Recall MCP brings structured, offline agent memory via SQLite
Recall is a local-first MCP server with 42 tools that gives AI agents structured, addressable memory stored in SQLite — no cloud, no LLM in the loop, and no manual babysitting required.
Score breakdown
Recall replaces ad-hoc agent memory approaches — full chat logs, vector indexes, or manually re-injected summaries — with a structured, self-updating graph that agents on multiple model families adopted autonomously without explicit prompting, removing the need to repeatedly re-inform agents of updated facts or resolved problems.
- 01Recall is a stdio MCP server with 42 tools that auto-shuts down when not in use
- 02Agents call `recall_compile` to get a ranked, word-budget-capped context packet instead of the full memory store
- 03Writes go through `recall_write`, which enforces schema validation, provenance stamping, and rollback support
Recall is a local-first MCP server posted by u/Empty-Poetry8197 that addresses a common pain point in agentic workflows: agent memory that either floods the context window with full chat history, relies on vector search, or requires manually re-injecting LLM-generated summaries. The server runs over stdio, exposes 42 tools, and auto-shuts down when not in use. Its core retrieval primitive is `recall_compile`, which returns a small, ranked context packet of tiered "addressed cells" rather than the entire memory store — ranked by evidence and capped to a word budget.
Facts are stored as addressable cells connected by programmable hyperedges rather than flat markdown files.
Memory writes go through `recall_write`, which enforces an admission firewall: schema validation, provenance stamping, and full rollback support. Facts are stored as addressable cells connected by programmable hyperedges rather than flat markdown files. Each cell carries an effective confidence score that recalculates automatically from the graph — factoring in who backed a claim, who challenged it, and whether that writer has a history of being wrong — with no LLM in the loop and no network required. Dropping in a contradicting cell causes scores to update on their own.
Setup is designed to be minimal: paste the MCP config once, type `/recall` in a project, and a per-project SQLite database spins up automatically. For team use, the single database file can be placed on a shared host so multiple contributors write through the same admission firewall. The server also supports tripwires that trigger automated alerts when changes affect deployment-ready state. The author notes the tool runs on Linux, macOS, and Windows, and that models across different vendors, families, and sizes adopted the tools autonomously once aware of them — compiling context at task start and writing back at task end without explicit prompting.
Key facts
- 01Recall is a stdio MCP server with 42 tools that auto-shuts down when not in use
- 02Agents call `recall_compile` to get a ranked, word-budget-capped context packet instead of the full memory store
- 03Writes go through `recall_write`, which enforces schema validation, provenance stamping, and rollback support
- 04Memory cells are addressable with programmable hyperedges; each carries a confidence score that recalculates from the graph with no LLM involved
- 05Runs entirely offline using Node's built-in SQLite — no database server, no account, no network required
- 06One SQLite file per project; for team use, the file can be shared on a host so all contributors write through the same firewall
- 07Tripwires can be set to send automated alerts when changes affect deployment-ready state
Topics
Summary and scoring are generated automatically from the original article. We always link back to the publisher and never republish images or paywalled content. Last processed Jun 12, 2026 · 10:05 UTC. How this works →