Cypher Agent
Cypher Agent
Guide 23 built an SQL Agent — an agent whose tools let
it discover a schema, sample rows, and run read-only SELECT queries over a
tabular KnowledgeBase. This guide builds the graph counterpart. A
Cypher Agent answers questions over a knowledge graph — nodes connected
by typed edges — by writing read-only Cypher
queries.
Tables vs. graphs
A tabular store answers questions about rows and columns: filters, aggregations, joins between a handful of tables. A graph store answers questions about connections: who is linked to whom, and through what path.
The classic example is the friend-of-friend query. In SQL that is a chain
of self-joins on a friendships table; the more hops, the more joins, and the
SQL grows with the question. In a graph it is a single pattern, and the number
of hops is just the length of the pattern:
MATCH (a:Person {name: 'Alice'})-[:Knows]->(f:Person)-[:LivesIn]->(c:City {name: 'Paris'})
RETURN f.name
When your questions are about relationships and paths, reach for a Cypher agent; when they are about typed tabular aggregates, reach for an SQL agent.
The three tools
synalinks.CypherAgent wraps a FunctionCallingAgent and pre-wires three
tools, all bound to a single graph KnowledgeBase:
| Tool | Purpose |
|---|---|
get_graph_schema() |
List every node and relation label, with their properties. |
get_node_sample(label, limit, offset) |
Fetch a few nodes of a label to see the data shape. Page-bounded by the agent's k. |
run_cypher_query(cypher_query) |
Execute a read-only MATCH ... RETURN query. Result sets are capped at k rows. |
The default instructions are seeded with the graph's node and relation labels,
so the agent knows what is available without spending a turn on
get_graph_schema for trivial questions.
Defining the graph
A graph KnowledgeBase is constructed with graph_uri= (a
Ladybug store here) plus entity_models
(the node types) and relation_models (the edge types). Entities subclass
synalinks.Entity and relations subclass synalinks.Relation, which carries a
typed subj (source node) and obj (target node).
Give each model a default label (matching its class name) so you can build
instances without repeating it:
class Person(synalinks.Entity):
label: str = synalinks.Field(default="Person", description="The entity label")
name: str = synalinks.Field(description="Person name")
class LivesIn(synalinks.Relation):
label: str = synalinks.Field(default="LivesIn", description="The relation label")
subj: Person
obj: City
Safety
Only read-only Cypher runs. The adapter scans the query (after stripping
comments and string literals) and rejects any write/admin keyword — CREATE,
MERGE, SET, DELETE, DETACH, REMOVE, DROP, ALTER, COPY,
INSTALL, LOAD — so the agent can explore the graph but never mutate it or
read files.
A note on the model
Function-calling agents need a model that emits native tool calls. Capable
local models such as qwen3 do; some smaller ones do not and will describe a
tool call in prose instead of emitting one. Prefer a tool-capable model (or a
hosted one) for the Cypher agent.
Key Takeaways
- Graphs for relationships: pick a Cypher agent when answers follow edges and paths; pick an SQL agent for typed tabular aggregates.
- Three tools:
get_graph_schema,get_node_sample,run_cypher_query, all bound to one graphKnowledgeBase. - Read-only: writes/admin Cypher is rejected by the engine.
data_model=makes the agent return a structured answer (here, the prose answer plus the Cypher it ran).
API References
Source
import asyncio
from dotenv import load_dotenv
import synalinks
class Person(synalinks.Entity):
label: str = synalinks.Field(default="Person", description="The entity label")
name: str = synalinks.Field(description="Person name")
class City(synalinks.Entity):
label: str = synalinks.Field(default="City", description="The entity label")
name: str = synalinks.Field(description="City name")
class LivesIn(synalinks.Relation):
label: str = synalinks.Field(default="LivesIn", description="The relation label")
subj: Person
obj: City
class Knows(synalinks.Relation):
label: str = synalinks.Field(default="Knows", description="The relation label")
subj: Person
obj: Person
class Query(synalinks.DataModel):
query: str = synalinks.Field(description="A natural-language question")
class CypherAnswer(synalinks.DataModel):
answer: str = synalinks.Field(description="The answer in natural language")
cypher_query: str = synalinks.Field(description="The Cypher that produced it")
async def main():
load_dotenv()
synalinks.clear_session()
synalinks.enable_logging()
# `graph_uri=` selects the graph adapter; `:memory:` is ephemeral.
knowledge_base = synalinks.KnowledgeBase(
graph_uri="ladybug://:memory:",
entity_models=[Person, City],
relation_models=[LivesIn, Knows],
embedding_model=synalinks.EmbeddingModel(model="ollama/all-minilm"),
)
await knowledge_base.update_relations(
[
LivesIn(subj=Person(name="Alice"), obj=City(name="Paris")),
LivesIn(subj=Person(name="Bob"), obj=City(name="Paris")),
LivesIn(subj=Person(name="Carol"), obj=City(name="London")),
Knows(subj=Person(name="Alice"), obj=Person(name="Bob")),
]
)
# Function calling needs a tool-capable model; qwen3 emits native tool calls.
language_model = synalinks.LanguageModel(model="ollama/qwen3:8b")
inputs = synalinks.Input(data_model=Query)
outputs = await synalinks.CypherAgent(
knowledge_base=knowledge_base,
language_model=language_model,
data_model=CypherAnswer,
max_iterations=8,
)(inputs)
agent = synalinks.Program(
inputs=inputs,
outputs=outputs,
name="cypher_agent",
description="A Cypher agent that answers questions about a knowledge graph.",
)
agent.summary()
result = await agent(Query(query="Who lives in Paris?"))
print("Answer:", result.get("answer"))
print("Cypher:", result.get("cypher_query"))
if __name__ == "__main__":
asyncio.run(main())
Run log
This guide calls synalinks.enable_logging(), so a full run traces every
module call. The log below is the unedited output of running the guide
above with local models.
Full run log — guides/26_cypher_agent.log
[Synalinks]
Call ID: b754f8c7-7e34-4469-82bb-f5bdd2436e72
Parent call ID: None
Module: EmbeddingModel
Module Name: embedding_model
Module Description: An embedding model API wrapper.
Data Model JSON:
[
{
"texts": [
"text"
]
}
]
[Synalinks]
Call ID: b754f8c7-7e34-4469-82bb-f5bdd2436e72
Parent call ID: None
Module: EmbeddingModel
Module Name: embedding_model
Module Description: An embedding model API wrapper.
Data Model JSON:
[
{
"embeddings": [
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]
}
]
(DEBUG) [Synalinks]
Call ID: 01290c7a-523f-4993-8524-d49a4b2fdcbc
Parent call ID: None
Module: CypherAgent
Module Name: cypher_agent
Module Description: A ready-to-use Cypher agent backed by a knowledge base.
Data Model JSON Schema:
[
{
"additionalProperties": false,
"properties": {
"query": {
"description": "A natural-language question",
"title": "Query",
"type": "string"
}
},
"required": [
"query"
],
"title": "Query",
"type": "object"
}
]
(DEBUG) [Synalinks]
Call ID: 8b02cc78-dbfb-4a85-b32b-fd908f68d9ce
Parent call ID: 01290c7a-523f-4993-8524-d49a4b2fdcbc
Module: Generator
Module Name: tool_calls_generator_cypher_agent
Module Description: Use a `LanguageModel` to generate a data model from an arbitrary input data model.
Data Model JSON Schema:
[
{
"additionalProperties": false,
"properties": {
"query": {
"description": "A natural-language question",
"title": "Query",
"type": "string"
}
},
"required": [
"query"
],
"title": "Query",
"type": "object"
}
]
(DEBUG) [Synalinks]
Call ID: 8b02cc78-dbfb-4a85-b32b-fd908f68d9ce
Parent call ID: 01290c7a-523f-4993-8524-d49a4b2fdcbc
Module: Generator
Module Name: tool_calls_generator_cypher_agent
Module Description: Use a `LanguageModel` to generate a data model from an arbitrary input data model.
Data Model JSON Schema:
[
{
"$defs": {
"ChatRole": {
"description": "The chat message roles",
"enum": [
"system",
"developer",
"user",
"assistant",
"tool",
"function"
],
"title": "ChatRole",
"type": "string"
},
"ToolCall": {
"additionalProperties": false,
"description": "A tool call, shaped like an OpenAI Chat Completions tool call.\n\nMirrors the wire envelope (`{id, type, function: {name, arguments}}`)\nexcept that `arguments` stays a parsed dict rather than a JSON-encoded\nstring, so modules and agents can read it directly. The string encoding\nis applied only at the wire edge (see `backend.pydantic.chat_completions`).",
"properties": {
"id": {
"description": "The id of the tool call",
"title": "Id",
"type": "string"
},
"type": {
"const": "function",
"default": "function",
"description": "The tool call type (always `function` today)",
"title": "Type",
"type": "string"
},
"function": {
"$ref": "#/$defs/ToolCallFunction",
"description": "The function invocation (name + arguments)"
}
},
"required": [
"id",
"function"
],
"title": "ToolCall",
"type": "object"
},
"ToolCallFunction": {
"additionalProperties": false,
"description": "The `function` payload of a tool call (name + parsed arguments).",
"properties": {
"name": {
"description": "The name of the function called",
"title": "Name",
"type": "string"
},
"arguments": {
"additionalProperties": true,
"description": "The arguments of the tool call",
"title": "Arguments",
"type": "object"
}
},
"required": [
"name",
"arguments"
],
"title": "ToolCallFunction",
"type": "object"
}
},
"additionalProperties": false,
"description": "A chat message",
"properties": {
"role": {
"$ref": "#/$defs/ChatRole",
"description": "The chat message role"
},
"reasoning_content": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "The reasoning/thinking content of the message. Keyed to match the litellm/DeepSeek `reasoning_content` chat-completion field (a provider extension, not part of the base OpenAI spec), so the message API stays a subset of the litellm-extended chat-completion message.",
"title": "Reasoning Content"
},
"thinking_blocks": {
"anyOf": [
{
"items": {
"additionalProperties": true,
"type": "object"
},
"type": "array"
},
{
"type": "null"
}
],
"default": null,
"description": "Opaque provider-native thinking blocks (e.g. Anthropic's signed `thinking_blocks`; a litellm extension, not part of the base OpenAI spec). Carried through verbatim on assistant-message re-injection so multi-turn tool-use round-trips preserve signatures. None for providers that emit reasoning only as text.",
"title": "Thinking Blocks"
},
"content": {
"anyOf": [
{
"type": "string"
},
{
"items": {
"additionalProperties": true,
"type": "object"
},
"type": "array"
},
{
"additionalProperties": true,
"type": "object"
},
{
"type": "null"
}
],
"default": null,
"description": "The content of the message",
"title": "Content"
},
"tool_call_id": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "The id of the tool call if role is `tool`",
"title": "Tool Call Id"
},
"tool_calls": {
"anyOf": [
{
"items": {
"$ref": "#/$defs/ToolCall"
},
"type": "array"
},
{
"type": "null"
}
],
"default": null,
"description": "The tool calls of the agent",
"title": "Tool Calls"
}
},
"required": [
"role"
],
"title": "ChatMessage",
"type": "object"
}
]
(DEBUG) [Synalinks]
Call ID: 8d4e0368-a39e-43ee-b2c2-585755d7ab5e
Parent call ID: 8b02cc78-dbfb-4a85-b32b-fd908f68d9ce
Module: Generator
Module Name: tool_calls_generator_cypher_agent
Module Description: Use a `LanguageModel` to generate a data model from an arbitrary input data model.
Data Model JSON Schema:
[
{
"additionalProperties": false,
"properties": {
"query": {
"description": "A natural-language question",
"title": "Query",
"type": "string"
}
},
"required": [
"query"
],
"title": "Query",
"type": "object"
}
]
(DEBUG) [Synalinks]
Call ID: 8d4e0368-a39e-43ee-b2c2-585755d7ab5e
Parent call ID: 8b02cc78-dbfb-4a85-b32b-fd908f68d9ce
Module: Generator
Module Name: tool_calls_generator_cypher_agent
Module Description: Use a `LanguageModel` to generate a data model from an arbitrary input data model.
Data Model JSON Schema:
[
{
"$defs": {
"ChatRole": {
"description": "The chat message roles",
"enum": [
"system",
"developer",
"user",
"assistant",
"tool",
"function"
],
"title": "ChatRole",
"type": "string"
},
"ToolCall": {
"additionalProperties": false,
"description": "A tool call, shaped like an OpenAI Chat Completions tool call.\n\nMirrors the wire envelope (`{id, type, function: {name, arguments}}`)\nexcept that `arguments` stays a parsed dict rather than a JSON-encoded\nstring, so modules and agents can read it directly. The string encoding\nis applied only at the wire edge (see `backend.pydantic.chat_completions`).",
"properties": {
"id": {
"description": "The id of the tool call",
"title": "Id",
"type": "string"
},
"type": {
"const": "function",
"default": "function",
"description": "The tool call type (always `function` today)",
"title": "Type",
"type": "string"
},
"function": {
"$ref": "#/$defs/ToolCallFunction",
"description": "The function invocation (name + arguments)"
}
},
"required": [
"id",
"function"
],
"title": "ToolCall",
"type": "object"
},
"ToolCallFunction": {
"additionalProperties": false,
"description": "The `function` payload of a tool call (name + parsed arguments).",
"properties": {
"name": {
"description": "The name of the function called",
"title": "Name",
"type": "string"
},
"arguments": {
"additionalProperties": true,
"description": "The arguments of the tool call",
"title": "Arguments",
"type": "object"
}
},
"required": [
"name",
"arguments"
],
"title": "ToolCallFunction",
"type": "object"
}
},
"additionalProperties": false,
"description": "A chat message",
"properties": {
"role": {
"$ref": "#/$defs/ChatRole",
"description": "The chat message role"
},
"reasoning_content": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "The reasoning/thinking content of the message. Keyed to match the litellm/DeepSeek `reasoning_content` chat-completion field (a provider extension, not part of the base OpenAI spec), so the message API stays a subset of the litellm-extended chat-completion message.",
"title": "Reasoning Content"
},
"thinking_blocks": {
"anyOf": [
{
"items": {
"additionalProperties": true,
"type": "object"
},
"type": "array"
},
{
"type": "null"
}
],
"default": null,
"description": "Opaque provider-native thinking blocks (e.g. Anthropic's signed `thinking_blocks`; a litellm extension, not part of the base OpenAI spec). Carried through verbatim on assistant-message re-injection so multi-turn tool-use round-trips preserve signatures. None for providers that emit reasoning only as text.",
"title": "Thinking Blocks"
},
"content": {
"anyOf": [
{
"type": "string"
},
{
"items": {
"additionalProperties": true,
"type": "object"
},
"type": "array"
},
{
"additionalProperties": true,
"type": "object"
},
{
"type": "null"
}
],
"default": null,
"description": "The content of the message",
"title": "Content"
},
"tool_call_id": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "The id of the tool call if role is `tool`",
"title": "Tool Call Id"
},
"tool_calls": {
"anyOf": [
{
"items": {
"$ref": "#/$defs/ToolCall"
},
"type": "array"
},
{
"type": "null"
}
],
"default": null,
"description": "The tool calls of the agent",
"title": "Tool Calls"
}
},
"required": [
"role"
],
"title": "ChatMessage",
"type": "object"
}
]
(DEBUG) [Synalinks]
Call ID: 01290c7a-523f-4993-8524-d49a4b2fdcbc
Parent call ID: None
Module: CypherAgent
Module Name: cypher_agent
Module Description: A ready-to-use Cypher agent backed by a knowledge base.
Data Model JSON Schema:
[
{
"additionalProperties": false,
"$defs": {
"ChatMessage": {
"additionalProperties": false,
"description": "A chat message",
"properties": {
"role": {
"$ref": "#/$defs/ChatRole",
"description": "The chat message role"
},
"reasoning_content": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "The reasoning/thinking content of the message. Keyed to match the litellm/DeepSeek `reasoning_content` chat-completion field (a provider extension, not part of the base OpenAI spec), so the message API stays a subset of the litellm-extended chat-completion message.",
"title": "Reasoning Content"
},
"thinking_blocks": {
"anyOf": [
{
"items": {
"additionalProperties": true,
"type": "object"
},
"type": "array"
},
{
"type": "null"
}
],
"default": null,
"description": "Opaque provider-native thinking blocks (e.g. Anthropic's signed `thinking_blocks`; a litellm extension, not part of the base OpenAI spec). Carried through verbatim on assistant-message re-injection so multi-turn tool-use round-trips preserve signatures. None for providers that emit reasoning only as text.",
"title": "Thinking Blocks"
},
"content": {
"anyOf": [
{
"type": "string"
},
{
"items": {
"additionalProperties": true,
"type": "object"
},
"type": "array"
},
{
"additionalProperties": true,
"type": "object"
},
{
"type": "null"
}
],
"default": null,
"description": "The content of the message",
"title": "Content"
},
"tool_call_id": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "The id of the tool call if role is `tool`",
"title": "Tool Call Id"
},
"tool_calls": {
"anyOf": [
{
"items": {
"$ref": "#/$defs/ToolCall"
},
"type": "array"
},
{
"type": "null"
}
],
"default": null,
"description": "The tool calls of the agent",
"title": "Tool Calls"
}
},
"required": [
"role"
],
"title": "ChatMessage",
"type": "object"
},
"ChatRole": {
"description": "The chat message roles",
"enum": [
"system",
"developer",
"user",
"assistant",
"tool",
"function"
],
"title": "ChatRole",
"type": "string"
},
"ToolCall": {
"additionalProperties": false,
"description": "A tool call, shaped like an OpenAI Chat Completions tool call.\n\nMirrors the wire envelope (`{id, type, function: {name, arguments}}`)\nexcept that `arguments` stays a parsed dict rather than a JSON-encoded\nstring, so modules and agents can read it directly. The string encoding\nis applied only at the wire edge (see `backend.pydantic.chat_completions`).",
"properties": {
"id": {
"description": "The id of the tool call",
"title": "Id",
"type": "string"
},
"type": {
"const": "function",
"default": "function",
"description": "The tool call type (always `function` today)",
"title": "Type",
"type": "string"
},
"function": {
"$ref": "#/$defs/ToolCallFunction",
"description": "The function invocation (name + arguments)"
}
},
"required": [
"id",
"function"
],
"title": "ToolCall",
"type": "object"
},
"ToolCallFunction": {
"additionalProperties": false,
"description": "The `function` payload of a tool call (name + parsed arguments).",
"properties": {
"name": {
"description": "The name of the function called",
"title": "Name",
"type": "string"
},
"arguments": {
"additionalProperties": true,
"description": "The arguments of the tool call",
"title": "Arguments",
"type": "object"
}
},
"required": [
"name",
"arguments"
],
"title": "ToolCallFunction",
"type": "object"
}
},
"properties": {
"messages": {
"default": [],
"description": "The list of chat messages",
"items": {
"$ref": "#/$defs/ChatMessage"
},
"title": "Messages",
"type": "array"
},
"answer": {
"description": "The answer in natural language",
"title": "Answer",
"type": "string"
},
"cypher_query": {
"description": "The Cypher that produced it",
"title": "Cypher Query",
"type": "string"
}
},
"required": [
"answer",
"cypher_query"
],
"title": "ChatMessages",
"type": "object"
}
]
Program: cypher_agent
description: 'A Cypher agent that answers questions about a knowledge graph.'
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━┓
┃ Module (type) ┃ Output Schema ┃ Vars # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━┩
│ input_module (InputModule) │ Query: │ 0 │
│ │ query: str │ │
├─────────────────────────────┼──────────────────────────────────┼─────────┤
│ cypher_agent (CypherAgent) │ ChatRole: Literal['system', │ 2 │
│ │ 'developer', 'user', │ │
│ │ 'assistant', 'tool', 'function'] │ │
│ │ │ │
│ │ ToolCallFunction: │ │
│ │ name: str │ │
│ │ arguments: dict │ │
│ │ │ │
│ │ ToolCall: │ │
│ │ id: str │ │
│ │ type: Literal['function'] = │ │
│ │ 'function' │ │
│ │ function: ToolCallFunction │ │
│ │ │ │
│ │ ChatMessage: │ │
│ │ role: ChatRole │ │
│ │ reasoning_content: str | None │ │
│ │ = None │ │
│ │ thinking_blocks: list | None = │ │
│ │ None │ │
│ │ content: str | list | dict | │ │
│ │ None = None │ │
│ │ tool_call_id: str | None = │ │
│ │ None │ │
│ │ tool_calls: list[ToolCall] | │ │
│ │ None = None │ │
│ │ │ │
│ │ ChatMessages: │ │
│ │ messages: list[ChatMessage] = │ │
│ │ [] │ │
│ │ answer: str │ │
│ │ cypher_query: str │ │
└─────────────────────────────┴──────────────────────────────────┴─────────┘
[Synalinks]
Call ID: 0180a8b2-e536-4565-bd29-02464543e48f
Parent call ID: None
Module: Functional
Module Name: cypher_agent
Module Description: A Cypher agent that answers questions about a knowledge graph.
Data Model JSON:
[
{
"query": "Who lives in Paris?"
}
]
[Synalinks]
Call ID: 81182d88-63cf-40cd-8431-f1c73f0c55fe
Parent call ID: 0180a8b2-e536-4565-bd29-02464543e48f
Module: CypherAgent
Module Name: cypher_agent
Module Description: A ready-to-use Cypher agent backed by a knowledge base.
Data Model JSON:
[
{
"query": "Who lives in Paris?"
}
]
[Synalinks]
Call ID: d2da4e84-a590-42e3-8225-10552a746554
Parent call ID: 81182d88-63cf-40cd-8431-f1c73f0c55fe
Module: Generator
Module Name: tool_calls_generator_cypher_agent
Module Description: Use a `LanguageModel` to generate a data model from an arbitrary input data model.
Data Model JSON:
[
{
"query": "Who lives in Paris?",
"messages": []
}
]
[Synalinks]
Call ID: 4f0c2269-9e8f-4df6-963d-e402d778e0de
Parent call ID: d2da4e84-a590-42e3-8225-10552a746554
Module: LanguageModel
Module Name: language_model
Module Description: A language model API wrapper.
Data Model JSON:
[
{
"messages": [
{
"role": "system",
"content": "<instructions>\nYou are a graph analyst with read-only access to a knowledge graph.\n\nAvailable node labels: ['City', 'Person']\nAvailable relation labels: ['Knows', 'LivesIn']\n\nPlan:\n1. If you don't already know the schema, call `get_graph_schema` first.\n2. When you need to inspect representative nodes, call `get_node_sample`.\n3. Build a single `MATCH ... RETURN` Cypher query and execute it with\n `run_cypher_query`. Iterate on the query (read the error, fix the\n Cypher, retry) until you have the data.\n4. Once you have an answer, stop calling tools and produce the final response.\n\nConstraints:\n- Only read-only Cypher is accepted. `CREATE`, `MERGE`, `SET`,\n `DELETE`, `DETACH DELETE`, `REMOVE`, `DROP`, `ALTER`, `COPY`,\n `INSTALL`, and `LOAD` are rejected by the engine — don't waste turns\n trying them.\n- Node and relation labels are case-sensitive PascalCase (e.g.\n ``Person``, ``LivesIn``); property names are snake_case.\n- Result sets are automatically capped server-side. If the result\n shows ``may_have_more=true``, refine the query (add filters or\n ``ORDER BY ... LIMIT n``) rather than asking for more rows.\n</instructions>\n"
},
{
"role": "user",
"content": "<input>\n{'query': 'Who lives in Paris?'}\n</input>\n<output>\n"
}
]
}
]
[Synalinks]
Call ID: 4f0c2269-9e8f-4df6-963d-e402d778e0de
Parent call ID: d2da4e84-a590-42e3-8225-10552a746554
Module: LanguageModel
Module Name: language_model
Module Description: A language model API wrapper.
Data Model JSON:
[
{
"role": "assistant",
"content": "",
"reasoning_content": "Okay, let's tackle the user's question: \"Who lives in Paris?\" First, I need to figure out how to get the information from the knowledge graph. The available node labels are City and Person, and the relations are Knows and LivesIn. \n\nThe user is asking about people who live in Paris, so the key here is the LivesIn relation. Since Paris is a city, I should check if there's a City node labeled Paris. Then, look for Persons that have a LivesIn relation pointing to that City node.\n\nBut wait, I should first confirm the schema to make sure I'm using the right labels and relations. Maybe the LivesIn relation is connected from Person to City. So the plan is to find all Person nodes where their LivesIn relation points to the City node with the name Paris.\n\nI need to construct a Cypher query that matches this. Starting with MATCH, perhaps something like MATCH (p:Person)-[:LivesIn]->(c:City) WHERE c.name = 'Paris' RETURN p.name. But I need to make sure that the City node's property is indeed named 'name' or whatever the correct property is. Wait, the user might have different property names. Oh, the constraints mention that property names are snake_case, so maybe the city's name is stored as 'name' or 'city_name'? Hmm, but without seeing the schema, I might have to assume. Alternatively, maybe the City node has a 'name' property. \n\nAlternatively, maybe the City node's label is 'City' and the property is 'name'. So the query would check for c.name = 'Paris'. But if the schema isn't known, perhaps I should first get the schema using get_graph_schema. Wait, the user hasn't provided any schema info yet. So maybe the first step is to call get_graph_schema to see what labels and properties are present. But the user's question is straightforward, and maybe the schema is already known. However, since the assistant might not have that info, it's safer to check.\n\nWait, the initial instructions say that if you don't know the schema, call get_graph_schema first. So maybe I should start by getting the schema to confirm the structure. But the user's question is about people living in Paris, so perhaps the schema already includes the necessary labels and relations. However, to be thorough, maybe I should first check the schema. But the user might not have provided that info, so the assistant needs to proceed with the available tools.\n\nAlternatively, maybe the assistant can proceed directly. Let's think: the available node labels are City and Person. The relations are Knows and LivesIn. So, the LivesIn relation is likely from Person to City. So, the query would be to find all Persons connected via LivesIn to a City named Paris. \n\nBut how to get the City's name? The City node might have a property like 'name' or 'cityName'. Since the user's question uses 'Paris' as the city name, the query would need to filter on that. So the Cypher query would be:\n\nMATCH (p:Person)-[:LivesIn]->(c:City)\nWHERE c.name = 'Paris'\nRETURN p.name\n\nBut if the property is not 'name', this might not work. However, given the constraints, the assistant might have to proceed with this query. Alternatively, if the schema isn't known, the assistant might need to first get the schema. But since the user hasn't provided any schema info, perhaps the assistant should proceed with the query as above.\n\nWait, but the initial plan says to first call get_graph_schema if the schema isn't known. So maybe the first step is to call get_graph_schema to confirm the structure. However, the user's question is straightforward, and maybe the assistant can proceed. But to be safe, perhaps the assistant should first check the schema. However, the user's input doesn't mention any schema, so maybe the assistant should proceed directly.\n\nAlternatively, maybe the assistant should first get the schema to confirm the properties. For example, if the City node's name is stored as 'city_name' instead of 'name', the query would fail. So, to avoid that, the assistant should first check the schema. But the user's question is about Paris, so maybe the assistant can proceed with the query. \n\nHmm, perhaps the assistant should first check the schema. Let me think: the available node labels are City and Person. The relations are Knows and LivesIn. So, the LivesIn relation connects Person to City. The City node likely has a property for the city name. So, the query would be as above. But to confirm, maybe the assistant should first get the schema. However, the user's question is straightforward, and the assistant might not have the schema info. \n\nIn this case, the assistant should proceed with the query. So, the final answer would be to run the Cypher query to find all Persons connected via LivesIn to a City named Paris. Therefore, the tool call would be to run_cypher_query with that query.\n",
"tool_calls": [
{
"id": "call_d3glzvzd",
"type": "function",
"function": {
"name": "run_cypher_query",
"arguments": {
"cypher_query": "MATCH (p:Person)-[:LivesIn]->(c:City) WHERE c.name = 'Paris' RETURN p.name"
}
}
}
]
}
]
[Synalinks]
Call ID: d2da4e84-a590-42e3-8225-10552a746554
Parent call ID: 81182d88-63cf-40cd-8431-f1c73f0c55fe
Module: Generator
Module Name: tool_calls_generator_cypher_agent
Module Description: Use a `LanguageModel` to generate a data model from an arbitrary input data model.
Data Model JSON:
[
{
"role": "assistant",
"content": "",
"reasoning_content": "Okay, let's tackle the user's question: \"Who lives in Paris?\" First, I need to figure out how to get the information from the knowledge graph. The available node labels are City and Person, and the relations are Knows and LivesIn. \n\nThe user is asking about people who live in Paris, so the key here is the LivesIn relation. Since Paris is a city, I should check if there's a City node labeled Paris. Then, look for Persons that have a LivesIn relation pointing to that City node.\n\nBut wait, I should first confirm the schema to make sure I'm using the right labels and relations. Maybe the LivesIn relation is connected from Person to City. So the plan is to find all Person nodes where their LivesIn relation points to the City node with the name Paris.\n\nI need to construct a Cypher query that matches this. Starting with MATCH, perhaps something like MATCH (p:Person)-[:LivesIn]->(c:City) WHERE c.name = 'Paris' RETURN p.name. But I need to make sure that the City node's property is indeed named 'name' or whatever the correct property is. Wait, the user might have different property names. Oh, the constraints mention that property names are snake_case, so maybe the city's name is stored as 'name' or 'city_name'? Hmm, but without seeing the schema, I might have to assume. Alternatively, maybe the City node has a 'name' property. \n\nAlternatively, maybe the City node's label is 'City' and the property is 'name'. So the query would check for c.name = 'Paris'. But if the schema isn't known, perhaps I should first get the schema using get_graph_schema. Wait, the user hasn't provided any schema info yet. So maybe the first step is to call get_graph_schema to see what labels and properties are present. But the user's question is straightforward, and maybe the schema is already known. However, since the assistant might not have that info, it's safer to check.\n\nWait, the initial instructions say that if you don't know the schema, call get_graph_schema first. So maybe I should start by getting the schema to confirm the structure. But the user's question is about people living in Paris, so perhaps the schema already includes the necessary labels and relations. However, to be thorough, maybe I should first check the schema. But the user might not have provided that info, so the assistant needs to proceed with the available tools.\n\nAlternatively, maybe the assistant can proceed directly. Let's think: the available node labels are City and Person. The relations are Knows and LivesIn. So, the LivesIn relation is likely from Person to City. So, the query would be to find all Persons connected via LivesIn to a City named Paris. \n\nBut how to get the City's name? The City node might have a property like 'name' or 'cityName'. Since the user's question uses 'Paris' as the city name, the query would need to filter on that. So the Cypher query would be:\n\nMATCH (p:Person)-[:LivesIn]->(c:City)\nWHERE c.name = 'Paris'\nRETURN p.name\n\nBut if the property is not 'name', this might not work. However, given the constraints, the assistant might have to proceed with this query. Alternatively, if the schema isn't known, the assistant might need to first get the schema. But since the user hasn't provided any schema info, perhaps the assistant should proceed with the query as above.\n\nWait, but the initial plan says to first call get_graph_schema if the schema isn't known. So maybe the first step is to call get_graph_schema to confirm the structure. However, the user's question is straightforward, and maybe the assistant can proceed. But to be safe, perhaps the assistant should first check the schema. However, the user's input doesn't mention any schema, so maybe the assistant should proceed directly.\n\nAlternatively, maybe the assistant should first get the schema to confirm the properties. For example, if the City node's name is stored as 'city_name' instead of 'name', the query would fail. So, to avoid that, the assistant should first check the schema. But the user's question is about Paris, so maybe the assistant can proceed with the query. \n\nHmm, perhaps the assistant should first check the schema. Let me think: the available node labels are City and Person. The relations are Knows and LivesIn. So, the LivesIn relation connects Person to City. The City node likely has a property for the city name. So, the query would be as above. But to confirm, maybe the assistant should first get the schema. However, the user's question is straightforward, and the assistant might not have the schema info. \n\nIn this case, the assistant should proceed with the query. So, the final answer would be to run the Cypher query to find all Persons connected via LivesIn to a City named Paris. Therefore, the tool call would be to run_cypher_query with that query.\n",
"tool_calls": [
{
"id": "call_d3glzvzd",
"type": "function",
"function": {
"name": "run_cypher_query",
"arguments": {
"cypher_query": "MATCH (p:Person)-[:LivesIn]->(c:City) WHERE c.name = 'Paris' RETURN p.name"
}
}
}
]
}
]
[Synalinks]
Call ID: f96c94fd-d01c-47b2-b70d-6f3bb00a89b7
Parent call ID: 4f0c2269-9e8f-4df6-963d-e402d778e0de
Module: Tool
Module Name: run_cypher_query
Module Description: Execute a read-only Cypher query and return the rows.
Keyword Arguments:
{
"cypher_query": "MATCH (p:Person)-[:LivesIn]->(c:City) WHERE c.name = 'Paris' RETURN p.name"
}
[Synalinks]
Call ID: f96c94fd-d01c-47b2-b70d-6f3bb00a89b7
Parent call ID: 4f0c2269-9e8f-4df6-963d-e402d778e0de
Module: Tool
Module Name: run_cypher_query
Module Description: Execute a read-only Cypher query and return the rows.
Data Model JSON:
[
{
"success": true,
"query": "MATCH (p:Person)-[:LivesIn]->(c:City) WHERE c.name = 'Paris' RETURN p.name",
"row_count": 2,
"row_cap": 50,
"results": "\"p.name\"\n\"Alice\"\n\"Bob\"",
"output_format": "csv",
"may_have_more": false
}
]
[Synalinks]
Call ID: 74405477-903f-4a10-9e48-4ddbcd2f7a7b
Parent call ID: f96c94fd-d01c-47b2-b70d-6f3bb00a89b7
Module: Generator
Module Name: tool_calls_generator_cypher_agent
Module Description: Use a `LanguageModel` to generate a data model from an arbitrary input data model.
Data Model JSON:
[
{
"query": "Who lives in Paris?",
"messages": [
{
"role": "assistant",
"content": "",
"reasoning_content": "Okay, let's tackle the user's question: \"Who lives in Paris?\" First, I need to figure out how to get the information from the knowledge graph. The available node labels are City and Person, and the relations are Knows and LivesIn. \n\nThe user is asking about people who live in Paris, so the key here is the LivesIn relation. Since Paris is a city, I should check if there's a City node labeled Paris. Then, look for Persons that have a LivesIn relation pointing to that City node.\n\nBut wait, I should first confirm the schema to make sure I'm using the right labels and relations. Maybe the LivesIn relation is connected from Person to City. So the plan is to find all Person nodes where their LivesIn relation points to the City node with the name Paris.\n\nI need to construct a Cypher query that matches this. Starting with MATCH, perhaps something like MATCH (p:Person)-[:LivesIn]->(c:City) WHERE c.name = 'Paris' RETURN p.name. But I need to make sure that the City node's property is indeed named 'name' or whatever the correct property is. Wait, the user might have different property names. Oh, the constraints mention that property names are snake_case, so maybe the city's name is stored as 'name' or 'city_name'? Hmm, but without seeing the schema, I might have to assume. Alternatively, maybe the City node has a 'name' property. \n\nAlternatively, maybe the City node's label is 'City' and the property is 'name'. So the query would check for c.name = 'Paris'. But if the schema isn't known, perhaps I should first get the schema using get_graph_schema. Wait, the user hasn't provided any schema info yet. So maybe the first step is to call get_graph_schema to see what labels and properties are present. But the user's question is straightforward, and maybe the schema is already known. However, since the assistant might not have that info, it's safer to check.\n\nWait, the initial instructions say that if you don't know the schema, call get_graph_schema first. So maybe I should start by getting the schema to confirm the structure. But the user's question is about people living in Paris, so perhaps the schema already includes the necessary labels and relations. However, to be thorough, maybe I should first check the schema. But the user might not have provided that info, so the assistant needs to proceed with the available tools.\n\nAlternatively, maybe the assistant can proceed directly. Let's think: the available node labels are City and Person. The relations are Knows and LivesIn. So, the LivesIn relation is likely from Person to City. So, the query would be to find all Persons connected via LivesIn to a City named Paris. \n\nBut how to get the City's name? The City node might have a property like 'name' or 'cityName'. Since the user's question uses 'Paris' as the city name, the query would need to filter on that. So the Cypher query would be:\n\nMATCH (p:Person)-[:LivesIn]->(c:City)\nWHERE c.name = 'Paris'\nRETURN p.name\n\nBut if the property is not 'name', this might not work. However, given the constraints, the assistant might have to proceed with this query. Alternatively, if the schema isn't known, the assistant might need to first get the schema. But since the user hasn't provided any schema info, perhaps the assistant should proceed with the query as above.\n\nWait, but the initial plan says to first call get_graph_schema if the schema isn't known. So maybe the first step is to call get_graph_schema to confirm the structure. However, the user's question is straightforward, and maybe the assistant can proceed. But to be safe, perhaps the assistant should first check the schema. However, the user's input doesn't mention any schema, so maybe the assistant should proceed directly.\n\nAlternatively, maybe the assistant should first get the schema to confirm the properties. For example, if the City node's name is stored as 'city_name' instead of 'name', the query would fail. So, to avoid that, the assistant should first check the schema. But the user's question is about Paris, so maybe the assistant can proceed with the query. \n\nHmm, perhaps the assistant should first check the schema. Let me think: the available node labels are City and Person. The relations are Knows and LivesIn. So, the LivesIn relation connects Person to City. The City node likely has a property for the city name. So, the query would be as above. But to confirm, maybe the assistant should first get the schema. However, the user's question is straightforward, and the assistant might not have the schema info. \n\nIn this case, the assistant should proceed with the query. So, the final answer would be to run the Cypher query to find all Persons connected via LivesIn to a City named Paris. Therefore, the tool call would be to run_cypher_query with that query.\n",
"tool_calls": [
{
"id": "call_d3glzvzd",
"type": "function",
"function": {
"name": "run_cypher_query",
"arguments": {
"cypher_query": "MATCH (p:Person)-[:LivesIn]->(c:City) WHERE c.name = 'Paris' RETURN p.name"
}
}
}
]
},
{
"role": "tool",
"content": {
"success": true,
"query": "MATCH (p:Person)-[:LivesIn]->(c:City) WHERE c.name = 'Paris' RETURN p.name",
"row_count": 2,
"row_cap": 50,
"results": "\"p.name\"\n\"Alice\"\n\"Bob\"",
"output_format": "csv",
"may_have_more": false
},
"tool_call_id": "call_d3glzvzd"
}
]
}
]
[Synalinks]
Call ID: 41426208-d25f-4d66-a14b-6c65e74bb715
Parent call ID: 74405477-903f-4a10-9e48-4ddbcd2f7a7b
Module: LanguageModel
Module Name: language_model
Module Description: A language model API wrapper.
Data Model JSON:
[
{
"messages": [
{
"role": "system",
"content": "<instructions>\nYou are a graph analyst with read-only access to a knowledge graph.\n\nAvailable node labels: ['City', 'Person']\nAvailable relation labels: ['Knows', 'LivesIn']\n\nPlan:\n1. If you don't already know the schema, call `get_graph_schema` first.\n2. When you need to inspect representative nodes, call `get_node_sample`.\n3. Build a single `MATCH ... RETURN` Cypher query and execute it with\n `run_cypher_query`. Iterate on the query (read the error, fix the\n Cypher, retry) until you have the data.\n4. Once you have an answer, stop calling tools and produce the final response.\n\nConstraints:\n- Only read-only Cypher is accepted. `CREATE`, `MERGE`, `SET`,\n `DELETE`, `DETACH DELETE`, `REMOVE`, `DROP`, `ALTER`, `COPY`,\n `INSTALL`, and `LOAD` are rejected by the engine — don't waste turns\n trying them.\n- Node and relation labels are case-sensitive PascalCase (e.g.\n ``Person``, ``LivesIn``); property names are snake_case.\n- Result sets are automatically capped server-side. If the result\n shows ``may_have_more=true``, refine the query (add filters or\n ``ORDER BY ... LIMIT n``) rather than asking for more rows.\n</instructions>\n"
},
{
"role": "user",
"content": "<input>\n{'query': 'Who lives in Paris?'}\n</input>\n<output>\n"
},
{
"role": "assistant",
"reasoning_content": "Okay, let's tackle the user's question: \"Who lives in Paris?\" First, I need to figure out how to get the information from the knowledge graph. The available node labels are City and Person, and the relations are Knows and LivesIn. \n\nThe user is asking about people who live in Paris, so the key here is the LivesIn relation. Since Paris is a city, I should check if there's a City node labeled Paris. Then, look for Persons that have a LivesIn relation pointing to that City node.\n\nBut wait, I should first confirm the schema to make sure I'm using the right labels and relations. Maybe the LivesIn relation is connected from Person to City. So the plan is to find all Person nodes where their LivesIn relation points to the City node with the name Paris.\n\nI need to construct a Cypher query that matches this. Starting with MATCH, perhaps something like MATCH (p:Person)-[:LivesIn]->(c:City) WHERE c.name = 'Paris' RETURN p.name. But I need to make sure that the City node's property is indeed named 'name' or whatever the correct property is. Wait, the user might have different property names. Oh, the constraints mention that property names are snake_case, so maybe the city's name is stored as 'name' or 'city_name'? Hmm, but without seeing the schema, I might have to assume. Alternatively, maybe the City node has a 'name' property. \n\nAlternatively, maybe the City node's label is 'City' and the property is 'name'. So the query would check for c.name = 'Paris'. But if the schema isn't known, perhaps I should first get the schema using get_graph_schema. Wait, the user hasn't provided any schema info yet. So maybe the first step is to call get_graph_schema to see what labels and properties are present. But the user's question is straightforward, and maybe the schema is already known. However, since the assistant might not have that info, it's safer to check.\n\nWait, the initial instructions say that if you don't know the schema, call get_graph_schema first. So maybe I should start by getting the schema to confirm the structure. But the user's question is about people living in Paris, so perhaps the schema already includes the necessary labels and relations. However, to be thorough, maybe I should first check the schema. But the user might not have provided that info, so the assistant needs to proceed with the available tools.\n\nAlternatively, maybe the assistant can proceed directly. Let's think: the available node labels are City and Person. The relations are Knows and LivesIn. So, the LivesIn relation is likely from Person to City. So, the query would be to find all Persons connected via LivesIn to a City named Paris. \n\nBut how to get the City's name? The City node might have a property like 'name' or 'cityName'. Since the user's question uses 'Paris' as the city name, the query would need to filter on that. So the Cypher query would be:\n\nMATCH (p:Person)-[:LivesIn]->(c:City)\nWHERE c.name = 'Paris'\nRETURN p.name\n\nBut if the property is not 'name', this might not work. However, given the constraints, the assistant might have to proceed with this query. Alternatively, if the schema isn't known, the assistant might need to first get the schema. But since the user hasn't provided any schema info, perhaps the assistant should proceed with the query as above.\n\nWait, but the initial plan says to first call get_graph_schema if the schema isn't known. So maybe the first step is to call get_graph_schema to confirm the structure. However, the user's question is straightforward, and maybe the assistant can proceed. But to be safe, perhaps the assistant should first check the schema. However, the user's input doesn't mention any schema, so maybe the assistant should proceed directly.\n\nAlternatively, maybe the assistant should first get the schema to confirm the properties. For example, if the City node's name is stored as 'city_name' instead of 'name', the query would fail. So, to avoid that, the assistant should first check the schema. But the user's question is about Paris, so maybe the assistant can proceed with the query. \n\nHmm, perhaps the assistant should first check the schema. Let me think: the available node labels are City and Person. The relations are Knows and LivesIn. So, the LivesIn relation connects Person to City. The City node likely has a property for the city name. So, the query would be as above. But to confirm, maybe the assistant should first get the schema. However, the user's question is straightforward, and the assistant might not have the schema info. \n\nIn this case, the assistant should proceed with the query. So, the final answer would be to run the Cypher query to find all Persons connected via LivesIn to a City named Paris. Therefore, the tool call would be to run_cypher_query with that query.\n",
"content": "",
"tool_calls": [
{
"id": "call_d3glzvzd",
"type": "function",
"function": {
"name": "run_cypher_query",
"arguments": {
"cypher_query": "MATCH (p:Person)-[:LivesIn]->(c:City) WHERE c.name = 'Paris' RETURN p.name"
}
}
}
]
},
{
"role": "tool",
"content": {
"success": true,
"query": "MATCH (p:Person)-[:LivesIn]->(c:City) WHERE c.name = 'Paris' RETURN p.name",
"row_count": 2,
"row_cap": 50,
"results": "\"p.name\"\n\"Alice\"\n\"Bob\"",
"output_format": "csv",
"may_have_more": false
},
"tool_call_id": "call_d3glzvzd"
}
]
}
]
[Synalinks]
Call ID: 41426208-d25f-4d66-a14b-6c65e74bb715
Parent call ID: 74405477-903f-4a10-9e48-4ddbcd2f7a7b
Module: LanguageModel
Module Name: language_model
Module Description: A language model API wrapper.
Data Model JSON:
[
{
"role": "assistant",
"content": "The people who live in Paris are:\n\n- Alice\n- Bob",
"reasoning_content": "Okay, let me process this. The user asked, \"Who lives in Paris?\" I constructed a Cypher query to find all Person nodes connected via LivesIn to a City named Paris. The tool response shows that the query returned two names: Alice and Bob. The results are in CSV format, with each name on a new line.\n\nI need to present these results clearly. The user probably expects a list of people living in Paris. Since the query used c.name = 'Paris', it's assuming the City node has a 'name' property. The response has two entries, so I should list them. Maybe the user wants just the names, so I'll format the answer as a list. Also, check if there's a possibility of more results, but the may_have_more flag is false, so no need to mention that. Just present the names clearly.\n"
}
]
[Synalinks]
Call ID: 74405477-903f-4a10-9e48-4ddbcd2f7a7b
Parent call ID: f96c94fd-d01c-47b2-b70d-6f3bb00a89b7
Module: Generator
Module Name: tool_calls_generator_cypher_agent
Module Description: Use a `LanguageModel` to generate a data model from an arbitrary input data model.
Data Model JSON:
[
{
"role": "assistant",
"content": "The people who live in Paris are:\n\n- Alice\n- Bob",
"reasoning_content": "Okay, let me process this. The user asked, \"Who lives in Paris?\" I constructed a Cypher query to find all Person nodes connected via LivesIn to a City named Paris. The tool response shows that the query returned two names: Alice and Bob. The results are in CSV format, with each name on a new line.\n\nI need to present these results clearly. The user probably expects a list of people living in Paris. Since the query used c.name = 'Paris', it's assuming the City node has a 'name' property. The response has two entries, so I should list them. Maybe the user wants just the names, so I'll format the answer as a list. Also, check if there's a possibility of more results, but the may_have_more flag is false, so no need to mention that. Just present the names clearly.\n"
}
]
[Synalinks]
Call ID: 5b4b7830-79fb-45e4-97b3-4829fcc825be
Parent call ID: 41426208-d25f-4d66-a14b-6c65e74bb715
Module: Generator
Module Name: final_generator_cypher_agent
Module Description: Use a `LanguageModel` to generate a data model from an arbitrary input data model.
Data Model JSON:
[
{
"query": "Who lives in Paris?",
"messages": [
{
"role": "assistant",
"content": "",
"reasoning_content": "Okay, let's tackle the user's question: \"Who lives in Paris?\" First, I need to figure out how to get the information from the knowledge graph. The available node labels are City and Person, and the relations are Knows and LivesIn. \n\nThe user is asking about people who live in Paris, so the key here is the LivesIn relation. Since Paris is a city, I should check if there's a City node labeled Paris. Then, look for Persons that have a LivesIn relation pointing to that City node.\n\nBut wait, I should first confirm the schema to make sure I'm using the right labels and relations. Maybe the LivesIn relation is connected from Person to City. So the plan is to find all Person nodes where their LivesIn relation points to the City node with the name Paris.\n\nI need to construct a Cypher query that matches this. Starting with MATCH, perhaps something like MATCH (p:Person)-[:LivesIn]->(c:City) WHERE c.name = 'Paris' RETURN p.name. But I need to make sure that the City node's property is indeed named 'name' or whatever the correct property is. Wait, the user might have different property names. Oh, the constraints mention that property names are snake_case, so maybe the city's name is stored as 'name' or 'city_name'? Hmm, but without seeing the schema, I might have to assume. Alternatively, maybe the City node has a 'name' property. \n\nAlternatively, maybe the City node's label is 'City' and the property is 'name'. So the query would check for c.name = 'Paris'. But if the schema isn't known, perhaps I should first get the schema using get_graph_schema. Wait, the user hasn't provided any schema info yet. So maybe the first step is to call get_graph_schema to see what labels and properties are present. But the user's question is straightforward, and maybe the schema is already known. However, since the assistant might not have that info, it's safer to check.\n\nWait, the initial instructions say that if you don't know the schema, call get_graph_schema first. So maybe I should start by getting the schema to confirm the structure. But the user's question is about people living in Paris, so perhaps the schema already includes the necessary labels and relations. However, to be thorough, maybe I should first check the schema. But the user might not have provided that info, so the assistant needs to proceed with the available tools.\n\nAlternatively, maybe the assistant can proceed directly. Let's think: the available node labels are City and Person. The relations are Knows and LivesIn. So, the LivesIn relation is likely from Person to City. So, the query would be to find all Persons connected via LivesIn to a City named Paris. \n\nBut how to get the City's name? The City node might have a property like 'name' or 'cityName'. Since the user's question uses 'Paris' as the city name, the query would need to filter on that. So the Cypher query would be:\n\nMATCH (p:Person)-[:LivesIn]->(c:City)\nWHERE c.name = 'Paris'\nRETURN p.name\n\nBut if the property is not 'name', this might not work. However, given the constraints, the assistant might have to proceed with this query. Alternatively, if the schema isn't known, the assistant might need to first get the schema. But since the user hasn't provided any schema info, perhaps the assistant should proceed with the query as above.\n\nWait, but the initial plan says to first call get_graph_schema if the schema isn't known. So maybe the first step is to call get_graph_schema to confirm the structure. However, the user's question is straightforward, and maybe the assistant can proceed. But to be safe, perhaps the assistant should first check the schema. However, the user's input doesn't mention any schema, so maybe the assistant should proceed directly.\n\nAlternatively, maybe the assistant should first get the schema to confirm the properties. For example, if the City node's name is stored as 'city_name' instead of 'name', the query would fail. So, to avoid that, the assistant should first check the schema. But the user's question is about Paris, so maybe the assistant can proceed with the query. \n\nHmm, perhaps the assistant should first check the schema. Let me think: the available node labels are City and Person. The relations are Knows and LivesIn. So, the LivesIn relation connects Person to City. The City node likely has a property for the city name. So, the query would be as above. But to confirm, maybe the assistant should first get the schema. However, the user's question is straightforward, and the assistant might not have the schema info. \n\nIn this case, the assistant should proceed with the query. So, the final answer would be to run the Cypher query to find all Persons connected via LivesIn to a City named Paris. Therefore, the tool call would be to run_cypher_query with that query.\n",
"tool_calls": [
{
"id": "call_d3glzvzd",
"type": "function",
"function": {
"name": "run_cypher_query",
"arguments": {
"cypher_query": "MATCH (p:Person)-[:LivesIn]->(c:City) WHERE c.name = 'Paris' RETURN p.name"
}
}
}
]
},
{
"role": "tool",
"content": {
"success": true,
"query": "MATCH (p:Person)-[:LivesIn]->(c:City) WHERE c.name = 'Paris' RETURN p.name",
"row_count": 2,
"row_cap": 50,
"results": "\"p.name\"\n\"Alice\"\n\"Bob\"",
"output_format": "csv",
"may_have_more": false
},
"tool_call_id": "call_d3glzvzd"
}
]
}
]
[Synalinks]
Call ID: f1234e74-9ee9-42ad-9ff8-32bd041adc5b
Parent call ID: 5b4b7830-79fb-45e4-97b3-4829fcc825be
Module: LanguageModel
Module Name: language_model
Module Description: A language model API wrapper.
Data Model JSON:
[
{
"messages": [
{
"role": "system",
"content": "<instructions>\nYou are a graph analyst with read-only access to a knowledge graph.\n\nAvailable node labels: ['City', 'Person']\nAvailable relation labels: ['Knows', 'LivesIn']\n\nPlan:\n1. If you don't already know the schema, call `get_graph_schema` first.\n2. When you need to inspect representative nodes, call `get_node_sample`.\n3. Build a single `MATCH ... RETURN` Cypher query and execute it with\n `run_cypher_query`. Iterate on the query (read the error, fix the\n Cypher, retry) until you have the data.\n4. Once you have an answer, stop calling tools and produce the final response.\n\nConstraints:\n- Only read-only Cypher is accepted. `CREATE`, `MERGE`, `SET`,\n `DELETE`, `DETACH DELETE`, `REMOVE`, `DROP`, `ALTER`, `COPY`,\n `INSTALL`, and `LOAD` are rejected by the engine — don't waste turns\n trying them.\n- Node and relation labels are case-sensitive PascalCase (e.g.\n ``Person``, ``LivesIn``); property names are snake_case.\n- Result sets are automatically capped server-side. If the result\n shows ``may_have_more=true``, refine the query (add filters or\n ``ORDER BY ... LIMIT n``) rather than asking for more rows.\n</instructions>\n"
},
{
"role": "user",
"content": "<input>\n{'query': 'Who lives in Paris?'}\n</input>\n<output>\n"
},
{
"role": "assistant",
"reasoning_content": "Okay, let's tackle the user's question: \"Who lives in Paris?\" First, I need to figure out how to get the information from the knowledge graph. The available node labels are City and Person, and the relations are Knows and LivesIn. \n\nThe user is asking about people who live in Paris, so the key here is the LivesIn relation. Since Paris is a city, I should check if there's a City node labeled Paris. Then, look for Persons that have a LivesIn relation pointing to that City node.\n\nBut wait, I should first confirm the schema to make sure I'm using the right labels and relations. Maybe the LivesIn relation is connected from Person to City. So the plan is to find all Person nodes where their LivesIn relation points to the City node with the name Paris.\n\nI need to construct a Cypher query that matches this. Starting with MATCH, perhaps something like MATCH (p:Person)-[:LivesIn]->(c:City) WHERE c.name = 'Paris' RETURN p.name. But I need to make sure that the City node's property is indeed named 'name' or whatever the correct property is. Wait, the user might have different property names. Oh, the constraints mention that property names are snake_case, so maybe the city's name is stored as 'name' or 'city_name'? Hmm, but without seeing the schema, I might have to assume. Alternatively, maybe the City node has a 'name' property. \n\nAlternatively, maybe the City node's label is 'City' and the property is 'name'. So the query would check for c.name = 'Paris'. But if the schema isn't known, perhaps I should first get the schema using get_graph_schema. Wait, the user hasn't provided any schema info yet. So maybe the first step is to call get_graph_schema to see what labels and properties are present. But the user's question is straightforward, and maybe the schema is already known. However, since the assistant might not have that info, it's safer to check.\n\nWait, the initial instructions say that if you don't know the schema, call get_graph_schema first. So maybe I should start by getting the schema to confirm the structure. But the user's question is about people living in Paris, so perhaps the schema already includes the necessary labels and relations. However, to be thorough, maybe I should first check the schema. But the user might not have provided that info, so the assistant needs to proceed with the available tools.\n\nAlternatively, maybe the assistant can proceed directly. Let's think: the available node labels are City and Person. The relations are Knows and LivesIn. So, the LivesIn relation is likely from Person to City. So, the query would be to find all Persons connected via LivesIn to a City named Paris. \n\nBut how to get the City's name? The City node might have a property like 'name' or 'cityName'. Since the user's question uses 'Paris' as the city name, the query would need to filter on that. So the Cypher query would be:\n\nMATCH (p:Person)-[:LivesIn]->(c:City)\nWHERE c.name = 'Paris'\nRETURN p.name\n\nBut if the property is not 'name', this might not work. However, given the constraints, the assistant might have to proceed with this query. Alternatively, if the schema isn't known, the assistant might need to first get the schema. But since the user hasn't provided any schema info, perhaps the assistant should proceed with the query as above.\n\nWait, but the initial plan says to first call get_graph_schema if the schema isn't known. So maybe the first step is to call get_graph_schema to confirm the structure. However, the user's question is straightforward, and maybe the assistant can proceed. But to be safe, perhaps the assistant should first check the schema. However, the user's input doesn't mention any schema, so maybe the assistant should proceed directly.\n\nAlternatively, maybe the assistant should first get the schema to confirm the properties. For example, if the City node's name is stored as 'city_name' instead of 'name', the query would fail. So, to avoid that, the assistant should first check the schema. But the user's question is about Paris, so maybe the assistant can proceed with the query. \n\nHmm, perhaps the assistant should first check the schema. Let me think: the available node labels are City and Person. The relations are Knows and LivesIn. So, the LivesIn relation connects Person to City. The City node likely has a property for the city name. So, the query would be as above. But to confirm, maybe the assistant should first get the schema. However, the user's question is straightforward, and the assistant might not have the schema info. \n\nIn this case, the assistant should proceed with the query. So, the final answer would be to run the Cypher query to find all Persons connected via LivesIn to a City named Paris. Therefore, the tool call would be to run_cypher_query with that query.\n",
"content": "",
"tool_calls": [
{
"id": "call_d3glzvzd",
"type": "function",
"function": {
"name": "run_cypher_query",
"arguments": {
"cypher_query": "MATCH (p:Person)-[:LivesIn]->(c:City) WHERE c.name = 'Paris' RETURN p.name"
}
}
}
]
},
{
"role": "tool",
"content": {
"success": true,
"query": "MATCH (p:Person)-[:LivesIn]->(c:City) WHERE c.name = 'Paris' RETURN p.name",
"row_count": 2,
"row_cap": 50,
"results": "\"p.name\"\n\"Alice\"\n\"Bob\"",
"output_format": "csv",
"may_have_more": false
},
"tool_call_id": "call_d3glzvzd"
}
]
}
]
[Synalinks]
Call ID: f1234e74-9ee9-42ad-9ff8-32bd041adc5b
Parent call ID: 5b4b7830-79fb-45e4-97b3-4829fcc825be
Module: LanguageModel
Module Name: language_model
Module Description: A language model API wrapper.
Data Model JSON:
[
{
"answer": "The people who live in Paris are Alice and Bob.",
"cypher_query": "MATCH (p:Person)-[:LivesIn]->(c:City) WHERE c.name = 'Paris' RETURN p.name"
}
]
[Synalinks]
Call ID: 5b4b7830-79fb-45e4-97b3-4829fcc825be
Parent call ID: 41426208-d25f-4d66-a14b-6c65e74bb715
Module: Generator
Module Name: final_generator_cypher_agent
Module Description: Use a `LanguageModel` to generate a data model from an arbitrary input data model.
Data Model JSON:
[
{
"answer": "The people who live in Paris are Alice and Bob.",
"cypher_query": "MATCH (p:Person)-[:LivesIn]->(c:City) WHERE c.name = 'Paris' RETURN p.name"
}
]
[Synalinks]
Call ID: 81182d88-63cf-40cd-8431-f1c73f0c55fe
Parent call ID: 0180a8b2-e536-4565-bd29-02464543e48f
Module: CypherAgent
Module Name: cypher_agent
Module Description: A ready-to-use Cypher agent backed by a knowledge base.
Data Model JSON:
[
{
"messages": [
{
"role": "assistant",
"reasoning_content": "Okay, let's tackle the user's question: \"Who lives in Paris?\" First, I need to figure out how to get the information from the knowledge graph. The available node labels are City and Person, and the relations are Knows and LivesIn. \n\nThe user is asking about people who live in Paris, so the key here is the LivesIn relation. Since Paris is a city, I should check if there's a City node labeled Paris. Then, look for Persons that have a LivesIn relation pointing to that City node.\n\nBut wait, I should first confirm the schema to make sure I'm using the right labels and relations. Maybe the LivesIn relation is connected from Person to City. So the plan is to find all Person nodes where their LivesIn relation points to the City node with the name Paris.\n\nI need to construct a Cypher query that matches this. Starting with MATCH, perhaps something like MATCH (p:Person)-[:LivesIn]->(c:City) WHERE c.name = 'Paris' RETURN p.name. But I need to make sure that the City node's property is indeed named 'name' or whatever the correct property is. Wait, the user might have different property names. Oh, the constraints mention that property names are snake_case, so maybe the city's name is stored as 'name' or 'city_name'? Hmm, but without seeing the schema, I might have to assume. Alternatively, maybe the City node has a 'name' property. \n\nAlternatively, maybe the City node's label is 'City' and the property is 'name'. So the query would check for c.name = 'Paris'. But if the schema isn't known, perhaps I should first get the schema using get_graph_schema. Wait, the user hasn't provided any schema info yet. So maybe the first step is to call get_graph_schema to see what labels and properties are present. But the user's question is straightforward, and maybe the schema is already known. However, since the assistant might not have that info, it's safer to check.\n\nWait, the initial instructions say that if you don't know the schema, call get_graph_schema first. So maybe I should start by getting the schema to confirm the structure. But the user's question is about people living in Paris, so perhaps the schema already includes the necessary labels and relations. However, to be thorough, maybe I should first check the schema. But the user might not have provided that info, so the assistant needs to proceed with the available tools.\n\nAlternatively, maybe the assistant can proceed directly. Let's think: the available node labels are City and Person. The relations are Knows and LivesIn. So, the LivesIn relation is likely from Person to City. So, the query would be to find all Persons connected via LivesIn to a City named Paris. \n\nBut how to get the City's name? The City node might have a property like 'name' or 'cityName'. Since the user's question uses 'Paris' as the city name, the query would need to filter on that. So the Cypher query would be:\n\nMATCH (p:Person)-[:LivesIn]->(c:City)\nWHERE c.name = 'Paris'\nRETURN p.name\n\nBut if the property is not 'name', this might not work. However, given the constraints, the assistant might have to proceed with this query. Alternatively, if the schema isn't known, the assistant might need to first get the schema. But since the user hasn't provided any schema info, perhaps the assistant should proceed with the query as above.\n\nWait, but the initial plan says to first call get_graph_schema if the schema isn't known. So maybe the first step is to call get_graph_schema to confirm the structure. However, the user's question is straightforward, and maybe the assistant can proceed. But to be safe, perhaps the assistant should first check the schema. However, the user's input doesn't mention any schema, so maybe the assistant should proceed directly.\n\nAlternatively, maybe the assistant should first get the schema to confirm the properties. For example, if the City node's name is stored as 'city_name' instead of 'name', the query would fail. So, to avoid that, the assistant should first check the schema. But the user's question is about Paris, so maybe the assistant can proceed with the query. \n\nHmm, perhaps the assistant should first check the schema. Let me think: the available node labels are City and Person. The relations are Knows and LivesIn. So, the LivesIn relation connects Person to City. The City node likely has a property for the city name. So, the query would be as above. But to confirm, maybe the assistant should first get the schema. However, the user's question is straightforward, and the assistant might not have the schema info. \n\nIn this case, the assistant should proceed with the query. So, the final answer would be to run the Cypher query to find all Persons connected via LivesIn to a City named Paris. Therefore, the tool call would be to run_cypher_query with that query.\n",
"content": "",
"tool_calls": [
{
"id": "call_d3glzvzd",
"type": "function",
"function": {
"name": "run_cypher_query",
"arguments": {
"cypher_query": "MATCH (p:Person)-[:LivesIn]->(c:City) WHERE c.name = 'Paris' RETURN p.name"
}
}
}
]
},
{
"role": "tool",
"content": {
"success": true,
"query": "MATCH (p:Person)-[:LivesIn]->(c:City) WHERE c.name = 'Paris' RETURN p.name",
"row_count": 2,
"row_cap": 50,
"results": "\"p.name\"\n\"Alice\"\n\"Bob\"",
"output_format": "csv",
"may_have_more": false
},
"tool_call_id": "call_d3glzvzd"
}
],
"answer": "The people who live in Paris are Alice and Bob.",
"cypher_query": "MATCH (p:Person)-[:LivesIn]->(c:City) WHERE c.name = 'Paris' RETURN p.name"
}
]
[Synalinks]
Call ID: 0180a8b2-e536-4565-bd29-02464543e48f
Parent call ID: None
Module: Functional
Module Name: cypher_agent
Module Description: A Cypher agent that answers questions about a knowledge graph.
Data Model JSON:
[
{
"messages": [
{
"role": "assistant",
"reasoning_content": "Okay, let's tackle the user's question: \"Who lives in Paris?\" First, I need to figure out how to get the information from the knowledge graph. The available node labels are City and Person, and the relations are Knows and LivesIn. \n\nThe user is asking about people who live in Paris, so the key here is the LivesIn relation. Since Paris is a city, I should check if there's a City node labeled Paris. Then, look for Persons that have a LivesIn relation pointing to that City node.\n\nBut wait, I should first confirm the schema to make sure I'm using the right labels and relations. Maybe the LivesIn relation is connected from Person to City. So the plan is to find all Person nodes where their LivesIn relation points to the City node with the name Paris.\n\nI need to construct a Cypher query that matches this. Starting with MATCH, perhaps something like MATCH (p:Person)-[:LivesIn]->(c:City) WHERE c.name = 'Paris' RETURN p.name. But I need to make sure that the City node's property is indeed named 'name' or whatever the correct property is. Wait, the user might have different property names. Oh, the constraints mention that property names are snake_case, so maybe the city's name is stored as 'name' or 'city_name'? Hmm, but without seeing the schema, I might have to assume. Alternatively, maybe the City node has a 'name' property. \n\nAlternatively, maybe the City node's label is 'City' and the property is 'name'. So the query would check for c.name = 'Paris'. But if the schema isn't known, perhaps I should first get the schema using get_graph_schema. Wait, the user hasn't provided any schema info yet. So maybe the first step is to call get_graph_schema to see what labels and properties are present. But the user's question is straightforward, and maybe the schema is already known. However, since the assistant might not have that info, it's safer to check.\n\nWait, the initial instructions say that if you don't know the schema, call get_graph_schema first. So maybe I should start by getting the schema to confirm the structure. But the user's question is about people living in Paris, so perhaps the schema already includes the necessary labels and relations. However, to be thorough, maybe I should first check the schema. But the user might not have provided that info, so the assistant needs to proceed with the available tools.\n\nAlternatively, maybe the assistant can proceed directly. Let's think: the available node labels are City and Person. The relations are Knows and LivesIn. So, the LivesIn relation is likely from Person to City. So, the query would be to find all Persons connected via LivesIn to a City named Paris. \n\nBut how to get the City's name? The City node might have a property like 'name' or 'cityName'. Since the user's question uses 'Paris' as the city name, the query would need to filter on that. So the Cypher query would be:\n\nMATCH (p:Person)-[:LivesIn]->(c:City)\nWHERE c.name = 'Paris'\nRETURN p.name\n\nBut if the property is not 'name', this might not work. However, given the constraints, the assistant might have to proceed with this query. Alternatively, if the schema isn't known, the assistant might need to first get the schema. But since the user hasn't provided any schema info, perhaps the assistant should proceed with the query as above.\n\nWait, but the initial plan says to first call get_graph_schema if the schema isn't known. So maybe the first step is to call get_graph_schema to confirm the structure. However, the user's question is straightforward, and maybe the assistant can proceed. But to be safe, perhaps the assistant should first check the schema. However, the user's input doesn't mention any schema, so maybe the assistant should proceed directly.\n\nAlternatively, maybe the assistant should first get the schema to confirm the properties. For example, if the City node's name is stored as 'city_name' instead of 'name', the query would fail. So, to avoid that, the assistant should first check the schema. But the user's question is about Paris, so maybe the assistant can proceed with the query. \n\nHmm, perhaps the assistant should first check the schema. Let me think: the available node labels are City and Person. The relations are Knows and LivesIn. So, the LivesIn relation connects Person to City. The City node likely has a property for the city name. So, the query would be as above. But to confirm, maybe the assistant should first get the schema. However, the user's question is straightforward, and the assistant might not have the schema info. \n\nIn this case, the assistant should proceed with the query. So, the final answer would be to run the Cypher query to find all Persons connected via LivesIn to a City named Paris. Therefore, the tool call would be to run_cypher_query with that query.\n",
"content": "",
"tool_calls": [
{
"id": "call_d3glzvzd",
"type": "function",
"function": {
"name": "run_cypher_query",
"arguments": {
"cypher_query": "MATCH (p:Person)-[:LivesIn]->(c:City) WHERE c.name = 'Paris' RETURN p.name"
}
}
}
]
},
{
"role": "tool",
"content": {
"success": true,
"query": "MATCH (p:Person)-[:LivesIn]->(c:City) WHERE c.name = 'Paris' RETURN p.name",
"row_count": 2,
"row_cap": 50,
"results": "\"p.name\"\n\"Alice\"\n\"Bob\"",
"output_format": "csv",
"may_have_more": false
},
"tool_call_id": "call_d3glzvzd"
}
],
"answer": "The people who live in Paris are Alice and Bob.",
"cypher_query": "MATCH (p:Person)-[:LivesIn]->(c:City) WHERE c.name = 'Paris' RETURN p.name"
}
]
Answer: The people who live in Paris are Alice and Bob.
Cypher: MATCH (p:Person)-[:LivesIn]->(c:City) WHERE c.name = 'Paris' RETURN p.name