Greplica

Product Introduction

1. Definition: Greplica is an open-source, persistent memory layer for AI coding agents and engineering teams. It is a technical tool that continuously extracts, stores, and retrieves contextual knowledge from codebases and development sessions.
2. Core Value Proposition: Greplica exists to eliminate the repetitive context-relearning that slows down AI coding assistants like Claude Code, Cursor, and GitHub Copilot. It provides a shared, searchable engineering memory, dramatically reducing token usage and improving task accuracy by grounding agents in historical decisions, constraints, and codebase structure.

Main Features

1. Persistent Knowledge Graph: Greplica works by analyzing past agent session transcripts and repository structure to build a durable knowledge graph. This graph stores entities as components (code modules), flows (workflows), and claims (specific decisions or constraints). It uses local or OpenAI embeddings for semantic search, allowing agents to query this memory before starting a task.
2. Dual Operational Modes: The product offers two distinct deployment models. Local Mode runs entirely offline using SQLite for storage and optional local embedding models, ensuring full data privacy with zero telemetry. Managed Mode connects to a cloud server (memory.autoloops.ai), enabling shared, synchronized memory across an entire team, different repository clones, and forks via GitHub OIDC authentication.
3. Automated Memory Updates & Integration: Greplica integrates directly into coding agent platforms via hooks and skills. It automatically captures useful learnings at the end of sessions (greplica-update-working-memory) and can bootstrap memory from historical transcripts. Commands like greplica graph context allow agents to retrieve relevant, actionable Markdown summaries of prior work at the start of any new task.

Problems Solved

1. Pain Point: AI coding agents waste significant time and computational resources (tokens) re-exploring the codebase in every new session, rediscovering constraints, subsystem boundaries, and past decisions that were already learned.
2. Target Audience: The primary users are software engineering teams utilizing AI coding assistants (e.g., Cursor, Claude Code, GitHub Copilot CLI), developer productivity engineers, and individuals working on complex, long-lived codebases where institutional knowledge is critical.
3. Use Cases: Essential scenarios include onboarding a new AI agent or developer to a large codebase, working on a subsystem with hidden constraints or failed historical approaches, and maintaining consistency across a team where multiple agents and developers are contributing to the same repository.

Unique Advantages

1. Differentiation: Unlike static documentation (which becomes stale) or siloed agent memory (which isn't shared), Greplica provides a dynamic, repo-grounded memory that is continuously updated and accessible across all sessions, agents, and team members. It is not a generic RAG tool but one specifically engineered for the software development lifecycle.
2. Key Innovation: Its core innovation is the structured, provenance-tracked knowledge graph built from actual development activity. The managed mode's reconciliation system, using GitHub's reusable workflows and OIDC attestation, allows secure, verifiable merging of memory from forks and branches without requiring broad repository access, a unique approach for collaborative AI-assisted coding.

Frequently Asked Questions (FAQ)

1. How does Greplica improve AI coding agent performance? Greplica reduces agent token usage and task time by providing immediate, relevant context. Benchmarks on the SWE-chat dataset show reductions of up to 75% in tokens and 38% in wall-clock time by preventing redundant code exploration and resurfacing critical constraints.
2. Is my code data secure with Greplica? In local mode, all data stays in your local SQLite database with no external calls. In managed mode, authentication uses GitHub OIDC; the reconciliation workflow only accesses specific, version-pinned code snippets for audit purposes and never performs full repository scans.
3. What coding agents and platforms does Greplica support? Greplica officially supports integration with Cursor, Claude Code, GitHub Copilot CLI, OpenHands, OpenCode, Factory Droid, and Antigravity through platform-specific skill and hook installations.
4. Can Greplica work with private or enterprise codebases? Yes. The open-source local mode is designed for private use. The managed server can be self-hosted, and the reconciliation system works with private GitHub repositories using the same OIDC security model.
5. How do I add existing project knowledge to Greplica's memory? You can use the greplica-fast-session-bootstrap process to ingest sanitized Markdown transcripts from past coding agent sessions (Codex, Claude, Copilot, OpenCode), populating the knowledge graph with historical context from day one.