Memory that abstains instead of guessing.
RE-call is agent memory on 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  ·  Try the demo  ·  Install and integrate

What is RE-call

RE-call is agent memory on your own PostgreSQL database. It indexes source documents with pgvector 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 A superseded claim is marked superseded; a result that does not clear the calibrated trust gate becomes ABSTAIN with a reason.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 Postgres full text retrieval. 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

RE-call builds an immutable, calibrated generation once and serves queries against that pinned generation. Solid arrows are the default path. Dashed arrows are opt in or reasoning only: the reranker, the entailment judge, graph expansion, and the answer provider. Every search, including the reasoning and fact paths, goes through trusted search, so nothing bypasses the pinned generation or the trust gate. Each box links to the file that implements it.

flowchart LR

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

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

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

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

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

node_caller(("Agent or user<br/>caller"))
node_postgres[("PostgreSQL + pgvector<br/>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_trusted_search -.->|"rejudges trusted"| node_entailment
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.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_entailment "https://github.com/GiulioDER/RE-call/blob/master/recall/entailment.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"

classDef toneNeutral fill:#f8fafc,stroke:#334155,stroke-width:1.5px,color:#0f172a
classDef toneBlue fill:#dbeafe,stroke:#2563eb,stroke-width:1.5px,color:#172554
classDef toneAmber fill:#fef3c7,stroke:#d97706,stroke-width:1.5px,color:#78350f
classDef toneMint fill:#dcfce7,stroke:#16a34a,stroke-width:1.5px,color:#14532d
classDef toneRose fill:#ffe4e6,stroke:#e11d48,stroke-width:1.5px,color:#881337
classDef toneIndigo fill:#e0e7ff,stroke:#4f46e5,stroke-width:1.5px,color:#312e81
classDef toneTeal fill:#ccfbf1,stroke:#0f766e,stroke-width:1.5px,color:#134e4a
class node_cli,node_mcp,node_sdk,node_caller toneBlue
class node_manifest,node_generations,node_parser,node_embedder,node_graph_build,node_calibration,node_postgres toneAmber
class node_trusted_search,node_gen_store,node_retriever,node_reranker,node_trust_gate,node_entailment toneMint
class node_reasoner,node_graph_expansion,node_answer toneRose
class node_provenance,node_ledger toneIndigo

Loading

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 capability	Where it fits	What it adds
Hosted embedder	Build and query	Remote model calls for embeddings. Query and corpus text may leave the environment.
Learned sparse retrieval, SPLADE	Hybrid retrieval	A learned term weighted retrieval leg in addition to dense vectors and Postgres full text.
Reranker	After candidate fusion	Reorders the fused candidates with a cross encoder.
Entailment judge	After the trust decision	Demotes high similarity near misses that do not answer the question.
Evidence Graph version one	Explicit reasoning retrieval	Adds 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 application	Evidence cards	Lets 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

Prerequisites: Python 3.11 or newer, Docker, and a Docker installation able to run PostgreSQL with pgvector.

pip install "recall-rag[fastembed]"
recall quickstart

The demo starts a throwaway database, indexes a small corpus included in the package, and runs three searches. It includes a normal answer, a stale claim that is returned as superseded, and a question that is refused. The demo uses development trust and changes no personal files.

Remove the demo database when finished:

recall quickstart --remove

Already have PostgreSQL with pgvector? Use recall quickstart --existing-dsn <dsn> instead. The demo is intentionally separate from a real install and is not calibrated for your data.

Install and integrate

For your own corpus, provide PostgreSQL with pgvector and run the guided setup wizard after installing recall-rag[fastembed]:

recall setup

It applies the schema, asks for the embedder and retrieval options, indexes the corpus, offers calibration, and registers the selected agent integration. When the wizard asks whether to calibrate, use a labeled query file that refers to the corpus you are installing. Calibration fitted to the bundled demo is only an example, not a certification for your data. The schema uses an ordered SQL migration path and pre-tenancy tables are migrated in place.

For Docker, an existing database, headless provisioning, manual calibration, and troubleshooting, see docs/INSTALLATION.md and docs/WIZARD.md.

Choose an integration

Use case	Install	Next step
CLI and Python	pip install "recall-rag[fastembed]"	Run recall setup, then use recall search or the Python API.
MCP, Claude Code, Claude Desktop, or Codex	pip install "recall-rag[fastembed,mcp]"	Run setup and follow the MCP guide. Host specific steps are below.
Claude Agent SDK	pip install "recall-rag[agent,fastembed]"	Use the in process integration in USING_WITH_AGENT_SDK.md.
LangChain or LlamaIndex	Install the matching extra	Use the adapters described in API.md.
Windows desktop UI	pip install "recall-rag[desktop]"	Run recall-install; the current release does not ship a standalone Windows binary. See the wizard guide.

Claude Code

Inside Claude Code, install the plugin after installing the Python package:

/plugin marketplace add GiulioDER/RE-call
/plugin install recall@re-call

The plugin supplies the MCP server, memory search skill, and lifecycle hooks. recall setup still needs to run against the project and database that Claude should use. The plugin keeps credentials out of the repository. Details and manual wiring are in plugin/README.md.

Codex

Run recall setup from the project. When Codex is detected, setup installs the Codex MCP server, plugin bundle, memory skills, and hooks into the user configuration. Restart Codex afterward. The Codex and Claude Code integrations share the same memo format and trust layer. See docs/CODEX_RECALL_INTEGRATION.md.

Claude Agent SDK

The SDK integration runs the same tools in process and does not start an MCP server:

from recall_agent import RecallAgentMemory

with RecallAgentMemory.from_env() as memory:
    options = memory.options()

See docs/USING_WITH_AGENT_SDK.md for the complete example and write-tool boundaries.

LangChain and LlamaIndex

Both adapters use the same trusted retrieval path. If trust abstains, they return no document by default, and returned documents retain verdict, confidence, cosine, and supersession metadata. See docs/API.md for the supported classes and methods.

If an install is not working

recall doctor

The doctor checks the interpreter, console scripts, embedder, Docker, database, pgvector, schema, configured table and tenant, calibration, and agent registration. It changes nothing and prints the repair command for each problem.

Product surface

Area	What ships
Retrieval and memory	Dense vectors plus Postgres full text with hybrid RRF, validity, calibrated confidence, provenance, trust verdicts, immutable generations, incremental indexing, pruning, and source erasure.
Structured facts	Citable evidence cards, provenance controller, append only fact ledger, current fact projection, and optional materialization outbox.
Quality	Real pgvector integration tests, type checking, linting, dependency audit, and a claim gate that checks published evidence in CI.

RE-call is not a hosted memory service, dashboard, or automatic truth extractor. It does not rewrite corpus metadata from an agent's inference. Reasoning is opt in, citation constrained, and review aware. See docs/PRODUCTION.md for deployment boundaries.

Read next

Need	Document
Why an agent needs persistent, trusted memory	docs/FOR_AGENTS.md
Full documentation map	docs/README.md
Install and provision	docs/INSTALLATION.md, docs/WIZARD.md
Python, CLI, and MCP reference	docs/API.md
Trust, architecture, and provenance	docs/WRITEUP.md, docs/PROVENANCE_CONTROLLER.md
Security and operations	docs/AUTH.md, docs/PRODUCTION.md, docs/OPERATING_MODES.md
Measurements and limits	docs/EVIDENCE.md, results/FINDINGS.md

Published numbers are tied to committed artifacts, and the claim gate checks them in CI. Benchmark interpretation and limits belong in docs/EVIDENCE.md, not in this overview.

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

RE-call is source available under the PolyForm Noncommercial License 1.0.0. Personal, educational, and noncommercial research use is permitted. Commercial use requires a separate written license from the copyright holder. See COMMERCIAL_LICENSE.md for the boundary between permitted use and commercial licensing, and preserve NOTICE when redistributing the software.