Nexus
Nexus is a lightweight knowledge management system for AI coding agents. It provides persistent memory and semantic search through tiered storage that preserves decisions, findings, and project knowledge across agent sessions. That knowledge compounds over time, becoming more valuable as the corpus grows.
Nexus includes RDR (Research-Design-Review), an integrated human-AI design and audit system. RDRs capture the reasoning behind technical decisions — problem, research, chosen approach, rejected alternatives — as structured, searchable documents that live in the repository alongside the code. Nexus indexes the RDR corpus so team members and their agents can quickly get up to speed on a project's design history and stay aligned as the codebase evolves.
What it does
Semantic search. Standard text search matches exact strings. Nexus matches by meaning: querying "how does authentication work" returns the auth middleware, the login handler, and the JWT validation — even when none contain the word "authentication."
nx index repo . # index current repo
nx search "error handling" # finds try/catch, Result types, error middleware, logging
nx search "auth" --hybrid # combine semantic + keyword matching
Persistent memory. Agents share ephemeral session context for inter-agent coordination, project-level decisions persist locally with full-text search, and cross-project knowledge is stored permanently with semantic retrieval.
nx scratch put "the bug is in the retry logic" # T1: inter-agent session context
nx memory put --project myapp --title "DB choice" "Chose Postgres over SQLite for concurrency"
nx store put --collection knowledge__myapp "API rate limit is 10k/min per the vendor docs"
Analytical queries. The query MCP tool handles catalog-aware scoped search in a single call — query(question="...", author="Fagin") searches only that author's collections. For complex multi-step analysis (compare, extract, generate), the /nx:query skill routes through three paths: direct query, template match, or planner dispatch.
# Filter indexed PDFs by bibliographic metadata
nx search "consensus protocols" --where bib_year>=2024 --where chunk_type=table_page
# Backfill bibliographic metadata on an existing collection
nx enrich knowledge__papers --delay 0.5
Decision tracking. RDR documents record the reasoning behind technical choices and are searchable alongside code, so prior decisions surface automatically during new design work.
Quick Start
Requires Python 3.12–3.13 and uv.
uv tool install conexus # install the nx CLI
nx doctor # verify installation
nx index repo . # index your repo (no API keys needed)
nx search "what does X do" # semantic search, fully local
Update: uv tool update conexus
Works immediately with local ONNX embeddings — no accounts, no API keys. For higher-quality cloud embeddings (Voyage AI), see the cloud setup instructions.
For Claude Code, install the plugin:
/plugin marketplace add Hellblazer/nexus
/plugin install nx@nexus-plugins
See Getting Started for the full walkthrough.
Three tiers, one lifecycle
Different information has different lifetimes. Together the three tiers form an integrated memory system that extends agent context across sessions and projects.
| Tier | Purpose | Storage | API keys? |
|---|
| Scratch (T1) | Inter-agent session context — coordination and knowledge sharing across agent invocations | In-memory ChromaDB | No |
| Memory (T2) | Project-level persistence with full-text search | Local SQLite + FTS5 | No |
| Knowledge (T3) | Permanent semantic knowledge — code, papers, docs, decisions searchable by meaning | Local ChromaDB (default) or ChromaDB Cloud + Voyage AI | No (local) / Yes (cloud) |