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RE-call MCP Memory Server

by GiulioDERversion 0.14.0i★ –iRuns on your computer
Registry nameio.github.GiulioDER/re-call

Install

Overview

Description from the maker (original English)

Agent memory on your own Postgres that flags superseded facts and abstains instead of guessing.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

  • Connection: Runs on your computer
  • Transport: stdio
  • Category: Search & AI memory
  • You need to fill in: RECALL_SERVING_DSN

Install

Pick the AI app you use, then copy the settings.

Before you install

  • Basic checks: 1 of 1 passed
  • Runs on your computer with your user’s permissions (files, network, apps).
  • You fill in yourself: RECALL_SERVING_DSN, RECALL_TENANT, RECALL_TRUST_MODE, RECALL_EMBEDDER, RECALL_TABLE, RECALL_MCP_TOOLS

Values marked secret are keys to your account. Never share them or paste them into a chat.

You need

  • PyPIPython’s uv installed (for the uvx command)

Which app do you use?

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "re-call": {
      "command": "uvx",
      "args": [
        "recall-rag"
      ],
      "env": {
        "RECALL_SERVING_DSN": "your-value",
        "RECALL_TENANT": "your-value",
        "RECALL_TRUST_MODE": "your-value",
        "RECALL_EMBEDDER": "your-value",
        "RECALL_TABLE": "your-value",
        "RECALL_MCP_TOOLS": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add re-call --env RECALL_SERVING_DSN=your-value --env RECALL_TENANT=your-value --env RECALL_TRUST_MODE=your-value --env RECALL_EMBEDDER=your-value --env RECALL_TABLE=your-value --env RECALL_MCP_TOOLS=your-value -- uvx recall-rag

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "re-call": {
      "command": "uvx",
      "args": [
        "recall-rag"
      ],
      "env": {
        "RECALL_SERVING_DSN": "your-value",
        "RECALL_TENANT": "your-value",
        "RECALL_TRUST_MODE": "your-value",
        "RECALL_EMBEDDER": "your-value",
        "RECALL_TABLE": "your-value",
        "RECALL_MCP_TOOLS": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "re-call": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "recall-rag"
      ],
      "env": {
        "RECALL_SERVING_DSN": "your-value",
        "RECALL_TENANT": "your-value",
        "RECALL_TRUST_MODE": "your-value",
        "RECALL_EMBEDDER": "your-value",
        "RECALL_TABLE": "your-value",
        "RECALL_MCP_TOOLS": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

  • RECALL_SERVING_DSN secret required env
    PostgreSQL serving DSN for the RE-call memory store.
  • RECALL_TENANT env
    Tenant id served by this local stdio server.
  • RECALL_TRUST_MODE env
    Set to development only for local uncalibrated corpora.
  • RECALL_EMBEDDER env
    The embedder the corpus was BUILT with, for example voyage:voyage-4 or BAAI/bge-large-en-v1.5. It must match: several models emit 1024 dimensions, so a mismatch does not error, it returns a confident…
  • RECALL_TABLE env
    The chunk table inside that database. `recall quickstart` indexes into quickstart_chunks so its sample corpus can never be retrieved beside real memory; `recall setup` uses chunks. The wrong value re…
  • RECALL_MCP_TOOLS env
    Optional. Serve a subset of the tools: a preset (all, search, read) or explicit names. Every tool definition is re-sent every turn, so serving ones nobody calls costs input tokens on every turn. Unse…

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account GiulioDER, which owns the repo

  • … Not checked yet

    Still maintained

    We have not read this repo from GitHub yet

  • … Not checked yet

    Has a licence

    We have not read this repo from GitHub yet

  • – Not applicable

    Endpoint responds

    This one runs on your computer; there is no internet endpoint

  • – Not applicable

    Sign-in with OAuth

    Runs on your computer and uses local settings instead

  • … Not checked yet

    No hidden instructions in tool descriptions

    Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

  • More stars than 0% of the other 7254 servers in this category
  • Published to the registry more recently than 48% of the other 7254 servers in this category
  • Basic checks equal to or better than 100% of the other 7254 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is RE-call MCP Memory Server safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

You need to fill in: RECALL_SERVING_DSN

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.14.0) reached the registry 42 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Context7

by upstash

Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

Computer or cloud★ 64,224MITUpdated today
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Codebase Memory

by DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

Runs on your computer★ 48,148MITUpdated 20 days ago
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Exa

by exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

Runs in the cloud★ 5,213MITUpdated last month
Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

Memory that abstains instead of guessing.

RE-call is agent memory in a local SQLite file or your own PostgreSQL with pgvector. Each result carries a verdict, confidence, and provenance, while unsupported questions are refused instead of answered by the nearest match.

What it is · For agents · How it works · Start with Lite · Install and integrate

What is RE-call

RE-call is agent memory in a local SQLite file or your own PostgreSQL database. It indexes source documents and keeps validity, lineage, confidence, and provenance attached to every result.

Vector search returns nearby text. RE-call also checks whether that text is current, supported, and trustworthy enough for the query. A superseded claim is marked superseded; a result that does not clear the calibrated trust gate becomes ABSTAIN with a reason. Declared supersession makes the current memory win over stale but similar memory.

The default path uses local embeddings plus hybrid dense and keyword retrieval. PostgreSQL uses its full text search; Lite uses SQLite FTS5. It needs no memory-layer LLM call. Additional retrieval, reasoning, and structured-fact modules are optional.

For the agent reading this, RE-call for agents explains the experience loop and how to use persistent memory as a trustworthy past rather than an unverified scratchpad.

How it works

On PostgreSQL, RE-call builds an immutable, calibrated generation and serves queries against that pinned generation. Lite binds calibration to its single-file corpus. Every search, including the reasoning and fact paths, goes through trusted search and the trust gate.

The diagram is interactive on the project site: click any box to see the source lines it describes. A step by step view of one recall_search call follows a single search through its refusal, abstention and answer paths. Both are generated from the sources in docs/diagrams.

Text version of the diagram, with each box linked to its file

Solid arrows are the default path. Dashed arrows are opt in or reasoning only: the reranker, graph expansion, and the answer provider. Each box links to the file that implements it.

flowchart LR

subgraph group_entry["Entry points"] node_cli["recall CLI commands"] node_mcp["MCP server tool handlers"] node_sdk["Agent SDK in process"] end

subgraph group_build["Generation build"] node_manifest["Source manifest immutable input"] node_generations["Generation manager build, validate, promote"] node_parser["Parse and chunk documents to chunks"] node_embedder["Embedder local or hosted API"] node_graph_build["Semantic graph entities, relations"] node_calibration["Calibration certified threshold"] end

subgraph group_serve["Trusted retrieval"] node_trusted_search["Trusted search query orchestrator"] node_gen_store["Generation store pinned generation"] node_retriever["Hybrid retriever dense, full text, RRF"] node_reranker["Reranker opt in"] node_trust_gate{{"Trust gate verdict or ABSTAIN"}} end

subgraph group_reason["Reasoning"] node_reasoner["Reasoning query evidence bundle, plan"] node_graph_expansion["Graph expansion one hop, before trust"] node_answer["Answer provider opt in LLM"] end

subgraph group_facts["Provenance and facts"] node_provenance["Provenance controller fact review"] node_ledger["Fact ledger append only"] end

node_caller(("Agent or user caller")) node_postgres[("PostgreSQL + pgvector generations, chunks, graph")]

node_caller -->|"runs"| node_cli node_caller -->|"tool calls"| node_mcp node_caller -->|"imports"| node_sdk node_sdk -->|"reuses handlers"| node_mcp node_cli -->|"generation build"| node_generations node_mcp -->|"ingest uploads"| node_generations node_generations -->|"reads sources"| node_manifest node_generations -->|"parses, chunks"| node_parser node_generations -->|"embeds chunks"| node_embedder node_generations -->|"builds graph"| node_graph_build node_generations -->|"requires certified"| node_calibration node_generations -->|"writes generation"| node_postgres node_graph_build -->|"writes graph"| node_postgres node_cli -->|"calibrate, publish"| node_calibration node_calibration -->|"stores threshold"| node_postgres node_cli -->|"search"| node_trusted_search node_mcp -->|"search, evidence"| node_trusted_search node_trusted_search -->|"pins generation"| node_gen_store node_gen_store -->|"reads active"| node_postgres node_trusted_search -->|"retrieves"| node_retriever node_retriever -->|"embeds query"| node_embedder node_retriever -->|"dense, full text"| node_gen_store node_retriever -.->|"reorders"| node_reranker node_trusted_search -->|"evaluates"| node_trust_gate node_mcp -->|"reasoning query"| node_reasoner node_reasoner -->|"retrieves"| node_trusted_search node_trusted_search -.->|"expands pre trust"| node_graph_expansion node_graph_expansion -.->|"same generation graph"| node_postgres node_reasoner -.->|"cited answer"| node_answer node_mcp -->|"apply fact"| node_provenance node_provenance -->|"fresh search"| node_trusted_search node_provenance -->|"appends"| node_ledger node_ledger -->|"stores"| node_postgres

click node_cli "https://github.com/GiulioDER/RE-call/blob/master/recall/cli.py" click node_mcp "https://github.com/GiulioDER/RE-call/tree/master/recall_mcp" click node_sdk "https://github.com/GiulioDER/RE-call/blob/master/recall_agent/memory.py" click node_manifest "https://github.com/GiulioDER/RE-call/blob/master/recall/manifest.py" click node_generations "https://github.com/GiulioDER/RE-call/blob/master/recall/generations.py" click node_parser "https://github.com/GiulioDER/RE-call/blob/master/recall/document.py" click node_embedder "https://github.com/GiulioDER/RE-call/blob/master/recall/embeddings.py" click node_graph_build "https://github.com/GiulioDER/RE-call/blob/master/recall/semantic_graph/_build.py" click node_calibration "https://github.com/GiulioDER/RE-call/blob/master/recall/calibration_v2.py" click node_trusted_search "https://github.com/GiulioDER/RE-call/blob/master/recall/trust.py" click node_gen_store "https://github.com/GiulioDER/RE-call/blob/master/recall/generation_store.py" click node_retriever "https://github.com/GiulioDER/RE-call/blob/master/recall/retriever.py" click node_reranker "https://github.com/GiulioDER/RE-call/blob/master/recall/rerank.py" click node_trust_gate "https://github.com/GiulioDER/RE-call/blob/master/recall/trust.py" click node_reasoner "https://github.com/GiulioDER/RE-call/blob/master/recall/reasoning.py" click node_graph_expansion "https://github.com/GiulioDER/RE-call/blob/master/recall_mcp/graph_expansion.py" click node_answer "https://github.com/GiulioDER/RE-call/blob/master/recall/answer_provider.py" click node_provenance "https://github.com/GiulioDER/RE-call/blob/master/recall/provenance_controller.py" click node_ledger "https://github.com/GiulioDER/RE-call/blob/master/recall/fact_ledger.py" click node_postgres "https://github.com/GiulioDER/RE-call/tree/master/recall/migrations"

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Ordinary recall search follows the direct path. Explicit reasoning accepts graph_expansion: auto is the default and resolves to bounded one hop expansion for every nonempty query; off keeps direct retrieval only; one-hop forces the graph path. The CLI uses --graph-expansion auto|off|one-hop; the MCP tool uses graph_expansion="auto"|"off"|"one_hop". Graph neighbors are generation bound, direct candidates remain first, and expanded candidates must clear the same trust boundary before they can support a cited answer.

The opt in choices attach to different points in the system:

Optional capabilityWhere it fitsWhat it adds
Hosted embedderBuild and queryRemote model calls for embeddings. Query and corpus text may leave the environment.
Learned sparse retrieval, SPLADEHybrid retrievalA learned term weighted retrieval leg in addition to dense vectors and Postgres full text.
RerankerAfter candidate fusionReorders the fused candidates with a cross encoder.
Evidence Graph version oneExplicit reasoning retrievalAdds bounded, generation-bound structural neighbors to reasoning retrieval. See graph_expansion above for controls; graph candidates pass through trust before a cited answer can use them.
Structured fact applicationEvidence cardsLets a reviewed fact pass through the provenance controller into the append only ledger.

For details, see the architecture writeup, provenance controller, and API reference.

Quickstart

For one project's memory on your machine, start with RE-call Lite. It stores memory in .recall/memory.db, refreshes changed memos automatically, and needs no Docker or database server.

Install 0.15.0 or later: Lite ships in the PyPI wheel from 0.15.0; earlier wheels do not contain it. Follow the Lite installation guide to create an environment and install the package, then run this from your own project with an existing folder of notes:

recall

Only the beginning is shown; read the rest in the repo

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