Open Source · Apache 2.0
Deploy autonomous agents on Kubernetes with evaluation-driven lifecycle, enterprise governance, and real-time monitoring. No vendor lock-in.
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The Problem
Demo agents are easy.
Production agents are a different story.
We've all seen the demo. An AI agent answers questions, calls tools, chains reasoning steps. The room is impressed. Then it reaches production.
No Quality Gates
Most tools let you deploy without measuring. An agent inventing policies, deleting databases, hallucinating answers — these aren't edge cases. They're what happens without operational discipline.
Vendor Lock-in
Proprietary platforms tie you to one model provider and one deployment model. Your agents become hostages to someone else's roadmap and pricing.
Too Light for Enterprise
Open-source chat UIs lack multi-tenancy, audit trails, guardrails, and the operational discipline required for regulated environments.
Why Récif?
Not another chat wrapper.
Récif is infrastructure for autonomous agents. A control tower, not a conversation UI.
Not a Chat UI
Récif isn't another ChatGPT wrapper. It's the control tower for autonomous agents that run in production. Deploy once, govern forever.
Agents Are First-Class
Agents don't live inside Récif — they live independently in their own containers. Corail agents are autonomous: they run their own runtime, their own model, their own tools. Récif is the ecosystem that connects, governs, and observes them — like a reef nurturing its corals.
Enterprise-Grade Governance
Scorecards, guardrails, versioned releases, audit trails. Know what your agents do, why, and how well.
Architecture
How it all connects.
Agents are autonomous containers. Récif is the control tower that governs them. They communicate via gRPC, deploy via GitOps, and run on Kubernetes.
End Users & Channels
Slack
REST API
Google Chat
WebSocket
Dashboard
↓ HTTP / WebSocket / Events
Corail Agents (autonomous containers)
Code Reviewer
claude-sonnet-4
Tools Skills Guards
Ticket Triage
gpt-4o
RAG Memory
Data Analyst
llama-3.3
Tools AG-UI
Security Scanner
claude-sonnet-4
Skills Guards
↓ gRPC Control Bridge
Récif Platform (control tower)
Dashboard
Next.js
REST API
Go / Chi
Operator
kubebuilder
Governance
Scorecards
AI Radar
Monitoring
Releases
Git-backed
↓ SQL / K8s API / GitHub API
Infrastructure
PostgreSQL
pgvector
Kubernetes
CRDs + Istio
Ollama
Local models
recif-state
Git repo
OCI Registry
Images
Istio Service Mesh
Built-in service mesh. Zero config.
Every agent runs with an Istio sidecar. mTLS encryption, traffic management, canary deployments, and full observability — out of the box. No competitor offers this.
Kiali — Service Graph (Canary Deployment)
Live Kiali graph showing canary deployment — v2 receives 10% of traffic while v1 handles 90%.
Canary Deployments
Deploy v2 on 10% of traffic. Compare scores, latency, error rates. Progressive rollout: 10% → 50% → 100%. Auto-rollback if quality degrades.
Full Observability
Kiali shows the service graph in real-time. See which agent talks to which DB, LLM, or tool. Latency, error rate, throughput — per agent, per version. Distributed tracing included.
mTLS Encryption
Every agent-to-agent and agent-to-service communication is encrypted automatically. Zero-trust networking with zero config. Certificate rotation handled by Istio.
Traffic Management
A/B test two models on the same agent. Blue/green deployments. Rate limiting per agent. Circuit breaker — if an agent crashes, traffic is cut instantly.
Features
Everything you need for production agents.
From model selection to governance, Récif handles the full lifecycle.
Multi-Model Runtime
Ollama, Anthropic, AWS Bedrock, Vertex AI, OpenAI. Switch providers without changing code.
Skills System
Anthropic-compatible skill packages. Import from GitHub, build custom, share across teams.
Knowledge Base & RAG
pgvector-powered retrieval. Connect Drive, Jira, Confluence, Databricks natively.
AI Radar & Monitoring
Real-time health, latency, token consumption, cost tracking, alerts per agent.
Governance & Guardrails
Scorecards, quality gates, guardrail policies, risk profiles. Enterprise compliance built-in.
GitOps Releases
Every config change is a Git commit. Immutable artifacts, diff, rollback, full audit trail.
No-Code + Custom Dev
Product teams create agents in minutes. Engineers scaffold projects with LangChain, CrewAI, AutoGen.
Canary Deployments & Evaluation
Deploy new versions to a subset of traffic. Evaluate with golden datasets before full rollout.
MCP & Integrations
Native MCP tool support. Plus HTTP, CLI, and custom tool types. Connect GitHub, Jira, Slack, AWS, GCP, and more.
Channels & Integrations
Connect everywhere. Integrate everything.
Communication Channels
Agents communicate through any channel. Deploy once, reach everywhere.
REST API Slack Google Chat WebSocket Custom
Platform Integrations
Connect to your existing tools. Agents inherit platform integrations automatically.
GitHub Jira Jenkins Slack AWS GCP Datadog Terraform
Get Started
Three steps to production agents.
From zero to governed agents in minutes, not months.
1
Deploy
One command. Kind + Helm locally, or Terraform for cloud. The full platform spins up in minutes.
cd deploy/kind && bash setup.sh cd deploy/terraform && terraform apply
2
Create
Create agents via the dashboard or define them as Kubernetes CRDs. Infrastructure as code, natively.
apiVersion: recif.io/v1 kind: Agent metadata: name: code-reviewer spec: model: claude-sonnet-4 provider: anthropic skills: - github-review - code-analysis
3
Govern
Monitor, evaluate, and control your agents at scale. Scorecards grade quality. Guardrails enforce policy.
Agent Fleet Overview
24 Agents 22 Healthy 1 Degraded 1 Down
code-reviewer97.112ms
ticket-triage93.445ms
data-analyst88.7230ms
Evaluation-Centric Platform
Evaluation is not a step. It's the architecture.
From data ingestion to user feedback, every component feeds the evaluation loop. No agent ships without proof. No regression goes undetected.
End-to-End Lifecycle
Ingest
Marée pulls docs from Drive, Jira, Confluence, S3
→
Dataset
Golden datasets with expected outputs + RAG context
→
Evaluate
14 MLflow scorers + LLM-as-judge per risk profile
→
Quality Gate
Governance scorecards block deploy if below threshold
→
Release
GitOps artifact: pending_eval → approved or rejected
→
Canary
10% traffic, Flagger webhook checks eval scores
→
Production
Live monitoring, sample-rate eval on real traffic
→
Feedback
User & expert annotations feed back into datasets
Negative feedback auto-appends to golden datasets — the loop never stops
Eval Run — code-reviewer v3 — Risk Profile: HIGH
14 Scorers — MLflow GenAI
Safety 98.2
Relevance 95.7
Correctness 91.4
Groundedness 93.1
Tool Accuracy 87.5
Cost $0.003
APPROVED — avg 94.3 ≥ threshold 90
Release v3 committed to recif-state. Applied to K8s CRD.
Feedback Loop — Live
👎
User rated 2/5 on trace tr_8f2k
"Wrong answer about leave policy"
Auto-appended to dataset
🔍
Expert annotation on trace tr_3m1n
Expected: "Employees get 25 days PTO + 10 sick days"
MLflow assessment
📈
Production sample — 10% eval rate
12 traces scored in last hour. Avg safety: 97.8
AI Radar
14 MLflow Scorers
Safety, Relevance, Correctness, Completeness, Fluency, Equivalence, Summarization, Guidelines, ExpectationsGuidelines, RetrievalRelevance, RetrievalGroundedness, RetrievalSufficiency, ToolCallCorrectness, ToolCallEfficiency.
+ Custom LLM-as-Judge + Register Your Own
Risk Profiles & Governance
LOW, MEDIUM, HIGH risk profiles select which scorers run. Governance scorecards grade 4 dimensions: Quality (35%), Safety (30%), Cost (20%), Compliance (15%). Policies enforce token limits, latency SLAs, blocked topics, daily cost caps.
Eval-Gated Releases
Every release starts as pending_eval. Corail runs scoring async, POSTs results to a callback. If scores pass governance thresholds → approved and applied to K8s. If not → rejected and auto-rollback.
Canary + Flagger Quality Gate
Deploy v2 on 10% of traffic. Flagger's webhook queries MLflow for live eval scores. If avg ≥ 60% → promote to 100%. If not → auto-rollback. Zero manual intervention.
Feedback → Dataset → Re-eval
User thumbs-down (score < 3/5) auto-appends the failing input to the agent's golden dataset. Expert annotations via MLflow assessments add expected outputs. Next eval run includes these cases. The agent gets better with every interaction.
Document Ingestion
Marée — feed your agents with knowledge.
A pluggable ingestion pipeline that transforms documents into searchable vector embeddings. From raw PDF to agent-ready knowledge in one command.
maree ingest --source drive --kb hr-knowledge
Source
Pull from Drive, Jira, Confluence, S3, Databricks, or local files
PDF DOCX HTML CSV
Processor
Extract text, tables, and images with Docling. OCR included.
Transformer
Chunk, clean, and prepare for embedding. Smart splitting preserves context.
Store
Embed with Ollama and store in PostgreSQL + pgvector. Ready for RAG.
Pluggable Pipeline
Each stage is replaceable. Swap Docling for Tika, switch from pgvector to Pinecone, add custom processors. The pipeline adapts to your stack.
Enterprise Connectors
Connect to where your knowledge already lives.
Google Drive Jira Confluence Databricks S3 Local Files
Docling-Powered Extraction
IBM's Docling handles complex documents: PDFs with tables, scanned images with OCR, DOCX with embedded formatting. Production-grade extraction, not toy parsing.
One Command
maree ingest \ --source drive \ --kb hr-knowledge \ --embedder ollama
Compare
How Récif compares.
Honest comparison with real competitors. Récif doesn't compete with chat UIs — it's a different category.
| Feature | Récif | Dify | LibreChat | OpenWebUI | CrewAI | Gemini Enterprise |
|---|---|---|---|---|---|---|
| Autonomous Agents | ✓ | ✓ | ~ | ~ | ✓ | ✓ |
| Eval-Gated Releases | ✓ | ✕ | ✕ | ✕ | ✕ | ~ |
| Canary Deployments | ✓ | ✕ | ✕ | ✕ | ✕ | ✕ |
| Service Mesh (Istio) | ✓ | ✕ | ✕ | ✕ | ✕ | ✕ |
| K8s CRDs + Operator | ✓ | ✕ | ✕ | ~ | ✕ | ~ |
| GitOps Releases | ✓ | ✕ | ✕ | ✕ | ✕ | ✕ |
| Governance & Scorecards | ✓ | ~ | ✕ | ✕ | ✕ | ✓ |
| Multi-Model (8+ providers) | ✓ | ✓ | ✓ | ✓ | ✓ | ~ |
| MCP Tools | ✓ | ✓ | ✓ | ~ | ✓ | ✓ |
| RAG / Knowledge Base | ✓ | ✓ | ~ | ✓ | ✓ | ✓ |
| Agent Memory | ✓ | ~ | ✕ | ✕ | ✓ | ✓ |
| Visual Workflow Builder | ✕ | ✓ | ✕ | ✕ | ~ | ~ |
| Multi-Tenant | ✓ | ✓ | ~ | ~ | ~ | ✓ |
| Open Source | ✓ | ✓ | ✓ | ✓ | ✓ | ✕ |
| Self-Hosted | ✓ | ✓ | ✓ | ✓ | ✓ | ✕ |
Enterprise
Why enterprises choose Récif.
Based on real feedback from teams evaluating Dify, LibreChat, OpenWebUI, CrewAI, and Gemini Enterprise.
🏢
One Platform, All Teams
LibreChat forces N separate instances — one per team, no shared governance, no cost visibility. Récif runs as a single platform with namespace-per-team isolation, centralized governance, and per-agent cost tracking.
🔓
No MCP Lock-in
Other platforms force you to recondition agents into MCP tools. Récif agents live in their own containers — bring your own framework, your own code, your own tools. The control bridge connects everything.
🛡️
Built-in Governance
Not bolted on — built in. Scorecards, guardrails, quality gates, versioned releases with Git audit trail. Know what your agents do, how well, and how much they cost.
🌊
Open Source Gemini Enterprise
The enterprise vision — a governed, centralized agent platform — without vendor lock-in. Any model (Ollama, Anthropic, Bedrock, Vertex), any cloud, Apache 2.0 license.
🎯
No-Code + Custom Dev
Product teams create agents in minutes with the no-code wizard. Engineering teams scaffold custom projects with their framework of choice. Both coexist in one platform.
👥
Multi-Tenant RBAC
Platform admins govern everything. Team admins manage their agents. Developers build and deploy. Viewers observe. Namespace isolation ensures no team can affect another.
Who is Récif for?
Built for everyone. Loved by engineers.
🚀
For Everyone — Mass Adoption
- Give every team access to AI agents without managing infrastructure
- No-code agent creation for product managers, analysts, support teams
- Marketplace of ready-to-use agents and skills
- Central governance ensures compliance without slowing teams down
- Cost tracking and budgets per team
- Chat with any agent directly from the dashboard
⚙️
For Engineers — Platform Teams
- Deploy custom agents with your own code, any framework
- GitOps-native: every change is a commit, every deploy is traceable
- Kubernetes-native: CRDs, operators, Helm charts, namespace isolation
- gRPC control plane, Istio service mesh, canary deployments
- Skills as code (Anthropic SKILL.md format)
- Scaffold projects: LangChain, CrewAI, AutoGen, or pure Corail
Open Source
Ready to govern your agents?
Deploy Récif in minutes. Join the community building the future of autonomous AI infrastructure.