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What it's for

Use cases ​

Each one is the same engine — memory, live data, and the graph that links them — doing real work for a coding agent. They build along the spine: memory first, then the live data it's grounded in, then the graph that connects it all, then anything else your org runs on.

Memory

Your agent remembers across sessions ​

Stop re-explaining your codebase. Architectural decisions, conventions, and gotchas persist and get recalled into every prompt — across sessions and machines.

bash
pensieve remember "Auth is Supabase JWT; prefer KQL over SQL in examples."

Onboard an engineer's agent in a day ​

Point a new hire's agent at the team's shared memory and the repo graph, sync once, and it already knows the architecture, the services, and the conventions.

bash
PENSIEVE_CLOUD_URL=https://pensieve.your-co.dev pensieve sync

Live data

Debug a prod incident from your editor ​

The agent recalls similar past incidents, queries live logs and traces, and walks the service graph to the failing call — without you leaving the IDE.

kql
otel_logs | where _timestamp > ago(15m) and severity_text == "ERROR"
| summarize n = count() by service_name | order by n desc

Ask your whole stack in plain English ​

"Why is checkout slow today?" → the agent turns it into KQL/SQL across logs, traces, data sources, and your operational databases, and streams the answer.

bash
pensieve query "p99 latency on /api/checkout in the last hour, by version"

The graph

Trace one customer across every service ​

From a customer to their auth events to the deploy that broke their checkout — one graph traversal, not five dashboards open side by side.

kql
context_edges | graph-traverse source "cus_42" from src to dst max-hops 3

Turn your repo into a queryable graph ​

Connect GitHub and the agent traverses code, PRs, issues, and people — and your memories link to the real files and services they're about.

bash
pensieve datasource add github your-org/your-repo --start

Anything

Make any source queryable ​

Webhooks, billing exports, CI outcomes, your own metrics — one POST and it's queryable, agent-readable data that prunes well from day one.

bash
curl -X POST $PENSIEVE/v1/ingest -H "X-Table: github_actions" --data-binary @runs.ndjson

Build your own ​

These are starting points, not a closed list — anything with a timestamp and a body becomes memory-adjacent, queryable data. The mental model is How pensieve works; the surfaces are Ingest, Query, and Connect your agent.

An open-source project · MIT licensed.