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# Graph-based agent definition (full-featured reference)
# Location: <loki-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. 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.
#
# Full documentation:
# https://github.com/Dark-Alex-17/loki/wiki/Graph-Agents
# ---------------------------------------------------------------------------
# Identity
# ---------------------------------------------------------------------------
name: example-graph-agent # Agent name (should match the directory name)
description: | # Free-form prose describing the workflow
A reference workflow: triage a request, retrieve context, branch on a
script decision, run either a sub-agent or an LLM 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: anthropic: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_loki.sh
- fetch_url_via_curl.sh
mcp_servers: # MCP servers an `llm` node may reference via `mcp:<server>`
- pubmed-search
conversation_starters: # Suggested prompts surfaced in the UI
- "Research LOINC code 2160-0"
# NOTE: `can_spawn_agents` is NOT a field here. It is DERIVED: a graph can
# spawn child agents iff it contains at least one `agent` node (this graph
# does -- see `deep_dive`).
# ---------------------------------------------------------------------------
# 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.
# ---------------------------------------------------------------------------
# Seed state (optional)
# Values placed into graph state before any node runs; reference anywhere via
# {{key}}. NOTE: `initial_prompt` is seeded automatically by Loki with the
# caller's prompt -- do not set it here.
# ---------------------------------------------------------------------------
initial_state:
audience: "clinician"
# 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 -- Loki fills it in from the key).
#
# Node types: agent | script | approval | input | llm | rag | 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 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 key topic:
{{initial_prompt}}
tools: [] # Tool whitelist. Omitted or [] = NO tools at all.
# A list narrows to EXACTLY those entries.
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_research` become {{topic}} etc).
topic: { type: string }
needs_research: { type: boolean }
required: [topic, needs_research]
state_updates: # {{output}} = this node's result (here, the parsed object)
triage_result: "{{output}}"
next: retrieve # REQUIRED for llm nodes: the success route
# --- rag node -----------------------------------------------------------
# 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}}"
sources: "{{output.sources}}"
next: decide
# --- script node --------------------------------------------------------
# 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.
decide:
id: decide
type: script
script: scripts/decide.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 no `_next`, in
# which case `next` is used). Because `deep_dive` is reached only via the
# script's dynamic `_next`, the startup validator will report it as an
# "unreachable" WARNING -- that is expected for `_next`-routed targets.
# --- agent node ---------------------------------------------------------
# Spawns a full Loki 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-researcher # Name of an existing Loki 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
# --- llm node with a narrowed tool whitelist ----------------------------
summarize:
id: summarize
type: llm
instructions: "You write concise 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_loki.sh # an exact global-tool / custom-tool name
- mcp:pubmed-search # `mcp:<server>` includes that server's functions
model: anthropic: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 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 result?"
default: "minor wording only" # 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 answer for {{topic}}.
Result so far: {{research}}
Requested refinement (if any): {{refinement}}
state_updates:
final_answer: "{{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_answer}}
Sources: {{sources}}
rejected_end:
id: rejected_end
type: end
output: "Request for {{topic}} was not approved."
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