Sessions Graph

Sessions Graph is the Context Graph component for session context and cross-session recall. It is the authority on (:Session) nodes in the Context Graph family. It stores free-form text assertions — called Memories — written explicitly by agents, and makes them searchable in future sessions.

Requires Memgraph ≥ 3.6 (text search is stable from that release).

Installation

pip install sessions-graph

To use with Agent Context Graph:

pip install sessions-graph[agent-context-graph]

To use session reconciliation (entity extraction from session content):

pip install sessions-graph[reconciliation]

Quick start

from sessions_graph import SessionsGraph

graph = SessionsGraph()  # connects via MEMGRAPH_URL / MEMGRAPH_USER / MEMGRAPH_PASSWORD env vars
graph.setup()  # creates constraints and the text index (run once)

# Write a memory
mem = graph.save_memory(
    user_id="alice",
    content="Prefers Python over TypeScript",
    session_id="s-abc123",  # optional — links memory to a session for provenance
)

# Retrieve all memories for a user
memories = graph.get_memories("alice")

# Search memories by content (full-text, powered by Tantivy)
results = graph.search_memories("alice", "Python")

# Update or delete
graph.update_memory(mem.memory_id, "Prefers Python, especially for data tooling")
graph.delete_memory(mem.memory_id)

Integration with Agent Context Graph

Wire the SessionsGraphConnector into an AgentLink to get automatic session provenance — the connector tracks the active session_id and user_id from SessionStartEvent so you can reference them when saving memories.

from sessions_graph import SessionsGraph
from sessions_graph.connector import SessionsGraphConnector
from agent_context_graph import AgentLink
from agent_context_graph.adapters.claude import ClaudeAdapter

graph = SessionsGraph()
graph.setup()

connector = SessionsGraphConnector(graph)
link = AgentLink()
link.add_connector(connector)

adapter = ClaudeAdapter(
    link,
    session_id="s-abc123",
    session_kwargs={"user_id": "alice"},
)

# During the session, save memories via the Python API:
graph.save_memory(
    user_id=connector.active_user_id,
    content="User works primarily in the ai-toolkit repository",
    session_id=connector.active_session_id,
)

Graph schema

(:User {user_id})
    ├─[:HAS_MEMORY]──▶ (:Memory {memory_id, user_id, content, created_at})
    │                          ▲                        │
    │            [:PRODUCED_MEMORY]              [:HAS_CHUNK]
    │                          │                        ▼
    └─[:HAD_SESSION]─▶ (:Session {session_id,   (:Chunk {hash, text})
                                  reconciliation_status,     ▲
                                  reconciled_at})  [:HAS_CHUNK]
                              │                        │
                        [:HAS_ACTION]                  │
                              ▼                        │
                        (:Action) ─────────────────────┘
                              │
                                              (:Entity)-[:MENTIONED_IN]->(:Chunk)

(:User)-[:HAD_SESSION]->(:Session) is written by SessionsGraphConnector on session start; it is the join key other Context Graph components hang off of.

(:Action) is owned by Actions Graph; (:Chunk) and the extracted entity nodes are owned by unstructured2graph. See Session reconciliation below for how they get linked.

Text search

Sessions Graph uses Memgraph text search (powered by Tantivy) for search_memories. The text index is created on setup():

CREATE TEXT INDEX memory_content_index ON :Memory(content);

Searches run as:

CALL text_search.search_all('memory_content_index', 'Python')
YIELD node AS m, score
WHERE m.user_id = 'alice'
RETURN m.content, score
ORDER BY score DESC
LIMIT 10;

The query string follows Tantivy query syntax.

Session reconciliation

A session's Actions Graph content (Messages, ToolCalls, ToolResults) and Memories are mostly opaque text today. Session reconciliation runs that content through unstructured2graph's chunk + entity-extraction pipeline -- LightRAG by default, overridable to another ExtractionBackend (e.g. GLiNER2) via reconcile_session(..., extraction_backend=...) -- turning it into queryable graph entities linked back to the session that produced them — see CONTEXT.md for the Session Reconciliation / Reconcilable Content / Reconciliation Status terminology.

The same pass also writes the session's episodic memory: an (:Episode {summary, summarized_at}) node linked via (:Session)-[:HAS_EPISODE]->(:Episode) (at most one per session — re-running reconciliation updates it rather than adding another), produced by a second, dedicated LLM call over the same deduped session text — a "what happened in this session" gist, not the structured entity graph. This is what a "what did we do last time?" recall query actually reads.

This requires the sessions-graph[reconciliation] extra and an LLM API key (OPENAI_API_KEY or ANTHROPIC_API_KEY) for LightRAG — see the lightrag-memgraph README.

Reconciliation never runs inside the SESSION_END hook itself. LightRAG entity extraction is LLM-backed and slow, and hook runtimes (Claude Code, Codex) enforce a timeout on hook commands. Instead:

• On SESSION_END, SessionsGraphConnector cheaply marks the session reconciliation_status = 'pending' — no LLM calls, safe inside the hook.

• The actual reconciliation run happens out-of-band, via the CLI:

  # Reconcile one session
  sessions-graph reconcile --session s-abc123
  
  # Sweep every session still marked 'pending' (e.g. from cron)
  sessions-graph reconcile --pending --limit 50
  
  # Optional: override LightRAG's working dir (default ./lightrag_storage)
  sessions-graph reconcile --pending --working-dir ./lightrag_storage
  

  The CLI runs with enforce_ontology=True: extracted entities get real type labels (:Person, :Organization, …) gated by unstructured2graph's default ontology, and anything outside it is kept but flagged ontology_conformant = false. See entity typing.

• Or, if you want it triggered automatically without a manual/cron step, opt in to a best-effort detached background process spawned right after a session ends. For hook-based runtimes (Claude Code, Codex), set this persistently:

  agent-context-graph config set reconcile.auto_reconcile true
  

  This writes [reconcile] auto_reconcile = true to ~/.config/context-graph/config.toml, which agent_context_graph.hooks.runner._add_sessions_graph_connector reads on every hook invocation via resolve_auto_reconcile() — no shell/session restart quirks, since hook subprocesses don't reliably inherit shell profile environment variables. Direct SDK integrations that construct SessionsGraphConnector themselves must instead pass SessionsGraphConnector(graph, auto_reconcile=True) explicitly — per ADR 0002 (config-file-only-hook-resolution), no ambient environment variable is consulted here. This is fire-and-forget — if the process dies before finishing (machine sleep, crash), the session stays pending and sessions-graph reconcile --pending is the reliable backfill.

Programmatically:

from sessions_graph import SessionsGraph
from lightrag_memgraph import MemgraphLightRAGWrapper

graph = SessionsGraph()
graph.setup()

lightrag_wrapper = MemgraphLightRAGWrapper()
await lightrag_wrapper.initialize(working_dir="./lightrag_storage")

summary = await graph.reconcile_session(
    "s-abc123",
    lightrag_wrapper=lightrag_wrapper,
    enforce_ontology=True,  # match the CLI: promote entity_type to real labels
)
print(summary.status, summary.texts_considered, summary.texts_deduped, summary.summary_written)

Label promotion is opt-in and mirrors unstructured2graph's flags: the default (enforce_ontology=False, promote_labels=False) leaves entities under the LightRAG workspace label with an entity_type property only; enforce_ontology=True restricts promotion to an ontology (pass ontology_path= for a custom one); promote_labels=True promotes every entity_type with no vocabulary. See unstructured2graph § entity typing.

Extracted entities land in the same LightRAG workspace as any documents ingested via unstructured2graph by default, so a person or concept mentioned both in a session and in an ingested document merges into one node. Pass entity_workspace= explicitly to reconcile_session() to isolate them instead.

Content is deduplicated by hash before ever reaching the LLM, so re-running a sweep over already-processed content never re-bills it. Each reconcilable unit (a message, tool call, tool result, or memory) is truncated to MAX_RECONCILABLE_CHARS (8000) before extraction, but a chatty session still has many units, so the first run can be substantial. Consider this before enabling auto_reconcile broadly.

Recall

recall(user_id, question) returns what one user's own past sessions hold about a question: the evidence, not an answer. The caller's model answers from it.

recalled = graph.recall("alice", "Which database did we pick for the cache?")
print(recalled.render(today="2026-10-05"))  # header with reading rules, then the rows
recalled.to_json()  # the same turns and facts as data

Five lanes, each over the user's own history only:

Then the turns the facts were read from are added. Turns come first, then facts, each group in time order. This is the hybrid retrieval context-graph-eval run --retrieval-strategy hybrid benchmarks, through this same code: 89/100 on LongMemEval's own judge (run g417-recall-r1, 100 questions, map #390).

Widths default to the benchmarked setup; RecallConfig.from_mapping(...) overrides them (e.g. from a config file's [recall] section). Similarity is computed exactly over the user's own vectors, which costs time linear in their history: about 0.6 s for a few hundred messages, 0.7 s at 10k, 6.7 s at 100k. Without MAGE, recall runs text search alone and says so in its result.

Harnesses reach it as the recall tool: sessions-graph registers it under agent-context-graph's agent_context_graph.tools entry point (sessions_graph.tool:RECALL), and agent-context-graph mcp serves it, taking the user and the [recall] overrides from the config file. See agent-context-graph § Recall.

Embeddings for recall

Recall searches a user's history by vector as well as by text, so three units get an embedding property, computed inside Memgraph by MAGE's embeddings module (no model runs on the host):

Each vector also records its embedding_model. A vector from a different model counts as missing and is replaced, so vectors from two models never mix. The default model is BAAI/bge-small-en-v1.5 (384 dimensions); change it with:

agent-context-graph config set recall.embedding_model <huggingface-model-name>

When it runs:

• Session end: SessionsGraphConnector always spawns a detached sessions-graph embed --session <id>, whether or not auto_reconcile is on. Embedding needs no LLM, and the hook never waits on the model.
• Reconciliation: after extraction, the session's new entities and edges are embedded. An embedding failure never fails the reconciliation.
• Catch-up: sessions-graph embed --pending embeds every session whose embedding failed, never ran, or used another model.

The outcome is on the Session: embedding_status is completed (with embedding_model) or failed (with embedding_error).

This needs Memgraph with MAGE (memgraph/memgraph-mage), with 2 GiB of memory or more for the default model. The model downloads inside Memgraph on first use. On plain memgraph/memgraph every session records failed, and agent-context-graph doctor --connector sessions-graph reports the embeddings check as failing.

sessions-graph embed --session s-abc123
sessions-graph embed --pending --limit 50 --model BAAI/bge-small-en-v1.5

API reference