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OpenHands (v1)

OpenHands re-architected: cleaner controller, refined memory condenser, improved tool dispatch. v1 of the All-Hands agent.

Category
agent-platform
Language
Python · TypeScript
Runtime
Python 3.12+ · Node
Providers
LiteLLM (any)
Agent loop
event-bus
Tool format
openai-fn / anthropic-tool_use
Memory
llm-summarize
Caching
cache_control-explicit
Sandbox
docker
Pedagogical
★★★★★
Flags
multi-agent · thinking · md-skills
Concepts
agent-loop ·tool-calling-formats ·memory-compression ·prompt-caching ·multi-agent-coordination ·guardrails ·provider-abstraction ·sandboxing
Verified
cloned
Upstream
github.com/All-Hands-AI/OpenHands

What it is

The v1 of OpenHands — re-architected from v0 with a cleaner controller, a pluggable memory condenser, and better tool dispatch. The cleanest case in the corpus for event-sourced agent state.

What’s worth studying

Three things make OpenHands v1 worth your time:

  1. Event-sourced architecture. Every action and observation is an immutable event in an append-only log. The “context for the next LLM call” is a derived view, not the source of truth. Replay debugging is free; audit logging is free; microagent triggers attach as event subscribers.
  2. Pluggable memory condenser. Strategies (LLM summarizing, no-op, etc.) implement a common interface and you swap them per workload. The summarizer prompt is tuned to preserve task IDs, file paths, and exact error messages — the preservation list is the design.
  3. Mock function-calling for non-native models. A fn_call_converter injects examples into the prompt for models without native tool calls and post-processes their output. Every model gets the same structured tool API. Powerful for cross-provider portability.

A fourth pattern — the temperature perturbation recovery for empty responses — lives in the LLM client wrapper. A simple cheap fix for a specific failure mode.

Drill-down

The full per-doc analysis lives below — these are the original numbered analyses, rendered as styled HTML. Pick a section to study deeper.