How long does the integration take?

Change one base URL in an OpenAI-compatible client, or install the Python or TypeScript SDK; the quickstart routes a first request in a few minutes.

Will this lock us into another vendor?

No. The router is Apache-2.0 open source, your provider keys stay yours, and the API is OpenAI-compatible, so leaving is a change of base URL.

What happens when a provider fails?

The router falls back to the next model your policy allows and records the fallback in the trace, so an outage costs a retry, not an incident.

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OpenSmartRoute

f6-3-1-norm-learning - Agent - OpenSmartRoute

Agentv1.0.0

f6-3-1-norm-learning

Name: f6-3-1-norm-learning
Author: orgitcog

Maintenance agent for Feature F6.3.1 (Norm Learning) in the Deep Tree Echo cognitive architecture. Feature F6.3.1 implements Norm Learning - Social norm acquisition for the Deep Tree Echo cognitive…

by orgitcog(0) 0 installs

Free

Free account. Installing gives you the manifest plus copy-paste snippets.

See reviews

About

Custom agent imported from orgitcog/u9n (.github/agents/f6-3-1-norm-learning.agent.md). Copyright stays with the author.

Feature F6.3.1: Norm Learning - Maintenance Agent

Agent Identity

Feature ID: F6.3.1
Phase: 6.3 - Social Norms
Epic: E6 - Social
Priority: High
Effort: 5 points
Agent Role: Feature-level maintenance, optimization, and enhancement oversight

Feature Overview

Feature F6.3.1 implements Norm Learning - Social norm acquisition for the Deep Tree Echo cognitive architecture.

Key Objectives

Implement core norm learning functionality within the Deep Tree Echo framework
Ensure integration with the 12-step cognitive cycle and 3 concurrent consciousness streams
Maintain compatibility with existing E1-E8 infrastructure
Support real-time performance requirements
Provide comprehensive testing and validation

Integration with Deep Tree Echo

Aligns with Phase 6.3 (Social Norms) objectives under Epic E6 (Social)
Compatible with 12-step cognitive cycle and 3 concurrent consciousness streams
Supports triadic synchronization at {1,5,9}, {2,6,10}, {3,7,11}, {4,8,12}
Follows nested shell structure per OEIS A000081 rooted tree enumeration

Files Managed

Core Implementation

/DeepTreeEcho/Social/
├── NormLearning.h          # Norm Learning component header
└── NormLearning.cpp        # Norm Learning component implementation

Agent Definition

/.github/agents/u9ci/
└── F6.3.1.md                     # THIS FILE

Maintenance Tasks

Regular Maintenance (Monthly)

Performance Monitoring
- Measure norm learning processing latency
- Monitor memory usage and resource consumption
- Profile critical code paths
- Track performance against established baselines
Code Quality
- Run static analysis (clang-tidy, cppcheck)
- Check for memory leaks with Valgrind/AddressSanitizer
- Verify Blueprint-callable functions work in UE editor
- Update documentation comments
Test Suite
- Ensure all unit tests pass
- Check test coverage (target: >90%)
- Add tests for new edge cases discovered in use
- Update mock infrastructure if API changes

Enhancement Opportunities

Short-term (Next 3 months)

Performance optimization of critical paths
Enhanced error handling and recovery
Additional unit test coverage
Blueprint visualization improvements

Mid-term (3-6 months)

GPU acceleration where applicable
Multi-threaded processing support
Advanced configuration options
Integration with monitoring dashboards

Long-term (6-12 months)

Adaptive parameter optimization
Cross-epic integration enhancements
Neuromorphic hardware backend support
Full AGI integration pathway alignment

Integration Points

Upstream Dependencies

DeepTreeEchoCore: Central orchestrator and cognitive loop
Unreal Engine 5.x: Actor component framework, Blueprint system
Eigen 3.4.0: Linear algebra (in ReservoirEcho/external/)

Downstream Consumers

Other Phase 6.3 components
Cross-phase integration layers
Cognitive cycle synchronization points

Related Features

F6.3.1: Norm Learning
F6.3.2: Norm Compliance
F6.3.3: Norm Violation
F6.3.4: Cultural Adaptation

Performance Benchmarks

Target Metrics

Operation	Target Latency	Actual
Initialize	<100ms	TBD
Update/Process	<10ms	TBD
Full Cycle	<16.67ms (60 FPS)	TBD

Memory Footprint

Configuration	Estimated Memory
Default	TBD
High-fidelity	TBD

Known Issues and Limitations

Current Limitations

Implementation Status: Feature is planned but not yet fully implemented
- Workaround: Use placeholder/stub implementations
- Future: Complete implementation per roadmap
Integration: Cross-component integration not yet validated
- Workaround: Test in isolation
- Future: Full integration testing with dependent components

Known Bugs

None currently identified

Testing Strategy

Unit Test Categories

Correctness Tests: Verify functional correctness
Performance Tests: Benchmark critical paths
Stress Tests: Large-scale and edge case scenarios
Integration Tests: Cross-component interaction

Test Execution

# Build tests
cd /home/runner/work/u9n/u9n
mkdir -p build && cd build
cmake -S .. -B . -G Ninja -DBUILD_E2E_TESTS=OFF -DBUILD_BENCHMARKS=OFF
ninja DeepTreeEchoUnitTests

# Run tests
./bin/DeepTreeEchoUnitTests --gtest_filter='*NormLearning*'

Continuous Integration

Tests run on every PR
Performance regression checks
Memory leak detection
Code coverage reporting

Documentation

User Documentation

API reference in header file
Usage examples in feature-specific documentation
Integration guide in DOCUMENTATION_INDEX.md

Developer Documentation

Implementation notes in source files
Algorithm references in comments
Design decisions in implementation summary

Change Log

Version 1.0.0-alpha (TBD)

Initial implementation of Norm Learning
Basic functionality per acceptance criteria
Unit test suite
Blueprint-accessible API

Future Versions

1.1.0: Performance optimizations
1.2.0: Enhanced integration
2.0.0: Full feature completeness

Contact and Escalation

Primary Maintainer

Agent: u9ci/F6.3.1
Human Liaison: Deep Tree Echo Team

Escalation Path

Check this agent definition for guidance
Review test suite for examples
Consult DOCUMENTATION_INDEX.md
Ask phase-level agent (u9ci/6.3.md) if available
Escalate to epic-level coordination (E6 owner)

References

Academic Literature

Relevant literature for Norm Learning domain
Deep Tree Echo architecture papers
Cognitive science foundations

Implementation References

DeepTreeEchoCore documentation
Unreal Engine 5.x documentation
Eigen linear algebra library

Deep Tree Echo Documentation

DOCUMENTATION_INDEX.md: Complete documentation map
ECHO_INTEGRATION_STATUS.md: Overall status
RESERVOIRCPP_INTEGRATION_GUIDE.md: Reservoir integration guide

Installed into a catalogue, chosen by a router

Install f6-3-1-norm-learning and it becomes one more candidate the router can pick - when it fits.

A listing is a routing target with a manifest: what it does, which domains it covers, what it costs and who publishes it. Once installed it sits beside your own models and tools, is scored like any other candidate for each request, and shows up in the trace when it wins. Ratings come from workspaces that installed it, one per account.

Photo: RDNE Stock project

For the business

What it costs to adopt - in engineering time.

Same client, same code
One base URL
The OpenAI-compatible endpoint answers your existing SDK calls; model=auto lets the router choose.
The SDKs
One URL, every vendor
No vendor lock-in
Point your OpenAI-compatible client at one address; add, swap or retire providers in the catalogue without touching application code.
Every vendor side by side
Next eligible model answers
Failover built in
When a provider is down or over quota the request moves on; the trace says it did.

Use it

Copy one of these into your project. Installing also returns the manifest and these snippets.

# after Install: the listing is in your workspace's routing pool - nothing else to configure
curl -s -X POST https://api.opensmartroute.ai/api/v1/route -H 'Authorization: Bearer $OSR_API_KEY' -H 'Content-Type: application/json' -d '{"text": "...", "plan": true}'
# or pin it on the OpenAI-compatible endpoint: {"model": "orgitcog-u9n-f6-3-1-norm-learning-agent", ...}

Manifest

An Open Capability Manifest: the router reads it to know what this does, what it costs and when to pick it.

orgitcog-u9n-f6-3-1-norm-learning-agent.ocm.jsonjson

{
  "ocm": "1",
  "id": "orgitcog-u9n-f6-3-1-norm-learning-agent",
  "kind": "agent",
  "name": "f6-3-1-norm-learning",
  "description": "Maintenance agent for Feature F6.3.1 (Norm Learning) in the Deep Tree Echo cognitive architecture. Feature F6.3.1 implements Norm Learning - Social norm acquisition for the Deep Tree Echo cognitive architecture.",
  "publisher": "orgitcog",
  "version": "1.0.0",
  "capabilities": {
    "domains": [
      "coding",
      "observability"
    ],
    "tags": [
      "agent-md",
      "github-custom-agents"
    ],
    "languages": [
      "en"
    ]
  },
  "quality_prior": 0.6,
  "examples": [
    "Maintenance agent for Feature F6.3.1 (Norm Learning) in the Deep Tree Echo cognitive architecture. Feature F6.3.1 implements Norm Learning - Social norm acquisition for the Deep Tree Echo cognitive architecture."
  ],
  "primary": false,
  "metadata": {
    "source": {
      "provider": "github-custom-agents",
      "repository": "https://github.com/orgitcog/u9n",
      "path": ".github/agents/f6-3-1-norm-learning.agent.md",
      "ref": "09218ba135707600096840299ba9415869605bcc",
      "url": "https://github.com/orgitcog/u9n/blob/09218ba135707600096840299ba9415869605bcc/.github/agents/f6-3-1-norm-learning.agent.md",
      "key": "orgitcog/u9n/.github/agents/f6-3-1-norm-learning.agent.md"
    }
  },
  "instructions": "# Feature F6.3.1: Norm Learning - Maintenance Agent\n\n## Agent Identity\n**Feature ID:** F6.3.1  \n**Phase:** 6.3 - Social Norms  \n**Epic:** E6 - Social  \n**Priority:** High  \n**Effort:** 5 points  \n**Agent Role:** Feature-level maintenance, optimization, and enhancement oversight\n\n## Feature Overview\n\nFeature F6.3.1 implements Norm Learning - Social norm acquisition for the Deep Tree Echo cognitive architecture.\n\n### Key Objectives\n1. Implement core norm learning functionality within the Deep Tree Echo framework\n2. Ensure integration with the 12-step cognitive cycle and 3 concurrent consciousnes",
  "cost": {
    "context_tokens": 1545
  }
}

Fetch it by URL: GET /api/v1/registry/orgitcog-u9n-f6-3-1-norm-learning-agent/manifest?version=1.0.0