> ## Documentation Index
> Fetch the complete documentation index at: https://icrl.dev/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Introduction

> ICRL for Python and TypeScript: self-improving agents that learn from successful trajectories

## What ICRL Is

ICRL (In-Context Reinforcement Learning) is a trajectory-learning framework for LLM agents.

It works by:

1. Running tasks in an environment.
2. Storing successful trajectories.
3. Retrieving similar prior steps during future runs.
4. Curating low-utility trajectories over time.

## What You Get

* Python package: `icrl`
* TypeScript package: `icrl`
* Python CLI: `icrl` (tool-calling coding assistant)
* TypeScript web demo: Next.js + Convex example

## Package Scope

* Python package focuses on:

  * ReAct loop (`Agent`, `ReActLoop`)
  * FAISS-backed `TrajectoryDatabase`
  * Built-in providers (`LiteLLMProvider`, `AnthropicVertexProvider`)
  * CLI and database utilities

* TypeScript package focuses on:
  * Same algorithmic abstractions (`Agent`, `TrajectoryDatabase`, `TrajectoryRetriever`, `CurationManager`)
  * Pluggable storage (`StorageAdapter`), including `FileSystemAdapter`
  * Built-in providers (`OpenAIProvider`, `AnthropicProvider`, `AnthropicVertexProvider`)

## How The Algorithm Runs

1. `reset(goal)` on environment.
2. Generate plan using retrieved examples.
3. Repeat reasoning/action/observation steps.
4. If successful in training mode, store trajectory.
5. Update retrieval feedback and run curation periodically.

## Next Steps

* Start with `/installation`
* Build first run at `/quickstart`
* Read algorithm details at `/core-concepts/icrl-algorithm`