# MemRL

> Use this tool when you need to improve task assistance in coding sessions through experience-based learning. MemRL captures and analyzes coding sessions, extracting intent and storing episodes with relevant data, to provide informed support for future tasks. It solves problems of inefficient coding assistance and limited knowledge retention, accepting inputs such as session transcripts and git diffs, and outputting relevant past experiences through semantic search.

Canonical page: https://skillsregistry.net/skills/anvanster-memrl  
JSON: https://api.skillsregistry.net/v1/skills/anvanster-memrl

## Description

A memory-augmented reinforcement learning system for Claude Code that captures coding sessions as episodes and learns from experience to improve future task assistance. Built in Rust, it automatically captures session transcripts, extracts structured intent using LLM analysis, and stores episodes with git diffs and error resolutions in both file-based storage and vector databases using LanceDB and FastEmbed. The system implements Bellman equation-based utility propagation to spread value from helpful episodes to similar ones, uses temporal credit assignment to reward episodes that preceded successful outcomes, and provides semantic search through embeddings to retrieve relevant past experiences.

## Trust

- **Trust score (0–1):** 0.00
- **Verification tier:** scanned
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** database
- **Updated:** 2026-09-28

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/anvanster-memrl)
- **Repository:** <https://github.com/anvanster/tempera>

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "anvanster-memrl"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/anvanster-memrl` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/anvanster-memrl/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt