Neural Context Protocol

PyPI

Bounded, trust-weighted memory for multi-agent systems. MCP-native. Agents share context without replaying full transcripts. Around 13x fewer tokens than raw replay — see Benchmarks.

The problem

Most agent memory systems treat context like an append-only log.

Six months of stored memory can cost more tokens on the first message than a brand-new user costs all week. Everything gets re-read at the same weight — a casual remark and a hard compliance rule fight for the same slot. There's little real consolidation, almost no decay, and retrieval usually means "search everything" instead of "pick the few things that matter right now."

The store turns into a landfill. Dilution starts to feel like forgetting.

NCP was built to stop that.

What NCP is

NCP is an agent-to-agent communication protocol for multi-agent systems — and, underneath it, a memory bus over MCP. It lets agents talk to each other, hand off work, and build on prior results without replaying transcripts or stuffing prompts.

MCP standardized how a single agent talks to its tools. NCP standardizes how agents talk to each other. It exposes one MCP endpoint that every host — Claude, Codex, OpenCode, Copilot, n8n, LangGraph, any Agent Plugins-compliant client, or a custom orchestrator — connects to as a peer. Each agent reads bounded, trust-weighted context, writes durable memory, and sends bounded signals (whispers) to other agents, all through the same protocol.

NCP is a bus, not an orchestrator. The orchestrator still decides who runs when; the bus owns what they know and share.

Problem	What NCP does
No shared channel between turns	One MCP memory bus every host can join
Transcripts grow and get re-read at equal weight	Bounded, scored context per turn
Good work disappears after a turn	Durable memory and decision traces
Multi-agent handoff is brittle	Whispers and shared pipeline memory
All context looks equally credible	Trust scores, drift markers, dissent, calibration
Memory becomes a landfill	Consolidation, feedback calibration, and decay
Token spend does not compound	Reusable, ranked memory across runs and agents
Teams want to use smaller models safely	Better engineered context for cheaper model calls

Why a memory bus

In a multi-agent system, the hard problem is not any single model — it is the channel between agents. Without one, every agent is an island: it re-reads context, re-discovers prior decisions, and leaves no reusable signal behind. Handoffs degrade into pasting full transcripts forward.

NCP is that channel. It is a bus, not just a store. Three properties make it one:

Bounded reads. Every agent gets a budget-bounded working context, not the whole history — so the channel scales as turns and agents grow.
Directed signals. Agents emit whispers to specific peers (handoffs, dissent, drift reports) without broadcasting full state.
Trust-aware transport. Every message on the bus carries a trust score and drift marker — self-reported, advisory inputs, not runtime-verified truth — so a receiving agent knows how much to believe what it reads. Calibration boosts what actually got used or produced good outcomes, lowers what drew dissent, and lets weak or outdated memory fade.

The payoff compounds at the organization level as token capital efficiency — the business value captured per dollar spent on model reasoning. Because work persists as reusable, trusted state instead of being thrown away at the end of each turn, token spend accrues into shared organizational memory rather than resetting: decisions, evidence, outcomes, trust signals, and cost records that future runs, teams, and pipelines draw on. Future agents — including cheaper or smaller models — stand on prior work without replaying the whole history. That does not make NCP a model router or eval platform; it is the context substrate those loops need. The Benchmarks section quantifies the effect.

Full feature set

Bounded retrieval is the entry point, not the whole story. The mechanisms below work together to keep shared memory small, trustworthy, and self-improving instead of turning into a landfill. Each links to the deeper section further down.

Token bloat

Bounded context assembly — every turn gets a budget-capped slice of context (conscious + retrieved + whispers), never the full history. See How agents talk over the bus.
Write-time noise filtering — strips ANSI codes, dedups repeated lines, and prunes boilerplate and empty fields before anything is stored. 33% aggregate token reduction on a fixed noisy-payload benchmark. See Signal filtering at write time.
Fan-in reduction — dedups near-duplicate claims across parallel workers before they reach a synthesis agent. 13% token reduction against an unbounded raw dump. See Fan-in reduction.
Consolidation — merges repeated or near-duplicate memory into fewer, stronger entries instead of leaving competing rows. See Retrieval and self-improving memory.

Context quality

Trust-weighted retrieval — blends lexical relevance (BM25), recency, and trust into one score, with penalties for drift and heavily re-derived generations. See Retrieval and self-improving memory.
Layered memory — every chunk is tagged episodic, procedural, semantic, social, or reasoning_trace, so retrieval can target the kind of memory a turn actually needs. See Memory layers.
Graph-aware retrieval and trust propagation — typed edges (caused_by, supersedes, supports, contradicts, refines, derived_from) let retrieval expand along relationships and let trust credit or debit a cause for what it produced. See Graph engineering.

Memory decay and the landfill problem

Self-improving calibration — ncp calibrate --feedback boosts chunks that keep proving useful, penalizes chunks that drew dissent, and lets weak or outdated memory decay instead of sitting at full weight forever. See Retrieval and self-improving memory.
Outcome-driven trust — ncp_record_outcome ties task success or failure directly to the chunks that informed it, so calibration is grounded in what actually worked, not just what got read.
Procedural self-refinement — a single named procedure can accumulate outcome evidence and evolve through an explicit, human-gated pipeline, instead of instructions going stale. See Procedural self-refinement.

Multi-agent coordination

Whispers — short, directed, bounded-TTL signals between specific agents (handoffs, dissent, drift notes) instead of broadcasting full state.
Cross-host handoffs — one agent hands its task to another host through the same protocol, carrying bounded context forward instead of a transcript. See Cross-agent handoffs.
Shared pipeline memory — every host on a pipeline_id reads and writes the same bounded, scored context.

Trust and accountability

Cryptographic agent identity — Ed25519 keypairs, with optional signed authorship verified against a registered public key. See Agent identity and reputation.
Per-agent reputation — a Beta-distribution posterior over "produces trustworthy memory," updated from calibration's trust deltas, that can optionally weight retrieval or gate whispers.
Decision traces and precedent — ncp_record_decision captures structured rationale; ncp precedents queries past decisions.

Operability at scale

Storage tiers — start on SQLite with zero extra services, move to pgvector + Redis for durable, cross-machine, multi-process coordination. See Storage tiers.
Cost and drift telemetry — ncp cost, ncp trust-drift, ncp explain, and a read-only web UI at /ui for turn timelines, chunk trust, whisper traffic, and the memory graph.
In-process library API — drive the same bus directly from an orchestrator via ncp.api, no server required. See Use NCP as a library.

Scoping note: NCP is the memory bus, not the orchestrator, and not the right default for simple single-agent or very short-lived tasks. See What NCP is (and isn't).

Hosts and plugins

NCP is MCP-native, so any host that can speak MCP can join the bus. Two packages make that concrete for the most common setups. Install whichever matches your client, or both — they point at the same running ncp serve instance and don't conflict.

Package	For	What it adds
`claude-plugin/`	Claude Code	Native plugin, installable via `/plugin install`. A `SessionStart` hook health-checks `ncp serve`, can autostart it, and injects the turn contract — including the mandatory subagent dispatch rule — automatically.
`agent-plugin/`	Any Agent Plugins 1.0.0-compliant client (Cursor, VS Code/Copilot, Codex CLI's plugin support, and others)	Vendor-neutral `plugin.json` + `mcp.json` + `skills/` package. Same MCP tools and skill guidance as the Claude plugin, without Claude-specific packaging.

Both declare the same ncp MCP server (http://127.0.0.1:4242/mcp) and the same tool surface. claude-plugin/ trades portability for lifecycle automation (autostart, health-check, session-start injection); agent-plugin/ trades that automation for working unmodified across any spec-compliant client — the Agent Plugins spec has no hook mechanism, so ncp serve must already be running, and setup is one step more manual. Each package's README documents its own install steps; the Portable Agent Plugin's also lists known gaps (no stdio transport, no autostart, no built-in auth-header injection) rather than presenting itself as more turnkey than it is.

For Codex CLI, OpenCode, GitHub Copilot, and n8n, see Quickstart and the matching examples/ directory.

Quickstart

pip install neural-context-protocol
ncp init
ncp serve --host 127.0.0.1 --port 4242 --cwd /path/to/project

For Claude Code, either install the packaged plugin:

/plugin marketplace add kulkarni2u/neural-context-protocol
/plugin install ncp@neural-context-protocol

or copy the config by hand:

cp examples/06_claude_code/mcp_servers.json .mcp.json

See examples/06_claude_code/README.md and claude-plugin/README.md.

For Codex CLI, copy examples/07_codex_cli/mcp_servers.json into your Codex MCP config location.

See examples/07_codex_cli/README.md.

For Codex CLI and OpenCode, register the same endpoint and copy the host's AGENTS.md turn contract — see examples/07_codex_cli/README.md and examples/09_opencode/README.md.

For GitHub Copilot (VS Code agent mode), copy examples/11_copilot/mcp.json to .vscode/mcp.json and examples/11_copilot/copilot-instructions.md to .github/copilot-instructions.md — see examples/11_copilot/README.md.

For n8n, NCP's MCP server must be reachable from your n8n instance with an auth token configured — see examples/08_n8n/README.md.

Portable Agent Plugin (vendor-neutral)

agent-plugin/ packages NCP to the open Agent Plugins 1.0.0 standard — plugin.json + mcp.json + skills/ in one directory — so the same package works with any compliant client (Cursor, VS Code/Copilot, or any other host implementing the spec), not just Claude Code:

pip install neural-context-protocol
ncp init
ncp serve --host 127.0.0.1 --port 4242 --cwd /path/to/project

Then load agent-plugin/ with whatever mechanism your client uses to load an Agent Plugin directory. It declares one streamable-http MCP server (http://127.0.0.1:4242/mcp) and two skills: ncp-core (the per-turn loop and tool reference) and ncp-multi-agent (whispers, subagent dispatch, cross-host coordination). See agent-plugin/README.md for setup details and known gaps (no stdio transport, no session-start hook/autostart — both intentional, explained there).

This is a different package from claude-plugin/: the Claude Code plugin is native-format and installable via /plugin install, with a SessionStart hook that health-checks and can autostart the bus. agent-plugin/ trades that lifecycle automation for portability across clients. Install whichever matches your client, or both — they point at the same running ncp serve instance and don't conflict.

ncp init creates .ncp/config.toml and a CLAUDE.md turn contract in the project root. When run interactively, it also detects installed claude, codex, and opencode CLIs and asks whether to add the matching NCP hook/setup files.

Zero-touch setup (route all agent comms through NCP)

For Claude Code, Codex CLI, and OpenCode you can go further than registering the server: setup files can start/check the bus automatically and instruct every session — and any subagents it dispatches — to use NCP as the agent-to-agent channel. For Claude Code, installing the ncp plugin (above) gets you this automatically; the steps below are the manual-copy equivalent.

mkdir -p .claude/hooks .claude/skills/ncp
cp examples/06_claude_code/settings.json             .claude/settings.json
cp examples/06_claude_code/hooks/ncp-session-start.sh .claude/hooks/
cp examples/06_claude_code/skills/ncp/SKILL.md        .claude/skills/ncp/
chmod +x .claude/hooks/ncp-session-start.sh

The setup files health-check 127.0.0.1:4242/healthz, start ncp serve if it's down, and inject the protocol instruction (including the mandatory subagent dispatch rule). Codex uses .codex/hooks.json; OpenCode uses a project plugin at .opencode/plugins/ncp.js. Hooks and contracts instruct hosts to use NCP — they don't enforce it; reliable coverage comes from registering the MCP tools, the always-loaded instructions, the dispatch template, and the session-start nudge together. See examples/06_claude_code/README.md, examples/07_codex_cli/README.md, and examples/09_opencode/README.md.

How agents talk over the bus

Instead of treating every model call as an isolated chat, NCP assembles a shared working context from three blocks every turn. Each block is a different channel on the bus:

[NCP:CONSCIOUS]     what this agent knows right now
[NCP:SUBCONSCIOUS]  relevant past, retrieved not replayed
[NCP:WHISPERS]      bounded signals from other agents

Memory survives restarts. The same runtime serves multiple hosts against the same store. Agents coordinate through bounded whispers without stuffing prompts.

Concrete example: a 3-agent bugfix on the bus

This is where the memory bus starts paying for itself.

Say you have a 30-module Java monorepo and a bug in PaymentProcessor.java. You run three agents on the same pipeline_id: analyzer, fixer, reviewer. They never see each other's transcripts — they communicate only through the bus.

analyzer reads the file, runs the affected tests, and publishes one distilled chunk instead of pasting a full stack trace into the next prompt:

NPE at PaymentProcessor.java:142.
root_cause: retryCount is null when payment_method=ACH and customer.tier=trial.
Guard missing before .intValue() call.

fixer does not receive the full transcript. It reads bounded context from the bus, retrieves that chunk by relevance, opens PaymentProcessor.java fresh with its own tools, applies the null guard, runs the targeted tests, and publishes the outcome:

Null guard applied at PaymentProcessor.java:142.
if (retryCount == null) retryCount = 0.
PaymentProcessorTest.testAchTrialRetry passes.

reviewer reads its own bounded context, sees the fix outcome, and receives a bounded whisper with the changed file list. If the fix is wrong, it emits a dissent whisper directed back to fixer with the specific issue — a targeted message on the bus, not a full-history replay.

By turn 20, a raw-replay workflow is dragging old stack traces, earlier tool output, and prior reasoning through every turn. The bus workflow is working from durable shared memory, current task context, and trust-weighted evidence.

Turn flow

flowchart TD
    A["Host calls ncp_get_context"]
    B["Assembler loads conscious state"]
    C["Resolve recent refs"]
    D["Retrieve top relevant chunks"]
    E["Drain bounded whispers"]
    F["Assemble bounded context"]
    G["Host runs provider turn"]
    H["Host persists durable memory"]

    A --> B --> C --> D --> E --> F --> G --> H

Architecture

flowchart LR
    A["Claude / Codex / OpenCode / n8n / other MCP hosts"]
    B["ncp serve<br/>HTTP/SSE MCP runtime"]
    C["Assembler<br/>bounded context + retrieval"]
    D["SQLite mode<br/>local-first store"]
    E["pgvector mode<br/>durable memory"]
    F["Redis<br/>whispers + fetch-session state"]

    A --> B
    B --> C
    C --> D
    C --> E
    C --> F

Every connected agent is a peer on the bus (A); ncp serve is the transport; the assembler and stores are the bus internals.

Memory layers

Memory on the bus is not a flat blob. Every chunk carries a required layer tag, drawn from a fixed set of five cognitively-named values, so you can filter retrieval by what kind of memory you want (ncp_fetch takes a layer filter, and ncp status / ncp viz report the distribution).

The valid layers are episodic, procedural, semantic, social, and reasoning_trace. Four of them are a writer-chosen convention — NCP stores and filters by the tag but does not enforce a meaning, so use them consistently with their usual sense:

Layer	Conventional use
`episodic`	What happened — events, observations, tool results from a turn
`procedural`	How to do something — repeatable steps and methods
`semantic`	Stable facts and definitions that outlive a single run
`social`	Agent-to-agent context — who said what, handoffs, dissent

reasoning_trace is the exception: it is set automatically — ncp_record_decision writes the decision rationale as a reasoning_trace chunk. Tagging memory consistently is what lets the bus retrieve "the decision rationale" or "the procedure" rather than just "a recent chunk."

Trust-aware transport

Most frameworks treat stored context as equally credible. The bus doesn't. Trust is part of the protocol, so a receiving agent always knows how much to believe a message.

Every memory chunk carries a base_trust score (derived from its src at write time) and a written_at_drift marker. Both base_trust and drift_score are self-reported, client-asserted advisory inputs — NCP does not yet compute drift itself. Retrieval scoring discounts chunks written during high-drift periods, and the CoherenceChecker reads the per-turn drift_score agents report and fires alerts when it crosses threshold. Agents emit world_check whispers to report drift back onto the bus. A runtime-computed drift signal is future work — see the north-star roadmap (WI-016).

ChunkSource:      user_verified | tool_result | agent_inferred | synthesis
base_trust:       float (0.0–1.0) — advisory weight applied at retrieval time
drift_score:      float (0.0–1.0) — self-reported coherence signal (advisory; not runtime-computed)
written_at_drift: float — drift level reported when this memory was written

The effect: each agent receives context ranked by how much it should believe it, not just by recency.

Per-chunk trust is only half the story. Trust on the bus also attaches to who wrote it — see agent identity and reputation below.

Agent identity and reputation

In a multi-agent system, "how much do I trust this message" depends on who sent it. NCP gives agents real, cryptographic identities, lets them optionally sign what they write, and tracks a reputation for each one. Reputation is computed and displayed by default; it can also weight retrieval and gate whispers, but only when an operator opts in (CAP-T4 — see below).

Cryptographic identity. ncp identity create generates an Ed25519 keypair; the identity ID is derived from the SHA-256 of the public key, and the secret key is written to a 0700 keystore (~/.ncp/keys, or NCP_KEYSTORE_DIR). Public keys are registered in the store; keys can be listed and revoked.

ncp identity create --label fixer   # prints the new identity_id
ncp identity list
ncp identity revoke <identity_id>

Optional authorship signing. ncp_write_memory and ncp_emit_whisper accept an optional signature over a canonical written_by | sha256(content) | pipeline_id payload; NCP verifies it against the author's registered public key, persists the result, and surfaces a verified marker in fetch results and the pidgin wire format. This is opt-in and off by default: it is gated behind [identity].require_signatures, which defaults to false, so unsigned writes still work and authorship is not authenticated unless an operator turns enforcement on. With require_signatures = true, writes that cannot be verified — including those from revoked identities — are rejected.

Reputation as a Beta posterior. Each identity carries a Beta distribution (alpha, beta) over "produces trustworthy memory." When ncp calibrate --feedback runs, the per-chunk trust changes it computes are rolled up to the chunk's author: trust gains become positive evidence, dissent-driven losses become negative evidence. A forget factor decays old evidence so reputation tracks recent behavior, and gain scales how fast evidence accrues. The reported score is the posterior mean; confidence rises with the number of observations.

ncp reputation             # score, confidence, and observation count per identity

Tune it under [reputation] in .ncp/config.toml (gain, forget, confidence_k) or via NCP_REPUTATION_*. An agent that has repeatedly produced disputed memory earns a lower reputation. Since Sprint 4 that score can also act on the bus — each piece is opt-in and off by default:

Outcomes as evidence (CAP-T3) — ncp_record_outcome records task success/failure against the chunks (or turn) that informed it; ncp calibrate --feedback consumes each outcome exactly once as the primary trust/reputation signal, ahead of the retrieval-count prior ([retrieval].usage_prior_weight).
Reputation-weighted retrieval (CAP-T4) — [retrieval].reputation_weight (default 0.0) blends the author's reputation confidence into chunk trust at ranking time, identically across the SQLite, pgvector, and async pgvector backends.
Whisper gating (CAP-T4) — [whispers].min_author_reputation (default 0.0) drops whispers from low-reputation authors at drain time. It gates on the claimed sender: sender identity is only as strong as [identity].require_signatures enforcement, which also stays off by default.
Work memoization (CAP-C3) — [memoization].enabled (default false) turns on ncp_lookup_memo/ncp_record_memo, a signature-keyed memo of completed work. It is lookup-only: NCP surfaces memo hits, misses, and an estimated tokens-saved figure in ncp status, and the host decides whether a memo lets it skip its own model call.

Retrieval and self-improving memory

Retrieval on the bus is hybrid multi-signal fusion, not pure recency or pure vector search. RetrievalPolicy (ncp/stores/retrieval.py) blends three signals with weights that must sum to 1.0:

score = w_lexical · BM25 + w_recency · recency + w_trust · base_trust
        (defaults 0.5 / 0.3 / 0.2; recency half-life 4h)

Two multiplicative penalties then shape the result:

Drift discount — chunks written while written_at_drift > 0.3 are scaled by (1 - drift).
Generation decay — every chunk carries a generation integer that increments as it is re-derived; the score is multiplied by generation_penalty_base ** generation (default 0.9), so heavily-rederived memory is naturally demoted in favor of primary sources.

Beyond scoring, retrieval can expand along caused_by edges — pulling in causally-linked chunks with a decay factor ([retrieval].edge_expansion) — and optionally rerank with a cross-encoder ([retrieval].rerank_*). Semantic vector retrieval is available via the [embedding] block but is off by default (enabled = false); turn it on to add embedding similarity to the fusion.

The self-improving loop closes through ncp calibrate --feedback (ncp/stores/calibration.py): chunks that keep getting retrieved gain trust (+feedback_weight · min(1, retrievals/10)), chunks that draw dissent lose it (-dissent_weight · min(1, dissents/3)), and a fraction of each net change propagates one hop along caused_by to credit or debit the cause. user_verified chunks are protected from automatic adjustment. Those same deltas feed the reputation rollup above.

Procedural self-refinement

ncp calibrate --feedback reweights trust on stored memory — it never touches the instructions an agent operates under. Procedural self-refinement (ncp/refine.py) closes that gap for a narrow, deliberately bounded case: a single named procedure — one chunk-sized block of operating instructions, not a whole multi-KB contract file — can accumulate outcome evidence and evolve through an explicit, human-gated pipeline.

ncp refine ingest null-guard-rule --content "Always null-check retryCount before calling intValue()."
ncp refine propose null-guard-rule        # evidence-backed candidate, not yet adopted
ncp refine apply <candidate_chunk_id>     # adopt it (promotes trust, optional --write-to file)
ncp refine rollback null-guard-rule       # revert to the prior version (new generation, nothing deleted)
ncp refine show null-guard-rule --history # walk every version

ncp refine propose is deterministic and additive-only: it never edits or removes existing instruction text, only appends deduplicated, frequency-ranked notes drawn from ncp_record_outcome failures (no model call). Writing a candidate does not adopt it — it's a new, low-trust chunk linked to its predecessor via supersedes. ncp refine apply is the human-gated adoption step, reusing the existing CAP-C5 supersede() machinery and calibrate manual-trust-override rather than duplicating either. ncp refine rollback never deletes or rewrites history: reverting writes a new generation whose content matches the prior version. Config under [refine]: min_failed_outcomes (default 3), max_bullets (default 5), promote_trust (default 0.80).

Graph engineering

Relationships between memories are first-class graph structure. Chunks are linked via typed directional edges (caused_by, supersedes, supports, contradicts, refines, derived_from), so retrieval and trust propagation can traverse relationships instead of treating memory as a flat scored pool.

At write time, the edges parameter on ncp_write_memory lets you specify chunk relationships (e.g., {"dst": "parent_chunk_id", "type": "caused_by"}). Retrieval can expand up to [retrieval].edge_max_hops (default 1) along [retrieval].edge_expansion_types (default ["caused_by"]), inheriting relevance with per-hop decay. Trust propagation walks edges up to [retrieval].propagation_max_hops (default 1), crediting or debiting causes for effects that proved useful or drew dissent. All defaults preserve legacy behavior exactly.

Export the relationship graph with ncp graph:

ncp graph --format dot

digraph ncp_graph {
  "chunk_abc123..." [label="chunk_abc\nepisodic", style=filled, fillcolor="#2e7d32"];
  "chunk_def456..." [label="chunk_def\nreasoning_trace", style=filled, fillcolor="#f9a825"];
  "chunk_abc123..." -> "chunk_def456..." [label="caused_by", style=solid];
  "chunk_ghi789..." -> "chunk_abc123..." [label="supports", style=dotted];
}

Node fillcolor indicates trust (green ≥0.8, amber 0.5–0.8, red <0.5); edge styles differ by type. JSON export includes stats and per-type edge counts. Add --as-of <epoch|ISO-8601> for a point-in-time view of the graph over the bi-temporal columns.

Two further graph capabilities are opt-in: [graph].infer_edges (default off) infers refines edges between similar chunks at write time with a deterministic similarity ratio — no model calls — marking them created_by="ncp:inferred"; and outcome credit recorded via ncp_record_outcome propagates along the caused_by chain during ncp calibrate --feedback, reported as a "via outcome propagation" count. See the graph engineering plan for the full model, multi-hop semantics, and compatibility details.

Signal filtering at write time

The bus is not a compression tool — but a memory bus should carry useful signal, not tool-output boilerplate.

When you call ncp_write_memory, NCP runs deterministic noise reduction before storing: it strips ANSI codes, collapses blank-line runs, dedups consecutive duplicate lines, removes tool-output boilerplate (progress bars, timing lines), and prunes null/empty JSON fields. The goal is context quality: stored chunks should be easier for future agents to retrieve, trust, and use.

This is reversible. The unfiltered original is preserved as a low-trust raw_ref chunk and retrievable on demand via ncp_fetch, so filtering does not destroy auditability.

The filter is conservative. It removes obvious noise where there is structural redundancy and leaves already-dense content mostly alone. On a fixed corpus of representative noisy agent payloads (chars_div4 token unit), aggregate reduction is 33% (537 -> 360 tokens), with per-category results:

Payload category	Token reduction
Duplicate-heavy logs	68%
Null/empty-heavy JSON tool results	59%
CLI output (ANSI + progress + timing)	5%
Stack-trace-style blobs	2%

This is deterministic signal filtering, not a model-quality change. See the compression benchmark doc.

Fan-in reduction

Write-time filtering strips boilerplate from one chunk at a time. It doesn't dedup across chunks — and a high-fanout burst (many parallel workers writing overlapping findings into one pipeline) is exactly the case where that matters: without dedup, a synthesis agent's bounded context can end up with several near-duplicate restatements of the same claim instead of that many distinct ones.

[retrieval].reduce_fanin_enabled (off by default) adds a deterministic reduction pass to context assembly for this case. When enabled, retrieval overfetches beyond the normal chunk cap, then — within any high-fanout cluster — merges near-duplicate claims down to the highest-trust version (reusing the same clustering ncp consolidate uses), drops malformed (empty) candidates, and flags surviving same-topic claims that diverge as contradictions. Contradictions are surfaced as a note:contradicts line in the assembled context for the reading agent to reason about; NCP groups and drops duplicates deterministically, but it never resolves a contradiction itself.

[retrieval]
reduce_fanin_enabled = true

On a deterministic 40-worker benchmark, 25% of NCP's own bounded top-k retrieval slots are near-duplicates of another slot in the same result with this off; enabling it merges those away and cuts tokens 13% against an unbounded raw dump of all 40 workers. See the fan-in reduction benchmark doc, including an honest account of the contradiction-flagging heuristic's false-positive rate.

What NCP is (and isn't)

NCP is the agent-to-agent memory bus and context protocol, not the orchestrator.

It sits underneath your existing agent framework — LangGraph (runnable example), CrewAI, AutoGen, or a custom orchestrator — and gives every connected host the same bounded, trust-weighted working memory. Agents can learn, share, dissent, hand off, and build on prior work without making the orchestrator own all context.

It is not a vector database. Not a model training framework. Not an orchestrator. Not the right default for simple single-agent or very short-lived tasks.

Host-native memory

Host-native memory is useful for continuity within a host or user's workflow, but its scope varies by provider: it may be machine-local or more broadly synchronized. NCP complements that provider-native continuity with an explicit shared repo/runtime agent-to-agent channel and, with a shared backend, cross-host collaboration.

NCP adds bounded retrieval, provenance, optional authenticated authorship, dissent, graph relationships, and explicit handoff to that shared context. Use it when you have 3+ agents, 10+ turns, and real shared state to preserve.

Benchmarks

Scenario	Baseline	Baseline tokens	NCP tokens	Result	Caveat
4-agent coding pipeline (40 turns)	sliding window	377	261	1.44x	Closest accounting comparison for a bounded recent-context baseline.
4-agent coding pipeline (40 turns)	raw replay	3,426	261	13.13x	Worst-case floor; the ratio scales with turn count.
4-agent coding pipeline (40 turns)	rolling summary	2,096	261	8.03x	Token accounting only; does not score summary quality.
6-role research pipeline (36 turns)	raw replay	3,277	267	12.27x	Worst-case floor for a deterministic synthetic research trace.
Cross-host handoff (Claude -> OpenCode)	window baseline	0.0 success	0.8 success	+0.8	Local harness with a noise-only control, not a distributed-host reliability study.
Needle recall at budget 4	sliding window	0.00	0.50	+0.50	Synthetic budget-stress recall check.
Task success at matched budget 400 (12 tasks, mock)	sliding window	0.00	1.00	+1.00	Context adequacy with a deterministic mock provider, not live model success.
HotpotQA-style multi-hop QA at matched budget 300 (15 tasks)	sliding window	0.00	1.00	+1.00	Synthetic, HotpotQA-shaped context-adequacy check — not the official HotpotQA dataset or PlugMem's own eval harness (see the benchmark's README).

MACE multi-agent coordination score (40 turns): 0.8915

Coding benchmark token unit: chars_div4; context budget: 340; pass gate: true. These are deterministic token-accounting benchmarks. The task-success row measures context adequacy at a matched token budget with a deterministic mock provider — whether the needed fact survives into a budget-bounded context (see the benchmark doc); run it with a live provider to measure real model task success. Provider-real quality-at-matched-budget evaluation lives in benchmarks/efficacy/ and compares NCP with sliding-window and rolling-summary controls (see the efficacy benchmark doc). The matched-budget construction and negation-aware scoring these benchmarks use are also available as a public API — ncp.eval — so you can build the same kind of eval against your own scenarios without vendoring benchmarks/.

A separate, complementary compression benchmark measures ingestion-time noise reduction on a fixed noisy-payload corpus: 33% aggregate token reduction (537 → 360, chars_div4, pass gate aggregate >= 0.20), ranging from 68% on duplicate-heavy logs down to 2% on already-dense stack traces (see the compression benchmark doc).

A third, complementary benchmark targets the many-parallel-workers-to-one-synthesizer fan-in case: with [retrieval].reduce_fanin_enabled off (today's default), 25% of NCP's own bounded top-k retrieval slots are near-duplicates of another slot in the same result on a deterministic 40-worker/4-topic corpus; enabling it merges the near-duplicates, drops malformed candidates, and flags likely contradictions for the reading model, at a 13% token reduction against an unbounded raw dump of all 40 workers (see [the f

Neural Context Protocol

At a glance

README

Neural Context Protocol

The problem

What NCP is

Why a memory bus

Full feature set

Token bloat

Context quality

Memory decay and the landfill problem

Multi-agent coordination

Trust and accountability

Operability at scale

Hosts and plugins

Quickstart

Portable Agent Plugin (vendor-neutral)

Zero-touch setup (route all agent comms through NCP)

How agents talk over the bus

Concrete example: a 3-agent bugfix on the bus

Turn flow

Architecture

Memory layers

Trust-aware transport

Agent identity and reputation

Retrieval and self-improving memory

Procedural self-refinement

Graph engineering

Signal filtering at write time

Fan-in reduction

What NCP is (and isn't)

Host-native memory

Benchmarks

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