# More Context Is Not Enough. AI Agents Need Memory They Can Trust. > Source: > Published: 2026-06-24 18:23:41+00:00 The agent does useful work in one session. It learns the shape of the project. It figures out which assumptions were wrong. It follows a correction, makes a decision, and gets closer to the real work. Then the session changes. The next run starts too cold. Old context comes back without the correction that changed it. The agent asks for the same setup again. It repeats an assumption that was already fixed yesterday. You end up managing the memory of the work instead of moving the work forward. That is the problem Pith is built for. Pith gives AI agents durable project memory they can trust when facts change. It is not trying to make an agent remember everything. That would be the wrong goal. Real projects are messy. Facts change. Decisions get reversed. A note that was useful last week can become stale after a release, a migration, a new customer constraint, or one correction from the human operator. The harder problem is not recall. The harder problem is knowing which memory is still useful. Longer context helps, but it does not solve continuity by itself. A long prompt can carry more text into a single run. It cannot automatically decide which prior facts survived a correction, which decision is now superseded, or which evidence should come back when the project resumes three days later. Developers working with agents already feel this. The friction shows up as small taxes: Those taxes compound. The more serious the workflow, the more expensive the memory gap becomes. If an agent is helping with a toy task, forgetting is annoying. If an agent is helping with a codebase, a release, a customer workflow, or a long-running research path, forgetting becomes operational drag. Pith is a local memory layer for AI agents that need durable project context. It keeps useful decisions, corrections, and project facts available across long-running work so agents do not have to restart from zero every session. The developer preview is built for builders experimenting with agent workflows, local-first memory, MCP-compatible clients, and AI coding tools. The current macOS preview supports a public install path, a local API, and client setup paths for different levels of automation. In the latest public release, Pith v1.0.3, the developer preview package refreshes client setup language and local API tooling. Claude Cowork and Codex are presented as the more automated setup paths. Claude Desktop, Claude Code, VS Code, and Cursor remain supported with clearer boundaries where manual steps, model tool choice, or verification checks may still apply. That distinction matters. A developer preview should tell you what is automated and what is still rough. If a memory layer is supposed to help agents handle real work, the setup path cannot pretend every client behaves the same way. Most AI memory discussions collapse into storage. Where do we put the notes? How do we search them? Which embedding model do we use? How large is the context window? Those questions matter, but they are not the full problem. The real question is whether the agent can trust the memory it retrieves. If a user corrected a fact yesterday, old memory should not quietly beat the correction today. If a decision was reversed, the agent should not revive the old decision just because it is semantically similar. If evidence exists for why a claim matters, the system should make that evidence inspectable instead of turning memory into vibes. This is where Pith is opinionated. The product is aimed at governed project memory: context that carries forward, but also has to survive changed facts, contradictions, and corrections. That is the difference between generic recall and memory that can support real work. The Pith developer preview is public for macOS builders. Install: ``` https://pith.run/install ``` Release: ``` https://github.com/pithrun/pith-core/releases/tag/v1.0.3 ``` Benchmark evidence: ``` https://pith.run/benchmarks ``` The benchmark page publishes scoped launch evidence for named memory benchmark lanes, with evidence files and caveats. Treat that proof the way it is intended: as inspectable evidence for specific lanes, not a universal claim that one memory system wins every workload. That boundary is deliberate. AI memory is not one problem. Different systems can look strong under different workloads, models, and evaluation setups. Pith should earn trust by making its claims narrow enough to inspect. Pith is not for casual traffic yet. The useful early users are builders with real agent workflows: people who have felt the cost of restarting context, re-explaining decisions, or cleaning up stale assumptions across repeated sessions. You are probably a good fit if: You are probably not the right fit if you want a polished consumer app, a managed team product, or a no-rough-edges onboarding path today. That will come later if the developer preview proves the core workflow. The bet behind Pith is simple: Agents that work on real projects need memory that behaves more like operational context and less like a pile of retrieved notes. They need to remember what changed. They need to carry corrections forward. They need to know when old context has become risky. They need enough evidence around memory that a developer can inspect why the agent is acting on it. That is not solved by a bigger prompt alone. It is a product problem, a systems problem, and a trust problem. Pith is the developer preview of that bet. If you are building agents and want memory that survives real work, try it here: ``` https://pith.run/install ```