linkedparticles

linkedparticles.org · docs.linkedparticles.org · PyPI · Apache-2.0

Particles is shared memory for humans and AI agents. Each particle is one claim, plus what you need to judge it: who said it, where, when, and how confident they were. Facts, opinions, and memories are all claims, recorded the same way as particles. Particles are not edited or deleted. Particles are superseded, retracted, or disputed in the open. How much to trust it is a perspective applied at query time, never baked into the record.

linkedparticles is the Engine of the Particles reference implementation: the package you install to actually run a belief store. Give it documents, pages, chat logs, or an agent's own session notes; get back claim-granularity beliefs, each carrying calibrated confidence, an uncertainty kind, a resolved subject, and provenance back to the exact source bytes. Ask a question and the answer cites the beliefs it was built from. Nothing is ever overwritten, so you can ask the store what it believed a year ago and why it stopped.

When your agent is wrong, you can see exactly why, and fix it at the source.

The Engine is a library first. The HTTP API, the CLI, the read-only MCP server, and the resident daemon are all surfaces over it.

Install

pip install linkedparticles

Python 3.11+. linkedparticles-core, the store-free Client layer, is pulled in automatically.

Sixty seconds

export ANTHROPIC_API_KEY=sk-ant-...
particles db init

# Deposit a source (file, URL, or literal text) into the append-only corpus
particles deposit https://en.wikipedia.org/wiki/Pluto

# Extract claim-granularity beliefs with confidence, subjects, and provenance
particles extract --all-pending

# Ask in natural language; the answer cites the particles behind it
particles query "Why is Pluto not a planet?"

# Health-check the store: contradictions, staleness, orphaned links
particles lint

Because nothing is overwritten, the store can replay its own history. --as-of is the assertion-time lens. It answers what did the store believe at T, and why did it stop, not what was true of the world at T:

particles query "How many planets are in the Solar System?" --as-of 2000-01-01
# → the belief as it stood then, and why it no longer stands: SUPERSEDED,
#   retired 2006-08-24, superseded by "Pluto is a dwarf planet."

There is a clickable version of that belief history on the front door. Step by step: getting started, then as-of time travel.

Wiring it into an agent

particles mcp serve                 # read-only MCP server (stdio), any MCP client
particles init claude-code          # session-start digest, session-end harvest
particles engine serve              # HTTP API, for a shared or remote engine
particles export obsidian ./vault   # also: anki, wiki, logseq, notion, graph, jsonl

It can also stand in for the reference memory server, and it plugs into LangChain as a retriever and a tool set. See Claude Code memory, swapping in for the reference memory server, and using from LangChain.

Does it hold up?

Measured on LongMemEval, the multi-session agent-memory benchmark, with a full-context oracle and a no-memory floor run as controls in the same harness:

Method, per-question-type breakdowns, comparator memories, and the budget-matched arm (including the arms where Particles loses) are on the benchmarks page, with the report JSONs of record beside them.

Security posture

A memory layer is an input-shaping attack surface: it does not merely hold data, it shapes what the agent believes and does next, so a poisoned claim is an instruction on a delay timer. Before this code was opened we ran an adversarial application-security audit over the whole package: the HTTP, CLI, and MCP surfaces, the filesystem writers, the egress layer, and the build pipeline. The verdict, verbatim: GO-WITH-FIXES, with 33 findings (2 High, 7 Medium, 20 Low, 4 Info). The ranked must-fix set merged the following day; the last open finding closed in v1.128.0.

What that bought, and what it did not:

• Prompt injection is contained, not eliminated. Every LLM call site that touches attacker-controllable text keeps trusted instructions in the system turn and wraps the untrusted material in a per-call, 128-bit-nonce data fence, with structural backstops behind it: a JSON contract enforced at the parser, a citation-id membership gate on synthesized prose. This raises the bar materially. It is hardening, not immunity.
• The model is never given tools, and no model output is executed. No function-calling; nothing the model emits becomes a shell command, a SQL fragment, or a fetch. An injection can at worst distort claims, never trigger actions.
• Outbound fetches are validated per hop. The host is resolved, the address is checked against a blocklist, and the connection is made to that vetted address, re-resolved and re-validated on every redirect. That closes DNS rebinding and redirect SSRF, not just the first lookup. The two fetches that run as subprocesses reach the same guarantee by pinning to addresses this process vetted.
• Fail-closed auth, and no raw SQL. The API refuses to boot on a non-loopback bind without a real bearer key, and the token comparison is constant-time. Every query in the data layer is typed ORM with bound parameters: no text(), no string-built SQL, no dynamic ORDER BY.
• Local-first by default. Loopback bind, a local SQLite file, telemetry off, and the MCP server as a locally-spawned stdio child rather than a network service. The only outbound traffic in the default posture is what you configured.

The real answer to the residual is epistemic rather than technical: every particle carries its provenance, source trust is a read-time lens that discounts a distrusted source without rewriting anything, and lint / review turn your rulings on contradictions into a reusable trust policy. The store is a record of what sources said, weighted by how much you trust them, not an oracle, so a poisoned source is something you can see, discount, and retract with the audit trail intact.

The limitations we ask you to read before relying on any of this are in SECURITY.md: the unauthenticated read surface, verbatim storage of whatever you deposit (secrets included), the single-operator trust model, and the MCP write boundary. SECURITY.md is also where to report a vulnerability; please report privately.

Why Particles?

Most Retrieval-Augmented Generation systems re-derive knowledge from scratch on every query, operating over raw text chunks without any persistent record of what was learned, how confidently it was held, or where it came from. Particles takes a different approach: rather than retrieving passages, it stores discrete, citable, revisable claims (natural-language sentences paired with structured metadata for confidence, provenance, and uncertainty) so that an agent's knowledge accumulates as an auditable ledger rather than evaporating between sessions. Corpus-entry-level provenance is required at Core, meaning every belief can be traced back to the source that produced it, and the particle store itself is a derived view that can be rebuilt from the corpus by re-running extractors, giving the system both durability and reproducibility.

The Particles standard defines the particle schema, the source-corpus model, the extraction protocol, and a set of operations (deposit, extract, query, lint, review, and reindex) that together form the spine along which an agent's knowledge is built, queried, audited, and revised one belief at a time. A developer working with this loop deposits a source into the append-only corpus, extracts claim-granularity particles with resolved subjects, queries by effective-confidence ranking, lints for contradictions and staleness, and reviews inconsistencies into a reusable trust policy. Because particles carry explicit confidence and provenance, Particles can answer queries that require finding relevant claims about a subject and synthesising them with provenance intact, rather than returning a ranked list of chunks whose epistemic status is opaque.