{"slug": "awf-ai-workflow-framework", "title": "AWF – AI Workflow Framework", "summary": "AWF, an open-source AI workflow framework, launched a three-part toolset: the Go-powered AWF CLI for YAML-defined, provider-agnostic workflows across OpenAI, Claude, Gemini and Ollama; ZPM, a Zig and Prolog reasoning layer exposing 22 MCP tools for facts, rules, queries, proofs and truth maintenance; and ztick, a Zig push-driven scheduler that fires jobs at requested times over TCP, webhooks, AMQP, Redis or AWF workflows. The framework claims deterministic replay of CLI workflows on identical inputs and provable ZPM deductions through Prolog inference chains, with per-step, per-workflow and per-run token tracking. AWF ships as static Go and Zig binaries with zero runtime dependencies, a native TCP protocol with TLS 1.3 and REST/OpenAPI 3.1.1, and positions itself as OSS end-to-end with no vendor lock-in.", "body_md": "AWF CLI orchestrates your AI workflows. ZPM gives your agents a memory they can reason over. ztick fires them at the precise moment you decide. Use one, use all three — they're designed to work together.\n\n01 — The Orchestrator\n\nCLI\n\nGo-powered, provider-agnostic, YAML-native. Define workflows once, run them from your terminal, wire them into CI. The foundation everyone builds on.\n\nZig + Prolog. Give your LLMs deterministic logical reasoning. 22 MCP tools for facts, rules, queries, proofs, and truth maintenance — usable as MCP server or direct CLI.\n\nZig-powered, push-driven scheduler. Cron in reverse: your apps send SET over TCP at the moment they decide, ztick fires at the requested time — into shells, webhooks, AMQP, Redis, or AWF workflows.\n\nNative TCP protocol with TLS 1.3 + REST/OpenAPI 3.1.1\n\nVendor-agnostic at the orchestration layer — OpenAI, Claude, Gemini, Ollama — and protocol-open at the reasoning layer via MCP. Zero lock-in, OSS end-to-end.\n\n02\n\nDeterministic AI\n\nCLI workflows replay identically on the same inputs; ZPM deductions are provable through Prolog inference chains. AI becomes as predictable as the rest of your stack.\n\n03\n\nProduction-Grade\n\nGo and Zig, static binaries, zero runtime dependencies. Structured logs, audit trails, and proof explanations across both products. Built for enterprise, not demos.\n\nChapter 03 — Why It Matters\n\nWhat you actually get\n\nConcrete problems the ecosystem solves, in plain terms.\n\n01\n\nShip in days, not months\n\nWorkflows in YAML, one static binary, zero SDK sprawl. Install, write, run — today.\n\n02\n\nYour AI bill stops being opaque\n\nEvery token tracked per step, per workflow, per run. Audit spend line by line — catch runaway agents before they run away.\n\n03\n\nSwap providers without a rewrite\n\nOpenAI raising prices? Switch to Claude. Need privacy? Drop in Ollama. Edit one line of YAML — your workflows don't care.\n\n04\n\nExplain every AI decision to audit\n\nCLI workflows replay identically. ZPM proves each conclusion through explicit inference chains. \"Why did the AI say X?\" finally has an answer.\n\n05\n\nStop your agents from making stuff up\n\nGive LLMs a knowledge base that reasons. Facts persist, rules apply, contradictions surface. \"The AI hallucinated\" becomes \"the assumption was wrong\".\n\n06\n\nRun in prod without a DevOps team\n\nTwo static binaries, zero runtime dependencies. Docker if you want, bare metal if you prefer. Any CI, any server, any laptop.\n\n…or, let's be honest\n\nThe reasons you'll actually end up installing it tonight.\n\nYour engineers were tired yesterday and prompted worse.\n\nYou read a wild thread on Reddit last Friday at 1 a.m.\n\nYour last AI demo segfaulted in front of the CEO.\n\nAccounting cried at the last vendor invoice.\n\nYour prompt is now 4,000 lines and nobody owns it.\n\n“ChatGPT said so” is not an architecture diagram.\n\nThe all-hands deck has the word agentic on slide 3.\n\nChapter 04 — The CLI\n\nAWF CLI\n\nThe foundation of the ecosystem. A lightweight, powerful tool to build and run AI workflows from your terminal.\n\n01\n\nLightning Fast\n\nWritten in Go for blazing performance. Every millisecond counts in production.\n\n02\n\nProvider Agnostic\n\nSwitch between OpenAI, Anthropic, or local LLMs without changing a line of code.\n\n03\n\nYAML Native\n\nDefine workflows in simple, version-controllable files. Human readable, machine precise.\n\n04\n\nProduction Ready\n\nBuilt-in validation and error handling for mission-critical deployments.\n\nDefine your entire workflow in a single YAML file. Human-readable, version-controllable, and production-ready.\n\nBuilt for pros\n\nWhen the demo ends and production begins\n\nLoops\n\nFor-each & while\n\nIterate over arrays, file lists, or API results with full workflow context inside each iteration.\n\ntype: foreach\n\nConversations\n\nMulti-turn agents\n\nNative session resume on Claude, Codex, Gemini, OpenCode. Token tracking and context injection across turns.\n\ntype: conversation\n\nResume\n\nPick up where it crashed\n\nEvery run has an ID. Interrupt it, fix it, resume from the failed step — no need to start over.\n\nawf resume <id>\n\nSub-workflows\n\nCompose like functions\n\nCall one workflow from another with typed input/output mapping. Build libraries of reusable pipelines.\n\ntype: call_workflow\n\nParallel\n\nFan out without fan-pain\n\nRun multiple steps concurrently with configurable strategies — all, any, race. Collect results in one place.\n\ntype: parallel\n\nRetry\n\nBackoff, baked in\n\nExponential, linear, or constant backoff per step. Flaky APIs and rate limits stop waking you up at 3 a.m.\n\nretry: {strategy: exponential}\n\nDiagrams\n\nExport the state machine\n\nGenerate Graphviz DOT for any workflow. Render it, ship it to architecture docs, impress the auditor.\n\nawf diagram <workflow>\n\nAudit trail\n\nJSONL, secrets masked\n\nPaired start/end entries per step, atomic writes, secret redaction, replayable history. Compliance happy.\n\nawf history\n\nSee it in action\n\nExecute your workflows with a single command. AWF provides detailed logging, step tracking, and clear completion status with workflow IDs for reproducibility.\n\nTerminal\n\n``` bash\n$ awf run code-review --input file=service.go --output=streaming --verbose=true\n\nregistered desktop notification backend\nregistered webhook notification backend\nRunning workflow: code-review\nstarting workflow workflow=code-review id=3eb274fa-186e-4070-a6a0-c26eea61ad28\nexecuting step step=read\nexecuting step step=analyze\nexecuting agent step step=analyze provider=claude\nexecuting step step=report\n[OUT] Analysis complete\nrecorded execution history workflow_id=3eb274fa-186e-4070-a6a0-c26eea61ad28 status=completed\nworkflow completed step=done status=completed\n  ✓ read: completed successfully\nWorkflow completed successfully in 36.6s\nWorkflow ID: 3eb274fa-186e-4070-a6a0-c26eea61ad28\n\n--- Execution Details ---\nStatus: completed\nSteps executed:\n  report:    completed (1ms)\n  read:      completed (1ms)\n  analyze:   completed (36.585s)\n```\n\nAWF provides detailed execution logs, validation feedback, and clear success/failure status for every workflow run.\n\nGet Started with AWF CLI\n\nThe core engine of the AWF ecosystem. Install it in seconds and start building.\n\nIf it's in your PATH, it's in your workflow. The CLI orchestrates any CLI utility with zero configuration.\n\nChapter 05 — Extend It\n\nPlugins & Workflow Packs\n\nThe CLI is a platform, not just a tool. Write your own step types or share a whole workflow as a reusable pack.\n\nPlugins\n\nWrite your own step types\n\nPlugins add new primitives to the workflow DSL — inject real-time data, call internal APIs, run custom tools. Distribute them as binaries, load them from anywhere.\n\nGive your LLM a memory that reasons. ZPM exposes the same 22 tools over the MCP protocol and as direct CLI subcommands — use it from Claude Code, Cursor, or straight from your shell.\n\nGet Started with ZPM\n\nThe reasoning engine of the AWF ecosystem. One binary, zero runtime dependencies.\n\nThe triggering layer of the ecosystem. A Zig-powered, push-driven scheduler — cron in reverse. Apps schedule jobs at the moment they decide, ztick fires at the requested time.\n\n01\n\nPush-Driven\n\nYour app sends SET over TCP at the moment it decides. ztick fires at the requested time. No polling, no fixed intervals, no drift.\n\n02\n\nMulti-Runner Dispatch\n\nShell, direct execve, HTTP webhook, AWF workflow, AMQP publisher, Redis command. One scheduler, every backend you already run.\n\n03\n\nTCP + REST + TLS\n\nNative TCP protocol with TLS 1.3, plus REST/JSON HTTP API with embedded OpenAPI 3.1.1. Token AUTH, namespace-scoped authorization.\n\n04\n\nDurable & Observable\n\nAppend-only logfile with binary framing and background compression. OpenTelemetry traces and metrics over OTLP/HTTP. Zero runtime dependencies.\n\nA tight TCP protocol or a REST endpoint — pick your interface. ztick chains directly into AWF workflows, so you can trigger orchestrations on demand: no cron, no drift, no surprises.\n\nGet Started with ztick\n\nThe triggering engine of the AWF ecosystem. One Zig binary, zero runtime dependencies.", "url": "https://wpnews.pro/news/awf-ai-workflow-framework", "canonical_source": "https://awf-project.ai/", "published_at": "2026-10-29 11:15:46+00:00", "updated_at": "2026-10-29 11:48:03.533323+00:00", "lang": "en", "topics": ["ai-agents", "ai-tools", "developer-tools", "agent-protocols", "artificial-intelligence"], "entities": ["AWF", "ZPM", "ztick", "OpenAI", "Anthropic", "Claude", "Gemini", "Ollama"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/awf-ai-workflow-framework", "markdown": "https://wpnews.pro/news/awf-ai-workflow-framework.md", "text": "https://wpnews.pro/news/awf-ai-workflow-framework.txt", "jsonld": "https://wpnews.pro/news/awf-ai-workflow-framework.jsonld"}}