+
+# Search with a relative query anchored to a known date
+results = client.search(
+    "what did I do last week?",
+    filters={"user_id": "jordan"},
+    reference_date="2025-03-21T00:00:00Z",
+)
+```
+
+```javascript JavaScript
+import { MemoryClient } from "mem0ai";
+
+const client = new MemoryClient({ apiKey: "your-api-key" });
+
+// Import a historical memory anchored to when it happened
+await client.add(
+  [{ role: "user", content: "I finished the Q1 review on March 10, 2025." }],
+  {
+    userId: "jordan",
+    timestamp: Math.floor(new Date("2025-03-10T00:00:00Z").getTime() / 1000),
+  }
+);
+
+// Search with a relative query anchored to a known date
+const results = await client.search("what did I do last week?", {
+  filters: { user_id: "jordan" },
+  referenceDate: "2025-03-21T00:00:00Z",
+});
+```
+</CodeGroup>
+
+<Tip>
+  `reference_date` is especially useful in automated tests and demos because it makes relative phrases like `last week` resolve consistently every time.
+</Tip>
+
+## Supported query patterns
+
+<AccordionGroup>
+  <Accordion title="Historical questions">
+    Examples: `last week`, `last month`, `in March 2025`, `on 2025-03-10`
+  </Accordion>
+  <Accordion title="Upcoming questions">
+    Examples: `upcoming`, `next week`, `tomorrow`, `what do I have coming up?`
+  </Accordion>
+  <Accordion title="Current-state questions">
+    Examples: `right now`, `currently`, `where do I work now?`
+  </Accordion>
+  <Accordion title="As-of questions">
+    Examples: `as of March 2025`, `where was I living as of 2024?`
+  </Accordion>
+  <Accordion title="Duration questions">
+    Examples: `how long have I lived here?`, `since when have I worked there?`
+  </Accordion>
+</AccordionGroup>
+
+## Verify the feature is working
+
+- Run a temporal search with a time-aware query (e.g., "what did I do last week?") and confirm the memory that fits the time window ranks first.
+- Use `reference_date` in test queries so relative phrases resolve consistently across runs.
+- For backfilled data, pass `timestamp` on `add()` to confirm the memory reflects the right point in time.
+
+## Best practices
+
+- Use explicit dates in source conversations when events or plans matter temporally.
+- Pass `timestamp` during historical imports so the ingestion time does not become the only time anchor.
+- Scope searches with `filters` so time-aware ranking operates inside the right user boundary.
+- Use `reference_date` in automated tests and reproducible demos.
+
+<CardGroup cols={1}>
+  <Card title="Memory Timestamps" icon="calendar" href="/platform/features/timestamp">
+    Anchor imported memories to when they actually happened.
+  </Card>
+</CardGroup>
+
+<Snippet file="get-help.mdx" />