RE-call MCP Memory Server

Memory that knows what it no longer believes.
RE-call is the retrieval engine I extracted from a research agent that had been running for months, after its memory outgrew its context window and it started confidently repeating conclusions it had already disproved.

Why RE-call  ·  Quickstart  ·  How it works  ·  Product surface  ·  Documentation  ·  Evidence

README with language menu  ·  Setup guide: install, configure and run RE-call

Why RE-call

Nearest-match retrieval cannot tell the difference between what is true and what merely reads like it. When a corpus keeps its history, and real agent memory does, the retracted claim and its correction are both retrievable, and the retracted one is often the nearer match. That is not a tuning problem. A ranker with no notion of validity has no way to prefer the correction.

RE-call came out of a production, long-running trading-research agent: months of operation, 792 typed memos, 6,469 chunks, re-indexed daily by a session-end hook. Every guard in this repository exists because that agent failed a specific way without it. See docs/CASE_STUDY.md.

It is for teams putting agent memory behind real applications, where a stale or unsupported memory is worse than no memory: keep the memory layer local by default, attach policy to every hit, calibrate the refusal threshold on your corpus, and let the application decide what to do with a result that is not trustworthy enough to answer from.

Capability	What it means in practice
Validity-aware retrieval	Superseded, expired, not-yet-valid, low-confidence, and not-entailed hits are surfaced as verdicts rather than flattened into ordinary search results.
Explicit abstention	When no valid result clears the calibrated threshold, callers receive an abstention with a reason instead of a nearest-neighbor guess.
Local operation	Ingest and retrieval run on PostgreSQL plus pgvector. Local embeddings are supported, so memory can be built and queried without a memory-layer LLM call.
Policy-driven configuration	Embedder, reranker, calibration, trust policy, and retrieval profile are selected to match legal, hardware, latency, quality, and cost requirements. The default is local and offline; higher-quality or hosted options are opt-in.
Production boundaries	Tenant IDs, row-level security, token-scoped MCP HTTP transports, erasure, quotas, timeouts, migrations, and observability are part of the shipped surface.
Reproducible evidence	Published numbers are tied to committed artifacts, and the claim gate checks them in CI.

Measured strengths:

Strength	Evidence boundary
Lower memory-layer cost	The LOCOMO head-to-head records no RE-call memory-layer LLM calls, while the comparator pays for extraction calls. See benchmarks/REVIEW.md.
External abstention check	On MTRAG, IBM's multi-turn RAG benchmark, RE-call is second on correct refusals among the recomputed systems and stays near the top answer-quality rows. See docs/MTRAG_BENCHMARK.md.
Validity beats nearest-match retrieval	Declared supersession makes the current memory win over stale but similar memory. The larger trust study is in results/FINDINGS.md.
Stronger than a plain vector store	Returned hits carry verdicts, confidence, provenance, tenant scope, and validity metadata. Plain top-k retrieval returns neighbors and leaves trust to the caller.
Clear limits	The evidence states where RE-call works, where it does not, and when a corpus-specific measurement is required.

The README is the product overview. For evidence behind these claims, start with docs/EVIDENCE.md, then use results/FINDINGS.md for the full interpretation and limits.

Quickstart

RE-call keeps memory in your own PostgreSQL with pgvector, so a database comes first.

Already running PostgreSQL with pgvector? Skip ahead and point the DSN at it.

Want a throwaway one? Save this as docker-compose.yml, then start it:

services:
  db:
    image: pgvector/pgvector:pg18
    environment:
      POSTGRES_USER: recall
      POSTGRES_PASSWORD: recall
      POSTGRES_DB: recall
    volumes:
      - recall_pgdata:/var/lib/postgresql
    ports:
      - "5432:5432"
    healthcheck:
      test: ["CMD-SHELL", "pg_isready -U recall"]
      interval: 2s
      timeout: 3s
      retries: 30

volumes:
  recall_pgdata:

docker compose up -d --wait

Then install, create the schema, and run the guided setup wizard. The wizard records the selected embedder, retrieval options, and an optional calibration that is fitted to your labeled queries and your corpus.

pip install "recall-rag[fastembed]"
python -m recall.cli --migration-dsn postgresql://recall:recall@localhost:5432/recall schema --dim 384 apply
python -m recall.cli setup

Those three run unchanged in PowerShell.

The schema command targets the default chunks table deliberately. Global migrations have to be applied there before any other table, so starting with --table something_else on a fresh database stops with SchemaTooOld. To add a separate index later, apply the default target first, then pass --table.

When the wizard asks whether to calibrate, it wants a labeled query file and the corpus those queries refer to. You do not have to build either to try it: both ship inside the installed package, next to each other.

python -c "import recall.eval, pathlib; print(pathlib.Path(recall.eval.__file__).parent)"

That prints a directory holding queries.json, a labeled set covering both answerable and unanswerable questions, and corpus/, the documents those questions are labeled against. Give the wizard those two paths and calibration runs end to end. Sources: recall/eval/queries.json and recall/eval/corpus/.

A calibration fitted that way belongs to that sample, not to your data. It shows the mechanism working and gives you a labeled file to copy the shape of. Calibration is per embedder and per corpus, so a new model or a substantially changed corpus needs calibrating again, and a threshold fitted on the sample should not be used to judge your own memory.

A labeled file needs at least one answerable and one unanswerable query, and every entry needs a query and an answerable key. Calibration refuses the file rather than fitting a threshold to one-sided evidence.

The distribution is recall-rag; the import is recall. The name recall on PyPI belongs to an unrelated package, so do not install both into the same environment.

Working from a clone:

pip install -e ".[fastembed]"

How it works

flowchart TB
    M["Memo: markdown plus frontmatter"] --> CH["Chunk"]
    CH --> EW["Embed locally"]
    EW -. "optional" .-> SP["SPLADE encode"]
    EW --> DB
    SP -. "optional" .-> DB

    Q["Query"] --> EQ["Query encoder"]
    EQ --> DB[("PostgreSQL plus pgvector")]

    DB --> DN["Dense vector search"]
    DB --> SL["Postgres full-text search"]
    DB -. "optional" .-> LS["Learned sparse search"]

    DN --> F["Reciprocal Rank Fusion"]
    SL --> F
    LS -. "optional" .-> F

    F -. "optional" .-> RR["Cross-encoder rerank"]
    RR --> GP
    F --> GP{"Gap check: calibrated threshold"}
    GP --> TR{"Trust layer: supersession, validity, confidence"}
    CAL["Calibration: fitted per embedder and corpus"] --> TR
    TR -. "optional" .-> EJ{"Entailment judge"}
    EJ --> OUT
    TR --> OUT["Verdict, confidence, provenance, or ABSTAIN"]

    TR -. "explicit opt-in" .-> RG["Reasoning graph projection"]
    DB -. "generation-bound" .-> RG
    RG --> IP["Inference proposals: review candidates"]
    TR --> RP["Reasoning policy plus budget"]
    IP --> RP
    RP --> RV{"Citation and trust validation"}
    RV --> ROUT["Cited answer, needs review, clarification, or ABSTAIN"]

Product surface

Area	Ships today
Retrieval	Dense, sparse, hybrid RRF, optional SPLADE, optional cross-encoder reranking, calibrated confidence, provenance, and trust verdicts.
Configuration	Guided setup, local and hosted embedder choices, retrieval cost profiles, optional reranking, strict or development trust policy, and per-corpus calibration.
Storage	PostgreSQL with pgvector, ordered SQL migration path, immutable generations, incremental indexing, pruning, and source-scoped erasure.
Agent integration	CLI, MCP server, LangChain retriever, LlamaIndex retriever, and injectable search seams for tests.
Reasoning	Explicit opt-in reasoning API, CLI, and MCP tools over trusted retrieval, generation-bound graph projections, proposal inspection, budgets, and citation validation.
Security	Tenant isolation, row-level security checks, serving and migration DSNs, bearer-token HTTP transports, scopes, quotas, and unsafe-DSN refusal.
Operations	Timeouts, reconnect policy, structured logging, counters, latency percentiles, and MCP stats.
Quality gates	Real pgvector integration tests, type checking, linting, dependency audit, claim-artifact checks, and regression fixtures for known failure modes.

Deliberately out of scope: an end-user dashboard, entity synthesis, high availability orchestration, automatic truth extraction from prose, and corpus rewrites from inference proposals. Reasoning is opt in, citation constrained, and review aware.

The ordered SQL migration path is versioned now, pre-tenancy tables are migrated in place, and runtime CREATE TABLE IF NOT EXISTS remains bootstrap only.

When not to use RE-call

Use something else if you need managed hosting, per-chunk ACLs, automatic truth extraction from prose, or a memory system that rewrites facts for you. RE-call is a retrieval library over your PostgreSQL database, not a hosted memory platform.

What this does not do

RE-call is a retrieval library with an opt-in reasoning layer, not a general reasoning system. It does not infer every missing supersession edge, prove that an on-topic memory answers a near-miss question, promote proposals into corpus truth, or replace database operations with a managed service. It returns the trust signals the caller needs, and it refuses to pretend that a nearest match is always usable evidence.

Use it

For an ad hoc local markdown folder, create a table for that index, index the corpus, and search it. If you did not calibrate during setup, use development mode only for local evaluation. Replace ./notes with your memo folder.

python -m recall.cli --table recall_notes \
  --migration-dsn postgresql://recall:recall@localhost:5432/recall \
  schema --dim 384 apply
RECALL_TRUST_MODE=development python -m recall.cli --table recall_notes index ./notes
RECALL_TRUST_MODE=development python -m recall.cli --table recall_notes search "what did we decide about caching?"
python -m recall.cli lint ./notes
python -m recall.cli check ./notes/new-memo.md --strict

PowerShell uses the same commands, but set development mode first when you are running an uncalibrated local evaluation:

$env:RECALL_TRUST_MODE = "development"

For production generation mode, build, validate, calibrate, and promote an immutable generation. Then query the tenant's active generation:

from recall.embeddings import FastEmbedEmbedder
from recall.generation_store import GenerationStore
from recall.trust import trusted_search

emb = FastEmbedEmbedder()
with GenerationStore(DSN, dim=emb.dim, tenant="acme", pool_size=8) as store:
    store.check_schema()
    result = trusted_search(store, emb, "what is the rate limit?")
    if result.abstained:
        ...  # say you do not know
    for hit in result.hits:
        hit.verdict
        hit.confidence
        hit.validity.superseded_by

Set RECALL_SERVING_DSN for application traffic and RECALL_MIGRATION_DSN only in the migration job. RECALL_DSN remains a deprecated development fallback for the serving DSN. See docs/MIGRATIONS.md. Configuration modes are summarized in docs/OPERATING_MODES.md.

Operational safety notes:

Topic	Rule
Test database	The test suite drops tables. It uses RECALL_TEST_DSN, never RECALL_DSN.
Default credentials	The MCP server refuses a non-local built-in recall:recall DSN unless RECALL_ALLOW_INSECURE_DSN=1 is set deliberately.
Tenancy	Set RECALL_TENANT or PgVectorStore(tenant=...). Use an unprivileged database role, because PostgreSQL superusers bypass RLS.

MCP

The MCP server uses the default chunks table. Apply that schema for the embedder the server will run, then point the client at recall_mcp.server.

python -m recall.cli --migration-dsn postgresql://recall:recall@localhost:5432/recall \
  schema --dim 384 apply

If an existing chunks table was created with another vector dimension, use a fresh database or an embedder with the matching dimension. The MCP stdio server does not take a --table flag.

{
  "mcpServers": {
    "recall": {
      "command": "python",
      "args": ["-m", "recall_mcp.server"],
      "env": {
        "RECALL_SERVING_DSN": "postgresql://...",
        "RECALL_TENANT": "acme",
        "RECALL_TRUST_MODE": "development"
      }
    }
  }
}

Omit RECALL_TRUST_MODE in production after you have built, calibrated, and promoted a generation. Local uncalibrated MCP work needs the explicit development setting because it has not gone through production calibration.

Tools: recall_search, recall_evidence, recall_index, recall_forget, and recall_stats. For non-English presentation, pass locale to recall_search or recall_evidence after enabling the optional translation endpoint. Localized text is additive and never replaces canonical evidence. The configuration is documented in docs/ENVIRONMENT.md.

Full guide: docs/USING_WITH_CLAUDE.md. Authentication and tenancy: docs/AUTH.md.

LangChain and LlamaIndex

pip install "recall-rag[langchain]"
pip install "recall-rag[llamaindex]"

from recall.integrations.langchain import RecallRetriever

retriever = RecallRetriever.from_store(store, emb, k=5)
docs = retriever.invoke("what is the rate limit?")

When the trust layer abstains, the adapters return no document by default. Returned documents carry trust metadata, including verdict, confidence, cosine, and supersession details.

Documentation

Start with docs/README.md.

Core documents:

Document	Purpose
docs/WRITEUP.md	Architecture and design rationale.
docs/API.md	Supported Python, CLI, and MCP surface.
docs/REPOSITORY_MAP.md	What is product, evidence, benchmark support, and archive.
docs/REASONING_OPERATIONS.md	Opt-in reasoning tools, traces, review policy, and operational behavior.
docs/AUTH.md	Authentication, scopes, and tenant isolation.
docs/MIGRATIONS.md	Migration roles, serving DSNs, and schema operations.
docs/OPERATING_MODES.md	Local, production, quality, hosted, and evaluation deployment modes.
docs/CALIBRATION.md	Calibration workflow and generation-aware serving.
docs/CASE_STUDY.md	Where the system came from and what is public versus private.
docs/RESEARCH_PROTOCOL.md	How benchmark runs are controlled and audited.

Release notes and upgrade warnings live in CHANGELOG.md.

Evidence

Start with benchmarks/README.md. The results directory has its own map at results/README.md.

The short version:

Question	Current evidence
Does declared supersession beat plain similarity search?	Yes, on the authored-edge cases measured in the trust and scale studies.
Can abstention be trusted everywhere?	No. It works on far gaps and fails on near-misses unless a stronger answerability layer is added.
Is retrieval quality universal?	No. Corpus shape dominates, and the measured recommendation is to benchmark your corpus before choosing an embedder.
Is the Mem0 comparison apples-to-apples?	The published head-to-head uses the same LOCOMO questions, generator, judge, and paired tests, with reader-tier limits stated in the benchmark review.
What does MTRAG add?	A third-party multi-turn benchmark with an official judge that gives full credit for correct refusal. RE-call does not top the benchmark, and that boundary is stated in docs/MTRAG_BENCHMARK.md.

Important benchmark documents:

Document	Purpose
results/FINDINGS.md	Interpretation, limits, and negative results.
results/RESULTS.md	Complete result tables.
results/ARTIFACTS.md	Checksum and artifact map for readers auditing a claim.
docs/MTRAG_BENCHMARK.md	MTRAG setup, results, and scope boundaries.
benchmarks/REVIEW.md	Adversarial review of the LOCOMO comparison.
benchmarks/PREREGISTRATION.md	Pre-registered rules for the main memory benchmark.
benchmarks/archive/preregistrations/README.md	Archived preregistrations for follow-up benchmark arms.

When not to use RE-call

Use something else if you need managed hosting, per-chunk ACLs, automatic truth extraction from prose, or a memory system that rewrites facts for you. RE-call is a retrieval library over your PostgreSQL database, not a hosted memory platform.

What this does not do

RE-call is a retrieval library with an opt-in reasoning layer, not a general reasoning system. It does not infer every missing supersession edge, prove that an on-topic memory answers a near-miss question, promote proposals into corpus truth, or replace database operations with a managed service. It returns the trust signals the caller needs, and it refuses to pretend that a nearest match is always usable evidence.

Reproduce

make eval
python -m recall.eval.scale --embedder hashing --filler 50000

Cloud rows require the relevant API keys. Local rows run key-free.

Citation

If you describe RE-call in a paper, post, talk, or README of your own, cite the project and credit Giulio D'Erme. Use CITATION.cff as the canonical citation source.

License

Apache 2.0 license. See LICENSE, and keep NOTICE with redistributed derivative works.