Metadata-Version: 2.4
Name: cognigraph
Version: 0.7.5
Summary: Dev Intelligence Layer — turn any codebase into a reasoning-ready knowledge graph. Works with any IDE, any AI tool, or plain terminal.
Project-URL: Homepage, https://cognigraph.dev
Project-URL: Documentation, https://github.com/quantamixsol/cognigraph#readme
Project-URL: Repository, https://github.com/quantamixsol/cognigraph
Project-URL: Changelog, https://github.com/quantamixsol/cognigraph/releases
Project-URL: Bug Tracker, https://github.com/quantamixsol/cognigraph/issues
Author-email: Harish Kumar <harish.kumar@quantamixsolutions.com>
License-Expression: Apache-2.0
License-File: LICENSE
License-File: NOTICE
Keywords: adaptive-activation,ai-coding,bayesian-learning,claude-code,codebase-reasoning,copilot,cursor,dev-intelligence,development-tools,distributed-reasoning,governance,graph-of-agents,knowledge-graph,lora,mcp,multi-agent,ontology,owl,pcst,reasoning,regulatory-ai,shacl,slm,vscode
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
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Description-Content-Type: text/markdown

# CogniGraph

### Dev Intelligence Layer — Graphs That Think

Turn any codebase into a reasoning-ready knowledge graph.
One command. Any IDE. Any AI tool. Zero cloud infrastructure.

[![PyPI version](https://badge.fury.io/py/cognigraph.svg)](https://pypi.org/project/cognigraph/)
[![Python 3.10+](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/downloads/)
[![License: Apache 2.0](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](LICENSE)
[![Tests: 554 passing](https://img.shields.io/badge/tests-554%20passing-brightgreen.svg)]()
[![MCP Compatible](https://img.shields.io/badge/MCP-compatible-8A2BE2.svg)]()
[![Patent: EP26162901.8](https://img.shields.io/badge/patent-EP26162901.8-orange.svg)](NOTICE)

---

> **What if your development environment understood your entire codebase — and kept learning?**
>
> CogniGraph transforms any codebase into a knowledge graph where every module, service, and config is a node backed by an autonomous LLM agent. Query it from any IDE, any AI tool, or plain terminal. One `pip install`, one `kogni init`, and your dev environment becomes intelligent.

---

## Quick Start

```bash
pip install cognigraph[api]
cd your-project
kogni init
```

That's it. CogniGraph scans your repo, builds a knowledge graph, and configures your IDE. Works with:

| IDE / Tool | Integration | Command |
|-----------|-------------|---------|
| **Claude Code** | MCP server + CLAUDE.md | `kogni init` (auto-detected) |
| **Cursor** | MCP server + .cursorrules | `kogni init --ide cursor` |
| **VS Code + Copilot** | MCP server + copilot-instructions | `kogni init --ide vscode` |
| **Windsurf** | MCP server + .windsurfrules | `kogni init --ide windsurf` |
| **Codex / Replit / JetBrains** | CLI + Python SDK | `kogni init --ide generic` |
| **Plain terminal** | Full CLI | `kogni init --ide generic` |
| **CI/CD pipelines** | Python SDK | `pip install cognigraph` |

No cloud account. No infrastructure. Your machine, your API keys, your data.

---

## What You Get

### CLI (any terminal, any IDE)
```bash
kogni run "What depends on the auth service?"     # Graph reasoning
kogni context auth-lambda                           # 500-token focused context
kogni inspect --stats                               # Graph statistics
kogni scan repo .                                   # Rebuild knowledge graph
kogni doctor                                        # Health check
kogni setup-guide                                   # Backend setup help
```

### Python SDK (any Python environment)
```python
from cognigraph import CogniGraph
from cognigraph.backends.api import AnthropicBackend

graph = CogniGraph.from_json("cognigraph.json")
graph.set_default_backend(AnthropicBackend(model="claude-haiku-4-5-20251001"))

result = graph.reason("How does GDPR conflict with the AI Act?")
print(result.answer)          # Multi-agent synthesized answer
print(f"Cost: ${result.cost_usd:.4f}")  # Transparent cost tracking
```

### MCP Tools (Claude Code, Cursor, VS Code, Windsurf)
| Tool | Purpose |
|------|---------|
| `kogni_context` | 500-token focused context (replaces 20-60K file reads) |
| `kogni_reason` | Multi-agent graph reasoning |
| `kogni_inspect` | Graph structure inspection |
| `kogni_preflight` | Pre-change safety check |
| `kogni_impact` | "What breaks if I change X?" |
| `kogni_lessons` | Surface past mistakes before you repeat them |
| `kogni_learn` | Teach the graph new knowledge |

---

## How It Works

```
Your Codebase ──→ kogni init ──→ Knowledge Graph (cognigraph.json)
                                        │
                   ┌────────────────────┼────────────────────┐
                   ▼                    ▼                    ▼
              CLI queries          MCP tools            Python SDK
              (any terminal)    (AI-powered IDEs)    (scripts, CI/CD)
                   │                    │                    │
                   └────────────────────┼────────────────────┘
                                        ▼
                              Graph-of-Agents Engine
                        (each node = autonomous LLM agent)
                                        │
                   ┌────────────────────┼────────────────────┐
                   ▼                    ▼                    ▼
              Anthropic            Ollama (free)         Any OpenAI-
              OpenAI               vLLM / llama.cpp      compatible
              AWS Bedrock          (local, private)       endpoint
```

**Key insight:** CogniGraph is model-agnostic. Use free local models (Ollama), cloud APIs (Anthropic, OpenAI), or enterprise backends (AWS Bedrock) — smart routing sends complex queries to capable models and simple ones to cheap models, all within your cost budget.

---

## 13 Innovations (Patent EP26162901.8)

| # | Innovation | What it does |
|---|-----------|-------------|
| 1 | **PCST Activation** | Sublinear subgraph selection — only wake relevant nodes |
| 2 | **MasterObserver** | Zero-cost transparency layer for reasoning traces |
| 3 | **Convergent Message Passing** | Agents talk until they agree, then stop |
| 4 | **Backend Fallback Chain** | Auto-fallback across models with cost budgets |
| 5 | **Hierarchical Aggregation** | Topology-aware answer synthesis |
| 6 | **SemanticSHACLGate** | 3-layer OWL-aware governance validation |
| 7 | **Constrained F1** | Joint quality + governance evaluation metric |
| 8 | **OntologyGenerator** | Auto-generate OWL+SHACL from documents |
| 9 | **Adaptive Activation** | Dynamic node selection from query complexity |
| 10 | **Online Graph Learning** | Bayesian edge weight updates from usage |
| 11 | **LoRA Auto-Selection** | Per-entity adapter matching |
| 12 | **TAMR+ Connector** | Retrieval-to-reasoning pipeline |
| 13 | **Multi-Resolution Embeddings** | Hybrid skill matching (regex + semantic) |

All 13 innovations are **free for every developer**. No license key required.

---

## Backends

| Backend | Models | Cost | Install |
|---------|--------|------|---------|
| **Ollama** | Any local model (Qwen, Llama, etc.) | **$0** (local) | `pip install cognigraph[api]` |
| **Anthropic** | Claude Haiku / Sonnet / Opus | $5 free credits | `pip install cognigraph[api]` |
| **OpenAI** | GPT-4o / GPT-4o-mini | $5 free credits | `pip install cognigraph[api]` |
| **AWS Bedrock** | Claude, Titan, Llama, Mistral | AWS Free Tier | `pip install cognigraph[api]` |
| **vLLM** | GPU inference + LoRA | $0 (your GPU) | `pip install cognigraph[gpu]` |
| **llama.cpp** | CPU GGUF models | $0 (your CPU) | `pip install cognigraph[cpu]` |

```bash
kogni setup-guide              # See all options with setup steps
kogni setup-guide ollama       # Free, local, no API key needed
kogni setup-guide anthropic    # Best quality, $5 free credits
kogni doctor                   # Verify everything works
```

---

## Pricing — 100% Free for Developers

CogniGraph follows the **open-core model**: everything a solo developer needs is free forever. We monetize team and enterprise collaboration features.

| | Community (Free) | Team | Enterprise |
|---|:---:|:---:|:---:|
| **Price** | **$0 forever** | $29/dev/month | Custom |
| All 13 innovations | ✓ | ✓ | ✓ |
| All MCP tools (7 tools) | ✓ | ✓ | ✓ |
| All backends (Ollama, Anthropic, OpenAI, Bedrock, vLLM) | ✓ | ✓ | ✓ |
| CLI + Python SDK + REST API | ✓ | ✓ | ✓ |
| Unlimited queries | ✓ | ✓ | ✓ |
| Auto-growing knowledge graph | ✓ | ✓ | ✓ |
| Session continuity workspace | ✓ | ✓ | ✓ |
| SemanticSHACL governance | ✓ | ✓ | ✓ |
| Multi-IDE support | ✓ | ✓ | ✓ |
| Commercial use | ✓ | ✓ | ✓ |
| Shared KG sync across team | — | ✓ | ✓ |
| Multi-developer coordination | — | ✓ | ✓ |
| Team analytics & insights | — | ✓ | ✓ |
| Custom ontologies | — | ✓ | ✓ |
| Private deployment | — | — | ✓ |
| Compliance & audit trail | — | — | ✓ |
| SLA support | — | — | ✓ |

**Why free?** We believe every developer deserves intelligent tooling regardless of budget. The innovations that save you tokens and time should not be behind a paywall. Teams pay for collaboration — individuals never pay.

---

## Benchmarks

| Metric | CogniGraph | Single-Agent Baseline | Improvement |
|--------|-----------|----------------------|-------------|
| Constrained F1 | **0.757** | 0.328 | **+131%** |
| Governance Accuracy | **99.7%** | N/A | — |
| Token Efficiency | **500 tokens/query** | 20-60K tokens | **40-120x** |

---

## Governance

The **SemanticSHACLGate** enforces 3-layer semantic validation on every reasoning output:

1. **Framework Fidelity** — agents cite correct regulatory frameworks
2. **Scope Boundary** — responses stay within assigned domain
3. **Cross-Reference Integrity** — proper attribution across domains

**MultiGov-30 benchmark: 99.7% governance accuracy** (FF: 100%, SB: 100%, CR: 98.3%).

---

## Patent & IP Notice

CogniGraph implements methods described in **European Patent Application EP26162901.8** (filed 6 March 2026, Quantamix Solutions B.V.). See [NOTICE](NOTICE) for details.

All 13 innovations are free to use under Apache 2.0. The patent protects the specific methods — you can use CogniGraph freely in any project, commercial or otherwise.

---

## Citation

```bibtex
@article{kumar2026cognigraph,
  title   = {CogniGraph: Governed Intelligence through Graph-of-Agents Reasoning
             over Knowledge Graph Topologies with Semantic SHACL Validation},
  author  = {Kumar, Harish},
  year    = {2026},
  institution = {Quantamix Solutions B.V.},
  note    = {European Patent Application EP26162901.8},
  url     = {https://github.com/quantamixsol/cognigraph}
}
```

---

## Contributing

See [CONTRIBUTING.md](CONTRIBUTING.md) for development setup, testing, and PR guidelines.

## License

[Apache 2.0](LICENSE) — use it commercially, modify it freely, just keep the attribution.
