La interfaz está localizada; las descripciones y documentos de las Skills se traducen automáticamente con IA a su idioma. El código, los comandos, rutas y términos técnicos se mantienen en el original, y prevalece el documento del autor.
Explorar Skills
Productividad · SKILL.md

core-sdk

core-sdk

Guides Graphiti Python SDK workflows for ingest, search, backends, and custom types.

Independent GitHub snapshot · Automated classification · Original documentation governs

Documentación de la Skill

GitHub

name: core-sdk description: "Guides Graphiti Python SDK workflows for ingest, search, backends, and custom types." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

core-sdk

Use this sub-skill for the Graphiti Python library itself: building temporal context graphs, choosing a backend driver, ingesting episodes, searching facts or nodes, and wiring custom entity or edge types.

Read first

  • references/api-reference.md for the verified Graphiti API, driver constructors, search recipes, and the node/edge objects you will handle.
  • references/workflows.md for the shortest working paths: quickstart ingest, search, custom types, provider selection, and tracing.
  • references/troubleshooting.md when the SDK import fails, a backend is missing, search returns validation errors, or the provider/model setup is unreliable.
  • scripts/quickstart_graphiti.py for a runnable smoke that adds a few sample episodes and prints search results.

What belongs here

This sub-skill owns the SDK questions people naturally ask directly:

  • "How do I create a Graphiti instance?"
  • "Should I use Neo4j or FalkorDB?"
  • "How do I add episodes or bulk episodes?"
  • "How do I search facts vs nodes?"
  • "How do I customize entity or edge types?"
  • "How do I add a single triplet without episode extraction?"
  • "How do I summarize a saga or build communities?"
  • "How do I use OpenAI-compatible providers, Azure OpenAI, Gemini, or local structured-output models?"

What does not belong here

Route these elsewhere:

  • REST route questions, queueing, or Docker deployment -> sub-skills/rest-service/
  • MCP tool questions, transports, or client integrations -> sub-skills/mcp-server/
  • Repo maintenance, test policy, or import packaging details -> root references

Typical workflow

  1. Choose the backend driver and credentials.
  2. Build indices and constraints.
  3. Add one episode or a small batch of episodes.
  4. Search with search() for facts or search_() for nodes and richer recipes.
  5. Optionally build communities, summarize a saga, or inspect provenance.
  6. Use the bundled quickstart script to confirm the environment.

API families to remember

  • Core client: Graphiti
  • Drivers: Neo4jDriver, FalkorDriver
  • Search recipes: NODE_HYBRID_SEARCH_RRF, EDGE_HYBRID_SEARCH_RRF, COMBINED_HYBRID_SEARCH_CROSS_ENCODER
  • Models: EntityNode, EpisodicNode, CommunityNode, SagaNode, EntityEdge, EpisodicEdge, CommunityEdge, HasEpisodeEdge, NextEpisodeEdge
  • Clients: OpenAIClient, OpenAIGenericClient, OpenAIEmbedder, OpenAIRerankerClient

Success criteria

A future agent should be able to use this sub-skill to:

  • install the right Graphiti package and backend extra,
  • create a driver and instantiate Graphiti,
  • add and search episodes without reopening the original repository,
  • choose the right search path or custom-type path,
  • and diagnose the predictable import, backend, or provider failure modes.
Skills guardadas