Durable Workflows for AI agents
Make your AI agents resilient to failure and outages
The diagrid package is an extension SDK for the open-source Dapr project to build durable, fault-tolerant AI agents. It integrates seamlessly with popular agent frameworks, wrapping them in Dapr Workflows to ensure agents can recover from failures, persist state across restarts, and scale effectively.
Get started with Diagrid Catalyst for free.
Community
Have questions, hit a bug, or want to share what you're building? Join the Diagrid Community Discord to connect with the team and other users.
Features
- Multi-Framework Support: Native integrations for LangGraph, CrewAI, Google ADK, Strands, PydanticAI, OpenAI Agents, Claude Agent SDK, LangChain, Smolagents, LangChain Deep Agents, and HolmesGPT.
- Durability: Agent state is automatically persisted in the database of your choice. If your process crashes, the agent resumes from the last successful step.
- Fault Tolerance: Built-in retries and error handling powered by Dapr.
- Observability: Deep insights into agent execution, tool calls, and state transitions.
Installation
Install the base package along with the extension for your chosen framework:
# For LangGraph
pip install "diagrid[langgraph]"
# For CrewAI
pip install "diagrid[crewai]"
# For Google ADK
pip install "diagrid[adk]"
# For Strands
pip install "diagrid[strands]"
# For Pydantic AI
pip install "diagrid[pydantic_ai]"
# For OpenAI Agents
pip install "diagrid[openai_agents]"
# For LangChain Deep Agents
pip install "diagrid[deepagents]"
# For Claude Agent SDK
pip install "diagrid[claude_agents]"
# For LangChain
pip install "diagrid[langchain]"
# For Smolagents
pip install "diagrid[smolagents]"
# For HolmesGPT (install in a dedicated environment — see note below)
pip install "diagrid[holmesgpt]"
Note:
diagrid[holmesgpt]is intentionally not part ofdiagrid[all]. HolmesGPT ships strict pins onfastapi,uvicorn,cachetools,mcp, andhttpx[socks]that conflict with the looser constraints used by the other agent extras. Install it in its own environment.
Prerequisites
- Python: 3.11 or higher
Examples & Quickstarts
Two paths to your first running agent:
- Managed quickstart templates —
diagridpy init my-project --framework langgraphbootstraps a Catalyst project, local kind cluster, Helm chart, and framework template in one command. Templates are cloned fromdiagridio/catalyst-quickstarts. - In-repo code samples — clone this repo and run any framework's example directly. See
examples/for the index, or jump straight to a framework:langgraph,crewai,adk,strands,openai_agents,claude_agents,pydantic_ai,deepagents,langchain,smolagents,holmesgpt.
Getting Started with Diagrid Catalyst
Diagrid Catalyst is a fully managed workflow engine for AI agents, built on the open-source CNCF Dapr Workflow project. It's the easiest way to test the different agentic integrations for free.
See quickstarts to get started in less than 5 minutes.
How It Works
This SDK leverages Dapr Workflows to orchestrate agent execution.
- Orchestration: The agent's control loop is modeled as a workflow.
- Activities: Each tool execution or LLM call is modeled as a durable activity.
- State Store: Dapr saves the workflow state to a configured state store (e.g., Redis, CosmosDB) after every step.
Your code can run anywhere (local machine, Kubernetes, EC2, etc.) while the fully managed workflow engine takes care of the agent's execution state, making it crash-proof and resilient to any outage or failure.
Release files for diagrid 0.4.6
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| diagrid-0.4.6.tar.gz | 177.5 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| diagrid-0.4.6-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 412.3 kB
Release files / diagrid-0.4.6.tar.gz
| Download URL | diagrid-0.4.6.tar.gz |
|---|---|
| Size | 177.5 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
92a4d6ef9dfdc3a14da068f0df1b343de3a5ea4c47ca2f7ba49c86c9630175ed
|
|
BLAKE2b-256 checksum How to use checksums |
ddd6bf2acb4f16df4ef7c32c14d95a0ce7943694f01bf23461a2ccbbc768a5f3
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Sep 15, 2026.
Transparency logRelease files / diagrid-0.4.6-py3-none-any.whl
| Download URL | diagrid-0.4.6-py3-none-any.whl |
|---|---|
| Size | 234.8 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
84a931927ef66ddac8f9ae4d1e940b4558d8889ae4d51badbd1a1b720769bfe8
|
|
BLAKE2b-256 checksum How to use checksums |
ab0bcff9596bcdcb89fba45ff17c00a6fea8710cf47d3f2246e3af82a44e8271
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Sep 15, 2026.
Transparency log