Power your agent
पब्लिशर का इंस्टॉल। PlugMCP एंडपॉइंट नहीं।
Host a copy with our keys and logs.
रजिस्ट्री में अभी कोई पैकेज्ड रिलीज़ नहीं है। रिपॉज़िटरी का README देखें.
रिमोट URL या आपकी मशीन पर कमांड? दोनों इंस्टॉल में क्या अंतर है
PlugMCP पर चलाएँ
A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.
लॉन्चपब्लिशर से
Core Storage
/mcp/<name> (see Multi-database routing)Give your AI agents persistent, collective memory — with deduplicating absorb, supersession lineage, semantic search, and a graph UI. Speaks MCP.
Absorb & Lineage
dry_run previewfollow modes: active, latest, full_history)memory_digest(topic) bundles relevant memories, open TODOs/issues, related edges, and source IDs into one retrievalSearch & Intelligence
Document Storage
Tools & Visualization
Two paths. pip is a local stdio child the client spawns. A container is a detached HTTP service you start with up; with MEMORA_DATABASES it serves multiple stores from one process. The LaunchAgent supervises the proxy, not the container — after a host restart the listener can come back while its upstream is still stopped. If you are running memora as a service, the container path is the install.
pip install memora-mcp
The PyPI package is memora-mcp (bare memora on PyPI is an unrelated project). Includes cloud storage (S3/R2) and OpenAI embeddings out of the box.
# Optional: local embeddings (offline, ~2GB for PyTorch)
pip install "memora-mcp[local]"
# Latest development version straight from git
pip install "git+https://github.com/agentic-box/memora.git"
Then spawn it from .mcp.json with "command": "memora-server" (see Configuration).
Default runtime is Apple's container CLI. Every container operation scripts/memora-instance.sh performs (build, up, status, logs, down) uses $MEMORA_CONTAINER_BIN (default container). The generated proxy process does not; it hardcodes container list.
Before the first build:
Install Apple's container CLI (signed pkg from its GitHub releases). It needs a Mac with Apple silicon running macOS 26 — Apple does not support older macOS versions for container.
Start the runtime — Apple's documented first command, which also installs a kernel if none is configured:
container system start
Clone this repo and cd into it:
git clone https://github.com/agentic-box/memora.git
cd memora
Copy the instance template. It ships with INSTANCE=myinstance so the later build/up/proxy lines match without renaming. Edit PORT and a backend (STORAGE_URI, VOLUME, or MEMORA_DATABASES):
cp instances/example.env instances/myinstance.env
Create the credential file and install the proxy the LaunchAgent will run. cred_args() requires a .mcp.json whose mcpServers.memora.env holds CLOUDFLARE_API_TOKEN (D1 access) and the embedding/LLM keys — up dies if that file is missing. The script looks for ~/.config/memora/credentials.mcp.json if that file exists, otherwise ~/repos/agentic-box/.mcp.json. Set CRED_SOURCE in the instance file to pick a path. Separately, proxy renders a plist whose executable is $MEMORA_PROXY_BIN (default ~/.local/libexec/memora/memora_proxy.py) and whose logs live in $MEMORA_LOG_DIR (default ~/.local/var/log) — nothing creates either on a fresh clone.
mkdir -p ~/.config/memora ~/.local/libexec/memora ~/.local/var/log
cp scripts/memora_proxy.py ~/.local/libexec/memora/
# real values; any key is fine, an absent file is not
# the default umask is permissive -- chmod 600 keeps other local accounts out
cat > ~/.config/memora/credentials.mcp.json <<'JSON'
{"mcpServers":{"memora":{"env":{"CLOUDFLARE_API_TOKEN":"REPLACE","OPENAI_API_KEY":"REPLACE"}}}}
JSON
chmod 600 ~/.config/memora/credentials.mcp.json
That JSON is the minimal correct config: both the LLM and embeddings use the
default OpenAI host with a real OpenAI key. Do not add
OPENAI_BASE_URL pointing at OpenRouter without the embedding pair from
Embeddings — OpenRouter has no embeddings
endpoint, every embed call 404s, and memora silently falls back to TF-IDF
keyword bags while looking healthy.
Then:
./scripts/memora-instance.sh build myinstance # tags IMAGE from myinstance.env (memora-pilot if IMAGE is unset)
./scripts/memora-instance.sh up myinstance # runs that same IMAGE
./scripts/memora-instance.sh proxy myinstance # render the LaunchAgent; run the printed launchctl
up does not publish a host port. The listener the workspace connects to is the proxy. proxy only renders a macOS LaunchAgent and prints the launchctl commands — it does not load the service. Run those printed commands.
The printed workspace URL is always http://127.0.0.1:<PORT>/mcp (the registry default). For a non-default store, append /<name> yourself — a bare /mcp on a registry silently binds MEMORA_DEFAULT_DB:
{"mcpServers": {"memora": {"type": "http", "url": "http://127.0.0.1:<PORT>/mcp/<store>"}}}
Proxy rationale, credentials, instance files, and MEMORA_CONTAINER_BIN: Container Deployment.
The server runs automatically when configured in Claude Code. Manual invocation:
# Default (stdio mode for MCP)
memora-server
# With graph visualization server
memora-server --graph-port 8765
# HTTP transport (alternative to stdio)
memora-server --transport streamable-http --host 127.0.0.1 --port 8080
Add to .mcp.json in your project root:
Local DB:
{
"mcpServers": {
"memora": {
"command": "memora-server",
"args": [],
"env": {
"MEMORA_DB_PATH": "~/.local/share/memora/memories.db",
"MEMORA_ALLOW_ANY_TAG": "1",
"MEMORA_GRAPH_PORT": "8765"
}
}
}
}
Cloud DB (Cloudflare D1) - Recommended:
{
"mcpServers": {
"memora": {
"command": "memora-server",
"args": ["--no-graph"],
"env": {
"MEMORA_STORAGE_URI": "d1://<account-id>/<database-id>",
"CLOUDFLARE_API_TOKEN": "<your-api-token>",
"MEMORA_ALLOW_ANY_TAG": "1"
}
}
}
}
With D1, use --no-graph to disable the local visualization server. Instead, use the hosted graph at your Cloudflare Pages URL (see Cloud Graph).
Cloud DB (S3/R2) - Sync mode:
{
"mcpServers": {
"memora": {
"command": "memora-server",
"args": [],
"env": {
"AWS_PROFILE": "memora",
"AWS_ENDPOINT_URL": "https://<account-id>.r2.cloudflarestorage.com",
"MEMORA_STORAGE_URI": "s3://memories/memories.db",
"MEMORA_CLOUD_ENCRYPT": "true",
"MEMORA_ALLOW_ANY_TAG": "1",
"MEMORA_GRAPH_PORT": "8765"
}
}
}
}
Add to ~/.codex/config.toml:
[mcp_servers.memora]
command = "memora-server" # or full path: /path/to/bin/memora-server
args = ["--no-graph"]
env = {
AWS_PROFILE = "memora",
AWS_ENDPOINT_URL = "https://<account-id>.r2.cloudflarestorage.com",
MEMORA_STORAGE_URI = "s3://memories/memories.db",
MEMORA_CLOUD_ENCRYPT = "true",
MEMORA_ALLOW_ANY_TAG = "1",
}
All 44 MCP tools register unconditionally, so every agent session is injected with the full ~12,700-token tool schema even when most tools are never called. MEMORA_TOOL_PROFILE exposes a subset per deployment so a gated tool is genuinely absent — missing from tools/list AND undispatchable (call_tool returns unknown-tool, not a hidden execution). The profile is applied and attested at startup; the active profile and exposed tool count are logged to stderr.
full. No existing deployment changes behaviour.full — a typo must not re-expose destructive maintenance tools (memory_rebuild_embeddings, memory_delete_batch) to every worker. Fail closed.memory_list is in leader but not agent. It was excluded from both while it cost 163-174s on a D1 store against memory_list_compact's 0.22s; #973 fixed that (now ~1.1s). It stays out of agent because a worker's read surface is deliberately narrow, not for speed.memora/tool_profile.py (two frozensets). Editing it is one line, not a sweep of 44 decorators._tool_manager._tools dict, so memora pins mcp>=1.27,<1.28 (the audited minor) and runs a startup attestation through the low-level registered MCP request handlers (_mcp_server.request_handlers[ListToolsRequest] / [CallToolRequest] — the actual dispatch callable real client requests use, not the FastMCP.list_tools / call_tool Python helpers) that refuses to start if the installed SDK routes listing/dispatch elsewhere (private-implementation drift). The pin is the static guard; the attestation is the runtime backstop. Bumping the upper bound requires re-running tests/test_tool_profile.py.agent would strip create_section/store_document/delete/digest/tags from the leader.memora-server (i.e. memora.server.main()) is the sole supported profiled serving path. A direct embedder that imports memora.server.mcp and calls mcp.run() themselves bypasses profiling entirely (the global mcp still holds all 44 tools); embedders who want profiling must call apply_tool_profile themselves or use main().# Leader deployment — exposes 19 tools
MEMORA_TOOL_PROFILE=leader memora-server
# Agent worker — exposes 12 tools
MEMORA_TOOL_PROFILE=agent memora-server
# Full (default) — all 44 tools, existing behaviour
memora-server
# Typo refuses to start:
# MEMORA_TOOL_PROFILE=agnt memora-server
# Error: unknown MEMORA_TOOL_PROFILE='agnt'; valid values: full, leader, agent
One memora process can serve every workspace. MEMORA_DATABASES is a JSON
registry of {name: storage URI}; a client reaches its store at /mcp/<name>.
The selector is the URL already in .mcp.json, not a tool argument — an optional
db on every tool is 44 chances to forget one, and every miss would write into
someone else's store.
Unset MEMORA_DATABASES is the old shape: one backend from MEMORA_STORAGE_URI
/ MEMORA_DB_PATH, one /mcp. Existing stdio deployments do not change.
Routing (streamable-http only):
The binding is sticky per MCP session, not per request. A session opened on
/mcp/alpha and reused against /mcp/beta still resolves to alpha. A client
cannot half-switch databases mid-conversation.
Malformed configuration refuses to start (it does not fall through to the
legacy database): bad JSON, a non-object, duplicate keys, an empty URI, a name
that is not one URL path segment, or MEMORA_DEFAULT_DB missing/unknown when
more than one database is listed.
Worked pair — run this, connect to this. A streamable-HTTP listener, not
an MCP command entry (that would spawn a stdio child that never speaks MCP
on stdio). Credentials live on the server process.
MEMORA_DATABASES='{"memora":"d1://<account-id>/<memora-db-id>","ob1":"d1://<account-id>/<ob1-db-id>"}' \
MEMORA_DEFAULT_DB=memora \
CLOUDFLARE_API_TOKEN='<token>' \
MEMORA_VECTOR_SCAN_PAGE_SIZE=100 \
memora-server --transport streamable-http --host 127.0.0.1 --port 8000 --no-graph
{
"mcpServers": {
"memora": {
"type": "http",
"url": "http://127.0.0.1:8000/mcp/ob1"
}
}
}
Container / proxy variant (this host's usual launcher, not the command
above): scripts/memora-instance.sh up myinstance starts the same HTTP server
inside a container and puts scripts/memora_proxy.py on 127.0.0.1:<PORT>
(8910 for the memora instance). The workspace URL is then
http://127.0.0.1:8910/mcp/ob1. See Container Deployment.
A registry may mix d1://, s3://, and local paths; parse_backend_uri
dispatches on the scheme.
memory_stats reports the bound database. It returns database (the name
this session actually resolved) and database_source (path,
registry_default, or unconfigured). A valid-but-wrong name in .mcp.json
is otherwise undetectable: every tool works, reads succeed, and writes land
silently in another project's store. Call memory_stats and check database
against the workspace you meant.
Health of a multi-database process: GET /health is liveness (no database I/O
— the only signal a supervisor may restart on). GET /health/db is an alert
surface (always HTTP 200; status is ok, degraded, unknown — no
snapshot yet, a refresh timed out, or evidence older than max staleness — or
error if the registry itself is unusable). GET /health/db/{name} is the
workspace-specific probe (200 or 503). Withdrawing the whole process because
one store is degraded takes the healthy ones down with it.
With MEMORA_DATABASES unset, a process still binds one database for its
lifetime (MEMORA_STORAGE_URI / MEMORA_DB_PATH). That is the original
one-store-one-container-one-port shape.
With MEMORA_DATABASES set, one container serves every workspace and
clients select a store by URL path (/mcp/<name>). See
Multi-database routing. scripts/memora-instance.sh
wants one of STORAGE_URI, VOLUME, or MEMORA_DATABASES per instance file
(load() requires at least one). If more than one is set, cmd_up uses
MEMORA_DATABASES, then STORAGE_URI, then VOLUME.
Dockerfile builds a credential-free image; scripts/memora-instance.sh deploys one
instance from instances/myinstance.env (or another named file). The script's runtime CLI is
$MEMORA_CONTAINER_BIN (default container — Apple's CLI). Every container
operation the script performs honours that override (build, up, status,
logs, down). The generated memora_proxy.py process hardcodes
container list, which is also why the proxy exists: that runtime reassigns
the container's IP on every start.
./scripts/memora-instance.sh build myinstance # build the image
./scripts/memora-instance.sh up myinstance # run the container
./scripts/memora-instance.sh proxy myinstance # render a LaunchAgent + print install commands
./scripts/memora-instance.sh status # every instance at a glance
Then point the workspace at it — the whole client config, with no secrets in it.
A registry instance needs the store in the path (/mcp/<name>); bare /mcp is
the registry default:
{"mcpServers": {"memora": {"type": "http", "url": "http://127.0.0.1:8910/mcp/ob1"}}}
Credentials never enter the image, the instance file, or the workspace's HTTP
config. They are read at run time from a separate credential config
($CRED_SOURCE — itself a .mcp.json holding only the mcpServers.memora.env
block) and injected with -e. If the instance file does not set
CRED_SOURCE, the script uses ~/.config/memora/credentials.mcp.json when that
file exists, otherwise ~/repos/agentic-box/.mcp.json. Pass through every
variable that file defines, not a hand-picked few: a container started with only
the embedding keys silently loses memory_absorb's LLM consolidation instead of
failing loudly.
Why the proxy exists — read this before deciding you do not need it. The
default runtime (Apple's container) reassigns a container's IP on every start, not just on recreate.
An MCP client reads its config once at startup, so a moved address does not produce an
error: it produces a permanent silent hang. scripts/memora_proxy.py holds a stable
127.0.0.1:<PORT> in front of the moving address and re-resolves per connection.
Two failure modes it distinguishes, which cost an outage to learn:
MEMORA_PROXY_STALE_GRACE
(300s). Conflating the two took every workspace offline while the containers were
answering normally on unchanged addresses.Set MEMORA_TOOL_PROFILE per instance (see Tool Profiles). Note the profile is
per container while roles are per agent: if one container serves a workspace's
leader and its workers, it needs the leader superset.
Deploy-time script variable (not a memora-server env var — it never reaches the process inside the container):
instances/README.md covers the config fields and launchd/README.md the supervised
proxy. REVERT.md documents restoring a workspace to the direct stdio server.
Memora supports three embedding backends:
Embeddings and the LLM are configured separately.
A partial split (only one MEMORA_EMBEDDING_* set) is rejected so one provider’s secret is never sent to another host.
Trap — OpenRouter has no embeddings endpoint. OpenRouter’s catalogue is chat/multimodal only (no embedding models). Do not point the embedding path at OpenRouter via OPENAI_BASE_URL (or a MEMORA base URL). That combination 404s every embed call; without MEMORA_EMBEDDING_STRICT=1 Memora falls back to TF-IDF and keeps answering, so the store fills with keyword bags while looking healthy. OpenRouter remains fine for the LLM only.
Worked example (LLM via OpenRouter, embeddings via Cloudflare Workers AI):
@cf/baai/bge-m3 is 1024-dimensional. Token needs Workers AI permission. Endpoint shape:
https://api.cloudflare.com/client/v4/accounts/<account_id>/ai/v1
{
"env": {
"MEMORA_EMBEDDING_MODEL": "openai",
"OPENAI_API_KEY": "<openrouter-key>",
"OPENAI_BASE_URL": "https://openrouter.ai/api/v1",
"MEMORA_LLM_MODEL": "deepseek/deepseek-chat",
"MEMORA_EMBEDDING_API_KEY": "<cloudflare-api-token-with-workers-ai>",
"MEMORA_EMBEDDING_BASE_URL": "https://api.cloudflare.com/client/v4/accounts/<account_id>/ai/v1",
"OPENAI_EMBEDDING_MODEL": "@cf/baai/bge-m3",
"MEMORA_EMBEDDING_STRICT": "1"
}
}
What this fix does (no oversell): embeddings and LLM can use different providers; a partial split is rejected; strict mode turns silent degradation into a hard, named failure.
Automatic: Embeddings and cross-references are computed automatically when you memory_create, memory_update, or memory_create_batch.
Manual rebuild required when the store fingerprint changes — not only MEMORA_EMBEDDING_MODEL, but also:
MEMORA_EMBEDDING_BASE_URL / host)OPENAI_EMBEDDING_MODEL, e.g. switching to @cf/baai/bge-m3)Fingerprint form: backend|model|repr (e.g. openai|@cf/baai/bge-m3|dense:1024). Legacy meta value openai alone is treated as a mismatch.
# After changing embedding model/endpoint, rebuild all embeddings
memory_rebuild_embeddings
# Then rebuild cross-references to update the knowledge graph
memory_rebuild_crossrefs
A built-in HTTP server starts automatically with the MCP server, serving an interactive knowledge graph visualization.
Access locally:
http://localhost:8765/graph
Remote access via SSH:
ssh -L 8765:localhost:8765 user@remote
# Then open http://localhost:8765/graph in your browser
Configuration:
{
"env": {
"MEMORA_GRAPH_PORT": "8765"
}
}
To disable: add "--no-graph" to args in your MCP config.
[Memory #ID] referencesNode size reflects connection count.
When using Cloudflare D1 as your database, the graph visualization is hosted on Cloudflare Pages - no local server needed.
Benefits:
?db= parameterSetup:
Create D1 database:
npx wrangler d1 create memora-graph
npx wrangler d1 execute memora-graph --file=memora-graph/schema.sql
Deploy Pages:
cd memora-graph
npx wrangler pages deploy ./public --project-name=memora-graph
Configure bindings in Cloudflare Dashboard:
DB_MEMORA → your databaseR2_MEMORA → your bucket (for images)Configure MCP with D1 URI:
{
"env": {
"MEMORA_STORAGE_URI": "d1://<account-id>/<database-id>",
"CLOUDFLARE_API_TOKEN": "<your-token>"
}
}
Access: https://memora-graph.pages.dev
Secure with Zero Trust:
memora-graph.pages.devSee memora-graph/ for detailed setup and multi-database configuration.
Ask questions about your knowledge base directly from the graph UI. The chat panel uses RAG (Retrieval-Augmented Generation) to search relevant memories and stream LLM responses with tool calling support.
[Memory #ID] references that focus the graph nodeConfigure the chat model:
Requires an OpenAI-compatible API (OPENAI_API_KEY + OPENAI_BASE_URL for local, OPENROUTER_API_KEY secret for Cloudflare). The chat model must support tool use (function calling).
Find and merge duplicate memories using AI-powered semantic comparison:
# Find potential duplicates (uses cross-refs + optional LLM analysis)
memory_find_duplicates(min_similarity=0.7, max_similarity=0.95, limit=10, use_llm=True)
# Merge duplicates (append, prepend, or replace strategies)
memory_merge(source_id=123, target_id=456, merge_strategy="append")
LLM Comparison analyzes memory pairs and returns:
verdict: "duplicate", "similar", or "different"confidence: 0.0-1.0 scorereasoning: Brief explanationsuggested_action: "merge", "keep_both", or "review"Works with any OpenAI-compatible chat API (OpenAI, OpenRouter, Azure, etc.) via OPENAI_BASE_URL. OpenRouter is fine for this LLM path; it does not provide embeddings — configure embeddings separately (see Semantic Search & Embeddings).
Store structured documents (research reports, architecture decisions, post-mortems) as searchable fragment trees:
# Store a markdown document — auto-parsed into typed fragments
memory_store_document(
content="# Research Report\n\n## Evidence Table\n| Claim | Confidence |\n...",
document_key="research/memora-enhancements-2026-04-08",
tags=["memora/research"]
)
# Returns: {root_id: 230, fragment_count: 100, node_map: {claim: [...], plan_item: [...], ...}}
# Retrieve the full document or specific fragment types
memory_get_document(document_key="research/memora-enhancements-2026-04-08")
memory_get_document(document_key="...", node_kinds=["claim"], content_mode="full")
# Delete a document and all its fragments
memory_delete_document(document_key="research/memora-enhancements-2026-04-08")
How it works: The parser splits markdown by structure — tables become individual claims, numbered lists become plan items, URL lists become references, and risk sections become risk fragments. Each fragment is independently searchable via memory_semantic_search while the full document is retrievable as a unit.
Fragment types: claim, plan_item, reference, section_chunk, risk
Integrity guards: Document fragments are protected from accidental modification:
memory_delete requires force=True for fragmentsmemory_merge refuses to merge fragmentsmemory_absorb excludes fragments from similarity matchingmemory_find_duplicates and memory_detect_supersessions skip fragmentsStructured tools for common memory types:
# Create a TODO with status and priority
memory_create_todo(content="Implement feature X", status="open", priority="high", category="backend")
# Create an issue with severity
memory_create_issue(content="Bug in login flow", status="open", severity="major", component="auth")
# Create a section placeholder (hidden from graph)
memory_create_section(content="Architecture", section="docs", subsection="api")
Analyze stored memories and surface actionable insights:
# Full analysis with LLM-powered pattern detection
memory_insights(period="7d", include_llm_analysis=True)
# Quick summary without LLM (faster, no API key needed)
memory_insights(period="1m", include_llm_analysis=False)
Returns:
MEMORA_STALE_DAYS; memory_insights default 14, graph UI default 30 — same variable, two consumers)OPENAI_API_KEY)Manage relationships between memories:
# Create typed edges between memories
memory_link(from_id=1, to_id=2, edge_type="implements", bidirectional=True)
# Edge types: references, implements, supersedes, extends, contradicts, related_to
# Remove links
memory_unlink(from_id=1, to_id=2)
# Boost memory importance for ranking
memory_boost(memory_id=42, boost_amount=0.5)
# Detect clusters of related memories
memory_clusters(min_cluster_size=2, min_score=0.3)
For offline viewing, export memories as a static HTML file:
memory_export_graph(output_path="~/memories_graph.html", min_score=0.25)
This is optional - the Live Graph Server provides the same visualization with real-time updates.
Browse memories directly in Neovim with Telescope. Copy the plugin to your config:
# For kickstart.nvim / lazy.nvim
cp nvim/memora.lua ~/.config/nvim/lua/kickstart/plugins/
Usage: Press <leader>sm to open the memory browser with fuzzy search and preview.
Requires: telescope.nvim, plenary.nvim, and memora installed in your Python environment.
Give your AI agents persistent, collective memory — with deduplicating absorb, supersession lineage, semantic search, and a graph UI. Speaks MCP.
टूल
अभी टूल सूची नहीं है। रिमोट सर्वर को पहले हेल्थ चेक के बाद सूची मिलती है।
FAQ
इस पेज पर Cursor, Claude Desktop, Claude Code या VS Code के लिए stdio कॉन्फ़िग कॉपी करें। यह पब्लिशर का इंस्टॉल इस्तेमाल करता है, PlugMCP प्रॉक्सी नहीं।
इस रिकॉर्ड में कोई ज़रूरी एनवायरनमेंट वेरिएबल दर्ज नहीं है।
Memora stdio पर लोकल चलता है।
पिछली जाँच 2026-11-11 को हुई। Give your AI agents persistent, collective memory — with deduplicating absorb, supersession lineage, semantic search, and a graph UI. Speaks MCP.
