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AutoMem

Memory framework

First committed by jack-arturo

Published in verygoodplugins/automem. Publisher page

827 GitHub stars

What it is

Long-term memory for AI assistants. Graph + vector store that recalls decisions, relationships, and context across sessions.
— the description by jack-arturo

AutoGineer overview, from the brain’s own files

AutoMem is a memory service designed to provide AI assistants with persistent, long-term recall. By combining a graph database (FalkorDB) for structured relationships and a vector database (Qdrant) for semantic search, it enables agents to retrieve not just similar snippets, but the context and reasoning behind past decisions. It supports a wide range of AI tools via the Model Context Protocol (MCP) and a REST API, allowing a single memory store to be shared across multiple platforms like Claude, Cursor, and GitHub Copilot. The system includes automated consolidation cycles to manage memory relevance and importance over time, ensuring that the agent's knowledge base remains efficient as history grows.

How it works

Memory model: Hybrid

AutoGineer overview, from the brain’s own files

AutoMem operates as a service with two primary storage layers: FalkorDB for storing memories as nodes with typed relationships, and Qdrant for storing vector embeddings. When an AI client requests a recall, the system performs a hybrid query that combines semantic similarity, graph traversal, and temporal alignment to rank results. A background enrichment pipeline and consolidation engine process memories to maintain relevance, while the MCP bridge allows various AI agents to interact with the memory service locally or remotely.

Folders
FolderRoleFiles
.githubagent-config9
docsdocs18
scriptsscripts39
teststests78
.railwayfolder2
automemfolder70
benchmarksfolder9
mcp-sse-serverfolder14

The agent reads

The agent writes

Requirements

Runtimes: Python

Databases: FalkorDB, MongoDB, PostgreSQL, Qdrant, Redis, SQLite

API keys (by name): ADMIN_API_TOKEN, AUTOMEM_API_TOKEN, AUTOMEM_TEST_ADMIN_TOKEN, AUTOMEM_TEST_API_TOKEN, CLASSIFICATION_API_KEY, OPENAI_API_KEY, OPENROUTER_API_KEY, QDRANT_API_KEY, VOYAGE_API_KEY

Works with: Claude Code, OpenAI Codex, GitHub Copilot, Cursor, Kiro, OpenCode, Amp, Cline, JetBrains Junie, Devin (ex-Windsurf)

Set it up

  1. This brain runs code automatically: 1 script. Review Makefile before opening the folder in Claude Code. No step below turns a hook on for you.

    • Makefile: echo "🔧 Setting up development environment..."; bash scripts/bootstrap_dev.sh; echo "✅ Virtual environment ready!"; echo "💡 Run 'source .venv/bin/activate' to activate"
  2. Not scanned by AutoGineerThe author’s README also describes installing it from a clone of its repository. Read its install section: AutoGineer did not scan that path. https://github.com/verygoodplugins/automem/blob/1a7ba1a6afe69835f62ad7f6013e33301d1ab83e/README.md#run-it
  3. First committed by jack-arturo. Published in verygoodplugins/automem. Licence: MIT License.

Usage

AutoGineer overview, from the brain’s own files

  • Connect local AI tools like Claude Desktop or Cursor to the AutoMem service via the MCP bridge.
  • Use the REST API to programmatically store decisions, preferences, and project context.
  • Query the memory service to retrieve the reasoning behind past technical choices or project milestones.
  • Deploy the memory service on local infrastructure or cloud platforms like Railway for persistent agent access.
“Why did we choose PostgreSQL?”
The agent retrieves the decision node, the rationale, and the considered alternatives, providing the context behind the architectural choice.
“What are our current preferences for message queues?”
The agent traverses the graph to identify the relationship between Kafka and RabbitMQ, surfacing the team's preference for 'boring technology'.

Documentation by jack-arturo

<p align="center"> <img src="https://automem.ai/img/G_Vd7EYWYAM_3SF.jpeg" alt="AutoMem" width="600" /> </p>

<p align="center"> <a href="https://www.npmjs.com/package/@verygoodplugins/mcp-automem"><img src="https://img.shields.io/npm/v/@verygoodplugins/mcp-automem?label=mcp-automem" alt="npm version" /></a> <a href="LICENSE"><img src="https://img.shields.io/github/license/verygoodplugins/automem" alt="License" /></a> <a href="https://automem.ai/discord"><img src="https://img.shields.io/badge/Discord-Join-5865F2?logo=discord&logoColor=white" alt="Discord" /></a> <a href="https://x.com/automem_ai"><img src="https://img.shields.io/badge/X-@automem__ai-000000?logo=x&logoColor=white" alt="X" /></a> <a href="https://automem.ai/benchmarks"><img src="https://img.shields.io/badge/LoCoMo%20(AMB)-85.1%25-success" alt="LoCoMo on the neutral Agent Memory Benchmark" /></a> <a href="benchmarks/EXPERIMENT_LOG.md#results"><img src="https://img.shields.io/badge/LongMemEval%20full-87.0%25-blueviolet" alt="LongMemEval full on AutoMem's internal harness: 87.0%" /></a> <a href="https://automem.ai/benchmarks"><img src="https://img.shields.io/badge/BEAM%2010M%20(AMB)-57.4%25-success" alt="BEAM 10M on the neutral Agent Memory Benchmark" /></a> <a href="https://railway.com/deploy/automem-ai-memory-service?referralCode=VuFE6g&utm_medium=integration&utm_source=github&utm_campaign=generic"><img src="https://img.shields.io/badge/Deploy%20on-Railway-0B0D0E?logo=railway&logoColor=white" alt="Deploy on Railway" /></a> </p>

<p align="center"> <strong>Long-term memory for AI assistants — fast, private, and yours.</strong> </p>

AutoMem

AutoMem gives your AI a memory that survives the chat.

Save the decisions, preferences, notes, and context that matter. The next time you open Claude, Cursor, Codex, ChatGPT, or another connected assistant, it can bring back the right details instead of making you repeat yourself.

Ask, “Why did we choose PostgreSQL?” and AutoMem can return the decision, the alternatives you considered, the principle behind it, and the work that followed — not just a pile of similarly worded snippets.

Why AutoMem feels different

No LLM call in the middle of recall. AutoMem looks up memories directly through its graph and vector index. That keeps normal retrieval fast and avoids an extra generative-LLM charge every time your assistant needs context. It still uses embeddings — from Voyage, OpenAI, or a local provider — and optional enrichment can add more structure over time.

More than vector search. A vector match finds something similar; AutoMem also records typed relationships between memories. It can follow the connections between a decision, its rationale, and its consequences, so your assistant has a better chance of returning the why, not only the words it recognizes.

One memory across your tools. Use the local MCP bridge with Claude Desktop, Cursor, Claude Code, Codex, Copilot, and more. For cloud agents, Remote MCP connects the same service to ChatGPT Developer Mode, Claude.ai, and ElevenLabs over HTTPS. Your memory is not locked to one chat app.

Own the data and the setup. Run AutoMem locally with Docker, on your own infrastructure, or as a small Railway service group. It exposes both MCP and a REST API, so it fits into the tools and workflows you already use.

Proven where long context gets hard

On the independent Agent Memory Benchmark's BEAM long-context tests, AutoMem scored 57.4% at 10 million source tokens while giving the answerer an average of only ~2.6–4.8k retrieved tokens. That is the kind of efficiency that lets memory stay useful as an agent's history grows.

The full picture — test setup, raw outputs, methodology, historical runs, and reproduction commands — is in automem.ai/benchmarks and benchmarks/EXPERIMENT_LOG.md.

How it works

AutoMem combines two storage layers behind a single API:

  • FalkorDB stores memories as nodes with 11 typed relationships between them. The graph is the canonical record.

  • Qdrant stores an embedding for every memory. Recall is a hybrid query — semantic similarity, graph traversal, temporal alignment, tag overlap, and importance — ranked by a 9-component score.

flowchart TB
    subgraph service [AutoMem Service Flask]
        API[REST API<br/>Memory Lifecycle]
        Enrichment[Background Enrichment<br/>Pipeline]
        Consolidation[Consolidation<br/>Engine]
        Backups[Automated Backups<br/>Optional]
    end

    subgraph storage [Dual Storage Layer]
        FalkorDB[(FalkorDB<br/>Graph Database)]
        Qdrant[(Qdrant<br/>Vector Database)]
    end

    Client[AI Client] -->|Store/Recall/Associate| API
    API --> FalkorDB
    API --> Qdrant
    Enrichment -->|11 edge types<br/>Pattern nodes| FalkorDB
    Enrichment -->|Semantic search<br/>1024-d vectors| Qdrant
    Consolidation --> FalkorDB
    Consolidation --> Qdrant
    Backups -.->|Optional| FalkorDB
    Backups -.->|Optional| Qdrant

If Qdrant is unavailable, the graph still serves recall in a degraded mode. If FalkorDB is down, the API returns 503 — the graph is the source of truth.

Multi-hop bridge discovery

Ask "why boring tech for Kafka?" and AutoMem doesn't just match the word "Kafka". It traverses the graph from the seed memories to find the bridge that connects them:

  • Seed 1: "Migrated to PostgreSQL for operational simplicity"

  • Seed 2: "Evaluating Kafka vs RabbitMQ for message queue"

  • Bridge: "Team prefers boring technology — proven, debuggable systems"

Both seeds carry an EXEMPLIFIES edge to the bridge memory. AutoMem ranks the bridge above the seeds and surfaces it in the recall response, so the assistant answers with your reasoning, not isolated facts. Tune via expand_relations, relation_limit, and expansion_limit on GET /recall.

11 authorable relationship types
TypeUse caseExample
RELATES_TOGeneral connectionBug report → Related issue
LEADS_TOCausal relationshipProblem → Solution
OCCURRED_BEFORETemporal sequencePlanning → Execution
PREFERS_OVERUser preferencesPostgreSQL → MongoDB
EXEMPLIFIESPattern examplesCode review → Best practice
CONTRADICTSConflicting infoOld approach → New approach
REINFORCESSupporting evidenceDecision → Validation
INVALIDATED_BYOutdated infoLegacy docs → Current docs
EVOLVED_INTOKnowledge evolutionInitial design → Final design
DERIVED_FROMSource trackingImplementation → Spec
PART_OFHierarchical structureFeature → Epic

Three more edge types are added automatically by the enrichment pipeline and consolidation engine: SIMILAR_TO, PRECEDED_BY, and DISCOVERED.

Memory consolidation, neuroscience-inspired

AutoMem implements biological memory consolidation cycles. Wrong rabbit holes fade naturally. Important memories with strong connections strengthen over time.

CycleFrequencyPurpose
DecayDailyExponential relevance scoring (age, access, connections, importance)
CreativeWeeklyREM-like processing that discovers non-obvious connections
ClusterMonthlyGroups similar memories, generates meta-patterns
ForgetOff by defaultArchives low-relevance memories (<0.2), deletes very old (<0.05)

Tune intervals via CONSOLIDATION_*_INTERVAL_SECONDS. See docs/ENVIRONMENT_VARIABLES.md.

For more on the recall scoring formula, enrichment internals, and how AutoMem differs from RAG and pure vector databases, see docs/COMPARISON.md.

Research foundation

AutoMem implements techniques from peer-reviewed memory research:

  • HippoRAG 2 (Ohio State, 2025) — graph + vector hybrid for associative memory

  • A-MEM (2025) — Zettelkasten-inspired dynamic memory organization

  • MELODI (DeepMind, 2024) — gist-based memory compression

  • ReadAgent (DeepMind, 2024) — episodic memory for context extension

Full writeups, findings, and how AutoMem implements each → docs/RESEARCH.md.

Run it

Railway (60 seconds)

Image: Deploy on Railway

Recommended Railway projects run AutoMem as a small service group: automem (the API), automem-graph-viewer (the standalone UI), falkordb (graph), qdrant (vectors), and mcp-automem (the MCP bridge for ChatGPT, Claude.ai, and ElevenLabs). Services use pre-built Docker images and auto-redeploy on :stable, so Railway does not spend compute rebuilding source.

→ Full setup: INSTALLATION.md

Docker Compose (local)
git clone https://github.com/verygoodplugins/automem.git
cd automem
make dev
ServiceURLPurpose
AutoMem APIhttp://localhost:8001Memory REST API
FalkorDBlocalhost:6379Graph database
Qdrantlocalhost:6333Vector database
FalkorDB Browserhttp://localhost:3000Local graph inspection UI

Database and browser ports bind to localhost by default; the API binding is unchanged.

→ Full setup: INSTALLATION.md

Python (development)
make install
source .venv/bin/activate
PORT=8001 python app.py

Requires Python 3.10+ (3.12 recommended). → INSTALLATION.md

Contributing: feature PRs target the develop branch. main only moves via validated release merges, so users deploying from main (e.g. Railway auto-deploys) see one deploy per release instead of one per PR.

Connect your AI

ClientModeSetup
Claude Desktop, Cursor, Claude Code, Codex, Copilot, AntigravityLocal MCP bridgenpx @verygoodplugins/mcp-automem setup
ChatGPT (developer mode), Claude.ai web/mobile, ElevenLabs AgentsRemote MCP (HTTPS)docs/MCP_SSE.md
Anything elseDirect REST APIdocs/API.md

The MCP bridge is published as @verygoodplugins/mcp-automem. It handles client-specific config (rules files, hooks, templates) and proxies to your AutoMem service — local or Railway.

Direct API call:

import requests

token = "your-automem-api-token"

requests.post(
    "https://your-automem.railway.app/memory",
    headers={"Authorization": f"Bearer {token}"},
    json={
        "content": "Chose PostgreSQL over MongoDB for ACID compliance",
        "type": "Decision",
        "tags": ["database", "architecture"],
        "importance": 0.9,
    },
)

Screenshots

<!-- CAPTURE CHECKLIST — replace each placeholder image once captured: 1. docs/img/graph-recall.png — FalkorDB browser at http://localhost:3000 showing a memory node with multiple typed edges visible (PREFERS_OVER, EXEMPLIFIES, RELATES_TO). Proves the graph is real. 2. docs/img/claude-recall.png — Claude Desktop conversation where the assistant cites a recalled memory inline, with the AutoMem MCP tool call visible in the trace. 3. docs/img/railway-services.png — Railway dashboard showing the production service group (automem, automem-graph-viewer, mcp-automem, qdrant, falkordb). A version exists as a GitHub user-attachment in INSTALLATION.md; capture a clean version and host it in-repo. Until each PNG exists, the table cells will render as broken-image icons on GitHub. -->

Screenshots will be added once the referenced in-repo image assets are available.

Known limitations

AutoMem is pre-1.0 and honest about its rough edges. The active ones for recall quality:

  • Tags are a hard gate, not a soft boost. Tags filter before scoring, so a memory missing the queried tag won't surface even on a perfect semantic match. Within a tag scope, high-importance off-topic memories can still over-rank — mitigated by the opt-in RECALL_RELEVANCE_GATE, not yet on by default (#130).

  • Temporal and preference updates. Recall doesn't yet reliably prefer the newest version of a conflicting fact, or fully resolve multi-session preference updates. The RECALL_RECENCY_BIAS=auto re-rank helps temporal-intent queries but stays opt-in pending broader validation (#158, #159).

  • The MCP SSE bridge doesn't forward state_mode. The HTTP recall API supports it; the SSE proxy doesn't pass it through yet (#172).

  • Entity-node synthesis is experimental and off by default. First-class Entity nodes (IDENTITY_SYNTHESIS_ENABLED) are gated off while people-entity word-pair noise is addressed (#181).

Docs, community, and license

Setup

API and integration

  • API reference — endpoints, scoring, enrichment

  • Remote MCP — ChatGPT, Claude.ai, ElevenLabs

  • Migrations — embedding dimensions, 0.16.0 data migrations, MCP SQLite import

Research and comparison

Operations

Community

Sibling repos

MIT licensed. Deploy anywhere. No vendor lock-in.

docs/API.md

AutoMem API Reference

This document lists the primary API endpoints and examples. JSON responses include status and primary payload fields for LLM-friendliness unless an endpoint explicitly returns a binary payload.

Authentication

  • All endpoints except /health require authentication via one of:

    • Authorization: Bearer <AUTOMEM_API_TOKEN> header (recommended)

    • X-API-Key: <AUTOMEM_API_TOKEN> header

    • ?api_key=<AUTOMEM_API_TOKEN> query parameter

  • Admin endpoints additionally require X-Admin-Token: <ADMIN_API_TOKEN> or X-Admin-Api-Key: <ADMIN_API_TOKEN>. Both headers are allowed by browser CORS preflight; configured VIEWER_ALLOWED_ORIGINS restrictions and admin authorization still apply.

  • GET /backup is admin-only and does not accept the regular API token by itself.

Health

  • GET /health

    • Returns overall status and service connectivity.

Memory

Explicit type values on POST, PATCH and batch writes must be non-empty strings. Known aliases normalize to canonical types (for example, insight → Insight, decision → Decision, analysis → Insight). Unknown custom strings remain unchanged for compatibility and produce a warning. Omit type to classify a new memory automatically or preserve an existing type on PATCH. Existing stored types are not migrated by this change.

  • POST /memory

    • Body: { "content": "...", "tags": ["tag"], "importance": 0.7, "metadata": {} }

    • Response: { "status": "success", "memory_id": "...", ... }

  • GET /memory/{id}

    • Response: { "status": "success", "memory": { ... } }

    • Errors: 404 if memory is missing, 500 on query failure, 503 if graph database is unavailable.

  • PATCH /memory/{id}

    • Body: any subset of fields (content, tags, importance, type, confidence, timestamp, metadata)

    • Response: { "status": "success", "memory_id": "..." }

  • DELETE /memory/{id}

    • Response: { "status": "success", "memory_id": "..." }

  • GET /memory/by-tag?tags=foo&tags=bar&limit=20&offset=0

    • Response: { "status": "success", "tags": ["foo","bar"], "count": N, "limit": 20, "offset": 0, "has_more": false, "memories": [...] }

  • DELETE /memory/by-tag?tags=foo&tags=bar

    • Response: { "status": "success", "tags": ["foo","bar"], "deleted_count": N }

  • POST /associate

    • Body: { "memory1_id": "...", "memory2_id": "...", "type": "RELATES_TO", "strength": 0.9 }

    • Batch body: { "associations": [{ "memory1_id": "...", "memory2_id": "...", "type": "RELATES_TO", "strength": 0.9 }] } (max 500)

    • Single response: { "status": "success", ... }

    • Batch response: { "status": "success"|"partial_success", "created_count": C, "failed_count": F, "succeeded": [...], "failed": [...], "summary": "C/M associations created successfully" }

    • Batch requests return 201 when every item succeeds and 207 when one or more items fail validation or reference missing memories.

    • type accepts only the 11 authorable semantic relationship types. System-generated labels such as SIMILAR_TO, PRECEDED_BY, and DISCOVERED are readable/filterable but cannot be created via this endpoint.

Recall

  • GET /recall

    • Basic parameters: query, limit, tags, exclude_tags, tag_mode (any|all), tag_match (prefix|exact, default prefix), time_query (e.g. "last week"), start, end, embedding

      • exclude_tags - Comma-separated list or multiple params to exclude memories containing ANY of these tags. Supports both exact and prefix matching (independent of tag_match). Example: exclude_tags=conversation_5 or exclude_tags=temp,draft

    • Tag semantics (per retrieval path):

      • tags is a hard scope filter on the vector, keyword, metadata-sidecar, and tag-only fallback paths: memories without a matching tag are excluded before scoring.

      • Graph/entity expansion bypasses the tag scope by default; pass expand_respect_tags=true to keep expanded results inside the scope.

      • context_tags is the soft-boost channel: it raises matching results' scores without excluding anything.

      • Within the tag-scoped pool, query-independent score components (importance, confidence, recency, tag overlap) can be ramped down for results with little topical evidence via the RECALL_RELEVANCE_GATE env var (default 0.0 = off; see ENVIRONMENT_VARIABLES.md).

      • scope_fallback (boolean, default false) - When tag-scoped results come up short of limit and a text/semantic query is present, fill the remaining slots from an unscoped vector search. Fills are appended after all scoped results (never interleaved with or displacing them) and each carries "outside_tag_scope": true. Fills get filter parity with the scoped path: exclude_tags, time filters, min_score, and current-state filtering (payload-level reasons plus graph-edge supersession) all still apply — only the tags scope is lifted. Candidates whose tags match the request's tags are never returned as fills: they are in-scope by definition (already returned, or dropped by a score filter) and are not resurrected.

    • Ordering: sort (or order_by) supports:

      • score (default) - hybrid relevance/importance ranking; exact score ties order newest-first

      • time_desc / time_asc - chronological ordering by updated_at/timestamp within the filter window (use for "what happened since X")

      • updated_desc / updated_asc - explicit alias (same ordering key as time_*)

    • Recency bias: recency_bias=auto|on|off (default from RECALL_RECENCY_BIAS env, ships off) - relative-recency re-rank for score ordering: candidate timestamps are min-max normalized across the result set and SEARCH_WEIGHT_TEMPORAL × relative_recency is added to each final score, so the newest version of a conflicting fact can outrank an older, heavier one. auto activates only when the query expresses temporal intent ("latest", "current", "what changed", ...). The response echoes "recency_bias": "on" whenever the mode activated for the request (on, or auto with temporal intent on a score-sorted, non-empty result set) — even if the re-rank was a no-op; results carry a temporal score component only when the re-rank actually adjusted scores (zero timestamp spread or zero SEARCH_WEIGHT_TEMPORAL echoes the flag without adding the component).

    • State filtering:

      • state_mode=current|history controls whether recall returns only currently valid state or full state history. Default: current.

      • current is equivalent to current_only=true: suppresses memories that are expired, not yet valid, archived, or invalidated/evolved by active replacements. Supersession chains (INVALIDATED_BY/EVOLVED_INTO) are resolved to their head — A→B→C surfaces C — bounded at 5 hops and cycle-safe; the surfaced head's state_replaces/relations[].from provenance still points at the originally suppressed memory.

      • history is equivalent to current_only=false: returns stale and future state when it matches the query/filter.

      • In current mode, inline relations[].memory payloads follow the same lifecycle rules. Obsolete or unverifiable targets retain only their id; edge type, strength, and provenance remain available. Use history mode to retrieve historical inline content.

      • current_only remains supported for backward compatibility and wins over state_mode when both resolve to a value. A malformed state_mode is still rejected with 400 even when current_only is supplied — the value is validated before precedence is applied.

      • state_debug=true includes suppression/replacement details in state_filter.

    • Context hints: context, language, active_path, context_tags, context_types, priority_ids

    • Metadata sidecar search: Text queries can admit bounded metadata candidates when the query strongly matches whitelisted metadata values. This is enabled by default and controlled by RECALL_METADATA_SEARCH_ENABLED; no extra request parameter is required.

    • Graph expansion:

      • expand_relations - Follow graph edges from seed results to related memories

      • expand_entities - Multi-hop reasoning via entity tags (finds "Amanda → Rachel" then "Rachel's job")

      • relation_limit - Max relations per seed (default: 5)

      • expansion_limit - Total max expanded memories (default: 25)

    • Expansion filtering (reduce noise in expanded results):

      • expand_min_importance - Minimum importance (0-1) for expanded memories. Seed results are never filtered, only expanded ones. Recommended: 0.3-0.5 for broad context, 0.6+ for focused results.

      • expand_min_strength - Minimum relation strength (0-1) to traverse during graph expansion. Only edges above this threshold are followed. Recommended: 0.3 for exploratory, 0.6+ for high-confidence connections.

    • Response: { "status": "success", "results": [...], "count": M, "state_mode": "current", "context_priority": {...} }

    • Echoed filters (for debugging): tags, exclude_tags, tag_mode, tag_match

    • When tags were passed, the response includes scope diagnostics: tag_scope: { "filtered": true, "pool_size_hint": <int|null>, "gated_low_evidence": <int> } — pool_size_hint is the post-tag-filter, pre-limit vector candidate count (null when no semantic query ran, e.g. tag-only recall), and gated_low_evidence counts returned results whose score components were ramped down by RECALL_RELEVANCE_GATE. Note the hint is capped by the vector fetch limit and sums per-query counts when the request decomposes into multiple queries (queries[]/auto_decompose), so the same memory can be counted more than once — treat it as a rough pool-size signal, not an exact count. scope_fallback: true is echoed when the fallback ran.

    • Echoed state: state_mode always reflects the resolved mode after current_only precedence. When current-state filtering runs, state_filter may include suppressed and replacement IDs, including INVALIDATED_BY, EVOLVED_INTO, and CONTRADICTS handling details when state_debug=true.

    • When expand_entities=true: includes entity_expansion: { enabled, expanded_count, entities_found }

    • When expand_relations=true: includes expansion: { enabled, seed_count, expanded_count, relation_limit }

Expansion Filtering Example:

# Get architecture decisions with related context, but filter out low-importance noise
GET /recall?query=database%20architecture&expand_relations=true&expand_min_importance=0.5&expand_min_strength=0.3

Exclude Tags Example:

# Get long-term memories from all conversations EXCEPT the current one
GET /recall?tags=user_1&exclude_tags=conversation_5&limit=3

# Exclude multiple conversations
GET /recall?tags=user_1&exclude_tags=conversation_5,conversation_6&limit=5

# Exclude temporary/draft memories
GET /recall?query=project%20plan&exclude_tags=temp,draft&limit=10

Current-State Recall Examples:

# Default: return active/current state only
GET /recall?query=current%20project%20plan

# Historical audit: include expired, future, invalidated, and evolved facts
GET /recall?query=project%20plan&state_mode=history
  • GET /memories/{id}/related

    • Query: relationship_types, max_depth (1..3), limit (<=200)

    • Default traversal uses the 11 authorable semantic relationship types.

    • Explicit opt-ins may include SIMILAR_TO, PRECEDED_BY, and DISCOVERED.

    • Response: { "status": "success", "related_memories": [...] }

  • GET /startup-recall

    • Returns critical lessons and system rules.

  • GET /analyze

    • Returns analytics (type counts, preferences, temporal insights, entities, confidence distribution).

Enrichment

Background and JIT enrichment merge derived tags and metadata into the latest stored memory using an atomic graph comparison, so a concurrent PATCH does not lose caller fields. A content edit during extraction cancels the stale result; background enrichment reports a retryable conflict and JIT skips that write. Qdrant updates touch only derived metadata keys and guard tag replacement against the source tags. Background relationship mutations are planned before the content/timestamp comparison. The source guard, relationship mutations, derived graph fields and completion flag then commit in one graph query, so a PATCH cannot interleave between them. A planned neighbor disappearing before commit is skipped without dropping the source row. Temporal counts and pattern observation/confidence increments represent unique per-memory evidence, so retries and forced reprocessing do not inflate them. A content or timestamp edit before commit rejects the obsolete plan. Graph and vector writes remain separate: a content edit after the graph commit can leave derived entity/enrichment metadata stale in Qdrant until payload repair or re-embedding, but does not replace the caller's content, tags or unrelated metadata keys. The remaining cross-store consistency work is tracked in #270.

  • GET /enrichment/status

    • Response: queue size, inflight/pending, stats, plus a classification block with type-classification counters (llm_attempts, llm_successes, fallbacks, pattern_classifications, last_error, last_error_at) for monitoring LLM-classification fallback rate, and a circuit block with open, circuit_open_skips, and recoveries for monitoring quota-circuit state.

  • POST /enrichment/reprocess

    • Body: { "ids": ["..."] } or query ?ids=a,b,c

    • Response: { "status": "queued", "count": N }

Admin

  • GET /backup

    • Headers: requires X-Admin-Token: <ADMIN_API_TOKEN> or X-Admin-Api-Key: <ADMIN_API_TOKEN>.

    • Query: optional include=falkordb,qdrant; defaults to both. Use include=falkordb or include=qdrant for a partial export.

    • Response: binary application/gzip attachment named automem-backup-<timestamp>.tar.gz.

    • Archive contents are restore-compatible: falkordb/falkordb_<timestamp>.json.gz and/or qdrant/qdrant_<timestamp>.json.gz.

    • Example: curl -H "X-Admin-Token: $ADMIN_API_TOKEN" "$AUTOMEM_API_URL/backup" -o snapshot.tar.gz

  • POST /admin/reembed

    • Headers: requires both API and Admin tokens.

    • Body: { "batch_size": 32, "limit": 100, "force": false }

    • Response: { "status": "complete", "processed": N, "failed": K }

Consolidation

  • POST /consolidate

    • Body: { "mode": "full"|"decay"|"creative"|"cluster"|"forget", "dry_run": true }

    • Response: { "status": "success", "consolidation": {...} }

  • GET /consolidate/status

    • Response: { "status": "success", "next_runs": {...}, "history": [...] }

Stream

  • GET /stream

    • Authenticated SSE feed of in-process memory operations. Each event is data: {json} with { "type", "timestamp", "data" }. Keepalive comments (: keepalive) are sent every 30 seconds.

    • Event types:

      • memory.store — single store (id, content_preview, type, importance, tags, size_bytes, elapsed_ms, count: 1) or batch store (count, ids, content_preview, elapsed_ms)

      • memory.recall — ranked recall (query, limit, result_count, elapsed_ms, tags)

      • memory.update — patch (id, content_preview, type, importance, tags, fields)

      • memory.delete — single delete (id, count: 1) or bulk-by-tag (count, tags)

      • memory.associate — single edge (memory1_id, memory2_id, relation_type, strength, count: 1) or batch (count, failed_count, relation_types)

      • enrichment.start / enrichment.complete / enrichment.failed

      • consolidation.run

    • Failed validation (4xx) does not emit. Watch with python scripts/automem_watch.py --url … --token …; see the scripts catalog for its usage and safety notes.

  • GET /stream/status

    • Response: { "subscribers": N }

Notes

  • Inclusion tag matching (tags) supports exact and prefix semantics; vector searches are filtered by tag conditions when provided. Inclusion prefix matching follows hierarchy segments separated by : or /: auth/login matches auth:login:password, while auth does not match auth-login. Exact matching retains delimiter spelling (auth/login and auth:login are distinct exact tags).

  • Legacy graph nodes without tag_prefixes retain a hierarchy-aware fallback. Legacy Qdrant points require their prefix payload to be backfilled for vector prefix filtering: run python scripts/backfill_tag_prefixes.py --dry-run, review the changes, then run without --dry-run against the intended graph and Qdrant collection. Use --force-qdrant if graph prefixes already exist but vector payloads need repair. No backfill runs automatically.

  • Exclusion filtering (exclude_tags) removes any memory containing ANY of the excluded tags. Exclusions retain raw string-prefix matching regardless of tag_match: exclude_tags=auth removes both auth:login and auth-login, and slash/colon spelling is not normalized for exclusions.

  • Time filtering accepts ISO timestamps (start, end) or a natural expression via time_query.

  • Context hints boost matching preferences (e.g., Python coding style) and guarantee at least one anchor memory when applicable; responses echo what was applied via context_priority.

Operational logging

  • Application events such as memory_stored, batch_stored and recall_complete append JSON fields to the existing timestamp/level/logger/message format. Fields include counts, latency and storage status; recall query text is omitted.

  • Werkzeug access logs redact URL query strings while retaining request methods, paths, protocols and response statuses. Vector-search debug logs omit query text.

  • Records without extra fields keep the plain format. Unsupported extra values use placeholders, and exception logs retain their tracebacks.

273 files · 249 not served here

Advantages

  • Avoids redundant LLM calls during recall by using direct graph and vector index lookups. evidence: README.md
  • Records typed relationships between memories to surface the 'why' behind decisions rather than just isolated facts. evidence: README.md
  • Provides a unified memory store accessible across multiple different AI tools and chat applications. evidence: README.md

Limitations

  • Tags act as a hard filter, meaning memories without the queried tag are excluded even if they are semantically relevant. evidence: README.md
  • The system does not yet reliably resolve conflicting facts or prioritize the newest version of information automatically. evidence: README.md
  • The MCP SSE bridge currently lacks support for passing through state_mode configurations. evidence: README.md

Security

Risk level: Medium risk

This brain runs

Git hooks (8)

  • .pre-commit-config.yaml: pre-commit hooks from https://github.com/psf/black at 24.4.2 (contributor tooling: runs only after pre-commit install) · uses the network
  • .pre-commit-config.yaml: pre-commit hooks from https://github.com/pycqa/isort at 5.13.2 (contributor tooling: runs only after pre-commit install) · uses the network
  • .pre-commit-config.yaml: pre-commit hooks from https://github.com/pycqa/flake8 at 7.1.1 (contributor tooling: runs only after pre-commit install) · uses the network
  • .pre-commit-config.yaml: pre-commit hooks from https://github.com/compilerla/conventional-pre-commit at v3.2.0 (contributor tooling: runs only after pre-commit install) · uses the network
  • .pre-commit-config.yaml: pre-commit hooks from https://github.com/pre-commit/pre-commit-hooks at v4.6.0 (contributor tooling: runs only after pre-commit install) · uses the network
  • .pre-commit-config.yaml: pre-commit hooks from https://github.com/Yelp/detect-secrets at v1.5.0 (contributor tooling: runs only after pre-commit install) · uses the network
  • .pre-commit-config.yaml: bash -c 'PYTEST_DISABLE_PLUGIN_AUTOLOAD=1 ./venv/bin/pytest tests/ --ignore=tests/benchmarks --ignore=tests/test_integration.py -x -q'
  • .pre-commit-config.yaml: bash -c 'command -v coderabbit >/dev/null 2>&1 || { echo "coderabbit CLI not installed, skipping"; exit 0; }; coderabbit review --type committed --plain'

Agent instructions that run or fetch something (39)

  • AGENTS.md: `make install`: Create `.venv` (and symlink `venv -> .venv`) and install dev deps.
  • AGENTS.md: `make dev`: Start local stack via Docker (FalkorDB, Qdrant, API).
  • AGENTS.md: `make test`: Run unit tests (fast, no services).
  • AGENTS.md: `make test-integration`: Start Docker and run full integration tests.
  • AGENTS.md: `make fmt` / `make lint`: Format with Black/Isort and lint with Flake8.
  • AGENTS.md: `make bench-eval BENCH=locomo-mini`: Run snapshot-based benchmark (~2 min).
  • AGENTS.md: `make deploy` / `make status`: Deploy/check Railway.
  • AGENTS.md: Quick health: `curl :8001/health`. · uses the network
  • AGENTS.md: Run `pre-commit install` and `make fmt && make lint` before committing.
  • AGENTS.md: Unit tests: `make test`.
  • AGENTS.md: Integration: `make test-integration` (requires Docker).
  • AGENTS.md: | Tier | Benchmark | Command | Runtime | Cost | When to use | |------|-----------|---------|---------|------|-------------| | 0 | Unit tests | `make test` | 30s | free | Every change | | 1 | LoCoMo-mini (2 convos, 304 Qs) | `make bench-eval BENCH=locomo-mini` | 2-3 min | free / ~$0.20 …[+355 chars]
  • AGENTS.md: `make bench-eval BENCH=locomo-mini CONFIG=baseline` — eval from snapshot (~2 min).
  • AGENTS.md: `make bench-compare BENCH=locomo CONFIG=<name> BASELINE=baseline` — A/B compare two configs.
  • AGENTS.md: `make bench-compare-branch BRANCH=<branch>` — compare a branch against baseline.
  • AGENTS.md: `make bench-ingest BENCH=locomo` — ingest + snapshot (run once per embedding change).
  • AGENTS.md: `make bench-health` — recall health check (score distribution, entity quality, latency).
  • AGENTS.md: Run `make bench-eval BENCH=locomo-mini` on `main` to confirm the current baseline.
  • AGENTS.md: Run the same eval on the branch.
  • AGENTS.md: Promote to `make bench-eval BENCH=locomo` (full) before merge.
  • AGENTS.md: `benchmarks/snapshots/` — Qdrant/FalkorDB snapshot data (gitignored, regenerate with `make bench-ingest`).
  • AGENTS.md: `benchmarks/results/` — per-run result JSONs (gitignored).
  • AGENTS.md: release-please and GHCR `:stable` then run on `main`.
  • CLAUDE.md: `GET /consolidate/status` - Check consolidation scheduler status and last run times
  • CLAUDE.md: Adds short summaries, timestamps (`enriched_at`), and per-run metrics under `metadata.enrichment`.
  • CLAUDE.md: # Run tests
  • CLAUDE.md: make test-integration # Starts services and runs integration tests
  • CLAUDE.md: make test-live # Same as above via Makefile
  • CLAUDE.md: JIT_ENRICHMENT_ENABLED=true # Run entity/summary extraction inline on recall for unenriched memories
  • CLAUDE.md: Install spaCy locally to improve entity extraction:
  • CLAUDE.md: pip install spacy
  • CLAUDE.md: python -m spacy download en_core_web_sm
  • CLAUDE.md: **Local Development** - Make changes and test with `make dev`
  • CLAUDE.md: **Unit Tests** - Verify with `make test`
  • CLAUDE.md: **Integration Tests** - Validate with `make test-integration`
  • CLAUDE.md: **Deploy to Railway** - Push changes with `make deploy`
  • CLAUDE.md: **Live Validation** - Test deployed instance with `make test-live`
  • CLAUDE.md: **Benchmarking** - Validate performance with `make test-locomo-live`
  • CLAUDE.md: # Run integration tests against Railway

Install scripts (1)

  • Makefile (make install): echo "🔧 Setting up development environment..."; bash scripts/bootstrap_dev.sh; echo "✅ Virtual environment ready!"; echo "💡 Run 'source .venv/bin/activate' to activate"
  • Medium risk: runs-git-hook-remote-exec (.pre-commit-config.yaml:9)
  • Medium risk: runs-git-hook-remote-exec (.pre-commit-config.yaml:16)
  • Medium risk: runs-git-hook-remote-exec (.pre-commit-config.yaml:23)
  • Medium risk: runs-git-hook-remote-exec (.pre-commit-config.yaml:30)
  • Medium risk: runs-git-hook-remote-exec (.pre-commit-config.yaml:38)
  • Medium risk: runs-git-hook-remote-exec (.pre-commit-config.yaml:50)
  • Medium risk: runs-instruction-instruction-network (AGENTS.md:23)
  • Low risk: network-instructions (.pre-commit-config.yaml:2)
  • Low risk: network-instructions (CLAUDE.md:299)
  • Low risk: network-instructions (Makefile:91)
  • Low risk: scripts-present (scripts/bootstrap_dev.sh)

Attribution and licence

First committed by
GitHub avatar of jack-arturojack-arturo
Publisher
Very Good Plugins (Community)
Source
verygoodplugins/automem · commit 1a7ba1a
Licence
MIT License (files served here)
  • ATTRIBUTION.md (written by AutoGineer)
Synced
Contributors
jack-arturo, mikemikimike, zackkatz, dexhunter

AutoGineer is not affiliated with or endorsed by jack-arturo or Very Good Plugins.

Install from source

249 of its files are not served here, so there is no ZIP. verygoodplugins/automem at 1a7ba1a