# AI use cases for Causal Memory (JingxuanC/causal-memory)

Repo: [JingxuanC/causal-memory](https://github.com/JingxuanC/causal-memory) | Use cases: 3 | Page: https://dedupedai.com/use-cases/jingxuanc-causal-memory/

causal-memory is a Rust MCP server that gives AI agents a memory of facts, temporal state and decision to outcome causal edges on one SQLite store, designed to survive context compaction.

This file holds every use case of this repo. Each one starts with a "## " heading and ends with its URL.

- Give your agent memory of why past decisions worked: https://dedupedai.com/use-cases/jingxuanc-causal-memory/give-agent-memory-of-why-decisions-worked/
- Ask what would happen before the agent acts: https://dedupedai.com/use-cases/jingxuanc-causal-memory/check-intervention-before-acting/
- Install the memory server non-interactively: https://dedupedai.com/use-cases/jingxuanc-causal-memory/install-causal-memory-non-interactive/

## How to give your agent memory of why past decisions worked with Causal Memory

Repo: [JingxuanC/causal-memory](https://github.com/JingxuanC/causal-memory) | Difficulty: Intermediate | Time: 30 minutes | Basis: From the README

Install the MCP server so the agent records decisions and their outcomes and recalls them later.

**Who it is for:** A developer whose agent re-fixes the same bug the wrong way every few sessions.

**The problem:** Causal context is the first thing lost when context is compacted.

**The outcome:** Recorded decision to outcome edges the agent can search.

### Steps

1. Install with `pip install causal-memory` (or build with `cargo build --release`)
2. Run `./install.sh` for the guided setup of the skill and MCP server
3. Restart your agent
4. Have the agent call record_decision and later search_causal

### Prompt to paste into your agent

```text
Install JingxuanC/causal-memory, record a decision and its outcome for <bug fix>, then query search_causal in a new session. Confirm the decision is returned.
```

### Caveat

The README says it does not beat mem0 on plain fact recall; its strength is causal recall.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/jingxuanc-causal-memory/give-agent-memory-of-why-decisions-worked/

## How to ask what would happen before the agent acts with Causal Memory

Repo: [JingxuanC/causal-memory](https://github.com/JingxuanC/causal-memory) | Difficulty: Advanced | Time: 30 minutes | Basis: From the README

Have your agent ask its memory what happened last time before it runs a risky command, and stop on a danger chain.

**Who it is for:** A platform engineer whose agent once broke a production login with a deploy and should never repeat it.

**The problem:** Agents forget past incidents after context compaction and confidently repeat the same risky action.

**The outcome:** Before a risky step the agent calls intervention_query and gets a DANGER chain that cites the recorded lesson, so you can stop or change the plan.

### Steps

1. Install with `./install.sh --yes` so the server and the bundled agent skill are both in place (the skill tells the agent when to call the tools)
2. When an action goes wrong, have the agent record the action and its outcome as a cause and effect pair in memory
3. Add a rule to your agent instructions: call intervention_query before deploys, migrations or deletes
4. Run the risky task again and read any DANGER chain the agent shows before it proceeds
5. Optionally preview memory cleanup with `causal-memory sleep --dry-run`

### Prompt to paste into your agent

```text
Before you run <risky command> on <system>, call causal-memory's intervention_query with the planned action. If it returns a DANGER chain, stop and show me the cited past outcome; otherwise proceed and record the result.
```

### Caveat

It only warns about outcomes that were recorded earlier, so a brand new store has nothing to warn with until you log a few real incidents.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/jingxuanc-causal-memory/check-intervention-before-acting/

## How to install the memory server non-interactively with Causal Memory

Repo: [JingxuanC/causal-memory](https://github.com/JingxuanC/causal-memory) | Difficulty: Beginner | Time: 15 minutes | Basis: From the README

Install the server, the agent skill and the MCP entry for every detected agent with one command and no prompts.

**Who it is for:** A developer setting up the same agent memory on a new laptop or a CI image, who cannot answer installer questions.

**The problem:** Interactive installers stall in scripts, and wiring an MCP server into Claude Code, Cursor and Codex by hand is three different config files.

**The outcome:** Causal Memory is built, registered with every detected client, and its tools show up after an agent restart.

### Steps

1. Run `git clone https://github.com/JingxuanC/causal-memory.git` and `cd causal-memory`
2. Run `./install.sh --yes` to accept all defaults (skill, server and MCP registration across detected clients)
3. Restart Claude Code, Cursor or your other agent so it reloads its MCP list
4. Ask the agent to list its MCP tools and confirm the Causal Memory tools are there
5. To undo it later, run `./install.sh --uninstall`

### Prompt to paste into your agent

```text
Install JingxuanC/causal-memory on <machine> with `./install.sh --yes`, then restart and list the MCP tools you can see. Report which clients were registered and confirm the database path it uses.
```

### Caveat

The installer builds with cargo, so a Rust toolchain must be present; without one, `pip install causal-memory` gives you the same server binary to register by hand.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/jingxuanc-causal-memory/install-causal-memory-non-interactive/