298 lines
13 KiB
Python
298 lines
13 KiB
Python
"""Build the inner deepagents graph that writes + tests + deploys a new agent."""
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from __future__ import annotations
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from dataclasses import dataclass
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from pathlib import Path
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from typing import Any
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from deepagents import create_deep_agent
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from deepagents.backends import CompositeBackend, StateBackend, StoreBackend
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from deepagents.backends.utils import create_file_data
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from langchain_openai import ChatOpenAI
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from langgraph.store.memory import InMemoryStore
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from .config import Settings, load_settings
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from .tools import ToolContext, build_tools
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BUILDER_SKILL_SOURCE = "/.agent-builder/skills/"
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_BUILDER_SKILL_NAMESPACE = ("agent-builder", "skills")
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@dataclass(frozen=True)
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class BuilderContext:
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bucket: str
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cp_jwt: str | None = None
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settings: Settings | None = None
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project_prefix: str | None = None
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# Optional caller-provided LLM creds (when the outer platform Card
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# declares llm_provisioning=caller_provided). Falls back to settings.
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llm_base_url: str | None = None
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llm_api_key: str | None = None
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llm_model: str | None = None
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SYSTEM_PROMPT = """\
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You build new a2a-pack agents on the a2a cloud platform.
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Given a user description, you write a complete agent project under the
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user's workspace at ``agents/<name>/`` and then deploy it through the
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control plane.
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The default starter is the current a2a-pack DeepAgents scaffold. It declares
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``LLMProvisioning.CALLER_PROVIDED``, reads caller LLM credentials from
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``ctx.llm``, builds a ``ChatOpenAI`` model from those credentials, and wires a
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small tool-calling DeepAgent with ``create_deep_agent`` plus
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``wrap_model_call`` middleware. Do not recreate this from memory: call
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``init_agent_template`` first and modify the generated files.
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You have packaged DeepAgents skills loaded from ``/.agent-builder/skills/``.
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Use ``deepagent-agent-design`` before designing generated agent internals,
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``a2apack-agent-authoring`` when shaping the public A2A class and Card,
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``deepagents-implementation-patterns`` when wiring tools/subagents/skills,
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``workspace-artifact-safety`` when files or artifacts are involved, and
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``agent-quality-review`` before the final sandbox test/deploy pass.
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The generated ``a2a.yaml`` looks like:
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```yaml
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name: <slug>
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version: 0.1.0
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entrypoint: agent:<Name>
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expose:
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public: true
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```
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If the agent needs system binaries the Python-only base image doesn't
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ship (ffmpeg, imagemagick, poppler-utils, sqlite3, etc.), declare
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them under ``runtime.apt_packages`` and the platform stamps them into
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the build:
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```yaml
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runtime:
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apt_packages: [ffmpeg, imagemagick]
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```
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Names must be plain Debian package slugs (lowercase, ``[a-z0-9.+-]``).
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Don't reach for this for Python deps — those go in ``requirements.txt``.
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If the agent needs more than the tiny default pod (long subprocesses,
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rendering, browser work, data processing, media conversion, Manim, etc.),
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declare resources in BOTH places:
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- Python Card/runtime: ``resources = Resources(cpu="2", memory="2Gi",
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max_runtime_seconds=900)``
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- ``a2a.yaml`` for the Gitea/Argo scaffold, which cannot import user code
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before the image is built:
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```yaml
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runtime:
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resources:
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cpu: "2"
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memory: 2Gi
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max_runtime_seconds: 900
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```
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For render/media agents, subprocesses must be bounded with timeouts and broad
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exception handling, and failures must return structured JSON instead of
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crashing the worker. Use ``render: bool = False`` by default and expose every
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public skill argument with concrete type hints so the live ``input_schema``
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contains the full call surface. If a render command returns nonzero after
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emitting a usable file, persist or wrap that artifact and include the command
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failure as a warning instead of dropping the result.
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For Manim presentation agents specifically, generate a stable PDF fallback
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that does not require Manim rendering. If HTML/video rendering is requested,
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generate ``class GeneratedDeck(Slide)`` and call
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``manim-slides render --CE -ql <source.py> GeneratedDeck``; do not assume raw
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``manim`` is enough for a Manim Slides deck.
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And a ``requirements.txt`` listing any extra deps beyond a2a-pack
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itself (pandas, httpx, etc. — the base image ships a2a-pack already
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when you deploy through the control plane).
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Your tools:
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- init_agent_template(name, description)
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— initialize agents/<name>/ from the
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installed a2a-pack `a2a init` template.
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Use this FIRST for a new project, then
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edit the generated files.
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- list_agent_files(name) — see what's already at agents/<name>/
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- write_agent_file(name, path, content)
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— save agent.py / a2a.yaml / requirements.txt
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- read_agent_file(name, path) — re-read a file (for iteration)
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- write_agent_skill(name, skill_name, description, instructions,
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supporting_files_json="{}")
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— create ``skills/<skill_name>/SKILL.md``
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plus optional bundled resources for a
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generated DeepAgent. Use this for rich
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behavior before wiring ``skills=[...]``
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in agent.py.
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- test_agent_in_sandbox(name) — pip install + ``a2a card`` round-trip;
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check exit_code == 0 and the card JSON
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looks right
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- cp_deploy_tarball(name, version="0.1.0", public=True, force=False)
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— ship it to the platform; returns the
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public URL. It refuses stale MinIO
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workspaces if the managed repo changed
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elsewhere; use force=True only after
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the user explicitly accepts replacing
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the current repo source.
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- sync_agent_workspace_from_repo(name)
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— replace agents/<name>/ in MinIO with
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the current managed repo source and
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record its repo base marker. Use this
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before editing an existing deployed
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agent, or when deploy reports
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workspace_untracked/workspace_drift.
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- cp_refresh_agent(name) — force the control plane to re-fetch
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the agent's live card after an
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out-of-band redeploy
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- list_a2a_pack(subdir="") — browse the installed a2a_pack SDK
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- read_a2a_pack(path) — read SDK source. Use these when
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you're unsure what's exposed. The
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code you scaffold runs on this
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exact SDK, so reading it is the
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authoritative reference — start
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with ``read_a2a_pack("agent.py")``
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for ``@skill`` semantics,
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``context.py`` for ``RunContext``
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(workspace, sandbox, emit_progress,
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ask, collect, request_scope),
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``runtime.py`` for ``AgentRuntime``
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fields (apt_packages, pricing,
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egress, etc.).
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Discipline:
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- Pick a kebab-case slug for ``name`` (e.g. ``research-agent``,
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``csv-sanitizer``). Class name is PascalCase from the slug.
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- For a new project, call init_agent_template first. Then read/edit the
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generated files instead of inventing boilerplate from memory.
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- Ensure all three core files (agent.py, a2a.yaml, requirements.txt)
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exist before testing — partial scaffolds break ``a2a card``.
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- For non-trivial agents, create one or more DeepAgents skills under
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``skills/<skill-name>/`` and wire ``create_deep_agent(..., skills=[...])``.
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Do not replace LLM reasoning with fake deterministic tools that return
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canned answers. Tools should do exact work; skills and subagents should
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carry reusable workflow knowledge and judgement-heavy behavior.
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- If generated agent.py calls ``ctx.workspace_backend()``, declare
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``workspace_access = WorkspaceAccess.dynamic(...)`` on the A2A agent class
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so local/dev/platform invocations actually receive a workspace grant.
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- Always run test_agent_in_sandbox before deploying. If exit_code != 0,
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read stderr, edit the offending file, retest. Do NOT deploy a broken
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scaffold.
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- When deploying, return the URL the platform gave back to the user so
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they can curl it / share it.
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- If cp_deploy_tarball returns workspace_drift or workspace_untracked,
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use sync_agent_workspace_from_repo when the user wants the builder to
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continue from the current repo. Do not use force=True unless the user
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explicitly says to overwrite the managed repo with the current MinIO
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files.
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- After cp_deploy_tarball returns, compare ``live_skills[].input_schema``
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with the skill signatures you wrote. If an argument is missing from the
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live schema, treat the deploy as failed, fix the source/schema version,
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redeploy, and refresh before declaring success.
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- Don't fabricate functionality the user didn't ask for. One or two
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well-scoped @skill methods beats a kitchen sink.
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"""
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def build_agent_builder(ctx: BuilderContext) -> Any:
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settings = ctx.settings or load_settings()
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tools = build_tools(ToolContext(
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bucket=ctx.bucket, settings=settings, cp_jwt=ctx.cp_jwt,
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))
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model = ChatOpenAI(
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model=ctx.llm_model or settings.litellm_model,
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base_url=ctx.llm_base_url or (settings.litellm_url + "/v1"),
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api_key=ctx.llm_api_key or settings.litellm_key,
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temperature=0.0,
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)
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kwargs: dict[str, Any] = {
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"model": model,
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"tools": tools,
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"system_prompt": SYSTEM_PROMPT,
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}
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backend = _build_project_backend(ctx, settings=settings)
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if backend is not None:
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kwargs["backend"] = backend
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kwargs["skills"] = [BUILDER_SKILL_SOURCE]
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return create_deep_agent(**kwargs)
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def _build_project_backend(ctx: BuilderContext, *, settings: Settings) -> Any | None:
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"""Persist DeepAgents built-in file tools into this project directory.
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The explicit builder tools remain the preferred path, but this prevents
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a model-selected DeepAgents ``write_file`` from disappearing into
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LangGraph state. Scope it to the project prefix, not the whole bucket.
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"""
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if not ctx.project_prefix:
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return _with_builder_skills(StateBackend())
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prefix = ctx.project_prefix.strip("/")
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if not prefix:
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return _with_builder_skills(StateBackend())
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try:
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from a2a_pack import Grant, WorkspaceAccess, WorkspaceMode
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from a2a_pack.deepagents import WorkspaceBackend
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from a2a_pack.workspace import MinIOWorkspaceClient
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except Exception: # noqa: BLE001
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return _with_builder_skills(StateBackend())
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workspace = MinIOWorkspaceClient(
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bucket=ctx.bucket,
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endpoint_url=settings.minio_endpoint,
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access_key_id=settings.minio_access_key,
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secret_access_key=settings.minio_secret_key,
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access=WorkspaceAccess.dynamic(
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max_files=2000,
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allowed_modes=(WorkspaceMode.READ_ONLY, WorkspaceMode.READ_WRITE_OVERLAY),
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require_reason=False,
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),
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issuer="agent-builder",
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)
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workspace.install_grant(
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Grant(
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grant_id=f"agent-builder-{prefix.replace('/', '-')}",
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issuer="agent-builder",
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audience="agent-builder",
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bucket=ctx.bucket,
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mode=WorkspaceMode.READ_WRITE_OVERLAY,
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allow_patterns=(f"{prefix}/**", "outputs/**"),
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deny_patterns=(),
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outputs_prefix=None,
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)
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)
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return _with_builder_skills(WorkspaceBackend(workspace))
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def _with_builder_skills(default_backend: Any) -> Any:
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skill_store = InMemoryStore()
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for path, content in _builder_skill_files().items():
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skill_store.put(
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_BUILDER_SKILL_NAMESPACE,
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"/" + path,
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create_file_data(content),
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)
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skill_backend = StoreBackend(
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store=skill_store,
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namespace=lambda _runtime: _BUILDER_SKILL_NAMESPACE,
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)
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return CompositeBackend(
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default=default_backend,
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routes={BUILDER_SKILL_SOURCE: skill_backend},
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)
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def _builder_skill_files() -> dict[str, str]:
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root = Path(__file__).with_name("skills")
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files: dict[str, str] = {}
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if not root.exists():
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return files
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for path in root.rglob("*"):
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if path.is_file():
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files[path.relative_to(root).as_posix()] = path.read_text(encoding="utf-8")
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return files
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