
Aidimag
io.github.AiDimagPublished by GitHub account AiDimagv1.0.22Updated Oct 24, 2026
Persistent, verified memory for AI coding agents: recall, capture, guardrails, review queue.
Overview
AIDimag gives a coding agent a persistent, evidence-verified memory of a git repository, stored locally in .aidimag/ and served over MCP.
- What you can do
- Search and recall repository memory, including path-scoped and trust-ranked results, plus session-start briefings and scratchpad notes for short-term session context. Propose new memories, capture facts live in chat, harvest session transcripts, and run a second critic grounded in verified memory. Mine commits, PRs, chat transcripts and pasted docs into a human-gated review queue; nothing is stored unreviewed. Fetch tickets from connected ticketing systems so ticket context sits next to mined proposals. Render verified memory into static context files for agents that do not use MCP.
- When to use it
- Use it when an assistant works repeatedly in the same codebase and should remember decisions, conventions, gotchas and failed approaches across sessions, and when stale or unverified facts would be worse than no memory. It suits teams that want memory reviewed before it is stored and checked against the code as it changes.
- Requirements
- Runs locally as an npm package via npx; Node 22 or later. AIDIMAG_REPO should point at the git repository to serve (otherwise it walks up from the working directory). AIDIMAG_LLM selects the extraction provider (openai, ollama or off) and is auto-detected; OPENAI_API_KEY is needed only when the OpenAI provider is used. Optional self-hosted team sync and ticketing integrations need their own credentials.
Installation
In SourceWeft
- Open Aidimag in the dashboard and add it to a workspace.
- Enable the server for the chats that should use its tools.
Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.
Other MCP clients
Follow the launch instructions in the repository.
README
AI Dimag — Verified Memory for AI Coding Agents
Your coding agent forgets your codebase. AIDimag doesn't.
[npm version] [CI] [VS Code Marketplace] [JetBrains Marketplace] [MCP Registry] [aidimag MCP server] [Product Hunt] [License] [Documentation] [Node]
Documentation • Why AIDimag? • Getting Started • Use Cases • Benchmarks • AI Dimag Cloud • Pricing
What is AI Dimag?
AI Dimag is a memory system for software engineering — not a general-purpose "AI memory" app. It gives any MCP-compatible agent (Claude, Cursor, Copilot, Windsurf…) a persistent memory of your codebase that survives across sessions — decisions, conventions, gotchas, failed approaches, guardrails, and reusable skills — stored as falsifiable claims with grounding evidence in .aidimag/ next to your code.
The subject of memory is your repository, not your preferences or chat history. Every capability — evidence, git-hook verification, guardrails, pre-commit checks, path-scoped recall, session scratchpad — exists to serve day-to-day development work.
The Difference: Claim-and-Verify, Not Store-and-Retrieve
Most memory systems store text and retrieve whatever is similar later — a stored fact is assumed true forever. That's dangerous in a codebase, where a confidently-retrieved stale fact is worse than no memory at all.
Every AI Dimag memory carries evidence (a shell check, an anchored commit, a test) that dim verify re-runs against the current repo — automatically, via git hooks, on every pull, checkout, and rebase. Beliefs that stop being true go STALE instead of silently misleading your AI.
Works with Every AI Tool
- MCP tools (Claude, Cursor, etc.) get real-time memory via the MCP server
- Non-MCP tools (Copilot, Windsurf, etc.) get static context files (
.cursorrules,CLAUDE.md,AGENTS.md, etc.)
Install
Requires Node 22+. Ships two equivalent binaries: dim (short) and aidimag.
Quick Start
One-command setup
Connect to Your AI Agent (MCP)
Add to your agent config (e.g. .mcp.json for Claude Code):
MCP Tools get memory_search, memory_propose, context_note (live in-chat fact capture), chat_harvest (live, tool-agnostic session harvesting with server-side secret redaction), memory_critique (a second critic grounded in verified memory), session-start briefings, session-end extraction, and more.
Non-MCP Tools: dim generate-context -f all renders verified memory into .cursorrules, CLAUDE.md, AGENTS.md, .windsurfrules, and .github/copilot-instructions.md (--auto keeps them refreshed).
Hermes Agent: dim hermes install registers aidimag as a native Hermes memory provider — one command, no pip, no venv. A single stdlib-only Python bridge delegates to the MCP server: session briefings are injected into the system prompt, recall is prefetched per turn, and session learnings become review-queue proposals (never silent writes). Then: hermes config set memory.provider aidimag.
Key Features
Human-Gated Capture
Commits, PRs, AI-chat transcripts (Claude Code, Codex, Copilot, Cursor), and pasted docs are mined into proposals. Nothing enters memory until you approve it in dim review (auto-triaged best-first, approve all --min-score 0.7 for batches).
Verification Lifecycle
STATIC_CHECK / COMMIT_REF / TEST_RESULT / EXEC_TRACE / HUMAN_ATTESTED evidence. Failing evidence flips memories to STALE and auto-drafts a recovery proposal. Confidence decays without re-confirmation.
Evidence Trust Gate
Shell-command evidence that arrives via team sync is never executed until you inspect and approve it (dim verify --trust).
Hybrid Semantic Recall
FTS5 keyword + vector KNN (OpenAI, local Ollama, or AWS Bedrock; auto-detected except Bedrock, which is explicit opt-in; works keyword-only with none).
Guardrails & Skills
Behavioral rules (never / ask-first / always) and step-by-step procedures, enforced by dim check (pre-commit) and memory_critique.
Team Mode, Self-Hosted
dim serve + dim sync: local-first replicas, device-code login, brain-scoped API keys, hashed credentials, cross-machine verification consensus.
Knowledgebase Inbox
Drop design docs / ADRs / PDFs / DOCX into knowledge/ and they're summarized into reviewed, pinned memories.
Scratchpad & Provenance Audit
dim scratch (and the scratchpad_* MCP tools) hold short-term session notes — TTL-expiring, never synced, never durable memory. dim audit lists memories resting on the weakest ground (agent-authored, evidence-free, stale, or long-unverified) so you can fix them up like a dependency audit for your repo's knowledge.
Web Dashboard & Extensions
dim ui — run checks, session briefings, bootstrap, harvest, and context generation from the browser — plus VS Code and IntelliJ extensions.
Ticketing Integration
Commits tell you what changed; tickets hold the why. aiDimag connects to your ticketing system so that context flows into your memory — ticket titles, types, and statuses appear next to mined proposals during dim review, and agents can fetch tickets via the ticket_get MCP tool.
Supported providers
Jira, GitHub Issues, Linear, GitLab Issues, Azure DevOps, ClickUp, Shortcut, YouTrack, Asana, Trello, Notion, Pivotal Tracker, a custom HTTP middleware, or Remote (team sync server — zero local credentials).
Quick start
Per-repo credential storage
Ticket credentials are stored per-repo in .aidimag/config.json under tickets.token (with file mode 0o600), matching the same pattern as cloud sync tokens. Credentials never leak between projects. You can also set the AIDIMAG_TICKET_TOKEN environment variable, which takes precedence over the config file.
Team-shared tickets (Remote provider)
One admin shares their ticket credential via the sync server (dim ticket share). Team members select "Remote (team sync server)" as their provider — they resolve tickets through the server and hold zero ticket credentials locally. When a cloud server is linked, the dashboard auto-discovers the team's ticket provider and shows a "Connect now" button.
Branch conventions
Define a branch-naming convention and have aiDimag warn or block on violations:
Full guide: Connecting tickets
How It Compares
AI Dimag follows a claim-and-verify model; other memory systems follow store-and-retrieve. The short version:
Full comparison: aidimag.com/comparison
vs. named tools
How aiDimag relates to the memory tools people usually ask about. These solve a different problem (remembering users and conversations); aiDimag remembers your repository and proves its memories are still true:
Chat-memory benchmarks (LoCoMo, LongMemEval, BEAM) score recall over conversation histories, so they don't apply to aiDimag — its memory subject is the repo. Instead aiDimag publishes its own reproducible suite (below), including the metric none of the chat benchmarks measure: does memory notice when the code changes?
Benchmarks
Reproducible performance and quality suites live in benchmark/
(npm run bench, npm run bench:quality). Headline results (Apple M4, Node 24,
10,000-memory brain — full tables at aidimag.com/benchmarks):
Documentation
Full documentation: aidimag.com
Contributing
Contributions welcome! See CONTRIBUTING.md for dev setup, project principles, and the PR checklist. All participation is governed by our Code of Conduct.
License & Pricing
AI Dimag is open source under the MIT License — free for everyone, any team size, forever. Use it, fork it, embed it.
The entire local-first product is free: CLI, MCP server, verification, guardrails, skills, IDE extensions, local dashboard, and self-hosted team sync (dim serve).
Want team sync without running a server? AI Dimag Cloud is an optional managed sync subscription — that's how the project stays funded and open source. See Pricing.
Built by Anup Khanal
Website • Documentation • Cloud • npm • License
Source: README.md at commit 6f62807
Tools
0Version history
1- v1.0.22LatestSep 16, 2026
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