Display Name
context-memory
Category
Agent Skills
Sub-Category
General
Primary Link
https://github.com/ErebusEnigma/context-memory
Author Name
ErebusEnigma
Author Link
https://github.com/ErebusEnigma
License
MIT
Other License
No response
Description
Persistent, searchable context storage across Claude Code sessions using SQLite + FTS5. Saves sessions with AI-generated structured summaries, topic extraction, and code snippet preservation. Two-tier full-text search returns results in under 50ms. Includes auto-save hooks, pre-compaction checkpoints, a web dashboard with analytics, and an MCP server for programmatic access. 364 tests across 12 modules, zero core dependencies, cross-platform.
Validate Claims
364 tests passing across 12 modules on Python 3.8, 3.11, and 3.12 via GitHub Actions CI. Install with git clone https://github.com/ErebusEnigma/context-memory && cd context-memory && python install.py. Test with /remember "test session" then /recall test to verify round-trip.
Specific Task(s)
Save a Claude Code session to persistent memory and search for it in a new session. Thereby remembering past context/important information.
Specific Prompt(s)
- /remember "Implemented user authentication with JWT" 2. In new session: /recall authentication
Additional Comments
Everyone reaches for fancier retrieval (vectors, embeddings, re-rankers) when the real issue is that the data wasn't stored well in the first place. If your summaries are rich and descriptive, even basic keyword search finds what you need. If your summaries are thin, no amount of vector math will recover what was never captured.
Recommendation Checklist
Display Name
context-memory
Category
Agent Skills
Sub-Category
General
Primary Link
https://github.com/ErebusEnigma/context-memory
Author Name
ErebusEnigma
Author Link
https://github.com/ErebusEnigma
License
MIT
Other License
No response
Description
Persistent, searchable context storage across Claude Code sessions using SQLite + FTS5. Saves sessions with AI-generated structured summaries, topic extraction, and code snippet preservation. Two-tier full-text search returns results in under 50ms. Includes auto-save hooks, pre-compaction checkpoints, a web dashboard with analytics, and an MCP server for programmatic access. 364 tests across 12 modules, zero core dependencies, cross-platform.
Validate Claims
364 tests passing across 12 modules on Python 3.8, 3.11, and 3.12 via GitHub Actions CI. Install with git clone https://github.com/ErebusEnigma/context-memory && cd context-memory && python install.py. Test with /remember "test session" then /recall test to verify round-trip.
Specific Task(s)
Save a Claude Code session to persistent memory and search for it in a new session. Thereby remembering past context/important information.
Specific Prompt(s)
Additional Comments
Everyone reaches for fancier retrieval (vectors, embeddings, re-rankers) when the real issue is that the data wasn't stored well in the first place. If your summaries are rich and descriptive, even basic keyword search finds what you need. If your summaries are thin, no amount of vector math will recover what was never captured.
Recommendation Checklist