ChatCortex is a framework for automated synthesis and optimization of AI agent architectures.
Instead of manually wiring LLM pipelines such as:
retrieval → LLM → verifier → tool
ChatCortex treats agent design as a multi-objective architecture search problem and automatically discovers architectures that optimize:
- Cost
- Latency
- Reliability
The project can be viewed as AutoML for AI Agents.
Modern AI agents are typically constructed through manual orchestration of:
- Large Language Models
- Retrieval systems
- Tools and APIs
- Verification modules
- Memory systems
This approach is often:
- brittle
- expensive
- difficult to optimize
- difficult to reproduce
ChatCortex introduces a formal synthesis framework where agent architectures are automatically generated from task specifications and evaluated under system constraints.
Long-term goal:
Automated synthesis of reliable AI agents from high-level intent.
Instead of designing agent pipelines manually:
Engineer → Manual Pipeline Design
ChatCortex enables:
Task Specification → Architecture Search → Pareto-Optimal Architectures
Architectures are evaluated across multiple objectives:
- minimize cost
- minimize latency
- maximize reliability
ChatCortex is organized as a layered architecture synthesis system.
TaskSpecification
↓
CapabilityRegistry
↓
Synthesis Engine
↓
AgentGraph (DAG)
↓
Execution Engine
↓
Telemetry
↓
Evaluation Harness
↓
Pareto Optimization
Each layer isolates a specific concern in automated agent architecture synthesis.
Formal representation of agent components such as:
- language models
- retrieval systems
- tools
- verification modules
- memory modules
Each component defines:
- capabilities
- cost per call
- latency
- reliability score
- privacy level
Components are immutable and purely declarative.
Central registry responsible for:
- component registration
- capability filtering
- privacy constraint enforcement
The registry does not perform optimization --- it only provides valid components for synthesis.
Defines the architecture synthesis problem.
Example:
from chatcortex import TaskSpecification
task = TaskSpecification(
required_capabilities=[
"retrieval",
"generation",
"verification"
],
max_cost=0.01,
max_latency=2000
)ChatCortex includes multiple architecture synthesis strategies.
- HeuristicSynthesizer --- Greedy deterministic architecture construction.
- RandomSynthesizer --- Random baseline for stochastic exploration.
- BeamSynthesizer --- Budget-aware approximate architecture search.
- ExhaustiveSynthesizer --- Computes the exact Pareto frontier.
- ProgressiveParetoBeamSynthesizer (v0.4.0) --- Depth-aware beam widening improving Pareto recovery.
Agent architectures are represented as Directed Acyclic Graphs (DAGs).
Aggregated metrics:
- total cost (additive)
- total latency (sequential assumption)
- reliability (multiplicative model)
Two execution modes:
Used for structural validation and reproducible testing.
Simulates real-world reliability using component success probabilities.
Supports:
- multiple tasks
- multiple synthesizers
- stochastic trials
- reproducible experiments
Outputs:
- average cost
- average latency
- success rate
- Pareto Coverage
- Hypervolume Loss
- Average Regret (cost, latency, reliability)
from chatcortex import TaskSpecification, BeamSynthesizer
task = TaskSpecification(
required_capabilities=[
"retrieval",
"generation",
"verification"
]
)
synth = BeamSynthesizer(beam_width=5)
architectures = synth.synthesize(task)
for arch in architectures:
print(arch.total_cost(), arch.total_latency())pip install chatcortexChatCortex is a controlled experimental platform for studying automated AI agent architecture synthesis.
Research areas:
- multi-objective optimization
- architecture search
- AI agent systems
- reliability-cost tradeoffs
- AutoML-style agent design
Tested on dense architecture spaces:
- 5-stage synthesis pipelines
- up to 95-point Pareto frontiers
- budget sweeps from 20 → 180 evaluations
- beam width sweeps from 3 → 15
Progressive Pareto Beam Widening demonstrates improved Pareto recovery compared to static beam strategies.
- component modeling
- capability registry
- task specification
- agent graph representation
- exhaustive architecture search
- exact Pareto frontier computation
- beam search synthesis
- Pareto-aware pruning
- progressive beam widening
- graph-structured agent synthesis
- real model / tool integrations
- enterprise optimization layers
ChatCortex is currently a research framework under active development.
MIT License
Developed by Siddharth Saraswat