# Axocoatl > Agentic runtime in Rust — persistent, supervised agents. Self-hosted, local-first, zero telemetry. Apache 2.0. - **Type:** MCP server - **Install:** `agentstack add mcp-axocoatl-axocoatl` - **Verified:** Pending review - **Seller:** [axocoatl](https://agentstack.voostack.com/s/axocoatl) - **Installs:** 0 - **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml) - **Latest version:** 0.1.0 - **License:** Apache-2.0 - **Upstream author:** [axocoatl](https://github.com/axocoatl) - **Source:** https://github.com/axocoatl/axocoatl - **Website:** https://axocoatl.ai ## Install ```sh agentstack add mcp-axocoatl-axocoatl ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # Axocoatl **The Rust runtime for self-coordinating multi-agent systems.** [](https://github.com/axocoatl/axocoatl/actions/workflows/ci.yml) [](https://crates.io/crates/axocoatl-cli) [](LICENSE) Kill the server mid-task — the agent restarts from its last checkpoint, not from zero. 100% local. Axocoatl runs persistent AI agents that coordinate through a **stigmergic event lattice** — agents activate when their dependencies complete, driven by pheromone-style signals with no central orchestrator. Built in Rust on the `ractor` actor model: low memory, fast cold start, provider-agnostic. --- ## 60-second quickstart ```bash # 1. Install (no Rust toolchain required) curl -fsSL https://raw.githubusercontent.com/axocoatl/axocoatl/main/scripts/install.sh | sh # 2. Interactive setup wizard — picks a provider, scaffolds a project axocoatl onboard # 3. Check your environment axocoatl doctor # 4. Start the daemon + API, then chat axocoatl dev axocoatl chat -a assistant ``` Prefer Cargo? `cargo install axocoatl-cli` (requires Rust 1.82+). > **Skipping `onboard`?** Copy [`axocoatl.example.yaml`](axocoatl.example.yaml) > to `axocoatl.yaml` — two agents and one workflow, fits on one screen. > The full `axocoatl.yaml` shipped in the repo is the larger demo (12 agents, > scheduled runs, MCP servers). --- ## Why Axocoatl | Capability | Axocoatl | AutoAgents | CrewAI | |---|:--:|:--:|:--:| | Language / runtime | Rust / actors | Rust / actors | Python | | **Stigmergic coordination** (no orchestrator) | ✅ | ❌ | ❌ | | HTN symbolic planning | ✅ | ❌ | ❌ | | Auction-based agent selection | ✅ | ❌ | ❌ | | Per-agent token budgets | ✅ | ❌ | partial | | 4-tier persistent memory + checkpointing | ✅ | partial | partial | | MCP client + server | ✅ | partial | ✅ | | A2A protocol | ✅ | ❌ | ❌ | | Provider-agnostic (Ollama/OpenAI/Anthropic/…) | ✅ | ✅ | ✅ | | Interactive onboarding + `doctor` | ✅ | ❌ | ❌ | The differentiator is the **coordination layer**: define agents with `depends_on`, and the event lattice cascades work through them automatically. ```yaml agents: - id: researcher provider: ollama model: llama3.2 depends_on: [] - id: summarizer provider: ollama model: llama3.2 depends_on: [researcher] # activates when researcher completes workflows: - id: research-and-summarize agents: [researcher, summarizer] entry_point: researcher ``` ```bash axocoatl workflow run research-and-summarize -i "What is photosynthesis?" ``` --- ## See it work **Give it a goal — it builds the team.** A coordinator agent decomposes the goal into subtasks, spawns a worker to fit each one, and runs them in parallel. No orchestration code, no glue. **Tell it once — it remembers.** Store a preference, open a brand-new conversation, and it still knows. Agent-editable core memory that persists across runs. **It never phones home.** Every socket the daemon opens is `127.0.0.1`; the only outbound call is your local model. Zero telemetry, zero external connections. --- ## Core concepts - **Agents** — persistent `ractor` actors with a provider, tools, 4-tier memory, and a token budget. Survive restarts via checkpointing. - **Hybrid memory recall** — relevant past exchanges are injected each turn, and the agent can also pull on demand: `recall_search` (semantic search over past sessions) and `recall_timeframe` (read a day's activity log). Tunable per agent. - **Agent-managed core memory** — editable blocks (`persona`, `human`, `project`, …) the agent curates via tools and that render into its prompt each turn (the MemGPT/Letta model). Per-agent by default, shareable across agents. A background "sleep-time" pass consolidates idle agents' memory automatically. - **Stigmergic coordination** — agents publish `TaskCompleted` events; an `EventLattice` accumulates pheromone signals and activates downstream agents when thresholds are crossed. No scheduler, no glue code. - **Coordinator role** — for explicit hierarchical work, an agent with `role: coordinator` decomposes a goal into subtasks (HTN or LLM), auctions them to worker agents, runs them in parallel, and synthesizes the results. The pass is resumable via checkpointing. - **Workflows** — declarative multi-agent DAGs via `depends_on` / `entry_point`. - **Providers** — Ollama, OpenAI, Anthropic, Mistral, Gemini, OpenRouter. No lock-in. - **Protocols** — MCP (discover, call, and expose tools — agents invoke external MCP tools through the daemon over a persistent connection) and A2A (agent interop). See the [docs site](https://docs.axocoatl.ai) for the full picture, the [marketing site](https://axocoatl.ai) for the positioning, or [`docs/ARCHITECTURE.md`](docs/ARCHITECTURE.md) and [`docs/TROUBLESHOOTING.md`](docs/TROUBLESHOOTING.md) for the in-repo quick reference. --- ## Roadmap - **Stronger sandbox isolation tiers** — the shipped sandbox is a hardened rootless Podman container (capabilities dropped, no-new-privileges, network-isolatable); microVM-class isolation (Firecracker) is planned. --- ## CLI ``` axocoatl onboard Interactive setup wizard axocoatl doctor Environment / dependency health check axocoatl init Scaffold a project non-interactively axocoatl validate Validate a config file axocoatl dev | serve Run daemon (+ IPC) / production server axocoatl chat -a Interactive chat axocoatl workflow list | run Inspect / execute multi-agent workflows axocoatl agents list|status|restart axocoatl tokens report Per-agent token usage axocoatl mcp servers|tools Inspect connected MCP servers/tools ``` ## HTTP API ``` GET /health POST /api/agents/{id}/execute GET /api/agents GET /api/agents/{id}/status POST /api/agents/{id}/restart GET /api/tokens/report GET /api/workflows POST /api/workflows/{id}/execute GET /api/mcp/servers GET /api/mcp/tools GET /ws (WebSocket streaming) ``` ## Examples Every example is runnable with a mock LLM — **no API keys needed** — unless noted. See [`examples/`](examples/). **Coordination & planning** - [`stigmergic-workflow`](examples/stigmergic-workflow) — the `EventLattice` + `depends_on` DAG. The running order *emerges* from pheromone signals crossing thresholds; no orchestrator decides it. - [`skills-lattice`](examples/skills-lattice) — event-driven Skills: one event fans out to every agent that `reacts_to` it (`emits`/`reacts_to`), distinct from a fixed DAG. - [`htn-planner`](examples/htn-planner) — symbolic HTN decomposition; compound tasks expand via methods and only unresolved frontiers reach the LLM. - [`crash-recovery`](examples/crash-recovery) — kill a multi-step workflow mid-run and resume from the checkpoint; completed steps are not re-run. **Memory & providers** - [`memory-recall`](examples/memory-recall) — agent-managed core memory, semantic recall, and sleep-time consolidation (Tiers 3–4); runs offline. - [`multi-provider`](examples/multi-provider) — per-agent provider selection: a cheap local model for simple steps, a frontier model for the hard one, with a per-tier cost breakdown. **Tools, protocols & integration** - [`tool-hooks`](examples/tool-hooks) — pre/post tool hooks that deny a path-traversal write, audit every call as JSON, and let the agent recover. - [`mcp-bridge`](examples/mcp-bridge) — call an external MCP tool over stdio through the real `McpToolRegistry`; plus how to expose agents as an MCP server. - [`a2a-server`](examples/a2a-server) — expose an agent over the A2A protocol (agent card + task endpoint) and call it from a client, in-process. - [`sandbox-session`](examples/sandbox-session) — the rootless Podman sandbox for agent tool execution: threat model, config knobs, and a live integration test (needs Podman). **Autonomy & config** - [`proactive-agents`](examples/proactive-agents) — agents that fire on a schedule or on an event (here, reacting to `AgentFailed`), not on a user prompt. - [`configs/`](examples/configs) — a gallery of minimal YAML configs for common recipes (research pipeline, feature dev, incident response, local-only, MCP). No Rust. **Foundations** - [`research-assistant`](examples/research-assistant), [`code-reviewer`](examples/code-reviewer), [`customer-support`](examples/customer-support) — agent coordination, token budgets, and session/checkpoint memory. ## Build from source ```bash git clone https://github.com/axocoatl/axocoatl cd axocoatl cargo build --release # binary: target/release/axocoatl cargo test --workspace # 415 tests ``` ## License Apache-2.0 — see [LICENSE](LICENSE). Changes: [CHANGELOG.md](CHANGELOG.md). ## Source & license This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [axocoatl](https://github.com/axocoatl) - **Source:** [axocoatl/axocoatl](https://github.com/axocoatl/axocoatl) - **License:** Apache-2.0 - **Homepage:** https://axocoatl.ai Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** yes - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: flagged — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/mcp-axocoatl-axocoatl - Seller: https://agentstack.voostack.com/s/axocoatl - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.