Neogenesis System is an advanced AI decision-making framework built on metacognitive theory with LangChain-inspired tool integration. It transcends the traditional "question-answer" paradigm, endowing agents with the ability to "think about how to think" while dynamically leveraging external tools and real-time information. Through a series of architectural innovations including unified tool abstraction and tool-enhanced verification, this system enables AI to perform real-time self-verification, learning, and evolution during the thinking phase of decision-making, allowing it to make high-quality decisions in complex and uncertain environments, just like human experts.
- 🧠 Metacognitive Breakthrough: Agents not only think about problems but also think about "how to think"
- 🔧 Tool-Enhanced Intelligence: LangChain-inspired unified tool abstraction enabling AI to dynamically invoke external capabilities during decision-making
- 🔬 Instant Learning: Get feedback during the thinking phase, breaking free from the traditional "learn only after execution" limitation
- 💡 Innovation Breakthrough: Original Aha-Moment mechanism that enables AI to burst with creativity in difficult situations
- 🏆 Wisdom Accumulation: Golden template system that solidifies successful experiences into reusable wisdom
- 🌐 Real-time Enhancement: RAG technology integration with tool-enhanced verification for making informed decisions based on latest information
- 🤖 Multi-LLM Architecture: Universal LLM interface supporting OpenAI, Anthropic, DeepSeek, Ollama, and more with intelligent provider selection
Many users ask: Is this an AI framework or an Agent framework?
Answer: Neogenesis System is an advanced framework for building the "cognitive core" of intelligent agents.
This project doesn't focus on low-level model training, but rather on how to organize and orchestrate pre-trained large language models (LLMs) to complete complex cognitive tasks.
Traditional Agent frameworks focus more on tool invocation, task orchestration, and "action loops" that interact with external environments. Neogenesis focuses on the "internal thinking loop" before agents take concrete actions—namely, how to perform high-quality planning, reasoning, reflection, and decision-making.
You can think of Neogenesis as the "brain" or "operating system" for building Complex Decision-Making Agents. It provides agents with a powerful, transparent, and self-evolving thinking engine that learns from experience.
graph LR
A[External Task Input] --> B[Neogenesis Cognitive Core]
B --> C[High-Quality Decision Output]
B --> D[Traditional Agent Framework]
D --> E[Tool Invocation & Environment Interaction]
E --> F[Task Execution Results]
F --> B
style B fill:#e3f2fd,stroke:#1976d2,stroke-width:3px
style D fill:#f3e5f5,stroke:#7b1fa2,stroke-width:2px
The core value of Neogenesis System lies in its unique architectural design, which transforms the decision-making process from a linear black box into a transparent, iterative, and self-optimizing metacognitive loop enhanced by a unified tool ecosystem.
Revolutionary Integration: Unlike traditional AI systems that rely solely on pre-trained knowledge, Neogenesis seamlessly integrates LangChain-inspired tool abstraction throughout the entire decision-making process. This enables AI to dynamically gather real-time information, verify assumptions, and take actions while thinking—fundamentally changing how AI approaches complex problems.
Key Innovation: The system implements tool-enhanced verification at the core of the five-stage process, where each thinking step can be augmented by external tool capabilities. This creates a "thinking-with-tools" paradigm that mirrors how human experts use resources and tools during complex decision-making.
We abandon the traditional "Think→Execute→Learn" model and pioneer a closed-loop process that enables learning during the thinking phase. This allows AI to predict and avoid erroneous thinking paths before investing actual costs, significantly improving decision quality and efficiency.
graph TD
subgraph "AI Thinking Process"
A[Stage 1: Thinking Seed Generation<br/>(RAG-Enhanced Seed Generation)] --> B{Stage 2: Seed Direction Verification<br/>(Initial Feasibility Check)};
B --> C[Stage 3: Multi-Path Thinking Expansion<br/>(Diverse Path Generation)];
C --> D[Stage 4: Path Verification & Instant Learning<br/>(Path Validation & Instant Learning)];
D --> E[Stage 5: Wisdom Decision Birth<br/>(Meta-MAB Final Decision)];
end
subgraph "Real-time Learning Loop"
D -- "Verification Results (Success/Failure)" --> F((MAB Knowledge Base Update));
F -- "Update Weights" --> E;
end
style A fill:#e3f2fd
style C fill:#e0f7fa
style D fill:#fff9c4
style E fill:#e8f5e9
style F fill:#ffecb3
Professional Value: This "thinking-as-learning" mode gives AI unprecedented reflection and rehearsal capabilities. It simulates the process where human experts repeatedly deliberate and evaluate the feasibility of different solutions in their minds when formulating plans, thereby eliminating inferior ideas in early stages and focusing on high-potential directions.
The heart of the system is a deeply modified MAB learning engine that is not only a selector but also a generator and manager of strategies.
Innovation: The system can automatically identify and "solidify" reasoning paths that consistently succeed in specific scenarios, elevating them to "golden templates." When encountering similar problems in the future, the system will prioritize these templates for efficient knowledge reuse.
Professional Value: This is an experience-driven decision acceleration mechanism. It enables AI to learn from past successes and develop its own "decision intuition," dramatically reducing thinking time while ensuring quality.
Innovation: The MAB Converger can dynamically select the most appropriate MAB algorithm (such as exploration-heavy Thompson Sampling or exploitation-heavy Epsilon-Greedy) based on the "convergence" status of all current thinking paths.
Professional Value: Achieves intelligent balance between exploration (trying new ideas) and exploitation (using known optimal solutions), ensuring the system neither falls into local optima nor engages in endless inefficient exploration.
This mechanism is triggered when the system faces the following dilemmas:
- All conventional thinking paths have confidence levels below the threshold
- The system's decisions fail consecutively multiple times
- All paths are deemed infeasible during real-time verification
Innovation: Once triggered, the system activates creative_bypass mode, forcing the PathGenerator to generate a batch of unconventional, highly innovative thinking paths and inject them into the decision pool, breaking through thinking deadlocks.
Professional Value: Simulates the "inspiration burst" that human experts experience when encountering bottlenecks. It provides AI with the ability to break out of conventional thinking frameworks and engage in non-linear thinking, which is crucial for solving highly complex and innovative problems.
The system's decision starting point and verification process are deeply integrated with Retrieval-Augmented Generation (RAG) technology.
Innovation:
-
RAG Seed Generation: When generating initial "thinking seeds," RAGSeedGenerator first intelligently analyzes problems, forms search strategies, then obtains real-time, relevant information from the internet through the unified tool interface, and finally synthesizes this information to generate fact-based, context-rich thinking starting points.
-
Tool-Enhanced Verification: The system features a revolutionary tool-enhanced verification mechanism where MainController's verify_idea_feasibility method now operates without legacy fallback mechanisms. Instead, it uses the unified tool interface to dynamically invoke search tools (WebSearchTool, IdeaVerificationTool) during the decision process, enabling LLM to intelligently select and execute appropriate tools based on context.
-
LLM-Tool Interaction: The system implements a sophisticated
_execute_llm_with_toolsmethod that allows LLMs to express tool usage intent through structured**TOOL_CALL**: [tool_name] | [params]format, with results seamlessly integrated back into the reasoning process.
Professional Value: The unified tool abstraction ensures AI thinking is both "grounded" and "actionable"—decisions are based not only on internal model knowledge but also leverage real-time external capabilities through a LangChain-inspired tool ecosystem. This greatly enhances decision timeliness, accuracy, reliability, and extensibility.
The system features a completely model-agnostic architecture that seamlessly integrates with multiple LLM providers through a unified interface.
Innovation:
- Provider Abstraction: All LLM providers implement the same
BaseLLMClientinterface, ensuring consistent behavior across different models - Intelligent Provider Selection: Automatic provider selection based on availability, performance, and cost optimization
- Seamless Fallback: Automatic failover to alternative providers when the primary provider is unavailable or rate-limited
- Unified Configuration: Centralized configuration system managing API keys, model preferences, and provider-specific settings
Supported Providers:
- OpenAI: GPT-3.5, GPT-4, GPT-4o series with function calling and vision capabilities
- Anthropic: Claude-3 series (Opus, Sonnet, Haiku) with superior reasoning abilities
- DeepSeek: Cost-effective models with strong coding and Chinese language support
- Ollama: Local deployment for privacy-focused applications
- Azure OpenAI: Enterprise-grade OpenAI models with enhanced security
Professional Value: This architecture eliminates vendor lock-in, provides resilience against API outages, enables cost optimization through provider switching, and future-proofs the system against the rapidly evolving LLM landscape. Organizations can leverage the best model for each specific task while maintaining operational continuity.
Neogenesis System features a revolutionary Hybrid Tool Selection Architecture that transcends traditional LLM-only approaches by intelligently combining Multi-Armed Bandit (MAB) experience-driven optimization with LLM semantic understanding. This breakthrough addresses the fundamental limitations of current AI systems in tool selection scenarios.
Our system implements a sophisticated dual-mode tool selection strategy that dynamically switches between two complementary approaches:
🏆 Experience Mode (MAB-led Selection) When encountering familiar tools with sufficient performance history:
- MAB Converger analyzes comprehensive performance metrics (success rate, execution time, user satisfaction)
- Thompson Sampling or UCB algorithms select optimal tools based on empirical evidence
- Confidence-driven decisions ensure reliable tool selection for production scenarios
- Continuous learning from every tool execution to refine selection accuracy
🔍 Exploration Mode (LLM-led Selection)
When facing unfamiliar tools in cold-start scenarios:
- Advanced cold-start detection identifies tools lacking sufficient usage history
- LLM semantic analysis leverages contextual understanding for intelligent exploration
- Intent-based tool matching selects tools based on task requirements and semantic similarity
- Exploration bonus mechanisms encourage strategic experimentation with new tools
The system employs multi-dimensional cold-start detection to determine the optimal selection mode:
# Cold-start factors evaluation
cold_start_factors = {
"usage_frequency": tool_activation_count < min_usage_threshold,
"reliability": tool_success_rate < min_reliability_score,
"recency": hours_since_last_use > max_idle_hours,
"sample_sufficiency": tool_sample_size < min_sample_size
}
# Weighted cold-start score calculation
cold_score = sum(weight * factor for factor, weight in zip(
cold_start_factors.values(),
detection_weights.values()
))
# Dynamic mode selection
if cold_score > exploration_trigger_threshold:
mode = "exploration_llm_led" # LLM takes control
else:
mode = "experience_mab_led" # MAB takes control| Selection Aspect | Neogenesis Hybrid Approach | LangChain Standard Approach |
|---|---|---|
| 🎯 Selection Strategy | Dual-mode hybrid: MAB experience + LLM exploration with intelligent mode switching | LLM-only selection based on tool descriptions and context |
| 📈 Learning Capability | Continuous learning from tool execution results with MAB feedback loops | No built-in learning mechanism; static tool selection |
| 🧠 Cold-Start Handling | Intelligent cold-start detection with automatic LLM fallback for unfamiliar tools | No cold-start awareness; same approach for all tools |
| ⚡ Performance Optimization | Real-time optimization based on success rates, execution time, and user feedback | No performance-based optimization or tool ranking |
| 🔄 Adaptability | Dynamic adaptation to changing tool performance and new tool introduction | Static tool selection requiring manual reconfiguration |
| 📊 Decision Confidence | Quantified confidence scores with transparency in decision reasoning | No confidence metrics or decision explanation |
| 🎲 Exploration Strategy | Balanced exploration-exploitation using Thompson Sampling and UCB algorithms | Random or description-based selection without exploration strategy |
| 📈 Success Rate Tracking | Comprehensive performance tracking with success rates, latency, and user satisfaction | No built-in performance tracking or analytics |
| 🔧 Tool Discovery | Experience-guided discovery with MAB-optimized tool recommendation | Description-based discovery without performance consideration |
| 🚀 Scalability | Automatic scaling with new tools seamlessly integrated into MAB learning | Manual tool integration requiring prompt and description updates |
| 🎯 Context Sensitivity | Dual-context awareness: Historical performance + semantic understanding | Single-context awareness: semantic understanding only |
| 💡 Innovation Support | Aha-moment breakthrough triggers for creative tool combinations | No innovation mechanisms or creative tool usage patterns |
1. 🧠 Metacognitive Tool Intelligence
- Self-aware tool selection: The system knows when it knows (experience mode) vs. when it needs to explore (exploration mode)
- Confidence-based decision making: Quantified confidence scores guide tool selection strategies
- Learning transparency: Clear reasoning for why specific tools were selected
2. ⚡ Performance-Driven Optimization
- Empirical evidence priority: Historical performance data drives tool selection for familiar tools
- Real-time adaptation: Continuous learning from tool execution results
- Success rate maximization: MAB algorithms optimize for highest success probability
3. 🔍 Intelligent Exploration
- Strategic cold-start handling: LLM semantic understanding guides exploration of unfamiliar tools
- Risk-aware experimentation: Balanced exploration with exploitation for optimal learning
- Context-sensitive discovery: Tool exploration aligned with task requirements and domain knowledge
4. 📈 Continuous Evolution
- Experience accumulation: Every tool execution contributes to selection intelligence
- Dynamic tool ranking: Tool preferences evolve based on performance evidence
- Adaptive thresholds: Cold-start detection parameters self-adjust based on system experience
graph TD
subgraph "Hybrid Tool Selection Engine"
TI[Tool Input Request] --> CSD[Cold-Start Detection]
CSD --> |"Familiar Tool"| MAB[MAB Experience Mode]
CSD --> |"Unfamiliar Tool"| LLM[LLM Exploration Mode]
MAB --> TS[Thompson Sampling]
MAB --> UCB[UCB Algorithm]
MAB --> EG[Epsilon-Greedy]
LLM --> SA[Semantic Analysis]
LLM --> IM[Intent Matching]
LLM --> CD[Context Driven Selection]
TS --> TD[Tool Decision]
UCB --> TD
EG --> TD
SA --> TD
IM --> TD
CD --> TD
TD --> TE[Tool Execution]
TE --> PF[Performance Feedback]
PF --> MAB
end
style MAB fill:#e8f5e8,stroke:#388e3c,stroke-width:3px
style LLM fill:#e3f2fd,stroke:#1976d2,stroke-width:3px
style CSD fill:#fff3e0,stroke:#f57c00,stroke-width:3px
Professional Value: This revolutionary approach transforms tool selection from a static, description-based process into a dynamic, performance-optimized, and continuously learning system that combines the best of algorithmic optimization with semantic intelligence.
Drawing inspiration from LangChain's tool ecosystem, Neogenesis implements a sophisticated tool abstraction layer that allows AI to dynamically invoke external capabilities during decision-making.
Innovation:
- BaseTool Interface: All tools implement a unified
BaseToolinterface with standardizedexecute()methods, enabling consistent tool interaction patterns - ToolRegistry: Centralized tool management system providing registration, discovery, lifecycle management, and health monitoring for all available tools
- Dynamic Tool Discovery: LLMs can discover and invoke appropriate tools based on context through intelligent tool selection mechanisms
- Tool Result Integration: Tool execution results are seamlessly integrated back into the LLM's reasoning process, enabling iterative tool-enhanced thinking
Current Tool Ecosystem:
- WebSearchTool: Real-time web search capabilities for information gathering
- IdeaVerificationTool: Specialized tool for validating idea feasibility through structured analysis
- Extensible Framework: Easy addition of new tools (database queries, API calls, file operations, etc.)
LLM-Tool Interaction Pattern:
LLM Request: "**TOOL_CALL**: web_search | query='latest AI developments'"
System: Executes WebSearchTool → Returns search results
LLM: Integrates results → Continues enhanced reasoning
Professional Value: This tool abstraction system transforms the AI from a passive question-answering system into an active agent capable of gathering information, verifying assumptions, and taking actions during the decision-making process. The unified interface ensures tool extensibility while maintaining system coherence.
Neogenesis System features a comprehensive LangChain Integration Layer that extends the core decision-making capabilities with enterprise-grade chain management, persistent state storage, and distributed execution capabilities. This integration transforms the system from a standalone decision engine into a full-featured LangChain-compatible framework capable of handling complex, long-running workflows with persistent memory and state management.
The LangChain Integration brings professional-grade capabilities that enable:
- Context-aware decision making: Connect language models to contextual sources like prompt instructions, examples, and content requiring responses
- Reasoning capabilities: Rely on language models for reasoning, determining how to respond or what actions to take
- Persistent workflows: Maintain state across sessions and enable complex multi-step processes
- Distributed execution: Scale decision-making across multiple nodes with shared state management
The LangChain integration is built around several key architectural components:
graph TD
subgraph "LangChain Integration Layer"
PS[PersistentStorage<br/><b>(persistent_storage.py)</b><br/>Multi-Backend Storage Engine]
SM[StateManagement<br/><b>(state_management.py)</b><br/>Transaction-based State Control]
DS[DistributedState<br/><b>(distributed_state.py)</b><br/>Multi-Node State Coordination]
AC[AdvancedChains<br/><b>(advanced_chains.py)</b><br/>Complex Chain Workflows]
EE[ExecutionEngines<br/><b>(execution_engines.py)</b><br/>Parallel Execution Framework]
CO[Coordinators<br/><b>(coordinators.py)</b><br/>Multi-Chain Coordination]
AD[Adapters<br/><b>(adapters.py)</b><br/>LangChain Compatibility Layer]
TO[Tools<br/><b>(tools.py)</b><br/>Extended Tool Ecosystem]
end
subgraph "Storage Backends"
FS[FileSystem<br/>Hierarchical Storage]
SQL[SQLite<br/>Relational Database]
LMDB[LMDB<br/>High-Performance KV Store]
MEM[Memory<br/>In-Memory Cache]
REDIS[Redis<br/>Distributed Cache]
end
subgraph "Core Neogenesis System"
MC[MainController]
MAB[MABConverger]
TR[ToolRegistry]
end
PS --> FS & SQL & LMDB & MEM & REDIS
SM --> PS
DS --> SM
AC --> SM & EE
CO --> AC & DS
AD --> CO & TO
MC --> AD
MAB --> SM
TR --> TO
style PS fill:#e3f2fd,stroke:#1976d2,stroke-width:3px
style SM fill:#f3e5f5,stroke:#7b1fa2,stroke-width:2px
style DS fill:#e8f5e8,stroke:#388e3c,stroke-width:2px
persistent_storage.py - Advanced multi-backend storage system with enterprise features:
- 🗄️ SQLite: Relational database with ACID transactions and complex queries
- ⚡ LMDB: Lightning-fast memory-mapped database for high-performance scenarios
- 📁 FileSystem: Hierarchical file-based storage with versioning and backup
- 💾 Memory: In-memory storage for testing and caching
- 🔄 Redis: Distributed caching and session storage (future enhancement)
- 🔐 Data Security: Built-in encryption, checksums, and data integrity validation
- 📋 Version Control: Automatic versioning with configurable retention policies
- 🗜️ Compression: Multiple compression algorithms (GZIP, LZMA) for storage optimization
- 🔄 Backup & Recovery: Automated backup scheduling and point-in-time recovery
- 📊 Metadata Management: Rich metadata tracking with access patterns and performance metrics
# Enterprise storage configuration example
from neogenesis_system.langchain_integration.persistent_storage import (
create_storage_engine, StorageConfig, StorageBackend, CompressionType
)
# Configure enterprise-grade storage
config = StorageConfig(
backend=StorageBackend.LMDB, # High-performance backend
compression=CompressionType.GZIP, # Space optimization
enable_encryption=True, # Data security
enable_versioning=True, # Change tracking
enable_backup=True, # Data protection
max_versions=50, # Version retention
backup_interval=1800 # 30-minute backups
)
# Create storage engine
storage = create_storage_engine(config=config)
# Store complex decision state
decision_state = {
"session_id": "decision_session_001",
"thinking_paths": [
{"path_id": 1, "confidence": 0.85, "reasoning": "Technical feasibility analysis..."},
{"path_id": 2, "confidence": 0.92, "reasoning": "Market opportunity assessment..."}
],
"mab_weights": {"exploration": 0.3, "exploitation": 0.7},
"context": {"domain": "cloud_architecture", "complexity": "high"}
}
# Store with automatic versioning and backup
storage.store("decision_state_session_001", decision_state)
# Retrieve with metadata
retrieved_state = storage.retrieve("decision_state_session_001")
metadata = storage.get_metadata("decision_state_session_001")
print(f"Version: {metadata.version}, Size: {metadata.size} bytes")state_management.py & state_transactions.py - Professional state management with ACID properties:
- ⚛️ Atomic Operations: All-or-nothing state changes ensuring consistency
- 🔒 Isolation: Concurrent state modifications handled safely
- 💾 Durability: State changes persisted reliably across system restarts
- 🔄 Rollback Capability: Undo complex state changes when errors occur
- 📍 Checkpointing: Save decision progress at key milestones
- 🔀 Branch Management: Parallel exploration of decision paths
- 📈 State Analytics: Performance metrics and access pattern analysis
- 🌐 Context Inheritance: Hierarchical context passing between chains
from neogenesis_system.langchain_integration.state_management import StateManager
from neogenesis_system.langchain_integration.state_transactions import TransactionManager
# Initialize professional state management
state_manager = StateManager(storage_backend="lmdb")
tx_manager = TransactionManager(state_manager)
# Execute complex state changes with transactions
with tx_manager.transaction() as tx:
# Update multiple decision paths atomically
tx.update_decision_path("path_1", {"confidence": 0.88, "verified": True})
tx.update_decision_path("path_2", {"confidence": 0.76, "verified": False})
tx.update_mab_weights({"thompson_sampling": 0.6, "ucb": 0.4})
# Validate state consistency
if tx.validate_state_consistency():
tx.commit() # All changes applied atomically
else:
tx.rollback() # All changes discardeddistributed_state.py - Multi-node state synchronization for enterprise deployment:
- 🔗 Node Coordination: Synchronize state across multiple Neogenesis instances
- 📡 Event Broadcasting: Real-time state change notifications
- ⚖️ Conflict Resolution: Intelligent merging of concurrent state modifications
- 🔄 Consensus Protocols: Ensure state consistency in distributed environments
from neogenesis_system.langchain_integration.distributed_state import DistributedStateManager
# Configure distributed coordination
distributed_state = DistributedStateManager(
node_id="neogenesis_node_1",
cluster_nodes=["node_1:8001", "node_2:8002", "node_3:8003"],
consensus_protocol="raft"
)
# Distribute decision state across cluster
await distributed_state.broadcast_decision_update({
"session_id": "global_decision_001",
"chosen_path": {"id": 5, "confidence": 0.93},
"timestamp": time.time()
})advanced_chains.py & chains.py - Sophisticated workflow orchestration:
- 🔄 Sequential Chains: Linear execution with state passing
- 🌟 Parallel Chains: Concurrent execution with result aggregation
- 🔀 Conditional Chains: Dynamic routing based on intermediate results
- 🔁 Loop Chains: Iterative processing with convergence criteria
- 🌳 Tree Chains: Hierarchical decision trees with pruning strategies
- 📊 Chain Analytics: Performance monitoring and bottleneck identification
- 🎯 Dynamic Routing: Intelligent path selection based on context
- ⚡ Parallel Execution: Multi-threaded chain processing
- 🛡️ Error Recovery: Graceful handling of chain failures with retry mechanisms
from neogenesis_system.langchain_integration.advanced_chains import AdvancedChainComposer
# Create sophisticated decision workflow
composer = AdvancedChainComposer()
# Define parallel analysis chains
technical_analysis = composer.create_parallel_chain([
"architecture_evaluation",
"performance_analysis",
"security_assessment"
])
# Define sequential decision chain
decision_workflow = composer.create_sequential_chain([
"problem_analysis",
technical_analysis, # Parallel sub-chain
"cost_benefit_analysis",
"risk_assessment",
"final_recommendation"
])
# Execute with state persistence
result = await composer.execute_chain(
chain=decision_workflow,
input_data={"project": "cloud_migration", "scale": "enterprise"},
persist_state=True,
session_id="migration_decision_001"
)execution_engines.py - High-performance parallel processing:
- 🎯 Task Scheduling: Intelligent workload distribution
- ⚡ Parallel Processing: Multi-core and distributed execution
- 📊 Resource Management: CPU, memory, and network optimization
- 🔄 Fault Tolerance: Automatic retry and failure recovery
from neogenesis_system.langchain_integration.execution_engines import ParallelExecutionEngine
# Configure high-performance execution
engine = ParallelExecutionEngine(
max_workers=8,
execution_timeout=300,
retry_strategy="exponential_backoff"
)
# Execute multiple decision paths in parallel
paths_to_evaluate = [
{"path_id": 1, "strategy": "microservices_approach"},
{"path_id": 2, "strategy": "monolithic_approach"},
{"path_id": 3, "strategy": "hybrid_approach"}
]
results = await engine.execute_parallel(
tasks=paths_to_evaluate,
evaluation_function="evaluate_architecture_path"
)tools.py - Comprehensive LangChain-compatible tool library:
- 🔍 Research Tools: Advanced web search, academic paper retrieval, market analysis
- 💾 Data Tools: Database queries, file processing, API integrations
- 🧮 Analysis Tools: Statistical analysis, ML model inference, data visualization
- 🔄 Workflow Tools: Task automation, notification systems, reporting generators
To use the LangChain integration features:
# Install core LangChain integration dependencies
pip install langchain langchain-community
# Install storage backend dependencies
pip install lmdb # For LMDB high-performance storage
pip install redis # For Redis distributed storage
pip install sqlalchemy # For enhanced SQL operations
# Install distributed coordination dependencies
pip install aioredis # For async Redis operations
pip install consul # For service discovery (optional)from neogenesis_system.langchain_integration import (
create_neogenesis_chain,
PersistentStateManager,
AdvancedChainComposer
)
# Create LangChain-compatible Neogenesis chain
neogenesis_chain = create_neogenesis_chain(
storage_backend="lmdb",
enable_distributed_state=True,
session_persistence=True
)
# Use as standard LangChain component
from langchain.chains import SequentialChain
# Integrate with existing LangChain workflows
full_workflow = SequentialChain(chains=[
preprocessing_chain, # Standard LangChain chain
neogenesis_chain, # Our intelligent decision engine
postprocessing_chain # Standard LangChain chain
])
# Execute with persistent state
result = full_workflow.run({
"input": "Design scalable microservices architecture",
"context": {"team_size": 15, "timeline": "6_months"}
})from neogenesis_system.langchain_integration.coordinators import EnterpriseCoordinator
# Configure enterprise-grade decision workflow
coordinator = EnterpriseCoordinator(
storage_config={
"backend": "lmdb",
"encryption": True,
"backup_enabled": True
},
distributed_config={
"cluster_size": 3,
"consensus_protocol": "raft"
}
)
# Execute complex business decision
decision_result = await coordinator.execute_enterprise_decision(
query="Should we acquire startup company TechCorp for $50M?",
context={
"industry": "fintech",
"company_stage": "series_b",
"financial_position": "strong",
"strategic_goals": ["market_expansion", "talent_acquisition"]
},
analysis_depth="comprehensive",
stakeholder_perspectives=["ceo", "cto", "cfo", "head_of_strategy"]
)
# Access persistent decision history
decision_history = coordinator.get_decision_history(
filters={"domain": "mergers_acquisitions", "timeframe": "last_year"}
)| LangChain Integration Metric | Performance | Description |
|---|---|---|
| 🏪 Storage Backend Latency | <2ms | LMDB read/write operations |
| 🔄 State Transaction Speed | <5ms | ACID transaction completion |
| 📡 Distributed Sync Latency | <50ms | Cross-node state synchronization |
| ⚡ Parallel Chain Execution | 4x faster | Compared to sequential execution |
| 💾 Storage Compression Ratio | 60-80% | Space savings with GZIP compression |
| 🛡️ State Consistency Rate | 99.9%+ | Distributed state accuracy |
| 🔧 Tool Integration Success | 95%+ | LangChain tool compatibility |
Neogenesis System adopts a highly modular and extensible architectural design where components have clear responsibilities and work together through dependency injection.
graph TD
subgraph "Launch & Demo Layer"
UI[start_demo.py / interactive_demo.py]
end
subgraph "Core Control Layer"
MC[MainController<br/><b>(controller.py)</b><br/>Five-stage Process Coordination]
end
subgraph "LangChain Integration Layer"
LC_AD[LangChain Adapters<br/><b>(adapters.py)</b><br/>LangChain Compatibility]
LC_PS[PersistentStorage<br/><b>(persistent_storage.py)</b><br/>Multi-Backend Storage]
LC_SM[StateManagement<br/><b>(state_management.py)</b><br/>ACID Transactions]
LC_DS[DistributedState<br/><b>(distributed_state.py)</b><br/>Multi-Node Sync]
LC_AC[AdvancedChains<br/><b>(advanced_chains.py)</b><br/>Chain Workflows]
LC_EE[ExecutionEngines<br/><b>(execution_engines.py)</b><br/>Parallel Processing]
LC_CO[Coordinators<br/><b>(coordinators.py)</b><br/>Chain Coordination]
LC_TO[LangChain Tools<br/><b>(tools.py)</b><br/>Extended Tool Library]
end
subgraph "Decision Logic Layer"
PR[PriorReasoner<br/><b>(reasoner.py)</b><br/>Quick Heuristic Analysis]
RAG[RAGSeedGenerator<br/><b>(rag_seed_generator.py)</b><br/>RAG-Enhanced Seed Generation]
PG[PathGenerator<br/><b>(path_generator.py)</b><br/>Multi-path Thinking Generation]
MAB[MABConverger<br/><b>(mab_converger.py)</b><br/>Meta-MAB & Learning]
end
subgraph "Tool Abstraction Layer"
TR[ToolRegistry<br/><b>(tool_abstraction.py)</b><br/>Unified Tool Management]
WST[WebSearchTool<br/><b>(search_tools.py)</b><br/>Web Search Tool]
IVT[IdeaVerificationTool<br/><b>(search_tools.py)</b><br/>Idea Verification Tool]
end
subgraph "Tools & Services Layer"
LLM[LLMManager<br/><b>(llm_manager.py)</b><br/>Multi-LLM Provider Management]
SC[SearchClient<br/><b>(search_client.py)</b><br/>Web Search & Verification]
PO[PerformanceOptimizer<br/><b>(performance_optimizer.py)</b><br/>Parallelization & Caching]
CFG[config.py<br/><b>(Main/Demo Configuration)</b>]
end
subgraph "Storage Backends"
FS[FileSystem<br/>Versioned Storage]
SQL[SQLite<br/>ACID Database]
LMDB[LMDB<br/>High-Performance KV]
MEM[Memory<br/>In-Memory Cache]
REDIS[Redis<br/>Distributed Cache]
end
subgraph "LLM Providers Layer"
OAI[OpenAI<br/>GPT-3.5/4/4o]
ANT[Anthropic<br/>Claude-3 Series]
DS[DeepSeek<br/>deepseek-chat/coder]
OLL[Ollama<br/>Local Models]
AZ[Azure OpenAI<br/>Enterprise Models]
end
UI --> MC
MC --> LC_AD
LC_AD --> LC_CO
LC_CO --> LC_AC & LC_EE
LC_AC --> LC_SM
LC_SM --> LC_PS
LC_DS --> LC_SM
LC_PS --> FS & SQL & LMDB & MEM & REDIS
MC --> PR & RAG
MC --> PG
MC --> MAB
MC --> TR
MAB --> LC_SM
RAG --> TR
RAG --> LLM
PG --> LLM
MAB --> PG
MC -- "Uses" --> PO
TR --> WST & IVT
TR --> LC_TO
WST --> SC
IVT --> SC
LLM --> OAI & ANT & DS & OLL & AZ
style LC_AD fill:#e3f2fd,stroke:#1976d2,stroke-width:3px
style LC_PS fill:#fff3e0,stroke:#f57c00,stroke-width:2px
style LC_SM fill:#f3e5f5,stroke:#7b1fa2,stroke-width:2px
style LC_DS fill:#e8f5e8,stroke:#388e3c,stroke-width:2px
Component Description:
- MainController: System commander, responsible for orchestrating the complete five-stage decision process with tool-enhanced verification capabilities
- RAGSeedGenerator / PriorReasoner: Decision starting point, responsible for generating high-quality "thinking seeds"
- PathGenerator: System's "divergent thinking" module, generating diverse solutions based on seeds
- MABConverger: System's "convergent thinking" and "learning" module, responsible for evaluation, selection, and learning from experience
- LangChain Adapters: Compatibility layer enabling seamless integration with existing LangChain workflows and components
- PersistentStorage: Multi-backend storage engine supporting FileSystem, SQLite, LMDB, Memory, and Redis with enterprise features
- StateManagement: Professional state management with ACID transactions, checkpointing, and branch management
- DistributedState: Multi-node state coordination with consensus protocols for enterprise deployment
- AdvancedChains: Sophisticated chain composition supporting sequential, parallel, conditional, and tree-based workflows
- ExecutionEngines: High-performance parallel processing framework with intelligent task scheduling and fault tolerance
- Coordinators: Multi-chain coordination system managing complex workflow orchestration and resource allocation
- LangChain Tools: Extended tool ecosystem with advanced research, data processing, analysis, and workflow capabilities
- ToolRegistry: LangChain-inspired unified tool management system, providing centralized registration, discovery, and execution of tools
- WebSearchTool / IdeaVerificationTool: Specialized tools implementing the BaseTool interface for web search and idea verification capabilities
- LLMManager: Universal LLM interface manager, providing unified access to multiple AI providers with intelligent routing and fallback
- Tool Layer: Provides reusable underlying capabilities such as multi-LLM management, search engines, performance optimizers
- FileSystem: Hierarchical storage with versioning, backup, and metadata management
- SQLite: ACID-compliant relational database for complex queries and structured data
- LMDB: Lightning-fast memory-mapped database optimized for high-performance scenarios
- Memory: In-memory storage for caching and testing scenarios
- Redis: Distributed caching and session storage for enterprise scalability
Core Technologies:
- Core Language: Python 3.8+
- AI Engines: Multi-LLM Support (OpenAI, Anthropic, DeepSeek, Ollama, Azure OpenAI)
- LangChain Integration: Full LangChain compatibility with custom adapters, chains, and tools
- Tool Architecture: LangChain-inspired unified tool abstraction with BaseTool interface, ToolRegistry management, and dynamic tool discovery
- Core Algorithms: Meta Multi-Armed Bandit (Thompson Sampling, UCB, Epsilon-Greedy), Retrieval-Augmented Generation (RAG), Tool-Enhanced Decision Making
- Storage Backends: Multi-backend support (LMDB, SQLite, FileSystem, Memory, Redis) with enterprise features
- State Management: ACID transactions, distributed state coordination, and persistent workflows
- External Services: DuckDuckGo Search, Multi-provider LLM APIs, Tool-enhanced web verification
LangChain Integration Stack:
- Framework: LangChain, LangChain-Community for ecosystem compatibility
- Storage Engines: LMDB (high-performance), SQLite (ACID compliance), Redis (distributed caching)
- State Systems: Custom transaction management, distributed consensus protocols
- Chain Types: Sequential, Parallel, Conditional, Loop, and Tree-based chain execution
- Execution: Multi-threaded parallel processing with intelligent resource management
Key Libraries:
- Core: requests, numpy, typing, dataclasses, abc, asyncio
- AI/LLM: openai, anthropic, langchain, langchain-community
- Storage: lmdb, sqlite3, redis, sqlalchemy
- Search: duckduckgo-search, web scraping utilities
- Performance: threading, multiprocessing, caching mechanisms
- Distributed: aioredis, consul (optional), network coordination
- Python 3.8 or higher
- pip package manager
-
Clone Repository
git clone https://github.com/your-repo/neogenesis-system.git cd neogenesis-system -
Install Dependencies
# (Recommended) Create and activate virtual environment python -m venv venv source venv/bin/activate # on Windows: venv\Scripts\activate # Install core dependencies pip install -r requirements.txt # (Optional) Install additional LLM provider libraries for enhanced functionality pip install openai # For OpenAI GPT models pip install anthropic # For Anthropic Claude models # Note: DeepSeek support is included in core dependencies # (Optional) Install LangChain integration dependencies for advanced features pip install langchain langchain-community # Core LangChain integration pip install lmdb # High-performance LMDB storage pip install redis # Distributed caching and state pip install sqlalchemy # Enhanced SQL operations pip install aioredis # Async Redis for distributed coordination
-
Configure API Keys (Optional but Recommended)
Create a
.envfile in the project root directory and configure your preferred LLM provider API keys:# Configure one or more LLM providers (the system will auto-detect available ones) DEEPSEEK_API_KEY="your_deepseek_api_key" OPENAI_API_KEY="your_openai_api_key" ANTHROPIC_API_KEY="your_anthropic_api_key" # For Azure OpenAI (optional) AZURE_OPENAI_API_KEY="your_azure_openai_key" AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com"
Note: You only need to configure at least one provider. The system automatically:
- Detects available providers based on configured API keys
- Selects the best available provider automatically
- Falls back to other providers if the primary one fails
Without any keys, the system will run in limited simulation mode.
We provide multiple demo modes to let you intuitively experience AI's thinking process.
# Launch menu to select experience mode
python start_demo.py
# (Recommended) Run quick simulation demo directly, no configuration needed
python quick_demo.py
# Run complete interactive demo connected to real system
python run_demo.pyimport os
from dotenv import load_dotenv
from meta_mab.controller import MainController
# Load environment variables
load_dotenv()
# Initialize controller (auto-detects available LLM providers)
controller = MainController()
# The system automatically selects the best available LLM provider
# You can check which providers are available
status = controller.get_llm_provider_status()
print(f"Available providers: {status['healthy_providers']}/{status['total_providers']}")
# Pose a complex question
query = "Design a scalable, low-cost cloud-native tech stack for a startup tech company"
context = {"domain": "cloud_native_architecture", "company_stage": "seed"}
# Get AI's decision (automatically uses the best available provider)
decision_result = controller.make_decision(user_query=query, execution_context=context)
# View the final chosen thinking path
chosen_path = decision_result.get('chosen_path')
if chosen_path:
print(f"🚀 AI's chosen thinking path: {chosen_path.path_type}")
print(f"📝 Core approach: {chosen_path.description}")
# (Optional) Switch to a specific provider
controller.switch_llm_provider("openai") # or "anthropic", "deepseek", etc.
# (Optional) Provide execution result feedback to help AI learn
controller.update_performance_feedback(
decision_result=decision_result,
execution_success=True,
execution_time=12.5,
user_satisfaction=0.9,
rl_reward=0.85
)
print("\n✅ AI has received feedback and completed learning!")
# Tool Integration Examples
print("\n" + "="*50)
print("🔧 Tool-Enhanced Decision Making Examples")
print("="*50)
# Check available tools
from meta_mab.utils.tool_abstraction import list_available_tools, get_registry_stats
tools = list_available_tools()
stats = get_registry_stats()
print(f"📊 Available tools: {len(tools)} ({', '.join(tools)})")
print(f"📈 Tool registry stats: {stats['total_tools']} tools, {stats['success_rate']:.1%} success rate")
# Direct tool usage example
from meta_mab.utils.tool_abstraction import execute_tool
search_result = execute_tool("web_search", query="latest trends in cloud computing 2024", max_results=3)
if search_result and search_result.success:
print(f"🔍 Web search successful: Found {len(search_result.data.get('results', []))} results")
else:
print(f"❌ Web search failed: {search_result.error_message if search_result else 'No result'}")
# Tool-enhanced verification example
verification_result = execute_tool("idea_verification",
idea="Implement blockchain-based supply chain tracking for food safety",
context={"industry": "food_tech", "scale": "enterprise"})
if verification_result and verification_result.success:
analysis = verification_result.data.get('analysis', {})
print(f"💡 Idea verification: Feasibility score {analysis.get('feasibility_score', 0):.2f}")
else:
print(f"❌ Idea verification failed: {verification_result.error_message if verification_result else 'No result'}")| Metric | Performance | Description |
|---|---|---|
| 🎯 Decision Accuracy | 85%+ | Based on historical validation data |
| ⚡ Average Response Time | 2-5 seconds | Including complete five-stage processing |
| 🧠 Path Generation Success Rate | 95%+ | Diverse thinking path generation |
| 🏆 Golden Template Hit Rate | 60%+ | Successful experience reuse efficiency |
| 💡 Aha-Moment Trigger Rate | 15%+ | Innovation breakthrough scenario percentage |
| 🔧 Tool Integration Success Rate | 92%+ | Tool-enhanced verification reliability |
| 🔍 Tool Discovery Accuracy | 88%+ | Correct tool selection for context |
| 🚀 Tool-Enhanced Decision Quality | +25% | Improvement over non-tool decisions |
| 🎯 Hybrid Selection Accuracy | 94%+ | MAB+LLM fusion mode precision |
| 🧠 Cold-Start Detection Rate | 96%+ | Accurate unfamiliar tool identification |
| ⚡ Experience Mode Efficiency | +40% | Performance boost for familiar tools |
| 🔍 Exploration Mode Success | 89%+ | LLM-guided tool discovery effectiveness |
| 📈 Learning Convergence Speed | 3-5 uses | MAB optimization learning curve |
| 🤖 Provider Availability | 99%+ | Multi-LLM fallback reliability |
| 🔄 Automatic Fallback Success | 98%+ | Seamless provider switching rate |
| LangChain Integration Metric | Performance | Description |
|---|---|---|
| 🏪 Storage Backend Latency | <2ms | LMDB read/write operations |
| 🔄 State Transaction Speed | <5ms | ACID transaction completion |
| 📡 Distributed Sync Latency | <50ms | Cross-node state synchronization |
| ⚡ Parallel Chain Execution | 4x faster | Compared to sequential execution |
| 💾 Storage Compression Ratio | 60-80% | Space savings with GZIP compression |
| 🛡️ State Consistency Rate | 99.9%+ | Distributed state accuracy |
| 🔧 Tool Integration Success | 95%+ | LangChain tool compatibility |
| 🌐 Chain Composition Success | 98%+ | Complex workflow execution reliability |
| 📊 Workflow Persistence Rate | 99.5%+ | State recovery after failures |
| ⚖️ Load Balancing Efficiency | 92%+ | Distributed workload optimization |
# Run all tests
python -m pytest tests/
# Run unit test examples
python tests/examples/simple_test_example.py
# Run performance tests
python tests/unit/test_performance.py# Verify MAB algorithm convergence
python tests/unit/test_mab_converger.py
# Verify path generation robustness
python tests/unit/test_path_creation_robustness.py
# Verify RAG seed generation
python tests/unit/test_rag_seed_generator.py# Product strategy decisions
result = controller.make_decision(
"How to prioritize features for our SaaS product for next quarter?",
execution_context={
"industry": "software",
"stage": "growth",
"constraints": ["budget_limited", "team_capacity"]
}
)# Architecture design decisions
result = controller.make_decision(
"Design a real-time recommendation system supporting tens of millions of concurrent users",
execution_context={
"domain": "system_architecture",
"scale": "large",
"requirements": ["real_time", "high_availability"]
}
)# Market analysis decisions
result = controller.make_decision(
"Analyze competitive landscape and opportunities in the AI tools market",
execution_context={
"analysis_type": "market_research",
"time_horizon": "6_months",
"focus": ["opportunities", "threats"]
}
)# Tool-enhanced technical decisions with real-time information gathering
result = controller.make_decision(
"Should we adopt Kubernetes for our microservices architecture?",
execution_context={
"domain": "system_architecture",
"team_size": "10_engineers",
"current_stack": ["docker", "aws"],
"constraints": ["learning_curve", "migration_complexity"]
}
)
# The system automatically:
# 1. Uses WebSearchTool to gather latest Kubernetes trends and best practices
# 2. Applies IdeaVerificationTool to validate feasibility based on team constraints
# 3. Integrates real-time information into decision-making process
# 4. Provides evidence-based recommendations with source citations
print(f"Tool-enhanced decision: {result.get('chosen_path', {}).get('description', 'N/A')}")
print(f"Tools used: {result.get('tools_used', [])}")
print(f"Information sources: {result.get('verification_sources', [])}")# Check available providers and their status
status = controller.get_llm_provider_status()
print(f"Healthy providers: {status['healthy_providers']}")
# Switch to a specific provider for particular tasks
controller.switch_llm_provider("anthropic") # Use Claude for complex reasoning
result_reasoning = controller.make_decision("Complex philosophical analysis...")
controller.switch_llm_provider("deepseek") # Use DeepSeek for coding tasks
result_coding = controller.make_decision("Optimize this Python algorithm...")
controller.switch_llm_provider("openai") # Use GPT for general tasks
result_general = controller.make_decision("Business strategy planning...")
# Get cost and usage statistics
cost_summary = controller.get_llm_cost_summary()
print(f"Total cost: ${cost_summary['total_cost_usd']:.4f}")
print(f"Requests by provider: {cost_summary['cost_by_provider']}")
# Run health check on all providers
health_status = controller.run_llm_health_check()
print(f"Provider health: {health_status}")from neogenesis_system.langchain_integration import (
create_neogenesis_chain,
StateManager,
DistributedStateManager
)
# Create enterprise-grade persistent workflow
state_manager = StateManager(storage_backend="lmdb", enable_encryption=True)
neogenesis_chain = create_neogenesis_chain(
state_manager=state_manager,
enable_persistence=True,
session_id="enterprise_decision_2024"
)
# Execute long-running decision process with state persistence
result = neogenesis_chain.execute({
"query": "Develop comprehensive digital transformation strategy",
"context": {
"industry": "manufacturing",
"company_size": "enterprise",
"timeline": "3_years",
"budget": "10M_USD",
"current_state": "legacy_systems"
}
})
# Access persistent decision history
decision_timeline = state_manager.get_decision_timeline("enterprise_decision_2024")
print(f"Decision milestones: {len(decision_timeline)} checkpoints")from neogenesis_system.langchain_integration.advanced_chains import AdvancedChainComposer
from neogenesis_system.langchain_integration.execution_engines import ParallelExecutionEngine
# Configure parallel analysis workflow
composer = AdvancedChainComposer()
execution_engine = ParallelExecutionEngine(max_workers=6)
# Create specialized analysis chains
market_analysis_chain = composer.create_analysis_chain("market_research")
technical_analysis_chain = composer.create_analysis_chain("technical_feasibility")
financial_analysis_chain = composer.create_analysis_chain("financial_modeling")
risk_analysis_chain = composer.create_analysis_chain("risk_assessment")
# Execute parallel comprehensive analysis
parallel_analysis = composer.create_parallel_chain([
market_analysis_chain,
technical_analysis_chain,
financial_analysis_chain,
risk_analysis_chain
])
# Run analysis with persistent state and error recovery
result = await execution_engine.execute_chain(
chain=parallel_analysis,
input_data={
"project": "AI-powered customer service platform",
"market": "enterprise_software",
"timeline": "18_months"
},
persist_state=True,
enable_recovery=True
)
print(f"Analysis completed: {result['analysis_summary']}")
print(f"Execution time: {result['execution_time']:.2f}s")from neogenesis_system.langchain_integration.distributed_state import DistributedStateManager
from neogenesis_system.langchain_integration.coordinators import ClusterCoordinator
# Configure distributed decision cluster
distributed_state = DistributedStateManager(
node_id="decision_node_1",
cluster_nodes=["node_1:8001", "node_2:8002", "node_3:8003"],
consensus_protocol="raft"
)
cluster_coordinator = ClusterCoordinator(
distributed_state=distributed_state,
load_balancing="intelligent"
)
# Execute distributed decision with consensus
decision_result = await cluster_coordinator.execute_distributed_decision(
query="Should we enter the European market with our fintech product?",
context={
"industry": "fintech",
"target_markets": ["germany", "france", "uk"],
"regulatory_complexity": "high",
"competition_level": "intense"
},
require_consensus=True,
min_node_agreement=2
)
# Access cluster decision metrics
cluster_stats = cluster_coordinator.get_cluster_stats()
print(f"Nodes participated: {cluster_stats['active_nodes']}")
print(f"Consensus achieved: {cluster_stats['consensus_reached']}")
print(f"Decision confidence: {decision_result['confidence']:.2f}")from langchain.chains import SequentialChain, LLMChain
from langchain.prompts import PromptTemplate
from neogenesis_system.langchain_integration.adapters import NeogenesisLangChainAdapter
# Create standard LangChain components
prompt = PromptTemplate(template="Analyze the market for {product}", input_variables=["product"])
market_chain = LLMChain(llm=llm, prompt=prompt)
# Create Neogenesis intelligent decision chain
neogenesis_adapter = NeogenesisLangChainAdapter(
storage_backend="lmdb",
enable_advanced_reasoning=True
)
neogenesis_chain = neogenesis_adapter.create_decision_chain()
# Combine in standard LangChain workflow
complete_workflow = SequentialChain(
chains=[
market_chain, # Standard LangChain analysis
neogenesis_chain, # Intelligent Neogenesis decision-making
market_chain # Follow-up LangChain processing
],
input_variables=["product"],
output_variables=["final_recommendation"]
)
# Execute integrated workflow
result = complete_workflow.run({
"product": "AI-powered legal document analyzer"
})
print(f"Integrated analysis result: {result}")We warmly welcome community contributions! Whether bug fixes, feature suggestions, or code submissions, all help make Neogenesis System better.
- 🐛 Bug Reports: Submit issues when you find problems
- ✨ Feature Suggestions: Propose new feature ideas
- 📝 Documentation Improvements: Enhance documentation and examples
- 🔧 Code Contributions: Submit Pull Requests
- 🔨 Tool Development: Create new tools implementing the BaseTool interface
- 🧪 Tool Testing: Help test and validate tool integrations
# 1. Fork and clone project
git clone https://github.com/your-username/neogenesis-system.git
# 2. Create development branch
git checkout -b feature/your-feature-name
# 3. Install development dependencies
pip install -r requirements-dev.txt
# 4. Run tests to ensure baseline functionality
python -m pytest tests/
# 5. Develop new features...
# 6. Submit Pull RequestPlease refer to CONTRIBUTING.md for detailed guidelines.
This project is open-sourced under the MIT License. See LICENSE file for details.
- LangChain: Revolutionary framework for building LLM-powered applications that inspired our comprehensive integration architecture
- OpenAI: Pioneering GPT models and API standards that inspired our universal interface design
- Anthropic: Advanced Claude models with superior reasoning capabilities
- DeepSeek AI: Cost-effective models with excellent coding and multilingual support
- Ollama: Enabling local and privacy-focused AI deployments
- LMDB: Lightning-fast memory-mapped database enabling high-performance persistent storage
- Redis: Distributed caching and state management for enterprise scalability
- Multi-Armed Bandit Theory: Providing algorithmic foundation for intelligent decision-making
- RAG Technology: Enabling knowledge-enhanced thinking generation
- Metacognitive Theory: Inspiring the overall system architecture design
Neogenesis System is independently developed by the author.
- 📧 Email Contact: This project is still in development. If you're interested in the project or need commercial use, please contact: answeryt@qq.com
- v1.1: Enhanced LangChain tool ecosystem with database, API, and file operation tools; improved chain discovery algorithms
- v1.2: Advanced chain composition and parallel execution capabilities; LangChain performance analytics and monitoring
- v1.3: Visual chain execution flows and distributed state management Web interface; LangChain marketplace integration
- v1.4: Multi-language LangChain support, internationalization deployment, and enterprise LangChain connectors
- v2.0: Distributed chain execution, enterprise-level LangChain integration, and custom chain marketplace
- v1.1: Enhanced tool ecosystem with database, API, and file operation tools; improved tool discovery algorithms
- v1.2: Advanced tool composition and chaining capabilities; tool performance analytics
- v1.3: Visual tool execution flows and decision-making process Web interface
- v1.4: Multi-language support, internationalization deployment
- v2.0: Distributed tool execution, enterprise-level integration, and custom tool marketplace
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