Agent Recall vs Piia Engram
A side-by-side look at the Agent Recall and Piia Engram MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.
Verdict: choose Agent Recall for knowledge & memory work as a local process; choose Piia Engram for knowledge & memory as a local process. You can also run both side by side in one mcpServers block.
Which MCP server should you choose?
Choose Agent Recall when
- You need dedicated capabilities in the Knowledge & Memory domain.
- You prefer a local stdio subprocess transport.
- Its auth model fits you: No auth required.
- You want tools like
session_start, session_end, remember.
Choose Piia Engram when
- You need dedicated capabilities in the Knowledge & Memory domain.
- You prefer a local stdio subprocess transport.
- You want tools like
get_user_context, wrap_up_session, memory_store.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Knowledge & Memory | Knowledge & Memory |
| Quality signal | 69/100 · Great | 61/100 · Good |
| Install command | Confirmed | Confirmed |
| Transport | Local subprocess (stdio) | Local subprocess (stdio) |
| Auth | No auth required | Not declared |
| Pricing | Free | Not declared |
| Required env vars | None listed | None listed |
| Verified owner | Community listing | Community listing |
| GitHub stars | 371 | 162 |
| Directory activity | 3 views 0 installs 0 upvotes | 2 views 0 installs 0 upvotes |
| Tools | 5 | 59 |
| Full listing | View Agent Recall | View Piia Engram |
Tools & capabilities
Agent Recall tools 5
session_start[ENTRY — call FIRST, before acting] Use when the user asks to start, load, continue, resume, or open memory for a project. Set mode='lite' for a ≤500-token briefing (good for fresh conversations where the agent will pull memory on demand via recall()).session_end[ON SAVE/EXIT — YOU must call this; nothing auto-saves] Use when the user asks to save, checkpoint, summarize, end, retain, or persist the current session. Optionally pass close_phase / open_phase to update the project pipeline narrative spine in the same call.remember[MID-SESSION WRITE — single fact/decision; saying it is not saving it] Use when the user asks to remember, store, note, or save a specific decision, fact, or insight.recall[RETRIEVE — use freely, any time] Use when the user asks to recall, search, find, or look up previous memory, context, or decisions.check[MID-SESSION — safe any time; for alignment, before risky decisions] Use when the user asks to validate understanding, verify alignment, or check if their interpretation matches the human's intent. Also call BEFORE a high-risk action — publish, deploy, delete, credential exposure, external send/message, or any other irreversible write — passing `action_description` (one sentence, what you're about to do). Returns matching corrections/rules/insights plus a `verdict`: `blocked` means an authoritative correction OVERRIDES the plan — read it before proceeding. To RECORD a durable human correction, pass `human_correction` as the STRUCTURED object {rule, why, applies_when} — a plain string is only STAGED for later review, never activated.
Piia Engram tools 59
get_user_contextStartup** — Load identity + knowledge at session start (supports `token_budget` for context size control)wrap_up_sessionSession end** — Save insights + sync at session endmemory_storeWriteback** — Unified write endpoint: routes to add_lesson / add_decision / add_playbook by `kindadd_lessonStore a reusable lesson learnedadd_decisionRecord a key decision with reasoningadd_playbookRecord an operational playbook (multi-step procedure with trigger keywords)search_knowledgeRetrieval** — Search lessons, decisions, and playbooks (supports `filters_json` for domain/tier/date filtering)get_relevant_knowledgeFind knowledge relevant to current projectget_recallReturn one structured identity + recent activity + relevant knowledge recall payloadget_knowledge_historyRead one item's revision history (superseded snapshots; exact by-version lookup)get_identity_cardOwner-gated export: write and return a Markdown identity card for non-MCP toolsupdate_identityPropose identity, preference, or quality-standard changes for local review (all modes)- +47 more on the listing page
Ready-to-paste client configs
Paste either block (or both) into your client config, e.g. claude_desktop_config.json or ~/.cursor/mcp.json. We only show a config when the install command is confirmed.
Agent Recall
{
"mcpServers": {
"agent-recall": {
"command": "npx",
"args": [
"-y",
"agent-recall-mcp"
]
}
}
}
Piia Engram
{
"mcpServers": {
"patdolitse-piia-engram": {
"command": "uvx",
"args": [
"piia-engram"
]
}
}
}
Frequently asked questions
Agent Recall is categorized under Knowledge & Memory and uses a local stdio subprocess. Piia Engram belongs to Knowledge & Memory and uses a local stdio subprocess. Pick Agent Recall for knowledge & memory work and Piia Engram for knowledge & memory.
Copy the mcpServers block from the config section on this page into your client's config (for example claude_desktop_config.json or ~/.cursor/mcp.json), then fully restart the client.
Agent Recall is listed as free. Auth: no auth required. Piia Engram has no declared pricing — check the project's own site. If a server needs environment variables such as API keys, define them under the env key in your client's MCP config.
Yes. MCP clients support multiple servers: add agent-recall and patdolitse-piia-engram as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, Agent Recall has a quality signal of 69/100 (Great) with 3 views, 0 installs and 371 GitHub stars. Piia Engram has 61/100 (Good) with 2 views, 0 installs and 162 GitHub stars.