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SymForce is an Apache-2.0 licensed library for robotics that combines symbolic mathematics with automatic code generation to build fast optimization solvers. It targets computer vision, SLAM, motion planning, and real-time control by eliminating handwritten derivatives and generating production-ready C++ code.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | symforce-org/symforce |
| Owner | symforce-org |
| Primary language | C++ |
| License | Apache-2.0 — OSI-approved |
| Stars | 1.6k |
| Forks | 171 |
| Open issues | 154 |
| Latest release | v0.11.0 (2026-06-01) |
| Last updated | 2026-06-01 |
| Source | https://github.com/symforce-org/symforce |
What symforce is
SymForce provides a symbolic toolkit (built on SymPy), a code generator for C++/multi-language targets, and a tangent-space factor-graph optimizer. It automatically computes Jacobians in tangent space, generates branchless code with minimal dependencies, and supports Lie group operations for geometric types—all designed to reduce runtime overhead and development friction in robotics applications.
Get the symforce source
Clone the repository and explore it locally.
git clone https://github.com/symforce-org/symforce.gitcd symforce# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Requires C++17 compiler and Python 3.9–3.12; pre-compiled wheels available for Linux/Mac, but building from source needed to access C++ headers (e.g., sym::Optimizer).
- Learning curve: symbolic math, Lie groups, tangent-space parameterization, and factor graphs are non-trivial. Recommend studying the RSS 2022 paper and official tutorials.
- Code generation step needed in development workflow: symbolic expressions → generated C++ code. This is not runtime code evaluation but a compile-time transformation.
- Generated code uses Eigen as a dependency; ensure your target platform and build system support it.
- Epsilon handling for singularities must be set early (symforce.set_epsilon_to_symbol()) to avoid numerical issues in expressions like atan2 and norms.
When to avoid it — and what to weigh
- Mature, Stable API Required — Project is at v0.11.0 (released 2026-06-01) with 154 open issues. Breaking changes may occur; not recommended if production stability and backwards compatibility are non-negotiable.
- General-Purpose Linear Algebra — SymForce is specialized for nonlinear robotics optimization and symbolic computation. For standard linear algebra, numerical solvers (Eigen, BLAS), or convex optimization, use dedicated libraries.
- Non-Robotics or Non-Geometric Problems — The API heavily emphasizes geometric types (Pose2/3, Rot2/3, Camera) and robotics workflows. If your problem does not involve symbolic geometry or tangent-space manifolds, overhead may not justify adoption.
- Strict Runtime or Licensing Constraints — Requires Python 3.9+ and C++17; generated code has external dependencies (Eigen). Apache-2.0 permits commercial use, but requires review of your distribution and modification practices.
License & commercial use
Apache License 2.0 (Apache-2.0). This is a permissive OSI-approved open-source license.
Apache-2.0 permits commercial use, modification, and distribution subject to: (1) including a copy of the license and NOTICE file, (2) documenting material changes, and (3) disclaiming warranties. Requires review if you distribute modified versions or integrate into proprietary products to ensure compliance with NOTICE and change documentation requirements.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Strong |
| License clarity | Clear |
| Deployment complexity | High |
| DEV.co fit | Good |
| Assessment confidence | High |
No security vulnerabilities or hardening claims found in the data. Standard considerations: (1) Code generation produces C++ that should be reviewed before deployment; (2) Symbolic math and floating-point arithmetic can introduce numerical instabilities if not carefully validated; (3) Zero dynamic memory allocation in generated code reduces some attack surface, but Eigen dependencies and Python binding security should be reviewed. No security audit details available.
Alternatives to consider
Ceres Solver
Mature, widely-adopted C++ nonlinear optimizer with automatic differentiation. Lacks symbolic geometry types and code generation; requires hand-written cost functions. Better for teams with existing C++ infrastructure and lower tolerance for pre-release software.
g2o (General Graph Optimization)
Specialized factor-graph optimizer for SLAM/SfM with a long track record. No symbolic math or code generation; steeper manual optimization tuning. Choose if you need battle-tested SLAM-only solutions without prototyping flexibility.
CasADi
General-purpose automatic differentiation and code generation for optimal control and optimization. Broader scope but less focus on robotics-specific geometry types and tangent-space optimization. Prefer if you need control-theoretic features over pure pose estimation.
Build on symforce with DEV.co software developers
SymForce eliminates handwritten derivatives and generates production C++ from symbolic math. Ideal for SLAM, bundle adjustment, and embedded real-time control. Start with Python prototyping, deploy with zero-allocation generated code.
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symforce FAQ
Can I use SymForce in a commercial product?
Do I need to write C++ to use SymForce?
What platforms does SymForce support?
How does SymForce handle singularities?
Build it with a software development company
DEV.co is a software development company delivering production systems to teams building on open source. Our engineers design, integrate, and ship across web, APIs, AI, data, and cloud. If symforce is part of your ai coding agents roadmap, we can implement, customize, migrate, and maintain it.
Accelerate Your Robotics Optimization
SymForce eliminates handwritten derivatives and generates production C++ from symbolic math. Ideal for SLAM, bundle adjustment, and embedded real-time control. Start with Python prototyping, deploy with zero-allocation generated code.