Bring your own agent

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Overview

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LangGraph

support/
  langgraph.json          {"graphs": {"support": "./src/support/graph.py:graph"}, "dependencies": ["."]}
  requirements.txt        extra packages (LangGraph and langchain-openai are preinstalled)
  src/support/graph.py    defines `graph`

from langgraph.graph import START, MessagesState, StateGraph
import veri_pyagent

model = veri_pyagent.chat_model()   # or ChatOpenAI(model=os.environ["VERI_MODEL_NAME"])

def answer(state: MessagesState):
    return {"messages": [model.invoke(state["messages"])]}

builder = StateGraph(MessagesState)
builder.add_node("answer", answer)
builder.add_edge(START, "answer")
graph = builder.compile()

kind = "environment"
name = "support-graph"
harness = "langgraph"

[model]
deployment_id = "1a2b3c4d5e6f"

[agent]
repo = { url = "https://github.com/acme/agents", ref = "v1.2.0", subdir = "support" }
entry = "support"

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CrewAI

crews/
  requirements.txt
  support_crew/__init__.py
  support_crew/crew.py    defines `build()` (or a @CrewBase class)

from crewai import Agent, Crew, Task
import veri_pyagent

def build() -> Crew:
    agent = Agent(role="Support engineer", goal="Answer the customer", backstory="...",
                  llm=veri_pyagent.crewai_llm(), mcps=veri_pyagent.mcp_servers() or None)
    task = Task(description="Earlier turns:\n{history}\n\nCustomer: {message}",
                expected_output="The reply to the customer", agent=agent)
    return Crew(agents=[agent], tasks=[task])

kind = "environment"
name = "support-crew"
harness = "crewai"

[model]
deployment_id = "1a2b3c4d5e6f"

[agent]
repo = { url = "https://github.com/acme/agents", ref = "main", subdir = "crews" }
entry = "support_crew.crew:build"

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Requirements and the sandbox

• The repository is cloned into /workspace/agent at the first message (ref is a branch, tag or commit; subdir is the agent’s directory). The repository must be public for now: private repositories need GitHub access, which is coming soon.
• agent.requirements (default: requirements.txt in the agent directory, when present) and, for LangGraph, the package names in langgraph.json dependencies are installed into /workspace/.veri/venv, on top of the preinstalled LangGraph, langchain-openai, langchain-mcp-adapters and CrewAI. The agent directory and its src/ are importable.
• A fetch, install or import that fails ends the first turn and fails the session with an error whose source is harness_setup; its message has the git, pip or Python error.
• Everything lives under /workspace, so a resumed, rolled-back or forked session neither clones nor installs again.
• Without agent.repo, entry names files already under /workspace, for example ones you uploaded with the session files API before the first message. Without an [agent] block at all, Veri’s built-in example agent runs.
• What your code prints goes to the harness log, not the transcript.

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The model

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Tools, MCP and permissions

• Your graph’s or crew’s own tools run as written. Veri sees their calls and results in the transcript but does not govern them.
• The environment’s MCP servers: await veri_pyagent.mcp_tools() (LangChain tools) or veri_pyagent.mcp_servers() (for crewai.Agent(mcps=...)). Their credentials are placeholders too. For crewai, address each MCP server by hostname, not IP address.
• Permission rules apply to those MCP tools only: mcp, mcp.<server> and mcp.<server>.<tool>, last match wins, with default = "allow". A denied tool is never handed to your agent. Shell, file and web rules are refused, and so is ask.

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Egress

• The repository’s host is allowed automatically, for git’s requests on that repository’s path.
• PyPI (pypi.org, files.pythonhosted.org) and GitHub are allowed by the default coding preset. With egress_preset = "locked_down", add them as egress_rules if you have requirements to install.
• Any other API your tools call needs an allowed_hosts entry or an egress_rules rule, and a secret scoped to its host for credentials.