NeMo Anonymizer

NeMo Curator is an AI data-curation and post-processing tool for preparing real-world data before model training. It is used to organize, filter, and refine training data, including workflows that generate and evaluate synthetic examples.

NAMS is a hosted, graph-native memory service from Neo4j Labs for LLM agents. Backed by Neo4j Aura, it captures messages, entities, tool calls, and reasoning traces into workspace-isolated graphs, with short-term conversation memory, long-term knowledge, synthesized observations, entity merging, hybrid vector and text search, and REST and MCP interfaces. Agents connect with a workspace API key, after which NAMS extracts and embeds memory automatically and supports graph, semantic, and three-tier context retrieval. Its skill-distillation feature runs a seven-stage Scope → Extract → Consolidate → Synthesize → Gate → Package → Publish pipeline that turns a scoped portion of the memory graph into a signed, provenance-grounded SKILL.md package. The process includes grounding, coverage, specification linting, PII redaction, human sign-off, typed procedure graphs, cryptographic attestation, composition of shared sub-procedures, and drift detection for stale or contradicted claims. A dashboard provides workspace and skill-distillation interfaces; the service also offers plugins for Claude Code, Gemini CLI, Codex, and OpenCode, and supports bring-your-own LLM keys or an external Neo4j deployment.

OptMem is a permanent-memory tool for AI agents. It installs as a dependency-free Python 3 script and integrates through a prompt block added to an agent's AGENTS.md or CLAUDE.md file. The agent uses `memo wake` at session startup, `memo note` to append one-line memories, `memo recall` for exact regular-expression searches, and `memo zoom` to navigate summaries. Raw memories are stored in an append-only `LOG.txt`; a binary tree of one-line summaries provides a compact reading view, with summaries rebuilt when needed. `memo nap` processes due merges, while `memo forget` discards a summary so the next nap can rebuild it. Memory can be kept in a configurable directory such as a synced folder or Git repository, and the system is designed to persist across sessions, context compaction, models, and vendors.

NVIDIA NeMo Data Designer is an open-source framework for generating synthetic datasets from scratch or from seed data. It builds datasets column by column using sampler columns, LLM-generated text or structured columns, image and other multimodal models, existing seed datasets, and dependency-aware field generation. Workflows can connect to local or remote MCP servers to use external tools and capture interaction traces. Outputs can be previewed, resumed, and monitored, then validated or scored with Python, SQL, custom validators, and LLM judges. The framework also supports plugins for custom columns, seed readers, and processors, and includes a command-line interface for configuring model providers and models. It is distributed as the `data-designer` Python package and its source repository is licensed under Apache License 2.0. The library includes optional aggregate telemetry for model names and token counts, which can be disabled with `NEMO_TELEMETRY_ENABLED=false`.

Meko is an agent-native persistence and data infrastructure layer from Yugabyte for storing context, knowledge, memories, conversations, and decision traces for multi-agent systems. It uses data packs to let agents keep private context, promote selected memories into shared spaces, and resume or hand off work across agents. Meko provides collective memory, shared agent knowledge, and end-to-end auditability of retrievals, memory updates, knowledge sharing, and decisions. It exposes the data layer through a single MCP endpoint and combines vector, SQL, graph, and search capabilities in a Postgres-compatible database. The service is designed to work with agent frameworks and clients including Claude, Codex, ChatGPT, Cursor, LangChain, and other MCP-compatible agents.