Positioning — Bigwave vs LangGraph, Autogen, CrewAI, OpenAI Assistants
Audience: developers evaluating agent frameworks.
Lens: comparison-not-attack. Every adjacent project is well-built and well-suited for some use case. Read this to pick the right tool — including not Bigwave.
TL;DR
| Project | Best when you want… | Where it's strongest |
|---|---|---|
| LangChain / LangGraph | A huge ecosystem of integrations and a mature graph DSL | Largest connector library, biggest community, well-funded |
| Autogen (Microsoft) | Multi-agent conversation patterns out of the box | Group-chat / planner-executor patterns, Microsoft research backing |
| CrewAI | Role-based "crew" abstractions with a low-code feel | Approachable mental model, opinionated defaults |
| OpenAI Assistants | A managed agent on the OpenAI stack | No infra, fastest path to a single-vendor demo |
| Bigwave | A self-hosted harness where every layer is swappable, with eval-as-promotion-gate built into the loop | Operator control, dual-license MIT/Apache, OpenAI- & Anthropic-compatible API server, no vendor lock-in |
One-line positioning: Bigwave is the open-source agent harness for teams who treat the loop as production infrastructure — self-hosted, swappable, observable, and gated by evals before promotion. We are not trying to out-ecosystem LangChain.
What Bigwave is, exactly
- Open-source agent harness, dual-licensed MIT OR Apache-2.0.
- Server-side ReAct loop, OpenAI-compatible
/v1/chat/completions+/v1/embeddings+ tool-calling (tools/tool_choice), and Anthropic Messages API (/v1/messages) — all via translation to the same internal serving layer. Does not cover OpenAI Batch, Files, or fine-tuning endpoints. - Python 3.12+, FastAPI backend, Vite/React UI, optional Docker.
bwCLI,install.sh, semver releases.- Gateway routing overhead: p99 < 2 ms (model-id resolution + quota check, CI-enforced — reproducible with
pytest tests/perf/). - Every layer is an explicitly pluggable ABC — serving engine, vector store, and training backend ship multiple concrete adapters today (5, 4, and 4 respectively); agent memory and tool registry follow the same ABC pattern with one adapter each today.
Four named wedges:
- Coding-agent harness — production-grade harness for shipping coding agents.
- Eval / promotion gate — evals run in the loop, gate which prompt/model versions get promoted to prod.
- Regulated industries — self-hosted, auditable, no third-party data path. Append-only audit log with Splunk HEC and Syslog export, workspace isolation fence, configurable retention — built and tested today.
- Chat voice consistency — keep brand voice consistent across long conversations and model swaps.
What Bigwave is not trying to be: the broadest connector hub, the easiest no-code agent builder, or a managed cloud product.
Comparison
vs LangChain / LangGraph
What LangGraph does well
- Largest connector and integration ecosystem in the space.
- Mature graph DSL for expressing complex flows with cycles, branches, and checkpoints.
- Backed by a well-funded company (LangChain, Inc.) with full-time maintainers.
- Strong observability story via LangSmith (paid).
Where Bigwave is different
- Every layer is an explicitly pluggable ABC — swap the serving engine, vector store, training backend, or gateway provider without touching the rest of the stack.
- Eval-as-promotion-gate is built into the loop, not a separate paid product.
- Self-hosted by default, dual-license MIT OR Apache-2.0. LangChain components have varied licensing depending on the specific package — always check your actual usage.
- Smaller surface area — we are intentionally not the connector-hub project.
When LangGraph is the right pick
- You want the broadest set of pre-built integrations.
- You're already invested in the LangChain ecosystem.
- You want LangSmith's hosted observability and are comfortable with that pricing.
- Your flows genuinely need graph-DSL expressiveness (cycles, multiple branches).
vs Autogen (Microsoft)
What Autogen does well
- Multi-agent conversation patterns (group chat, planner/executor) are first-class.
- Strong Microsoft research backing and academic credibility.
- Good for prototyping multi-agent architectures quickly.
Where Bigwave is different
- Single-loop, server-side execution — not a conversational multi-agent abstraction. Many production agent deployments don't actually need N agents talking; they need one loop that's deterministic and audit-logged.
- OpenAI-compatible and Anthropic Messages API-compatible server; Autogen is a library with its own client conventions.
- Eval/promotion gate and training loop are built in; Autogen doesn't ship either.
When Autogen is the right pick
- Your problem genuinely benefits from multiple agents conversing (debate, planner-executor, role-play).
- You want a research-grounded framework to extend.
vs CrewAI
What CrewAI does well
- "Crew" / role abstraction is approachable — fast first-day mental model.
- Opinionated defaults reduce the time-to-first-demo.
- Active community, strong newcomer ergonomics.
Where Bigwave is different
- No role/crew abstraction is imposed. The loop is the primitive; orchestration is the operator's call.
- More aimed at production operators than at the first-time agent builder.
- Eval gate, audit logging, swappable persistence are higher priorities for our wedges than role ergonomics.
When CrewAI is the right pick
- The role/crew mental model fits your domain.
- You're optimizing for time-to-first-demo over long-run operator control.
vs OpenAI Assistants
What OpenAI Assistants does well
- Zero-infra path to a working agent on the OpenAI stack.
- Tight integration with OpenAI's tool ecosystem (file search, code interpreter).
- Easiest demo if you're already all-in on OpenAI.
Where Bigwave is different
- Self-hosted; your data and prompts don't leave your infra.
- Multi-provider — OpenAI, Anthropic, and any compatible endpoint, swappable by config.
- No vendor lock-in to a single model provider's roadmap.
- Auditable for regulated industries — fully open source, MIT OR Apache-2.0.
When OpenAI Assistants is the right pick
- You're committed to OpenAI as the sole provider.
- You don't want to run any infra at all.
- Your compliance posture is fine with OpenAI's data-handling.
Honest weaknesses (what Bigwave does not have, today)
We do not pretend to be ahead where we are not:
- Connector breadth: LangChain has ~10× our pre-built integration count. If you need a Slack/Notion/Confluence/Salesforce/etc. tool you can
pip install, they win on inventory. - Hosted observability dashboard: LangSmith is a more polished, hosted product. We rely on logs + your own observability stack, plus our open-source eval runner.
- Multi-agent conversation primitives: Autogen has more sophisticated group-chat patterns out of the box.
- Community size: all four adjacent projects have larger communities. We're 5 days old (public 2026-05-06).
- Battle-tested at scale: LangGraph and OpenAI Assistants have well-documented production deployments at large companies. Bigwave's production deployments are in design-partner pilot stage.
If any of those gaps are dealbreakers for your use case, pick the project that closes them. We'd rather you succeed with the right tool than struggle with ours.
Feature matrix (summary)
| Bigwave | LangGraph | Autogen | CrewAI | OpenAI Assistants | |
|---|---|---|---|---|---|
| Self-hosted by default | ✓ | ✓ | ✓ | ✓ | ✗ |
| OpenAI-compatible API server | ✓ | ✗ (library) | ✗ (library) | ✗ (library) | ✓ |
| Anthropic-compatible API server | ✓ | ✗ | ✗ | ✗ | ✗ |
| Eval gate refuses regressed deploys | ✓ | ✗ | ✗ | ✗ | ✗ |
| Deterministic guardrails | ✓ | ✗ | ✗ | ✗ | partial |
| Audit log + workspace isolation | ✓ | ✗ | ✗ | ✗ | ✗ |
| Fine-tuning / DPO in the loop | ✓ | ✗ | ✗ | ✗ | ✗ |
| Pre-built connector ecosystem | minimal | very large | medium | medium | OpenAI only |
| Multi-agent conversation patterns | minimal | strong | strong | medium | ✗ |
| License | MIT / Apache-2.0 | MIT | MIT | MIT | proprietary |
Why these comparisons land for our wedges
| Wedge | Adjacent that also fits | Why Bigwave is differentiated |
|---|---|---|
| Coding-agent harness | LangGraph, Autogen | Server-side ReAct loop + eval gate means you can A/B prompt/model pairs and only promote winners. |
| Eval / promotion gate | (no direct adjacent) | Eval-as-gate is in the loop, not a separate paid product. Least-crowded wedge. |
| Regulated industries | (none are self-hosted-first MIT/Apache + audit-logged) | Dual-license MIT OR Apache-2.0 + self-hosted + append-only audit log with Splunk HEC / Syslog export, workspace isolation, configurable retention — built today, not roadmap. |
| Chat voice consistency | LangGraph + custom prompt mgmt | Voice/style is gated by evals before deploy. Comparable adjacents require you to bolt that on. |