jido_gralkor

Drop-in long-term memory for a Jido agent. One Hex package: the Jido plugin and ReAct tools on top of an embedded Gralkor memory adapter — Graphiti driven directly from the BEAM via Pythonx, with no separate Gralkor service to deploy. Storage uses either an embedded FalkorDB child or a remote FalkorDB deployment.

You write your agent's prompt, model, and business tools. jido_gralkor covers session identity, recall, capture, the memory_search / memory_add ReAct tools, a small helper that pins tool_choice to memory_search on the first ReAct iteration so the agent itself authors its memory queries, a graceful-shutdown flush, a context-rotation primitive for long-running agents, Destinations for named memory placement and extraction, Lenses for ingestion, and Reflections for asynchronous post-ingestion synthesis.

This is the canonical home for new Gralkor development: Gralkor is Jido-first. As of 3.0.0 the former :gralkor_ex Hex package is folded into this one, and the legacy :gralkor and :gralkor_ex packages direct consumers here. Consumers need only {:jido_gralkor, "~> 6.0"} for the whole memory stack.

Install

def deps do
  [
    {:jido_gralkor, "~> 6.0"}
  ]
end

Then fetch:

mix deps.get

The package requires Elixir ~> 1.18 and directly depends on :jido, :jido_ai, :pythonx, :req_llm, :jason, and :yaml_elixir. On the first native-runtime boot, Pythonx materialises a managed Python 3.12 environment with graphiti-core and falkordblite; consumers do not install Python themselves, but the boot needs package-download access and a writable cache.

Required configuration

Four things the consumer must set up.

1. A FalkorDB backend. Graphiti runs in-process via Pythonx and connects to FalkorDB either as an embedded falkordblite child or over the network. Pick one:

# Embedded — falkordblite spawns a redis-server grandchild under this dir
export GRALKOR_DATA_DIR=/var/lib/<your-app>/gralkor   # writable
export GOOGLE_API_KEY=...                             # the default LLM and embedder are Google

Native Graphiti supports google: and openai: models, and each of its two roles picks its provider independently. GRALKOR_LLM_MODEL selects the LLM (default google:gemini-3.1-flash-lite) and GRALKOR_EMBEDDER_MODEL selects the embedder (default google:gemini-embedding-2-preview). The cross-encoder/reranker has no spec of its own and follows the LLM role's provider.

Mixing the two roles is supported. An OpenAI LLM with a Google embedder builds an OpenAI LLM client, an OpenAI reranker, and a Google embedder:

export GRALKOR_LLM_MODEL=openai:gpt-4.1-mini
export GRALKOR_EMBEDDER_MODEL=google:gemini-embedding-2-preview
export OPENAI_API_KEY=...   # the llm role selected openai
export GOOGLE_API_KEY=...   # the embedder role selected google

Set only the credential(s) for the providers your two specs actually select: an all-Google pair needs GOOGLE_API_KEY alone, an all-OpenAI pair needs OPENAI_API_KEY alone. Startup raises ArgumentError before any inference client is constructed when a spec names a provider outside :openai / :google (naming both specs and the supported providers), or when the credential for a provider a spec selects is missing or blank (naming the variable and the role, "llm" or "embedder"). Nothing checks that an LLM and an embedder from different providers are otherwise compatible — embedding dimensions and the like are yours to keep consistent.

Direct ReqLLM calls are provider-portable regardless — for example, the focused interpretation functional suite uses OpenAI without starting Graphiti.

# Remote — point at a managed FalkorDB. config/runtime.exs
config :jido_gralkor,
  falkordb: [
    host: System.fetch_env!("FALKORDB_HOST"),
    port: String.to_integer(System.fetch_env!("FALKORDB_PORT")),
    username: System.get_env("FALKORDB_USERNAME"),
    password: System.get_env("FALKORDB_PASSWORD"),
    ssl: System.get_env("FALKORDB_SSL") == "true"
  ]

Remote wins when both are set. :ssl defaults to false; set true for FalkorDB Cloud or any TLS-fronted endpoint. Misconfigured :falkordb (non-keyword, missing host/port, blank host, non-positive port) raises ArgumentError at app start.

2. In-memory client in tests. Swap the adapter for the in-memory twin:

# config/test.exs
config :jido_gralkor,
  client: Gralkor.Client.InMemory,
  destination_storage: Gralkor.Destination.Storage.InMemory,
  lens_storage: Gralkor.Lens.Storage.InMemory,
  reflection_storage: Gralkor.Reflection.Storage.InMemory

Start the legacy client twin once in test/test_helper.exs:

{:ok, _} = Gralkor.Client.InMemory.start_link()
ExUnit.start()

Lens tests should also start a fresh storage process in setup so state is isolated:

setup do
  start_supervised!(Gralkor.Lens.Storage.InMemory)
  start_supervised!(Gralkor.Reflection.Storage.InMemory)
  :ok
end

When the client and storage layers use these in-memory adapters, the native supervision tree (Pythonx → GraphitiPool → CaptureBuffer) does not start and Lens or Reflection storage calls do not reach Graphiti. No FalkorDB backend is required.

3. Jido.Thread.Plugin on your use Jido supervisor. The plugin reads session_id from agent.state[:__thread__].id, so the thread plugin must be active:

defmodule MyApp.Jido do
  use Jido,
    otp_app: :my_app,
    default_plugins: [Jido.Thread.Plugin, Jido.Identity.Plugin]
end

4. A non-blank human name in agent state. Before any completed or failed turn is captured, populate agent.state[:user_name] with the current human's name (for example, from the request's tool context in on_before_cmd/2). The plugin deliberately has no generic "User" fallback: a missing or blank value raises ArgumentError before capture.

:jido_gralkor auto-supervises its native runtime (Python → GraphitiPool → CaptureBuffer) when a FalkorDB backend is configured — no separate Gralkor.Server to wire into your supervision tree, and no readiness gate to add. By the time Application.start/2 returns, Gralkor.Client is ready.

Configuration reference

Everything :jido_gralkor reads, in one place. Nothing else is configurable — Python, the venv, and the Graphiti client are internal concerns with no consumer-facing knobs.

Application environment (config :jido_gralkor, …)

# config/runtime.exs — everything optional, shown with its default
config :jido_gralkor,
  interpret_max_output_tokens: 2000,
  recall_deadline_ms: 12_000

The implicit "operator" Lens and legacy capture/5, memory_add/3, and recall/4 need no ontology configuration. They use the packaged operator Destination and its library-owned Gralkor.DefaultOntology. Application-specific extraction schemas belong on registered Destinations referenced by named Lenses or Reflections.

Environment variables

Plugin mount options

{JidoGralkor.Plugin, %{agent_name: "Susu", ingestion_lens: "observations", …}}

Per-turn, tool_context[:lens] overrides :ingestion_lens for that query; the plugin retains the selection on the request's thread entry so later capture stays bound to it.

JidoGralkor.ContextRotator.rotate_now/2

A complete configuration

Everything above, in one deployment. Three files.

Ontologies are modules referenced by Destinations. Define an ontology as ordinary compiled Elixir in your own lib/, then reference its module from each Destination that should extract with it. Lenses and Reflections reference those Destinations by name; neither repeats the ontology or address.

# lib/my_app/ontologies.ex — compiled code. Named by Destination definitions below.
defmodule MyApp.Ontology do
  use Gralkor.Ontology, entities: :strict, relationships: :scoped

  entity Teammate, "A person the agent works with." do
    field :handle,   :string, required: true, doc: "stable login handle"
    field :timezone, :string,                 doc: "IANA tz"
  end

  entity WorkingPreference, "A way a teammate prefers to work." do
    field :description, :string, required: true
  end

  from Teammate do
    prefers WorkingPreference do
      field :since, :string, doc: "date first observed"
    end
  end
end

# config/runtime.exs
import Config

config :jido_gralkor,
  # Backend — pick one. Remote wins if both are present.
  falkordb: [
    host: System.fetch_env!("FALKORDB_HOST"),
    port: String.to_integer(System.fetch_env!("FALKORDB_PORT")),
    username: System.get_env("FALKORDB_USERNAME"),
    password: System.get_env("FALKORDB_PASSWORD"),
    ssl: System.get_env("FALKORDB_SSL") == "true"
  ],

  destinations: [
    [name: "observations", address: "operator/observations", ontology: MyApp.Ontology],
    [name: "decisions", address: "operator/decisions", ontology: MyApp.Ontology],
    [name: "release-knowledge", address: "global/release-knowledge"]
  ],

  # The Lens registry selects ingestion behavior and a Destination.
  lenses: [
    [
      name: "observations",
      destination: "observations",
      ingestion: Gralkor.Lens.Ingestion.Store
    ],
    [
      name: "decisions",
      destination: "decisions",
      ingestion: MyApp.DecisionIngestion
    ]
  ],

  # Optional custom Reflection declarations. Omit this key to use the two
  # packaged declarations shown here.
  reflections: [
    [
      name: "generalisations",
      destination: "generalisations",
      chain_of_thought: "priv/reflections/generalisations.yaml"
    ],
    [
      name: "erl",
      destination: "experiential-learning",
      chain_of_thought: "priv/reflections/erl.yaml"
    ]
  ],

  # Tuning — optional, shown at its default.
  interpret_max_output_tokens: 2000,
  recall_deadline_ms: 12_000

# lib/my_app/chat_agent.ex — the mount selects among the registered names.
plugins: [
  {JidoGralkor.Plugin,
   %{
     agent_name: "Susu",
     ingestion_lens: "observations",
     search_destinations: ["decisions", "generalisations"]
   }}
]

That mount writes captured turns and memory_add calls through the "observations" Lens to its Destination, and concurrently searches the selected "decisions" and "generalisations" Destinations. After a flushed ingestion has completed across its intended Lenses, each declared Reflection is scheduled independently over the completed lensed representations.

Ontology placement. The packaged operator Destination uses Jido Gralkor's open Gralkor.DefaultOntology; packaged experiential learning uses Gralkor.Reflection.ERLOntology. Applications attach custom ontology modules to explicitly registered Destinations. If an older deployment set config :jido_gralkor, :ontology, remove it and create a named Destination referenced by a Lens or Reflection instead.

Wire it on your agent

defmodule MyApp.ChatAgent do
  use Jido.Agent,
    name: "my_chat",
    schema: [user_name: [type: :string, required: true]],
    strategy:
      {Jido.AI.Reasoning.ReAct.Strategy,
       tools: [
         JidoGralkor.Actions.MemorySearch,
         JidoGralkor.Actions.MemoryAdd,
         # ... your other tools
       ],
       system_prompt: """
       You are a helpful assistant with long-term memory.

       Use memory_search when answering benefits from past context.
       Use memory_add to record explicit insights you want to preserve
       beyond the conversation that's already being auto-captured.
       """,
       request_transformer: MyApp.ChatAgent.RequestTransformer},
    default_plugins: %{__memory__: false},
    plugins: [
      {JidoGralkor.Plugin,
       %{
         agent_name: "Susu",
         ingestion_lens: "observations",
         search_destinations: ["observations", "generalisations"]
       }}
    ]

  # Optional: pin tool_choice to memory_search on iteration 1 so the agent
  # itself authors a focused recall query in-thread.
  defmodule RequestTransformer do
    @behaviour Jido.AI.Reasoning.ReAct.RequestTransformer

    @impl true
    def transform_request(_request, state, _config, _runtime_context) do
      {:ok, JidoGralkor.ReAct.maybe_force_memory_search(%{}, state)}
    end
  end
end

The plugin claims Jido's :__memory__ slot. On ai.react.query, it plants :session_id (when a thread is committed), :agent_name, the selected :lens, and :search_destinations on the signal's tool_context. Recall itself is the LLM's job — JidoGralkor.ReAct.maybe_force_memory_search/2 is the cheapest way to force it on iteration 1. Capture runs automatically on completion and failure: the ReAct event trace is normalised into Gralkor's canonical [%Gralkor.Message{role, content}] shape via JidoGralkor.Canonical — user for the user query, behaviour for intermediate thinking / tool calls / tool results, assistant for the final answer on completed turns, or a terminal "request failed: …"behaviour on failed turns so the failure stays visible to downstream distillation.

Set tool_context[:lens] on an individual query to override ingestion_lens for that turn. The plugin retains the selection on the request's Jido thread entry, making it authoritative for both memory_add and later completion or failure capture after ReAct has released its transient tool context. The host strategy's configured tools and complete tool context are carried into post-ingestion Reflection execution.

The plugin reads user_name per-turn from agent.state[:user_name]. Populate it before each request (for example, via on_before_cmd/2 from the signal's tool_context) so distill renders user lines under the correct human identity. Missing and blank names raise; there is no generic fallback.

What happens at runtime

Session identity.session_id is the current Jido thread id (read from agent.state[:__thread__].id, populated by Jido.Thread.Plugin). The plugin does not mint its own identifier — Jido's thread lifecycle is the single source of truth.

Destinations. Every Lens and Reflection references a registered Destination. Its operator/path or global/path address resolves the Graphiti graph ID, and its ontology governs extraction. Multiple Lenses and Reflections may save to the same Destination. Replacement writes inject _gralkor_lens into supplied nodes and relationships so a replaceable Lens changes only its own content at that Destination.

Post-ingestion Reflections. A successful flush first completes every intended Lens ingestion and retains the actual zero, one, or many outputs each Lens stored, with a shared evidence identifier linking representations of the same submitted information. When at least one representation was stored, the declared Reflections are then scheduled asynchronously. Each Reflection runs independently, so one failure does not prevent another from completing or storing its artefact.

First-turn bootstrap. On the very first query of a fresh agent, the thread isn't yet committed (the ReAct strategy's ThreadAgent.append runs after the plugin hook). The plugin plants :agent_name plus configured :lens and :search_destinations, but no :session_id; completed and failed turn capture are both skipped with a warning until a committed thread supplies that identity. memory_search called in that same first turn short-circuits with an explicit "did not run" non-result so the LLM cannot read an empty payload as "no memory exists" and confidently lie.

Death-triggered flush.JidoGralkor.Lifecycle is an optional Jido.AgentServer.Lifecycle implementation. When wired as lifecycle_mod: on the agent, graceful termination of the AgentServer calls the configured client's flush/1 callback for the active thread so an orphaned agent doesn't strand its capture buffer. The plugin mount alone does not enable this lifecycle. No idle-timer machinery — Jido's AgentServer owns :idle_timeout directly.

{:ok, pid} =
  MyApp.Jido.start_agent(
    MyApp.ChatAgent,
    id: "operator-42",
    initial_state: %{user_name: current_user.name},
    lifecycle_mod: JidoGralkor.Lifecycle
  )

Context rotation.JidoGralkor.ContextRotator.rotate_now/2 synchronously flushes the active session via flush_and_await/2, installs a fresh Jido thread, and seeds the rotated thread with the most-recent :keep_last_n pre-flush entries plus any turns that landed during the flush. It returns :ok when there is no committed thread and {:error, reason} when state reading, flushing, or thread installation fails. The agent process is never stopped. Use it from a /new chat command or a small wrapper GenServer that fires on an interval.

Error contracts. Invalid configuration, invalid Lens requests, and automatic plugin-capture failures raise. Valid explicit Gralkor.Client.ingest/1, replace/1, search/1, and adapter operations return tagged success/error tuples; the ReAct search action propagates those errors. The asynchronous memory_add action logs background failures and still returns immediately, as described below.

memory_add is async. The tool returns "Ingesting." immediately and does the storage call in a background Task. Graphiti's entity/edge extraction can take tens of seconds; you don't want the agent waiting. Failures are logged; best-effort storage is the contract.

Configure Lenses

A Lens is an application-owned memory channel with :operator or :global scope. Its write mode is either append, which sends content through an ingestion process, or whole-graph replacement, which replaces the graph content owned by that Lens.

Appending is the default write mode. An appending Lens supplies an ontology and the ingestion process Gralkor invokes when content is sent through it; write: :append may be stated explicitly or omitted.

The ontology is a module you compile into your own application — declared once in lib/, then named by each Destination that should extract with it:

# lib/my_app/ontology.ex
defmodule MyApp.Ontology do
  use Gralkor.Ontology, entities: :strict, relationships: :scoped

  entity Teammate, "A person the agent works with." do
    field :handle,   :string, required: true, doc: "stable login handle"
    field :timezone, :string,                  doc: "IANA tz"
  end

  entity WorkingPreference, "A way a teammate prefers to work." do
    field :description, :string, required: true
  end

  from Teammate do
    prefers WorkingPreference do
      field :since, :string, doc: "date first observed"
    end

    trusts Teammate
  end
end

Register Destinations first, then point as many Lenses as your application needs at them. Several Lenses may use the same Destination:

# config/runtime.exs
config :jido_gralkor,
  destinations: [
    [name: "observations", address: "operator/observations", ontology: MyApp.Ontology],
    [name: "decisions", address: "operator/decisions", ontology: MyApp.Ontology],
    [name: "systems", address: "operator/systems", ontology: MyApp.Ontology]
  ],
  lenses: [
    [
      name: "observations",
      destination: "observations",
      ingestion: Gralkor.Lens.Ingestion.Store
    ],
    [
      name: "decisions",
      destination: "decisions",
      ingestion: MyApp.DecisionIngestion
    ]
  ]

A replaceable Lens declares write: :replace_graph and the graph format it accepts instead of :ingestion:

config :jido_gralkor,
  lenses: [
    [
      name: "systems",
      destination: "systems",
      write: :replace_graph,
      graph_format: :property_graph
    ]
  ]

Destination addresses control visibility: operator/path resolves a separate graph for each operator, while global/path resolves the same graph for every operator.

Gralkor.Lens.Ingestion.Store is the built-in straight-through process. A consumer can define any other ingestion process by implementing one callback:

defmodule MyApp.DecisionIngestion do
  @behaviour Gralkor.Lens.Ingestion

  @impl true
  def ingest(request, store) do
    with {:ok, decisions} <- MyApp.Decisions.extract(request.content) do
      Enum.reduce_while(decisions, :ok, fn decision, :ok ->
        case Gralkor.Lens.Store.add(store, decision, request.source_description) do
          :ok -> {:cont, :ok}
          {:error, reason} -> {:halt, {:error, reason}}
        end
      end)
    end
  end
end

The callback receives the original %Gralkor.Ingest{} request and a Lens-bound %Gralkor.Lens.Store{}. It decides whether to make zero, one, or many writes and can use Gralkor.Lens.Store.add/3 and search/3. The selected Destination supplies the graph address and ontology. Client.ingest/1 accepts appending Lenses and raises for replaceable Lenses; Client.replace/1 accepts replaceable Lenses and raises for appending Lenses.

The plugin mount chooses how an agent uses the registered Lenses:

{JidoGralkor.Plugin,
 %{
   agent_name: "Susu",
   ingestion_lens: "observations",
   search_destinations: ["observations", "generalisations"]
 }}

• ingestion_lens receives memory_add calls and automatic capture unless a turn supplies tool_context[:lens].
• search_destinations is an optional list of registered Destination names. An empty list searches the packaged "operator" Destination.

Consumers that ingest, replace, or search outside an agent call the same public boundary directly:

:ok =
  Gralkor.Client.ingest(%Gralkor.Ingest{
    operator_id: "operator-42",
    lens: "decisions",
    content: "We chose Friday.",
    source_description: "release planning"
  })

{:ok, memories} =
  Gralkor.Client.search(%Gralkor.Search{
    operator_id: "operator-42",
    query: "When should we release?",
    destinations: ["decisions", "generalisations"],
    max_results: 20
  })

:ok =
  Gralkor.Client.replace(%Gralkor.Replace{
    operator_id: "operator-42",
    lens: "systems",
    graph: %Gralkor.Graph{
      format: :property_graph,
      data: %{
        nodes: [
          %{id: "payments", labels: ["System"], properties: %{name: "Payments"}},
          %{id: "ledger", labels: ["System"], properties: %{name: "Ledger"}}
        ],
        relationships: [
          %{
            from: "payments",
            to: "ledger",
            type: "DEPENDS_ON",
            properties: %{protocol: "events"}
          }
        ]
      }
    }
  })

Search names Destinations directly. Searches run concurrently while results retain configured Destination order. max_results defaults to 20, must be a positive integer, and applies independently to every Destination. Result types are :facts, :nodes, :episodes, and :artefacts; each returned item identifies its Destination. Node searches accept entity_types, fact searches accept edge_types, and artefact searches may narrow by artefact_id. The memory_search action returns the attributed list as JSON.

:property_graph is the supported replacement format. Every node requires a unique, non-blank string :id, a list of non-blank string :labels, and a :properties map. Every relationship requires :from and :to identifiers naming supplied nodes, a non-blank string :type, and a :properties map. This payload is the whole current graph for the Lens; partial node and relationship operations are not supported.

Replacement changes only content owned by that Lens at its Destination. Gralkor overwrites any supplied _gralkor_lens property with the selected Lens name on every inserted node and relationship. Content saved through another Lens or Reflection, or carrying no Lens ownership, remains unchanged. An empty graph removes all graph content owned by the selected Lens. The supplied graph format must match the Lens's configured :graph_format.

Invalid Lens names, write modes, formats, and graph data raise ArgumentError; graph data is fully validated before storage mutation begins. Once a valid replacement starts, deletion and insertion are not transactional: an import error is returned, and content already removed or inserted is not rolled back.

Registry and plugin configuration fail fast for blank, duplicate, reserved, retired, or malformed Lens definitions and for unknown Lens names. The retired "default" Lens name raises with guidance to use "operator"; it is not an alias. If no Lens configuration is used, the implicit "operator" Lens preserves the existing operator group and uses Jido Gralkor's built-in generic extraction contract.

Ontology DSL

Each Destination ontology is a module declared with Gralkor.Ontology:

• entity Foo do field … end declares an entity. field :name, :type, opts supports :string | :integer | :float | :boolean, plus required: true and doc: (rendered as the Pydantic field description).
• entity Foo, "when to extract one" do … end adds a description, rendered as the extracted type's own description. Graphiti's extractor reads it to decide when to mint the entity, so a type whose name alone is ambiguous — Preference, Pattern, Learning — is extracted far more reliably with one. The description must be a literal string.
• from Source do verb Target [do field … end] end declares outgoing relationships from Source. The verb's name becomes the edge type in graphiti (prefers → "PREFERS", relates_to → "RELATES_TO"). The optional do block carries edge properties.
• Same verb in multiple from blocks becomes one edge type with multiple endpoint pairs.
• entities: :strict excludes graphiti's generic Entity extraction — only your declared types survive. entities: :open lets graphiti extract generic Entity nodes alongside yours.
• relationships: :scoped populates graphiti's edge_type_map from your declared (src, dst) pairs, so named edges only fire between declared endpoints. relationships: :open drops the map; graphiti's default applies. Either way, graphiti always extracts edge candidates — generic fall-through edges between unconstrained pairs are not closed off.
• Both opts are required at use — no defaults; pick deliberately.

Protected field names. Entity and edge type names are unrestricted — pick whatever suits your domain. Field names are not: graphiti rejects any custom entity attribute whose name collides with a field on its own EntityNode, namely uuid, name, group_id, labels, created_at, summary, attributes, and name_embedding. The DSL does not currently catch this at compile time, so field :name, :string compiles and then raises EntityTypeValidationError from Python on the first write through the Lens that selected the ontology. Name fields for what they hold — handle, title, statement — rather than reaching for name or summary.

On each store write, graphiti receives the selected Destination ontology's entity_types, edge_types, edge_type_map, and excluded_entity_types, translated from the module's compile-time payload.

Configure Reflections

A Reflection is an asynchronous post-ingestion process over completed lensed representations. It is declared by name, registered Destination, and a repository YAML Chain of Thought. Reflections are not Lenses: Lens definitions remain independent views for absorbing information, while Reflections operate over the successful results after every intended Lens has finished.

The package supplies two declarations by default:

• generalisations uses priv/reflections/generalisations.yaml and the packaged generalisations Destination at global/generalisations.
• erl uses priv/reflections/erl.yaml and the packaged experiential-learning Destination at operator/experiential-learning. That Destination carries Gralkor.Reflection.ERLOntology, whose Learning entity declares optional problem_kind, approach, success, and lesson fields.

Set :reflections to replace those defaults with application declarations, and set :reflection_root when their YAML paths resolve from somewhere other than the installed application root. A Reflection's Destination ontology governs extraction, whether it is the generic default or an application schema.

config :jido_gralkor,
  reflection_root: File.cwd!(),
  reflections: [
    [
      name: "release-review",
      destination: "release-knowledge",
      chain_of_thought: "priv/reflections/release-review.yaml"
    ]
  ]

Each YAML file contains an ordered, non-empty steps list. A step declares a label, natural-language directions, and an exact structured output schema. Later directions may interpolate prior outputs with {{output_name}}. At runtime each step receives the completed ingestion's lensed representations, the host agent's tools, and its full tool context. The model may direct tool calls described by the custom directions; tool results return to the same step before it produces its structured output. That output is validated exactly, made available to later interpolation, and the final step becomes one stored %Gralkor.Reflection.Artefact{} with its supporting evidence identifiers.

Multiple Reflections and Lenses may save to the same Destination. Search selects Destinations directly:

{:ok, artefacts} =
  Gralkor.Client.search(%Gralkor.Search{
    operator_id: "operator-42",
    query: "What release approaches have worked?",
    destinations: ["experiential-learning"],
    result_type: :artefacts,
    max_results: 20
  })

{:ok, [artefact]} =
  Gralkor.Client.search(%Gralkor.Search{
    operator_id: "operator-42",
    query: "",
    destinations: ["experiential-learning"],
    result_type: :artefacts,
    artefact_id: "reflection-123"
  })

result_type: :artefacts returns final Reflection artefacts from the selected Destinations, and artefact_id optionally narrows the lookup to one exact artefact. Each artefact carries its declaring Reflection.

Testing against the in-memory twin

Gralkor.Client.InMemory is a real implementation of Gralkor.Client (not a mock) that stores canned responses and records every call. Your agent's integration tests can hit it without any network:

For Lens-aware calls, pair it with Gralkor.Lens.Storage.InMemory as shown in Required configuration; Client.ingest/1 and search/1 use the Lens storage boundary directly.

setup do
  Gralkor.Client.InMemory.reset()
  :ok
end

test "agent recalls stored context" do
  Gralkor.Client.InMemory.set_recall({:ok, "<gralkor-memory>known fact</gralkor-memory>"})
  Gralkor.Client.InMemory.set_capture(:ok)
  # ... exercise your agent, assert on responses, inspect recorded calls
end

The same Gralkor.ClientContract macro suite is run against both the in-memory twin and the production Gralkor.Client.Native adapter, so both satisfy an identical contract.

Maintainers can exercise the interpretation prompt against a real model with mix test.functional test/functional/interpret_epistemic_humility_test.exs. The suite loads OPENAI_API_KEY from .env, uses OpenAI gpt-5.6-sol through ReqLLM, and starts no Graphiti or FalkorDB runtime. It verifies source preservation across varied accounts, conflict handling without truth adjudication, restraint when provenance is absent, and relevance filtering.

What's in the library

The Jido glue:

• JidoGralkor.Plugin — use Jido.Plugin, state_key: :__memory__, singleton: true. Handles ai.react.query (planting session, agent, selected Lens, and Destinations to search) and ai.request.completed / ai.request.failed (capture).
• JidoGralkor.ReAct — maybe_force_memory_search/2 helper. Folds tool_choice: %{type: "function", function: %{name: "memory_search"}} into ReAct overrides on iteration 1; passes through unchanged on iterations 2+.
• JidoGralkor.Canonical — normalises a Jido/ReAct turn into the canonical [%Gralkor.Message{role, content}] shape.
• JidoGralkor.Lifecycle — Jido.AgentServer.Lifecycle impl whose sole job is the death-triggered flush.
• JidoGralkor.ContextRotator — synchronous rotate_now/2 for in-life context consolidation.
• JidoGralkor.Actions.MemorySearch — the ReAct tool that calls Gralkor.Client.search/1 for configured Destinations and falls back to legacy recall/4 in implicit-operator plugin mode. It short-circuits when no thread is committed or the query is blank.
• JidoGralkor.Actions.MemoryAdd — fire-and-forget ReAct tool.
• JidoGralkor.Actions.MemoryBuildIndices — admin tool. Description tells the LLM DO NOT CALL unless the user asked. Whole-graph index rebuild.
• JidoGralkor.Actions.MemoryBuildCommunities — admin tool. Same DO NOT CALL guard. Runs Graphiti community detection on this agent's group.

The embedded Gralkor adapter (under lib/gralkor/):

• Gralkor.Client — adapter behaviour plus the public ingest/1, replace/1, and Destination-based search/1 boundary.
• Gralkor.Client.Native — production adapter; wires Recall, CaptureBuffer, GraphitiPool, and req_llm.
• Gralkor.Client.InMemory — test twin.
• Gralkor.Destination and Gralkor.Destination.Registry — first-class named addresses and extraction ontologies shared by Lenses and Reflections. The full agreed model is in DESTINATIONS.md.
• Gralkor.Lens, Gralkor.Lens.Replaceable, Gralkor.Ingest, Gralkor.IngestedRepresentation, Gralkor.Replace, Gralkor.Graph, Gralkor.Search — resolved ingestion models, completed-ingestion representation, and consumer request values.
• Gralkor.Lens.Store / Gralkor.Lens.Storage.Graphiti — append, replacement, and search capabilities with collision-safe local/shared-global Graphiti placement.
• Gralkor.Lens.Ingestion.Store — the built-in straight-through ingestion process.
• Gralkor.Reflection, Gralkor.Reflection.Registry, Gralkor.Reflection.ChainOfThought, Gralkor.Reflection.Runner, and Gralkor.Reflection.Scheduler — validated YAML declarations and asynchronous ordered execution after completed Lens ingestion.
• Gralkor.Reflection.Artefact, Gralkor.Reflection.Store, and the Graphiti/InMemory Reflection storage modules — exactly-one-artefact persistence at referenced Destinations.
• Gralkor.Ontology — compile-time DSL for declaring graphiti custom-entity ontologies (entity/field/from/verb macros).
• Gralkor.Application, Gralkor.Python, Gralkor.GraphitiPool, Gralkor.CaptureBuffer, Gralkor.Recall, Gralkor.Distill, Gralkor.Interpret, Gralkor.Format, Gralkor.Config, Gralkor.Message, and Gralkor.InterpretParseFailed — the embedded capture, recall, interpretation, and Graphiti pipelines.

The behavioural contract lives in test-trees/. Functional trees describe each application-visible feature, and the Journey tree describes the broad whole-application workflow. CLAUDE.md carries the maintainer-facing mental model and project guidance.

Publishing (maintainers)

:jido_gralkor is published to the public Hex registry as a package owned by the elimydlarz Hex user. Releases use that user's API key (HEX_TOKEN) loaded from the workspace .env; see the workspace publish skill for the full release flow.

$publish patch   # or minor | major | current

The skill runs the full suite before changing release state, verifies or transfers the package to personal ownership, lets trunk-sync synchronize the version commit and default branch, publishes through the personal Hex token, creates the lightweight jido-gralkor-v<version> tag through GitHub's API, and verifies both remote refs. Copy .env.example to .env and provide HEX_TOKEN plus a repository-scoped GH_TOKEN with Contents write permission.

License

MIT.