SessionFS starts with portable AI coding sessions: capture the work, resume it in another tool, hand it to a teammate or agent, then distil it into shared memory and audit.
The foundation is the portable .sfs session: captured from local tools, resumable across runtimes, shareable with teammates, and compiled into knowledge.
Background daemon captures every session from 10 local tools — Claude Code, Codex, Gemini, Copilot, Cursor, Amp, Cline, Roo Code, Kilo Code, Antigravity — into the portable .sfs format. Zero developer behaviour change.
Start in Claude Code, resume in Codex. Auto-launch + full transcript via --append-system-prompt-file. Four-tool resume; 10-tool capture.
Sessions distil into claims, decisions, patterns, and bugs. Auto-compiled wiki with backlinks. Agents read AND write — the next session inherits everything the last one learned.
Shared architecture, conventions, and key decisions — loaded into every AI session automatically. Explain once, never repeat.
Ask a question across the sessions and project knowledge you can already read — “where did we fix the SSO redirect?” — and get an answer that cites the sessions it used. Ask about one project or a whole project group. Dashboard, terminal or MCP, on your own model key.
See every file an agent created, edited or deleted in a session, with line counts and a diff per file, rebuilt from the captured tool calls of all 10 tools. Redacted secrets stay redacted.
Rules and personas follow every tool, every session, every teammate.
One canonical rules document, compiled to CLAUDE.md, codex.md, .cursorrules, copilot-instructions.md, and GEMINI.md. Resume syncs the target tool's rules file before launch.
Portable AI roles per project (atlas, sentinel, scribe, …). ASCII name regex, soft-delete preserves history, server-side guard refuses delete while non-terminal tickets reference. Every plan.
Every captured manifest records which persona + ticket + rules version was active. Sync propagates the tags into sessions.persona_name + sessions.ticket_id, so every audit query can trace any line of code back to the agent identity that produced it.
Handoffs move captured sessions between people and agents. Tickets carry the work, personas claim it, and AgentRuns enforce CI policy on the way out.
Server-enforced FSM (suggested → open → in_progress → blocked → review → done). Acceptance criteria, dependencies, comments. Assign to a persona or directly to a teammate, who gets a “Your turn” notification. Active-ticket bundle tags every captured session with the assigned persona. Every plan.
Auditable execution record for one persona run, with CI-friendly policy enforcement (severity × fail_on → exit_code). GitHub Actions + GitLab CI workflows shipped. Replay a run with an edited prompt or context and compare findings between runs.
Cloud-agent-ready via integration docs: AWS Bedrock action group, Google Vertex AI function-calling schema, and a closed-OPERATIONS dispatch table for custom API clients. Same personas + tickets, hosted-LLM-side.
Hand off a session to a teammate with full context. Session data copies to recipient — full conversation, workspace state, git snapshot, status stepper.
Group related projects, grant reader, editor or admin access to the whole group, and optionally restrict project visibility to what each member owns or reaches through a group. Search knowledge and read an overview across the group, filtered to what each person can read.
Two-way Jira sync: status changes and comments move both ways, link tickets to existing issues, with custom story templates and one evidence comment when a ticket completes. Publish wiki pages and project context to Confluence, without overwriting pages edited there.
Built in. The foundation under everything else — verify, sanitise, audit.
Cross-references every AI claim against actual tool outputs. When the AI says "test passes" but exit code is 1 — that's a critical contradiction, flagged with full evidence + CWE mapping.
Server-side scan of every archive file. 14 PHI patterns + 20 secret patterns. BLOCK / REDACT / WARN modes. Org-level policy via settings JSON; dashboard surfaces findings per-session.
Every captured session carries instruction provenance, persona, ticket, and the AgentRun that signed off. The dashboard's Agent Runs tab is a queryable audit log; severity + policy verdict + findings JSON are all stored per run.
Start with local memory, then add shared knowledge, tickets, and governance as your team adopts agents.