Particles Engine Py MCP Server

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 Particles are superseded, retracted, or disputed in the open.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.

Extraction and the written answers call a language model. The quickstart below uses Anthropic, and any OpenAI-compatible endpoint works too, hosted or local (Ollama, llama.cpp, vLLM): each purpose names its provider in config.yaml (named providers). Deposit, the session digest and structural queries make no model call at all.

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:

The oracle and floor rows come from a separate control run in the same harness, the day before the three runs the Particles row averages; the benchmarks page names the report of record for each row.

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.

Fifteen adversarial audits have covered this code, and each returned GO-WITH-FIXES. The verdicts are quoted as given.

Not every release in those ranges contains the releases numbered before it: 1.212.0, 1.214.8 and 1.215.0 were tagged before their merge. Every release from 1.215.1 on contains every lower release. Nor does a release number prove its commit passed CI: 1.222.3, 1.222.4 and 1.222.5 were tagged on commits whose CI run failed, and the release tagger now tags a version only after its CI run passes.

Three of the fix rounds changed the design rather than a defect. An engine with no key serves reads only: it refuses every write, from loopback too, and the particles command line, the Claude Code hooks and the MCP servers write the local store in-process and need no key. Each client can hold a key of its own whose role bounds it: a reader key writes nothing, an agent key writes only as its own principal, and PARTICLES_API_KEY and every per-client operator key carry the operator's authority, by design, so give an operator key only to clients you would give that authority. The local refresh trusts a store, its config file or a rule file only when you alone can write it.

A cloned repository cannot configure Particles, or give a stdio MCP server or a Claude Code hook its store, unless a relative path you set points into it (a relative PARTICLES_CONFIG for the command line, a relative DATABASE_URL for an MCP server) or particles init claude-code --store default runs inside it. A store it ships, found by a path relative to the directory, cannot direct a refresh to a file on your disk, and that store's blob directory cannot direct a blob read or write to another file. No HTTP route reads a file the caller names on the engine's disk. A web page cannot send writes to an engine on loopback, or read from one that runs without a key, and a request refused for its key or its origin takes no lock on the store. The job that holds the PyPI publishing identity runs no code from a package index: it downloads the built files, checks them against the digests the build job recorded, and runs the pinned upload action.

Not every finding is fixed, and this page does not list them all. The latest audit's report names what stays open; the Low and Info findings the audits deferred are tracked by area until they are fixed; and SECURITY.md states each known exception beside the guarantee it qualifies, with the limits we have accepted. The fourteenth delta audit was the last: security review now runs on each security-relevant change, plus one full audit per release. The standing limits a deployment should plan around are by design: the read surface is open by default, an operator key carries the operator's authority, every key reads every project's beliefs, keys are not scoped to a store, and Particles is a single-operator tool with no multi-tenant isolation.

What holds today, and what does not:

• Prompt injection is contained, not eliminated. Outside the benchmark harness, every model prompt that carries deposited, stored or harvested text keeps its instructions in the system turn and wraps that text in a per-call fence marked with a 128-bit random nonce. A test finds every call to the model's entry points, including calls through a wrapper that passes a system turn on, and fails the build on one that has not been classified, or that is classified as fenced and sends no system turn. A call reached another way, such as through a function passed as a value, is outside what that test can find, and what each fenced prompt keeps inside its fence rests on behavioural tests that drive each prompt builder with a claim trying to close its fence. Behind the fence sit structural checks: a JSON contract enforced at the parser for the probes and extractors, and a gate that rejects synthesized prose citing a claim it was not given. A projection manifest's framing and direction are not fenced: they reach the synthesis instructions as written, so project only with manifests you trust. This raises the bar materially. It is hardening, not immunity.
• The model has no tools, but its verdicts change what is stored. The model is never offered function calling, and nothing it emits is run as code or SQL or used as a fetch destination. Its output still has effects: subject names it extracts become Wikidata search terms; its verdicts retire claims (an older value a newer claim updates, a claim from the same passage that the re-anchor pass restates in words the model wrote, the less trusted side of a contradiction between sources of different trust) and return held claims to active; a document that declares it replaces another retires the claims of the one it names, within a collection an operator key can name; each of these is recorded in the event log; and the beliefs it extracts are written into Claude Code's MEMORY.md, which an agent with tools reads as instructions. The exporters write stored text, and the article prose the model writes, with its markup inert. An injection can distort claims, and a distorted claim can steer an agent.
• Fetches of URLs from deposited content are checked on every hop. The host is resolved, each address is checked against a blocklist, and the connection goes to that vetted address, again on every redirect, which closes DNS rebinding and redirect SSRF. The two fetches that run as subprocesses are pinned to addresses this process vetted, neither follows a redirect on its own, neither inherits a proxy or CA setting, curl expands no URL glob, and the git clone runs under no system or global git config, so no filter driver (git-lfs among them) runs for the cloned files; that last point rests on reasoning, since git-lfs was not installed where it was audited. Endpoints you configure yourself (a model provider, a remote engine, a telemetry collector) are held to the network.egress setting but not to the blocklist, since a LAN or local model server is a legitimate target.
• Fail-closed auth, and no SQL built from input. With no key (PARTICLES_API_KEY unset or empty, and no per-client key), the engine refuses to start on a non-loopback bind, or on a bind it cannot read; a script that calls uvicorn.run is not refused at startup and relies on the per-request bounds. It then serves only a loopback client that names a loopback host or one listed in api.allowed_hosts, which turns away a DNS-rebinding page, and serves that client reads only. With a key, every write needs a key whose role allows it. The legacy key's comparison is constant-time, and a per-client key is checked by the digest of its secret. The data layer issues typed ORM statements with bound parameters; its few literal SQL statements take no input, and a LIKE pattern that carries text is escaped.
• Local by default, with outbound defaults you should know. The engine binds to loopback, stores to a local SQLite file, keeps telemetry off, and serves MCP as a locally spawned stdio child. Out of the box it also contacts the model provider you configure, Wikidata for subject lookups during extraction (these carry extracted entity names), and the Hugging Face Hub, to download the embedding model and, while outbound traffic is open, to check for a newer copy when a process loads it. A deposited Reddit, Hacker News or Mastodon post is followed to the link it names, and a Reddit deposit reads the Arctic Shift archive. The scheduled consolidation and the daemon, when you turn them on, also re-fetch the URLs you deposited. Settings come only from the file PARTICLES_CONFIG names or ~/.particles/config.yaml, never from a config.yaml in the directory a command runs in; a relative PARTICLES_CONFIG is read against that directory, and only the command line accepts one. The store is a different matter: when nothing names one, the particles command line and engine serve open ./particles.db in the directory they run in, while an MCP server started with neither DATABASE_URL nor PARTICLES_CONFIG opens the store ~/.particles/config.yaml names, else ~/.particles/particles.db, and a Claude Code hook refuses a store with a relative path unless its pinned config points it at a remote engine. Set network.egress to none or allowlist to restrict outbound traffic, and deny network access at the operating system for a guarantee.

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 read surface left open by default, what each key role may do and the operator's authority an operator key carries, what a working directory can still supply, verbatim storage of whatever you deposit (secrets included), model verdicts that retire claims, project scope as a relevance filter rather than isolation, the single-operator trust model, the MCP write boundary, and the limits of the release supply chain. To report a vulnerability, use this repository's private vulnerability reporting (Security, Report a vulnerability); SECURITY.md has the other channel.

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.

Particles is a minimal interoperable substrate for claim-granularity agent knowledge: narrower than a formal-ontology knowledge graph such as RDF/SPARQL and more structured than augmented markdown. Anchoring every particle to a subject is what makes this precision possible: the Subject store is the standard's catalogue of real-world entities against which particles are indexed, aligning beliefs to canonical entities that can, in principle, be mapped to external ontologies. A developer choosing Particles is therefore choosing a substrate where each belief is a first-class object with an identity, a confidence score, a source, and a subject, not a position in a vector space.

Design rationale

The foundation of Particles is the guarantee that nothing already written is ever overwritten. The Source Corpus is constituted as an append-only archive of all materials from which particles have been or may be extracted, meaning that every ingested source and every derived claim accumulates in place rather than displacing what came before. Provenance is carried by each particle from the moment of its creation, and when a source must be handled in a mutable mode rather than a purely append-only one, the system does not silently absorb the change: particles whose provenance location falls within the changed regions are flagged with PROVENANCE_STALE, making the disturbance visible rather than burying it. Supersession and retraction therefore propagate deterministically through the provenance graph, touching every claim whose origin is implicated, so the ledger remains a faithful record of what was asserted and when, never a quietly revised version of the past.

Trust in Particles is not a property baked into a stored record but a lens applied at the moment of reading. The raw confidence value is stored immutably, calibrated at the time a particle is created and never subsequently altered; what changes with each query is the effective confidence, which is computed at query time rather than stored. Trust weighting, recency decay, and the as-of instant are all applied per observer at query time, never written back into the record. This means that a belief does not need to be retracted or rewritten to sink in the rankings as its source ages or as the extractor that produced it loses credibility: the stored particle remains intact while the effective confidence computed over it falls, allowing stale beliefs to recede naturally without any mutation of the underlying ledger. When as_of is set to a past instant T, the query evaluates the store on the assertion-time axis (what the store believed at T) rather than on any world-time validity dimension, so the same immutable corpus can answer questions about present belief and historical belief without ambiguity.

Contradictions in Particles are treated as first-class citizens of the knowledge graph rather than anomalies to be suppressed. When two sources or two principals disagree, the conflict-resolution ladder surfaces an INCONSISTENCY record for review instead of silently overwriting either side, preserving both competing claims in the ledger and making the disagreement legible to any downstream consumer. Contestedness, the property that marks a particle as being in tension with one or more others, must be computed at read time and is never stored; like effective confidence, it is a read-time lens rather than a durable annotation, which means that as new particles arrive or old ones are superseded, the contested status of any given claim is always freshly derived from the current state of the graph rather than from a stale flag written at some earlier moment.

Memory in Particles maintains itself through a periodic, automated health discipline. The Lint operation performs health checks for contradictions, stale claims, orphan pages, and missing cross-references, scanning the corpus on a scheduled basis so that problems accumulate into visible findings rather than silently degrading the quality of the belief store. Because nothing is overwritten and contradictions are surfaced rather than resolved by fiat, Lint operates on a stable substrate: it traverses the same immutable record that queries read, and its findings feed back into the trust-policy review cycle, closing the loop between ingestion, querying, and the ongoing stewardship of the ledger's integrity.

Architecture

A Particles deployment is organized around two distinct storage subsystems: a Source Corpus store and a Particle store. The Source Corpus store is an append-only object store, compatible with a local filesystem, an S3-compatible backend, or a content-addressed store, while corpus entries and snapshot metadata are held in queryable form, either relational or document store. Together these two subsystems form the durable foundation on which all operations act.

The Core SDK exposes both a clean Python API and a CLI, and the six core operations (Deposit, Extract, Query, Lint, Reindex, and Review) each map to a CLI subcommand and a POST API endpoint in the reference SDK. A developer therefore has two symmetric surfaces for every operation: a scriptable command line suitable for pipelines and a programmatic endpoint suitable for application integration.

The journey from a raw source to a set of particles begins with Deposit, which writes the source into the append-only corpus, and continues with Extract, which drives the extraction pipeline. That pipeline selects the most specific registered extractor whose applicability specification matches the deposited source; the general extractor is used if and only if no domain-specific extractor applies. Once particles exist in the Particle store, Query retrieves them ranked by effective confidence, Lint checks for contradictions and staleness, Reindex rebuilds derived indexes, and Review consolidates inconsistencies into a reusable trust policy. Each step in this sequence is a discrete, named operation rather than an implicit side effect, which means a developer can invoke any stage independently and observe its inputs and outputs in isolation.

The Why Particles?, Design rationale, and Architecture sections are cited projections of a particle store, not hand-authored prose. Each marked block is rendered from docs/projection/readme.yaml and the corpus bundle committed beside it, and the trailer under each block names the exact claims it was built from. Edit the manifest or the underlying particles and re-render, never the prose between the sentinels. Check it yourself: python scripts/projection_drift.py restores the bundle into a throwaway store, re-derives the deterministic render, and fails if any block has drifted from the claims it cites.

This is the project eating its own dogfood: the README argues for claim-granularity knowledge with provenance, and is itself assembled that way.

Documentation

Everything below is served at docs.linkedparticles.org.

The standard itself (whitepaper, technical specification, and the normative schema, context, and vocabulary artifacts) lives at linkedparticles.org.

The three repositories

Deployment

Container and chart artifacts live under deploy/. The engine ships as a single-writer service; see running in a container.

Contributing

See CONTRIBUTING.md and ARCHITECTURE.md. Contributions are accepted under a Developer Certificate of Origin sign-off; there is no CLA.

License

Apache-2.0. See LICENSE and NOTICE.