Version control meets edge orchestration for AI systems.

Build AI workflows that you actually control. No proprietary platforms, no vendor lock-in, no serverless sprawl, no duct tape and bailing wire "workflow automation." Just Git, YAML, and Cloudflare Workers. Fast, developer-first, infinite scale.

You have 50+ prompts, 20+ agents, and dozens of SQL queries scattered across your codebase. Changing one breaks three others. Rollbacks mean digging through Git history. A/B testing means deployment hell. And every "AI orchestration platform" wants you to give up control for their black box.

You know there's a better way.

Version every AI component independently. Deploy any combination from any point in history.

# Each component versions independently
edgit tag create extraction-prompt v1.0.0
edgit tag create company-agent v2.1.0
edgit tag create validation-sql v0.5.0

# Deploy optimal versions from different timelines
edgit deploy set extraction-prompt v0.1.0 --to prod  # Ancient but perfect
edgit deploy set company-agent v3.0.0 --to prod      # Latest stable
edgit deploy set validation-sql v2.5.0 --to prod     # Optimal performance

# Instant rollback
edgit deploy set extraction-prompt v0.1.0 --to prod  # < 50ms globally

No merge conflicts. No monorepo versioning. No JSON state files.

All version data lives in Git tags. Your CI/CD deploys to Cloudflare KV. Every version ever created is instantly accessible at the edge.

→ Get Started with Edgit

Compose AI workflows in YAML. Execute at 200+ global locations. Sub-50ms cold starts.

# ensembles/company-intelligence.yaml
name: company-intelligence

state:
  schema:
    companyData: object
    analysis: object

flow:
  - member: fetch-company-data
    state:
      set: [companyData]
    input:
      domain: ${input.domain}
    cache:
      ttl: 3600

  - parallel:
    - member: analyze-financials
      state:
        use: [companyData]
        set: [analysis]

    - member: fetch-competitors
      state:
        use: [companyData]

No DAG builders. No UI-driven workflows. No central orchestrator bottleneck.

Define workflows in Git. Deploy to Cloudflare Workers. State management, caching, and parallelization built in.

→ Learn About Conductor

# Version components with Edgit
components:
  extraction-prompt: v1.0.0
  company-analyzer: v2.1.0

# Load as Conductor members
members:
  - type: Think
    component: extraction-prompt@v1.0.0
    config:
      model: gpt-4
      cache_ttl: 3600

# Orchestrate in ensembles
ensembles:
  - name: company-intel
    members: [fetch, analyze, score]
    deploy: cloudflare-workers

Mix optimal component versions. Test combinations locally. Deploy atomically to the edge.

Managed service with generous free tier for:

Prompt & Component Management

• Visual editor for prompts, SQL, configs
• Version and tag changes with one click
• A/B test and multivariant experiments
• Let analysts iterate without touching code

Workflow & Deployment

• Visual workflow builder (React Flow)
• Deploy to production from the UI
• Real-time collaboration across teams
• Observability dashboards

The Difference: Your data lives in Git, not our database. We're the UI layer. You own the source of truth.

Think PromptLayer's editing experience, but Git-native instead of proprietary storage.

Cloudflare Workers, KV, D1, R2, and AI Gateway are the primitives. No centralized compute.

Configuration, orchestration, and versioning live in Git. CLI and SDK are thin layers around Git operations.

Multi-layer caching with TTL control. Member-level cache settings. Cache-first thinking reduces costs and latency.

LLM and API members produce machine-readable, type-safe output validated via JSON schema.

Every execution emits structured logs and metrics. Debugging and transparency are effortless.

Core tooling (Edgit, Conductor) is open source. Cloud is proprietary—we charge for the UI and managed service, not the runtime.

Edgit: v0.1.8 (active development)

• ✅ Component versioning
• ✅ Deployment management
• ✅ AI-powered commits
• ✅ Smart detection

Conductor: v0.0.1 (building now)

• 🚧 Core runtime
• 🚧 State management
• 📋 Scoring system
• 📋 MCP integration

Cloud: Design phase (managed service with generous free tier)

npm install -g @ensemble-edge/edgit
cd your-repo
edgit init

edgit tag create my-prompt v1.0.0
edgit deploy set my-prompt v1.0.0 --to staging

edgit build --target cloudflare
edgit deploy --to cloudflare

npm install @ensemble-edge/conductor
conductor init
conductor deploy

Modern AI development looks like this:

1. Ship v1.0.0 of your app
2. Change a prompt
3. Everything becomes v2.0.0
4. Original prompt trapped in Git history
5. Can't A/B test old vs new
6. Can't mix optimal versions
7. Rollback means reverting the entire codebase

This is insane for systems with 100+ independently evolving components.

Ensemble Edge treats AI components like they deserve: individually versioned, independently deployable, infinitely composable.

Then orchestrates them at the edge where they execute fast and scale infinitely.

📚 Complete Documentation - Comprehensive guides and tutorials

• Edgit - Component versioning system
• Conductor - Edge orchestration framework
• Cloud - Managed service (coming soon)
• Getting Started - Quick start guides for all products
• Examples - Real-world implementations

We're building in public. Watch development at:

• Edgit Repository - Component versioning
• Conductor Repository - Edge orchestration
• Documentation - Mintlify docs site

Issues, PRs, and feedback welcome.

• Edgit: MIT
• Conductor: Apache 2.0
• Documentation & Examples: MIT
• Ensemble Cloud: Proprietary (managed service)

Open core, closed cloud. Use it, fork it, build on it.

Built by engineers who believe AI tooling should be as solid as the infrastructure it runs on.

No buzzwords. No hand-holding. Just tools that work.