# AI memory
AI memory lets an AI agent use what happened in earlier sessions: what you
decided, what it did and what it learned about your work.
Context Graph is Memgraph's AI memory. It records the sessions of your coding
agents, such as Claude Code and Codex, as a graph in Memgraph, and serves them
back to every connected agent in later sessions.
It connects to the
[supported agents](https://memgraph.com/docs/ai-ecosystem/ai-memory/agents) with no code to write.
[Custom agents](https://memgraph.com/docs/ai-ecosystem/ai-memory/custom-agents), built on the Claude
Agent SDK or the OpenAI Agents SDK, can use the same memory.
Every connected agent gets:
- **Recording.** Hooks in the agent write each session to the graph as it
happens. You use the agent as before.
- **Recall.** A `recall` tool, served over the Model Context Protocol (MCP),
returns what your past sessions hold about a question. Each session also
starts with a short note on where you left off in the current project.
- **Memory**, if you turn it on. A `memory` tool replaces the agent's own
memory, so the notes the agent saves are stored in the graph and shared by
all your agents.
Context Graph is open source, part of the
[Memgraph AI Toolkit](https://github.com/memgraph/ai-toolkit/tree/main/context-graph).
## Get started
One command installs Context Graph, finds or starts Memgraph and connects the
agents you choose. It shows its plan and changes nothing until you confirm.
**Linux/macOS**
```bash
curl -sSL https://install.memgraph.com/ai-memory | bash
```
**Windows**
```powershell
iwr https://install.memgraph.com/ai-memory/windows -useb | iex
```
See [Get started](https://memgraph.com/docs/ai-ecosystem/ai-memory/get-started) for the prerequisites,
the choices setup offers and how to check that sessions are recorded.
## How it works
Context Graph works in three phases, shown in the diagram: during a session,
it records each event and serves recall from Memgraph; after the session,
reconciliation builds a knowledge graph from what was recorded.
### During a session
- **Hooks.** The `memgraph-context-graph` hooks run in the agent, whether a
coding agent or a custom agent, on each event: a prompt, a reply, a tool
call or its result. They drop text marked as private and redact secrets
before anything is stored.
- **Connectors.** The hooks pass each event to the connectors, which write it
to Memgraph. Each one records one part of a session:
- `actions-graph`: prompts, replies, tool calls and their results;
- `skills-graph`: the agent skills a session used;
- `sessions-graph`: the session, its user and project, and the memories the
agent saved;
- `resources-graph`, optional: the GitHub resources a session read.
On Memgraph Enterprise, `context-graph-spaces` adds
[shared spaces](https://memgraph.com/docs/ai-ecosystem/ai-memory/shared-spaces) for a team.
- **Memgraph.** Users, sessions, actions, skills and memories form one graph,
and the knowledge graph links to them. See
[The graph](https://memgraph.com/docs/ai-ecosystem/ai-memory/graph).
- **MCP server.** The MCP server reads Memgraph and serves the `recall` and
`memory` tools to the agent. See
[Recall and memory](https://memgraph.com/docs/ai-ecosystem/ai-memory/recall-and-memory).
### After a session
When a session ends, [reconciliation](https://memgraph.com/docs/ai-ecosystem/ai-memory/graph#add-a-knowledge-graph)
extracts the people, organizations, projects and other entities it mentions,
and the facts that connect them, into a knowledge graph. It runs on your
machine:
1. **Ontology.** The ontology is the schema of the knowledge graph: the entity
types and fact types to look for, such as a person who works for an
organization. It's a model in HyGM, the AI Toolkit's graph schema package.
Every user starts with a default ontology, and you can write your own.
2. **Extract.** GLiNER2, a local extraction model, finds the entities and
facts of those types in the session's text. It needs no LLM API key.
3. **Store.** The entities and facts are stored in Memgraph with their
embeddings, linked to the messages they came from. Recall uses them to find
facts spread over many sessions.
**Ontology learning** is optional. With an Anthropic or OpenAI API key in the
configuration, Context Graph learns new entity and fact types from your
sessions. It's the only part of Context Graph that sends session text off
your machine.
## In this section
- [Get started](https://memgraph.com/docs/ai-ecosystem/ai-memory/get-started): Install Context Graph,
connect your agents and check that sessions are recorded.
- [Recall and memory](https://memgraph.com/docs/ai-ecosystem/ai-memory/recall-and-memory): How agents
read their past sessions, and how to make Context Graph their memory.
- [The graph](https://memgraph.com/docs/ai-ecosystem/ai-memory/graph): What your agents recorded, how
to browse and query it, and how to add a knowledge graph of entities and
facts.
- [Privacy](https://memgraph.com/docs/ai-ecosystem/ai-memory/privacy): What is recorded, how to keep
text out of the graph, and how to delete sessions.
- [Supported agents](https://memgraph.com/docs/ai-ecosystem/ai-memory/agents): The files setup changes
for each agent, and how the agents differ.
- [Custom agents](https://memgraph.com/docs/ai-ecosystem/ai-memory/custom-agents): Use the same memory
in an app built on the Claude Agent SDK or the OpenAI Agents SDK.
- [Shared spaces](https://memgraph.com/docs/ai-ecosystem/ai-memory/shared-spaces): Share memory between
the members of a team, on Memgraph Enterprise.
- [Configuration](https://memgraph.com/docs/ai-ecosystem/ai-memory/configuration): The configuration
file, recall and extraction settings, your own Memgraph, updating and
uninstalling.