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Cognee Cli

OrganizationPopular
topoteretes
cognee-cli

Use when the user wants to drive cognee from the terminal with cognee-cli — remember/recall/forget/improve memory commands, managing datasets and config, or database migrations.

Overview

Publishertopoteretes
Repositorycognee
Skill namecognee-cli
Stars
32K
Forks
3.3K
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by topoteretes on GitHub. Read the source before you install it.

Installation

Install the Cognee Cli AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/topoteretes/cognee/tree/main/.agents/skills/cognee-cli
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/topoteretes/cognee.git /tmp/cognee
mkdir -p .claude/skills
cp -r /tmp/cognee/.agents/skills/cognee-cli .claude/skills/cognee-cli
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cognee Cli in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Cognee Cli on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Cognee Cli is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Use the cognee CLI

cognee-cli ships with the package (entry point in cognee/cli/_cognee.py; each command lives in cognee/cli/commands/). Every command has --help for its flags, but only a few (demo, memify, eval, serve, push, upgrade, downgrade, stamp, and search with one CODE example) include usage examples — for the memory commands use the examples in this file. Needs LLM_API_KEY configured, same as the SDK.

Core flow

The memory commands are the primary surface as of cognee 1.x:

bash
cognee-cli remember "Your text here"         # also accepts file paths / URLs
cognee-cli remember ./docs --dataset-name my_project
cognee-cli recall "Your question"            # query the graph
cognee-cli recall "keyword" --query-type CHUNKS
cognee-cli forget --all                      # wipe local state

remember is ingest + graph build in one step (add + cognify under the hood); --background/-b runs the cognify stage in the background, and --dry-run estimates LLM tokens/cost without ingesting. recall takes --datasets/-d, --top-k/-k (default 10), and --session-id/-s.

forget targets --dataset, --dataset-id, --data-id (needs a dataset), or --everything/--all — one unified command replacing the older delete and empty-dataset paths. --memory-only (with a dataset) drops the graph and vectors but keeps the raw files, so the data can be rebuilt.

forget --all does not ask for confirmation. It deletes every dataset immediately, even on a non-interactive stdin. The legacy delete --all prompts Delete ALL data from cognee? [y/N] first, so switching to forget silently drops that safety net — script it with care.

--query-type accepts 10 of the SDK's 20 SearchType values — the list in cognee/cli/config.py:SEARCH_TYPE_CHOICES: HYBRID_COMPLETION, GRAPH_COMPLETION, RAG_COMPLETION, CHUNKS, CHUNKS_LEXICAL, SUMMARIES, CODE, CYPHER, GRAPH_REPORT, SKILLS. The rest (TEMPORAL, TRIPLET_COMPLETION, GRAPH_COMPLETION_COT, AGENTIC_COMPLETION, NATURAL_LANGUAGE, …) are SDK-only, e.g. cognee.recall(q, query_type=SearchType.TEMPORAL).

When --query-type is omitted the CLI uses HYBRID_COMPLETION (DEFAULT_SEARCH_TYPE), whereas the SDK's cognee.recall() auto-routes between search types. --top-k defaults to 10 on the CLI and 15 in the SDK.

Session memory and enrichment

Session entries are currently written from the SDK — cognee.remember(..., session_id="chat_1") — not the CLI (cognee-cli remember has no session flag). The CLI side of session memory is reading and bridging:

bash
cognee-cli recall "question" -s chat_1       # session cache first: without -d/-t
                                             # this searches the session directly
cognee-cli sessions get                      # retrieve session Q&A history
cognee-cli improve -d my_project -s chat_1   # bridge session content into the graph
cognee-cli improve -d my_project             # enrich/index the graph (no session)
cognee-cli feedback ...                      # attach feedback to results

improve also takes --node-name, --feedback-alpha (learning rate in (0, 1]; default IMPROVE_FEEDBACK_ALPHA, 0.1), --build-global-context-index, --build-truth-subspace (both opt-in stages; the truth subspace needs -s), and --background/-b. It prints one line per stage — name, status (completed / already_completed / skipped / errored) and the skip reason (e.g. no_session_ids, lock_held, triplet_embedding_disabled). remember/improve build their graphs through cognify(), so cognify-level settings (e.g. CONTRADICTION_DETECTION=true) apply to them too.

Legacy / lower-level commands

add, cognify, search, memify, and delete still ship and are what the memory commands call underneath. Use them only to drive a single stage in isolation; prefer remember/recall/forget/improve otherwise.

bash
cognee-cli add "text" && cognee-cli cognify  # what `remember` does in one step
cognee-cli search "question"                 # `recall` minus routing/scope/session sources
cognee-cli memify -d my_project              # custom extraction/enrichment tasks
cognee-cli delete --all                      # superseded by `forget --all`

Management

bash
cognee-cli datasets list                     # dataset operations
cognee-cli config get [key] [--show-secrets] # view one/all settings (API keys masked by default)
cognee-cli config set <key> <value>          # set + persist to ./.env in the cwd
cognee-cli config unset <key>                # reset a key to its default (also persisted)
cognee-cli -ui                               # launch API server + UI (see cognee-server skill)
cognee-cli serve --url http://localhost:8000 # connect CLI/SDK to a running instance

Database migrations

cognee has two migration chains: the relational schema (Alembic, in cognee/alembic/) and the graph/vector data chain (slugs registered in cognee/modules/migrations/registry.py). Both run automatically — at API server startup and on the first write (remember, add, cognify, improve, …) in an SDK/CLI process — unless ENABLE_AUTO_MIGRATIONS=false. So you rarely need these commands; they are for inspecting state, disabled auto-migration, and rollbacks. There is no migrate command.

bash
cognee-cli current                    # stamped revision per database (per dataset
                                      # with access control on)
cognee-cli history                    # the data-migration chain, newest first
cognee-cli upgrade                    # relational to head, then data chain to head
cognee-cli upgrade <slug>             # data chain up to and including <slug>
cognee-cli upgrade --alembic <rev>    # pin the relational (Alembic) target
cognee-cli downgrade <slug|base>      # REWRITES DATA; revision is required,
                                      # prompts unless --force; --dataset <uuid>
                                      # (repeatable) limits it
cognee-cli stamp <head|base|slug>     # set the stored revision WITHOUT running
                                      # anything; prompts unless --force;
                                      # --dataset <uuid> (repeatable) limits it

The positional revision is always a data-chain slug; the relational target goes through --alembic. downgrade leaves the relational schema alone unless you pass --alembic. upgrade runs even when ENABLE_AUTO_MIGRATIONS=false. --alembic-path (or COGNEE_ALEMBIC_PATH) points at a custom Alembic scripts directory.

Gotchas

  • The CLI initializes cognee lazily; the first command in a fresh environment is slow (DB + model setup), later ones are fast.
  • remember (and add) without --dataset-name targets the default dataset main_dataset; recall/search operate across your accessible datasets unless a dataset is given.
  • forget refuses to run bare — pass --dataset, --dataset-id, --data-id (with a dataset), or --everything/--all.
  • Session commands (recall -s, sessions get, improve -s) require CACHING=true (the default) — with it off, session reads return nothing and SDK session writes raise. To cut read latency and token cost while keeping session memory, cognee-cli config set AUTO_FEEDBACK false — by default cognee makes one structured-output LLM call per answered query to self-tune its memory.
  • memify requires one of the arguments -d/--dataset-name --dataset-id
  • config set/config unset write to the .env file in whatever directory you run the command from (creating it if missing). config reset (reset all keys) is still not implemented.
  • Which .env actually wins is not always the cwd one. At import, cognee calls dotenv.load_dotenv(override=True), which resolves relative to the cognee package location, not your working directory. In a source/editable checkout (uv pip install -e .) a .env at the repo root therefore shadows the .env in the directory you ran from — and because override=True, it also beats variables you exported. Symptom: config set appears to do nothing, or the CLI connects to a backend you thought you had overridden. To test against different settings, move the repo .env aside, or set values programmatically after import (cognee.config.set_*). (Under python -c the cwd .env does win, because dotenv falls back to the cwd when __main__ has no __file__ — which is why the same command can behave differently as a script vs. -c.)

Frequently asked questions

What does the Cognee Cli AI skill do?

Use when the user wants to drive cognee from the terminal with cognee-cli — remember/recall/forget/improve memory commands, managing datasets and config, or database migrations.

Why use Cognee Cli on TypingMind?

Because you install it once and use it with any model. Cognee Cli is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Cognee Cli in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/topoteretes/cognee/tree/main/.agents/skills/cognee-cli. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Cognee Cli?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Cognee Cli?

As many as you like. As long as a model supports skills, you can use Cognee Cli with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Cognee Cli AI skill free?

Yes. It is published on GitHub by topoteretes under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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