ChestGraph

ChestGraph is the technological core of Open Village: a graph-based agreement discovery engine that gives organisations and communities a technical foundation where resources, capacity, services or obligations connect through coordinated participation of multiple parties.

Traditional systems typically handle two-party relationships: buyer and seller, provider and client, or obligor and obligee.

Real collaboration is often more complex. One party may supply a resource or service valuable to another, while its own commitment only becomes possible through fulfilment by a third or fourth party.

ChestGraph examines the structure of the full network and looks for agreement structures in which participants can close their commitments together, on their own terms. That is what lets locally non-matching links become globally closable agreements.

How the model works

1. Actors and chests
2. Graph building
3. Matching
4. Clearing
5. Multi-party agreement

Every actor has a chest. In the chest they can define what resource or capacity they offer; what resource or capacity they seek; what value those resources or commitments represent; under what conditions an agreement is acceptable; and which commitments they are willing to accept, and on what terms.

The chest stays open until it finds an agreement that matches the conditions recorded in it.

The system represents possible commitments and connections as a directed graph. Nodes are participants; edges are possible relationships or commitments between them.

The ChestGraph algorithm inspects this graph and looks for closed agreement structures in which participants’ commitments can be linked, and every involved party’s predetermined conditions are met. The goal is to recognise network configurations from which a genuinely executable multi-party agreement can be formed.

Locally non-matching, globally closable links

In a bilateral system a link typically closes when one party can directly satisfy the other’s need. ChestGraph examines the same problem at another level.

A single link on its own only partially meets a given expectation. Together, the full network forms a closed structure.

For ChestGraph the relevant question is therefore: is there a relationship structure in which the agreement can be fulfilled for every participant at once? That is the basis of multi-party agreement discovery.

A multi-party agreement example

Five parties hold complementary resources.

• The first offers web development and seeks graphic design.
• The second offers graphic design and seeks copywriting.
• The third offers copywriting and seeks translation.
• The fourth offers translation and seeks marketing.
• The fifth offers marketing and seeks web development.

1. Web development
2. Design
3. Copy
4. Translation
5. Marketing
6. Web development

In a bilateral system these links stay isolated. ChestGraph recognises the closed five-party network in which every participant’s exchange conditions can be met, and treats it as one connected multi-party agreement — not five independent transactions.

Where value appears relative to the traditional process

Most collaboration and exchange flows are built on two-party deals: a link forms when one party’s offer directly matches the other’s demand.

In real economic networks the value is often already present — it is just not visible as a direct link. Needed resources are spread across parties, and relationships turn into separate negotiations. Participants spend time and money on search, intermediaries and separate transactions, while possible agreements already in the network may remain hidden.

ChestGraph aims to surface that hidden relational value: among existing resources, capacity and commitments it looks for a structure in which they can connect into one closable agreement.

Asymmetric clearing

A multi-party agreement can also mean exchange of different value or type. One participant may commit a defined amount of work while accepting another type of service or resource in return.

Asymmetric clearing lets obligations of different value or type connect in one shared agreement structure, as long as the agreement’s conditions produce a balance acceptable to every involved party. That matters especially in networks where a participant does not necessarily receive value from the same party they fulfil for.

Graph matching is also framed by two further conditions

• Temporal fit — commitments must complete within an acceptable time window; value alignment alone does not resolve a timing conflict.
• Resource compatibility — value equivalence does not mean automatic substitutability; participants themselves define which resource types they accept as equivalent.

Clearing’s purpose is to determine how commitments in the network — under these conditions — can connect into a single closable structure.

Collateralised multi-party agreements

One of the central questions in multi-party collaboration is securing fulfilment: in a linked agreement, a single non-performance can affect the whole structure.

In the settlement layer around ChestGraph, commitments can carry collateral conditions. As part of the agreement, participants can define under what collateral a commitment is considered secured — financial deposit, pre-locked resource, verifiable fulfilment capacity, or another form of security accepted in that business process.

ChestGraph’s role is to determine whether a given network structure meets the agreement’s conditions; actual collateral handling and settlement live in a separate infrastructure layer. If a participant fails to perform, the settlement layer handles collateral under predetermined rules — compensation, partial refund, or another resolution path fixed in the agreement.

Collateral is thus one of the base mechanisms of the execution layer for reliable multi-party agreements.

Trust, settlement and dispute resolution

Multi-party agreements rest on verifiable commitments. Each commitment can carry a condition that defines when it is considered fulfilled — the settlement layer manages those commitments and their related collateral.

In a dispute, participants can predefine the resolution model. A decision may come from a designated trust circle, participant voting, an independent adjudicator, or a community / DAO-style mechanism. Based on the decision, the settlement layer releases, holds or uses collateral for compensation under the fixed rules.

Graph-based agreement discovery and the execution/trust layer are separable — so ChestGraph remains usable across different organisational and technical environments, with different trust models.

Fewer intermediaries, more closable agreements

ChestGraph holds related offers, demands, commitments and conditions in one shared graph — examining the structure of the full network rather than isolated links. That makes it possible to recognise agreements that a traditional bilateral approach would leave hidden.

The model applies beyond classic barter, for example linking obligations, multilateral invoice settlement, aligning capacity, connecting resource ledgers, or forming service-chain agreements across organisations.

Part of the intermediary role can thus be replaced by algorithmic agreement discovery: the system examines options already available in the network and looks for closable structures among them.

ChestGraph technology layers

The system around ChestGraph is built from several separable layers.

1. Business applicationBusiness rules and user experience
2. ChestGraphGraph building, agreement discovery and clearing
3. SettlementCollateral handling and dispute resolution, as well as multilateral contract creation and management

ChestGraph’s role is representing the network, searching for agreement structures, and checking closability against conditions. The settlement layer handles execution conditions for agreements already formed.

That separation lets ChestGraph stay usable behind different business applications and different execution infrastructures.

As a practical limit, an agreement cycle is currently capped at roughly 20–30 participants — keeping closure stable and fast even in dense, complex networks. The engine is also event-driven: on every new node or edge registration it validates the structure immediately and locally, rather than relying on large periodic batch runs.

Measured performance

ChestGraph is a working system measured in practice: graph-search performance was also tested in a real runtime environment on a standard Linux container with 2 vCPU / 4 GB RAM. The measurement looked at agreement-discovery runtime across different graph sizes.

As graph size grows, the search space expands — the engine handles this with limited-depth traversal narrowed to relevant subgraphs, avoiding unnecessary combinatorial load. That is why detection time stays predictable at low resource cost, even in networks of hundreds or a thousand nodes.

Possible application areas

• Multi-party B2B supplier and partner networks
• Multilateral clearing of invoices and obligations
• Capacity and resource sharing across organisations or communities
• Cross-organisation service and fulfilment chains
• Closed, trust-based economic communities
• Decentralised collaboration models

How ChestGraph relates to Open Village

ChestGraph is one of Open Village’s foundational technology building blocks: it provides the graph-based operating model that different applications can build on.

As a general technology layer it makes available multi-party agreement discovery, closability checks against conditions, clearing of asymmetric obligations, linking of commitments, and support for collateralised settlement flows.

ChestGraph gives a shared technological base for Open Village’s future applications.

More on the theoretical background: Open Village — a digital model of a village