Cognetivy
Open-source, local-first state layer that turns AI coding agent runs into versioned, inspectable workflows.
The process gap around AI coding agents is real: you get output you can't reproduce and reasoning you can't trace. Cognetivy closes that with a local .cognetivy/ workspace, schema-backed collections carrying citations and derived_from provenance, and a Studio view that compares workflow versions and runs side by side. The MIT license and the absence of a model inside the engine mean it adds structure, not another bill. The tradeoff is architectural, not commercial: state is single-workstation, Studio is read-only, and you need an MCP-compatible agent for any of it to do work. If you want a hosted canvas with shared dashboards, look at hosted orchestration platforms instead.
Verified 13h ago · liveness 72/100 · cite: rightaichoice.com/tools/cognetivy
- Developers on Cursor, Claude Code, or OpenClaw who need repeatable, traceable agent runs
- Small teams comfortable in a CLI that want auditable agent output stored locally and versioned in Git
- Product managers running deep research that has to be comparable run over run
- Researchers who need persistent collections with citations and derived_from provenance
- Teams that need hosted, shared collaboration dashboards and remote access
- Users who want a GUI-only setup with no command-line step
- Environments that can't persist a local .cognetivy/ folder, including some CI runners
We scan live Reddit threads, YouTube comments, X posts, G2 reviews and other communities — and hand you an honest verdict in under a minute.
- Honest verdict, not marketing
- Real pros & cons from real users
- Attributed quotes with receipts
3 free scans · no card needed
Skip Cognetivy if your team needs a shared, hosted dashboard with remote access, or if you need the platform itself to schedule and supervise agents rather than record runs your agent already drives.
Running it globally and from any directory rather than one project at a time means installing with npm install -g cognetivy; the npx path is scoped to the directory you run it from.
Cognetivy is MIT-licensed and you run it locally, and the seed data records a single $0/mo Open Source plan covering unlimited local workflows, runs, and collections with Studio included. That places it below hosted agent-observability and orchestration platforms that bill per seat or per run — but you are not buying a hosted service, so the comparison is really about where the cost of maintenance lands.
In short
Cognetivy — Open-source, local-first state layer that turns AI coding agent runs into versioned, inspectable workflows. Best for Developers on Cursor, Claude Code, or OpenClaw who need repeatable, traceable agent runs, Small teams comfortable in a CLI that want auditable agent output stored locally and versioned in Git, Product managers running deep research that has to be comparable run over run. Free to use.
What's new in Cognetivy
Checked 6 days agoAcross the latest 2 updates: 1 launch and 1 news mention.
Structured Workflows, Local State, and Why We Built It
Covers recent Cognetivy changes across templates, Studio UX, and traceability, framing the project's focus on structured workflows, local-first state, and auditability.
Welcome to Cognetivy — Why we built an open-source state layer for AI coding assistants
Introduces Cognetivy as an open-source state layer for AI coding assistants, built around turning agent chaos into structured, local workflows.
What people actually say about Cognetivy — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
6 mentions across 3 sources (Hacker News, GitHub, Lemmy) · researched Aug 7, 2026.
Average across the 3 sources that answered — each source counts once, not each post.
- +Local-first, so all workflow state stays in your repo.
- +MIT open-source and vendor-independent, no lock-in.
- +Structured DAG workflows reduce agent randomness.
- +Run tracking with events and duration improves traceability.
- +Schema-backed collections persist and export to CSV.
- −Community feedback is thin and mostly self-promotional.
- −Requires understanding of DAGs and workflow concepts.
- −No cloud sync or collaboration features for teams.
- −May be overkill for simple, single-shot coding tasks.
- −Young tool with potential for breaking changes.
- • Self-hosting and maintenance time
- • No official support or SLAs
Viability Score
How well maintained and how widely used is Cognetivy? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Versioned workflow DAGs defined as nodes (prompts) connected by input/output collections
- Workflow validation rejects cycles; each node can declare required_skills and required_mcps
- Run tracking with status, duration, and current node state
- NDJSON event timeline per run: step_started, step_completed, run_completed, and custom events
- COGNETIVY_NEXT_STEP hints returned to the agent with action, node_id(s), and hint
- Explicit parallel node execution via run_nodes_parallel
- Schema-backed collections scoped to a workflow (kinds plus item_schema)
- Optional citations, derived_from, and reasoning fields for traceability on every collection kind except run_input
- Studio read-only browser UI on localhost:3742
- Compare workflow versions and runs side by side in Studio
- Local-first storage in .cognetivy/, versionable with Git
- No LLM inside the engine and no cloud requirement
- Agent skills plus MCP server connection options
- One-command setup via npx cognetivy, plus global install with cognetivy install <target>
- 18 pre-built use-case templates organized by persona, applied during onboarding or from Studio
About Cognetivy
Cognetivy is an MIT-licensed state layer for AI coding assistants. Where most agent tooling lives inside a chat window, Cognetivy sits between your editor and the model: you define a workflow once as a versioned DAG file, your agent reads it and executes each step, and every run, event, and artifact lands in a local .cognetivy/ folder in your project. There is no cloud account and no LLM inside the engine. A workflow is a single connected directed acyclic graph of nodes — prompts connected by input/output collections, with optional required_skills and required_mcps per node; cycles are rejected at validation. A run is one execution of that workflow with a given input, so you can start many runs from one definition and compare them. Runs are logged with status, duration, and an NDJSON event timeline (step_started, step_completed, run_completed, plus custom events), and the engine returns a COGNETIVY_NEXT_STEP hint so your agent knows whether to run a node, run nodes in parallel, or complete the run. Collections are schema-backed and persist across runs, with optional citations, derived_from, and reasoning fields so outputs chain back to sources. You inspect all of that in Studio, a read-only browser UI on localhost:3742. Setup is one command — npx cognetivy — which creates the workspace, prompts you to pick your coding agent, installs the Cognetivy skill, optionally applies a workflow template, and opens Studio. It ships 18 pre-built use cases grouped by persona, from competitor analysis for marketing teams to BMAD development cycles for engineers. Docs list setup paths for Claude Code, Cursor, OpenClaw, GitHub Copilot, Gemini CLI, OpenAI Codex, Amp, Cursor Agent CLI, OpenCode, Factory Droid, and Qwen Code, plus any MCP client.
Behind the Verdict
Cognetivy makes a narrow, defensible bet: the hard part of agent work isn't the model, it's the missing record of what the model did. Everything else in the product follows from that. Workflows are DAGs of prompt nodes connected by typed input/output collections — the engine validates them and rejects cycles, so a workflow can't quietly become a tangle. Each node can declare required_skills and required_mcps, which means the definition itself carries the environment it expects. Runs are first-class: start one with an input file, get back a COGNETIVY_NEXT_STEP object naming the action, node_id(s), and a hint, then drive it with run step. Parallelism is explicit — when the action is run_nodes_parallel you spawn one sub-agent per runnable node. The event log is NDJSON, one file per run, with step_started, step_completed, run_completed, and whatever custom events you append. Collections are the part that ages well. Schema is workflow-scoped (kinds plus item_schema), and every kind except run_input supports optional citations, derived_from, and reasoning. Populate those and you get an artifact trail that points back to the source and the chain of thinking — the thing most agent sessions lose the moment the context window rolls. Studio is deliberately read-only. On localhost:3742 you view the workflow DAG, node prompts, and collection schema; browse runs, run detail, and the event timeline; inspect collections and their items with traceability; and compare workflow versions or apply templates from the gallery. Workflows themselves are versioned JSON files driven by the agent, not forms you fill in — that's a real line in the sand, and it's the reason Studio stays inspect-only. Because .cognetivy/ is just a folder, all of it versions with Git, and you can diff a workflow change against the runs that came after it. Setup is one command: npx cognetivy creates the workspace, asks which coding agent(s) you use, installs the Cognetivy skill where needed, optionally applies a workflow template, and opens Studio. A global npm install adds cognetivy install <target> for Cursor, Claude Code, OpenClaw, or workspace. Docs cover per-agent setup across Claude Code, Cursor, OpenClaw, GitHub Copilot, Gemini CLI, OpenAI Codex, Amp, Cursor Agent CLI, OpenCode, Factory Droid, and Qwen Code, plus any standard MCP client. The 18 pre-built use cases are grouped by persona and load straight from onboarding — competitor analysis, BMAD development cycles, deep product research, content marketing strategy, investor and funding research, academic literature review, PR review against a checklist, and bug triage. Where it doesn't fit: teams that need a shared, hosted dashboard with remote access. Nothing here is shared for you — state is local, and there's no cloud component to log into. Projects that can't persist a .cognetivy/ folder, including some CI runners, can't use it at all. It also requires an MCP-compatible agent to do anything useful, so it is not standalone
Researching Cognetivy? Get your full AI stack in 60 seconds.
Free, no signup — tell us your goal and get tools matched to your budget & existing stack.
Real-world workflow fit
Concrete scenarios for the personas Cognetivy actually fits — and what changes day-one when you adopt it.
Run npx cognetivy from the project root, pick your agent during onboarding, and apply the PR review template so it becomes the current workflow. Ask your agent to start a run against an open pull request; the engine returns COGNETIVY_NEXT_STEP and the agent works the checklist node by node, writing findings into a collection.
Outcome: A completed PR review whose findings live in a schema-backed collection with citations, plus an NDJSON event log in .cognetivy/ that you can commit to Git alongside the code change.
Define a product research workflow once, with nodes that gather sources and one that synthesizes them, then start a fresh run each week with a new topic input file. Open Studio with cognetivy studio to compare this week's run against the last.
Outcome: Runs are directly comparable because they came from the same workflow definition, and the reasoning and derived_from fields show which source produced which conclusion.
Use the academic deep research template, then extend the collection schema with a kind for sources so each item carries its own citations and reasoning. Populate the fields as the agent works rather than after the fact.
Outcome: A persistent, schema-backed collection that survives across runs and can be browsed in Studio with traceability intact, instead of research trapped in a chat history.
Use Cases
- Run structured competitor analysis with a workflow that gathers sources and summarizes findings into a persistent collection.
- Execute BMAD cycles with full observability and persistence across runs.
- Turn scattered product research into a schema-backed knowledge base you can compare run over run.
- Build a content marketing calendar from audience research with citable sources per item.
- Assemble a targeted investor pipeline with deep, structured profiles and traceable reasoning.
- Conduct academic deep research with systematic literature reviews and citations.
- Run code or PR reviews against a structured checklist that writes artifacts into collections.
- Automate bug triage with a template-driven workflow picked during onboarding.
Limitations
- All state lives in the local .cognetivy/ folder — there is no cloud component and no LLM in the engine, so nothing is shared or hosted for you.
- Studio is read-only: you inspect runs, collections, and events, but workflows are defined as versioned JSON files and driven by the agent.
- Cognetivy requires an MCP-compatible agent to do anything useful, so it is not standalone.
- Projects that cannot write a .cognetivy/ folder, including some CI environments, can't use it.
- There is no built-in remote access or shared team dashboard.
- Docs are public, but release notes live on GitHub rather than in-product.
as of 2026-10-11
Verification history
We have re-verified Cognetivy 9 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 9 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Cognetivy tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Open Source
$0/mo
Ideal for
Developers and small CLI-comfortable teams who want agent run capture in their own repo without a hosted subscription — solo devs through small product teams.
What this tier adds
Starting tier and only tier: MIT-licensed, everything local in .cognetivy/, unlimited local workflows, runs, and collections, Studio included, no usage fees.
Where the pricing makes sense
The company stage and team size where Cognetivy's pricing actually pencils out — and where peers do it cheaper.
Cognetivy is MIT-licensed and you run it locally, and the seed data records a single $0/mo Open Source plan covering unlimited local workflows, runs, and collections with Studio included. That places it below hosted agent-observability and orchestration platforms that bill per seat or per run — but you are not buying a hosted service, so the comparison is really about where the cost of maintenance lands.
Setup time & first value
How long it actually takes to get something useful out of Cognetivy — broken out by persona, not the marketing-page minute.
One command gets you to first value: npx cognetivy creates the .cognetivy/ workspace, prompts you to choose your coding agent, installs the skill, optionally applies a workflow template, and opens Studio — the installer is built to finish in under a minute. Realistically, developers reach a first useful run within that session. Product managers and researchers who skip the templates should budget
Switching to or from Cognetivy
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From chat-history-only agent sessions: install via npx cognetivy, then have your agent create a workflow from the tasks you currently retype in prompts, and start capturing runs instead of transcript scrolling.
- →From hosted agent-orchestration platforms: keep the hosted platform for scheduled jobs, and move repeatable inspection-heavy work into a Cognetivy workflow so the run record lives in your repo.
- →From manual research spreadsheets: use the deep product research or academic template as the collection schema, then let the agent populate it so citations and derived_from land with each item.
- ↗To hosted orchestration platforms: expect to rebuild workflows as platform-specific pipelines, since Cognetivy workflow files are local JSON driven by your agent rather than a hosted scheduler definition.
- ↗To ad-hoc prompting: your .cognetivy/ folder stays useful as a historical record, but new work stops producing comparable runs the moment you go back to freeform prompts.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Cognetivy”, and we withheld 6: 6 could not be judged, because “Cognetivy” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Cognetivy.
Official links
Featured Head-to-Head Comparisons
Cognetivy vs Locus Robotics
These tools solve completely different problems. Choose Locus Robotics if you run a mid-to-large warehouse and need to automate physical picking, putaway, and replenishment with scalable AMRs. Choose Cognetivy if you're a developer or researcher who uses AI coding agents and wants structured, traceable workflows stored locally. There is no overlap—pick the one that matches your domain.
Cognetivy vs Truleo
If you’re a law enforcement agency drowning in siloed data, Truleo’s CJIS-compliant platform with jail call analysis and report writing automation is purpose-built for you — but it’s paid and targeted. If you’re a developer or researcher using AI coding agents and need structured, repeatable, local workflows for tasks like competitor analysis or deep research, Cognetivy’s free, open-source state layer is a no-brainer. They serve entirely different domains; pick based on your role.
Cognetivy vs Presto Voice
Presto Voice is the clear choice for QSR chains wanting to automate drive-thrus and boost revenue via upselling, especially with recent adoption by Dairy Queen. Cognetivy is ideal for technical users who need repeatable, traceable workflows for AI coding agents, but it's free and local-only—aimed at a completely different audience. Your pick depends on whether you run restaurants or write code.
Popular in Agent Frameworks & Orchestration
Temporal AI
Durable execution platform that keeps AI agents and long-running workflows alive through crashes, retries, and abandoned sessions.
Frequently Asked Questions
Used Cognetivy? Help shape our editorial sentiment research.