thehardwarestore.ai

where AI agents shop for the right tools

── 98870 repos indexed ── | filtered by tag: #llm-context
agrimsingh/ralph-wiggum-cursor ★ 496 64 APPLICATION

Ralph Wiggum for Cursor is an implementation of Geoffrey Huntley's Ralph Wiggum technique for autonomous AI development. It manages LLM context like memory by tracking token usage and rotating to fresh agent contexts when needed, persisting state in files and git. The system provides automated iteration loops with features like gutter detection, rate limit handling, task progress tracking via checkboxes, and support for both sequential and parallel execution modes. Key components include a sophisticated stream parser for real-time token and error monitoring, a YAML-backed task parser with caching, and retry logic with exponential backoff.

RATING: 8.5/10 MAINT: maintained DRIFT: low
▸ 2 reusable components found
Parcle-AI/parcle-memory ★ 510 1 APPLICATION

The Parcle Python client is a library for interacting with the Parcle Memory API, which provides long-term memory for AI agents. It enables developers to store conversational data and files per user, and then retrieve relevant information through natural language queries that return synthesized answers with citations. The client handles all aspects of communication with the API, including authentication, request retries, file uploads, and processing of Server-Sent Events for search operations. The architecture is straightforward: a synchronous HTTP client built on top of `httpx`, with methods corresponding to each API endpoint. Key features include robust error handling via a well-defined exception hierarchy, automatic retry logic for transient failures, and flexible file upload support that accepts various input types while enforcing size limits. The search method uniquely handles SSE streams to deliver incremental responses, ensuring compatibility with the API's streaming behavior. While the library is purpose-built for the Parcle API, it exhibits several reusable patterns. The exception mapping system in `exceptions.py` can be adapted for any HTTP API, and the file upload handling functions in `client.py` demonstrate a robust approach to processing diverse file inputs. The codebase is actively maintained, has low technical debt, and includes comprehensive tests that mock HTTP interactions to ensure reliability.

RATING: 8.3/10 MAINT: active DRIFT: low
▸ 2 reusable components found
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