
Aidimag
io.github.anup-khanalPublished by GitHub account anup-khanalv1.0.20Updated 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, with path-scoped and trust-ranked results
- Propose new memories and capture live in-chat facts or session notes for review
- Harvest session transcripts and critique work against verified memory and guardrails
- Hold short-term scratchpad notes that expire and are never synced
- Fetch tickets from a connected ticketing system to add context to proposals
- Deliver session-start briefings and session-end extraction
- When to use it
- Use it when an AI coding agent repeatedly loses context about a repository's decisions, conventions, gotchas and failed approaches across sessions, and you want those facts checked against the code rather than trusted blindly.
- Requirements
- Local process run with npx (Node 22+). AIDIMAG_REPO should point at the git repository to serve; it defaults to walking up from the working directory. Extraction features use AIDIMAG_LLM (openai, ollama or off, auto-detected); the OpenAI provider needs OPENAI_API_KEY. Semantic search can use OpenAI, local Ollama or AWS Bedrock, or run keyword-only.
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- v1.0.20LatestSep 16, 2026
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