ocas-finchOCAS self-improvement orchestrator (Darwin's finch — adaptive evolution). Mines session JSONL files to detect corrections, breakthroughs, methodologies, course-changes, and behavioral directives (Always/Never). Routes each finding to the optimal storage tier: MEMORY.md, skill files, reference files, or Chronicle KG. Compacts MEMORY.md by routing entries to the correct tier. Part of the OCAS System Evolution Layer alongside Mentor, Fellow, and Forge. NOT for real-time behavioral adaptation, skill evaluation, or skill creation.
Install via ClawdBot CLI:
clawdbot install indigokarasu/ocas-finchGrade Fair — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Accesses system directories or attempts privilege escalation
/proc/Calls external URL not in known-safe list
https://github.com/<agent-handleUses known external API (expected, informational)
api.github.comAudited Oct 6, 2026 · audit v1.0
Generated Oct 6, 2026
Teams running long-lived AI agents whose MEMORY.md files hit capacity thresholds need automated compaction. Finch mines session JSONL logs for corrections, directives, and breakthroughs, then routes each finding to the optimal storage tier (memory, skill, reference, KG). This prevents manual memory surgery that causes bloat and context loss.
Organizations deploying multiple AI agents across cron jobs, email, calendar, and kanban integrations need cross-signal health monitoring. Finch scans seven signal sources to detect cron errors, unreachable workspace mounts, and MCP tool failures before they cascade. Operational gaps are surfaced as GAP findings rather than silent failures.
Software teams that capture lessons-learned, runbooks, and methodology corrections during development sessions can use Finch to distill that knowledge into structured skill files and reference documents. Findings are routed to the right location, keeping skill packages coherent and preventing PII leakage into public repos via automated checks.
Regulated industries using AI agents need auditable trails of behavioral directives (Always/Never rules) and course-changes detected during operations. Finch's tiered routing and PII scanning scripts (check_no_pii.py) provide a compliance-friendly mechanism to store methodology insights without leaking sensitive data into shared artifacts.
Platforms building multi-agent systems with distinct roles (self-improvement, evaluation, skill creation) need a clear responsibility boundary to avoid tool overlap. Finch handles session mining and memory routing while deferring evaluation to Mentor and skill creation to Forge, enabling maintainable OCAS-style architecture.
Offer Finch as a hosted service for AI agent teams, charging per connected agent or per session-mined volume. Value comes from automated memory compaction, scan health dashboards, and tier-routing analytics that reduce manual ops overhead. Free tier for individual developers, paid tiers for teams and enterprise fleet management.
Sell deployment and integration services to enterprises wiring Finch into their existing cron, email, calendar, and workspace stacks. Includes custom signal-source connectors, MCP fallback configuration, and PII-safe publication pipelines. Recurring support contracts fund ongoing scan-gotcha maintenance and new integration adapters.
Keep the core orchestrator MIT-licensed and open-source to build community adoption, while monetizing a managed cloud that handles cron scheduling, multi-profile storage backends, and Chronicle KG hosting. Enterprises pay for managed uptime, compliance reporting, and cross-profile analytics without running infrastructure.
💬 Integration Tip
Wire Finch into your existing cron scheduler first and run a dry scan against real session JSONL files before enabling auto-routing; verify googleapiclient or Composio fallbacks are configured, since MCP tools often fail via tool_call() in cron contexts.
Scored Oct 6, 2026
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