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

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

Short answer: Install with pip install causal-memory (or build with cargo build --release); then run ./install.sh for the guided setup of the skill and MCP server; Restart your agent. All steps.

Who it is forA developer whose agent re-fixes the same bug the wrong way every few sessions.

The problemCausal context is the first thing lost when context is compacted.

The outcomeRecorded decision to outcome edges the agent can search.

What you need first

  • Rust toolchain, or pip for the prebuilt package
  • An MCP client such as Claude Code

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 for your agent

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.

Use this in your AI agent

Ask your agent

Using the Deduped MCP, get the setup steps for JingxuanC/causal-memory and "Give your agent memory of why past decisions worked"

Add Deduped to Claude Code once

claude mcp add --transport http deduped https://dedupedai.com/mcp

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