Announcing AgentHelm v0.3.0: Production-Ready AI Agent Orchestration

Source: Dev.to

Introduction

After months of iteration, I’m excited to release AgentHelm v0.3.0, a significant step toward making AI agents production‑ready.

Building AI agents is easy. Running them reliably in production is hard. You need to handle:

• Multi‑step execution with failure recovery
• Memory persistence across sessions
• Cost and token tracking
• Observability and debugging
• Tool orchestration at scale

Most agent frameworks focus on the first mile (getting an agent to work) but neglect the last mile (keeping it running reliably).

Plan‑First Approach

Instead of letting agents run wild, AgentHelm introduces a plan‑first workflow:

Task → Plan Generation → Human Review → Execution

• PlannerAgent generates a structured plan with steps and dependencies.
• You review the plan.
• The Orchestrator executes it, parallelising where possible.

If something fails mid‑execution, the Saga pattern triggers compensating actions that automatically roll back completed steps.

MemoryHub

Memory is no longer an afterthought. MemoryHub provides:

Zero‑config by default, but it scales to production with Redis and a networked Qdrant instance.

Command‑Line Interface

Everything works from the command line:

# Run a one‑off task
agenthelm run "Analyze this quarter's sales data"

# Generate a plan and save it
agenthelm plan "Build a customer support bot" -o plan.yaml

# Dry‑run execution of a saved plan
agenthelm execute plan.yaml --dry-run

# Interactive chat mode
agenthelm chat

# List recorded traces
agenthelm traces list

Every tool execution is traced. Traces can be exported to Jaeger for visualization, and costs are tracked across 20+ LLM providers with built‑in pricing.

AgentHelm also connects to Model Context Protocol servers, allowing you to use their tools directly in your agents.

Installation

pip install agenthelm
agenthelm init
agenthelm chat

Roadmap (v0.4.0)

• Web dashboard for trace visualization
• Advanced conflict resolution in multi‑agent workflows
• Policy engine for budget and constraint enforcement
• Webhook integrations

Open Source

AgentHelm is open source. Contributions are welcome.

Documentation
GitHub