The LLVM for AI Agents

Write once.
Deploy anywhere.

KodaIR is a universal intermediate representation for AI agents. Any framework in, any cloud out, without rewriting a line of code.

How It Works
Any framework or cloud agent
PydanticAI LangGraph CrewAI Bedrock Vertex AI Foundry
↓
KodaIR
↓
Any framework, cloud, or infrastructure
PydanticAI LangGraph CrewAI AWS GCP Azure On-Prem
The Problem
79%

Multi-environment agents

Most enterprises run AI agents across two or more cloud environments, with no portability between them.

$2-5M

Migration cost

Rewriting 50 agents from one cloud to another takes 12 to 18 months of engineering and millions in spend.

0%

Portability today

No tool exists that translates an AI agent from one framework or cloud to another. Every migration is hand-coded.

The Architecture

N+M, not N×M.

Parsers normalize each framework into a canonical IR. Generators emit cloud-native deployment artifacts from the same IR. Add a framework, update one parser. Add a cloud, update one generator.

Parsers (Input)
PydanticAI
LangGraph
CrewAI
OpenAI Agents
Anthropic Agents
Cloud-native agents
AWS Bedrock
GCP Vertex AI
Azure Foundry
→
KodaIR
5-layer canonical representation
L1Agent Definitionidentity
L2Orchestrationthe hard layer
L3State Schematyped
L4ToolsMCP-native
L5Memorystate-sync
→
Generators (Output)
Any framework
PydanticAI, LangGraph, CrewAI, OpenAI Agents, Anthropic Agents
Any cloud
AWS Bedrock, GCP Vertex AI, Azure Foundry
Any infrastructure
On-Prem, K8s
The IR

Five layers. One hard one.

The IR captures what frameworks express and what clouds need to execute. Each layer has a fidelity score. Gaps are filled by Semantic Polyfills, synthesized equivalents built from cloud-native primitives.

1

Agent Definition

identity · model · prompt
Name, capability class, system prompt, model constraints. The IR captures requirements like reasoning-large or fast, and each generator resolves to the best model on its target platform.
2

Orchestration Logic

graphs · crews · loops · handoffs
The moatNormalizes graph-based state machines, role-based crews, handoff flows, and agentic loops into a single execution representation. This is the compiler theory problem, and where the deepest IP lives.
3

State Schema

typed state · reducers · dependencies
The typed state flowing through execution. Pydantic convergence gives us 60 to 70% parser automation here. LangGraph, CrewAI, and PydanticAI all meet on Pydantic as their canonical data model.
4

Tool & Integration

normalized definitions · MCP-native
Tool schemas, execution bindings, retry policies, side-effect classification. MCP and A2A solve the communication layers while KodaIR inherits that leverage and focuses on what they don't cover.
5

Memory & Persistence

checkpointing · session state · history
The hard layerMoving code is straightforward. Moving the memory of a live agent, active conversations, checkpoints, and session state, is the problem nobody else addresses. Dual-write orchestration ensures zero memory loss during cutover.
How It Works

From locked in to portable in minutes.

A real scenario that plays out at enterprises every quarter.

A company runs 40 customer service agents on AWS Bedrock. After a merger, the combined entity standardizes on GCP. Engineering estimates 14 months and $3M to rewrite. With KodaIR, the migration takes days.
1
Connect your cloud
Point KodaIR at your existing AWS Bedrock agents. No code changes, no SDK installs. KodaIR reads your agents as they are.
2
Translate to the IR
KodaIR parses each agent into the universal Agent-IR, capturing orchestration logic, tools, state, and memory across all five layers.
3
Pick your target
Choose GCP Vertex AI, Azure Foundry, or any supported cloud. KodaIR generates cloud-native deployment artifacts for your target.
4
Deploy and validate
KodaIR deploys your agents natively on the target cloud and runs behavioral equivalence tests to confirm they work identically.
5
Optimize continuously
Once portable, KodaIR monitors cost and performance across clouds. If pricing shifts, agents can migrate automatically.
Under the hood
Step 1
Bedrock Ingester pulls live agent config via boto3: model, action groups, Lambda bindings, knowledge bases, guardrails
Step 2
Compiler Agent classifies orchestration pattern, extracts state schema, normalizes tool definitions, scores fidelity per layer
Step 3
Generator Agent emits GCP Agent Builder config, Cloud Function stubs, Firestore schema, IAM bindings. Semantic Polyfills fill feature gaps
Step 4
Verifier Agent runs test suite against both source and target, compares tool calls, output structure, and semantic similarity
Step 5
Sentinel Agent monitors cross-cloud cost and latency feeds, triggers autonomous migration when thresholds breach
The Endgame

Translation is the wedge.
Autonomous migration is the platform.

Once agents are portable, they can move themselves. KodaIR's Sentinel layer monitors cost, latency, and compliance, then migrates workloads across clouds without human intervention.

1
Cost spike detected
on AWS Bedrock
→
2
Sentinel evaluates
GCP pricing and latency
→
3
Agent-IR translates
deploys natively to GCP
→
4
State-sync cutover
zero memory loss
→
5
Continuous optimization
across all clouds

Your AI workload has economic agency over where it runs.

Framework portability.
Cloud-native performance.
Zero lock-in.

KodaIR is in early development. We are working with design partners who run agents across multiple clouds. If that is you, let's talk.