Releases·Tool Release
Dev ships COGEXT, a free MIT-licensed local commitment extractor for AI agents, on PyPI
A developer released cogext-primitive, a free, MIT-licensed Python tool that extracts commitments from agent output, parses deadlines, and tracks them through a state machine in local SQLite. It is the offline entry point to COGEXT, an accountability layer for AI agents whose paid cloud version performs external verification via Gmail, GitHub, and webhooks. The tool is precision-tuned rather than recall-tuned, deliberately skipping vague or compound promises based on an audit of 120 agent outputs.
17 engagement2 sourcesThu, Sep 24, 2026, 03:17 PM
AI Overview
- COGEXT (pip install cogext-primitive) extracts agent commitments, parses deadlines, and tracks them through an open → due → overdue → fulfilled state machine, fully offline
- The extractor skips compound promises, pronoun-shaped objects, and vague claims — a precision-over-recall design choice backed by an audit of 120 agent outputs
- The tool is free, MIT licensed, requires no API key, cloud, or signup, with code on github.com/yaminbinyoosuf/cogext-primitive
- Extracted commitments store in local SQLite, with 48 tests passing
- A paid cloud version at cogextai.com adds external verification via Gmail, GitHub, and webhooks; the offline extractor is the free entry point
Related
Products·building
COGEXT — API that extracts and tracks commitments made by AI agents
COGEXT is an API that takes text produced by AI agents, extracts promises as structured commitments with confidence scores and deadlines, and tracks each through a 12-state lifecycle with webhook events. It addresses the problem that agents hallucinate, forget, and contradict themselves, giving developers a way to hold agents accountable to what they said. Aimed at teams building agent systems.
COGEXT is an API that takes text produced by AI agents, extracts promises as structured commitments with confidence scores and deadlines, and tracks each through a 12-state lifecycle with webhook events. It addresses the problem that agents hallucinate, forget, and contradict themselves, giving developers a way to hold agents accountable to what they said. Aimed at teams building agent systems.
1 engagement1 sourceredditWed, Sep 2, 2026, 09:39 PM
Releases·Tool Release
Built a verification layer for AI agents catches them lying about what they did
An independent developer launched COGEXT v2.0, an accountability layer that independently verifies whether AI agents actually completed commitments they claim, checking Gmail, GitHub, or custom webhooks before allowing a "fulfilled" state. The release targets a problem many agent builders report: agents claim actions silently fail, discovered only when clients complain.
An independent developer launched COGEXT v2.0, an accountability layer that independently verifies whether AI agents actually completed commitments they claim, checking Gmail, GitHub, or custom webhooks before allowing a "fulfilled" state. The release targets a problem many agent builders report: agents claim actions silently fail, discovered only when clients complain.
17 engagement2 sourcesredditMon, Sep 14, 2026, 01:52 PM
Products·launched
Commitment-tracking REST API that uses Claude to extract promises from unstructured text
A REST API that accepts unstructured text (emails, Slack messages, call transcripts) and returns structured commitments with deadlines and confidence scores, using Claude as the extraction engine. A 12-state lifecycle tracks each promise over time, with webhook events when commitments go overdue or get fulfilled. Aimed at people who need automated follow-through tracking on communications.
A REST API that accepts unstructured text (emails, Slack messages, call transcripts) and returns structured commitments with deadlines and confidence scores, using Claude as the extraction engine. A 12-state lifecycle tracks each promise over time, with webhook events when commitments go overdue or get fulfilled. Aimed at people who need automated follow-through tracking on communications.
3 engagement1 sourceredditThu, Sep 3, 2026, 12:51 PM
Releases·Tool Release
Developer publishes COGEXT open standard for AI agent commitment tracking; LangChain not yet compliant
A developer published an open spec, COGEXT, for tracking promises AI agents make to users (e.g. 'I'll send the report by Friday') across run boundaries. Observability tools like LangSmith show traces, tokens, and latency but not whether commitments were later kept, which the spec addresses. The author proposes a LangChain callback handler and a pip-installable compliance checker, but notes zero current adoption, including LangChain.
A developer published an open spec, COGEXT, for tracking promises AI agents make to users (e.g. 'I'll send the report by Friday') across run boundaries. Observability tools like LangSmith show traces, tokens, and latency but not whether commitments were later kept, which the spec addresses. The author proposes a LangChain callback handler and a pip-installable compliance checker, but notes zero current adoption, including LangChain.
4 engagement1 sourceredditSat, Sep 26, 2026, 10:50 AM
Products·launched
intent-continuity: automatic reuse of historical requirements for coding agents
A pure-Python library that discovers, verifies, and applies relevant historical requirements for coding agents. It uses no embeddings, vector database, or LLM calls. It helps developers keep coding agents consistent with prior project intents.
A pure-Python library that discovers, verifies, and applies relevant historical requirements for coding agents. It uses no embeddings, vector database, or LLM calls. It helps developers keep coding agents consistent with prior project intents.
31 engagement1 sourcegithubThu, Sep 10, 2026, 01:59 AM