86 lines
3.7 KiB
Markdown
86 lines
3.7 KiB
Markdown
# agent-builder
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The platform's own meta-agent. Generates, tests, and deploys new
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a2a-pack agents from a natural-language description.
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## What it does
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The single `build(name, prompt)` skill:
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1. Builds an inner `deepagents` LangGraph configured with project tools and
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packaged builder skills:
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- `deepagent-agent-design` — when to use DeepAgents skills, subagents,
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workspace backends, and deterministic tools
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- `a2apack-agent-authoring` — how to shape `agent.py`, public `@skill`
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schemas, runtime metadata, pricing, resources, and auth
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- `deepagents-implementation-patterns` — concrete `create_deep_agent`
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wiring for `ctx.llm` credentials, skills, tools, and subagents
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- `workspace-artifact-safety` — workspace grants, artifacts, bounded
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subprocesses, and scope expansion
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- `agent-quality-review` — pre-sandbox and pre-deploy checks for generated
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agents
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- `list_agent_files(name)` / `write_agent_file(name, path, content)`
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/ `read_agent_file(name, path)` — boto3 → MinIO, scoped to
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`agents/<name>/` in the caller's workspace
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- `write_agent_skill(...)` — creates valid DeepAgents
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`skills/<skill-name>/SKILL.md` bundles for generated agents, so rich
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behavior lives in progressive-disclosure skills instead of fake tools
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- `init_agent_template(name, description, frontend="react"|"static"|"none")`
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— starts from the current `a2a-pack` scaffold and can include packed
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frontend templates under `frontend/` for app-like agents
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- `test_agent_in_sandbox(name)` — tarballs the workspace project,
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spins a microVM with the public `a2a-pack` wheel installed, runs
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`a2a card` to verify the scaffold is valid, and prints frontend metadata
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when a packed app is declared
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- `cp_deploy_tarball(name, version, public)` — POSTs the tarball
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to `/v1/agents/from-tarball` on the user's behalf using their
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forwarded CP JWT
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2. Streams the graph's tool calls back to the dashboard as
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`agent_progress` events (so the user watches the build happen in
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real time, not in silence).
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3. Returns the live URL when the new agent finishes deploying.
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## What it needs forwarded
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Three platform-managed bits land on the inner skill via `RunContext`:
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| Field | Source | Required? |
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| `ctx.workspace.bucket` | grant minted by the orchestrator | yes |
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| `ctx.llm` | caller's saved LLM credential, routed by the platform (Card declares `llm_provisioning=platform`) | yes |
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| `ctx.cp_jwt` | caller's CP JWT (Card declares `wants_cp_jwt=True`) | yes — used by `cp_deploy_tarball` |
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The user opts into all three when they pick `agent-builder` from the
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marketplace — the dashboard already surfaces these on the agent card.
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## Pricing
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`$0.10 / call` during the current pricing model. The builder uses your saved
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LLM credential. The deployed agent is yours forever; subsequent invocations of
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a generated LLM agent also use the caller's saved LLM credential via `ctx.llm`.
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## How to call it
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In the dashboard chat (Workspace tab):
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```
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use agent-builder.build to make a new agent named "csv-sanitizer" that takes a CSV path, strips whitespace from every column, deduplicates rows, and writes the cleaned file back next to the original
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```
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The orchestrator will discover agent-builder, hand off with the
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required forwards, you'll watch the inner deepagents scaffold + test +
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deploy in real time, then get a live URL.
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## Local dev
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```bash
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cd apps/agent-builder
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python3 -m venv .venv
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.venv/bin/pip install -e ../a2a -r requirements.txt
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.venv/bin/a2a card # see what the Card looks like
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.venv/bin/a2a validate
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```
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You can't run the full skill locally without a workspace bucket + CP
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JWT — those come from the platform at invoke time.
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