Notre avis
Atlas permet d'ingérer des fichiers de documentation et de code dans une mémoire sémantique persistante via Qdrant et les embeddings Voyage, pour retrouver du contexte entre sessions.
Points forts
- Recherche sémantique rapide avec des embeddings de haute qualité (Voyage-3-large)
- Indexation duale (sémantique et temporelle) pour des requêtes précises
- Ingestion récursive de répertoires avec filtrage automatique des fichiers non pertinents
Limites
- Nécessite un serveur Qdrant local et une clé API Voyage, ce qui alourdit la configuration
- La mémoire persistante dépend de la base vectorielle locale, non partagée entre machines
- Les fichiers non supportés sont ignorés sans avertissement explicite
Utilisez Atlas lorsque vous avez besoin de conserver un contexte important entre sessions Claude Code ou de retrouver rapidement des décisions passées dans votre projet.
Évitez Atlas si votre projet est petit et que le contexte tient dans une session, ou si vous ne pouvez pas exécuter de services Docker localement.
Analyse de sécurité
PrudenceThe skill instructs users to run Docker, set API keys, and execute bun scripts that interact with a local vector database. While no destructive commands are present, the use of network services and secret management introduces moderate risk, appropriate for a 'caution' rating.
- •Requires running Docker (network service) and setting environment variable VOYAGE_API_KEY, potentially exposing credentials.
- •Scripts run with `bun` can access file system and network, but are expected for the tool's purpose.
Exemples
Remember the API architecture we discussed last time and store it in Atlas for future sessions.Find all mentions of authentication patterns in our project using Atlas.Ingest all the .md files in the docs folder into Atlas so I can query them later.name: atlas description: Ingest project documentation and code into persistent semantic memory (Qdrant + Voyage embeddings). Use when user wants to remember context across sessions, ingest docs, or search previous work. Requires Qdrant running locally and VOYAGE_API_KEY set. allowed-tools: Bash(bun:*)
Atlas - Persistent Semantic Memory
Atlas provides automatic context ingestion and retrieval using Voyage embeddings + Qdrant vector database. Solves the context overflow problem by storing knowledge persistently across sessions.
Quick Start
Prerequisites
- Qdrant running locally:
docker run -d -p 6333:6333 qdrant/qdrant
- VOYAGE_API_KEY set (get from https://voyageai.com):
export VOYAGE_API_KEY="your-key-here"
- Verify setup:
curl http://localhost:6333/health
Ingesting Context
Store files in Atlas memory for persistent retrieval:
Ingest Single File
cd ~/production/atlas
bun run --filter @inherent.design/atlas atlas ingest /path/to/file.md
Ingest Directory (Recursive)
cd ~/production/atlas
bun run --filter @inherent.design/atlas atlas ingest /path/to/docs/ --recursive
Ingest Multiple Paths
cd ~/production/atlas
bun run --filter @inherent.design/atlas atlas ingest README.md src/index.ts docs/ -r
What gets ingested:
- Supported:
.md,.ts,.tsx,.js,.jsx,.json,.yaml,.qntm,.rs,.go,.py,.sh,.css,.html - Ignored:
node_modules,.git,dist,build,coverage,.atlas
Processing:
- Chunks text (768 tokens, 13% overlap) for semantic coherence
- Embeds with Voyage-3-large (1024-dim)
- Stores in Qdrant with dual-indexing (semantic QNTM keys + temporal timestamps)
- Preserves original text for future consolidation
Searching Context
Retrieve relevant context semantically:
Basic Search
cd ~/production/atlas
bun run --filter @inherent.design/atlas atlas search "typescript patterns"
Limited Results
cd ~/production/atlas
bun run --filter @inherent.design/atlas atlas search "memory consolidation" --limit 10
Temporal Filtering (Since Date)
cd ~/production/atlas
bun run --filter @inherent.design/atlas atlas search "sleep patterns" --since "2025-12-25"
Chronological Timeline
cd ~/production/atlas
bun run --filter @inherent.design/atlas atlas timeline --since "2025-12-01"
When to Use This Skill
Use Atlas when:
- User asks to "remember this across sessions"
- Project context is too large for single session
- User wants to search previous work/decisions
- Documentation needs to be queryable
- Building on previous research or code
Examples:
- "Remember the API architecture we discussed"
- "What did we decide about the database schema?"
- "Find all mentions of authentication patterns"
- "Ingest all the .atlas research files"
Architecture
Built on .atlas research (Steps 1-4 + Sleep Patterns):
Stack:
- Voyage-3-large embeddings (1024-dim, 9.74% better than OpenAI)
- Qdrant HNSW index (M=16, int8 quantization, 4x compression)
- RecursiveCharacterTextSplitter (semantic boundaries)
- Dual-indexing (QNTM semantic keys + RFC 3339 timestamps)
Production Config (from Step 3 research):
- Recall@10: >0.98
- Latency: 10-50ms (p95)
- Memory: 1.4GB RAM + 5GB disk per 1M vectors
Technical Details
For implementation details, see:
- docs/architecture.md - Complete technical architecture
Packages:
@inherent.design/atlas-core- Core library (embeddings, storage, search)@inherent.design/atlas- Command-line interface@inherent.design/atlas-mcp- MCP server for Claude Code integration
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