> ## Documentation Index
> Fetch the complete documentation index at: https://docs.cognee.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Session Persistence
> Persist cached conversation sessions into the knowledge graph
## When to use this
You want to persist cached conversation sessions into the knowledge graph so the Q\&A history becomes part of the searchable graph. In the current API, the user-facing way to do this is `cognee.improve(dataset=..., session_ids=[...])`.
Use the lower-level Memify pipeline in this guide when you need direct control over only the session persistence step.
**Before you start:**
* Complete [Quickstart](/getting-started/quickstart) to understand basic operations
* Ensure you have [LLM Providers](/setup-configuration/llm-providers) configured
* Have an existing knowledge graph created with `remember()`
* [Caching must be enabled](/core-concepts/sessions-and-caching#cache-adapters) and at least one session must exist (created by prior `cognee.recall()` calls with a `session_id`)
## Code in Action
```python theme={null}
import asyncio
import cognee
from cognee import SearchType
from cognee.memify_pipelines.persist_sessions_in_knowledge_graph import (
persist_sessions_in_knowledge_graph_pipeline,
)
from cognee.modules.users.methods import get_default_user
async def main():
await cognee.remember(
"Alice moved to Paris in 2010. She works as a software engineer.",
dataset_name="session_demo",
self_improvement=False,
)
# Build session history with recall
await cognee.recall(
query_type=SearchType.GRAPH_COMPLETION,
query_text="Where does Alice live?",
datasets=["session_demo"],
session_id="demo_session",
)
await cognee.recall(
query_type=SearchType.GRAPH_COMPLETION,
query_text="What does she do for work?",
datasets=["session_demo"],
session_id="demo_session",
)
# Persist the session into the graph
user = await get_default_user()
await persist_sessions_in_knowledge_graph_pipeline(
user=user,
session_ids=["demo_session"],
dataset="session_demo",
)
asyncio.run(main())
```
## What Just Happened
1. **Remember** — builds a knowledge graph from your text.
2. **Recall with `session_id`** — runs two recalls that accumulate Q\&A history in the session cache under `"demo_session"`.
3. **`get_default_user()`** — retrieves the authenticated user. This pipeline requires a `User` object with write access.
4. **`persist_sessions_in_knowledge_graph_pipeline(user, session_ids, dataset)`** — reads the cached session data and writes it into the knowledge graph.
## What Changed in Your Graph
* New nodes are created from the session Q\&A history, grouped under the `user_sessions_from_cache` node set.
* The session data is processed internally into graph memory, so entities and relationships from the session content become part of the graph.
* Persistence is incremental: each run only ingests the Q\&A entries added since the last successful persist for that session. Re-running on an unchanged session adds nothing new, so you can safely call it repeatedly as a session grows without re-embedding or duplicating the earlier history.
## Additional Information
* Runnable session persistence demo available on our [GitHub](https://github.com/topoteretes/cognee/blob/main/examples/demos/conversation_session_persistence_example.py)
- **`user`** (`User`, required) — authenticated user with write access. Obtain via `await get_default_user()`.
- **`session_ids`** (`Optional[List[str]]`) — list of session IDs to persist. If `None`, no sessions are extracted.
- **`dataset`** (`str`, default: `"main_dataset"`) — the dataset to write session data into.
- **`run_in_background`** (`bool`, default: `False`) — run asynchronously and return immediately.
Two tasks run in sequence, coordinated by a per-`(user, session)` persist watermark:
1. **`extract_user_sessions`** — reads Q\&A data from the `SessionManager` for the specified `session_ids`, then consults the persist watermark (the number of Q\&A entries already persisted for that session) and yields only the entries added since the last successful run. A session with no new entries yields nothing, so re-running on an unchanged session does no ingestion work.
2. **`cognify_session`** — calls `cognee.add` and `cognee.cognify` on the extracted entries, writing the results into the graph under the `user_sessions_from_cache` node set. Only after both succeed does it advance that session's watermark. If cognify fails, the watermark is left untouched and the same entries are retried on the next run (add-level content-hash deduplication keeps the retry safe).
The watermark is stored as an internal session-context row via the `SessionManager` — it reuses the existing session cache and introduces no new backend, configuration, or credentials.
* **No sessions found** — caching must be enabled and `recall()` with a `session_id` must have been run first. See [Sessions and Caching](/core-concepts/sessions-and-caching).
* **Error: no graph data found** — run `cognee.remember(..., dataset_name=...)` before calling this pipeline.
* **LLM errors** — verify that your LLM provider is configured. See [LLM Providers](/setup-configuration/llm-providers).
* **Permission errors** — the user must have write access to the target dataset. See [Permissions](/core-concepts/multi-user-mode/permissions-system/datasets).
Bridge sessions with the current improvement workflow
Learn how sessions and caching work in Cognee
Query the enriched graph with v1.0 retrieval