Documentación de Dweve Nexus
Complete documentation covering agent architectures, orchestration systems, and coordination patterns will be available in all supported languages upon our public launch. This preview demonstrates core concepts and system capabilities.
Dweve Nexus
A production-ready framework for building sophisticated multi-agent artificial intelligence systems with neural-symbolic reasoning, multi-modal perception, and distributed cognition capabilities.
What is Dweve Nexus?
Dweve Nexus recognizes a fundamental truth about intelligence: the hardest problems require collaboration. A single model, no matter how sophisticated, faces inherent limitations in reasoning depth, knowledge breadth, and computational capacity. Nexus orchestrates networks of specialized agents, each equipped with sophisticated cognitive capabilities: multi-modal perception across text, audio, images, and structured data; neural-symbolic reasoning combining pattern recognition with logical inference; intelligent action planning; multi-tier memory systems; and comprehensive safety mechanisms. Agents collaborate through structured protocols to tackle challenges that would overwhelm any individual system.
This is not simple prompt chaining or sequential API calls. Nexus provides a comprehensive framework built on six cognitive subsystems: the Agent System manages autonomous decision-making through goal-directed behavior and knowledge integration; the Perception System processes information across 31 specialized feature extractors spanning sentiment analysis, entity recognition, voice analysis, object detection, scene understanding, schema detection, and data quality checking; the Reasoning System integrates symbolic logic (forward/backward chaining, Prolog, RETE) with neural pattern recognition through eight reasoning patterns (abductive, analogical, causal, counterfactual, decision, deductive, inductive, metacognition); the Action System executes plans through tool integration and effect prediction; the Memory System maintains coherent state across episodic, semantic, procedural, and working memory tiers with automatic consolidation; and the Safety System enforces six defense layers including intent verification, bounded autonomy, content moderation, ethics enforcement, anomaly detection, and runtime monitoring.
Neural-symbolic integration forms the intellectual foundation. Knowledge graphs store structured facts and relationships, queryable through sophisticated graph operations. Vector stores enable semantic similarity search over embeddings, with adapters for Pinecone and Weaviate. The RAG pipeline ingests documents, chunks strategically, retrieves relevant context, and assembles prompts. Bidirectional translation bridges symbolic and neural representations. Symbolic reasoning engines (Prolog, RETE, truth maintenance systems) provide logical inference. Explainability mechanisms trace decisions through their complete reasoning provenance. This hybrid architecture delivers both the flexibility of neural networks and the rigor of logical systems.
Communication follows industry standards. Native support for Google's Agent-to-Agent (A2A) protocol enables interoperability through JSON-RPC 2.0 messaging. Agents discover each other's capabilities, route messages intelligently, handle errors gracefully, and maintain circuit breakers for resilience. Model Context Protocol (MCP) integration connects agents to external context sources, tools, and resources. Protocol bridges translate between A2A, MCP, and custom formats. The framework handles serialization, deserialization, retry logic, delivery confirmation, and streaming for continuous data transfer.
Why multi-agent systems?
Single language models face fundamental scaling constraints. Context windows, while growing, cannot hold unlimited information. Reasoning chains, while impressive, degrade as depth increases. Knowledge, while broad, cannot match domain specialists in every field. Multi-agent systems transcend these limitations through collaboration. Instead of one model attempting everything, specialized agents contribute focused expertise. A research agent gathers information. An analysis agent identifies patterns. A critique agent challenges assumptions. A synthesis agent integrates findings. The collective intelligence emerges from orchestrated interaction.
This architectural approach unlocks capabilities impossible for single models. Parallel exploration searches solution spaces simultaneously rather than sequentially. Multiple agents explore different hypotheses, different research directions, different solution strategies in parallel, then converge on the most promising paths. Diverse perspectives prevent groupthink and blind spots. When agents approach problems from different angles with different priors, the synthesis reveals insights no single perspective captured.Specialized expertise allows each agent to excel in its domain rather than compromise across all domains. A medical diagnosis agent masters clinical reasoning. A legal analysis agent understands precedent and statute. A financial modeling agent speaks the language of markets. Together, they address multidisciplinary challenges.
Beyond capability, multi-agent architectures provide transparency and control. Instead of a black-box model producing opaque outputs, you observe explicit agent interactions: who said what, which evidence was cited, how disagreements resolved, where consensus formed. This visibility enables debugging complex reasoning chains, ensuring compliance with regulatory requirements, and building user trust through explainable decisions. The conversation between agents becomes the audit trail.
Core components: Six cognitive subsystems
Nexus architecture mirrors biological intelligence through six integrated cognitive subsystems. Each provides specialized capabilities that compose into emergent agent intelligence far exceeding the sum of parts.
1. Agent System: Autonomous decision-making
Goal-directed behavior drives agent actions through explicit goal hierarchies. Agents maintain goal stacks with priorities, dependencies, and success criteria. The planning system decomposes high-level objectives into executable action sequences. Progress monitoring detects when plans fail and triggers replanning. Goals can be dynamic, spawning sub-goals as situations evolve or opportunities emerge.
Knowledge integration connects agents to neural-symbolic knowledge systems. Agents query knowledge graphs for structured facts, search vector stores for semantic similarity, invoke RAG pipelines for context retrieval, and apply symbolic reasoning engines for logical inference. The integration is bidirectional: agents both consume knowledge and contribute new discoveries back to shared knowledge bases.
Cognitive cycle implements the perception-reasoning-action loop at the core of intelligent behavior. Each cycle begins with perception (processing sensory input and messages), continues through reasoning (updating beliefs, evaluating goals, selecting actions), and concludes with action execution (invoking tools, sending messages, updating state). The cycle operates continuously, adapting to environmental changes in real-time.
2. Perception System: 31 multi-modal feature extractors
Text perception (12 extractors) analyzes linguistic content across multiple dimensions. Sentiment analysis detects emotional tone and opinion polarity. Entity recognition extracts named entities (people, organizations, locations, dates). Topic modeling identifies thematic content. Keyword extraction surfaces salient terms. Language detection and translation enable multilingual understanding. Intent classification determines user goals. Text summarization condenses lengthy content. Question answering extracts specific information. Relationship extraction identifies connections between entities. Readability analysis assesses complexity. Style analysis characterizes writing patterns.
Audio perception (6 extractors) processes acoustic information. Speech-to-text transcription converts spoken language. Voice analysis extracts speaker characteristics, emotion, and stress indicators. Music information retrieval identifies genre, tempo, key. Sound event detection recognizes environmental audio. Speaker diarization segments audio by speaker. Acoustic scene classification categorizes environments.
Image perception (7 extractors) understands visual content. Object detection localizes and classifies objects in images. Scene understanding interprets spatial relationships and context. Facial recognition and analysis extract identity and expressions. OCR extracts text from images. Image captioning generates natural language descriptions. Visual question answering combines vision and language understanding. Image similarity measures visual resemblance.
Structured data perception (6 extractors) analyzes formatted information. Schema detection infers structure from semi-structured data. Data quality assessment identifies completeness, consistency, and accuracy issues. Statistical analysis computes distributions and correlations. Anomaly detection flags unusual patterns. Time series analysis models temporal dynamics. Graph analysis examines network structures and connectivity.
3. Reasoning System: Neural-symbolic integration
Eight reasoning patterns provide diverse cognitive capabilities. Abductive reasoning infers the best explanation for observations.Analogical reasoning transfers knowledge from similar domains. Causal reasoning models cause-and-effect relationships. Counterfactual reasoningexplores "what if" scenarios. Decision reasoning evaluates options under uncertainty. Deductive reasoning derives logical conclusions from premises.Inductive reasoning generalizes from examples. Metacognition reasons about reasoning itself, monitoring and controlling cognitive processes.
Symbolic reasoning engines provide logical inference capabilities. Forward chaining derives new facts from known facts and rules. Backward chaining works backward from goals to find supporting evidence. Prolog integration enables logic programming. RETE algorithm provides efficient pattern matching for production systems. Truth maintenance systems track logical dependencies and handle belief revision. Constraint propagation enforces consistency across related variables.
Neural pattern recognition complements symbolic reasoning with learned associations. Deep neural networks identify patterns in high-dimensional data. Embedding models map concepts to vector spaces where similarity has geometric meaning. Attention mechanisms focus on relevant information. The neural and symbolic components exchange information bidirectionally: symbolic systems guide neural attention, while neural systems suggest symbolic hypotheses.
4. Action System: Intelligent execution
Tool integration connects agents to external capabilities. Agents invoke APIs, run scripts, query databases, manipulate files, and control external systems. The tool registry catalogs available capabilities with type signatures and usage examples. Tool calls include retry logic, timeout handling, and error recovery. Agents learn tool usage patterns from examples and feedback.
Plan execution coordinates multi-step action sequences. Plans specify action dependencies, partial ordering constraints, and conditional execution paths. The execution engine schedules actions respecting dependencies, monitors progress, detects failures, and triggers replanning when necessary. Plans can include human-in-the-loop approval points for critical decisions.
Effect prediction models action consequences before execution. Forward simulation estimates outcomes under different action choices. Risk assessment evaluates potential negative impacts. Contingency planning prepares responses to anticipated failures. This predictive capability enables safer autonomous operation by avoiding actions likely to cause harm.
5. Memory System: Four-tier knowledge architecture
Episodic memory stores specific experiences and events with temporal context. Each episode captures what happened, when, where, and why. Agents recall past experiences similar to current situations, learning from history. Episodic retrieval uses semantic similarity, temporal proximity, and contextual relevance to surface applicable memories.
Semantic memory maintains factual knowledge independent of specific experiences. Facts, concepts, relationships, and schemas populate a structured knowledge base. Semantic knowledge generalizes across episodes, extracting patterns that apply broadly. The knowledge graph representation enables sophisticated querying and inference.
Procedural memory encodes skills and procedures as executable knowledge. How to perform tasks, apply techniques, and follow protocols resides in procedural memory. Skills improve through practice as the system refines action sequences based on outcomes. Procedural knowledge transfers across contexts when structural similarity exists.
Working memory holds currently active information with limited capacity. Active goals, current context, intermediate results, and attention focus occupy working memory. The system implements cognitive load management, prioritizing critical information when capacity constraints bind. Working memory contents guide perception (what to notice) and action (what to do next).
6. Safety System: Six-layer defense architecture
Intent verification (Layer 1) validates that agent goals align with user intentions. Before executing actions with significant consequences, the system confirms understanding through clarifying questions. Ambiguous requests trigger disambiguation dialogues. This layer prevents well-intentioned but misunderstood actions.
Bounded autonomy (Layer 2) constrains agent capabilities to permitted boundaries. Capability restrictions limit which tools agents can invoke. Resource quotas prevent runaway consumption. Temporal bounds constrain execution duration. Spatial boundaries restrict agent influence. These constraints create safe operational envelopes.
Content moderation (Layer 3) filters inputs and outputs for harmful content. Input filtering blocks malicious prompts attempting to manipulate agent behavior. Output filtering prevents generation of harmful, biased, or inappropriate content. The moderation system covers toxicity, violence, explicit content, personally identifiable information, and regulated domains.
Ethics enforcement (Layer 4) implements value alignment through explicit ethical principles. Ethical rules encode requirements like respect for autonomy, prevention of harm, fairness, and transparency. Before taking actions, agents evaluate ethical implications. Ethical conflicts trigger escalation to human oversight.
Anomaly detection (Layer 5) identifies unusual behavior patterns suggesting compromise or malfunction. Statistical models learn normal behavior distributions. Deviations trigger alerts and potentially halt execution pending investigation. Anomaly detection catches threats that explicit rules miss.
Runtime monitoring (Layer 6) provides continuous observability into agent behavior. Detailed logging captures decisions, actions, and reasoning chains. Metrics track performance, resource usage, and safety violations. Dashboards visualize agent activities in real-time. Audit trails enable forensic analysis after incidents.
Deployment and production infrastructure
Container-based deployment packages agents and their cognitive subsystems as Docker containers. Each container includes the agent runtime, required perception extractors, reasoning engines, memory systems, and safety monitors. Container orchestration through Kubernetes manages agent lifecycle, handles failures through automatic restarts, and distributes workload across cluster nodes.
Service mesh integration provides sophisticated networking for inter-agent communication. The mesh handles service discovery (agents find each other dynamically), load balancing (distribute messages across agent instances), circuit breaking (prevent cascade failures), retry logic (automatic recovery from transient errors), and distributed tracing (track requests across agent boundaries).
Monitoring and observability provide comprehensive visibility into multi-agent system behavior. Metrics track agent CPU and memory usage, message throughput and latency, reasoning step execution time, perception extractor performance, memory system size, and safety violation frequency. Distributed tracing visualizes complete request flows across agents.
Getting started
The documentation covers comprehensive multi-agent system development across 15 major sections:
Begin with the Getting Started guide in the navigation menu, or jump directly to specific sections using the comprehensive structure above.
Integration interfaces
DweveScript DSL
DweveScript provides a declarative domain-specific language for defining multi-agent workflows, agent behaviors, coordination protocols, and safety policies. The language compiles to optimized execution plans with static analysis guarantees. Write agent orchestrations as high-level specifications rather than imperative code. The DSL supports agent definitions, workflow composition, conditional logic, parallel execution, error handling, and policy constraints.
Python client library
Python client libraries enable programmatic control of multi-agent systems. Create agents, define tasks, monitor execution, and retrieve results through a clean Python API. The library handles communication with the Nexus runtime, serialization of complex data structures, and provides async/await interfaces for non-blocking operations. Ideal for integration with existing Python workflows, notebooks, and automation pipelines.
REST API
RESTful HTTP API provides language-agnostic access to Nexus capabilities. Create and manage agents, submit workflow definitions, query system state, and retrieve results through standard HTTP endpoints. OpenAPI specifications document all endpoints. JSON payloads ensure broad compatibility. Supports authentication, rate limiting, and webhook notifications for asynchronous operations.
gRPC API
High-performance gRPC interface enables efficient communication for performance-critical applications. Binary Protocol Buffers serialization, bidirectional streaming, and multiplexing reduce overhead. Protocol Buffer definitions allow client generation for any language with gRPC support. Ideal for high-throughput scenarios, real-time agent coordination, and microservice integration where milliseconds matter.
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