Orchestrate distributed swarms of AI agents that collaboratively solve complex tasks.
This project has been archived.
The maintainers of this project have marked this project as archived. No new releases are expected.
Project description
Hivemind
Distributed AI Swarm Runtime
Hivemind is a distributed AI swarm runtime for coordinating large numbers of AI agents across complex tasks. Orchestrate multi-agent systems with a swarm execution model: tasks are decomposed into subtasks, executed in parallel, and coordinated through a scheduler and dependency graph.
Install: PyPI package is
hivemind-ai; CLI command ishivemind.
Features
- Planner → Scheduler → Executor → Agents — DAG-based task execution with configurable parallelism
- 120+ tools — Research, coding, data science, documents, experiments, memory, filesystem
- Memory & knowledge graph — Episodic, semantic, research, artifact memory; entity/relationship extraction
- Provider routing — OpenAI, Anthropic, Azure, Gemini (model name → provider)
- EventLog, replay, telemetry — Structured events for debugging and metrics
- CLI & TUI —
hivemind run,hivemind research,hivemind analyze,hivemind tuiwith dashboard
Architecture
Planner
↓
Scheduler
↓
Executor
↓
Agents → Tools → Memory → Knowledge Graph
Quickstart
Install (Python 3.12+):
pip install hivemind-ai
# or: uv add hivemind-ai
Run a task:
hivemind run "Summarize swarm intelligence in one paragraph."
Use in code:
from hivemind.swarm.swarm import Swarm
swarm = Swarm(worker_count=4, worker_model="gpt-4o-mini", planner_model="gpt-4o-mini", use_tools=True)
results = swarm.run("Analyze diffusion models and write a one-page summary.")
Set API keys via environment or ~/.config/hivemind/config.toml (see Configuration below).
CLI usage
| Command | Description |
|---|---|
hivemind run "task" |
Run swarm with the given task |
hivemind tui |
Launch terminal UI (prompt + output + dashboard) |
hivemind research papers/ |
Literature review on a directory of papers |
hivemind analyze repo/ |
Analyze repository architecture |
hivemind memory [--limit N] |
List memory entries |
TUI usage
hivemind tui
- Prompt — Type a task and press Enter or r to run.
- Output — Response and step status (e.g. “Planning…”, “Step 2 of 5…”).
- Dashboard (d) — Tasks, swarm graph, memory, logs.
- Keys:
rRun,dDashboard,EscUnfocus,oOutput,qQuit.
Examples
| Workflow | Command |
|---|---|
| Literature review | hivemind research papers/ or uv run python examples/research/literature_review.py [dir] |
| Repository analysis | hivemind analyze . or uv run python examples/coding/analyze_repository.py [path] |
| Dataset analysis | uv run python examples/data_science/dataset_analysis.py [path-to.csv] |
| Document intelligence | uv run python examples/documents/analyze_documents.py [dir] |
| Parameter sweep | uv run python examples/experiments/parameter_sweep.py --params '{"lr":[0.01,0.1]}' |
Outputs go to examples/output/. Run from project root when using script paths.
Demo GIF
To create a demo GIF showing swarm execution and task progress:
- Start the TUI:
hivemind tui - Use a screen recorder (e.g. asciinema, LICEcap, or terminal GIF tools) to record:
- Typing a task in the prompt (e.g. “Summarize swarm intelligence in one paragraph”)
- Pressing Enter to run
- The spinner and step status (Planning…, Executing step 1 of N…)
- The final response appearing
- Optionally pressing d to open the Dashboard (tasks, swarm graph, memory, logs)
- Export the recording as a GIF and add it to the README or docs (e.g.
).
Example asciinema:
asciinema rec demo.cast
# run: hivemind tui, then run a task and optionally open dashboard
# exit TUI (q), then Ctrl-D to stop rec
# convert: asciinema-agg demo.cast demo.gif # or use asciinema’s playback + another tool for GIF
Configuration
Config order: env > project .hivemind/config.toml > user ~/.config/hivemind/config.toml > defaults.
Minimal TOML (keys in env):
[default]
worker_model = "gpt-4o-mini"
planner_model = "gpt-4o-mini"
events_dir = ".hivemind/events"
data_dir = ".hivemind"
Env overrides: HIVEMIND_WORKER_MODEL, HIVEMIND_PLANNER_MODEL, HIVEMIND_EVENTS_DIR, HIVEMIND_DATA_DIR, plus provider keys (OPENAI_API_KEY, AZURE_OPENAI_*, etc.). See docs/providers.md and docs/development.md.
Documentation
| Doc | Description |
|---|---|
| Introduction | What Hivemind is, problem it solves, core concepts |
| Architecture | Planner, Scheduler, Executor, Agents, Tools, Memory, events, telemetry, replay |
| Swarm runtime | Task lifecycle, flow, code snippets |
| Tools | Tool architecture, registry, runner, creating tools, categories |
| Memory | Memory types, store, retrieval, embedding search, knowledge graph |
| Providers | Provider routing, model spec, Azure |
| CLI | All CLI commands and parameters |
| TUI | Layout, panels, keyboard shortcuts |
| Examples | Example workflows and commands |
| Development | Project structure, adding tools/providers, setup |
| Contributing | Setup, testing, code style, PR guidelines |
| FAQ | Common questions |
Contributing
Contributions are welcome. See CONTRIBUTING.md for setup, testing, and PR guidelines.
License
Hivemind is licensed under GPL-3.0-or-later. See LICENSE for the full text.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file hivemind_ai-0.1.0.tar.gz.
File metadata
- Download URL: hivemind_ai-0.1.0.tar.gz
- Upload date:
- Size: 2.4 MB
- Tags: Source
- Uploaded using Trusted Publishing? Yes
- Uploaded via: twine/6.1.0 CPython/3.13.7
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
e92031e847451e5f060e6020c0d004def3ee8cedbeaecfd9c8af20b40302ab58
|
|
| MD5 |
b6eb5acbcdc64c89b890390c7a6f08e8
|
|
| BLAKE2b-256 |
fa78a86e4e1b0571538648b17aac0826b18fa20122cc3c0b86dccaae18a5b4d7
|
Provenance
The following attestation bundles were made for hivemind_ai-0.1.0.tar.gz:
Publisher:
pypi-publish.yml on rithulkamesh/hivemind
-
Statement:
-
Statement type:
https://in-toto.io/Statement/v1 -
Predicate type:
https://docs.pypi.org/attestations/publish/v1 -
Subject name:
hivemind_ai-0.1.0.tar.gz -
Subject digest:
e92031e847451e5f060e6020c0d004def3ee8cedbeaecfd9c8af20b40302ab58 - Sigstore transparency entry: 1059477917
- Sigstore integration time:
-
Permalink:
rithulkamesh/hivemind@431f631797f65bfe32a74120e6d7bb89dd55d83f -
Branch / Tag:
refs/tags/v0.1.0 - Owner: https://github.com/rithulkamesh
-
Access:
public
-
Token Issuer:
https://token.actions.githubusercontent.com -
Runner Environment:
github-hosted -
Publication workflow:
pypi-publish.yml@431f631797f65bfe32a74120e6d7bb89dd55d83f -
Trigger Event:
release
-
Statement type:
File details
Details for the file hivemind_ai-0.1.0-py3-none-any.whl.
File metadata
- Download URL: hivemind_ai-0.1.0-py3-none-any.whl
- Upload date:
- Size: 206.8 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? Yes
- Uploaded via: twine/6.1.0 CPython/3.13.7
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
fc5a1d9df4b9dba00b4106e0f54e22b3a32189949f245cd709af2dc4d8f1afb0
|
|
| MD5 |
8b13564080e316b6ca01e48e50cdf6a9
|
|
| BLAKE2b-256 |
5adaafcf9ca2c5d7f1a0a2a2035a52cbe175ea375f51fe27a37b34861d3522d4
|
Provenance
The following attestation bundles were made for hivemind_ai-0.1.0-py3-none-any.whl:
Publisher:
pypi-publish.yml on rithulkamesh/hivemind
-
Statement:
-
Statement type:
https://in-toto.io/Statement/v1 -
Predicate type:
https://docs.pypi.org/attestations/publish/v1 -
Subject name:
hivemind_ai-0.1.0-py3-none-any.whl -
Subject digest:
fc5a1d9df4b9dba00b4106e0f54e22b3a32189949f245cd709af2dc4d8f1afb0 - Sigstore transparency entry: 1059477918
- Sigstore integration time:
-
Permalink:
rithulkamesh/hivemind@431f631797f65bfe32a74120e6d7bb89dd55d83f -
Branch / Tag:
refs/tags/v0.1.0 - Owner: https://github.com/rithulkamesh
-
Access:
public
-
Token Issuer:
https://token.actions.githubusercontent.com -
Runner Environment:
github-hosted -
Publication workflow:
pypi-publish.yml@431f631797f65bfe32a74120e6d7bb89dd55d83f -
Trigger Event:
release
-
Statement type: