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<img src="https://capsule-render.vercel.app/api?type=rect&color=0:6b46c1,100:2b6cb0&height=120&section=header&text=MODELROUTE&fontSize=48&fontColor=ffffff&fontAlignY=58" width="100%" alt="MODELROUTE"/>

MODELROUTE

Local model router / proxy across Ollama, vLLM, and cloud with fallback

<img src="https://readme-typing-svg.demolab.com?font=Fira+Code&size=18&duration=3500&pause=1000&color=6B46C1&center=true&vCenter=true&width=720&lines=Local+model+router++proxy+across+Ollama+vLLM+and+cloud+with+;Self-hostable+%C2%B7+MCP-native+%C2%B7+CI-ready+%C2%B7+polyglot" width="720"/>

AI Agents & LLMOps — build, route, evaluate, and secure agents.

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pip install cognis-modelroute
modelroute scan .            # → prioritized findings in seconds

Contents

• Why modelroute? · Features · Quick start · Example · Architecture · AI stack · How it compares · Integrations · Install anywhere · Related · Contributing

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Why modelroute?

AI infra

modelroute is single-purpose, scriptable, and self-hostable: point it at a target, get prioritized results in the format your workflow already speaks (table · JSON · SARIF), gate CI on it, and let agents drive it over MCP.

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Features

• ✅ Resolve
• ✅ Build Request
• ✅ Estimate Tokens
• ✅ Messages Tokens
• ✅ Dispatch
• ✅ List Models
• ✅ List Providers
• ✅ Runs on Linux/macOS/Windows · Docker · devcontainer
• ✅ Ports in Python, JavaScript, Go, and Rust (ports/)

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Quick start

pip install cognis-modelroute
modelroute --version
modelroute scan .                       # scan current project
modelroute scan . --format json         # machine-readable
modelroute scan . --fail-on high        # CI gate (non-zero exit)

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Example

$ modelroute scan .
  [HIGH    ] MOD-001  example finding             (./src/app.py)
  [MEDIUM  ] MOD-002  another signal              (./config.yaml)

  2 findings · risk score 5 · 38ms

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Architecture

flowchart LR
  A[Input: file / dir / API] --> B[Collectors]
  B --> C[Rules / Analyzers]
  C --> D[Scorer]
  D --> E{Reporters}
  E --> F[Table]
  E --> G[JSON / SARIF]
  E --> H[MCP tool -. drives .-> AI agents]

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Use it from any AI stack

modelroute is interoperable with every popular way of using AI:

• MCP server — modelroute mcp (Claude Desktop, Cursor, Cognis.Studio, uncensored-fleet)
• OpenAI-compatible / JSON — pipe modelroute scan . --format json into any agent or LLM
• LangChain · CrewAI · AutoGen · LlamaIndex — wrap the CLI/JSON as a tool in one line
• CI / scripts — exit codes + SARIF for non-AI pipelines

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How it compares

	Cognis modelroute	LiteLLM
Self-hostable, no account	✅	varies
Single command, zero config	✅	⚠️
JSON + SARIF for CI	✅	varies
MCP-native (AI agents)	✅	❌
Polyglot ports (JS/Go/Rust)	✅	❌
Open license	✅ COCL	varies

Built in the spirit of LiteLLM, re-framed the Cognis way. Missing a credit? Open a PR.

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Integrations

Pipes into your stack: SARIF for code-scanning, JSON for anything, an MCP server (modelroute mcp) for AI agents, and a webhook forwarder for SIEM/Slack/Jira. See docs/INTEGRATIONS.md.

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Install — every way, every platform

pip install "git+https://github.com/cognis-digital/modelroute.git"    # pip (works today)
pipx install "git+https://github.com/cognis-digital/modelroute.git"   # isolated CLI
uv tool install "git+https://github.com/cognis-digital/modelroute.git" # uv
pip install cognis-modelroute                                          # PyPI (when published)
docker run --rm ghcr.io/cognis-digital/modelroute:latest --help        # Docker
brew install cognis-digital/tap/modelroute                             # Homebrew tap
curl -fsSL https://raw.githubusercontent.com/cognis-digital/modelroute/main/install.sh | sh

Linux	macOS	Windows	Docker	Cloud
scripts/setup-linux.sh	scripts/setup-macos.sh	scripts/setup-windows.ps1	docker run ghcr.io/cognis-digital/modelroute	DEPLOY.md (AWS/Azure/GCP/k8s)

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Related Cognis tools

• agentsmith — Config-first scaffolding and orchestration for multi-agent workflows
• skillhub — Local skill registry and installer for AI agents
• toolguard — Runtime allowlist and policy for agent tool-calls
• evalbench — Offline LLM / agent eval harness with regression gates
• ragkit — Batteries-included local RAG pipeline — ingest, index, serve
• memorybank — Portable long-term memory store for agents, exposed over MCP

Explore the suite → 🗂️ all 170+ tools · ⭐ awesome-cognis · 🔗 cognis-sources · 🤖 uncensored-fleet · 🧠 hermes

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Contributing

PRs, new rules, and demo scenarios are welcome under the collaboration-pull model — see CONTRIBUTING.md and SECURITY.md.

⭐ If modelroute saved you time, star it — it genuinely helps others find it.

License

Source-available under the Cognis Open Collaboration License (COCL) v1.0 — free for personal, internal-evaluation, research, and educational use; commercial / production use requires a license (licensing@cognis.digital). See LICENSE.

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<div align="center">_{<b><a href="https://cognis.digital">Cognis Digital</a></b> · one of 170+ tools in the <a href="https://github.com/cognis-digital/cognis-neural-suite">Cognis Neural Suite</a> · <i>Making Tomorrow Better Today</i>}</div>