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docs: updated the graph.example.yaml
2026-06-05 11:53:19 -06:00

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# Graph-based agent definition (full-featured reference)
# Location: <coyote-config-dir>/agents/<agent-name>/graph.yaml
#
# A graph agent is defined by this file alone. An agent directory contains
# either a config.yaml (a normal LLM-loop agent) or a graph.yaml (a graph
# agent), never both. The presence of graph.yaml is what makes the agent
# a graph agent.
#
# This file is a reference: it documents every available field, themed
# around a deep web research workflow with parallel retrieval. It is not
# a runnable agent as-is. The `agent:`, `script:`, and `documents:` values
# point at things that would need to exist for a real agent. For a real,
# runnable deep-research graph agent, see assets/agents/deep-research/.
#
# Full documentation:
# https://github.com/Dark-Alex-17/coyote/wiki/Graph-Agents
# ---------------------------------------------------------------------------
# Identity
# ---------------------------------------------------------------------------
name: deep-research-example # Agent name (should match the directory name)
description: | # Free-form prose describing the workflow
A reference workflow: triage a research request, retrieve local
context, branch on a script decision, run either a sub-agent or an
LLM research step, then gate the result behind human approval.
version: "1.0" # Graph schema version. Only "1.0" is accepted.
# ---------------------------------------------------------------------------
# Agent-level config (all optional)
# The same knobs a normal agent's config.yaml carries. In a graph agent they
# live here instead of in a config.yaml.
# ---------------------------------------------------------------------------
model: claude:claude-sonnet-4-6 # Default model for `llm` nodes that don't override it
temperature: 0.0 # Default sampling temperature for `llm` nodes
top_p: null # Default sampling top-p for `llm` nodes
global_tools: # Tool universe an `llm` node's `tools:` whitelist draws from
- web_search_coyote.sh
- fetch_url_via_curl.sh
mcp_servers: # MCP servers an `llm` node may reference via `mcp:<server>`
- ddg-search
# ---------------------------------------------------------------------------
# Skills policy (optional)
# Skills only attach to `llm` nodes inside a graph. Both fields are optional.
#
# skills_enabled: master switch for skills across every `llm` node in the
# graph. false here turns skills off entirely, regardless of
# per-node settings. Omitting it inherits the agent / global
# cascade (default true).
# enabled_skills: the *universe* of skill names any `llm` node in this graph
# may reference in its own `enabled_skills`. The validator
# rejects per-node entries outside this list at load time.
# Omit to inherit the agent / global cascade.
#
# Per-node usage is documented on the `triage` llm node below. There is no
# auto-load: the model uses `skill__list` / `skill__load` / `skill__unload` to
# bring skills in as it needs them, exactly like in normal-agent contexts.
# ---------------------------------------------------------------------------
skills_enabled: true
enabled_skills:
- code-review
- git-master
- ai-slop-remover
conversation_starters: # Suggested prompts surfaced in the UI
- "Research the current state of WebAssembly outside the browser"
# ---------------------------------------------------------------------------
# Agent variables (optional)
# Declared the same way as a normal agent's config.yaml `variables:` block.
# Each variable becomes available to:
# - LLM nodes via the template form `{{name}}` once seeded into state
# (see initial_state below).
# - Script nodes via the env var `LLM_AGENT_VAR_<UPPER_NAME>`.
# Values may be overridden at runtime with
# `coyote -a <agent> --agent-variable <name> <value> "..."`.
# ---------------------------------------------------------------------------
variables:
- name: project_dir
description: |
Absolute path to the project directory.
default: "."
# ---------------------------------------------------------------------------
# Execution settings (all optional)
# ---------------------------------------------------------------------------
settings:
max_loop_iterations: 100 # Per-node visit cap. If one node id is entered more
# than this many times, execution aborts. Default 100.
timeout: 600 # Optional wall-clock cap (seconds) on the whole run,
# checked between node transitions.
log_state_snapshots: true # Log state before each node (debug/trace). Default true.
validate_before_run: true # Run the graph validator at startup. Default true.
max_concurrency: 4 # Cap on simultaneously running branches in any
# super-step (static fan-out OR a `map` node).
# Default 4. Per-`map` overrides this. See Parallel
# Execution below.
# ---------------------------------------------------------------------------
# Reducers (optional, required whenever two parallel branches write the same
# state key in the same super-step; otherwise the validator errors at load).
#
# A reducer says how two values for the same key get merged. Built-ins:
# append list += [value] (single value appended to a list)
# extend list += value (a list) (list-of-lists flattened by one level)
# concat "a\nb" (string join with newline separator)
# sum a + b (numeric add; ints stay ints)
# max max(a, b)
# min min(a, b)
# merge {**a, **b} (dict union, RHS wins on key collision)
# overwrite last-write-wins (explicit opt-in; B's value replaces A's)
#
# Keys not listed here have an implicit "single writer per super-step" rule:
# the validator rejects any graph where two parallel branches both write a
# key with no reducer.
# ---------------------------------------------------------------------------
reducers:
sources: append # The diamond below writes `sources` from both
# branches; append accumulates them into a list.
context: concat # Each branch contributes prose; concat joins them.
# ---------------------------------------------------------------------------
# Seed state (optional)
# Values placed into graph state before any node runs; reference anywhere via
# {{key}}.
#
# Note: `initial_prompt` is seeded automatically by Coyote with the
# caller's prompt. So there's no need to set it here.
# ---------------------------------------------------------------------------
initial_state:
audience: "general reader"
# Seed an empty default for any key that a strict field (a node prompt /
# instructions / question / End output) references but that is only set on
# some paths. `refinement` is set only if the `refine` input node runs;
# seeding it "" keeps `finalize`'s strict prompt from failing on the
# approve-directly path.
refinement: ""
# ---------------------------------------------------------------------------
# Entry point
# ---------------------------------------------------------------------------
start: triage # ID of the first node to run (must exist in `nodes`)
# ---------------------------------------------------------------------------
# Nodes
# Each node is keyed by its id. The `id:` inside a node must match its key
# (it may also be omitted and thus Coyote fills it in from the key).
#
# Node types: agent | script | approval | input | llm | rag | map | end
# ---------------------------------------------------------------------------
nodes:
# --- llm node -----------------------------------------------------------
# A one-shot LLM call (with an optional bounded tool-call loop). Runs in a
# fresh isolated context. Tools are strictly opt-in (see `tools`).
triage:
id: triage
type: llm
description: Classify the research request and extract its topic.
instructions: | # Optional system prompt (templated against state)
You triage research requests for a {{audience}} audience.
prompt: | # Required user prompt (templated against state)
Classify this request and extract the core research topic:
{{initial_prompt}}
tools: [] # Tool whitelist. Omitted or [] = no tools at all.
# A list narrows to exactly those entries.
# --- Skills on llm nodes (optional) ------------------------------------
# `enabled_skills` narrows what this node's model can see / load via the
# built-in `skill__list` / `skill__load` / `skill__unload` meta-tools.
# Must be a subset of the graph-level `enabled_skills` (the validator
# catches violations at load time). `skills_enabled: false` would
# disable skills entirely for this node (no meta-tools exposed).
# Nothing is auto-loaded: the model decides when to load a skill.
skills_enabled: true # Whether skills are enabled on this llm node; defaults to 'true'
enabled_skills:
- ai-slop-remover
output_schema: # Optional JSON Schema. The output is parsed to JSON
type: object # and its top-level object keys auto-merge into state
properties: # (so `topic` / `needs_deep_dive` become {{topic}} etc).
topic: { type: string }
needs_deep_dive: { type: boolean }
required: [topic, needs_deep_dive]
state_updates: # {{output}} = this node's result (here, the parsed object)
triage_result: "{{output}}"
# --- Polymorphic `next` -----------------------------------------------
# A single string runs the next node sequentially (e.g. `next: retrieve`).
# A list runs all listed nodes in parallel as one BSP super-step
# (for more info on BSP, see https://en.wikipedia.org/wiki/Bulk_synchronous_parallel).
# Their writes are merged via `reducers:` at the join. Branches converge
# implicitly when they all route to the same downstream node (here,
# `synthesize`). See the diamond:
#
# triage
# / \
# retrieve web_search (run concurrently)
# \ /
# synthesize (join; fires once after both finish)
next: [retrieve, web_search]
# --- rag node (parallel branch 1 of the diamond) ------------------------
# Hybrid (vector + keyword) retrieval against a per-node knowledge base.
# The knowledge base is built once, at agent load time, into
# <agent-dir>/retrieve.yaml (named after this node's id).
retrieve:
id: retrieve
type: rag
documents: # Required. Files, directories, URLs, loader paths.
- ./knowledge/ # relative paths resolve against the agent directory
- https://example.com/reference
query: "{{topic}}" # Retrieval query (templated). Default: {{initial_prompt}}.
top_k: 5 # Chunks to retrieve. Default = the KB's own top_k.
timeout: 120 # Retrieval timeout in seconds. Default 120.
# Knowledge-base build config (optional; used only when the KB is first
# built). When embedding_model + chunk_size + chunk_overlap are all set,
# the KB builds with no interactive prompts (works in non-interactive runs).
embedding_model: openai:text-embedding-3-small
chunk_size: 1000
chunk_overlap: 100
reranker_model: null # Optional reranker for hybrid-search results
batch_size: 100 # Optional embedding-request batch size
state_updates: # {{output}} = { context: <str>, sources: [<path>, ...] }
context: "{{output.context}}" # writes `context` -> `reducers.context = concat`
sources: "{{output.sources}}" # writes `sources` -> `reducers.sources = append`
next: synthesize # Joins with web_search at `synthesize`.
# --- llm node (parallel branch 2 of the diamond) ------------------------
# Runs concurrently with `retrieve`. Both branches write `context` and
# `sources`; the validator confirms both keys have a reducer declared, and
# the BSP scheduler merges them at the join.
web_search:
id: web_search
type: llm
instructions: "You are a web researcher. Cite every claim."
prompt: "Web research: {{topic}}. Return findings and sources."
tools:
- web_search_coyote
- mcp:ddg-search
output_schema:
type: object
properties:
context: { type: string }
sources:
type: array
items: { type: string }
required: [context, sources]
# When `output_schema` is set, top-level keys auto-merge into state, so
# `context` and `sources` are produced without needing `state_updates`.
next: synthesize # Joins with retrieve at `synthesize`.
# --- script node (the diamond's join; also dispatches) -----------------
# Runs a .sh / .py / .ts script. The script receives state via the
# GRAPH_STATE env var (inline JSON) or GRAPH_STATE_FILE (path to a JSON
# file, used when state exceeds 32 KiB). Exactly one is set. It must print
# a single JSON object on stdout: keys merge into state, and the reserved
# `_next` key (if present) overrides routing.
#
# The script also receives these env vars (parity with bash tools called
# from normal agents):
# GRAPH_STATE / GRAPH_STATE_FILE state payload (one of the two is set)
# LLM_ROOT_DIR coyote config dir
# LLM_PROMPT_UTILS_FILE path to .shared/prompt-utils.sh
# LLM_AGENT_DATA_DIR this agent's data directory
# LLM_AGENT_VAR_<NAME> one per declared `variables:` entry
# PATH with coyote's functions bin dir prepended
# CLICOLOR_FORCE / FORCE_COLOR so child tools emit ANSI colors
# The script's working directory is coyote's invocation CWD (not the agent
# directory), matching the behavior of bash tools.
#
# This node fires once: after both `retrieve` and `web_search` finish.
# The BSP scheduler dedups the two incoming edges into a single frontier
# entry, applies the staged branch writes through the reducers, then runs
# this node against the merged state. Inside the script, `context` is the
# concatenated text of both branches and `sources` is the combined list.
synthesize:
id: synthesize
type: script
script: scripts/synthesize.py # Path relative to the agent directory
timeout: 30 # Seconds. Default 30.
state_updates: # Applied after the stdout JSON is merged
decided_for: "{{topic}}"
next: summarize # Default route if the script emits no `_next`
fallback: summarize # Route taken if the script fails (crash / bad JSON)
# This script is expected to emit `_next: deep_dive` (or `_next: subjects_map`
# to demonstrate the map node below), or no `_next` (then `next` is used).
# Targets reached only via the script's dynamic `_next` get an
# "unreachable" warning from the validator. This is expected for `_next`-routed
# targets.
# --- agent node ---------------------------------------------------------
# Spawns a full Coyote sub-agent and waits for it. The child uses its own
# tool stack. Agent nodes have no `tools:` field. No schema hint is
# injected even when `output_schema` is set (unlike llm nodes).
deep_dive:
id: deep_dive
type: agent
agent: deep-research # Name of an existing Coyote agent to spawn
prompt: | # User message sent to the child (templated)
Research {{topic}} in depth. Existing context:
{{context}}
timeout: 600 # Optional wall-clock cap, seconds. Default 300.
output_schema: # Optional. Same extraction as llm nodes
type: object
properties:
summary: { type: string }
findings:
type: array
items: { type: string }
required: [summary, findings]
state_updates:
research: "{{output}}"
next: review # Required for agent nodes
# --- map node (Dynamic fan-out. Think: LangGraph's `Send` API) ----------------
# Spawns one parallel sub-branch per item in `over`. Each sub-branch runs
# the node referenced by `branch:` with the item bound to `as:`. Outputs
# collect into the array named by `collect_into:`, preserving input order.
#
# Reach via `synthesize`'s `_next: subjects_map`. The producer is expected
# to have written a list at `subjects` (e.g. an upstream LLM node with an
# `output_schema` returning {"subjects": ["a", "b", "c"]}).
subjects_map:
id: subjects_map
type: map
over: "{{subjects}}" # Required. List expression resolved from state.
# Empty list is allowed. It means no branches spawn,
# and thus `collect_into` is written as [].
as: subject # Required. Per-branch state key holding the
# current item. Read with {{subject}} inside
# the branch node's prompt.
branch: research_subject # Required. Node id to invoke per item.
# Must point to an llm | agent | rag | script
# node satisfying the map branch contract:
# - no `next:` (atomic, joined at map exit)
# - no `state_updates:` other than via the
# map's `collect_into` channel
# - no `output_schema:` (top-level merge
# would clash with collect_into)
# Validator enforces all three.
collect_into: subject_findings # Required. State key for the array of
# per-branch outputs, in input order
# (not spawn-finish order).
max_concurrency: 3 # Optional per-map cap. Defaults to
# settings.max_concurrency above.
output_key: output # Optional. State key the branch's output
# appears under. Default "output". Useful
# only if the branch reads its own bound
# name back (rare).
next: aggregate_subjects # Where to go after all sub-branches finish.
# Branch node for subjects_map. Each invocation receives a different
# `subject` in state. The branch is "atomic", meaning it cannot route on
# its own; the surrounding `map` joins after all invocations finish.
research_subject:
id: research_subject
type: llm
instructions: "Research one subject deeply for a {{audience}} audience."
prompt: "Research {{subject}}: pull the key facts and one citation."
tools:
- web_search_coyote
# No `next:`, `state_updates:`, or `output_schema:` here. Map branches
# have a strict contract (see `subjects_map.branch` comment).
# Aggregator that runs after the map joins. Reads the collected list.
aggregate_subjects:
id: aggregate_subjects
type: llm
instructions: "Combine N per-subject reports into one cohesive summary."
prompt: |
Per-subject reports (in original input order):
{{subject_findings}}
state_updates:
research: "{{output}}"
next: review
# --- llm node with a narrowed tool whitelist ----------------------------
summarize:
id: summarize
type: llm
instructions: "You write concise research summaries for a {{audience}} audience."
prompt: "Summarize the topic {{topic}}, using your tools as needed."
tools: # Narrow whitelist: exactly these entries, nothing else
- web_search_coyote # an exact global-tool / custom-tool name
- mcp:ddg-search # `mcp:<server>` includes that server's functions
model: claude:claude-haiku-4-5 # Optional per-node model override
temperature: 0.3 # Optional per-node sampling override
max_attempts: 2 # Retry count on transient errors only. Default 1.
max_iterations: 10 # Tool-call-loop turn cap. Default 10.
fallback: review # Route here if all attempts fail
timeout: 300 # Optional node wall-clock cap, seconds (unset = no timeout)
state_updates:
research: "{{output}}"
next: review # Required for llm nodes: the success route
# --- approval node ------------------------------------------------------
# Human-in-the-loop checkpoint. `user__ask` always offers a free-form
# "type your own answer" option, so `on_other` is required.
review:
id: review
type: approval
question: |
Proposed research result for {{topic}}:
{{research}}
Approve?
options: # The listed choices shown to the user
- "yes"
- "no"
routes: # Map each listed option to its next node
"yes": finalize
"no": rejected_end
on_other: refine # Required: route for ANY answer not in `routes`
state_updates:
decision: "{{choice}}" # {{choice}} = the chosen option or the free-form text
# --- input node ---------------------------------------------------------
# Collects a free-form string from the user.
refine:
id: refine
type: input
question: "What should be changed about the research result?"
default: "tighten the summary" # Optional: used if the user submits empty input.
# Note: a substituted default is not re-validated,
# so make sure it would satisfy `validation`.
validation: "len(input) > 0" # Optional length predicate: len(input) <op> N,
# <op> in > >= < <= == . Length only -- no regex.
state_updates:
refinement: "{{input}}" # {{input}} = the user's text
next: finalize # Required for input nodes: the success route
# --- llm node (final synthesis) -----------------------------------------
finalize:
id: finalize
type: llm
prompt: |
Produce the final research report for {{topic}}.
Result so far: {{research}}
Requested refinement (if any): {{refinement}}
state_updates:
final_report: "{{output}}"
next: done
# --- end nodes ----------------------------------------------------------
# Terminate the graph. `output` (templated, lenient interpolation) becomes
# the graph's final result. A graph needs at least one `end` node.
done:
id: done
type: end
state_updates: # Optional: applied before `output` is rendered
status: "completed"
output: |
[{{status}}] {{final_report}}
Sources: {{sources}}
rejected_end:
id: rejected_end
type: end
output: "Research on {{topic}} was not approved."
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