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Assign different AI models to different agents, run iterative loops, and let agents collaborate with structured output.\n\n```aflow\nworkflow code_quality\n  description: \"Iterative code review with writer, tester, and critic\"\n  version: \"1.0.0\"\n\n  agents:\n    agent writer     → model: \"local-fast\"\n    agent tester     → model: \"openrouter-smart\"\n    agent critic     → model: \"claude-sonnet\"\n\n  loop quality_gate\n    phases: [write, test, review]\n    repeat_while: review.verdict == \"needs_work\"\n    max_iterations: 5\n\n  done when: review.confidence >= 0.85\n```\n\n## Why AgentFlow?\n\n| | Traditional (LangGraph, CrewAI) | AgentFlow DSL |\n|---|---|---|\n| **Define workflows** | Python code (~40 lines boilerplate) | `.aflow` file (~20 lines) |\n| **Multi-model** | Manual provider switching | Per-agent model aliases |\n| **MCP integration** | Write MCP server code | Automatic — each workflow = MCP tool |\n| **Git-friendly** | Code + config scattered | Single `.aflow` file |\n| **Reviewable** | Need Python knowledge | Readable by anyone |\n| **Safety** | Manual guardrails | `irreversible: true` approval gate |\n| **Transparency** | Custom logging | Execution receipt + tool declaration |\n\nRun workflows on your **Claude subscription credit** (`provider: agent-sdk`) instead of pay-as-you-go API billing, poll long runs over MCP without timeouts, and gate irreversible actions (money, deploys, deletions) behind explicit approval.\n\n## Quick Start\n\n### 1. Install\n\n```bash\nnpm install -g @anhonestboy/agentflow\n```\n\n### 2. Configure\n\n```bash\nagentflow init\n```\n\nInteractive wizard: choose providers (Claude, OpenRouter, Ollama), configure model aliases, save API keys.\n\n### 3. Write a workflow\n\nCreate `my-workflow.aflow`:\n\n```aflow\nworkflow blog_post\n  description: \"Generate and refine a blog post\"\n  version: \"1.0.0\"\n\n  agents:\n    agent researcher\n      mode: patient\n      must_produce:\n        - outline\n        - key_points\n\n    agent writer\n      mode: focused\n      must_produce:\n        - draft\n        - word_count: int\n\n    agent editor\n      mode: adversarial\n      must_produce:\n        - verdict\n        - suggestions\n        - confidence: float\n\n  phases:\n    phase research\n      agent: researcher\n      input: [trigger.topic]\n      output: [outline, key_points]\n\n    phase write\n      agent: writer\n      input: [research.outline, research.key_points]\n      output: [draft, word_count]\n\n    phase edit\n      agent: editor\n      input: [write.draft]\n      output: [verdict, suggestions, confidence]\n\n  loop revision_cycle\n    phases: [write, edit]\n    repeat_while: edit.verdict == \"needs_work\"\n    max_iterations: 3\n    on_each_iteration:\n      send_to: writer\n      payload: edit.suggestions\n\n  done when: edit.confidence >= 0.8 and edit.verdict == \"approved\"\n```\n\n### 4. Run\n\n```bash\nagentflow check my-workflow.aflow     # Validate\nagentflow run my-workflow.aflow --input 'topic=\"AI in photography\"'\n```\n\n### 5. Add to Claude Code\n\n```bash\nagentflow mcp-config\n```\n\nCopy the JSON output to your Claude Code MCP settings. Your workflow is now a tool — call it directly from Claude Code.\n\n## Supported Providers\n\n| Provider | Status | Notes |\n|---|---|---|\n| **Claude** (Anthropic) | ✅ | Native SDK, multi-round tool use, API key (pay-as-you-go) |\n| **Claude Agent SDK** | ✅ | Subscription auth — uses your plan's monthly Agent SDK credit |\n| **OpenRouter** | ✅ | 315+ models, automatic provider routing |\n| **DeepSeek** | ✅ | Native API (`deepseek-chat`, `deepseek-reasoner`), no OpenRouter key needed |\n| **Ollama** | ✅ | Local execution, no API key needed |\n\nConfigure model aliases for cost optimization — use cheap models for drafting, frontier models for review:\n\n```json\n{\n  \"models\": {\n    \"local-fast\":       { \"provider\": \"ollama\",      \"model\": \"qwen3:8b\" },\n    \"openrouter-smart\": { \"provider\": \"openrouter\",  \"model\": \"google/gemini-2.5-flash\" },\n    \"deepseek-chat\":    { \"provider\": \"deepseek\",    \"model\": \"deepseek-chat\" },\n    \"claude-sonnet\":    { \"provider\": \"claude\",      \"model\": \"claude-sonnet-4-5\" },\n    \"claude-plan\":      { \"provider\": \"agent-sdk\",   \"model\": \"claude-sonnet-4-5\" }\n  }\n}\n```\n\nThe `deepseek` provider talks to DeepSeek's native OpenAI-compatible API at `https://api.deepseek.com` and reads the key from `DEEPSEEK_API_KEY` — no OpenRouter account required. Example models: `deepseek-chat` (V3) and `deepseek-reasoner` (R1).\n\n### Run on your Claude subscription (no API credits)\n\nThe `agent-sdk` provider routes agents through the [Claude Agent SDK](https://platform.claude.com/docs/en/agent-sdk/overview) using your Claude login instead of an API key. Usage draws from your plan's [monthly Agent SDK credit](https://support.claude.com/en/articles/15036540-use-the-claude-agent-sdk-with-your-claude-plan) (Pro $20, Max 5x $100, Max 20x $200 — starting June 15, 2026), not from pay-as-you-go API billing.\n\nSetup:\n\n```bash\nnpm install @anthropic-ai/claude-agent-sdk   # optional dependency\nclaude login                                  # Claude Code must be logged in\n```\n\nThen point an agent (or alias) at the provider:\n\n```aflow\nagent writer\n  model: \"claude-plan\"\n```\n\nNote: if `ANTHROPIC_API_KEY` is set, the Agent SDK would silently bill your API account instead — AgentFlow unsets it for `agent-sdk` agents so usage stays on the subscription credit.\n\n## CLI Reference\n\n```bash\nagentflow init                     # Interactive setup wizard\nagentflow check <file>             # Validate workflow + summary\nagentflow run <file> --input '…'   # Execute with real LLMs\nagentflow run <file> --mock        # Execute with mock agents (no API key needed)\nagentflow run <file> --approve-irreversible   # Authorize irreversible phases\nagentflow run <file> --output-dir <dir>       # Where phase outputs go (or $AGENTFLOW_OUTPUT_DIR)\nagentflow run <file> --state-dir  <dir>       # Where <uuid>.state.json goes (or $AGENTFLOW_STATE_DIR)\nagentflow compile <file>           # Compile to IR JSON\nagentflow validate <file>          # Validate only (no summary)\nagentflow mcp-config               # Print MCP server config for Claude Code\nagentflow models                   # List configured models + connectivity\nagentflow resume <file> --instance <uuid>  # Resume a paused/interrupted workflow\n```\n\n### Exit codes (`run` / `resume`)\n\n`run` and `resume` return an honest exit code so scripts and orchestrators can branch on the outcome:\n\n| Code | Meaning |\n|---|---|\n| `0` | Workflow **completed** |\n| `1` | Workflow **failed** (or an unexpected terminal state) — a summary of `failed_steps` is printed |\n| `2` | Workflow **paused** at an irreversibility gate or a `human_action_required` phase — the resume command is printed |\n\nEvery run also prints a stable, parseable cost line:\n\n```\n💰 total_cost_usd=0.012300 total_prompt_tokens=4500 total_completion_tokens=1200 cost_known=true\n```\n\n`cost_known=false` means no executor reported a dollar cost (e.g. local Ollama, or an unpriced model). Anthropic and DeepSeek costs come from a small static pricing map (override with `AGENTFLOW_PRICING_JSON='{\"model\":{\"input\":<usd/1M>,\"output\":<usd/1M>}}'`); OpenRouter reports its real cost via usage accounting. When cost is known, `max_cost` aborts the workflow once the accumulated cost exceeds the cap — including cost spent on schema-validation retries.\n\nA budget-aborted run is reported as **failed (exit 1)**, not paused: the receipt records a `budget` failed step, and the saved state remains **resumable** (`agentflow resume` continues from the last checkpoint, e.g. after raising `max_cost`).\n\n### State & output directories\n\nBy default `<uuid>.state.json` is written to the current directory and outputs to `./output/<workflow-id>`. When AgentFlow runs inside a target repo (e.g. via flow), set `--state-dir` / `$AGENTFLOW_STATE_DIR` and `--output-dir` / `$AGENTFLOW_OUTPUT_DIR` to keep the working tree clean. `resume` reads state from the same configured directory.\n\n### Tools are `claude`-only\n\nOnly the `claude` provider actually executes agent `tools` (`file_write`, `file_read`, `shell_exec`, `test_runner`). Declaring tools on an `openrouter`/`deepseek`/`ollama`/`agent-sdk` agent — or naming a tool the registry doesn't implement — is a **validation error (S14)**: the model would otherwise hallucinate the tool results. Either move the agent to a `claude` model or drop the tools.\n\n## Language Reference\n\n### Agents\n\n```\nagent <id>\n  model: \"<alias>\"         # Model alias from config (default: \"auto\")\n  mode: <mode>             # focused | adversarial | reliable | precise | strict | patient | objective\n  tools: [<name>, ...]     # Built-in tools: file_write, file_read, shell_exec, test_runner\n  must_produce:\n    - <name>               # Required output field (string)\n    - <name>: float        # Typed output field\n  constraint: \"<rule>\"     # Natural language constraint\n```\n\n### Phases\n\n```\nphase <id>\n  agent: <agent_id>\n  input: [<ref>, ...]          # trigger.field or phase_id.output\n  output: [<name>, ...]\n  inject_context: \"<path>\"     # Optional: inject file content into agent context\n  timeout: 30min               # For human_action_required phases\n```\n\n### Loops\n\n```\nloop <id>\n  phases: [<phase_id>, ...]\n  repeat_while: <condition>    # review.verdict == \"needs_work\"\n  max_iterations: <n>\n  on_each_iteration:\n    send_to: <agent_id>\n    payload: <ref>             # Feedback to inject\n  on_max_exceeded:\n    escalate_to: <agent_id>\n    message: \"<...>\"\n```\n\n### Conditions\n\n```aflow\n# Comparison\nreview.confidence >= 0.85\n\n# Logical\nreview.verdict == \"approved\" and review.confidence >= 0.85\nnot (review.verdict == \"needs_work\")\n```\n\n## Architecture\n\n```\n.aflow file\n    │\n    ▼\n Tokenizer ──► Parser ──► Compiler (AST → IR)\n                              │\n                    ┌─────────▼──────────┐\n                    │   Validator (S1-S14) │\n                    └─────────┬──────────┘\n                              │\n                    ┌─────────▼──────────┐\n                    │  WorkflowRunner     │\n                    │  ┌───────────────┐  │\n                    │  │ ExecutorResolver│  │\n                    │  │ ┌─────────────┐│  │\n                    │  │ │ Claude      ││  │\n                    │  │ │ OpenRouter  ││  │\n                    │  │ │ Ollama      ││  │\n                    │  │ └─────────────┘│  │\n                    │  └───────────────┘  │\n                    └─────────┬──────────┘\n                              │\n                    ┌─────────▼──────────┐\n                    │  MCP Server         │\n                    │  (stdio JSON-RPC)   │\n                    └─────────┬──────────┘\n                              │\n                     Claude Code / Cursor\n```\n\n## Examples\n\n| File | Description |\n|---|---|\n| `examples/blog-post.aflow` | Researcher → writer → editor with revision loop |\n| `examples/code-quality.aflow` | Writer → tester → critic with quality gate loop |\n| `examples/code-quality-with-plan.aflow` | Extended with planning phase |\n| `examples/custom-domain.aflow` | 7-phase domain provisioning workflow |\n\n## Development\n\n```bash\ngit clone https://github.com/anhonestboy/agentflow.git\ncd agentflow\nnpm install\nnpm run build\nnpm test          # 231 tests, 24 suites\nnpm run dev -- check examples/code-quality.aflow\n```\n\n## Roadmap\n\n- **v1.1** — VS Code extension (syntax highlighting, LSP)\n- **v1.2** — Parallel phase execution\n- **v1.3** — Workflow registry & sharing\n- **v2.0** — Web visualizer, CI/CD integration\n\n## License\n\nMIT — see [LICENSE](LICENSE) for details.\n","readmeFilename":"README.md"}