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 graphs, Lenses for ingestion, and Reflections for consumer-invoked 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, "~> 8.0"} for the whole memory stack.

Install

def deps do
  [
    {:jido_gralkor, "~> 8.0"}
  ]
end

Then fetch:

mix deps.get

The package requires Elixir ~> 1.18 and has runtime dependencies on :jido, :jido_ai, :pythonx, and :jason. 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.

# 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.

Embedded runtimes admit one add_episode mutation at a time through their shared local connection while searches remain concurrent. The remote backend retains concurrent writes. To allow a long embedded query more than the 60-second default read timeout, configure the timeout in milliseconds:

config :jido_gralkor,
  embedded_falkordb_socket_timeout_ms: 120_000

The setting is passed to the embedded Redis client as socket_timeout; it is ignored by remote FalkorDB. A zero, negative, or non-integer value raises ArgumentError when the embedded runtime starts.

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

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.Destination.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 shared storage 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. Each JidoGralkor.Plugin also contributes one linked JidoGralkor.Runtime child beneath its consuming Jido.AgentServer; that child owns the agent's domain configuration and admitted Reflection work. Graceful application shutdown waits for active Lens flush work and flushes buffered capture before CaptureBuffer stops.

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,
  embedded_falkordb_socket_timeout_ms: 60_000,
  recall_deadline_ms: 12_000

Mounted-agent Destinations, Lenses, and Reflections come only from each plugin mount's :runtime_config, described below. Legacy direct client arities retain application-environment Destination and Lens registries as compatibility surfaces, but mounted agents never fall back to them. :destination_storage remains the single storage boundary for both search and Reflection output.

The retired :reflection_storage setting causes startup to fail. Configure :destination_storage for artefact persistence and declare each Reflection's Destination output in the agent's :runtime_config.

The implicit "operator" Lens and legacy capture/5, memory_add/3, and recall/4 need no ontology configuration. The Lens uses the packaged operator Destination and the library-owned Gralkor.DefaultOntology. Application-specific extraction schemas belong on appending Lenses or Reflections.

recall/4 presents every fact returned by memory search verbatim and in order inside an untrusted memory block, retaining any source wording carried by each fact. Recall makes no second inference call: the consuming agent decides how to interpret the returned memory with its own model.

Environment variables

Plugin mount options

{JidoGralkor.Plugin,
 %{
   agent_name: "Susu",
   ingestion_lens: "observations",
   runtime_config: %{destinations: [], lenses: [], reflections: []}
 }}

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.

Search selection is invocation-local, not a plugin mount option. memory_search accepts optional destinations and lenses; the removed :search_destinations mount option raises with migration guidance.

Replace the complete configuration for one running agent with JidoGralkor.Runtime.replace(agent_server_pid, runtime_config). Runtime-targeted APIs accept the owning AgentServer PID. Validation and resolution complete before the three collections become active as one snapshot; an error leaves the old snapshot untouched. If the target has no available Gralkor runtime, the operation raises instead of falling back to application compatibility configuration. If that runtime fails, its linked AgentServer terminates and the consumer's supervisor must start a replacement agent with the current durable configuration.

JidoGralkor.ContextRotator.rotate_now/2

A complete configuration

Everything above, in one deployment. Three files.

Ontologies are modules referenced by writers. Define an ontology as ordinary compiled Elixir in your own lib/, then select it on each appending Lens or Reflection Destination output that should extract with it. Destinations only name graphs.

# lib/my_app/ontologies.ex — compiled code. Named by writer 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"
  ],

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

# lib/my_app/chat_agent.ex — the mount owns this agent's complete domain configuration.
plugins: [
  {JidoGralkor.Plugin,
   %{
     agent_name: "Susu",
     ingestion_lens: "observations",
     runtime_config: %{
       destinations: [],
       lenses: [
         %{
           name: "observations",
           destination: "global",
           write: :append,
           ontology: MyApp.Ontology,
           ingestion: Gralkor.Lens.Ingestion.Store
         },
         %{
           name: "decisions",
           destination: "global",
           write: :append,
           ontology: MyApp.Ontology,
           ingestion: MyApp.DecisionIngestion
         }
       ],
       reflections: [
         %{
           name: "release-review",
           outputs: [
             %{kind: :destination, destination: "global"}
           ],
           chain_of_thought: %{
             steps: [
               %{
                 label: "review",
                 directions: "Review the supplied release evidence.",
                 output: %{"assessment" => "string", "approved" => "boolean"}
               }
             ]
           }
         }
       ]
     }
   }}
]

That mount writes captured turns and memory_add calls through the "observations" Lens to global. Memory search independently defaults to every accessible Destination registered for this agent and may narrow each call by Destination and Lens. Ingestion does not invoke Reflections; a consumer or consumer-owned scheduled job submits a named Reflection asynchronously when its workflow requires synthesis.

Ontology placement. Appending Lenses and Reflection Destination outputs default to Jido Gralkor's open Gralkor.DefaultOntology. Packaged ERL explicitly uses Gralkor.Reflection.ERLOntology. Applications attach custom ontology modules to their writers. If an older deployment set config :jido_gralkor, :ontology, remove it and select the module on each Lens or Reflection Destination output that needs it.

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",
         runtime_config: %{
           destinations: [],
           lenses: [
             %{
               name: "observations",
               destination: "global",
               write: :append,
               ontology: MyApp.Ontology,
               ingestion: Gralkor.Lens.Ingestion.Store
             }
           ],
           reflections: []
         }
       }}
    ]

  # 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, and the selected ingestion :lens on the signal's tool_context. Search selectors come from each memory_search call. 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 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 every Reflection Destination output references a registered Destination, which resolves to one logical graph ID: global, operator/<operator id>, or an application Destination's exact shared name. Application names beginning operator/ are reserved for operator-local graphs and rejected. At the Graphiti boundary, each logical ID is encoded exactly once as g_ followed by the lowercase hexadecimal encoding of every original byte. Appending Lenses and Destination outputs govern their own extraction. Multiple writers 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 there.

The physical encoding replaces the former lossy - and / to _ normalisation. Graphs stored under old physical names are not read or migrated automatically; migrate them only from known logical Destination/operator IDs, or re-ingest their source content, because underscores cannot recover the original ID.

Reflection invocation. Lens-aware capture requires a non-blank operator before buffering, and CaptureBuffer assigns each buffered ingestion one cryptographically collision-resistant ID that it reuses across flush retries. Capture and ordinary ingestion stop after Lens ingestion; neither invokes a Reflection. The consuming application owns the event, job, or schedule that selects a configured Reflection and submits it with Gralkor.Client.reflect/5. Submission returns its replay-stable invocation ID immediately; the agent-owned runtime independently produces and delivers the artefact and reports the terminal outcome through the invocation callback.

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 the configured ingestion :lens, but no :session_id; completed and failed turn capture are both skipped with a warning until a committed thread supplies that identity. memory_search still searches for the current operator because public Search does not depend on conversation-session identity.

Death-triggered flush.JidoGralkor.Lifecycle is an optional Jido.AgentServer.Lifecycle implementation. When wired as lifecycle_mod: on the agent, graceful termination schedules the configured client's flush/1 for the active thread before termination returns. Lens definitions are resolved from one runtime snapshot before the ingestion worker starts, so that worker no longer depends on the agent runtime remaining alive; termination does not wait for ingestion itself. 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, automatic plugin-capture failures, non-PID runtime targets, and unavailable targeted runtimes raise. Runtime-targeted operations never redirect to application compatibility configuration. Valid runtime-targeted Gralkor.Client.ingest/2, replace/2, search/2, reflect/5, 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 ingestion channel that targets a Destination. An appending Lens selects its extraction ontology; its write mode sends content through an ingestion process. A whole-graph replacement Lens replaces its own graph content at the Destination.

An appending Lens declares write: :append, its Destination, and the ingestion process Gralkor invokes when content is sent through it.

The ontology is a module you compile into your own application — declared once in lib/, then named by each appending Lens 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

Point as many Lenses as your application needs at global, operator, or an application Destination. Several Lenses may use the same Destination with different ontologies:

runtime_config = %{
  destinations: [],
  lenses: [
    %{
      name: "observations",
      destination: "global",
      write: :append,
      ontology: MyApp.Ontology,
      ingestion: Gralkor.Lens.Ingestion.Store
    },
    %{
      name: "decisions",
      destination: "global",
      write: :append,
      ontology: MyApp.Ontology,
      ingestion: MyApp.DecisionIngestion
    }
  ],
  reflections: []
}

A replaceable Lens declares write: :replace_graph and a Destination instead of :ingestion or :ontology:

runtime_config = %{
  destinations: [],
  lenses: [
    %{
      name: "systems",
      destination: "global",
      write: :replace_graph
    }
  ],
  reflections: []
}

Destination names control visibility: operator resolves a separate operator/<operator id> graph for each operator; global and application Destination names resolve to one shared graph each.

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; the Lens supplies the ontology. Runtime-targeted Client.ingest/2 accepts appending Lenses and raises for replaceable Lenses; Client.replace/2 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",
   runtime_config: runtime_config
 }}

• ingestion_lens receives memory_add calls and automatic capture unless a turn supplies tool_context[:lens].

Search is independent of that mount. With only a query, memory_search searches episodes in every accessible registered Destination. Optional selectors narrow one invocation; names are ORed within each list and the two lists intersect:

%{
  query: "What did earlier rollouts teach us?",
  destinations: ["global", "release-notes"],
  lenses: ["observations", "decisions"]
}

This includes episodes only when their Destination is either selected Destination and their originating Lens is either selected Lens. Selecting a Lens never adds its Destination. A Lens selector applies only to episode results; direct callers may still explicitly request facts, nodes, or Reflection artefacts without a Lens selector.

Consumers pass the owning AgentServer PID whose runtime configuration should resolve each named operation. A search resolves all selected Lenses and Destinations from one atomic snapshot:

:ok =
  Gralkor.Client.ingest(agent_server, %Gralkor.Ingest{
    id: "release-planning-2026-08-28",
    operator_id: "operator-42",
    lens: "decisions",
    source_kind: :conversation,
    content: "We chose Friday.",
    source_description: "release planning"
  })

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

:ok =
  Gralkor.Client.replace(agent_server, %Gralkor.Replace{
    operator_id: "operator-42",
    lens: "systems",
    graph: %Gralkor.Graph{
      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"}
        }
      ]
    }
  })

Every ingestion requires a non-blank operator_id, a non-blank replay-stable id, and deterministic provenance through source_kind. Both identifiers are validated before Lens storage begins. A consumer may use that ingestion ID as a related Reflection invocation ID; Gralkor.Reflection.Runner derives the artefact ID from the operator ID, invocation ID, and Reflection name. Reusing an ingestion ID does not deduplicate the Lens episode write.

The supported source kinds are:

• :conversation accepts speaker-attributed text and becomes a Graphiti message episode.
• :document accepts text and becomes a Graphiti text episode.
• :structured_record accepts a JSON-compatible map or list, which Gralkor JSON-encodes for a Graphiti JSON episode.

source_kind says where the information came from; it is not a credibility, truth, opinion, or speculation rating. Gralkor validates the kind and its content shape before invoking a Lens or Graphiti. Automatic turn capture declares :conversation; callers using Gralkor.Ingest or memory_add/4 declare the kind themselves, while legacy memory_add/3 remains a document-text compatibility call. The same Graphiti extraction call receives static guidance to retain attribution and epistemic wording such as uncertainty or speculation—Gralkor does not run a second presentation-classification inference. Fact results resolve their originating episodes and append the episode identifier, source kind, and source description on recall.

Appending Lens episodes record writer provenance by suffixing their source description with [lens: <Lens name>]; Reflection episodes use the exact source description reflection:<Reflection name>. Both registries reject names containing the Lens delimiter so these forms stay unambiguous. This episode-writer provenance is separate from _gralkor_lens, which owns replacement-graph nodes and relationships.

Search defaults to stored episodes across every accessible registered Destination: the current operator's private operator/<operator id> logical graph plus every shared Destination. Supplying destinations narrows the graphs; supplying lenses narrows writers; OR applies within either list and both dimensions must match when both are present. Destination searches run concurrently while results retain selected Destination order. max_results defaults to 20, must be a positive integer, and applies independently after writer filtering in every Destination. Each episode identifies its Destination plus its originating Lens or declaring Reflection; raw legacy episodes that carry neither trusted writer marker are omitted before that limit rather than assigned invented provenance. Direct callers may explicitly request :facts, :nodes, or :artefacts without Lens selectors; node searches accept entity_types, fact searches accept edge_types, and artefact searches may narrow by artefact_id. The memory_search action returns attributed episode results as JSON.

%Gralkor.Graph{nodes:, relationships:} is the sole replacement representation. 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. No graph-format option exists.

Invalid Lens names, write modes, 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.

Runtime 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 writes to operator/<operator id> and uses Jido Gralkor's built-in generic extraction contract.

Ontology DSL

Each writer 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 names. Runtime configuration and the application-compatibility Lens registry reject custom entity kinds named Entity, Episodic, or Community, which are Graphiti core labels; Person is not reserved. Graphiti also rejects a custom entity attribute whose field name collides with its own EntityNode: uuid, name, group_id, labels, created_at, summary, attributes, and name_embedding. The DSL does not currently catch field collisions 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 Lens or Reflection 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 a named synthesis process declared in one agent's runtime configuration with an inline structured Chain of Thought and exactly one Destination output. The consumer decides when it runs; after admission, that agent's JidoGralkor runtime owns production, Destination delivery, retry, abandonment, and the invocation callback.

The package supplies two declarations by default:

• generalisations declares a global Destination output using Gralkor.DefaultOntology.
• erl declares an operator Destination output using Gralkor.Reflection.ERLOntology, whose Learning entity declares optional problem_kind, approach, success, and lesson fields.

Package-owned definitions are always installed alongside the consumer's runtime_config.reflections; consumers cannot replace them. A Destination output's optional :ontology defaults to Gralkor.DefaultOntology.

runtime_config = %{
  destinations: [],
  lenses: [],
  reflections: [
    %{
      name: "release-review",
      outputs: [
        %{
          kind: :destination,
          destination: "global",
          ontology: MyApp.ReleaseOntology
        }
      ],
      chain_of_thought: %{
        steps: [
          %{
            label: "review",
            directions: "Review the supplied release evidence.",
            output: %{"assessment" => "string", "approved" => "boolean"}
          }
        ]
      }
    }
  ]
}

A consumer submits a configured Reflection through the AgentServer that owns it. Submission returns the replay-stable invocation ID without waiting for inference, storage, or callback delivery:

operator_id = "operator-42"
invocation_id = "release-2026-09-02"

invocation = %{
  id: invocation_id,
  operator_id: operator_id,
  invocation_context: %{reason: "release decision"},
  representations: completed_representations
}

review_consumer = self()
callback = fn result -> send(review_consumer, {:release_review, result}) end

{:ok, ^invocation_id} =
  Gralkor.Client.reflect(
    agent_server,
    "release-review",
    invocation,
    callback,
    tools: host_tools,
    tool_context: host_tool_context
  )

The callback eventually receives a map with :invocation_id, the terminal :outcome, and :artefact when production succeeded. A successful delivery reports outcome: :delivered. A non-retryable production failure reports {:production_failed, reason}. Abandonment reports {:abandoned, %{stage: :production | :delivery, reason: reason}}; delivery abandonment still includes the produced artefact.

Each admitted invocation progresses independently. A 5xx failure from inference, packaged-generalisation related-memory retrieval, or Destination delivery retries with exponential backoff until success or twenty-four hours from the first failed attempt. A 4xx failure is abandoned immediately. Unfinished work terminates with the agent without invoking its callback; the consumer owns durable scheduling and may resubmit after its supervisor starts a replacement agent.

Each inline Chain of Thought contains an ordered, non-empty steps list. A step declares a non-blank label, natural-language directions, and an exact non-empty structured output schema. Output types are string, boolean, integer, recursively typed arrays such as Array<string>, and exact objects such as { content: string; level: integer }. Later directions may interpolate only prior outputs with {{output_name}}. Each step receives the invocation identity and context, completed lensed representations, host tools, and tool context. The final step becomes one %Gralkor.Artefact{} whose fields are exactly id and payload.

Before packaged generalisation inference begins, Gralkor performs one default episode search through the targeted agent runtime across every accessible registered Destination. The same search supplies related Lens-authored observations and prior Reflection-authored generalisations. The packaged prompt directs a new generalisation with no lineage to use level 1 and an evolved generalisation to use one level above its highest lineage snapshot. Generalisation lineage is validated only by the structured output contract; Gralkor preserves the model-produced values without comparing them with the related-memory input.

Programmatic requests and consumer-owned scheduled jobs use the same Client.reflect/5 path. Gralkor does not own cron, calendar scheduling, or durable job state.

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

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

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

result_type: :artefacts returns artefacts from the selected Destinations, and artefact_id optionally narrows the lookup to one exact artefact. The deterministic UUID derives from the operator ID, invocation ID, and Reflection name, but the artefact itself contains no producer identity. Before extraction, Graphiti waits for a graph uniqueness constraint and acquires a graph-backed UUID claim with a renewable lease, so independent application runtimes serialize equal work and reject conflicting immutable content before Graphiti's upsert can overwrite it. Lease expiry and renewal use the graph server's clock; every ownership transfer advances a generation. For a claimed deterministic UUID, the episode, every extracted entity and edge, and its extraction-completion marker persist in one generation-fenced FalkorDB query, so loss of ownership aborts the complete graph-effect set before it commits. Until that marker exists, canonical lookup retains the episode only as resumable storage state and artefact search excludes it. Repeating an equal marked write is a successful confirmation without extraction; an equal unmarked deterministic episode re-enters normal extraction without rerunning the Runner. Artefact search uses ranked completed episodes to select up to the requested number of artefact identifiers, then enumerates every completed episode carrying those identifiers independently of BM25. It collapses equal historical duplicates by artefact ID, preserves the selected ranking, and reports an artefact conflict if any completed duplicate under a selected ID disagrees.

Reflection artefact episodes written before the extraction-completion marker existed remain hidden because an absent marker cannot distinguish a fully extracted legacy write from a partial write. Invoke the original Reflection again with the same stable invocation ID and deliver its artefact to the same Destination to establish the marker; unmarked legacy episodes remain non-searchable. An operator-authored migration may mark a legacy episode only after independently verifying that its extraction completed and its immutable artefact content is the intended canonical value. This does not migrate an episode left under the former physical graph-ID encoding.

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 ingest and replacement calls, pair it with Gralkor.Lens.Storage.InMemory as shown in Required configuration. Destination search uses Gralkor.Destination.Storage.InMemory instead.

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 shared Gralkor.ClientContract macro suite exercises the in-memory twin. The production Gralkor.Client.Native adapter proves the same public port separately in its adapter tests.

Maintainers can use the project test aliases according to the feedback speed and runtime boundary they need:

mix test              # Unit and Integration; excludes Functional and Journey
mix test.unit         # Unit
mix test.integration  # Integration
mix test.functional   # Functional; may call real model providers
mix test.journey      # Journey
mix test.fast         # stale/affected Unit and Integration
mix test.changed      # stale/affected Unit, Integration, and Functional; excludes Journey
mix test.all          # Unit, Integration, Functional, Journey, and Node tests

Functional tests require their documented provider credentials and can send test inputs to external model providers. mix test.fast is the routine local feedback command; use mix test.changed only when the affected Functional boundary is intentionally available.

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, and selected ingestion Lens) 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 always calls runtime-targeted Gralkor.Client.search/2 for the current operator, using optional Destination and Lens selectors from that invocation. It works before a thread is committed and short-circuits only a blank query.
• 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 operator/<operator id> graph.

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

• Gralkor.Client — adapter behaviour plus runtime-targeted ingest/2, replace/2, search/2, capture, and asynchronous reflect/5 boundaries; legacy application-registry arities remain compatibility surfaces.
• Gralkor.Client.Native — production adapter; wires Recall, CaptureBuffer, and GraphitiPool.
• Gralkor.Client.InMemory — test twin.
• Gralkor.Destination and Gralkor.Destination.Registry — first-class named graphs 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 for exact Destination graph identities.
• Gralkor.Lens.Ingestion.Store — the built-in straight-through ingestion process.
• JidoGralkor.Runtime — the per-AgentServer atomic configuration snapshot and supervisor for admitted Reflection production, Destination delivery, retry, and callback.
• Gralkor.Reflection, Gralkor.Reflection.ChainOfThought, and Gralkor.Reflection.Runner — structured declarations validated by the agent runtime and the inner ordered-inference engine.
• Gralkor.Artefact and Gralkor.Destination.Storage — producer-independent artefacts and their canonical Destination storage boundary.
• 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.Format, Gralkor.Config, and Gralkor.Message — the embedded capture, recall, 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 loads .env and 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, and creates the lightweight jido-gralkor-v<version> tag through GitHub's API. Final verification requires that immutable tag to resolve to the published commit and the remote default branch to contain it, so later trunk-sync bookkeeping commits do not invalidate a completed release. Copy .env.example to .env and provide the configured inference credential, HEX_TOKEN, and a repository-scoped GH_TOKEN with Contents write permission.

License

MIT.