cognigraph 0.7.6

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.

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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.

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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.

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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

REST API (any HTTP client — Copilot, Postman, custom tools, bots)

# Start the server
kogni serve                          # localhost:8000

# Query from anything that speaks HTTP
curl -X POST http://localhost:8000/reason \
  -H "Content-Type: application/json" \
  -d '{"query": "What depends on the auth service?"}'

{
  "answer": "The auth service is depended on by...",
  "confidence": 0.87,
  "cost_usd": 0.0023,
  "latency_ms": 1250.5
}

Endpoints: /reason (single query), /reason/batch (up to 50), /graph/stats, /nodes/{id}, /health
Auth: API key via X-API-Key header or Bearer token
Docs: Interactive Swagger UI at http://localhost:8000/docs
Full reference: docs/api-reference.md

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

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How It Works

Your Codebase ──→ kogni init ──→ Knowledge Graph (cognigraph.json)
                                        │
         ┌──────────┬──────────┬────────┼────────┐
         ▼          ▼          ▼        ▼        ▼
       CLI      REST API    Python   MCP      Direct
    (terminal)  (HTTP)      SDK      Server   JSON read
         │          │          │        │        │
         ▼          ▼          ▼        ▼        ▼
    Any IDE    Any tool     Scripts  Claude   Custom
    terminal   Copilot      CI/CD   Cursor   parsers
               Postman      Jupyter VS Code
               Slack bots   Replit  Windsurf

The knowledge graph is the product. Once built, query it however you want:

Access Method	Use When	Example
kogni run	Quick terminal query	kogni run "what calls payments?"
kogni serve	Any HTTP client needs access	curl localhost:8000/reason
Python SDK	Scripts, notebooks, pipelines	graph.reason("query")
MCP Server	AI-powered IDE with MCP support	Auto-available after kogni init
Read JSON	Custom integration, any language	Parse cognigraph.json directly

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.

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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.

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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

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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.

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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

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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%).

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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.

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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}
}

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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.