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Add LangGraph adapter with tests for export functionality
- Implemented and functions in to convert agent configurations into LangGraph Python code. - Created a comprehensive test suite in to validate the output of the export functions, ensuring correct handling of agent metadata, skills, tools, skillflows, and hooks. - Added helper functions for YAML rendering and agent directory structure creation to facilitate testing. - Ensured compatibility with existing agent structures by supporting both and directories.
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‎README.md‎

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@@ -367,6 +367,8 @@ Adapters are used by both `export` and `run`. Available adapters:
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| `openclaw` | OpenClaw format |
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| `nanobot` | Nanobot format |
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| `cursor` | Cursor `.cursor/rules/*.mdc` files |
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| `langgraph` | LangGraph `StateGraph` Python module (skills → nodes, skillflows → edges) |
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| `deepagents` | LangChain DeepAgents harness (`create_deep_agent(...)`) — skills, tools, sub-agents |
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```bash
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# Export to system prompt

‎examples/deepagents/README.md‎

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# DeepAgents example
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A research agent with a fact-checker sub-agent, demonstrating the DeepAgents adapter:
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- `agent.yaml` — manifest (model, skills, tools, sub-agent declaration)
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- `SOUL.md` — agent identity, embedded in the generated `system_prompt`
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- `skills/web-research/`, `skills/summarize/` — passed via `skills=["./skills"]`; DeepAgents loads each `SKILL.md` natively
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- `tools/web-search.yaml` — emitted as a `@tool` function bound into `tools=[...]`
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- `agents/fact-checker/` — emitted as a `SubAgent` dict in `subagents=[...]`
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- `expected_output.py` — the Python module the adapter produces
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DeepAgents is a higher-level harness on top of LangGraph — there is no graph
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wiring: the model decides when to plan, when to delegate to sub-agents, and
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when to call tools. If you need explicit per-step edges, use the `langgraph`
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adapter instead.
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## Regenerate
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```bash
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gapman export --dir examples/deepagents --format deepagents --output examples/deepagents/expected_output.py
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```
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## Run the generated agent
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```bash
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pip install deepagents langchain-anthropic
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python examples/deepagents/expected_output.py
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```
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The generated file leaves tool implementations as `NotImplementedError` stubs — replace them with your own logic before invoking.

‎examples/deepagents/SOUL.md‎

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# Research Assistant
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You are a research assistant. Find authoritative sources, extract the relevant
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facts, and summarize them honestly. Delegate to the `fact-checker` sub-agent
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whenever a claim warrants independent verification. Cite every claim.

‎examples/deepagents/agent.yaml‎

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spec_version: "0.1.0"
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name: research-assistant
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version: 1.0.0
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description: Research assistant with a fact-checker sub-agent
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author: gitagent-examples
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license: MIT
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model:
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preferred: anthropic:claude-sonnet-4-5
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skills:
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- web-research
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- summarize
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tools:
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- web-search
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agents:
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fact-checker:
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description: Verifies factual claims against authoritative sources

‎examples/deepagents/agents/fact-checker/SOUL.md‎

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# Fact-Checker
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You are a pedantic fact-checker. Treat every claim as unverified until you
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locate a primary source. If a claim cannot be substantiated, say so plainly.

‎examples/deepagents/agents/fact-checker/agent.yaml‎

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spec_version: "0.1.0"
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name: fact-checker
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version: 1.0.0
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description: Verifies factual claims against authoritative sources

‎examples/deepagents/expected_output.py‎

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"""
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DeepAgents definition for research-assistant v1.0.0
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Generated by gitagent export --format deepagents
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"""
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from __future__ import annotations
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from deepagents import create_deep_agent
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from langchain_core.tools import tool
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# Agent metadata
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AGENT_NAME = "research-assistant"
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AGENT_VERSION = "1.0.0"
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MODEL = "anthropic:claude-sonnet-4-5"
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# System prompt (SOUL.md + RULES.md + DUTIES.md + compliance)
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SYSTEM_PROMPT = """# research-assistant
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Research assistant with a fact-checker sub-agent
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# Research Assistant
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You are a research assistant. Find authoritative sources, extract the relevant
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facts, and summarize them honestly. Delegate to the `fact-checker` sub-agent
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whenever a claim warrants independent verification. Cite every claim.
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"""
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# Hooks (pre_tool_use scripts run before every tool call)
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def _run_pre_tool_use_hooks(tool_name: str) -> None:
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"""No pre_tool_use hooks configured in hooks/hooks.yaml."""
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return None
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# NOTE: other hook events (post_tool_use, on_session_start, etc.) have no
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# DeepAgents equivalent and are intentionally skipped.
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# NOTE: memory/ has no direct DeepAgents equivalent — wire a checkpointer if needed.
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# Tools (from tools/*.yaml)
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@tool
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def web_search(query: str) -> str:
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"""Search the public web for a query"""
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_run_pre_tool_use_hooks("web-search")
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raise NotImplementedError("Implement tool: web-search")
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TOOLS = [web_search]
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# Skills (skills/<name>/SKILL.md — DeepAgents loads these natively)
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# Pointing skills= at the directory lets DeepAgents discover every SKILL.md
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# without us having to inline the skill content into SYSTEM_PROMPT.
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SKILLS = ["./skills"]
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# For reference, the skills available in this agent:
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# - summarize: Condense the gathered sources into a faithful, cited summary
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# - web-research: Search the web for sources relevant to the user's question
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# Sub-agents (agents/<name>/ → SubAgent dicts)
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fact_checker_subagent = {
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"name": "fact-checker",
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"description": "Verifies factual claims against authoritative sources",
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"system_prompt": """# fact-checker
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Verifies factual claims against authoritative sources
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# Fact-Checker
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You are a pedantic fact-checker. Treat every claim as unverified until you
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locate a primary source. If a claim cannot be substantiated, say so plainly.
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""",
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"tools": TOOLS,
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}
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SUBAGENTS = [fact_checker_subagent]
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# Agent
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agent = create_deep_agent(
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model=MODEL,
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system_prompt=SYSTEM_PROMPT,
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tools=TOOLS,
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skills=SKILLS,
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subagents=SUBAGENTS,
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)
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if __name__ == "__main__":
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result = agent.invoke({"messages": [{"role": "user", "content": "Hello"}]})
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for message in result["messages"]:
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print(message)

‎examples/deepagents/skills/summarize/SKILL.md‎

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---
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name: summarize
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description: Condense the gathered sources into a faithful, cited summary
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---
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Produce a concise summary of the gathered material. Every factual claim must
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be attributed to a specific source URL. If sources disagree, surface the
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disagreement explicitly.

‎examples/deepagents/skills/web-research/SKILL.md‎

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---
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name: web-research
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description: Search the web for sources relevant to the user's question
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allowed-tools: web-search
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---
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Use the `web-search` tool to find authoritative sources. Capture the URL of
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every source consulted; downstream skills will cite them.

‎examples/deepagents/tools/web-search.yaml‎

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name: web-search
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description: Search the public web for a query
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input_schema:
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type: object
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properties:
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query:
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type: string
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description: The search query
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required:
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- query

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