agentgraph-compiler 0.2.1

Compile structured YAML configurations into production-grade LangGraph multi-agent architectures.

GraphForge 🛠️🦜🔗

Compile structured YAML configurations into production‑grade LangGraph multi‑agent architectures.

GraphForge eliminates LangGraph boilerplate by letting you declare your agent topology in YAML. Define nodes, chains, tools, edges, sub‑agents, and supervisors — the compiler scaffolds a complete, modular, runnable Python codebase ready for production.

📖 Full Developer Wiki → — complete reference for every YAML field, node type, edge type, and generated output.

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🚀 Key Features

• Declarative Multi‑Agent Workflows — Define the full agent graph (nodes, edges, sub‑agents, supervisor routing) in simple YAML.
• TypedDict State (LangGraph‑native) — Generates TypedDict classes with Annotated[..., add_messages] reducers by default. Pydantic BaseModel is available via state_type: pydantic.
• Supervisor Orchestration — type: supervisor nodes generate an LLM-driven routing scaffold with configurable route_field and compile-time validation.
• Hierarchical Parent-Scoped Output — Sub-agent code is generated inside subdirectories under the parent source_dir (e.g. my_agent/adaptive_rag/).
• All LangGraph Edge Types — standard, conditional, supervisor_route, tool_loop, fan_out (Send API), and interrupt_before human‑in‑the‑loop.
• Parallel Fan-Out via Send API — type: fan_out dispatches one node to multiple inputs simultaneously using langgraph.types.Send.
• Async Node Scaffolding — async_fn: true generates async def node functions with notes on awaiting LLM/tool calls.
• Multi-Model LLM Support — provider: openai | anthropic | ollama | groq per chain or supervisor node with model-specific imports scaffolded.
• MCP Tool Client Stubs — source: mcp in the tools block scaffolds a Model Context Protocol client.
• Checkpointing — memory.type: sqlite | in_memory wires SqliteSaver / MemorySaver into workflow.compile().
• Real-time Streaming — Generated main.py uses app.stream() with thread config for node-by-node event output.
• Mermaid Flowcharts — Auto-generates flowchart.md with nested subgraph rendering and distinct supervisor styling.
• CLI — forge compile, forge validate, forge init, forge ingest, plus --dry-run preview.

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🛠️ Quick Start

1. Install

Option A — PyPI (once published)

pip install agentgraph-compiler
# or
uv add agentgraph-compiler

Option B — Directly from GitHub (available now, no clone needed)

pip install agentgraph-compiler

Option C — Clone for local development

git clone https://github.com/cvaws/GraphForge.git
cd GraphForge
uv sync

After any install, the forge CLI is available in your environment:

forge --help

2. Initialize a Project

uv run forge init

Creates:

• configs/config.yaml — Global settings (models, vector store, embedding)
• configs/agents/agent.yaml — Starter agent workflow spec

3. Configure Environment

cp .env.example .env
# Fill in OPENAI_API_KEY (and optionally LANGCHAIN_API_KEY for tracing)

4. Validate Configuration

uv run forge validate configs/agents/agent.yaml

Runs Pydantic schema validation and checks all edge targets exist as declared nodes — without writing any files.

5. Compile Agent Topology

# Preview without writing files
uv run forge compile configs/agents/agent.yaml --dry-run

# Compile Python scaffold
uv run forge compile configs/agents/agent.yaml

6. Run the Agent

uv run python my_agent/main.py "How do I build multi-agent graphs with GraphForge?"

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💡 Try an Example

Six ready-to-compile examples cover every supported LangGraph pattern — all configs live in configs/agents/examples/:

Example	Pattern
simple_rag/	Single-agent RAG with conditional edges
multi_agent/	Supervisor + 2 sub-agents via graphRef
react_agent/	ReAct tool_loop + MCP tool client
parallel_fanout/	Parallel fan_out via Send API
human_in_loop/	interrupt_before + approve/revise cycle
async_agent/	async_fn: true async nodes

# Example: compile and run the ReAct agent
uv run forge compile configs/agents/examples/react_agent/agent.yaml
uv run python react_agent/main.py "What is the current price of Bitcoin?"

See configs/agents/examples/README.md for compile/run instructions for all examples.

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📖 CLI Reference

Command	Usage	Description
compile	forge compile <path> [--dry-run]	Compiles YAML into Python scaffolds under source_dir.
validate	forge validate <path>	Schema validation + edge target reference checks.
init	forge init [directory]	Scaffolds starter configs/config.yaml and agent.yaml.
ingest	forge ingest [--config path]	Ingests URLs from config.yaml into Qdrant vector store.

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⚙️ Global Configuration (configs/config.yaml)

Centralize model providers, vector store settings, and ingestion URLs:

models:
  chat:
    name: gpt-4o
    temperature: 0.5
  embedding:
    name: text-embedding-3-small
    provider: openai

vector_store:
  name: qdrant
  url: "http://localhost:6333"
  collection: "agent"

ingestion:
  urls:
    - "https://lilianweng.github.io/posts/2023-06-23-agent/"

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📖 YAML Reference

Graph Config

version: "1.0"

graph:
  source_dir: "my_agent"               # Output directory for scaffolded Python code
  state_type: typeddict                 # "typeddict" (default, LangGraph-native) | "pydantic"
  state_schema: "graphs.state.AgentState"
  state_fields:
    messages: "List[BaseMessage]"       # → Annotated[List[BaseMessage], add_messages]
    next: "str"                         # Supervisor routing field
    context: "dict"
  shared_fields: [messages, context]    # Fields readable/writable by sub-agents
  memory:
    type: sqlite                        # "sqlite" | "in_memory" | omit for stateless
    db_path: ".checkpoints/agent.db"

Node Types

nodes:
  # Standard node — calls a Python function
  my_node:
    module: "graphs.nodes.my_node"
    function: "my_node"
    chains: ["my_chain"]              # Chains to import into the node
    description: "Does something useful."

  # Async node
  async_node:
    module: "graphs.nodes.async_node"
    async_fn: true                    # Scaffolds `async def async_node(state)`

  # Supervisor node — LLM-driven router
  supervisor:
    type: supervisor
    provider: openai                  # openai | anthropic | ollama | groq
    llm: gpt-4o
    route_field: next                 # State field the supervisor writes to (default: "next")
    system_prompt: "templates/supervisor.prompt"
    routes_to: [rag_agent, code_agent]

  # Sub-agent reference — compiles recursively, nested under parent source_dir
  rag_agent:
    graphRef: "sub-agents/adaptive-rag.yaml"
    description: "Adaptive RAG sub-agent."

Chain Config

chains:
  retrieval_grader:
    module: "graphs.chains.retrieval_grader"
    runnable: "retrieval_grader"
    provider: anthropic                         # Per-chain provider
    llm: claude-3-5-haiku-20241022
    temperature: 0.0
    prompt: "retrieval_grader"                  # Loads templates/retrieval_grader.prompt
    tools: [web_search]                         # Binds tool imports into chain scaffold
    description: "Binary relevance grader."

Edge Types

edges:
  # Standard directed edge
  - type: standard
    start: retrieve
    end: grade_documents

  # Conditional edge with a router function
  - type: conditional
    start: grade_documents
    router:
      module: "routers.router"
      function: "route_after_grading"
    mappings:
      web_search: web_search
      generate: generate

  # Supervisor routing — dispatches based on state[route_field]
  - type: supervisor_route
    start: supervisor
    routes_to: [rag_agent, code_agent]
    end: post_process

  # Tool loop — LLM node ↔ ToolNode
  - type: tool_loop
    start: llm_node
    tool_node: tools
    end: END

  # Fan-out — parallel dispatch via Send API
  - type: fan_out
    start: coordinator
    target: worker              # Node to fan out to (runs in parallel)
    field: tasks                # State field (iterable) to fan out over
    key: task                   # Key name in each Send payload
    end: aggregator

  # Human-in-the-loop — pause before this node
  - type: standard
    start: generate
    end: human_review
    interrupt_before: true

Tool Config

tools:
  web_search:
    module: "graphs.tools.web_search"
    function: "web_search"
    description: "Queries Tavily for web results."

  # MCP Tool Client stub
  my_mcp_tool:
    module: "graphs.tools.my_mcp_tool"
    function: "my_mcp_tool"
    source: mcp
    server_url: "http://localhost:8000/mcp"
    description: "Calls remote MCP server."

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📊 Generated Flowchart

GraphForge auto-generates flowchart.md for every compiled agent:

flowchart TD
    START([START]) --> supervisor{{"Supervisor: supervisor\n(Routes request to sub-agent)"}}
    supervisor -.->|rag_agent| rag_agent__START
    supervisor -.->|code_agent| code_agent__START

    subgraph rag_agent ["rag_agent (Adaptive RAG Sub-Agent)"]
        direction TB
        rag_agent__START --> retrieve["retrieve\n(Pulls document chunks from vector store)"]
        retrieve --> grade_documents["grade_documents"]
        grade_documents --> generate["generate"]
    end

    subgraph code_agent ["code_agent (Code Generator Sub-Agent)"]
        direction TB
        code_agent__START --> plan["plan\n(Breaks task into plan)"]
        plan --> write_code["write_code"]
    end

    rag_agent__END --> post_process["post_process\n(Formats final response)"]
    code_agent__END --> post_process
    post_process --> END([END])

    classDef supervisorStyle fill:#d35400,stroke:#fff,stroke-width:2px,color:#fff;
    class supervisor supervisorStyle;
    classDef nodeStyle fill:#2980b9,stroke:#fff,stroke-width:2px,color:#fff;
    class post_process nodeStyle;

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📁 Generated Output Structure

forge compile configs/agents/agent.yaml scaffolds the full hierarchy under source_dir:

my_agent/                               ← Parent source_dir
├── flowchart.md                        ← Mermaid diagram of the multi-agent DAG
├── main.py                             ← Runnable entrypoint with app.stream()
├── graphs/
│   ├── __init__.py
│   ├── graph.py                        ← Compiled StateGraph + checkpointer
│   ├── state.py                        ← TypedDict AgentState with add_messages reducer
│   ├── chains/
│   │   └── post_process_chain.py
│   └── nodes/
│       ├── supervisor.py               ← LLM routing scaffold
│       └── post_process.py
│
├── adaptive_rag/                       ← Sub-agent nested under my_agent/
│   ├── graphs/
│   │   ├── graph.py
│   │   ├── state.py
│   │   └── nodes/
│   └── routers/
│
└── code_agent/                         ← Sub-agent nested under my_agent/
    ├── graphs/
    └── routers/

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🔍 Observability (LangSmith)

Enable full step-by-step tracing in .env:

LANGCHAIN_TRACING_V2=true
LANGCHAIN_API_KEY=your_langchain_api_key
LANGCHAIN_PROJECT=GraphForge-Agents

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🧪 Tests

uv run pytest -v

CI runs on every push via .github/workflows/ci.yml.

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🤝 Contributing

See CONTRIBUTING.md for dev setup, how to add new edge/node types, and the PR process.

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📦 License

Released under the Apache 2.0 License. Version 0.2.0.