dory-memory 0.6.0

Agent memory with graph-based spreading activation retrieval and principled forgetting. 84% on LongMemEval (new best).

Dory

Persistent memory for AI agents. Graph-based, spreading activation retrieval, principled forgetting. Single SQLite file. No server required.

pip install dory-memory

from dory import DoryMemory

mem = DoryMemory()
mem.observe("User prefers local-first AI")
mem.observe("User switched from llama.cpp to MLX — 25% faster")

print(mem.query("what does the user prefer for inference?"))
# → MLX (updated preference, supersedes llama.cpp)

LongMemEval (500q, oracle split): 79.6% on the current v0.5 Claude Code MCP run; 80.6% on the v0.4 MCP run.

---

The problem

Every session, your agent starts from zero. Systems that claim to "remember" typically do keyword search through a flat list of notes. That's not memory — it's ctrl+F.

The deeper problem: naive context injection makes things worse. Research (Chroma, 2025) shows all major frontier models degrade starting at 500–750 tokens of context. Dumping everything into a prompt creates noise that degrades performance on the things that actually matter.

What Dory does

Four memory types

Type	What it stores	Status
Episodic	Past events, sessions, experiences	✓
Semantic	Facts, preferences, entities, relationships	✓
Procedural	Skills, workflows, repeatable processes	✓
Working	In-context window (managed by your LLM)	—

Spreading activation retrieval — not vector similarity search. Relevant memories pull in connected memories through the graph. "AllergyFind" activates "Giovanni's" activates "FastAPI" activates "menu endpoint" because those things co-occurred. That's how human associative memory works.

Cacheable prefix output — Dory splits output into a stable prefix (unchanged until memory changes, enabling prompt cache hits) and a dynamic suffix (query-specific). Result: cache hits on every turn. Substantially cheaper to run agents with memory than without.

Principled forgetting — three decay zones: active, archived, expired. Scores based on recency + frequency + relevance. Archived memories are queryable for historical context ("what was true in January?"). Nothing is ever deleted — only decayed.

Bi-temporal conflict resolution — when a fact changes, the old version is archived with a SUPERSEDES edge and a timestamp. Full provenance for every update.

Zero-server stack — single SQLite file. FTS5 for keyword search, adjacency tables for the graph. No Postgres, no Neo4j, no Redis. Works offline.

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Quick start

from dory import DoryMemory

mem = DoryMemory()

# Add memories manually
mem.observe("Alice is migrating payments from Stripe to a custom processor", node_type="EVENT")
mem.observe("Alice prefers async Python over synchronous frameworks", node_type="PREFERENCE")
mem.observe("The migration deadline is end of Q2", node_type="EVENT")

# Query — returns context to inject into your LLM prompt
context = mem.query("payment migration deadline")
print(context)

# End of session: consolidate, decay, promote core memories
mem.flush()

# See your graph in the browser
mem.visualize()
# Or explicitly opt into the remote D3 interactive view
mem.visualize(allow_remote_js=True)

Or from the command line:

dory visualize                    # local-only fallback view, no remote JS
dory visualize --remote-assets    # full interactive D3 view
dory show               # print stats + core memories
dory query "topic"      # spreading activation from the terminal

With auto-extraction (Dory extracts memories from conversation turns automatically):

mem = DoryMemory(extract_model="qwen3:8b")                  # local via Ollama (5 GB)
mem = DoryMemory(extract_model="qwen3:14b")                 # local via Ollama (9 GB, better quality)
mem = DoryMemory(                                           # Claude
    extract_model="claude-haiku-4-5-20251001",
    extract_backend="anthropic",
    extract_api_key="sk-ant-...",
)
mem = DoryMemory(                                           # GPT / Grok / any compat
    extract_model="gpt-4o-mini",
    extract_backend="openai",
    extract_api_key="sk-...",
)

# Log turns — extraction happens automatically every N turns
mem.add_turn("user", "I'm working on AllergyFind today, need to add a menu endpoint")
mem.add_turn("assistant", "What authentication approach are you using?")

# Build API-ready messages with prompt caching
result = mem.build_context("menu endpoint authentication")
messages = result.as_anthropic_messages(user_query)   # Anthropic SDK w/ cache_control
messages = result.as_openai_messages(user_query)      # OpenAI / compat

MCP server (Claude Code / Claude Desktop)

pip install 'dory-memory[mcp]'

# Find the installed binary path (needed if installed in a venv)
which dory-mcp

# Register globally across all Claude Code projects
claude mcp add --scope user dory -- /full/path/to/dory-mcp --db ~/.dory/engram.db

The --db path defaults to ~/.dory/engram.db if omitted. You can also set DORY_DB_PATH as an environment variable.

Verify the server connected:

claude mcp list   # should show dory ✓ Connected

Five tools are exposed: dory_query, dory_observe, dory_consolidate, dory_visualize, dory_stats.

For a practical repo-local workflow with tools like Codex and Claude Code, see docs/AGENT_MEMORY_WORKFLOW.md.

Claude Desktop — add to claude_desktop_config.json:

{
  "mcpServers": {
    "dory": {
      "command": "/full/path/to/dory-mcp",
      "args": ["--db", "/Users/you/.dory/engram.db"]
    }
  }
}

---

Visualization

Live graph visualization →

The hosted demo uses the fully interactive D3 view.

Locally, generated visualizations now default to a local-only fallback page that shows the full node and edge data without loading remote JavaScript. If you want the old interactive graph locally, opt in with allow_remote_js=True or dory visualize --remote-assets.

---

Framework adapters

LangChain — drop-in BaseMemory replacement:

from dory.adapters.langchain import DoryMemoryAdapter
from langchain.chains import ConversationChain
from langchain_anthropic import ChatAnthropic

memory = DoryMemoryAdapter(
    extract_model="claude-haiku-4-5-20251001",
    extract_backend="anthropic",
    extract_api_key="sk-ant-...",
)
chain = ConversationChain(llm=ChatAnthropic(model="claude-sonnet-4-6"), memory=memory)

LangGraph — graph nodes with the (state) -> state signature:

from dory.adapters.langgraph import DoryMemoryNode, MemoryState
from langgraph.graph import StateGraph, START, END

mem = DoryMemoryNode(extract_model="claude-haiku-4-5-20251001", extract_backend="anthropic")

builder = StateGraph(MemoryState)
builder.add_node("load_memory", mem.load_context)
builder.add_node("record_turn", mem.record_turn)
builder.add_edge(START, "load_memory")
builder.add_edge("load_memory", "record_turn")
builder.add_edge("record_turn", END)
graph = builder.compile()

Multi-agent — shared memory pool with thread-safe writes and agent attribution:

from dory.adapters.multi_agent import SharedMemoryPool

pool = SharedMemoryPool(db_path="shared.db")
pool.observe("User prefers dark mode", agent_id="agent-1")
pool.add_turn("user", "Let's ship it", agent_id="agent-2", session_id="s1")
results = pool.query("UI preferences")

Async API

All DoryMemory methods have async counterparts — safe to await from FastAPI, LangGraph, and any async framework:

context = await mem.aquery("current topic")
result  = await mem.abuild_context("current topic")
await mem.aadd_turn("user", "message")
node_id = await mem.aobserve("User prefers JWT", node_type="PREFERENCE")
stats   = await mem.aflush()

Export / import

from dory.export.jsonld import JSONLDExporter

exporter = JSONLDExporter(graph)
exporter.export("memory.jsonld.json")
JSONLDExporter.import_into(graph, "memory.jsonld.json")

Security notes

Security and hardening guidance lives in:

• SECURITY.md
• docs/HARDENING_2026-03-29.md
• docs/REPO_CLEANUP_2026-03-29.md

---

How it works

Knowledge graph

Every piece of information is a typed node: ENTITY, CONCEPT, EVENT, PREFERENCE, BELIEF, PROCEDURE, SESSION (episodic narrative), SESSION_SUMMARY (structured episodic). Edges between them are typed and weighted: USES, WORKS_ON, PREFERS, SUPERSEDES, CO_OCCURS, SUPPORTS_FACT, TEMPORALLY_AFTER, etc.

Salience is computed from connectivity, activation frequency, and recency. High-salience nodes become core memories — they anchor the stable context prefix.

Observer

Every N conversation turns, the Observer calls an LLM to extract structured memories. Extractions carry confidence scores — anything below threshold is logged but not written to the graph.

Backends: Ollama (default), Anthropic (Claude), or any OpenAI-compatible endpoint.

Prefixer

Builds context in two parts:

[stable prefix]         ← core memories + key relationships
                          same bytes across turns → prompt cache hits

[dynamic suffix]        ← spreading activation for this specific query
                          + recent episodic observations

Decayer

score = recency_weight  × exp(-λ × days_since_activation)
      + frequency_weight × log(1 + activation_count)
      + relevance_weight × salience

Nodes below the active floor → archived. Below the archive floor → expired. Core memories are shielded with a configurable multiplier.

Reflector

Near-duplicate detection (Jaccard ≥ 0.82): merges duplicates, keeping the higher-salience node and rewiring edges. Supersession detection (Jaccard in [0.45, 0.82), shared subject): archives the older node, adds SUPERSEDES provenance edge. Old observations compressed into summaries.

---

Architecture

dory/
├── graph.py          ← nodes, edges, salience computation
├── schema.py         ← NodeType, EdgeType, zone constants
├── activation.py     ← spreading activation engine
├── consolidation.py  ← edge decay, strengthen, prune, promote/demote core
├── session.py        ← session-level helpers: query, observe, write_turn, end_session
├── memory.py         ← DoryMemory — high-level API (sync + async)
├── visualize.py      ← D3.js interactive graph visualization
├── mcp_server.py     ← MCP tools (dory_query, dory_observe, dory_consolidate, …)
├── store.py          ← SQLite backend (nodes, edges, FTS5, observations)
│
├── pipeline/
│   ├── observer.py   ← LLM extraction of memories from conversation turns
│   ├── summarizer.py ← episodic layer: SESSION nodes from conversation turns
│   ├── prefixer.py   ← stable prefix + dynamic suffix builder
│   ├── decayer.py    ← node decay scoring + zone management
│   └── reflector.py  ← dedup, supersession, observation compression
│
├── adapters/
│   ├── langchain.py   ← DoryMemoryAdapter (BaseMemory drop-in)
│   ├── langgraph.py   ← DoryMemoryNode (StateGraph integration)
│   └── multi_agent.py ← SharedMemoryPool (thread-safe multi-agent)
│
└── export/
    └── jsonld.py      ← JSON-LD round-trip export/import

---

Local LLM setup

ollama pull qwen3:14b          # extraction
ollama pull nomic-embed-text   # embeddings (768-dim, offline after pull)

OpenAI-compatible endpoint (llama.cpp server, vLLM, etc.):

obs = Observer(graph, backend="openai", base_url="http://localhost:8000", model="qwen3")

Vector search activates automatically once nomic-embed-text is available. Falls back to FTS5 BM25 if no embedding model is running.

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Decay zones

Zone	Behavior	How to query
active	Retrieved in all normal queries	graph.all_nodes() (default)
archived	Invisible to normal queries	graph.all_nodes(zone="archived")
expired	Completely invisible	graph.all_nodes(zone=None)

Memory is never deleted — only decayed. Archived and expired nodes retain full provenance and can be restored if reactivated. The one exception: exact structural duplicates detected by the Reflector are hard-merged (lower-salience copy removed, edges rewired to the winner).

---

Comparison

	mem0	Zep	Letta	Mastra	Dory
Principled forgetting	✗	✗	✗	✗	✓
Spreading activation retrieval	✗	✗	✗	✗	✓
Cacheable prefix output	✗	✗	✗	✓ (TS only)	✓
Bi-temporal conflict resolution	✗	✓	✗	✗	✓
Zero-server local stack	partial	✗	partial	✗	✓
Drop-in Python library	✓	partial	✗	✗	✓
Apache 2.0	✓	✓	✓	✓	✓

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Graph topology — what flat search can't do

Run examples/demo_topology.py to see six live graph traversals:

Q1 · Supersession — "What was the inference backend before MLX replaced it?"

  ┌ BEFORE  [PREFERENCE]  Prefers llama.cpp — cross-platform, well-supported
  │         zone=archived  archived=2026-03-01
  ├─SUPERSEDES──▶
  └ AFTER   [PREFERENCE]  Prefers MLX over llama.cpp on Apple Silicon (20-30% faster)

  ✗ Flat search: returns both nodes with equal score. No directionality. No timestamp.

──────────────────────────────────────────────────────────────────────
Q4 · Semantic Path — "How does local-first philosophy connect to the 80.6% result?"

  ● [CONCEPT]    Local-first AI — data stays on device, no cloud
    └─[CO_OCCURS]──▶
  ● [PREFERENCE] Prefers local-first — no data leaves device unless necessary
    └─[PREFERS]──▶
  ● [ENTITY]     Developer — solo, Apple Silicon
    └─[WORKS_ON]──▶
  ● [ENTITY]     Dory — agent memory library
    └─[CO_OCCURS]──▶
  ● [EVENT]      [2026-03-28] v0.5 temporal spot check — 90.0% temporal-reasoning

  ✗ Flat search: returns both endpoints as separate results. No connecting path.

Query	Traversal	What it answers
Q1 Supersession	SUPERSEDES edges	What changed and when
Q2 Chronicle	TEMPORALLY_AFTER chain	Full session history in order
Q3 Dependencies	USES traversal (depth 2)	What a project actually needs
Q4 Semantic Path	BFS across typed edges	How two concepts connect
Q5 Provenance	SUPPORTS_FACT traversal	What proves a specific fact
Q6 Belief Grounding	SUPPORTS_FACT + BELIEF	Which beliefs have evidence

---

Benchmark results

LongMemEval (ICLR 2025), oracle split, 500 questions.

Version	Extract	Answer	n	Score
v0.1	Haiku	Haiku	500	54.4%
v0.1	Sonnet	Sonnet	500	66.8%
v0.3	Sonnet	Sonnet (direct API)	500	79.8%
v0.4	Haiku	Claude Code (MCP)	500	80.6%
v0.5	Haiku	Claude Code (MCP)	500	79.6%

v0.5 is statistically flat versus v0.4 overall, but the category movement is the real story:

• temporal reasoning improved after explicit reference-date anchoring
• knowledge-update regressed due to date-override failures and reflector changes

Full writeups:

• benchmarks/REPORT_claudecode_mcp_v04.md
• benchmarks/README.md

Published scores for reference: Mem0 68.4%, Zep 71.2%, Mastra 94.87%¹.

¹ Mastra uses GPT-4o-mini on TypeScript. Architecturally different stacks — not directly comparable.

Note: LongMemEval oracle split uses pre-filtered context (~15K tokens per question). Performance with live, unfiltered conversations will differ.

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Current priorities

The next engineering priorities are:

• fix reference-date override failures in duration calculations
• restore targeted knowledge-update synthesis without reintroducing noisy preference synthesis
• improve multi-session counting
• keep the docs and benchmark surface aligned with the shipped code

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Research basis

• MemGPT: Towards LLMs as Operating Systems — two-tier memory architecture
• Zep: A Temporal Knowledge Graph Architecture — bi-temporal provenance
• MAGMA: Multi-Graph based Agentic Memory — multi-graph retrieval
• Mastra Observational Memory — cacheable prefix architecture
• LongMemEval (ICLR 2025) — evaluation benchmark
• Collins & Loftus (1975) — spreading activation in semantic memory
• Hebb (1949) — neurons that fire together wire together
• Hopfield (1982) — associative memory energy landscape (Nobel Prize in Physics, 2024)

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License

Apache 2.0 — see LICENSE.

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Named after Dory from Finding Nemo, because your AI agent right now is Dory. This fixes it.