chatcortex 0.2.0

Experimental platform for automated synthesis and evaluation of LLM-based agent systems

ChatCortex

ChatCortex is a research-oriented framework for automated synthesis and multi-objective optimization of LLM-based agent architectures.

It is designed as a controlled experimental platform for modeling, generating, evaluating, and optimizing tool-augmented AI agent systems.

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Vision

ChatCortex aims to eliminate manual agent wiring and ad-hoc prompt engineering by introducing:

• Formal task modeling
• Constraint-aware synthesis
• Multi-objective optimization
• Exact Pareto frontier computation
• Deterministic and stochastic execution simulation

Long-term goal:

Automated architecture synthesis of reliable AI agents from high-level intent.

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Architecture Overview

ChatCortex is organized into layered research components:

1. User / Intent Layer (future)
2. TaskSpecification (formal model)
3. Synthesis Engine (greedy / exhaustive)
4. AgentGraph (DAG representation)
5. Execution Engine (deterministic / probabilistic)
6. Telemetry
7. Evaluation Harness
8. Pareto Optimization

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Core Components

1️. Component Metadata

Formal, immutable representation of:

• Models
• Tools
• Memory modules
• Verification modules

Each component defines:

• Capabilities
• Cost
• Latency
• Reliability
• Privacy level

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2️. TaskSpecification

Defines a task formally:

• Ordered required capabilities
• Hard constraints (max cost, latency, privacy)
• Multi-objective weights

Separates feasibility from optimization preference.

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3️. Synthesizers

HeuristicSynthesizer

Greedy deterministic builder selecting best component per stage.

ExhaustiveSynthesizer

Explores full Cartesian architecture space and enables exact Pareto frontier computation.

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4️. AgentGraph

Directed Acyclic Graph (DAG) representation of agent architecture.

Aggregates:

• Total cost (additive)
• Total latency (sequential assumption)
• Aggregate reliability (multiplicative)

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5️. Execution Engine

Supports:

• Deterministic mode (structural validation)
• Probabilistic mode (reliability simulation)
• Fixed random seed for reproducibility

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6️. Evaluation Harness

Runs experiments across:

• Multiple tasks
• Multiple synthesizers
• Multiple stochastic trials

Produces:

• Average cost
• Average latency
• Success rate

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7️. Pareto Optimization

Exact multi-objective Pareto frontier computation across:

• Cost (minimize)
• Latency (minimize)
• Reliability (maximize)

Provides ground-truth optimal architecture trade-offs.

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Research Positioning

ChatCortex is intended as:

• A systems-AI research framework
• A controlled environment for architecture optimization experiments

It emphasizes:

• Reproducibility
• Formal modeling
• Separation of concerns
• Experimental rigor

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Roadmap

Phase 1 (Complete)

• Formal modeling
• Heuristic synthesis
• Execution simulation
• Evaluation harness

Phase 2 (Complete)

• Exhaustive architecture search
• Exact 3-objective Pareto optimization

Phase 3 (Planned)

• Heuristic search (beam search, evolutionary refinement)
• Statistical robustness analysis
• Intent-to-task automation layer
• Real model/tool integration

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Installation

pip install chatcortex

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Status

ChatCortex is currently a research framework under active development.

It is not yet a production agent orchestration library.

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License

MIT License

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Developed by Siddharth Saraswat