eng-server
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Alternatives to eng-server
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- FlicenseNot gradedqualityAmaintenanceEnables AI coding agents to read and write persistent memory directly during sessions, storing decisions, preferences, error patterns, research, and cross-project guidelines in local SQLite databases. Supports semantic search so relevant project and global context can be recalled by meaning, fully offline and without API keys.-
- AlicenseNot gradedqualityDmaintenanceProvides coding agents with persistent graph-backed memory, storing decisions, reasons, and contradictions across sessions so conversations pick up where they left off.Apache 2.0
- AlicenseNot gradedqualityAmaintenanceEnables AI agents and LLMs to persist facts, preferences, and observations in a local SQLite store and to build and traverse subject-predicate-object knowledge graphs across sessions. It provides fuzzy keyword search and graph queries so assistants retain context and recall prior project history without repeating prompts.1MIT
- AlicenseAqualityBmaintenanceEnables AI agents to maintain a persistent knowledge graph of a project, providing dependency context, impact analysis, side-effect discovery, and session recording for more informed coding decisions.5MIT
- AlicenseNot gradedqualityAmaintenanceEnables AI coding agents to store decisions, preferences, and lessons in a local SQLite store, then recall the most relevant ones on demand. Runs fully local — no cloud, API keys, or per-query cost — and also exposes forget and consolidate operations.2Apache 2.0
- AlicenseAqualityCmaintenancePersistent, local-first graph memory for AI coding agents. Provides durable cross-session memory via a local SQLite knowledge graph with typed relationships.615 PyPI1MIT
TDQS
Scored across 12 tools
Tools divide into distinct workflow stages (session start, prepare, commit placements, commit step, search, conflicts), and descriptions explicitly clarify which call stores versus stages data. Some overlap exists between ecg_commit_placements and ecg_commit_step, and between ecg_cancel_step/ecg_discard_proposal, which could confuse ordering, but the descriptions distinguish them.
All tools use a consistent ecg_ prefix followed by verb_noun snake_case (ecg_prepare_placements, ecg_commit_step, etc.). A few names like ecg_stats and ecg_search_context are noun/short forms, but the convention is otherwise predictable.
12 tools is well within a healthy range and each maps to a distinct step in the memory workflow. The set is neither thin nor bloated for the stated purpose.
The surface covers the full lifecycle: session init, preparation, staging, provenance, consent, commit, cancellation, search, conflict listing/resolution, and stats. No obvious CRUD or lifecycle gaps remain.