AI Agents & LLMOps — build, route, evaluate, and secure agents.
pip install cognis-ragkit
ragkit scan . # → prioritized findings in secondsReal, reproducible output from the tool — runs offline:
$ ragkit-emit --version
ragkit 0.1.0$ ragkit-emit --help
usage: ragkit [-h] [--version] [--format {table,json}]
{index,search,ask,stats} ...
Local RAG pipeline: ingest, index, serve.
positional arguments:
{index,search,ask,stats}
index build an index from files/directories
search retrieve top-k chunks for a query
ask extractive cited answer for a query
stats show index statistics
options:
-h, --help show this help message and exit
--version show program's version number and exit
--format {table,json}
output formatBlocks above are real
ragkitoutput — reproduce them from a clone.
Sample result format (illustrative values — run on your own data for real findings):
{
"Findings": [
{
"ID": "12345",
"Title": "Suspicious Activity Detected",
"Description": "An unknown entity accessed our network.",
"Severity": "High"
},
{
"ID": "67890",
"Title": "Malware Infection Found",
"Description": "A virus was detected on a workstation.",
"Severity": "Medium"
}
]
}
ragkit is a local, dependency-light RAG pipeline: ingest + TF-IDF index, search, and extractive cited answers. Console script: ragkit.
- Install:
pipx install ragkit # or: pip install ragkit - Build an index from files or directories of
.txt/.mddocuments (written to.ragkit/index.jsonby default):ragkit index ./docs --chunk-size 80 --overlap 20
- Search for the top-k most relevant chunks:
ragkit search "how do retries work" --top-k 5 - Ask for an extractive, cited answer and read it as JSON (the
--formatflag is global, before the subcommand):ragkit --format json ask "what is the retention policy" --top-k 3 | jq '.answer, .citations'
- Inspect the index in CI/automation to confirm it built and is fresh:
ragkit --format json stats | jq '.documents, .chunks'
- Why ragkit? · Features · Quick start · Example · Architecture · AI stack · How it compares · Integrations · Install anywhere · Related · Contributing
self-host RAG
ragkit 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.
- ✅ Tokenize
- ✅ Chunk Text
- ✅ Ingest Paths
- ✅ Build Index
- ✅ Save Index
- ✅ Load Index
- ✅ Answer
- ✅ Runs on Linux/macOS/Windows · Docker · devcontainer
- ✅ Ports in Python, JavaScript, Go, and Rust (
ports/)
pip install cognis-ragkit
ragkit --version
ragkit scan . # scan current project
ragkit scan . --format json # machine-readable
ragkit scan . --fail-on high # CI gate (non-zero exit)$ ragkit scan .
[HIGH ] RAG-001 example finding (./src/app.py)
[MEDIUM ] RAG-002 another signal (./config.yaml)
2 findings · risk score 5 · 38ms
flowchart LR
IN[sources] --> P[ragkit<br/>curate + validate]
P --> OUT[query / analysis]
ragkit is interoperable with every popular way of using AI:
- MCP server —
ragkit mcp(Claude Desktop, Cursor, Cognis.Studio, uncensored-fleet) - OpenAI-compatible / JSON — pipe
ragkit scan . --format jsoninto 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
| Cognis ragkit | RAGFlow | |
|---|---|---|
| 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 RAGFlow, re-framed the Cognis way. Missing a credit? Open a PR.
Pipes into your stack: SARIF for code-scanning, JSON for anything, an MCP server (ragkit mcp) for AI agents, and a webhook forwarder for SIEM/Slack/Jira. See docs/INTEGRATIONS.md.
pip install "git+https://github.com/cognis-digital/ragkit.git" # pip (works today)
pipx install "git+https://github.com/cognis-digital/ragkit.git" # isolated CLI
uv tool install "git+https://github.com/cognis-digital/ragkit.git" # uv
pip install cognis-ragkit # PyPI (when published)
docker run --rm ghcr.io/cognis-digital/ragkit:latest --help # Docker
brew install cognis-digital/tap/ragkit # Homebrew tap
curl -fsSL https://raw.githubusercontent.com/cognis-digital/ragkit/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/ragkit |
DEPLOY.md (AWS/Azure/GCP/k8s) |
agentsmith— Config-first scaffolding and orchestration for multi-agent workflowsskillhub— Local skill registry and installer for AI agentstoolguard— Runtime allowlist and policy for agent tool-callsevalbench— Offline LLM / agent eval harness with regression gatesmemorybank— Portable long-term memory store for agents, exposed over MCPpromptpack— Versioned prompt / template registry with A/B and rollbacks
Explore the suite → 🗂️ all 170+ tools · ⭐ awesome-cognis · 🔗 cognis-sources · 🤖 uncensored-fleet · 🧠 engram
PRs, new rules, and demo scenarios are welcome under the collaboration-pull model — see CONTRIBUTING.md and SECURITY.md.
{} composes with the 300+ tool Cognis suite — JSON in/out and a shared
OpenAI-compatible /v1 backbone. See INTEROP.md for the
suite map, composition patterns, and reference stacks.
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.