RE-call MCP Memory Server

Trustworthy memory for AI agents.
RE-call gives retrieval results confidence, provenance, validity, tenant isolation, and an explicit abstention path when the memory does not support an answer.

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

Why RE-call

Most memory systems optimize for the nearest match. Agent memory needs a stricter contract: the retriever must say whether a memory is current, where it came from, how confident it is, and when the corpus does not contain an answer.

RE-call is built around that contract.

It is for teams putting agent memory behind real applications: support copilots, internal research agents, compliance assistants, and long-running workflow agents where a stale or unsupported memory is worse than no memory. The buyer story is simple: 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Validity-aware retrieval	Superseded, expired, not-yet-valid, low-confidence, and not-entailed hits are surfaced as verdicts rather than flattened into ordinary search results.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

Run the guided setup wizard for your own corpus. The wizard records the selected embedder, retrieval options, and an optional calibration that is fitted to your labeled queries and your corpus.

docker compose up -d --wait
pip install "recall-rag[fastembed]"
python -m recall.cli --table recall_quickstart \
  --migration-dsn postgresql://recall:recall@localhost:5432/recall \
  schema --dim 384 apply
python -m recall.cli setup

PowerShell:

docker compose up -d --wait
pip install "recall-rag[fastembed]"
python -m recall.cli --table recall_quickstart `
  --migration-dsn postgresql://recall:recall@localhost:5432/recall `
  schema --dim 384 apply
python -m recall.cli setup

When the wizard asks whether to calibrate, provide a labeled query JSON and the corpus directory. Use recall/eval/queries.json as the input shape. Calibration is per embedder and per corpus, so a new model or substantially changed corpus should be calibrated again.

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"]

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.
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, graph reasoning, entity synthesis, high availability orchestration, and automatic truth inference from prose.

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.

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.

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/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, graph reasoning, 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, not a general reasoning system. It does not infer every missing supersession edge, prove that an on-topic memory answers a near-miss question, 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.