Introducing SybreSpace
AI agents that work together
SybreSpace is an agent-native orchestration platform. Not a task queue with AI bolted on — a system built from the ground up to coordinate multiple AI agents with durable state, live steering, automated code review, and cross-provider support.
AI agents are powerful alone. Together, they're transformative.
One agent can write code. But what about coordinating a planner, an implementer, and a reviewer — all working on the same codebase, at the same time, using different AI providers? That's orchestration. And existing tools weren't designed for it.
Agent frameworks like CrewAI and LangGraph treat agents as ephemeral functions in a pipeline. Coding GUIs like T3 Code isolate agents into separate workspaces so they can't collide. SybreSpace treats agents as stateful collaborators — with their own context windows, provider preferences, lifecycle states, and the ability to be steered mid-execution — all working on the same codebase simultaneously.
Generic Orchestration
Agents as functions
- Invoke a function, get a result
- No awareness of context windows
- No mid-execution steering
- Build review loops yourself
- Single-provider per workflow
SybreSpace
Agents as collaborators
- Persistent sessions with full context
- Live token tracking and compaction
- Redirect agents mid-turn
- Built-in multi-round auto-review
- Mix Claude, Codex, Gemini, and more
How SybreSpace orchestration works
A data-driven Conductor routes work between agents based on durable workflow state — not hardcoded logic. Change orchestration policies without changing code.
Define
Create a workflow template — Planner, Implementer, Reviewer — or any N-slot combination. Each slot gets a role, provider, and model.
Dispatch
The Conductor routes work to the first incomplete slot by priority. Agents receive full context: the goal, prior work, and any reviewer feedback.
Coordinate
As each agent completes its turn, the Conductor evaluates workflow state and routes to the next agent. Review loops, handoffs, and escalations happen automatically.
Deliver
Work moves through review gates, aggregates results, and surfaces the finished product for user approval. Every step is auditable.
Core capabilities
Everything you need to run production multi-agent workflows — built in, not bolted on.
Multi-Provider Orchestration
Run Claude, Codex, Gemini, Grok, Qwen, DeepSeek, and more in the same workflow. The right model for each task — planner on Opus, implementer on Sonnet, reviewer on a different provider entirely.
Live Agent Steering
Redirect a running agent mid-turn — not just queue a signal for later. True injection for Claude and Codex, safe-boundary steering for OpenAI-compatible providers. Change direction without losing context.
Automated Code Review
Built-in multi-round review loops. Reviewer gives feedback, implementer fixes, next round begins. Stuck-loop detection, max-round policies, and write-restricted reviewer sessions prevent runaway cycles.
Durable Workflow State
All orchestration state lives in SQLite — never in memory. Sessions survive crashes, restarts, and cold starts. The Conductor routes from durable state, not ephemeral signals.
N-Slot Workflow Templates
Define any orchestration pattern without code changes. Two-agent implementer/reviewer? Three-slot planner/implementer/reviewer? Custom five-agent pipeline? Just add a workflow definition.
Conflict Detection
Track which files each agent modified across concurrent sessions. Detect merge conflicts before they happen — not after. Essential when multiple agents edit the same codebase.
Context-Aware Scheduling
Live token tracking per session. Automatic prompt cache management to reduce costs. Context compaction triggers at model-aware thresholds. Three-stage checkpointing for orchestrator sessions.
Durable Message Queue
Orchestrator notifications are queued durably with smart aggregation — multiple sub-completions batched into one combined prompt. Deduplication, delivery tracking, and pause-aware holds.
Safety Guardrails
Consecutive nudge limits, no-progress detection via similarity analysis, roadblock loop caps, cost and time budgets, and bundle-level blocking. Agents that get stuck get escalated, not retried forever.
Every workflow pattern, one architecture
SybreSpace doesn't force you into one orchestration model. The same Conductor engine powers solo work, master/sub decomposition, multi-agent bundles, automated review, and manual workstreams — all through typed session entries and data-driven workflow definitions.
Standalone Sessions
Single-agent work with full lifecycle management, context tracking, and provider flexibility.
Master Orchestrator
Decompose complex work into N parallel sub-sessions. Monitor progress, detect conflicts, aggregate results.
Multiagency Bundles
N-slot agent teams with priority routing, phase management, slot authority, and handoff templates.
Auto-Review
Automated review rounds with accept/reject/fix cycles, stuck-loop detection, and audit trails.
Workstreams
Manual parent containers for organizing related sessions into user-managed status buckets.
User Steering
Step in at any time. Message any agent, take over a session, or pause the entire workflow. You're always in control.
The landscape today
Existing tools each solve a piece of the multi-agent puzzle. None solve the whole thing.
CrewAI, LangGraph, AutoGen
Define agent roles and chain them into workflows. Powerful composability, large ecosystems.
What's missing
- Agents are ephemeral functions, not persistent sessions
- No awareness of context windows or token costs
- No file-level conflict detection
- No built-in code review loops
- You build everything from primitives
Cline, OpenCode, Goose, T3 Code, Amp
AI coding tools adding sub-agent spawning — fan out tasks to parallel workers and collect results.
What's missing
- Spawn-and-collect — no lifecycle management
- Worktree isolation prevents real collaboration
- No orchestration templates or workflow definitions
- No automated review loops or quality gates
- No cross-agent conflict detection
Letta, OpenAI Agents SDK
Give agents persistent memory and identity. Agents learn and improve over time.
What's missing
- Single-agent focus — not multi-agent coordination
- No codebase-aware orchestration
- No conflict detection between agents
- No workflow templates or review automation
- No live mid-turn steering
Agent-native orchestration platform
Coordinates multiple AI agents as a system — with durable state, live steering, automated review, and conflict detection. All on the same codebase.
Unified by design
- Persistent, steerable agent sessions
- Same-codebase concurrent editing
- Multi-round automated code review
- Cross-agent file conflict detection
- 9+ AI providers in the same workflow
Feature-by-feature comparison
| Capability | Agent Frameworks | Coding Agents | Stateful Platforms | SybreSpace |
|---|---|---|---|---|
| What is an agent? | A function in a pipeline | A CLI with sub-workers | A stateful entity | A steerable, durable session |
| Multi-agent coordination | Sequential/parallel chains | Spawn workers, collect results | Single-agent focus | Orchestrated collaboration |
| Same-codebase editing | Not designed for code | Isolated worktrees | Not designed for code | Same tree, conflict-aware |
| Multi-provider support | Via LiteLLM adapters | Multi-provider per agent | Limited | 9+ providers, mixed workflows |
| Live mid-turn steering | Breakpoints only | Reply to running agent | Memory edits only | True injection + safe-boundary |
| Automated code review | Build it yourself | Manual / per-edit approval | Not included | Multi-round with guardrails |
| Conflict detection | Not included | Prevented via isolation | Not included | File-level attribution |
| Context & cost management | Not applicable | Provider-dependent | Memory management | Live tokens + cache optimization |
| Stuck-loop prevention | Manual implementation | Manual restart on failure | Manual implementation | Similarity + nudge limits |
| Workflow templates | Code-defined graphs | Not applicable | Hardcoded patterns | Data-driven, no code changes |
| State durability | Checkpoint-dependent | Thread persistence | Durable memory | SQLite, crash-safe |
Every tool in this space gets something right. CrewAI's role-based composition. LangGraph's checkpoint model. Letta's persistent memory. Cline's SDK-native subagents. Goose's meta-orchestrator vision. OpenCode's mixed-model teams. SybreSpace brings these ideas together into a single platform purpose-built for coordinating AI agents on shared work — with the durable lifecycle management, conflict detection, review automation, and safety guardrails that real multi-agent collaboration requires.
Design principles
The architectural decisions behind SybreSpace — and why they matter for real multi-agent collaboration.
Durable, event-driven
All workflow state lives in SQLite. The Conductor observes durable events, not in-memory signals or prompt heuristics. Crash, restart, cold-start — state is always there.
Type-driven routing
Session type determines Conductor behavior. An orchestration sub follows the master flow. An auto-review session has its own lifecycle. No ambiguity, no special cases.
Data-driven policy
Workflow definitions, transitions, and routing rules are data in SQLite tables — not code. Add a new orchestration pattern by inserting rows, not writing functions. Change policies at runtime.
No silent fallbacks
Invalid transitions fail visibly. Stuck agents get escalated with clear diagnostics. Errors surface, they don't hide. When something goes wrong, you know immediately.
User owns terminal state
Only the user can mark work as "finished." The orchestrator's ceiling is "ready for approval." Agents suggest completion — humans decide.
Local-first architecture
SQLite is the primary read path for speed. MySQL backs up durably for cross-machine sync. Your orchestration state lives on your hardware, not in someone else's cloud.
What you can build with SybreSpace
Any workflow where multiple AI agents need to coordinate, review, and build on each other's work.
Multi-Agent Software Development
A planner breaks down the task. An implementer writes the code. A reviewer checks the work. The orchestrator coordinates all three — with automatic conflict detection when they touch the same files.
Automated Code Review Pipelines
Every code change gets reviewed by an AI agent before it reaches you. Multi-round feedback loops, write-restricted reviewers, and stuck-loop detection ensure quality without manual babysitting.
Cross-Provider AI Workflows
Use Claude for planning, Codex for implementation, and Gemini for review — all in the same orchestrated workflow. Each agent uses the model best suited to its role.
Large-Scale Task Decomposition
Break massive projects into sub-tasks. Assign each to a specialized agent. Monitor progress, detect conflicts, and aggregate results — all with a single master orchestrator session.
Ready to orchestrate your AI agents?
SybreSpace is part of the Sybre platform. Self-hosted, locally owned, and designed for teams that want AI agents to work together — not just work alone.