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Releases: cchinchilla-dev/agentloom

v0.4.0

15 Apr 21:06
e5d39a8

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Full Changelog: v0.3.0...v0.4.0

v0.3.0

12 Apr 12:23
715e15e

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Full Changelog: v0.2.0...v0.3.0

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v0.2.0

30 Mar 10:33
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Full Changelog: v0.1.2...v0.2.0

v0.1.2

26 Mar 20:37
87f09bc

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Bug Fixes

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Full Changelog: v0.1.1...v0.1.2

v0.1.1

22 Mar 17:53
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Full Changelog: v0.1.0...v0.1.1

v0.1.0

22 Mar 16:13
3fc241c

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First public release

Production-ready agentic workflow orchestrator with native observability, resilience, and cost control.

Highlights

  • YAML and Python DSL workflow definitions — DAGs with sequential + parallel steps
  • Step types: llm_call, tool, router (conditional), subworkflow
  • Provider gateway with automatic fallback (OpenAI, Anthropic, Google, Ollama)
  • Resilience — circuit breaker, rate limiter, and retry with exponential backoff per provider
  • Budget enforcement — hard stop when USD limit exceeded
  • Cost tracking per step, model, and provider
  • Observability — OpenTelemetry traces + Prometheus metrics (optional, pip install agentloom[all])
  • CLIrun, validate, visualize (ASCII + Mermaid), info
  • Checkpointing — save and resume workflow state to disk

Design decisions

  • httpx over provider SDKs — minimal dependencies (~5 core)
  • anyio over raw asyncio — structured concurrency via task groups
  • str.format_map over Jinja2 — one fewer dependency for prompt templates
  • Observability optional — NoopSpan/NoopTracer pattern, zero overhead when not installed
  • Pydantic v2 — validation and serialization for all models

Stats

  • 392 tests, mypy strict, ruff clean
  • 16 workflow examples included

Full Changelog: https://github.com/cchinchilla-dev/agentloom/commits/v0.1.0