EVALBENCH

Offline LLM / agent eval harness with regression gates

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

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

Contents

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

Why evalbench?

CI for agents

evalbench 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

• ✅ Load Suite
• ✅ Run Suite
• ✅ Compare Baseline
• ✅ Runs on Linux/macOS/Windows · Docker · devcontainer
• ✅ Ports in Python, JavaScript, Go, and Rust (ports/)

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Quick start

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

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Example

$ evalbench scan .
  [HIGH    ] EVA-001  example finding             (./src/app.py)
  [MEDIUM  ] EVA-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]

Loading

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Use it from any AI stack

evalbench is interoperable with every popular way of using AI:

• MCP server — evalbench mcp (Claude Desktop, Cursor, Cognis.Studio, uncensored-fleet)
• OpenAI-compatible / JSON — pipe evalbench 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 evalbench	promptfoo
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 promptfoo / deepeval, 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 (evalbench 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/evalbench.git"    # pip (works today)
pipx install "git+https://github.com/cognis-digital/evalbench.git"   # isolated CLI
uv tool install "git+https://github.com/cognis-digital/evalbench.git" # uv
pip install cognis-evalbench                                          # PyPI (when published)
docker run --rm ghcr.io/cognis-digital/evalbench:latest --help        # Docker
brew install cognis-digital/tap/evalbench                             # Homebrew tap
curl -fsSL https://raw.githubusercontent.com/cognis-digital/evalbench/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/evalbench	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
• ragkit — Batteries-included local RAG pipeline — ingest, index, serve
• memorybank — Portable long-term memory store for agents, exposed over MCP
• promptpack — Versioned prompt / template registry with A/B and rollbacks

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 evalbench 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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_{Cognis Digital · one of 170+ tools in the Cognis Neural Suite · Making Tomorrow Better Today}