Projectmem
Local-first coding agent memory that records issues, failed attempts and fixes inside your repo and feeds them back to Claude Code, Cursor or Codex at session
The pre-commit warning is the reason to try Projectmem — `pjm precheck` fires at commit time, before you have spent the afternoon, and no closer competitor ships a judgement layer that says "you already tried this two days ago." The v0.3.3 release fixed precheck degrading as event logs grow, which was the obvious way this design breaks on real repositories; v0.3.4 is the current version headlining "memory that stays small." Git hooks doing the logging means there is no discipline tax. The trade-off is that you must live in git and be willing to run a Python install and hand-edit an MCP config. Against Mem0 or Letta, pick this one for judgement over storage.
Verified 4d ago · liveness 60/100 · cite: rightaichoice.com/tools/projectmem
- Developers running Claude Code, Cursor or Codex daily across multiple git repositories
- Solo devs and small teams who want cross-project memory without a cloud service or account
- Anyone who has watched an agent re-propose an approach that already failed and wants a commit-time warning
- Developers comfortable with pip installs and hand-editing an MCP client config
- Developers whose work isn't tracked in git — the automatic capture layer has nothing to read
- Teams that need centrally pushed configuration instead of per-developer local setup
- Workflows that depend on a persistent memory server or database rather than stdio plus local files
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Skip Projectmem if your code doesn't live in git — the entire capture layer is git hooks, so a repo-less workflow leaves the event log empty and the agent with nothing to read.
Projectmem is MIT licensed and costs $0 with no account, no cloud and no subscription, so the comparison isn't tier pricing but the tokens you were already burning: the vendor documents 5,000–20,000 tokens per session re-reading source files versus about 800–1,500 over MCP. Hosted memory services that charge a monthly seat fee buy you a managed store and team features; Projectmem trades that for a Python dependency and a config file you own.
In short
Projectmem — Local-first coding agent memory that records issues, failed attempts and fixes inside your repo and feeds them back to Claude Code, Cursor or Codex at session. Best for Developers running Claude Code, Cursor or Codex daily across multiple git repositories, Solo devs and small teams who want cross-project memory without a cloud service or account, Anyone who has watched an agent re-propose an approach that already failed and wants a commit-time warning. Free to use.
What's new in Projectmem
Checked 4 days agoAcross the latest 1 update: 1 changelog entry.
Viability Score
How well maintained and how widely used is Projectmem? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Append-only event log stored in a .projectmem/ directory inside your repository
- Git hooks auto-classify commits: reverts become failed attempts, fix commits become fix events
- pjm precheck warns about failed approaches on staged files before a commit lands
- pjm wrap injects a token-budgeted memory block into an agent session
- Native MCP server exposing 17 tools across all registered projects
- One MCP server configuration serves every repository, no --root and no cwd
- pjm doctor finds unregistered projects with existing memory and flags misconfigured clients
- Cross-project memory at ~/.projectmem/global/ carries library gotchas between repositories
- pjm score grades memory ROI A+ to F with debugging hours, tokens and dollars saved
- Works with Claude Code, Cursor, Codex, Antigravity and other MCP agents
- Universal Mode: distilled markdown files for agents without MCP
- Interactive local dashboard with 6 tabs run via pjm dashboard --serve
- Stale-entry detection with git verification: flags staleness, never deletes history
- CLAUDE.md export for agents working without MCP
- 25 CLI commands, installed with pip install -U projectmem
About Projectmem
Projectmem is a local-first memory layer for AI coding agents. It writes an append-only event log into a `.projectmem/` directory inside each repository — the issues you hit, the attempts that failed, the fixes that landed, the decisions you made — and serves that history back to Claude Code, Cursor, Codex, Antigravity and other MCP-compatible agents at the start of a session. Setup is four steps and roughly five minutes: `pip install -U projectmem`, `pjm doctor` to find projects you already have, one MCP server entry in your client config, then `pjm init` per repo. No `--root`, no cwd — a single stdio MCP server covers every project. The parts that change behaviour run without you remembering them. Git hooks auto-classify commits: every revert becomes a failed attempt, every fix commit a fix event. `pjm precheck` reads a staged file's failure history before the commit lands and names the dead ends already tried plus decisions that have gone stale. `pjm wrap claude` injects a token-budgeted memory block into the session, and the vendor documents it working with Claude Code, Cursor and Aider, with clipboard paste as the fallback elsewhere. Memory also follows you between repositories. `~/.projectmem/global/` keeps library gotchas — the vendor's own example is an SQLAlchemy async-session leak learned in one project being injected before line one of the next. `pjm score` grades the layer A+ to F and reports debugging hours saved, tokens prevented and dollars protected, with JSON output for CI. The vendor publishes its own arithmetic: 5,000–20,000 tokens re-reading source files per session versus roughly 800–1,500 over MCP, or ~2,500 in markdown-only Universal Mode. It is MIT licensed, runs 100% local with no telemetry and no account, and the MCP server is a stdio subprocess your client spawns — no database, no cloud. The trade-off is that the capture layer is git hooks, so it only knows about work that lives in git.
Behind the Verdict
Projectmem takes a different position from most AI memory tooling. Mem0 and Letta are memory stores — you put things in, you get things out, and the value depends on what your application does with them. Projectmem is built around a narrower claim: your coding agent should know what already failed in this repository, and it should be told at the moment that matters. That moment is the commit. `pjm precheck` reads the failure history of your staged files before the commit lands and names the dead ends you already tried plus decisions that have gone stale. Pair it with the git hooks — every revert becomes a failed attempt, every fix commit a fix event — and you get automatic capture with zero manual logging. That combination is the real product. The MCP server, the 17 tools, the dashboard, `pjm score`: those are delivery and measurement around it. The delivery side is genuinely well thought through. One MCP server entry serves every project because there is no `--root` and no cwd pinning, which is the thing that usually forces you into per-repo configuration. The transport is stdio, so there is no database and no persistent server to keep alive. Memory is a plain `events.jsonl` you can grep. `pjm doctor` looks where code lives — `~/Developer`, `~/code`, cloud folders, every drive on Windows — and flags a client still pinned to a single repo, which the vendor calls the most common reason a new project stays invisible to your agent. Cross-project memory at `~/.projectmem/global/` carries library gotchas between repositories: the vendor's example is an SQLAlchemy async-session leak learned once getting injected before line one of the next project. The token math is published rather than asserted. A session without memory burns 5,000–20,000 tokens re-reading source files; reading distilled memory over MCP costs about 800–1,500; markdown-only Universal Mode sits around 2,500; `pjm wrap` pre-injection is 500–2,000 depending on the budget you set. There is also a compounding effect the vendor is honest about: when the warning lands, the agent skips a failed debugging round it would otherwise have spent about 3,000 tokens on. Where it does not fit. The capture layer is git hooks, so projects outside git get nothing — and commit hygiene matters, because squashing, rebasing and force-pushing degrade the event log. Setup needs Python plus a hand-edited MCP client config. Everything lives on your machine, so moving laptops means moving files yourself. And the headline numbers — up to 50%+ tokens saved, roughly 12 hours of debugging saved per month — are produced by the vendor's own `pjm score`, not independently measured. Treat them as a measurement framework you can run yourself rather than a third-party benchmark. The honest framing is that Projectmem makes a modest, verifiable claim rather than a broad one: it reduces repeated mistakes in git repositories, it proves it with a score you can compute, and it costs nothing and sends nothing anywhere. If
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Real-world workflow fit
Concrete scenarios for the personas Projectmem actually fits — and what changes day-one when you adopt it.
Installs with pip install -U projectmem, runs pjm doctor to find existing projects, adds the stdio MCP server entry once, then runs pjm init in each repo. Types pjm wrap claude before a session to inject a token-budgeted memory block.
Outcome: The agent starts the session already knowing which approaches failed last week instead of re-reading source files to rebuild context, cutting the ~5,000–20,000 token discovery pass to roughly 800–1,500.
Stages changes and runs pjm precheck, or lets the auto-installed git hook run it. Precheck reads the staged files' failure history and names the dead ends already tried, plus decisions that have gone stale.
Outcome: The failed approach is caught before the commit lands rather than after an afternoon of debugging, and the agent skips a roughly 3K-token failed debugging round.
A library gotcha learned in project A is written to ~/.projectmem/global/. Starting work in project B, the global memory injects that gotcha before line one of the new session.
Outcome: A lesson learned once — the vendor's example is an SQLAlchemy async-session leak — becomes default knowledge in the next repository without being re-learned.
Use Cases
- Stopping Claude Code from re-reading the same source files at the start of every session
- Warned at commit time that an approach you are about to try already failed two days ago
- Carrying a learned library gotcha from one repository into the next via global memory
- Giving Cursor a token-budgeted context block before a session instead of paying for discovery
- Measuring how many debugging hours and tokens a memory layer actually prevented
- Running a fully local MCP memory server with no cloud dependency or telemetry
- Keeping an agent's context under ~1,500 tokens per session instead of 5,000–20,000
- Exporting memory into CLAUDE.md for agents that cannot speak MCP
Models Under the Hood
as of 2026-09-09
Limitations
- The capture layer is git hooks, so projects outside git get nothing.
- Setup requires Python and hand-editing an MCP client config, and the vendor notes that a client still pinned to a single repo is the most common reason a new project stays invisible to your agent.
- Memory fidelity depends on commit hygiene — squashing, rebasing and force-pushing degrade the event log.
- The headline figures (up to 50%+ tokens saved, ~12h debugging saved per month, ~$ protected) are produced by the vendor's own pjm score, not independently measured — the MCP-versus-baseline token ranges are documented methods, but the outcomes are self-reported.
- Universal Mode (markdown only) costs about 2,500 tokens per session versus roughly 800–1,500 over MCP, so agents without MCP support get a thinner saving.
- Nothing syncs off your machine, so moving to another laptop means moving the files yourself.
as of 2026-10-14
Verification history
We have re-verified Projectmem 8 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 8 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Where the pricing makes sense
The company stage and team size where Projectmem's pricing actually pencils out — and where peers do it cheaper.
Projectmem is MIT licensed and costs $0 with no account, no cloud and no subscription, so the comparison isn't tier pricing but the tokens you were already burning: the vendor documents 5,000–20,000 tokens per session re-reading source files versus about 800–1,500 over MCP. Hosted memory services that charge a monthly seat fee buy you a managed store and team features; Projectmem trades that for a Python dependency and a config file you own.
Setup time & first value
How long it actually takes to get something useful out of Projectmem — broken out by persona, not the marketing-page minute.
Roughly five minutes once, then one command per repo. Install takes a pip command; pjm doctor scans where code lives; the MCP server entry goes into your client config once and serves every project because there is no --root or cwd; then pjm init per repository. Budget extra time if you're new to hand-editing MCP client configs, and remember to fully quit and reopen the client after the config
Switching to or from Projectmem
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From no memory layer at all: run pjm doctor to discover existing projects, register anything it missed, add the single MCP server entry, then pjm init per repo — the log starts empty and fills from your next commits.
- →From Mem0 or Letta: keep them for chat-level memory and add Projectmem alongside for repo-scoped failure and decision history delivered over the same MCP client.
- →From a CLAUDE.md file you maintain by hand: use the CLAUDE.md export so agents working without MCP read the same distilled memory.
- ↗To a general memory store like Mem0 or Letta: your events.jsonl is plain text, so the history exports without a proprietary format to unwind.
- ↗To no memory layer: delete the .projectmem/ directory and remove the MCP server entry — nothing lives in a cloud account to cancel.
- ↗To another machine: copy the .projectmem/ directories and ~/.projectmem/global/ yourself, since nothing syncs.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Projectmem”, and we withheld 6: 6 could not be judged, because “Projectmem” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Projectmem.
Official links
Tools that pair well with Projectmem
Common stack mates teams adopt alongside Projectmem, with the specific reason each pairing earns its keep.
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Featured Head-to-Head Comparisons
Projectmem vs Spider Cloud
Spider Cloud and Projectmem serve entirely different needs. Spider Cloud is a web scraping API optimized for AI agents needing fresh external data—ideal for RAG and LLM context. Projectmem is a local memory layer that gives coding agents persistent project context across sessions. Choose Spider Cloud if your agent needs to fetch and structure live web content; choose Projectmem if you want your coding assistant to remember past fixes and architectural decisions without leaving your machine.
Projectmem vs Voyage Ai
Choose Voyage AI if you need enterprise-grade embedding models for accurate retrieval over finance or legal documents and have budget to negotiate. Choose Projectmem if you're a developer wanting a free, privacy-first memory layer for AI coding agents like Claude Desktop or Cursor — it's perfect for personal or small-team use.
Projectmem vs Temporal Ai
If you need durable, fault-tolerant orchestration for AI agents or microservices—and are willing to pay for cloud or manage infrastructure—Temporal is the clear choice. If you simply want a local memory layer so your AI coding agent remembers past context across sessions, Projectmem is free, private, and fits your needs perfectly. They serve fundamentally different purposes; choose based on whether you need reliable execution or persistent context.
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Frequently Asked Questions
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