Agents

Butterbase Agents

A Butterbase agent is a declarative graph of LLM and tool nodes — not a free-running chat loop. The runtime traverses the graph, calls tools (builtin / MCP / function), and resolves the end node's output_template. State, rate limits, and budgets are enforced by the control plane.

When to use

• The user wants to add a workflow that combines an LLM with tool calls (DB writes, storage reads, MCP servers, app functions).
• The user wants to expose an agent endpoint to end users (visibility: public or authenticated).
• Debugging a failing agent run (look at list_agent_runs, then get_agent_run).
• Registering an external MCP server for the agent to use.

Don't use for plain LLM chat completions — use the ai skill (manage_ai / /v1/ai/chat). Agents are for stateful, multi-step, tool-using workflows.

Concepts

graph_spec (validated by validate_agent_spec before anything is persisted)

| Field | Required | Notes | |---|---|---| | spec_version | yes | Literal "1". | | entry | yes | ID of the first node. | | nodes | yes | Record { id → node }. | | edges | yes | [{ from, to }]. Both endpoints must exist in nodes. | | tools | yes | { builtin: [], mcp_servers: [], functions: [] } — declares what nodes can call. | | limits | yes | max_steps (1–200), max_tool_calls (0–500), max_parallel_tools (1–16), timeout_seconds (5–3600), human_timeout_seconds (60–7×24×3600). |

Node types:

• llm — model, system_prompt, input_template, output_key, tools: [toolRef], optional temperature (0–2), max_tokens.
• tool — tool_ref, args_template (record), output_key.
• end — output_template (string; can interpolate {{output_key}} values).

toolRef is a discriminated union by source:

• { source: 'builtin', name }
• { source: 'mcp', server_id, name }
• { source: 'function', name }

Each may carry mode_override (read_only | read_write) and exposed_to_override (developer_only | end_user).

Builtin tools (always available, no setup)

| Name | Purpose | Args | |---|---|---| | query_table | Select rows (RLS enforced) | table, filter, limit (≤200) | | insert_row | Insert | table, values | | update_row | Update by id | table, id, patch | | delete_row | Delete by id | table, id | | read_storage | Get object (≤5 MB) | key | | write_storage | Put object (≤1 MB b64) | key, content_base64, content_type? | | auth_user_lookup | Find a user | email OR id |

All builtins respect role: end_user runs as butterbase_user with their user id (RLS applies); developer_only runs as butterbase_service.

MCP servers

Register before referencing in graph_spec.tools.mcp_servers. Transports: sse, http, streamable_http. The control plane probes on register (calls listTools()), stores status='healthy'|'unhealthy'. Re-probe with the same endpoint after a server URL change.

Access & limits

| Field | Default | Notes | |---|---|---| | visibility | private | private (owner only), authenticated (any app user), public (anyone, with rate limits). | | max_runs_per_user_per_hour | null | null = unlimited. | | max_runs_per_ip_per_hour | null | Primary public-agent throttle. | | max_runs_per_app_per_hour | null | App-wide cap. | | daily_budget_usd | null | Hard kill once exceeded. | | max_concurrent_runs | null | | | safety_acknowledged | false | Required true if visibility ≠ private AND any node calls a write tool (insert_row, update_row, delete_row, write_storage, or a write-mode MCP/function tool). |

Procedure

Designing a new agent

1. Sketch the graph in prose first. "User asks X → LLM rephrases → query_table for context → LLM answers → end." Concrete node IDs.
2. Write the spec as a JSON file in the repo (e.g. agents/.json) — versioning it in git makes templates portable and lets butterbase repo push carry it to clones.
3. Validate without persisting — call validate_agent_spec (MCP) or pass the file to a validate_agent_spec call. Surface any Zod issues to the user with field paths.
4. Register MCP servers if used: agent_mcp_servers table (MCP-tool wrapper TBD; use the dashboard or POST /v1//agent-mcp-servers directly). Wait for status: healthy.
5. Create — create_agent with name, graphspec, defaultmodel, access fields. If visibility ≠ 'private' and any write tool is reachable, require the user to explicitly say "yes, I acknowledge" and set safety_acknowledged: true.
6. Smoke — invoke_agent with a small input. Poll get_agent_run until terminal. Show the user the run timeline (steps, tool calls, final output).

Editing

• update_agent is a PATCH. Pass only changed fields. Bumping graph_spec revalidates; runs in flight against the old spec finish unmolested.
• Disabling an agent: update_agent { status: 'disabled' } — new runs return 403, existing runs keep going.

Debugging a failing run

1. list_agent_runs filtered by agent name, then get_agent_run(run_id) for the event timeline.
2. Check error.code: validation_failed (spec issue), tool_error (named tool, named arg), budget_exceeded, rate_limited, timeout.
3. For tool errors, re-run the same args_template with the underlying tool directly (select_rows, invoke_function, etc.) to confirm the issue is in the tool's surface, not the agent runtime.
4. For human_input_required checkpoints, resume with resume_agent_run(run_id, user_input).

CLI

• butterbase agents list / get / create -f spec.json / update -f patch.json / delete — read/write specs from files. Useful for version-controlling agents alongside app code.

Anti-patterns

• ❌ Skipping validate_agent_spec. Zod issues are clearer than the runtime errors you get from a bad spec at first invocation.
• ❌ Setting visibility: public with write tools and no rate limits. The control plane will refuse without safety_acknowledged: true, but you should also set per-IP limits and a daily budget.
• ❌ Putting secrets in system_prompt or args_template. Read them from ctx.env inside a function tool instead — agent specs are visible to anyone who can read the agent.
• ❌ Letting an LLM node call itself recursively without a max_steps ceiling. Always cap.
• ❌ Forgetting that builtin DB tools respect RLS. If query_table returns empty, the calling role probably can't see the rows — check exposed_to.
• ❌ Treating agents as part of clone replay. Agent records are not copied when an app is cloned — bundle the spec JSON in the repo (agents/*.json) and document recreation in the README.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: butterbase-ai
• Source: butterbase-ai/butterbase-skills
• License: MIT

Install and usage instructions live in the source repository linked above.