cognigraph 0.7.5

Dev Intelligence Layer — turn any codebase into a reasoning-ready knowledge graph. Works with any IDE, any AI tool, or plain terminal.

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.

---

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

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)

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)

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]

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 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

@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 for development setup, testing, and PR guidelines.

License

Apache 2.0 — use it commercially, modify it freely, just keep the attribution.