Understanding Palim
Palim is the shared, persistent knowledge base for the AI tools you connect.
Palim connects MCP-compatible AI tools to a shared knowledge base. Connected agents save a compact work state after substantial results by default. Complete chats are stored only when the agent uses palim_save_session. Another connected tool can retrieve the saved content later.
The four building blocks
| Building block | Best for | Content |
|---|---|---|
| Context snapshot | Hand-offs between AI tools | A compact work state without the complete message history |
| Session | Traceable chat history | A full conversation with messages, summary, and metadata |
| Memory | Durable facts and decisions | A single piece of information that can be edited or deleted |
| Brain | An overview of recurring topics | A topic view derived from saved content in the dashboard |
Proactive checkpoints are the default
The Palim MCP server instructs connected agents to call palim_save_context after important results, decisions, or changes of direction. This stores a compact, self-contained summary – not the complete message transcript. Trivial conversations, secrets, and content you asked not to persist should not be stored.
A typical workflow
- Connect at least one AI client to Palim.
- Work on a substantial task. The connected agent will normally create a context checkpoint afterward.
- Open another connected tool later.
- Ask: “What did we decide about the auth flow in Project Atlas?”
Palim searches the stored content and returns matching results to the client. The AI then decides how to use those results in its response.
You can still request a checkpoint explicitly: “Capture the current state for my other AI tools.” To store the complete chat instead, say: “Save this complete chat to Palim.”
What search supports today
Palim uses PostgreSQL full-text search and text matching. You can also filter by date range, tags, project, and source. Semantic embedding search is not implemented yet.
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