shadowgraph
Local-first decision memory for AI agents — records what was chosen, what was rejected, why, and when to reconsider. MCP, CLI, and HTTP. Zero runtime dependencies.
Documentation
ShadowGraph
Local-first decision memory for AI agents. ShadowGraph remembers what an agent decided, what it
rejected, why it rejected it, and when that decision should be reconsidered.
> Status: Technical Preview / Early Access. Install from GitHub — it is not on npm.
> See Limitations and Technical Preview status.
Why it matters
Chat memory remembers the conversation. It loses the decision.
Ask an agent three months later why the project uses SQLite and the useful part is already gone:
- The choice may survive in a summary. The rejected alternative and the reason for rejecting it do not.
- A fact changes — the deployment goes from single-user to multi-user — and nothing reopens the decision.
- The same approach fails again, because the failed attempt was never recorded as a failed attempt.
ShadowGraph stores that reasoning as structured, inspectable data instead of prose: what was chosen,
what was rejected, why, the assumptions and evidence behind it, failed attempts, outcomes,
provenance, confidence history, and the conditions that should trigger a rethink.
The promise is deliberately narrow: **important AI decisions should survive sessions and stay
explainable, reviewable, and reconsiderable.**
Who it is for: developers building agents on MCP, a CLI, or a local HTTP API who need
consequential decisions to outlive a session. It is a decision store, not a transcript store, and it
keeps everything on your machine.
Quick Start — 5 minutes
Requirements
- Node.js 20+ (the optional SQLite backend needs Node 22.5+ for `node:sqlite`)
- No runtime npm dependencies, no build step, no account, no network calls
1. Install
ShadowGraph is not published to npm. During the Technical Preview, install it from this
repository. A global install puts `shadowgraph` on your `PATH`, which is what MCP clients need:
npm install --global github:LiLara-AI/shadowgraphOr clone and run from source
git clone https://github.com/LiLara-AI/shadowgraph.git
cd shadowgraph
npm install
node src/cli.js setup
node src/cli.js doctorReplace `shadowgraph` with `node src/cli.js` in every command below.
> `npm install shadowgraph-unified-plugin` does not work and fails with `E404`. The package is
> `private: true` and unpublished, and the registry name is not reserved. This README will change if
> publication is ever approved.
2. JSON arguments and your shell
Every ShadowGraph command takes a single JSON argument, so quoting depends on your shell. Pick
the row for the shell you are actually using — this is the most common reason a first command fails:
| Shell | Form | Example |
|---|---|---|
| bash / zsh / Git Bash (macOS, Linux, WSL) | single quotes, plain JSON | `shadowgraph recall '{"project":"demo"}'` |
| Windows PowerShell | single quotes, `\"` inside | `shadowgraph recall '{\"project\":\"demo\"}'` |
| Windows `cmd.exe` | double quotes, `\"` inside | `shadowgraph recall "{\"project\":\"demo\"}"` |
The examples below use the bash form. All three are tested on every command in this README.
3. Initialize a store
mkdir shadowgraph-demo
cd shadowgraph-demo
shadowgraph setup
shadowgraph doctor`setup` creates `.shadowgraph/data.json` in the current directory, so run it where you want the
store to live. It never rewrites an existing store. `doctor` then checks Node compatibility, storage
readability and writability, graph validity, and the MCP entry point.
Run `setup` before `doctor`: on a fresh directory `doctor` reports `Storage is not initialized`
and exits `1` until a store exists. That is expected, not a failed install.
4. Record a decision, restart, and get it back
shadowgraph decision '{"project":"checkout-service","title":"Choose the datastore","chosen":"SQLite","confidence":0.8,"alternatives":[{"label":"PostgreSQL","reasonRejected":"Single-user local deployment does not justify running a server","reopenWhen":[{"key":"deployment","operator":"equals","value":"multi-user"}]}]}'
shadowgraph fact '{"project":"checkout-service","key":"deployment","value":"single-user","sourceClass":"human_confirmed","confidence":1}'
shadowgraph search '{"query":"datastore","project":"checkout-service"}'Each command runs in a new process and reopens the store from disk, so the `search` result comes
back across a real restart, not from in-memory state. You now have a decision that carries its
rejected alternative, the reason it was rejected, and the condition that should reopen it.
The demo: a decision that reopens itself
This is the whole point of ShadowGraph, in three commands. Continue in the same directory.
The decision is settled, so there is nothing to reconsider yet:
shadowgraph review '{"project":"checkout-service"}'[]Now the world changes. The deployment becomes multi-user:
shadowgraph fact '{"project":"checkout-service","key":"deployment","value":"multi-user","sourceClass":"human_confirmed","confidence":1}'Restart and ask again — passing only the project, never the triggering fact:
shadowgraph review '{"project":"checkout-service"}'[
{
"decisionId": "decision_1788079304730_yjawcg",
"title": "Choose the datastore",
"reason": "deployment",
"alternativesToReconsider": [
"PostgreSQL"
]
}
]ShadowGraph read the stored fact, matched it against the rule saved with the decision, and surfaced
the alternative that had been rejected for a reason that no longer holds. Your decision IDs will
differ; nothing else does.
That is decision memory: not "what did we talk about", but **"what did we decide, what did we rule
out, and does that still hold?"**
For the same story through MCP, the HTTP API, and the JavaScript API — plus recording failed
attempts and outcomes — see the decision-memory demo.
Key capabilities
Decision memory. Decisions carry the chosen approach, rejected alternatives with their reasons,
assumptions, evidence, and structured `reopenWhen` rules. Outcomes (successful, mixed, failed,
unknown) feed back into confidence.
Reconsideration. `review()` evaluates reopen rules against *stored* facts, so it works after a
restart without the caller re-supplying what changed. Review signals are persisted and
acknowledgeable.
Failed-attempt memory. Attempts record the approach, the result, the environment, and the
lesson, so an agent can discover that something was already tried and why it did not work.
Provenance you can audit. Every claim carries a `sourceClass` — `agent_claimed`,
`tool_observed`, `human_confirmed`, or `production_verified` — which records *what was claimed*
about an observation's origin, never proof of it. Ordinary tool input cannot create `verified`; that
requires a separately configured Ed25519 verifier.
Scoped memory and temporal recall. `remember()` / `recall()` store preferences, profiles, goals,
instructions, procedures, episodes, and notes under a project plus optional `userId` / `agentId` /
`runId`. Facts, memories, and relations are bi-temporal, so you can ask what was true `asOf` a past
moment. Retrieval fuses lexical, vector, graph-distance, and temporal signals and declares which
signals were unavailable rather than silently degrading.
Project and scope isolation. Omitted project and scope mean the `default` project and all-null
scope — never every project or every user. Purge is previewable, logical by default, and explicitly
irreversible in hard mode.
Explainable retrieval. Results expose raw scores, ranks, and reasons, and every bounded response
declares its total, pages, and omitted scope. Nothing is silently summarized away.
Local-first and privacy
Everything is a local file. The HTTP server binds to `127.0.0.1` and rejects non-local browser
origins. There is no cloud service, no account, no telemetry, and no analytics — ShadowGraph makes
no outbound network request unless you explicitly configure one.
The two opt-ins that can send data off the machine are both off by default:
- Embeddings. No endpoint is configured. A localhost OpenAI-compatible server works once
configured; a remote endpoint additionally requires `SHADOWGRAPH_ALLOW_REMOTE_EMBEDDINGS=1`,
because that means memory and query text leave your machine.
- Markdown export. `markdown-sync` writes plaintext copies you control. ShadowGraph cannot find
or delete those copies later — see Storage, backup, and deletion.
For shared local use, set a Bearer token:
SHADOWGRAPH_API_TOKEN="use-a-random-token-at-least-16-characters" shadowgraph serveThen send `Authorization: Bearer use-a-random-token-at-least-16-characters` with every request. This
is defense in depth for a local deployment, not a public-internet security model. See
Interfaces
MCP
shadowgraph mcpCompact mode is recommended: it advertises 12 workflow tools while the full graph, memories, facts,
alternatives, and outcomes stay stored at full fidelity. Compact mode is a tool-advertisement
choice, not lossy storage.
SHADOWGRAPH_MCP_COMPACT=1 shadowgraph mcpTool metadata follows the revision `initialize` negotiates: a session negotiated at `2025-03-26`
also receives behavioural annotations, and one at `2025-06-18` or `2025-11-25` also receives output
schemas and structured results, while a session at `2024-11-05` keeps the tool members and
serialized results it always had. A request for a revision this server does not implement is
answered with `2025-11-25`, the latest it does. See the
MCP compatibility guide for the full table.
The 12 compact tools are `shadowgraph_context`, `shadowgraph_remember`, `shadowgraph_recall`,
`shadowgraph_record_decision`, `shadowgraph_record_attempt`, `shadowgraph_record_fact`,
`shadowgraph_record_outcome`, `shadowgraph_retrieve`, `shadowgraph_search`, `shadowgraph_review`,
`shadowgraph_validate`, and `shadowgraph_maintain`. Full mode advertises 27 — see the
MCP compatibility guide for the complete inventory, every protocol
revision, and verified client behaviour.
AI tool setup
Install globally first so the client can find `shadowgraph` on its `PATH`:
npm install --global github:LiLara-AI/shadowgraph
shadowgraph setup
shadowgraph doctorClaude Code (user scope):
claude mcp add --scope user --env SHADOWGRAPH_MCP_COMPACT=1 --transport stdio shadowgraph -- shadowgraph mcpCursor (`.cursor/mcp.json` or `~/.cursor/mcp.json`):
{"mcpServers":{"shadowgraph":{"type":"stdio","command":"shadowgraph","args":["mcp"],"env":{"SHADOWGRAPH_MCP_COMPACT":"1"}}}}Codex:
codex mcp add shadowgraph --env SHADOWGRAPH_MCP_COMPACT=1 -- shadowgraph mcpHermes Agent:
hermes mcp add shadowgraph --command shadowgraph --connect-timeout 30 --env SHADOWGRAPH_MCP_COMPACT=1 --args mcpVerified file forms for all four live in `integrations/`. Set an absolute
`SHADOWGRAPH_FILE` in the client environment when one store must be shared across working
directories.
CLI
The commands you will actually use:
shadowgraph setup
shadowgraph doctor
shadowgraph context '{"project":"my-app"}'
shadowgraph decision '{"project":"my-app","title":"Choose the datastore","chosen":"SQLite"}'
shadowgraph fact '{"project":"my-app","key":"deployment","value":"local","sourceClass":"human_confirmed"}'
shadowgraph attempt '{"solution":"Rewrite everything","result":"Regression"}'
shadowgraph outcome '{"decisionId":"DECISION_ID","outcome":{"status":"failed","lessons":["Assumption was wrong"]}}'
shadowgraph review '{"project":"my-app"}'
shadowgraph search '{"query":"database","project":"my-app"}'
shadowgraph remember '{"project":"my-app","memoryType":"preference","key":"editor","text":"Prefers VS Code"}'
shadowgraph recall '{"project":"my-app","query":"development environment"}'Full command list
`setup` · `doctor` · `serve` · `mcp` · `stats` · `list` · `search` · `retrieve` · `recall` ·
`remember` · `markdown-sync` · `context` · `review` · `maintain` · `signals` · `ack` · `validate` ·
`repair-plan` · `backup` · `restore` · `decision` · `attempt` · `fact` · `outcome` · `status` ·
`link` · `traverse` · `redact` · `supersede` · `purge-preview` · `purge` · `journal` · `rebuild` ·
`confidence-evidence`
Full argument shapes are in the API reference.
HTTP API
shadowgraph serve
curl http://127.0.0.1:8787/healthA read-only dashboard is served at `http://127.0.0.1:8787/dashboard`. It talks only to the same
local origin, and a token entered there is kept in page memory only — never in cookies, local
storage, or ShadowGraph data.
All HTTP endpoints
GET /health GET /stats GET /records
GET /search?q=&project= GET /review-signals GET /validate
GET /journal
POST /decisions POST /attempts POST /memories
POST /recall POST /facts POST /outcomes
POST /review POST /context POST /status
POST /relationships POST /traverse POST /redact
POST /supersede POST /maintain POST /retrieve
POST /review-signals/ack POST /repair-plan POST /backup
POST /restore POST /rebuild POST /confidence-evidence
POST /projects/purge-preview
DELETE /projects`/redact` returns a privacy-safe export and never mutates. `/repair-plan` is always non-destructive
and returns `{apply:false, actions:[...]}`. `/projects/purge-preview` shows deletion counts without
changing storage. The server returns `401` when token auth is enabled and missing, `403` for
disallowed browser origins, `404` for missing decisions or routes, and `413` for oversized bodies.
JavaScript
import { createShadowGraph } from 'shadowgraph-unified-plugin';
const graph = createShadowGraph();
graph.addDecision({
project: 'checkout-service',
title: 'Choose the datastore',
chosen: 'SQLite',
confidence: 0.8,
alternatives: [{
label: 'PostgreSQL',
reasonRejected: 'Single-user local deployment does not justify running a server',
reopenWhen: [{ key: 'deployment', operator: 'equals', value: 'multi-user' }]
}]
});
graph.addFact({
project: 'checkout-service',
key: 'deployment',
value: 'multi-user',
sourceClass: 'human_confirmed',
confidence: 1
});
// review() reads stored facts, so this also works in a fresh process after an
// export/save and load. Do not pass the triggering fact again.
console.log(graph.review({ project: 'checkout-service' }));The bare-specifier import resolves when ShadowGraph is a dependency of your project
(`npm install github:LiLara-AI/shadowgraph`). With a `--global` install, use the CLI, HTTP, or MCP
surfaces instead, or import from the installed path.
Storage, backup, and deletion
JSON is the zero-dependency default and stores a versioned graph in `.shadowgraph/data.json`. Set
`SHADOWGRAPH_FILE` to relocate it. Set `SHADOWGRAPH_STORAGE=sqlite` on Node 22.5+ for the WAL-backed
relational adapter. Current exports use schema 5; schemas 1 through 4 remain importable.
State and journal are written in one atomic operation, every save and restore for a destination
shares a cross-process lock fence, and a stale write is rejected with a revision conflict rather
than silently lost. `backup` takes a consistent snapshot; `restore` validates domain and journal
consistency before replacing live state, and rolls back on failure. This is process-level rollback
safety, not a claim of crash or power-loss durability. The full guarantees — lock timeouts,
stale-lock recovery, revision arithmetic, and restore artifact reporting — are in the
API reference and the
Deletion is explicit and previewable:
shadowgraph purge-preview '{"project":"release-demo"}'
shadowgraph purge '{"project":"release-demo"}'
shadowgraph purge '{"project":"another-project","mode":"hard"}'Logical purge (the default) removes project content from the live projection and keeps an auditable,
payload-free purge skeleton. Hard purge physically deletes journal entries, creates a declared gap,
and cannot be undone.
Purge cannot delete external Markdown exports. ShadowGraph has no way to find plaintext copies
in arbitrary workspaces, Git history, cloud sync, backups, or removable media. Delete those
separately.
Limitations and Technical Preview status
ShadowGraph 0.40.0 is a Technical Preview / Early Access release. It is not Beta and not stable.
- Interfaces and the storage schema may still change. Do not use it for data you cannot
reproduce.
- Not on npm. The package is deliberately `private: true`. No npm publication, Git tag, or
GitHub release has been created, and none is authorized.
- No comparative benchmark has been measured. Comparative benchmark infrastructure was executed,
but no arm was measured because no common local/free LLM and embedding endpoint was available. No
comparative performance, quality, token, cost, or 'best' claim is supported. ShadowGraph makes no
claim of being faster, cheaper, lower-token, more accurate, or better than any other memory
system. See the benchmark report.
- Security review status. An AI-assisted independent security review of commit `4a5e076` (tree
`62c1918e`) was completed on 2026-08-30 by Antigravity Assistant (Gemini 3.7 Flash), with a PASS
result and no unresolved findings. No human third-party security audit has been performed. See
- No default extractor, background watcher, or hosted sync. ShadowGraph records what you tell it
to record.
- Single maintainer. No paid support, no patch SLA, and no bug bounty.
Feedback and support
Technical Preview feedback is the point of this release. Please tell us when something breaks.
| What | Where |
|---|---|
| Bug or incorrect behaviour | Open a bug report |
| Feature or capability request | Open a feature request |
| Security vulnerability | Report it privately — never in a public issue |
| Questions, ideas, "is this useful?" | Discussions |
During the preview, these reports are the most valuable:
- Installation problems — anything between `npm install --global` and a green `doctor`.
- MCP client compatibility — which client, which mode, and what it did or did not discover.
- Memory usefulness — did recalled context actually change what your agent did?
- Confusing workflows — where the docs or a command shape sent you the wrong way.
- Missing decision-memory use cases — decisions you wanted to store and could not.
- Performance — where it felt slow, and roughly how large the store was.
ShadowGraph has no telemetry and collects nothing automatically, so a report from you is the only
signal there is. When pasting output, redact anything private: decision and memory content is your
data, and `shadowgraph doctor` output is usually enough.
Documentation
| Document | What it covers |
|---|---|
| Decision-memory demo | The full worked example through CLI, MCP, HTTP, and JavaScript |
| API reference | JavaScript, CLI, HTTP, and MCP surfaces |
| Unified memory guide | `remember` / `recall`, scoping, temporal recall, Markdown sync |
| MCP compatibility | Protocol revisions, tool inventory, verified client behaviour |
| Contracts | Authoritative guarantees: provenance, lifecycle, journal, completeness, search, confidence, SQLite restore |
| Architecture decisions | ADR-0006 (memory kernel), ADR-0007 (journal baseline placement) |
| Vision and principles | What ShadowGraph is for, and what it deliberately will not do |
| Benchmark report | Honest results — no arm was measured; no comparative claim is supported |
| Security policy | Threat model, review status, and how to report a vulnerability privately |
| Contributing | Development setup and pull-request expectations |
| Changelog | Release history |
Checks
npm run check
npm test
npm run check:integrations
npm run check:mcp
npm audit --omit=dev
npm run check:package
npm run smoke:packageGitHub Actions covers Ubuntu and Windows on Node 20, 22, and 24. SQLite gates run only where
`node:sqlite` exists. The strict official MCP Inspector runs full and compact gates, the pinned Glama
`mcp-proxy@6.4.3` gate proves the revision this server negotiates with it and that the tool list
reaches an HTTP scanner unchanged, and the package smoke test runs from a real clean install in
every matrix cell.
License
MIT. See LICENSE.
Frequently asked questions
What is shadowgraph?
shadowgraph is Local-first decision memory for AI agents — records what was chosen, what was rejected, why, and when to reconsider. MCP, CLI, and HTTP. Zero runtime dependencies.
How do I install shadowgraph?
Open the GitHub repository and follow its README. Most MCP servers are added to your client's MCP config, then called by your agent.
Is shadowgraph open source?
Yes — it is hosted on GitHub at https://github.com/LiLara-AI/shadowgraph and has 1 stars.
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